Routing in mobile wireless sensor network: a survey

Telecommun SystDOI 10.1007/s11235-013-9766-2

Routing in mobile wireless sensor network: a survey

Getsy S Sara · D. Sridharan

© Springer Science+Business Media New York 2013

Abstract The Mobile Wireless Sensor Network (MWSN)is an emerging technology with significant applications. TheMWSN allows the sensor nodes to move freely and they areable to communicate with each other without the need fora fixed infrastructure. These networks are capable of out-performing static wireless sensor networks as they tend toincrease the network lifetime, reduce the power consump-tion, provide more channel capacity and perform better tar-geting. Usually routing process in a mobile network is verycomplex and it becomes even more complicated in MWSNas the sensor nodes are low power, cost effective mobile de-vices with minimum resources. Recent research works haveled to the design of many efficient routing protocols forMWSN but still there are many unresolved problems like re-taining the network connectivity, reducing the energy cost,maintaining adequate sensing coverage etc. This paper ad-dresses the various issues in routing and presents the stateof the art routing protocols in MWSN. The routing proto-cols are categorized based on their network structure, stateof information, energy efficiency and mobility. The classifi-cation presented here summarizes the main features of manypublished proposals in the literature for efficient routing inMWSN and also gives an insight into the enhancements thatcan be done to improve the existing routing protocols.

G.S Sara (B) · D. SridharanDepartment of Electronics & Communication Engineering,College of Engineering, Anna University,Guindy Chennai 600 025, Indiae-mail: [email protected]

G.S SaraShalom, 13&14B, Adinaath Avenue, Santhoshapuram, Chennai,Tamil Nadu 600 073, Indiae-mail: [email protected]

Keywords Survey · Mobile wireless sensor network ·Routing · Mobility and energy efficiency

1 Introduction

A mobile wireless sensor network consists of sensor nodesthat have the ability to move within the network [101].A sensor node is a tiny device that includes three basic com-ponents: a sensing subsystem for data acquisition from thephysical surrounding environment, a processing subsystemfor local data processing and storage and a wireless commu-nication subsystem for data transmission [28, 118]. Prelimi-nary studies show that introducing mobility in wireless sen-sor network is advantageous [73, 79, 86, 114, 122]. Mobilitycan be achieved by equipping the sensor nodes with mobi-lizers for changing their locations [28] or the sensors canbe made to self propel via springs [12, 123] or wheels [15]or they can be attached to transporters like vehicles, an-imals, robots [65] etc. Sometimes the sensor nodes maymove due to the environment (ocean or air) in which theyare placed [100]. The recent year researches prove that mo-bile wireless sensor networks outperform the static wire-less sensor networks as they offer the following advantages[8, 28, 101, 123]:

• A sparse architecture may be considered for a mobile sen-sor network design

• MWSN has a dynamic topology which reflects in thechoice of other characteristic properties such as routing,MAC level protocols and physical characteristics

• In static WSN, an initially connected network can turninto a set of disconnected subnet works due to hardwarefailure or energy depletion but in MWSN, the nodes canbe used to reorganize the network

mailto:[email protected]


G.S Sara, D. Sridharan

Fig. 1 Mobile Wireless SensorNetwork

• The lifetime of a sensor network can be increased usingmobile sensor nodes [67, 92]

• Mobile sensors can relocate after initial deployment toachieve the desired density requirement and to reduce theenergy holes in the network

• Mobility can reduce energy consumption during commu-nication [92]

• MWSN has more channel capacity as compared to staticWSN [7]

• Better targeting can be achieved using MWSN• Data fidelity can be achieved by MWSN by reducing the

number of hops owing to which the probability of errordecreases

Figure 1 explains the architecture of three tier MobileWireless Sensor Network [101]. The sensor nodes are de-ployed randomly in the network. These nodes can commu-nicate with each other and the mobile agents. The mobileagents can move anywhere, at any time and they are respon-sible for collecting sensed data and forwarding them to thefixed network consisting of Access Points.

A few of the applications where mobile wireless sensornetwork can be employed include a group of ornithologists

monitoring the ecology of migratory birds, a mobile worker(might be a robot or human) equipped with sensor(s) col-lecting and transmitting data to the sink about agricultureproduction, smart city, e-voting, intelligent traffic system,firefighters moving through a burning building etc. [60, 101,124, 128]. Some of the applications may need the support ofmobile sinks such as soldiers equipped with Personal DigitalAssistants (PDA) moving in battlefield for enemy detectionand a rescuer who is equipped with PDA moving in a disas-ter area searching for survivors.

But introducing mobility in a wireless sensor network isvery challenging as path breakage happens frequently due tochannel fading, shadowing, interference, node mobility andnode failure. Preconstruction of message delivery networkswill not be of much help here as the topology changes toofrequently. Frequent location-updates from a mobile nodecan lead to excessive drain of the sensor node’s battery sup-ply and also can increase collisions [75]. Owing to these,factors like mobility of nodes, bandwidth restrictions andlimited resources etc., have to be considered in designingMWSN. Based on the type of communication, two kinds ofMWSN exist [101]:


Infrastructure Network—A mobile unit is connected withthe nearest base station that is within its communication ra-dius to contact

Infrastructure less Network—In this type of network, nofixed router is needed and all mobile units are capable ofmovement. They are self organizing with the capability toestablish communication in an arbitrary manner.

Routing is the act of moving a data packet from sourceto destination. The route of each message destined to thebase station in a sensor network is crucial in terms of net-work life time. Long routes can increase the network delaywhile routing via the shortest route can always cause the in-termediate nodes to drain the energy supply soon, leadingto network partitioning. The best routing protocol is the onethat covers all states of a specified network and never con-sumes too much of network resources. Sensor nodes havelimited energy supply and minimizing the power consump-tion is crucial in Mobile Wireless Sensor Network.

Mobility in tandem with energy efficiency in a wirelesssensor network endows with significant challenges for rout-ing [25, 48, 58, 95].

• It is impossible to build a global addressing scheme forthe deployment of a large number of sensor nodes as itleads to an increase in overhead maintenance

• The highly dynamic nature of MWSN and the frequentchange in network topology and communication links,due to node mobility or link faults, make routing a realchallenge [37]

• Energy processing and storage capacities of mobile sen-sor nodes are limited. Due to the dynamic nature of thenetwork topology, various nodes will deplete their energysupplies and drop out of the network leading to the parti-tioning of the network

• Multiple sensors may sense the occurrence of an event si-multaneously and generate data. This data traffic has to beaggregated to improve the energy and bandwidth utiliza-tion

• Sensor networks are usually application specific. The de-sign requirements of a MWSN change with application

• Knowing the position of mobile sensor nodes is importantsince data collection is based on location. Routing proto-col can be designed to aid this

• The routing protocol should be adaptable to the self orga-nizing nature of the nodes

• Dynamic clustering architecture should be tailored by therouting protocol as depletion of power from cluster headcan be prevented; thereby extending the network’s life-time

• Randomized path choice should be imbibed as multiplepaths to a destination with low overheads can help in bal-ancing the network load and tolerating the failure of nodes

• Good thresholds can be set for sensor nodes for energy,time delay and to transfer the sensed data. This wouldsave energy by limiting unnecessary transmissions

1.1 Routing protocols

Several routing protocols are proposed by many researchers,based on various criteria and design issues. No routing pro-tocol can be termed as perfect, as each routing protocol maybe suitable for some application but may have loopholeswhen judged in some other perspective. The routing pro-tocols of MWSN can be mainly classified based on theirnetwork structure, state of information, mobility and energyefficiency techniques (Fig. 2).

• Based on the network structure, they are further catalogedas Direct Communication Routing, Flat based Routing,and Hierarchical routing. In direct communication rout-ing, a sensor node sends data directly to sink. The powerof the sensor node drains very quickly here if the networkarea is large and the number of collision too increases.Therefore direct communication routing is hardly used inmobile wireless sensor network. The flat based routingprotocol assign the same functionality to all nodes [48].It is very simple and efficient for small networks. It isfurther categorized into Opportunistic Routing (OR) andBest Path routing [131]. The idea behind opportunisticrouting is that for each destination, a set of next hop can-didates are selected and each of them is assigned a pri-ority according to its closeness to the destination. Whena packet needs to be forwarded, the highest priority nodeis chosen as the next hop. The best path routing schemeattempts to find the best path and forwards packets to thecorresponding next hop. The Hierarchical protocols dy-namically organize the nodes in the network into parti-tions called clusters and the clusters are further aggre-gated into larger partitions called super clusters and soon. The cluster heads aggregate the data; thereby reducingthe data and saving energy [120]. They are further catego-rized as flat hierarchy, cluster based hierarchy and zonebased hierarchy. In flat based hierarchy all nodes havesame capabilities but different responsibilities. In clusterbased hierarchy, the physical network is transformed intoa virtual network of interconnected clusters. Each clusterhas cluster heads which make control decisions for clustermembers. The zone based hierarchy increases the scal-ability by shrinking the topology reorganization scope.Zones are created and the flat scheme is applied to eachzone [51].

• Based on the state of information, the routing protocolsare grouped into topology based routing and locationbased routing. The topology based routing protocols usethe principle that every node in the network maintainslarge scale topology information [94]. They can be againclassified as proactive routing, reactive routing and hybridrouting. Proactive routing also known as pre-computedrouting or table driven routing, calculates the route toall destinations apriori and stores the information about


Fig. 2 Taxonomy of routing protocols in MWSN

the links and network topology changes in a routing ta-ble. The nodes here periodically update their routing ta-bles. Reactive routing or on demand routing computes theroute to a destination only when it is needed using routediscovery process and route maintenance. Hybrid rout-ing utilizes the functionality of both proactive and reac-tive routing. Location based routing protocols make useof position information of nodes to route data. They areyet again ordered under the location updates as time basedlocation update routing protocol, distance based locationupdate routing protocol and predictive distance based lo-cation update routing protocol [51]. In the time based lo-cation update scheme, each node periodically sends a lo-cation update to a location server. In distance based up-date scheme, each node tracks the distance it has movedsince its last update and sends its location update when-

ever the distance exceeds a certain threshold. In the pre-dictive distance based, also called as dead reckoning, thenode reports to the location server both its position andvelocity. Based on this information and the mobility pat-tern, the location of the node can be predicted.

