A Heuristic Routing Protocol for Wireless Sensor

A Heuristic Routing Protocol for Wireless SensorNetworks in Home Automation

Xiao Hui LiEngineering Research Center of

Metallurgical Automationand Measurement Technology,

Ministry of Education,Wuhan University

of Science and Technology,Wuhan, Hubei, 430081

Email: [email protected]

Seung Ho HongDepartment of Electronics,

Information and System Engineering,Ubiquitous Sensor Network Research Center

Hanyang University, 1271 Sa-3-dong,Ansan, 426-791, Korea


Kang Ling FangEngineering Research Center of

Metallurgical Automationand Measurement Technology,

Ministry of Education,Wuhan University

of Science and Technology,Wuhan, Hubei, 430081


Abstract—Although numerous routing protocols have beendesigned for wireless sensor networks, not all are suitable forwireless sensor networks in home automation (WSNHA). In thispaper, we evaluate some popular wireless sensor network routingprotocols for their suitability to WSNHA. We also propose agreedy-algorithm heuristic routing (GAHR) protocol and an A*algorithm for route finding. We compared the performance ofthis heuristic routing with the ad-hoc on-demand distance-vectorprotocol using a simulation. The simulation results showed thatthis routing protocol dramatically reduced the routing overheadand the average packet delay without impacting reliability,and demonstrated that WSNHA-GAHR adapts automatically tochanges in the network topology.

I. INTRODUCTION

Home automation (HA) systems are increasingly used toincrease the comfort level of residents and provide distributedcontrol of heating, ventilation, air conditioning (HVAC), andlighting to help reduce costs and save energy.

The wireless sensor network (WSN) is a promising networktechnology that generally consists of a number of small sensornodes with sensing, data processing, and wireless communi-cations capabilities[1]. The advance of technology means thatsmart sensor nodes and actuators may be hidden in appliancessuch as vacuum cleaners, microwave ovens, refrigerators, andhome entertainment devices. These sensor nodes inside devicesin the home can interact with each other and with externalnetworks via the Internet or satellite. They allow residents tomanage devices in their homes more easily, both locally andremotely. WSNs are usually used in HA systems because oftheir characteristics of self-organization, high sensing fidelity,low cost, and rapid deployment. We refer to the combinationof HA and WSNs as WSNHA.

Using WSNs in HA systems provides several advantagesthat cannot be achieved using other networks[2].

• Reduced installation costsThis benefit is the most attractive. Installation costs are dra-matically reduced because there is no cost for cabling orinstallation.

• Easy deployment

The position of the sensor nodes need not be engineered or pre-determined. They can be mounted almost anywhere, especiallyin dangerous places where cabling may not be feasible.

• Good scalability

Some extension of the network may be necessary to cater tonew or changed requirements. WSNs are self-organized andcan be changed or extended without additional cabling.

• Easy integration with mobile user interface device

Associating mobile user interface devices such as personaldigital assistants (PDAs) and smart phones with base stationsbecomes possible everywhere and at any time with wirelessnetworks. Typical examples include an engineer who connectsto the network to perform a particular management task.

Although these advantages are very attractive, WSNspresent certain challenges related to practical design andimplementation in HA systems. Many routing, power man-agement, and data dissemination protocols have been speciallydesigned for WSNs where energy awareness is a central designissue. The focus, however, has been on routing protocolstailored to the application and network architecture. It is thusnecessary for routing designers to meet the requirements ofWSNHA systems

The remainder of this paper is organized as follows. Section2 analyzes the characteristics of WSNHA and the requirementsfor the WSNHA-oriented routing protocol. Section 3 comparesthree existing categories of WSN routing protocols. Section 4describes the design of the WSNHA greedy-algorithm heuris-tic routing (WSNHA-GAHR) protocol. Section 5 describesthe evaluation of the routing protocol performance throughsimulation, and Section 6 presents the conclusions.

II. WSNHA

A. WSNHA characteristics

HA is a mature technology, and many articles describe itscharacteristics in detail[3], [4]. In general, WSNHA devicescan be divided into three categories: sensors, actuators, andmobile user interface devices.