• Energy efficient routing protocols in MWSN can bebroadly categorized based on when the energy optimiza-tion is performed. A mobile sensor node consumes itsbattery energy not only when it actively sends or receivespackets but also when it stays idle listening to the wire-less medium for any possible communication requestsfrom other nodes. Thus, energy efficient routing proto-cols must minimize either the active communication en-ergy required to transmit and receive data packets or theenergy during inactive periods. For protocols that belongto the former category, the active communication energy


can be reduced by adjusting each node’s radio power justenough to reach the receiving node but not more than that.For protocols that belong to the latter category, powersaving approach can be used. Each node can save theinactive energy by switching its mode of operation intosleep/power-down mode or simply turn it off when thereis no data to transmit or receive. This leads to considerableenergy saving, especially when the network environmentis characterized with low duty cycle of communicationactivities. However, it requires well-designed routing pro-tocol to guarantee data delivery even if most of the nodessleep and do not forward packets for other nodes. Anotherimportant approach to optimize active communication en-ergy is load distribution. While the primary focus of theabove two approaches is to minimize energy consumptionof individual nodes, the main goal of the load distributionmethod is to balance the energy usage among the nodesand to maximize the network lifetime by avoiding over-utilized nodes when selecting a routing path. Energy effi-cient design is a new area of research which investigatesthe approaches to save battery life [26].

• Depending on the applications, the nodes that have to bemobile are decided. The routing protocols should supportthe mobility management accordingly. Based on the im-pact of mobility on nodes in the network, the routing al-gorithms are cataloged as• Routing only when the sink is mobile• Routing when a few nodes act as mobile relays• Routing when all the nodes are mobile• Routing when a few nodes are stationary

• Biologically cooperative routing is nowadays being widelytested on MWSN and found to have remarkable adaptiv-ity, reliability and robustness in Mobile Wireless SensorNetwork. These include nature inspired techniques likeAnt Colony Optimization, Bee Colony Optimization. Cel-lular Automata, Genetic algorithms etc. to find the opti-mal path for routing.

2 Classification based on network structure

2.1 Flat based routing protocols

When a flat based routing protocol is applied, all the sensornodes in the network are treated equally. They are mainlydata centric routing protocols. The nodes collaborate to per-form the routing task by sending queries to certain regionsand collecting data from the sensors located in that region.The attribute based naming is usually used here to stipulatethe properties of data. In static sensor network many rout-ing protocols are flat-based namely, SPIN [35] and DirectedDiffusion [42] etc. They try to save energy through negoti-ation and elimination of redundant data [48]. But due to thehigh mobility of nodes, they can’t be used in MWSN as thelink failure is very high.

2.2 Opportunistic routing

It selects a set of next hop neighbors and assigns a prior-ity based on certain characteristics. It is mainly a post de-cided routing. It may exploit the broadcast nature of wirelesstransmissions and dynamically selects a next hop per packetbased on loss conditions. It reduces the number of transmis-sions needed for reliable delivery of a packet as it avoids re-transmissions as long as the packet makes progress towardsthe destination. But there is jeopardy of duplicate forward-ing by multiple candidates unaware of others’ transmissions.Hence the opportunistic routing performs well only if thehighest priority candidate that received the packet forwardsit [131].

Guangcheng and Xiaodong et al. [31] suggests an oppor-tunistic routing for mobile wireless sensor networks basedon Received Signal Strength Indicator (RSSI). The Oppor-tunistic Probability (OP) based on the RSSI of the sink’sbeacon packets and Mobility Vector (mv) is established andthe best node with the highest OP available at that instantis used to store and relay packets at each hop, after packetsare broadcasted. The sink sends out beacon packets period-ically with higher power. The other nodes establish and up-date their OP value with beacon packets’ RSSI informationusing the equation

OP′is = OPis + (

1/|RSSI|) × α + mvi × c (1)

where OP′is—Updated OP value; OPis—Current OP value;

α—constant; |RSSI|—RSSI absolute value of the beaconpacket which was just received; c—Positive constant; mvi—node i’s mobility vector.

The node that has to send the data, broadcasts pack-ets which include OPis value to the sink in its header. Thenodes in the Forward Candidate Set examine the header ofevery successfully decoded packet. If the OP value in thepacket header is lower than its OP, the node buffers and for-wards the packet. The success delivery ratio of OR-RSSIwas found to be three times better than Tiny AODV [29] asit is not based on the existing path and has more opportu-nity to deliver packets successfully. But the delay incurredby OR-RSSI is observed to be very high.

Lian et al. [68] have proposed a Receiver based Oppor-tunistic Forwarding Protocol (ROF) for MWSN that doesnot need to establish global routing between source nodeand sink but allows the neighbor nodes of the sender to con-tend for the forwarding right under certain conditions andpermits only the contention winner to forward the data. Theforwarding right is calculated based on the distance to sink,sink node’s extra coverage area and the node’s residual en-ergy. Nodes which obtain the forwarding right, forwards thedata packets while others discard the packets. Forwardingdelay and communication consumption of ROF is low whenthe number of nodes is small and is moderate when there is


Fig. 3 Kalman filter and statepredictor at mobile sink

an increase of nodes. It is less influenced by the nodes’ speedbecause ROF shortens the time of forwarding right con-tention through dual channel communication mechanism.

The node’s residual energy is considered when calculat-ing the forwarding priority in ROF and so it balances energyutilization better. This protocol brings large forwarding de-lay in low density network.

Andrea et al. [2] suggested an opportunistic routing pro-tocol based on estimation of mobility of sink. Here the datapackets are forwarded from a static information source tomobile sink through a multihop WSN. While the sourceand the sensor nodes are located at fixed positions, the mo-bile sink estimates and tracks its state using Kalman filter(Fig. 3) [2]. Mobile sink transmits a STATE UPDATE mes-sage containing the current estimate of its state to all thenodes. Nodes in the sensor set {Si} that successfully decodethis packet enter into a distributed contention procedure toselect the node that has to forward the information from thesender to the sink. Basic geographic routing is performedto deliver data. Mobility Prediction Routing (MPR) savesenergy by triggering STATE UPDATE message only whenneeded. It minimizes the traffic required to reliably track thesink by matching the frequency of STATE UPDATE trans-mission to the actual movement pattern of the sink. MPRoffers high reliability even when the network has to supporta high level of traffic and is able to deliver information muchquicker. So this protocol is very suitable for applications thathave high constraints in terms of latency. Higher value ofsink acceleration induces low packet delivery ratio.

The algorithm by Branislav et al. [6] is based on infor-mation potentials which can be adapted using a simple it-erative distributed computation. The mobility graph is usedto encode knowledge about likely mobility pattern withinthe network. It is extracted from training data and is usedto predict future relay nodes for the mobile node. Predictiverouting scheme is implemented here to optimize data deliv-ery in sensor network. The simulation results show that the

quality of service achieved is significantly higher even if thequality of prediction is unrealistically low. Its performancedegrades to the original routing algorithm in case of wrongprediction, as the predicted gradient value is discarded andthe existing gradient field is adapted to the new relay node ifthe predicted node is different from the actual node.

2.3 Best path routing

These types of routing protocols try to identify the best pathbetween source and destination using some metrics and for-ward packets to the next hop. It triggers many packet retrans-missions and path discoveries. Le et al. [64] proposed thePAGER-M algorithm which utilizes the location informationof the sensor node and the base station to assign each sensornode a cost, which is close to a sensor’s Euclidean length ofthe shortest path to the base station. A packet is forwardedto the base station using greedy forwarding whenever pos-sible. The cost for each sensor is assigned using shadowspread phase and cost spread phase. This helps to reduce thetransmission failures caused by mobility. It is observed thatPAGER-M achieves an average delivery ratio >99 % withbeacon interval 3–4 seconds. When the number of nodesare increased, the average delivery ratio of PAGER-M ishigher than AODV [93] routing protocol. This is becausethe path length of AODV is one hop more than PAGER-M.Due to the conservative choice of forwarding destinations inPAGER-M, it has an average path length. The sending nodechooses the closest and safest neighbor. Due to the long bea-con broadcast interval of PAGER-M, the routing overhead issignificantly lower.

Kihun et al. [59] have explained the Location based En-ergy Efficient Intersection Routing protocol (EELIR). Dur-ing the start of the advertisement phase, the routing rangeof a node is limited by the transmitting node. It is done byforming a segment and intersection of two circles. The seg-ment is the minimum distance from node to sink. The first


circle has the node as center with the radius r equal to thenode’s maximum transmission distance. The second centeris any point that lies in the segment. The origins of two cir-cles are discovered and intersection of the two circles formlimited routing space. The sender node ‘A’ transmits adver-tisement message to neighbor nodes. In the conditional replyphase, the neighboring nodes of ‘A’ calculate whether to re-ply or not to the advertisement message using

d1 =√

(xz − xa)2 + (yz − ya)2 (2)

d2 =√

(xz − xb)2 + (yz − yb)2 (3)

where location of source node (xa, ya), location of neighbornode (xb, yb).

If d1 < r & d2 < r : true, then the nodes will transmita reply message which contains the energy level informa-tion and minimum distance of the node to the sink. Duringthe route selection phase, node A estimates the next nodefrom the received reply messages and chooses the best routeto destination. Simulation results show that nodes transmitdata to sink without flooding. So delivery ratio is increasedwhen compared with other flooding based routing and lo-cation aided routing but the element of delay is induced.Therefore more research is needed to reduce the delay time.

Anycast based hybrid routing protocol [57] is primarilyan AODV based routing with major modifications to supportanycasting, distributed regions and the ability to accommo-date multiple path cost metrics. Route discovery is initiatedwith the expanding ring RREQ mechanism. Any node withactive sink information can generate RREP and data trans-mission is done using the best path. Link Layer Notificationis used to detect link failures. Due to the removal of certainintermediate nodes, the delivery rate of Anycast AODV isbetter than AODV [93] routing protocol. Classical AODVtries to find a route to a single sink, thus losing more data ascompared to Anycast AODV.

2.4 Hierarchical routing

Flat network architecture will not be apposite if the networksize is large as the sink gets overloaded when the number ofnodes is increased. This becomes a bottleneck, causing de-lay in communication owing to which there may be a chanceof packet loss. Single gateway architecture is not scalablefor a larger set of sensors covering a wider area of inter-est, since the sensors are typically not capable of long haulcommunication [58]. Network clustering can provide solu-tion to these problems. The nodes in the network are dy-namically organized into clusters based on certain parame-ters like distance, residual energy etc. A hierarchy in sensornodes is created when a subset of nodes have more responsi-bilities than other nodes in the network. In hierarchical rout-ing, some nodes play a passive role like listening to traffic

while others play active role like traffic relaying, neighbor-hood management etc. [51]. Instead of transmitting a datadirectly to the sink, all the nodes transmit their data to the re-spective cluster heads also called as aggregators [105]. Thecluster head performs data aggregation and removes redun-dant information. The main objective of this type of routingis to achieve energy efficiency. It also helps to reduce theorganization complexity overhead of the network which isproportional to the number of nodes in the network.

2.4.1 Flat hierarchy

In flat hierarchy, all the nodes in the network have the samepotential but they have different responsibilities. The Robustcooperative Routing Protocol (RRP) proposed by Xiaoxia etal. [117] consists of multiple nodes with the same packetattempting to deliver it to another node cooperatively. Theauthors have assumed that all nodes have the same transmis-sion range and a path has already been established between asource and a destination. This is called intended path. Nodeson intended paths are called the intended nodes. A guardnode is at least a neighboring node of two intended nodes.When an intended node fails to receive a packet from its in-tended upstream node, guard nodes who have successfullyreceived the packet by using the Wireless Broadcast Ad-vantage (WBA) will forward the packet to the downstreamnodes without waiting for the routing instruction. The packetis delivered either to the intended downstream node if reach-able or to the node that lost the packet. Guard links canimprove the reliability and reduce the end to end delay atthe cost of spending more energy in overhearing at guardnodes. Traditional alternative routing methods have to waitfor the timeout at the network layer and then find the alter-native path to replace the failed path but RRP can forwardthe packet at the MAC layer and hence reduces the transferdelay at the intermediate nodes on the path. RRP outper-forms Destination Sequence Distance Vector (DSDV) [11]and Adhoc On Demand Multipath Distance Vector Routing(AOMDV) [76] in terms of packet delivery ratio due to its re-sponsiveness to topology changes and as the robust path bearimplicit geographic information about intended path. Theycan react quickly to link failure through cooperation. End toend delay of RRP increases with link error probability be-cause of longer latency for selecting an available path andmore retransmissions. Instead of relying on retransmissionat MAC layer and searching for new paths, RRP delivers thepacket over the most reliable path located in the robust path.

M-Geocast proposed by Lynn et al. [75] is a robust andenergy efficient geometric routing protocol with multiplemobile sinks. One of the mobile sinks is chosen as the mas-ter sink and it acts as location service provider, data collectorand dissemination server. Simple geographic routing is used


Fig. 4 Three layer mobile nodearchitecture

by all nodes to send message to the master sink. Two opti-mization techniques namely, path history projection and ge-ographic void prediction are applied in this algorithm. Sim-ulation analysis has shown that M-Geocast successfully de-livers more than 99 % of all events even as sinks increase.Though the node speed is increased, delay incurred by M-Geocast remains stable because geometric routing does notincur additional overhead regardless of its speed, as long asthe location information of the destination remains the same.

2.4.2 Cluster hierarchy

In this architecture, the sensors organize themselves intoclusters and each cluster has a cluster head. The clusterheads process, aggregate and forward the information viaother cluster heads to the base station [62]. The nodes’ bat-tery life is perked up; thereby improving the network life-time but the cluster head sometimes becomes the bottle necksince all communications pass through it [51].

Zhi-Feng et al. [130] have designed three layered mobilenode architecture to organize all the sensors in the mobilewireless sensor network. In order to reduce the complex-ity of the sensor network, the data collection, routing tablemaintenance and data processing responsibilities are placedon different set of nodes. There are three types of sensornodes performing different functions and having differentcapabilities (Fig. 4). The Sensor nodes or S nodes have lim-ited storage and processing capacity. They can’t communi-cate with each other and can move randomly with wind and

water. They can send data to adjacent Fusion node (F node)within one hop. F nodes are in charge of maintaining routingtable, receiving and fusing data from S nodes and transmit-ting data to Control nodes (C nodes) by the shortest path.The C nodes act as data warehouse, gateways and connect tointernet. The shortest path routing protocol based on Floydalgorithm [99] is used by the F nodes to perform the routing.Shortest Path (SP) avoids frequent cluster head election andsaves large amount of energy.

It is seen that nodes in LEACH [36] die much quicklythan nodes in SP. Blind spots are reduced using SP. Lifetimeof nodes using SP routing protocol is increased 13 timeswhen compared with LEACH.

Lan et al. [63] have modified the LEACH routing proto-col [36] to support mobility of nodes. The sensing area isdivided into sub areas and location of cluster heads is opti-mized. Let n be the number of sensors in a given sub areawhere node i has x–y coordinate and the distance to clusterhead is xi , yi , di .

Cji is the cost function for the node i to be cluster head

node of cluster j .

Cji = v∗

i ×√(

xi − xcj

)2 − (yi − yc

j

)2 (4)

v∗i = vt if vi < vt ; else v∗

i = vi

where vi is the velocity of node i, vt is the threshold veloc-ity, xc

j and ycj are the optimal location for the cluster head

in the sub area. The node with the smallest Cji is chosen


to be the cluster head of cluster j . The calculation is doneby base station and broadcasted to all nodes. After receiv-ing data from sensor nodes, the cluster head aggregates thedata and sends the processed data back to base station. M-LEACH increases both the network life time and numberof data packets received at the base station. Data deliveryis about 8 % better than LEACH when dealing with nodemobility.

2.4.3 Zone hierarchy

It is an extension to the flat scheme. The network is dividedinto different zones. By shrinking the topology reorganiza-tion scope, the scalability can be increased. This is achievedas each zone performs distributive routing. Some protocolsare able to create non overlapping zones while others are not.It reduces the reorganization complexity inferred by node’smovement [51].

Cluster based Routing Protocol for Mobile Sensor Net-works was proposed by Liliana et al. [71]. It designs a rout-ing protocol for high density wireless sensor networks wherethe clusters are formed based on the mobility patterns ofsensors but the overhead caused by the mobility informa-tion is not very high. All the sensor nodes are assumed tobe homogeneous and they are location aware. The base sta-tion is assumed to be stationary. The sensor field is dividedinto different square zones. Each zone has a unique zoneID corresponding to the coordinates of the origin point ofthe area it covers in the plane. The zone size determines theneighboring nodes of a sensor node. Each zone has a zonehead that acts as the gateway between sensors in the clusterand the base stations or other cluster heads. The routing isdone only by the zone head. The destination node collectsseveral paths when it receives Route Request (RREQ) mes-sages from the source node in a period of time and selectsthe most stable path and sends Route Reply (RREP) mes-sages back along the path. This routing protocol can assurebetter routing stability.

Getsy et al. [27] proposed a multipath hybrid routing thatcan be designed for dynamic energy deficient mobile wire-less sensor network where energy dissipation reduction andreliable transmission of data is a must. Despite the real shapeof the sensor field, the entire area is circumscribed into a bigsquare and then divided into different zones called precincts.Each zone consists of a head node called fusion node orprecinct head which is selected based on the surplus en-ergy of the node. Every node within a precinct communi-cates with the precinct node using single hop communica-tion. When an event is detected the sensor node first com-municates with the fusion node. The fusion node checks ifthe destination is within its precinct. If so, proactively theevent is sent to the destination. This is called as intra precinctrouting. To forward the data to other precincts, inter Precinct

routing is employed which uses the Energy Aware SelectionMechanism (EA) and Maximal Nodal surplus Energy esti-mation technique. The hybrid routing concept employed inthis algorithm helps to reduce the wastage of bandwidth andcontrol overhead. It reduces the control traffic produced byperiodic flooding of routing information as seen in proactiverouting [90].

2.4.4 Grid based routing

In the proposed protocol by Jae Min Choi et al. [46], a gridis constructed in the sensor field. All the sensors store theirown location information and related grid ID through GPS.A cluster is formed on the basis of grid ID. Initially the clus-ter heads are selected randomly. The mobile sink selects fourof the nearest neighbor cluster heads at the maxim to formthe Agent Cluster head (ACH). When an event takes place ina sensor field, the source detects the event, constructs the an-nouncement packet and transmits it to the cluster heads thatthe source belongs to. The cluster head does the data merg-ing of the announcement packet and sends the data in the di-rection of the stored ACH. The ACH forwards the packet bymeans of location information to the mobile sink. The pro-posed protocol has reduced the number of control packetsbecause it has one grid construction and cluster configura-tion.

Grid Based Energy Efficient Routing (GBEER) [60] ad-dresses the problem of packet transmission from multiplesources to multiple mobile sinks in large scale sensor net-work. The sensing field is divided into grid structure (Fig. 5)and sensor nodes decide their cells based on the location in-formation and the header is selected randomly. To advertisethe data detected by a sensor node, the header sends data an-nouncement packet to other headers. In order to efficientlyadvertise and request the data, the header employs the con-cept of quorum [33, 34]. The sensor node which detectsan event becomes the source and it generates a Data An-nouncement (DA) packet and sends it to its cell header usinggreedy geographical forwarding [5]. The header aggregatesthe data and compresses these packets. Then it forwards theDA packet through the announcement quorum to which itbelongs. While propagating the DA packet through the an-nouncement quorum, each header stores the packet forward-ing information. The simulation results show that GBEERis not affected by speed of sinks. The Two Tier Data Dis-semination model (TTDD) [20] has slightly higher averagedelivery success ratio than GBEER.

2.5 Discussion

The simplicity of flat routing protocol makes it an attrac-tive choice of routing for small networks. As the networkbecomes large, route hop count increases, link breakage


Fig. 5 Data announcement anddata request in GBEER

happens frequently and end to end delay increases, it willnot be able to support high mobility as the link failure be-comes very severe due to mobility. This routing techniqueintroduces heavy overhead which consumes more networkcapacity. Occasionally routing information about remotenodes becomes inaccurate due to long transmission time re-quired by flat routing technique [70].

The Opportunistic Routing (OR) can exploit the exis-tence of many good candidates for making forward progress.It performs better when the loss probability is high andtherefore this type of routing is more suitable at high datarates. On using this routing, the success delivery ratio ishigher as compared to best path routing since OR is notbased on existing path and it has more opportunity to deliverpackets successfully. Receiver based opportunistic routingperforms well in terms of communication and storage cost.The OR routing achieves better robustness against sink mo-bility because it adapts the beaconing interval to the mobilitypattern of the sink. The OR offers high reliability even whenthe network has to support a high level traffic and is able todeliver information to multiple mobile sinks much quickerwhich makes it more suitable for applications that have highconstraints in terms of latency [2]. But when the networkdensity is low, the OR does not perform well at high trans-mission rates due to the reduction in the number of links in

the network as there will not be enough good candidates foropportunistic forwarding [131].

Simulation analysis done by Guangcheng and Xiaodong[31] prove that the number of control packets can be reducedon using best path routing. They reduce a bulk of routingoverhead if the beacon broadcast interval is long [64]. Itis also observed that when the number of source or sink isvaried, the best path routing’s latency becomes substantiallyhigher because RREQ flooding required here to find the bestpath generates significant traffic subsequently inducing ad-ditional contention. The average message latency graduallyincreases here as the node speed increases because the routeentries cached by this type of routing become no longervalid [75]. So the best path routing is not always an opti-mum solution for MWSN. On the other hand opportunisticrouting with priority based contention forwarding and dualchannel mechanism promises a better routing protocol forMWSN with higher data rates and lesser collisions as Prior-ity based routing decreases the control packet and communi-cation cost and also as the time required for forwarding rightcontention can be shortened using dual channel mechanismto OR [68].

One can achieve a comparatively stable network topol-ogy by systemizing the network into clusters or groups [83,120]. To an extent, the dynamic property of the networktopology can be limited to a cluster using hierarchy routing.