978-1-4244-3693-4/09/$25.00 ©2009 IEEE

Sensors distributed throughout a house collect physicaldata such as temperature, humidity, motion, and light level.Energy efficiency is a prime consideration in the design ofWSNHA sensors because they are usually battery powered.Although static and mobile sensor nodes can coexist in thesame house, their ratio depends on the requirements of thespecific application. Most WSNHA sensors are static.

Actuators are attached to the objects they control, such aslamps, refrigerators, and air-conditioners, to receive commandsand carry out assigned tasks. HA control functions are usuallyembedded in actuators. Actuator nodes generally have fixedlocations and are powered by house current.

Mobile user interface devices such as PDAs and smartphones are able to access the network for control or monitoringpurposes. These handheld devices are usually highly mobileand only communicate sporadically.

The classification of nodes is an important characteristic ofWSNHA, as it helps define the network structure. WSNHAcoverage is generally small, and the distribution of sensornodes depends on the house layout and the application.

B. WSNHA-oriented routing protocol requirements

It is extremely important to understand the requirementsbefore starting the design of an appropriate routing protocolfor WSNHA. The most important requirements of WSNHAare as follows.

• High energy efficiencySome battery-powered sensor nodes do not easily accommo-date battery recharging or frequent battery replacement. Thisrequires that routing protocols consider energy efficiency.

• Low storage and simple algorithmDue to their low cost, sensor nodes usually have limited mem-ory, which requires routing protocols to have low informationstorage requirements.

• High dependency on sensor node distributionNodes are distributed at random in HA. This randomness mayaffect the performance of the routing algorithms.

• Self-adaptation to network topology changesWhen a sensor node fails, it leaves the network and causes achange in the network topology. Adding or moving appliancesor decorative walls in the home may cause blind regions inthe network. Sensor nodes in blind regions cannot maintainconnectivity with the network, and this also leads to a changein the network topology. The routing protocol should thereforeadapt easily and automatically to changes in network topology.

• Relaxed requirement for real-time behaviorWSNHA systems do not have the same strict requirements forreal-time data transmission as industrial networks.

• Relaxed requirement for scalabilityWSNHA coverage is generally small and thus does not requirehigh scalability.

• Relaxed requirements for coping with mobilityAside from some low-velocity mobile sensor nodes and userinterface devices, most sensor nodes in WSNHA are in fixedlocations. Highly mobile nodes are not a concern.

III. COMPARISONS OF ROUTING PROTOCOLS FOR WSNS

In this section, we evaluate the popular WSN routing proto-cols for WSNHA suitability. This information is drawn fromthe original papers for the routing protocols and others[5],[6] that present comparisons of some protocols. Because allrouting protocols for WSNs focus on high energy efficiency,only, the other requirements of HA are examined. In general,WSN routing protocols can be classified as flat-based routing,hierarchical-based routing, or location-based routing depend-ing on the network structure.

A. Flat-based routing

Because flat-based routing is focused on how to routemessages for a large number of sensor nodes, it uses floodingas its main routing technology. Typical common flat-basedrouting protocols include directed diffusion[7], SPIN[8], rumorrouting[9], and GBR[10].

• Advantages

Flat-based routing has low storage requirements and a simplealgorithm. Its performance is not dependent on sensor nodedistribution, and it provides good scalability.

• Disadvantages

First, flat-based routing is suited to large networks. BecauseWSNHA involves small networks, the complicated attribute-based naming mechanism is not necessary. Second, floodingcreates much delay, which does not work well with WSNHA.Third, flooding in WSNHA causes needless energy consump-tion, as data are forwarded to every sensor.

B. Cluster-based routing

Cluster-based routing is an efficient way to lower energyconsumption within a cluster. The number of messages trans-mitted to the base station is reduced by data aggregationand fusion. Cluster-based routing is mainly two-layer routingwhere one layer is used to select cluster heads and the otherlayer is used for routing. High-energy nodes in cluster-basedrouting can be used to process and send information, whereaslow energy nodes can be used to perform sensing in closeproximity to the target. Typical common cluster-based routingprotocols include LEACH[11], PEGASIS[12], TEEN[13], andTTDD[14].

• Advantages

Cluster-based routing has a good energy efficiency and net-work lifetime extension.

• Disadvantages

First, the cluster formation algorithm is not particularly easy toimplement in cluster-based routing. Second, the clustering al-gorithm is based on a distributed algorithm, which incurs extraoverhead. WSNHA does not require the level of complexityof the cluster formation algorithm.

C. Location-based routing

In location-based routing, sensor nodes are addressed bytheir locations. Location-based routing makes full use oflocation information to reduce energy consumption. Typicalcommon location-based routing protocols include GAF[15]and GEAR[16].

• AdvantagesLocation-based routing has good energy efficiency due to thereduction in flooding, and it can be easily implemented withthe help of location information.

• DisadvantagesFirst, the method of obtaining the location information de-pends on the application. Second, its performance depends onthe sensor node distribution. Using location-based routing inWSNHA requires addressing these two drawbacks.

From the factors described, we conclude that location-based routing protocols are most suitable for WSNHA for thefollowing reasons:

1) The routing algorithm in location-based routing is lesscomplicated and more easily implemented than that incluster-based routing protocols.

2) The energy efficiency is better than that of flat-basedrouting protocols because of the reduction in flooding.

3) Obtaining location information can be easily imple-mented in WSNHA. We can make full use of locationinformation to find routes and reduce energy consump-tion.

WSNHA systems are generally small, and most of the nodesare static. Sensor nodes in HA are easily addressed by location.The static node location information can be determined duringnetwork deployment and stored in appropriate node locationtables. When a new static node joins the network, it broadcastsits location information to the other static nodes, which updatetheir location tables accordingly. Static sensors and actuatorscan maintain the location information for all static nodes usingthis limited flooding technology.

Even though mobile nodes do not know their own locations,they can send a Hello message periodically to detect newstatic neighbors and maintain a table of current neighbors.The mobile nodes tell all actuators the location informationof their static neighbors. Although the location informationfor a mobile node cannot be obtained directly, we can querythe actuators to obtain the location information for the mobilenode’s static neighbors. Thus, the actuators maintain not onlythe location information for all static nodes, but also thelocation information for the neighbors of the mobile nodes.Note that the overhead for location information is not verybig because the WSNHA is small.

IV. WSNHA-GAHR

Because we have the location information of all the nodes,the main problem to be solved is how to use that locationinformation to find a route. WSNHA-GAHR is a heuristicrouting method that is improved by a greedy algorithm andA* heuristic path finding. It can find an optimal route from

the source to the destination while simultaneously recordingchanges in the network topology. The advantages of WSNHA-GAHR are that it automatically adapts well to changes innetwork topology and can adjust automatically regardless ofthe density of node distribution.

A. Greedy algorithm

A greedy algorithm is any algorithm that follows theproblem-solving metaheuristic of making the locally optimumchoice at each stage with the hope of finding the globaloptimum[17]. That is, it always makes the choice that looksbest at the moment. Greedy algorithms are simple and straight-forward. They are shortsighted in their approach in the sensethat they make decisions on the basis of the information athand without worrying about the effect these decisions mayhave in the future. They are easy to design and implement andare generally quite time efficient.

If we use a greedy algorithm to find the route from thesource to the destination, we must choose a selection functionthat tells which of the candidate selections is the most promis-ing. That is, we must know what the optimum next hop is ateach node in route finding. Obviously, each node would like toforward the packet to the neighbor closest to the destination.Suppose that node X has N one-hop neighbors. The selectionfunction is

f = min(d1, d2, ..., dN )

, where di is the distance between the ith neighbor of X andthe destination, for i ∈ [1, N ].

Before describing in detail how to forward the packet to thedestination, we make the following assumptions:

1) Each route query packet contains the destination address.2) Each node knows its own location and the locations of its

neighbors by means of a simple neighbor Hello protocol.Suppose that NTable is the neighbor table of X and thedestination is D. Each node can then use Algorithm 1 toforward the packet.