Table 1 Pros and Cons of Flatbased Routing and HierarchicalRouting in MWSN

Routing methodology Advantages Disadvantages

Flat Based Routing • Good for small networks• Opportunistic routing

can achieve betterrobustness against sinkmobility.

• Offers high reliability• OR can be used for

applications with highdelay constraints

• Best path routing helps toreduce control packets

• Does not support highmobility

• In low density networkOR does not performwell at high transmissionrates

• Latency of best pathrouting increases asnode speed increases

Hierarchical Routing • Reduces unnecessaryrouting packets

• Hybrid routing reduceswastage of bandwidth,minimizes control trafficand decreases collision

• Complexity occurs dueto selection andmaintenance of clusterheads

Only the stable and high level information are mainly prop-agated across a long distance to avoid unnecessary routingoverhead [54, 83]. A hierarchical cluster can be deployedto achieve some kind of resource reuse such as frequencyreuse and code reuse [44, 54, 120]. It also helps to achievereduction in interference by using different spreading codesacross clusters [44]. The data fusion and aggregation em-ployed in hierarchical routing helps to reduce energy con-sumption within the network. The overhead and complexityin this type of routing protocol comes from the selection andmaintenance of cluster head [70]. Hybrid routing usuallyadopted in zone routing reduces the wastage of bandwidth.Periodic flooding of routing information to the sink is re-duced here, which in turn reduces the control traffic [27, 90].The scope of future research using hierarchical routing forMWSN lies in the design of a hybrid routing protocol thatincorporates frequency reuse or code reuse modus operandialong with simple and efficient data aggregation technique.Table 1 summarizes the major advantages and disadvantagesof flat based routing and hierarchical routing in MWSN.

3 Classification based on state of information

3.1 Topology based routing

The topology refers to the network layout or network shape.It is defined as the set of communication links betweennode pairs used explicitly or implicitly by routing mecha-nism [97]. A proper topology control algorithm is needed toimprove network lifetime, reduce interference, increase net-work capacity and utilization, reduce end to end delays andincrease the robustness to frequent node failures [89]. Topol-ogy based routing involves a hop wise route creation fromsource to destination. Topology control finds its justification

either in proactive protocols by reducing the periodic up-dates of their routing tables or in broadcasting protocols byusing hierarchical routing methods [51]. It tries to minimizethe broadcasting overhead and power to reach all nodes inthe network. But the complexity of this routing occurs dueto the movement of the nodes which destabilizes the networkthereby increasing route repair and route error. The topologybased routing tries to minimize the number of links betweennodes seeing to it that there is no hindrance to the networkconnectivity and also minimizes the power needed for trans-missions. If a node creates and maintains the topology, thenthe algorithm belongs to Centralized Topology Control. Inrouting algorithms, if a subset of nodes create and maintainthe topology, then they belong to Decentralized Topologycontrol [23].

3.1.1 Proactive routing

Routing protocols that store information in a routing tableeven before it is needed are called proactive routing proto-cols or table driven routing protocols. They keep a track ofroutes for all destinations in the network. They experienceminimum initial delay as a route can be immediately ob-tained from the routing table.

The CEER [110] is a hop count based routing approach.It utilizes the color theory based dynamic localization al-gorithm. The network model consists of four anchors thatcollect and aggregate data received from cluster heads. Eachanchor floods its RGB values and average hop distance toeach sensor node periodically so that each sensor node cancalculate its hop count to the anchor and adjust its RGB val-ues based on the hop count. During data transmission, thecluster head selects the one hop neighbor that is closer to anearby anchor than itself as the next possible hop by com-paring the RGB values. This continues till the data reaches


the server via the anchor. In OR-RSSI [31] powerful beaconpackets which are periodically sent by the sink are utilizedby the other nodes to establish opportunistic probability val-ues (OP). Once the packets are broadcast by the source, thebest node with the highest OP available at that instant is usedto store and relay packets at each hop.

In [6], the authors use the information potential basedrouting [72] to deliver messages from any node in the net-work to the sink. Each node communicates to its 1-hopneighbors to compute and maintain the information poten-tial. The Gauss Seidel iterative [91] method is used in whicheach node holds its own potential values and periodically up-dates it by using the current average value of its neighbors.

3.1.2 Reactive routing

Reactive or On Demand routing protocols acquire the rout-ing information only when it is needed. They consume lessbandwidth for maintaining the route tables at each node.The latency for many applications will drastically increase.In [60], the sensor node which detects an event, generatesthe data announcement packet and sends it to the cell header.The header propagates the Data Announcement (DA) packetthrough the announcement quorum. The sink sends the DataRequest (DR). When the header with the DA-packet receivesthe DR packet, it retrieves the DA packet and checks the datageneration time to decide whether the data is stale or not. Ifthe data is valid, the header sends the DR-Pkt to the source’sheader which in turn forwards the DR-Pkt to the source. Thesource generates the data packet and forwards it to the sink.

3.1.3 Hybrid routing

Hybrid routing protocol limits the scope of proactive pro-cedure to the node’s local neighborhood, but the searchthroughout the network, although global, is done by query-ing only a subset of the network nodes. The network is di-vided into clusters or zones. Within a cluster region, the rout-ing protocol continuously evaluates the route so that whena packet needs to be forwarded, the route is already knownand can be used immediately. Between clusters or zones, thecommunication is via reactive routing [10].

In Anycast based Lightweight routing protocol [57], thesink advertises a HELLO message periodically and thenodes receiving this cache the information with a timestamp. On an event trigger, the nodes first check their cachefor any available sink. If no sink is available, route discoveryis initiated with the expanding ring RREQ mechanism. Thenodes with the active sink information generate RREP anddata transmission is started. Energy Efficient Mobile Wire-less Sensor network [27] is a multipath hybrid routing pro-tocol. During intra precinct routing, the nodes within a zonecommunicate with the fusion head periodically and their in-formation is stored in a routing table at the fusion node. For

inter precinct routing, the fusion nodes enables the selectionof best paths from the computation of maximal nodal sur-plus energy.

3.2 Location based routing

Routing protocols that deliver packets to nodes based ontheir geographic locations are the Location based routingprotocols. They assume that all nodes in their network areaware of their geographic locations. The routing destinationis specified either as a node with a given location or as ageographic region. Each packet holds a bounded amount ofadditional routing information to record where it has been inthe network [21]. There are various criteria which assure thatthe location information of sensor nodes plays a very impor-tant role for routing. For example, location awareness be-comes crucial to calculate the distance between two particu-lar nodes so that energy consumption can be estimated. Thequery can be directly diffused to a particular region whichin turn reduces the number of transmissions [58]. The sen-sor nodes can be addressed by means of their locations etc.

3.2.1 Time based

Lynn et al. [75] have designed a time based location updaterouting called M-Geocast (Fig. 6). It marks each packet withthe location information of its destination. The forwardingnode makes a locally optimal greedy choice by selecting oneof its neighbors that is closest to the destination. Each nodehas the location information of all its neighbors throughneighbor discovery process. In the neighbor discovery pro-cess, each node periodically broadcasts its location informa-tion to its neighbor using a MAC level broadcast includingits own identifier. When a neighboring stationary node hearsa beacon, it generates its beacon just once to inform its lo-cation to the newcomer. This is called adaptive beaconing.This allows each node to keep track of their neighbor’s loca-tion even during movement while suppressing the unneces-sary beacon transmissions. Location information of mastersink while on movement is propagated throughout the sen-sor field by periodic flooding.

The Elastic Routing Protocol proposed by Yu et al. [124]is a novel geographic routing scheme for mobile sinks inwireless sensor network.

The sink sends periodic beacon messages informing itslocation to its neighboring nodes and also informs its currentlocation to the node from which it received the last packetby greedy forwarding. The source obtains the location of themobile sink and forwards continuous data packets to the sinkby greedy forwarding.

3.2.2 Distance based

In [64], the authors have proposed a distance based routingprotocol called Partial partitioning Avoiding GEographic


Fig. 6 Routing through aMaster Sink in M-Geocast

Routing—Mobile (PAGER-M). It utilizes the location in-formation of sensor nodes and base station to assign eachsensor node a cost. The cost is calculated based on the sen-sor’s Euclidean distance of shortest path to the base station.Greedy forwarding [109] is used to forward a packet to thebase station by the sensor node. If there are concave nodes,the greedy forwarding fails, which can be overcome by for-warding a packet to a neighbor using the high cost to lowcost rule.

The proposed routing protocol in [46] assumes that allnodes know their own location through Global PositioningSystem (GPS). In the sensor area, grid is constructed andclusters are formed based on a grid ID. The mobile sinkselects the nearest four cluster heads and designates themas Agent Cluster Header (ACH). It computes its distance toACH using the grid ID. All the ACH’s transmit their loca-tion information to the CHs related to them. The sink trans-mits control packets which contains its location informationto the ACHs alone. When an event occurs, the source con-structs announcement packets and transmits it to the clus-ter head that the source belongs to. The CH sends data inthe direction of the stored ACH. Data transmission is donethrough the shortest distance. If a sink moves and ACH isshifted, the previous ACHs wait till it receives the locationinformation of a new ACH and then transmits the data.

3.2.3 Predictive distance based

M-LEACH [63] assumes all nodes to be location aware us-ing GPS or other location detect scheme and the sensornodes are grouped into clusters. The distance between thecluster head and node is calculated. Using the predicted dis-tance, clustering and routing is done. The cluster heads are

chosen based on their locations such that they minimize thetotal power attenuation. Each node sends its location, veloc-ity and energy level to the base station which is stationary.The BS selects the cluster head. The cluster head broadcastsAdvertisement messages using Carrier Sense multiple Ac-cess (CSMA) MAC protocol (Fig. 7) [63]. Cost of node m

to join cluster j

Cma = Wij × dmi (5)

where Wij —willingness to accept a new node into the clus-ter; dmi—distance between node m and cluster head i.

A node will choose the cluster with the smallest Cma tojoin. A node calculates it’s next allocated time slots basedon the number of nodes in the cluster.

The Receiver based Opportunistic Forwarding protocol(ROF) [68] allows the neighbor nodes to forward the data.It chooses the node with the smallest distance to sink, ex-tra coverage area and surplus residual energy to forward thedata.

Forwarding priority of a node is calculated as

Pri =(

{|d − di |/R} × {Ei/E0} × {ri/R}, d − di > 0;0, d − di ≤ 0

)

(6)

where d—distance between source and sink; di & ri—distance of node i to the sink & source respectively; Ei—residual energy of node i; E0—initial energy of node i; R—node’s communication distance.

The proposed scheme in [59] is a location based en-ergy efficient intersection routing protocol. The source nodemakes a limited routing space and transmits the advertise-ment message. The neighboring nodes calculate d1 and d2

and transmit a reply message only if d1 and d2 are less than


Fig. 7 M-LEACH

its routing space r . The source node gives priority to nodesof high energy level and short Pn (minimum distance be-tween node A and sink node S).