Algorithm 1: Greedy route finding

Input: NTable, DOutput: the next hop (nexthop)/* dX→D is the distance between X and D *//* initialize the minimum distance and the

next hop */dmin = dX→D;nexthop = X;foreach neighbor ni in NTable do

/* di is the distance between ni and D */computes di ;if di < dmin then

dmin = di;nexthop = ni;

endend

After analyzing Algorithm 1, we can see that if all di aregreater than the original dmin, i.e., if all the neighbors arefarther away, then the next hop cannot be determined correctly

and the greedy algorithm will not work. This special case isdescribed in Fig.1.

X

Y

r

r

Node in WSNHA

D

S

n1

n2

n3

Fig. 1. A special case

If node S finds a route to node D, S chooses the node n1,which is the closest neighbor to D, as its next hop accordingto Algorithm 1. Similarly, n1 chooses n2 as its next hop.However, when n2 chooses the next hop, it will choose itselfas its next hop because n2 only has one one-hop neighbor anddn1D is greater than dn2D. This would obviously put n2 intoan infinite loop. We can use a heuristic algorithm called A*to chose the right route in this special case.

B. A* route finding

A* [18], [19], [20] is a best-first, graph-search algorithmthat finds the least-cost path from a given initial node to onedestination node. It uses a distance-plus-cost heuristic function(usually denoted h(x)) to determine the order in which thesearch visits the nodes in the graph. The distance-plus-costheuristic is a sum of two functions: the path-cost function(usually denoted d(x), which may or may not be a heuristic)and an admissible heuristic estimate of the distance to the goal(usually denoted c(x)). The path-cost function d(x) is the costfrom the starting node to the current node.

Because the c(x) part of the h(x) function must be anadmissible heuristic, it must not overestimate the distance tothe goal. Thus, for an application like routing, c(x) mightrepresent the straight-line distance to the goal as that isphysically the smallest possible distance between any twonodes. A* incrementally searches all routes leading from thestarting node until it finds the shortest path to the goal. Likeall informed search algorithms, it first searches the routes thatappear to be most likely to lead toward the goal. What setsA* apart from the greedy algorithm is that it also takes thedistance already traveled into account; the d(x) part of theheuristic is the cost from the start, and not simply the localcost from the previously expanded node.

We use a two-dimensional region and draw a grid structurefor the network topology in Fig.2 to describe how to apply theA* algorithm to route finding. The side length of the grid is 1.The r is the radio signal distance with length

√2. A circle with

S as its center and r as its radius is the transmission range ofthe radio signal region; the transmission range of every node is

assumed to be the same. We can assign each node a heuristicvalue h, which represents the heuristic estimate cost from thisnode to the destination. We refer to h(X) as the heuristic valueof X.

S

D

n1

n2

n3

n4 n5

n6 n7

1 2r=

Fig. 2. A* route finding

In Fig.2, node S finds a route to node D. Initially from allof its neighbors (n1, n2, and n3), S chooses the one that hasthe smallest heuristic value as its next hop.

h(n1) =√

37, h(n2) = 4, h(n3) =√

17,where n2 is the neighbor closest to D. So the next hop of S

is n2. After S picks next hop n2, it sets its own heuristic valueh(S) to dSn2 + h(n2) and broadcasts this new heuristic valueto its neighbors. The dSn2 is the distance between S and n2,which corresponds to the path-cost function in the distance-plus-cost heuristic function. Therefore, h(S) = 1 + 4 = 5.

After S forwards a packet to n2, n2 finds its next hop fromits neighbors in a similar manner. It has two neighbors, n3

and S.h(n3) =

√17, h(S) = 5.

The next hop of n2 is n3, so n2 sets its own heuristic valueh(n2) to dn2n3 +h(n3) and broadcasts this new heuristic valueto its neighbors. Therefore, h(n2) = 1 +

√17.

If S later receives a packet destined to the same D, theheuristic values of its neighbors become:

h(n1) =√

37, h(n2) = 1 +√

17, h(n3) =√

17Thus, this time S will forward the packet directly to n3

instead of to n2. The first packet was transmitted along thedotted route, but after h(n2) changed leading to the next hopchange, the second packet was transmitted along the solidroute.

Using A*, this heuristic value h, which represents theestimated cost from the local node to the destination, willchange according to the node distribution and the change ofnetwork topology.

The detailed algorithm is described in Algorithm 2. Supposethat every node maintains three tables:

1) Ntable records the neighbors’coordinates of the localnode.

2) Htable records the heuristic value between the lo-cal node and the destination node. It has two fields,destination and hvalue, which denotes the heuristicvalue.

3) Ltable records a learned cost between the localnodes’neighbors and the destination node. It has threefields, destination, neighbor and cost, which denotesthe learned cost.

Each node can use Algorithm 2 to forward the packet.

Algorithm 2: A* route finding

Input: NTable, Htable, Ltable, DOutput: the next hop (nexthop)/* dn1→D is the distance between n1 and D,

and n1 denotes the first neighbor of thelocal node X */

/* initialize the minimum distance and thenext hop */

costmin = dn1→D;nexthop = n1;foreach neighbor ni in NTable do

/* find the learned cost from ni to D inLtable */

/* not find for initialization */flag = false;foreach record RLj in Ltable do

if RLj .neighbor == ni andRLj .destination == D then

/* find the learned cost to D */flag = true;cost = RLj .cost;

endend/* if not find, add a new record in Ltable

*/if flag == false then

add a new record RLnew;RLnew.destination = D;RLnew.cost = dni→D;cost = RLnew.cost;

endif cost < costmin then

costmin = cost;nexthop = ni;

endend/* update Htable *//* not find for initialization */flag = false;hvalue = dX→nexthop + costmin

foreach record RHi in Htable doif RHi.destination == D then

/* find the heuristic value to D */flag = true;RHi.hvalue = hvalue;break;

endend/* if not find, add a new record in Htable

*/if flag == false then

add a new record RHnew;RHnew.destination = D;RHnew.hvalue = hvalue;

endsend the broadcast of hvalue for D to its neighbors;

The greedy algorithm can find the optimal route and A* canadjust the route according to the change of network topology.

The main idea of WSNHA-GAHR is to combine the greedyalgorithm with the A* algorithm. If obstacles such as walls arepresent in HA, WSNHA-GAHR can adjust automatically withchanges in the heuristic value. When some nodes fail, the nodedistribution becomes sparse, and again, WSNHA-GAHR canadjust automatically by changing the heuristic value. WSNHA-GAHR thus exhibits a good ability to adapt automatically tochanges in the network topology.

V. EVALUATING THE PERFORMANCE OF WSNHA-GAHRBY SIMULATION

We used NS2 to compare the performance of GAHR and ad-hoc on-demand distance-vector (AODV) routing[21], which isa widely used on-demand routing protocol. The network layerof ZigBee[22] supports AODVjr routing, a variation of AODV.AODV uses flooding for route discovery, a bandwidth-costlyapproach. We developed the source code for WSNHA-GAHRand used IEEE 802.15.4 for the physical and MAC layers inour simulation. We assumed a signal propagation radius of 10m according to the two-ray ground reflection model.

A. Performance measurement

We choose three metrics for analyzing the performance ofWSNHA-GAHR and comparing it to AODV.

1) Packet delivery ratioThis is the ratio of the number of data packets receivedto the number originally sent. This metric indicates thereliability of the routing protocol.

2) Routing overheadThis is the number of routing command packets. Thismetric reflects how much bandwidth is occupied by therouting command packets. It also indirectly reflects theenergy consumption of the routing protocol. The typesof command packets depend on the routing protocol.

3) Average packet delayThis is the average one-way latency for successfullytransmitting a packet from the source to the destination.It reflects the reaction time of the routing protocol.

B. Simulation scenarios

We generated three different scenarios to compare theperformance of WSNHA-GAHR with that of AODV usingsensor fields composed of static sensors and mobile nodes.The movement of mobile nodes can change the topology ofthe networks. The velocity of the mobile nodes was assumedto be 0.5 m/s, and the source node generated one data packetevery 5 s. Each simulation lasted 1000 s. The three differentscenarios are described below.