3.3 Discussion

The topology control decisions are usually based on thelink state information of a node and its neighbors. Inapttopologies diminish the network capacity by limiting thespatial reuse of communication channels and reduce the net-work robustness. The topology control preserves the net-work power by exploiting the spatial orientation of networknodes. It provides a good management over network assetssuch as battery power and minimizes the redundancy in net-work communication. It utilizes either the global topologyinformation or partial link state information for topologyadaptation decisions. This requires that the route informa-tion has to be refreshed and updated often.

It is observed from the simulation results [23] that cen-tralized topology control algorithms take longer time to con-verge to an optimum topology. The distributed topologycontrol algorithms just maintain the local view of topologyand link state information. They thereby reduce the bottleneck of the centralized controller and the overheads for dis-tributing the link state information to the controller [23]. Itis also analyzed that topology control routing protocols guz-zle a substantial amount of bandwidth. It is opined that theproactive routing protocols show a remarkable performanceunder high traffic load and variable traffic pattern. They per-mit the reduction of latency time at the cost of energy.

Compared to the reactive routing protocols, the proac-tive routing protocols have 50 % lesser latency per packet

as shown by the simulation study of Tai-jung et al. [110].But when the mobility of nodes is rapid, their performancedegrades due to high energy depletion leading to breakagein the network connectivity. Reactive routing protocol per-forms well under low or medium mobility of nodes and con-tinuous traffic load. If a fluctuating traffic which produceshigher route discovery constitutes the network, then the re-active routing protocols lose their advantage over proactiverouting.

A good topology control algorithm should address is-sues like topology control overheads, latency, schedulingand mobility. Most of these algorithms do not include thenode mobility for topology adaptation. Future work can befocused towards attaining the knowledge about the nodemovement pattern which would render better stability andadaptivity to the network.

Location based approach stays close to the perfect packetdelivery of 100 % for all distances [38]. These routing proto-cols are scalable and re-silent to topology changes becausethey don’t need route discovery and maintenance but peri-odic beaconing to update the location creates a lot of con-gestion. As long as the location information of the destina-tion is valid, the delay incurred by the location based routingprotocol remains stable even under high node mobility.

Table 2 summarizes the various features of Locationbased Routing Protocols in MWSN. Restricted directionalflooding to update a node’s location can be used for appli-cations that require high reliability and fast message deliv-ery. But this may not be an ideal solution if the network islarge as multiple copies of each packet are broadcast at thesame time. The inclusion of certain rules as in opportunistic


Table 2 Categorization of location based routing protocols in MWSN

Protocol Type Scalability Locationupdating

Implementationcomplexity

Localization Robustness Processingoverhead

Powerusage

M-Geocast Time based Good Periodic Moderate No High Less Low

Elasticrouting

Time based Good Periodic Less No High Less(increases ifpacketgenerationratedecreases)

Low

PAGER-M Distancebased

Good Event based Moderate No High Less Low

ACH Distancebased

Limited Event based High Yes Medium Less Fair

M-Leach PredictiveDistancebased

Limited Event based High Yes Medium High Low

ROF PredictiveDistancebased

Limited Event based Moderate No High Medium Fair

EELIR PredictiveDistancebased

Good Event based Less No High High Fair

routing protocol to forward location updates like forwardinglocation update to prioritized multiple neighbors rather thansimply forwarding it to one hop neighbor nearest to destina-tion is more advantageous.

The Quorum concept [60] can be utilized for locationupdate services. It helps to re-silence against unreachablebackbone nodes if the number of nodes is large at the inter-section of two quorums. The main disadvantage is that thecost of position update and queries becomes very high if thequorum sets are large [81]. In location based routing thatutilizes the grid structure to identify the position of node;strong route maintenance is available even if the grid headmoves because another node from the same grid replaces itvia handoff procedure.

Many of the location based routing [63] use GPS to lo-cate the node’s position. The network infrastructure setupwith GPS is very expensive. Research should devise new lo-cation update services which are cost effective to find themobile node’s position. Majority of the position based rout-ing protocol consider the nodes as neighbors if the Euclideandistance between them is equal to the transmission radius.This is inappropriate to an extent and future research cantake into consideration the irregular transmission radius ofa node due to obstacles and noise, unidirectional links anddifferent node’s transmission radii [69].

Another major issue is that most of the existing locationbased routing methods assume that the location informationof a node is known and never make a mention of how thelocations are known. It would be better if the methodolo-

gies that provide cost effective and energy efficient locationupdates are explored further.

4 Classification based on energy efficient routingtechniques

Sensor nodes are energy constrained [115] and have limitedcomputing power [116]. So it may not be able to run so-phisticated routing protocols. The life time of a sensor nodedepends mainly on the power supply from a finite batterysource. Stability of a sensor network is proportional to thelongevity of the lifetime of sensor nodes. Therefore the sen-sor nodes should be able to survive with a small finite sourceof energy [48, 49]. It is observed that wireless communica-tion consumes a large portion of the battery power [108].The transmitter dissipates energy to run the radio electron-ics and the power amplifier and the receiver dissipates en-ergy to run the radio electronics [119]. Due to the character-istics of random deployment and low cost of sensor nodes,it becomes difficult and unnecessary to recharge them oncetheir energies are depleted [61]. Nodes nearer to the sinkdeplete their energy quickly, thereby making the sink un-reachable. Processing overhead and delay at nodes increasesif the hop count increases due to the fact that the packetshave to be buffered at more nodes on the route. This even-tually leads to packet drop. Packet drop will cause retrans-mission which increases energy consumption [88, 126]. En-ergy wastage occurs when a node receives more than one


packet at the same time due to collision and subsequent re-transmission. Overhearing of packets that are destined forother nodes incurs tremendous amount of energy depletion.The control packet overhead adds to increased transmissionleading to energy expenditure. Idle listening and over emit-ting increases energy wastage [41]. All these factors neces-sitate the formation of innovative routing techniques to elim-inate energy inefficiencies and reduce energy consumption.Therefore energy consumption presents a major challengefor routing protocols and algorithms.

4.1 Energy aware routing

Minimum active communication energy can be achieved ei-ther by transmission power control or load distribution. Theenergy efficient routing protocols that try to reduce the trans-receiving energy of a node by adjusting its radio power justenough to reach the receiving node, avoid routing of packetsthrough nodes with low residual energy and optimize flood-ing of routing information over the network are called theenergy aware routing protocols.

4.1.1 Transmission power control

Kihun Kim et al. [59] have proposed an energy efficient rout-ing protocol that minimizes the transmission power by usinglocation information and the energy levels of sensor nodes.The source node knows the location of the sink. It trans-mits an advertisement message to the neighbor nodes. Theneighbor nodes calculate whether to reply or not for the re-ceived advertisement message. If it replies, then it includesits residual energy level information and distance to sink.On receiving reply from all neighboring nodes, the sourcenode gives priority to node of high energy level and shorterdistance to sink. These nodes require minimum transmissionpower to forward the data. This protocol also performs bet-ter than other flooding based routing protocol and locationaided routing as it collects information about neighbors onlywhen nodes need route discovery; thereby reducing the net-work overhead and energy consumption.

It is pragmatic from the work of Andrea et al. [2] thatthe transmission energy consumption is minimized by trig-gering STATE UPDATE messages only when needed. It ex-hibits only a slight increase in energy consumption underharsh conditions because it minimizes the required traffic toreliably track the sink by matching the frequency of STATEUPDATE transmissions to the actual movement pattern ofthe mobile station.

The simulations done by Yu et al. [124] reveal that theelastic routing exploits the overhearing feature of wirelesstransmission for location propagation. The node extracts thesink location information in an overheard packet before dis-carding it and thereby minimizes the transmission of packets

for location propagation. The results show that this extrac-tion does not consume significant energy as compared to theoverhead of other protocols for location update of a mobilesink. The link availability based QOS aware routing proto-col utilizes the energy consumption estimate to make energyefficient routing decisions [85].

The power consumption at each hop is written as

Ei(Ti) = εrBTi + εtBTi + εddni BTi + εi(1 − 2Ti)B (7)

for i = 1,2, . . . , k.εr , εi , εt and εd—coefficients of energy consumption in

receiving, idle, transmitting, dissipation respectively; n—path loss component; B—bit rate of wireless channel; Ti—traffic on link between hops i and i + 1.

If the hop distance di is chosen as a constant, then Ei

becomes a constant for a given traffic.Therefore total energy spent on the route with a fixed dis-

tance of d isk∑

i=1

Ei = kEi (8)

This means that the total energy as a routing metric isequivalent to hop count. This value is embedded in eachnode along a path into the route request. It is analyzed fromthe simulation that link breakages are reduced to about 25 %as compared to AODV for high mobility nodes and therebythey avoid energy loss due to retransmissions.

4.1.2 Load balanced routing

Xiaoxia Huang et al. [117] have proved that cooperativerouting using guard nodes consume lesser energy than noncooperative routing.

Expected energy consumption for successful deliveryfrom node i − 1 to i via cooperation,

E = {[Er × (Ne + 1)(1 − p)

]/(1 − pNe+1)}

+ {Et/

(1 − pNe+1)} (9)

Expected energy consumption for successful delivery fromnode i − 1 to i via non cooperative routing

En = Er(Ne + 1) + Et/(1 − p) (10)

Since 0 < p < 1,

Et/(1 − pNe+1) ≤ Et/(1 − p);

[Er × (Ne + 1)(1 − p)

]/(1 − pNe+1) ≤ Er(Ne + 1)

(11)

Therefore;

E ≤ En (12)

where Et —energy consumption in transmission; Er—energy consumption in reception; Ne—neighbor nodes; p—link error probability.


In situations where multiple nodes with the same packetattempt to deliver it to other nodes, cooperative robust rout-ing [99] is used The energy consumption per bit of robustrouting is lower than AOMDV [76] at relatively low mobil-ity.

The CEER algorithm [110] utilizes the cluster head toaggregate the data and adopts the dynamic path finding tofind a neighbor node with maximum energy to route the datato a nearby anchor. Energy Saving Dynamic Source Routing(ESDSR) uses local broadcast to find a path and chooses thebest route with maximum expected life but the source nodedoes not aggregate data of its neighbor node to send data tothe destination. So CEER algorithm saves more energy ascompared to ESDSR [87] as the network density increases.

The receiver based opportunistic forwarding protocol[68] considers the residual energy of nodes into the calcula-tion of forwarding priority. It tries to forward data via nodesthat have higher residual energies. This balances the networkenergy consumption and improves the forwarding reliability.The CAEE routing protocol [77] uses the in-network storagewhich utilizes the sleeping nodes located near the routingnodes to buffer the data. It is observed that the increase inmini sinks (sleeping nodes) avoids congestion and balancesthe energy consumption.