In Scenario 1, the sensor field was 20 × 21m. There were30 nodes, including one mobile node. We chose three source-destination pairs randomly from the deployed sensors.

In Scenario 2, the sensor field was 30×30m. There were 60nodes, including three mobile nodes. We chose nine source-destination pairs at random from the sensor field.

In Scenario 3, the sensor field was 40 × 40m. There were90 nodes, including five mobile nodes. We choose 11 source-destination pairs at random from the sensor field.

The data packets in each scenario could be sent from a staticsource to a static destination, from multiple static sources toa single static destination, from a mobile source to a mobiledestination, from a mobile source to a static destination, orfrom a static source to a mobile destination.

C. Simulation results and analysis

In Scenario 1, the three source nodes sent a total of 560data packets. In AODV, the destinations successfully received375 of these, for a packet delivery ratio of 66.96%. InWSNHA-GAHR, the destinations successfully received 531data packets, for a packet delivery ratio of 94.82%. In Scenario2, the nine source nodes sent a total of 1718 data packets. InAODV, the destinations successfully received 773, for a packetdelivery ratio of 44.99%, whereas in WSNHA-GAHR, thedestinations successfully received 1544, for a packet deliveryratio of 89.87%. In Scenario 3, the 11 source nodes sent a totalof 2097 data packets. In AODV, the destinations successfullyreceived 892, for a packet delivery ratio of 42.54%, whereas inWSNHA-GAHR, the destinations successfully received 1796,for a packet delivery ratio of 85.65%. Fig.3 shows a compar-ison of the packet delivery success of AODV and WSNHA-GAHR for the different scenarios. The packet delivery ratioof WSNHA-GAHR was higher than that of AODV for allscenarios.

30 60 900

500

1000

1500

2000

2500

Network size / Amount of nodes

Am

ount of packets

Amount of packets delivered

Amount of packets received by using WSNHA-GAHR

Amount of packets received by using AODV

Fig. 3. Comparison of packets received by using WSNHA-GAHR and AODV

Because no RTS/CTS mechanism exists in 802.15.4 MAC,high traffic will inevitably lead to high packet collision rateand low packet delivery ratio. The number of mobile nodesincreased as the network size increased, and this caused morefrequent changes in the network topology. AODV requiredfrequent flooding of command packets to detect these changesin the network topology, and this led to a high packet collisionrate in the MAC layer and low packet delivery ratio in theapplication layer. Under the same conditions, WSNHA-GAHRonly broadcast one-hop heuristic values to notify the neighbornodes when the network topology changed. The commandpacket traffic was smaller than that of AODV. The WSNHA-GAHR packet collision rate was not as high as that of AODV,

so the WSNHA-GAHR packet delivery ratio was higher thanthat of AODV for all scenarios.

Fig.4 shows the number of routing packets generated byAODV and WSNHA-GAHR for different network sizes. InScenario 1, the routing overhead of WSNHA-GAHR wasslightly higher than that of AODV. This was apparently dueto the WSNHA-GAHR one-hop hello packets. The AODVrouting overhead in Scenario 1 was 15.78% less than that ofWSNHA-GAHR. In Scenarios 2 and 3, however, the routingoverhead of WSNHA-GAHR was less than that of AODV.Statistically, WSNHA-GAHR reduced the routing overhead by59.69% and 64.14% compared to that of AODV in Scenarios2 and Scenario 3, respectively.

30 60 900.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

2.3x 10

4


Am

ount of co

mm

and p

ack

ets

WSNHA-GAHR

data2

Fig. 4. Comparison of routing overhead by using WSNHA-GAHR andAODV

In AODV, the main command packets are the route requestbroadcast packets. The network traffic increased and thenetwork topology changed frequently in Scenarios 2 and 3because the numbers of source-destination pairs and mobilenodes were higher. The change in network topology led to anincrease in the number of route request broadcast packets usedto update the route. However, because AODV uses floodingto broadcast the route request packets, the number of routerequest broadcast packets in AODV increased markedly. InWSNHA-GAHR, the main command packets included one-hop hello packets and one-hop heuristic value broadcast pack-ets. In the initial stage of WSNHA-GAHR, one-hop hellopackets were transmitted to create the neighbor table for everynode. At steady state, the neighbor tables of these static nodesdid not change, and the number of hello packets decreasedbecause most of nodes in the network were static. Only veryfew mobile nodes required sending one-hop hello packets todetect their new neighbors periodically. Similarly, the one-hopheuristic value broadcast packets were sent only when thenetwork topology changed. The WSNHA-GAHR commandpackets were transmitted on demand, and they were all one-hop packets instead of being flooded to the whole network.The increase of network traffic did thus not lead to a markedincrease of command packets.