Seong-Yong Choi et al. [102] have introduced a PowerAware Heuristic (PAH) algorithm to achieve robust energyefficient dynamic routing for a mobile sink in a multihopsensor network. PAH uses the Maximum Move Hop Count(MMHC) and Average Residual Energy (ARES) around thesink. It is seen through simulation that the sink’s movementreduced workload on the nodes within the energy hole andso energy consumption was even among the nodes.

4.2 Energy conserving routing protocol

The inactivity energy consumed by a sensor node can be re-duced by switching the node’s mode of operation into sleep/power down mode or by simply turning it off when there isno data transmission or reception. The routing protocols thattry to minimize this inactivity energy due to idle listening,overhearing etc., are the power saving routing protocols. Yetanother efficient method to save energy consumption is todesign the sensor nodes or sensor network setup such thatthey are able to save energy owing to their hardware de-sign [26]. For example, by recharging node’s battery usingsolar energy.

4.2.1 Power save approach

Majid Nabi et al. [78] use the gossiping strategy [24] asa routing protocol for multi hop data transmission to thenearest sink node. An efficient MAC layer which reducesdelay and saves energy is used along with the gossiping

process. The nodes store data from the other nodes in thecache. It forwards a few data which are randomly chosenfrom the cache along with its own sensed data and propa-gates the packet to its neighbors. The Mobile Cluster MAC(MCMAC) protocol dedicates a part of the active slots to themobile clusters (MCS) and the other part to the static nodesin the network. Each slot is assigned to only one node in thecluster. If there are many clusters they share the MCS partusing hybrid contention based and schedule based channelaccess mechanism. The simulation results show that there isimprovement with respect to existing protocols in terms ofapplication level latency and reliability as the optimizationmethods use the application level QoS metrics of the net-work to decrease the power consumption overhead withoutworsening latency.

Bashir and Jalel [4] proposed an adaptive Mobility awareand Energy efficient MAC (MEMAC) protocol along withDynamic Source Routing. The mobility prediction algo-rithm utilizes the first order autoregressive model (AR-1)[127] to predict the current mobility state of a node from itsprevious mobility state. It partitions the network into clus-ters which are dynamically formed. The frames are handledduring multiple phases using hybrid scheme of TDMA andCSMA. The frame length is adjusted based on the mobilityinformation of sensor nodes and the number of nodes thathave data to send. This avoids wasting slots by excludingnodes that are expected to leave or join the cluster and thosenodes that have no data to send using TDMA schedule.

4.2.2 Energy efficient design

The M-Geocast protocol [75] is designed in such a way thatwhen there are multiple sinks, only one sink is selected asmaster sink. The master sink alone periodically updates itslocation information. The other sinks only intimate the mas-ter sink about their location. All the nodes send their data tomaster sink and it in turn forwards the data to the other sinks.So M-Geocast consumes less energy when the number ofsinks are increased as compared to AODV and Geocast. Thisis due to the fact that it requires location update from a sin-gle master sink while AODV and Geocast requires substan-tial overhead due to RREQ flooding and location broadcastfrom multiple sinks respectively.

The GBEER algorithm [60] constructs a permanent gridstructure using global location information after the sensornodes are deployed. Data requests and reply are sent to thesource and the sink respectively along the grid. This makesthe communication overhead caused by the sink’s mobilityto be limited to the grid cell. No additional energy consump-tion occurs here due to multiple events because only one gridstructure is built independent of the event. This consumesless energy than TTDD [20] because in TTDD the grid struc-ture varies in proportion to the number of sources. GBEER’s


energy consumption is not affected by the speed of the sink.In [31], the sink periodically sends a beacon packet that goesthrough longer distance with higher power. Only a few bea-con packets are sent here, which saves much energy. Thetransmission power of each node is reduced as the beaconpacket from the sink consists of the information needed forrouting.

In the Shortest path routing protocol [130], the F nodesare designed to have very powerful batteries than the Snodes. F nodes maintain the routing table, receive and fusethe data from S nodes and transmit data to C nodes via theshortest path. The S nodes which are mobile with lesser bat-tery capacity are not stressed here. Power control is used toinvert the power loss. On simulation it is observed that theaverage residual energy of S nodes remains more than 90 %of their total energy as the S nodes only collect data andtransmit them to the fusion node within one hop; therebyimproving the total network’s energy.

4.3 Discussion

The topical research activities (Table 3) on wireless sensornetwork are towards the reduction of energy consumptionby sensor nodes. Mobility based energy efficient routing isa relatively new field of study that is posing a lot of chal-lenges. The mobile node generates a large number of over-heads to broadcast its location updates to the other nodes.This increases the overall energy consumption of the net-work. Most of the energy aware routing protocol tries to re-duce the transmission power required by a node by reducingthe overhead messages. Instead of broadcasting these con-trol packets to all nodes at all time, on demand broadcastingseems ideal [20, 59]. Nevertheless, the on demand routingprotocols use control packets like RREQ, RREP etc. to dis-cover a path. For an energy aware routing, many solutionsare proposed which utilize the transmission power as a met-ric to find an energy efficient path. However, it is shown byCao et al. [9] that if energy drain rate is considered as a met-ric rather than transmission power or if min-max algorithmsare used, then better energy efficiency can be obtained. En-hanced energy efficiency can also be obtained by distribut-ing the load among different nodes [77] or by the usage ofbackup routes which leads to lesser packet loss and robust-ness against mobility and fading but there are some openissues to be considered and successfully addressed for loadbalancing to be implemented in real deployment of mobilewireless sensor network. The increase in total overhead andpacket disorder has to be minimized to reduce the complex-ity of the routing protocol.

The simulation study in [32] shows that in a system ofN nodes, cooperation with symmetric power allocation canreduce delay by a factor of 1/C, where C is the total powerbudget for the system. The node’s power can also be saved

by turning off the radio when not in use. Yet introducingthe sleep schedule to a node poses a great challenge to datarouting as synchronization between the sender and receiverbecomes a must. The sleeping node can miss the communi-cation opportunity which will result in longer delivery de-lay and lower energy efficiency [32]. Scheduled communi-cation scheme becomes a very complex task with randommobile nodes having imperfect clocks. The decentralizedfashion of nodes’ wake up and sleep time is another solutionfor achieving power hoard as it is more scalable and eas-ier to implement but the delay incurred by these power savemethods lead to heavy congestion and packet loss. A hy-brid method of applying scheduled sleep time for nodes withlow mobility and less congestion in addition to unscheduledsleep time for nodes with high mobility offers a better powersave approach.

It is analyzed that cross layer mechanism with a cooper-ation between MAC and network layer when incorporatedwith the routing helps to achieve better energy efficiency.Further research can be focused towards applying reinforce-ment learning methods to achieve good energy aware rout-ing decision and by designing the network in such a way thatload can be balanced energy efficiently with lesser overhead.The design of mobile sensor nodes can also be done in sucha way as to utilize nature’s power to recharge their batteries.

5 Classification based on mobility of nodes

Multihop paths are traversed by packets from the sensornodes to reach the sink [28]. The nodes closer to the sink areburdened with more packets to be relayed leading to earlyenergy depletion in static WSN. Adjusting protocol param-eters such as coding rates or initiating new routes along theexisting topology may not allow the network to meet the newtraffic [1]. Mobility has been found beneficial to replenishenergy resources [96] and also to reallocate resources [22].Mobile nodes can change their location on sensing that theneighboring node’s energy is depleted. This helps to avoidlink errors, contention overhead and forwarding during rout-ing. Using mobile sensor nodes, shorter hop by hop data de-livery can be achieved [123]. This helps to reduce the prob-ability of error which increases with increasing number ofhops that a data packet has to travel. The real challenge ofnetwork routing in MWSN occurs due to the fact that it isnot easy to grasp the whole network topology which keepson changing dynamically.

5.1 Routing only when the sink is mobile

Because of the mobility of the sink, the set of sensors locatednear the sink changes over time. This does not stress thesensor nodes that are closer to sink, thereby balancing the


Table 3 Comparison of energy efficient routing protocols in MWSN

Protocol Category Energy con-sumptionrate

Energy reserve Networklifetime

End to enddelay

Collisionavoidancemechanism

Deliveryratio

Data Ag-gregation

MPR TPC Very low Satisfactory Good Less CSMAbased onBEB

High No

Elasticrouting

TPC Low Satisfactory Very good Less Overhearing High No

EELIR TPC Low Supplementary Good Moderate(decreasewith time)

– Highinitially &dropseventually

No

REDM TPC Medium Supplementary Very good Less – Medium No

LABQ TPC Low Satisfactory Good Less – No

RRP LB Low(increasesas nodemobilityincreases)

Satisfactory Good Moderate ModifiedIEEE802.11 withRTS/CTS

Very high No

CEER LB Low Satisfactory Good (badif networkarea issmall)

Less CSMA/CA – Yes

ROF LB Low(increasesas nodedensityincreases)

Supplementary Good Less Dualchannelbasedforwardingrightcontentionmechanism

High No

CAEE LB Low (if no.of sinks ismore)

Supplementary Very good – In networkstorage

– Yes

MCMAC PS Medium Supplementary Good Less TDMA +CSMA

High No

MEMAC PS Low Supplementary Good Moderate &Constant

TDMA +CSMA

High No

M-Geocast

EED Low Satisfactory Good Less – High butdegradeswith nodespeed

No

GBEER EED Low Satisfactory Good – – Moderate Yes

SP EED +TPC Low Satisfactory Very good – – – Yes

TPC—Transmission Power Control; LB—Load Balanced; PS—Power Save; EED—Energy Efficient Design

energy consumption and prolonging the network life time.When the sinks are moving, they usually monitor the routingprocess with the sources. If the sinks have moved after theyasked data, they will not be able to solve the routing issuesthemselves. In these cases, the data should be transmittedvia the relay sensor nodes or other neighboring sink nodes[46, 80].

Sink can follow three types of mobility patterns inMWSN [77].

5.1.1 Random mobility

In this case the sink follows a random path in the sensor fieldand implements a pull strategy for data collection from thesensor nodes. Data can be requested from either one hop ork hop neighbors of the sink [77].

The Elastic routing [124] assumes all sensors to be staticexcept the sinks which can move freely in the network. Eachnode obtains its location information via GPS or other loca-tion services [52, 113, 125]. The mobile sink sends beacon


messages to announce its current location to neighbor sen-sor nodes. Each node searches its neighbor list for the sinkbefore forwarding the data packet. The packet is forwardeddirectly to the sink without further calculation if the sink isavailable in the neighbor’s list [106]. On movement, the sinkchecks if it has moved out of range of the last forwardingnode. In that case it informs its current location to the lasthop forwarding node by unicasting. The other nodes over-hear this transmission and reset the location information ofthe sink in the received data packet to the new location.

Andrea et al. [2] have considered a sensor network sce-nario where a set of static nodes {si} with known geographicpositions, a mobile sink that moves with time varying speedv(t) along an unknown trajectory r(t) through the sensorfield and a source S that is located at a known positionare deployed. The Mobility Prediction Routing algorithmis used here. The mobile sink transmits a STATE UPDATEmessage containing the current estimate of its state follow-ing the standard 802.11 CSMA mechanism based on BinaryExponential Backoff (BEB) [40]. The basic geographic rout-ing is applied for further communications. Ioannis Chatgi-giannakis et al. [43] have used the random walk mobilitymodel for the sink to collect data from the sensors. Theyhave demonstrated that by using sink’s random mobility, theenergy spent in relaying traffic is reduced and the networklifetime is extended.

In the Grid Based Energy Efficient Routing [60], the mo-bile sinks have a random trajectory. The sensor node gener-ates data based on an event and sends it to the header. Theheader sends the data announcement packet to other head-ers. When the mobile sink which is moving randomly needsthe data, it sends data request to the nearest header via localflooding. The data request packet is then forwarded to thesource’s header which then transmits data to the sink.

5.1.2 Predictable/fixed path mobility

The mobility trajectory of sink here is along a known fixedpath. CAEE routing protocol [77] utilizes the discrete sinkmobility along a fixed path in the sensor network. The datacollector (DC) node collects and stores the data from thesensors in the mini sink. The mobile sink periodically visitsthe minisinks and collects the data from the DC node. Themobility path of the sink is along the periphery of the sensornode. Jun et al. [56] has described a scheme that is basedon discrete mobility of the sink where the sink’s pause timeis greater than its mobility time in the sensor field. Theyhave shown that longest lifetime for the sensor network canbe achieved if the mobile trajectory of the sink is along theperiphery of the sensor field. Their results also show thata better routing strategy is to use a combination of roundroutes and short paths.

Branislav et al. [6] have used a sensor node closer to mo-bile sink as the relay node. The relay node acts as the data

sink for all the traffic to the mobile sink. Routes are setupusing the gradient of information potentials. The mobilitygraph is extracted from the radio signal strength (RSSI)traces of users in the environment. The future relay nodesare predicted with the help of mobility graph. The routing al-gorithm updates information potentials for both the currentand predicted relay node, guaranteeing that new informationpotential is ready once it is needed.

5.1.3 Controlled mobility of sink

The main challenge in controlled mobility is to design sen-sor network protocols that can exploit mobile componentseffectively and solve the navigational problems for mobileelements [1]. Stefano Basagni et al. [107] define a MixedInteger linear Programming (MILP) analytical model whosesolution determines those sink routes that maximize networklifetime. The Greedy Maximum Residual Energy (GMRE)heuristic is used to move the sink to a new location withhighest residual energy. The sink greedily selects the sitewithin dMAX surrounded by nodes that have the maximumenergy left. After spending a time t minutes at a site, the sinkevaluates whether to move to its adjacent site. Two sites areadjacent if their distance ≤ dMAX. It evaluates by calculatingthe residual energy at nodes around each of potential futuresites and compares with the residual energy at the currentsite. Sink moves to the site with the highest residual energyby querying the sentinel. The sentinel sensor node measuresthe residual energy at a site by flooding.

In the Reactive Sink Mobility algorithm [14], the sinkmoves opportunistically based on a form of feedback fromthe network. The sink is allowed to move around within anarea of the order of a square wave length. A gradient basedreactive routing protocol along with connectivity discoveryprocess is implemented. The experimental evidence demon-strates that limited sink mobility increases the fault toleranceof a sensor network and enhances the existing load balanc-ing properties. It increases load balancing by using reliabil-ity feedback. The authors [66] have proposed a routing pro-tocol that utilizes the sink’s mobility along with the existingrouting protocol IPV6 Routing Protocol for Low Power &Noisy Network (RPL). It is a distributed and weighted strat-egy that improves the network lifetime by moving the sinkstowards the leaf nodes. The sinks are moved based on threeparameters namely—energy (ei ), number of hops (hk

i ) andnumber of neighbors (bi ).

Weight Wi = βhki × ei + γ bi (13)

β and γ are coefficients of normalization. By moving thesinks according to the weighted approach, nodes playing therelay nodes change and the data packets become more reli-able.


5.2 All nodes are mobile

The variant of geometric routing protocol called M-Geocast[75] considers the case of fully mobile sensor network whereany node can move anytime. It designates one of the sinksas master sink which acts as a location service provider anddata collection and dissemination server. It utilizes simplegeographic forwarding to send messages to master sink. It ispragmatic from the simulation results that M-Geocast’s de-lay remains stable even as the node’s speed increases due tothe fact that no additional overhead is incurred due to mo-bility as long as location information of the destination re-mains valid. It is observed that M-Geocast consistently de-livers more than 96 % of all the events for most of the cases.The routing hole which occurs in most geometric routingscheme is reduced here.

Chang et al. [110] have proposed a routing algorithmwhere all the nodes are mobile. A node on arriving at a newlocation sends an anchor information request to neighbornodes. If a neighbor node has the anchor’s RGB values, ittransmits the information to the new node. The new nodecalculates and selects the smallest Dik value to the Kth an-chor. Then it updates its RGB values and transmits it to theserver. The position of the node i will then be updated inthe location database. This algorithm avoids topology-holeproblem. It efficiently chooses a better routing path with en-ergy awareness.

Zou et al. [64] have designed a routing protocol for a sen-sor network where all the sensor nodes move randomly withrandom velocities within the sensing field. A cost functionwhich has a value close to the Euclidean length of the short-est path to the base station is assigned to each sensor node.Greedy forwarding is used to forward a packet and when apacket reaches sensor nodes near local minimums, the highcost to low cost rule is applied. A sensor node is providedwith multiple forwarding candidates to reduce transmissionfailure. The beacon interval is prolonged and randomized toreduce the interference and routing overhead.

The Robust Cooperative routing protocol [117] considersa network scenario where all the nodes are mobile. It pro-vides robustness against node mobility. A node learns thepartial path information by overhearing ongoing transmis-sion. If it hears transmissions correctly from two intendednodes that belong to the same flow, indicated by source anddestination, it becomes the guard node. Through cooperativerouting a new path is set up with small overhead when manynodes move away. This protocol chooses the best path byutilizing path diversity in robust path.

5.3 Few nodes act as relay nodes

Aman et al. [1] proposed a protocol where a mobile mi-croserver which acts as relay node, moves across the net-work to route data from static sensor nodes to the sink. Ini-tially the microserver transmits the queries on behalf of the

sink. It broadcasts the queries as it moves. Each node withinits range finds the shortest distance to the microserver fromthe various queries it has received. The nodes respond tothe query that has arrived via the shortest path. This helpsto achieve sustainability of the network by reducing relayoverheads.

Shah et al. [103, 104] analyzes three tier architecture ofsensor network (Fig. 8) comprising of a top tier of WANconnected devices, a middle tier of mobile transport agentscalled as MULES and a bottom tier made up of fixed wire-less sensor nodes. The top tier acts as the access point. Themovement of the MULE is random.

The MULES upload the data from the sensor nodes andcarry it to the access point.

They have large storage capacities, renewable power andability to communicate with the sensors, other MULES andaccess points. The static sensor nodes communicate usingshort range radio. The key advantages of MULE architec-ture are robustness and scalability. They increase reliabilityby acting as the redundant access point and create a faulttolerant design.

Venkitasubramaniam et al. [111] have considered a net-work where n sensors communicate to a mobile Accesspoint (AP) over a common channel. The networking func-tions of the sensors are shifted to a set of interconnectedsuper nodes which act as the mobile access point here.They are observed to have lesser power and bandwidth con-straints. The opportunistic ALOHA that uses channel stateinformation in conjunction with orthogonal code divisionmultiple access is utilized by mobile AP.

5.4 Few nodes are stationary

Luca Borsani et al. [74] have proposed HAT mobile proto-col where the nodes are divided into two categories namelyfixed and mobile nodes. Fixed nodes create the routing treeand mobile nodes join the routing tree as leaves. This proto-col limits the impact of signaling overhead required to sup-port the handover procedure thereby reducing energy con-sumption due to network mobility. It also reduces packet er-ror rate.

The authors in [130] utilize the S nodes with limited stor-age and random mobility to collect data. The F nodes arestationary and they receive and fuse the data from S nodesand send it to the C nodes which are data ware house ofmultilayer mobile WSN.

5.5 Discussion

Table 4 compares the different routing protocols based onthe type of mobility imbibed. In a static sensor network, thenodes with the best channel to the base station have a heavierload than their peers due to relay traffic. This reduces their


Fig. 8 Mobile wireless sensornetwork with mobile relays

network lifetime leading to hot spot problem [14]. Imbibingmobility in sensor network helps to alleviate this problem atthe cost of finding an efficient route. It is demonstrated byexperiments that the traffic experienced by the most heavilyloaded node is reduced by a factor of three with an arbitrarymobile strategy [56]. The routing protocol should be pliableto the self organizing nature of the nodes. Random mobilityprovides improved data capacity [17, 30] and networkingperformance [18, 55, 103]. But the latency of data transfercannot be bounded deterministically and delivery itself canbe in jeopardy if the data is cleared from the buffer [1].

It is observed that if the mobility of some nodes can becontrolled, then these nodes can be moved to optimize en-ergy efficiency of the network [6, 16, 39, 112] or else if themobility can only be predicted but not controlled, then thismobility can be utilized to transport data [6, 47, 50]. It ispragmatic that controlled mobility of the sink improves thenetwork lifetime up to six times when compared with thestatic sink and up to two times on comparing with the ran-dom sink mobility [107]. Controlled mobility also helps toachieve power efficiency of the network [19]. Jun et al. [56]has suggested that peripheral movement of the sink helps inbetter routing of data. On using a mobile agent it is observedthat they can move closer to the node to collect data and for-ward it to the sink. This helps in conserving energy sincedata is transmitted over fewer hops thus reducing the numberof transmitting packets. By reducing the number of hops, theprobability of transmission error and also collision reduces.The sensor nodes can reduce their transmission range to thelowest value required to reach the mobile infrastructure [43].

Routing efficiency is improved when the sink velocityis increased since in unit interval of time, the mobile sinkcan meet more sensors and gather more information. Butif the mobility speed is high, the sink may not be able tocollect a potentially long packet. During sink movement itbecomes important to reduce the traffic required to reliablytrack the sink in order to reduce the energy spent [2]. If thesink moves with a relatively higher speed, the data packetsdelivered to the sink will take a flexural path as seen in Elas-tic routing [124] leading to higher packet delivery delay.

It is observed that if the location information of the nodesis known, then it will not affect the routing much even if thenode’s speed increases as not much additional overhead isincurred [75].

Future work should focus on issues like timely andenergy efficient discovery of mobile nodes, transmissionscheduling, finding optimum value of mobile node velocity,reduction of traffic to track the mobile node, data transferbetween mobile node and static sensor node.