Fig.5 shows the average packet delay for different network

sizes. The average packet delay of WSNHA-GAHR wassmaller than that of AODV for all scenarios. Statistically,WSNHA-GAHR was able to reduce the delay by 70.43%,53.79%, and 10.11% compared to that of AODV in Scenarios1, 2, and 3, respectively.

30 60 900

0.03

0.06

0.09

0.12

0.15

0.18

0.21

0.24

0.27

0.3


Ave

rage p

ack

et dela

y (s

)

30 60 90

0

10

20

30

40

50

60

70

80

90

100


Pack

et deliv

ery

ratio

(%

)

Packet delivery ratio of WSNHA-GAHR

Packet delivery ratio of AODV

Average packet delay of AODV

Average packet delay of WSNHA-GAHR

Fig. 5. Comparison of average packet delay by using WSNHA-GAHR andAODV

In AODV, if no valid path for the destination is available inthe routing table of the source, the source starts route discoveryto find one before sending the data packets to the destination.The source broadcasts route request packets to initiate routediscovery for the destination. The source can send data packetsonly after it finds a valid path to the destination, so the delayof a packet from the source to the destination includes thedelay to find the route plus the packet transmission delay.WSNHA-GAHR transmits the data packets as a best effort.It does not create a route before data transmission and onlymakes full use of location information to transmit the datapackets directly while it is finding the optimal route. In otherwords, creating the route and transmitting the data packets takeplace in parallel. In AODV, creating the route and transmittingthe data packets do not take place in parallel. This is theunderlying reason for the smaller average packet delay ofWSNHA-GAHR than of AODV in every scenario.

We would expect that the average packet delay wouldincrease as the network size increases because the trafficload will be greater. Fig.5 shows that the average packetdelay of AODV decreased as the network size increased. Thisoccurred because we did not count the delay of the packetsthat were not successfully delivered in this delay analysis.The delay of those packets is considered to be infinite. Fig.5shows that the packet delivery ratio of both WSNHA-GAHRand AODV decreased as the network size increased, but thepacket delivery ratios of AODV were less than 70% in allscenarios. Because we neglected the undelivered packets thathave infinite delay and only counted the packets deliveredsuccessfully, the average packet delay of AODV decreasedwhen the network size increased. If we count the delay ofpackets that were not successfully delivered, the difference indelay between WSNHA-GAHR and AODV is even larger.

VI. CONCLUSIONS AND FUTURE WORK

We have developed WSNHA-GAHR, a new kind of routingprotocol based on the analysis of WSNHA characteristicsand requirements. We simulated three different scenarios tocompare the performance of WSNHA-GAHR with that ofAODV. The simulation results for packet delivery ratio, routingoverhead, and average packet delay show that WSNHA-GAHR performed better than AODV. We will create a physicalimplementation to obtain actual experimental WSNHA-GAHRperformance data in future research.

ACKNOWLEDGMENT

This work was partially supported by a grant from BasicResearch Program of the Ubiquitous Sensor Network ResearchCenter (USNRC) funded by the Gyeonggi Regional ResearchCenter (GRRC) plan, and a grant from NSF(Natural ScienceFoundation) of educational agency of Hubei Prov. China undergrant number B20071106.

REFERENCES

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wirelesssensor networks: a survey,” Computer Networks, no. 38, pp. 393–422,2001.

[2] H. Q. Xu, S. H. Hong, and Z. Wang, “Routing Algorithm for WirelessSensor Networks in Home Automation: Review and Solutions,” inProceedings of Future Generation Communication and Networking(FGCN), Sanya, 2008.