6 Biologically cooperative routing

Nature inspired routing protocols provide remarkable adap-tation, reliability and robustness in various environments,even under hostility. The swarm intelligence concept im-bibed in the cooperative routing reduces the control traffic,the complexity of an individual node and increases the ro-bustness to changes in network topology [13]. Ant colony


Table 4 Comparison of routing protocols based on mobility

Scheme Mobilenode

Mobilitypattern

Sinkmovement

Locationtracking

Mobilenodespeed

Networkdensity

Routing

F. Yu[124]

Sink Random Randompath

GPS Variable High Elastic

AndreaMunari [2]


MobilityPredictionalgorithm

Adaptive Medium Mobilityprediction

KisukKweon[60]


Quorum Variable Low Grid based

Majid I[77]

Sink Predictable Peripheralpath

Mini sink Constant High Collisionavoidance &energyefficient

BranislavKusi [6]

Sink Predictable Predictedpath

Mobilitygraph basedon RSSI

Constant Medium Informationpotentialbased

StefanoBasagni[107]

Sink Controlled Predictedpath

GreedyMaximumResidualenergyapproach

Constant Medium Shortestpath withGMRE

DanielePuccinelli[14]


ConnectivityDiscoveryprocess

Adaptive Low Gradientbased

Leila BenSaad [66]


Weightedapproach

Variable Medium IPv6 routing

Lynn Choi[75]

All nodes Random Randompath

Master sink Variable Low Geometricrouting

Le Zou[64]


Euclideanlength

Variable Low Greedyforwarding

XiaoxiaHuang[117]


Overhearingfeature

Variable Low Cooperative

AmanKansal [1]

Relaynode

Random NA Querying Controlled Low Shortestpath

R.C. Shah[103]

Relaynode

Random NA NA Variable Low Mule

Zhi FengDuan[130]

Sensornode

Random Fixed path Fusion node Variable Medium Shortestpath

LuccaBorsani[74]

Sensornode

Random NA Handovertechniqueand routingtable data

constant Medium Tree based

optimization technique, insect population inspired wirelesssensor network, bee colony algorithm, cellular automata andgenetic algorithms can be utilized to create optimum routingalgorithm for mobile wireless sensor network. ACO algo-rithms are a kind of metaheuristic search algorithm. It usesthe swarm intelligence concept [98].

GPS/Ant like routing algorithm (GPSAL) based on GPSand mobile software agents initiates the ant’s behavior for

routing in a mobile network. The route discovery is accel-erated using mobile software agents modeled as ants whichare responsible for collecting and spreading updates aboutthe data location information of the mobile hosts. Ants com-municate with each other by using the stigmergy technique.Stigmergy means the indirect communication of concernedindividuals through changing environment. In on demandrouting algorithm (ARA) [84], the route discovery is per-


formed by flooding forward ants to the destination as well asestablishing the reverse links to the source. This is similar toDynamic Source Routing (DSR). The routes’ maintenancein ARA does not need any other extra particular messagesduring transmission. Pheromone operating rule is given by,

ϕj (t) = (1 − ρ)ϕij (t) +∑

�ϕkij (t) (14)

where ϕj (t)—pheromone trail from node i to j ; ρ—decayfactor; �ϕk

ij (t)—quantity of pheromone laid on edge (i, j)

by ant k.The integration of ant based routing and AODV routing

protocol [84] enhances the node connectivity and decreasesthe end to end delay as well as route discovery latency. Thedeployment of ants in AODV increases the node connectiv-ity and reduces the amount of route discovery.

A Tracking Range Based ant Colony routing protocol(TRAC) [121] uses the tracking range of mobile sensornodes to split the search path into two parts: indefinite pathand definite path. The message from base node is first sendprobabilistically through indefinite path until the trackingrange of mobile destination sensor node is reached. Thenthe message is sent through the definite path in the track-ing range. With the mobility of the mobile destination node,the length of the indefinite path is decreased, thus reduc-ing the entire path traveled by the artificial ants. Termite isa routing protocol that is based on the principles of swarmintelligence [82]. As packets are dispatched from a sourceto a destination, each packet follows a preference (bias) to-wards its destination while the packet will follow the up-dated preference back to its source. The bias is known as thepheromone. In termite routing, an exponential decay equa-tion for pheromone is adopted as

ϕij (t) = e−t ϕij (t) + �ϕij (t) (15)

where t is elapsed time; ϕij (t)—pheromone in networkmemory; �ϕij (t)—current update.

Authors in [98] have proposed a parallel and distributedreporting cell planning algorithm to locate the mobile termi-nal to route the incoming calls based on cellular automata.Cellular Automata (CA) represents a system of distributed,locally interacting cells that evolve according to a set ofrules. Genetic algorithm is used to discover efficient CAtransition rules. Zhanshan and Axel [129] have considered amobile wireless sensor network as analogous to a flying in-sect population in several aspects. The interaction betweenindividuals, either insects or WSN nodes can be capturedwith evolutionary game theory models in which individualsare players and reliability is the fitness of each player.

Genetic algorithms (GA) are particular class of evolu-tionary algorithms which rely on techniques inspired by evo-lutionary biology such as inheritance, mutation, selectionand recombination. These algorithms are implemented us-ing computer simulations in which a population of abstract

representations of candidate solutions is transformed into anoptimization problem [45, 53]. Ataul Bari et al. [3] have pro-posed a genetic algorithm based approach for energy effi-cient routing in two-tiered sensor networks i.e. sensor nodesand relay nodes with higher power. The relay nodes performthe routing here. An efficient solution, based on the GeneticAlgorithm for scheduling the data gathering of relay nodesis used here in conjunction with the routing. Experimentalresults clearly demonstrate that this approach significantlyincreases the lifetime of the network (by nearly 200 % onaverage), compared to traditional routing schemes that donot consider energy dissipation of the nodes.

6.1 Discussion

“The natural systems through evolution have producedhighly complex systems showing globally coordinated in-formation processing with no global coordination” [98]. Theswarm intelligence concept used in biologically cooperativerouting for mobile wireless sensor network helps to reducecontrol traffic by collecting network information from over-heard packets. It helps to reduce the complexity of an indi-vidual node by modeling all routing functions mathemati-cally. It increases robustness of the system with respect tochanges in network topology due to the emergent routingbehavior from node interactions. By applying ant colonyoptimization to routing, fair energy usage can be accentu-ated by adding the battery information of the node in theant packets. The disadvantage of ACO is that it does notmaintain the local connectivity because of which the sourcekeeps sensing packets even during link failure leading to alarge number of unsuccessful transmissions. Another majordisadvantage of ACO is that if there are insufficient numbersof routes, then the nodes have to buffer packets till an ant ar-rives. It is observed that ACO in combination with AODVincreases node connectivity as the probability of receivingreplies quickly from neighboring nodes is high here. Con-gestion problem is alleviated quite well and a better networkload balance is achieved here as compared to ACO. End toend delay of packet transmission is mitigated. Connectionestablishment time due to processing delay for route discov-ery is minimized using ACO with AODV.

In Termite routing, nodes maintain knowledge of most ofthe destinations. So minimum number of control packets areneeded. Routes are repaired automatically. It is not energyefficient or bandwidth efficient as large data packets explorethe network. The application of evolutionary game theory torouting can make the network more reliable and fault toler-ant [129]. The cellular automata systems can translate theglobal criteria of reporting cells’ problem to local transitionrules of cellular unit. Using genetic algorithm it is possibleto construct heterogeneous networks by allowing the nodesto transmit at different power levels [45]. Recent research


activities for GA based routing in static sensor network haveshown a very significant outcome. These works can be fur-ther extended to solve the combined problem of mobilityand routing.

7 Conclusion

Routing in a mobile wireless sensor network is one of thedemanding issues of the recent years. Topical research pro-gresses have made a promising evolution in MWSN routing.In this paper, we have presented a broad survey of the up todate routing protocols proposed in the literature for MWSN.We have cataloged them based on their network structure,state of information, mobility and energy efficiency. Theserouting protocols have their own pros and cons and most ofthem are application oriented. Based on our observation, wecan suggest that the flat based routing is a suitable choicefor simple network but for a large network the hierarchi-cal routing seems apt. We have also observed that the hybridrouting protocols that exploit the proactive and reactive rout-ing techniques will provide efficient routing solutions in adynamic topology network if the issues related to mobility,processing overheads and end to end delay are eased. Thelocation based routing protocols are lucrative but they mustuse cost effective methods to perform location updating. Wehave also analyzed that the cross layer combination of en-ergy aware routing technique with the application of energyconserving method will tend to provide a proficient solutionfor energy efficient routing in MWSN. The mobility of sen-sor nodes helps to reduce issues like routing holes, hot spotproblem and energy-hole problem etc. But there is still roomfor developing the modus operandi for efficient discovery ofnodes and proper scheduling of transmission. The inclusionof QoS parameters as routing metrics will help to improvisethe efficacy of the routing algorithm. We perceive that thebiological cooperative routing techniques or nature inspiredrouting techniques are emerging as a new area of researchwhich promises for a more adaptive and robust routing innear future. Not much of work has been done to ensure se-cure routing in mobile wireless sensor network. Security is-sues in routing are another class of approach that will bedrawing the attention of researchers in the coming years.

Acknowledgements This paper is supported by the Junior ResearchFellowship for Engineering and Technology under University GrantsCommission, India. We would like to thank the anonymous reviewersfor their valuable suggestions towards the improvisation of this paper.

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Getsy S Sara received her B.E. de-gree with distinction in Electronics& Communication from BharathiarUniversity, India in 2004 and M.Edegree with distinction in DigitalCommunication and Network Engi-neering from Anna University, In-dia in 2006. Currently she is pur-suing her Ph.D. degree in the Fac-ulty of Information & Communica-tion Engineering, Anna UniversityChennai, India. Her research inter-ests include wireless ad hoc net-working, sensor networks, energyefficient routing protocols and com-

munication systems. She is an IEEE student member.

D. Sridharan received his B. Tech.degree and M.E. degree in Electron-ics Engineering from Madras Insti-tute of Technology, Anna Univer-sity in the years 1991 and 1993 re-spectively. He got his Ph.D. degreein the Faculty of Information andCommunication Engineering, AnnaUniversity in 2005. He is currentlyworking as Associate Professor inthe Department of Electronics andCommunication Engineering, CEGCampus, Anna University, Chennai,India. He was awarded the YoungScientist Research Fellowship by

SERC of Department of Science and Technology, Government of In-dia. His present research interests include Internet Technology, Net-work Security, Distributed Computing and Wireless Sensor Networks.He is a life member of Institution of Electronics and Telecommunica-tion Engineers (IETE), Indian Society for Technical Education (ISTE)and Computer Society of India (CSI).

Documents

Routing in mobile wireless sensor network: a survey