[3] C. Reinisch, W. Kastner, G. Neugschwandtner, and W. Granzer, “Wire-less technologies in home and building automation,” in the 5th IEEEInternational Conference on Industrial Informatics, 2007, pp. 93–98.

[4] W. Kastner, G. Neugschwandtner, S. Soucek, and H. Newman, “Com-munication systems for building automation and control,” Proceedingsof the IEEE, vol. 93, no. 6, pp. 1178–1203, 2005.

[5] J. N. AL-Karaki and A. E. Kamal, “Routing techniques in wirelesssensor networks: a survey,” IEEE Wireless Communication, vol. 1, pp.6–28, 2004.

[6] K. Akkaya and M. Younis, “A survey on routing protocols for wirelesssensor networks,” Journal of Ad Hoc Networks, vol. 3, pp. 325–349,2005.

[7] C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva,“Directed diffusion for wireless sensor networking,” IEEE/ACM Trans-actions on Networking, vol. 11, no. 1, pp. 2–16, 2003.

[8] J. Kulik, W. R. Heinzelman, and H. Balakrishnan, “Negotiation-basedprotocols for disseminating information in wireless sensor networks,”Wireless Networks, vol. 8, pp. 169–185, 2002.

[9] D. Braginsky and D. Estrin, “Rumor routing algorithm for sensornetworks,” in the First Workshop on Sensor Networks and Applica-tions(WSNA), 2002.

[10] C. Schurgers and M. B. Srivastava, “Energy efficient routing in wirelesssensor networks,” in Military Communication Conference, 2001 (MIL-COM 2001) Communications for Network-Centric Operations: Creatingthe Information Force, vol. 1, Los Angeles, 2001, p. 357.

[11] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “Anapplication-specific protocol architecture for wireless microsensor net-works,” IEEE Transactions on Wireless Networking, vol. 1, pp. 660–670,2002.

[12] S. Lindsey and C. Raghavendra, “PEGASIS: Power-efficient gathering insensor information systems,” in the IEEE Aerospace Conference, vol. 3,2002, pp. 1125–1130.

[13] A. Manjeshwar and D. P. Agarwal, “TEEN: A protocol for enhanced ef-ficiency in wireless sensor networks,” in the 1st International Workshopon Parallel and Distributed Computing Issues in Wireless Networks andMobile Computing, San Francisco, 2001.

[14] F. Y. et al, “A two-tier data dissemination model for large-scale wirelesssensor networks,” in the proceedings of Mobicom02, 2002.

[15] Y. Yu, J. Heidemann, and D. Estrin, “Geography-informed energyconservation for ad hoc routing,” in the 7th Annual ACM/IEEE Inter-national Conference on Mobile Computing and Networking (MobiCom01), Rome, Italy, 2001, pp. 70–84.

[16] Y. Xu, D. Estrin, and R. Govindan, “Geographical and energy-awarerouting: A recursive data dissemination protocol for wireless sensornetworks,” UCLA Computer Science Department Technical Report,UCLA-CSD TR-01-0023, Tech. Rep., 2001.

[17] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introductionto Algorithms, Second Edition. The MIT Press, 2001.

[18] P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristicdetermination of minimum cost paths,” IEEE Transactions on SystemsScience and Cybernetics (SSC), vol. 4, no. 2, pp. 100–107, 1968.

[19] J. Pearl, Heuristics: Intelligent Search Strategies for Computer ProblemSolving. Addison-Wesley, 1984.

[20] D. Rina and J. Pearl, “Generalized best-first search strategies and theoptimality of A*,” Journal of the ACM, vol. 32, no. 3, pp. 505–536,1985.

[21] C. E. Perkins and E. M. Royer, “Ad hoc On-Demand Distance VectorRouting,” in The Proceedings of the 2nd IEEE Workshop on MobileComputing Systems and Applications, New Orleans, 1999, pp. 90–100.

[22] ZigBee Specification, ZigBee Alliance Std. Document 053 474r17, 2007.

Documents

A Heuristic Routing Protocol for Wireless Sensor