Research Article Load Balanced Congestion Adaptive Routing ...downloads.hindawi.com/journals/ijdsn/2014/532043.pdflevels of the research. e main weaknesses of MANET are such as limited

Research ArticleLoad Balanced Congestion Adaptive Routing forMobile Ad Hoc Networks

Jung-Yoon Kim,1 Geetam S. Tomar,2,3 Laxmi Shrivastava,4

Sarita Singh Bhadauria,4 and Won-Hyoung Lee1

1 Department of Image Engineering, Chung-Ang University, Chung-Ang Cultural Arts Center Office No. 503,Dongjak-Gu, Seoul 156-756, Republic of Korea

2Machine Intelligence Research Labs, Gwalior 474011, India3 Department of Electrical and Computer Engineering, University of West Indies, St. Augustine, Trinidad and Tobago4Department of Electronics, Madhav Institute of Technology and Science, Gwalior 474005, India

Correspondence should be addressed to Won-Hyoung Lee; [email protected]

Received 14 March 2014; Accepted 20 April 2014; Published 13 July 2014

Academic Editor: Tai hoon Kim

Copyright © 2014 Jung-Yoon Kim et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Inmobile ad hoc networks the congestion is amajor issue, which affects the overall performance of the networks.The load balancingin the network alongside the congestion is another major problem inmobile ad hoc network (MANET) routing due to difference inlink cost of the route. Most of the existing routing protocols provide solutions to load balancing or congestion adaptivity separately.In this paper, a congestion adaptive routing along with load balancing, that is, load balanced congestion adaptive routing (LBCAR),has been proposed. Transferring of load from congested nodes to less busy nodes and involvement of other nodes in transmissionthat can take part in route can improve the overall network life. In the proposed protocol two metrics, traffic load density and linkcost associated with a routing path, have been used to determine the congestion status. The route with low traffic load density andmaximum life time is selected for packet transmission using this protocol. Performance of the network using LBCAR has beenanalyzed and compared with congestion adaptive routing protocol (CRP) for packet delivery ratio, average end-to-end delay, andnormalized routing overhead.

1. Introduction

It is due to the effect of advancements in wireless technologythat all communication systems are going wireless. It isexpected to have all such systems and devices connected withcertain network. The wireless networks of the day are one ofthe major areas of communication, which are flooded withmultimedia and other allied services with various data types.It is also true that wired communication systems cannot bedone away with as it has high bandwidth and uncomparablereliability. The main difference between wireless and wirednetworks is only in communication channel and their modeof communication. In past, most common networks wereonly wired communication networks having fixed infrastruc-ture and evenwireless networks used to have fixed infrastruc-ture and control like cordless telephone, cellular networks,Wi-Fi, microwave and satellite communication, and so forth.

Ad hoc wireless networks are kind of infrastructure lessnetwork in which two or more devices are equipped withwireless communication and networking capability alongwith routing capabilities even in mobility. Ad hoc networksdo not have fix topologies to cover a large area. Thesetopologies may change dynamically and unpredictably at anymoment as nodes might be on mobility. Traditional routingprotocols that are normally used for internet based wirelessnetworks cannot be applied directly to ad hoc wirelessnetworks because some common assumptions are not validin all cases for such dynamically changing networks and maybe not true for mobile nodes.The availability of bandwidth isan important issue of ad hoc networks. Thus, these networktypes present a difficult challenge in the design of routingprotocols, where each node participates in routing by for-warding data dynamically based on the network connectivity.As network uses wireless channel for communication, the

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 532043, 10 pageshttp://dx.doi.org/10.1155/2014/532043

2 International Journal of Distributed Sensor Networks

links are affected by propagation loss, shadow fading, andmultipath Rayleigh fading. In ad hoc networks, due to themovement of mobile nodes and the multipath effect, a packettransmission is subjected to Rayleigh fades. If the signal-to-noise ratio (SNR) at some stages becomes lower than acertain threshold, packets will contain excessive number oferrors and will be dropped due to high noise. This cause ofpacket loss degrades the network performance significantly.Thus signal-to-noise ratio (SNR) is a good indicator of linkquality and can be determined from the hardware. DifferentSNRs cause different bit error rates (BERs).Themathematicalformula for calculating BER [1] is given as follows:

BER = 0.5 × erfc(√𝑃𝑟×𝑊

𝑁 × 𝑓

) , (1)

where 𝑃𝑟is the received power,𝑊 is the channel bandwidth,

𝑁 is noise power, 𝑓 is transmission bit rate, and erfc isthe complementary error function. Most wireless networkstypically measure SNR as performance parameter. SNR [1]may be calculated by

SNR = 10 log𝑃𝑟

𝑁

. (2)

The above formula is applicable when only one packet isreceived by the receiver. If more than one packet arrives atthe receiver, the SNR is calculated by

SNR = 10 log𝑃𝑟

𝑁 + ∑

𝑛

𝑖=1𝑃𝑟𝑖

. (3)

∑

𝑛

𝑖=1𝑃𝑟𝑖is the interference component and 𝑃

𝑟𝑖is the signal

strength of the packets at the receiver. 𝑛 is the number ofpackets that arrive at the receiver simultaneously; 𝑖 refers tointerference [1].

When a sending node is broadcasting packets, it piggy-backs its transmissions power𝑃. On receiving the packets, theintended node measures the signal strength received whichholds the following relationship for free-space propagationmodel [2] as follows:

𝑃𝑟= 𝑃𝑡(

𝜆

4𝜋𝑑

)

2

𝐺𝑡𝐺𝑟, (4)

where 𝜆 is wavelength of the carrier, 𝑑 is the distance betweensender and receiver, 𝐺

𝑡and 𝐺

𝑟are unity gain of transmitting

and receiving omnidirectional antennas, respectively, and 𝑃𝑡

and 𝑃𝑟are transmitted and received powers, respectively.

The wireless ad hoc network shown in Figure 1 considersmobile nodes, which are not supported by an external deviceor control mechanism and have their communication rangeaccording to coverage area of the individual node. It may beseen that sending and destination nodes are connected usingmultihop communication and thus need congestion free pathto achieve reliable communication.

Devices in mobile ad hoc networks should be ableto detect the presence of other devices and perform thenecessary setup to facilitate communications and the sharing

Destination

SourceTransmission range

Figure 1: Wireless ad hoc network concept.

of data and services. However, due to limitation of band-width and sharing common channel for all the nodes, thecongestion has become more challenging in the wireless adhoc networks [3]. Congestion is always considered to be themain factor for degrading the performance of the networkand leads to packet losses and bandwidth degradation andalso leads into wastage of time and energy by invoking con-gestion recovery algorithms. Various techniques have beendeveloped in attempt to minimize congestion and to increasethe capacity of wireless ad hoc network. There have beensome protocols proposed, which inform the transmittingnodes about the current level of network congestion and helptransmitting stations to reroute or delay their transmissionaccording to congestion levels and protocols used. A loadbalancing technique shares the traffic load evenly among allthe nodes that can take part in transmission, which has beenproposed recently [4] and proposed to enhance the overallcapacity and throughput of the network. Transferring of loadfrom congested nodes to less busy nodes and involvementof other nodes in transmission that can take part in routecan improve the overall network life as per the proposal. Anumber of congestion adaptive and load balanced algorithmshave been proposed separately, but, in this paper, a congestionadaptive routing along with load balancing, that is, loadbalanced congestion adaptive routing (LBCAR), has beenproposed, which has considered twometrics, traffic load den-sity and lifetime associated with a routing path, to determinethe congestion status and weakest node of the route and theroute with low traffic load density and maximum lifetimeis selected for packet transmission. The proposed scheme isexpected to adapt to the sudden changes and level of trafficload and to find suitable path even in congestion scenario andnode energy constraints. The proposed algorithm is suitablefor burst traffic also and performs well for higher trafficconditions in the network.

The remainder of the paper is organised as follows.Review of all four routing protocols is presented in

Section 2. The detailed observation on constraint environ-ment is discussed in Section 3. Section 4 depicts congestionand some congestion control mechanism.The proposed con-gestion control protocol is presented in Section 5. Section 6has simulation parameter, Section 7 has analysis of sim-ulation results for proposed congestion adaptive protocol.Finally, Section 8 concludes this paper and defines topics forfurther research.

International Journal of Distributed Sensor Networks 3

2. Related Work

A wireless MANET has become most promising and rapidlygrowing area, which is based on a self-organized and rapidlydeployable network. Due to its flexible features, MANETattracts different real-world application areas where thenetworks topology changes very quickly. However, it hascertain drawbacks, which are being taken care of at variouslevels of the research. The main weaknesses of MANET aresuch as limited bandwidth, battery power, computationalpower, and security. Research is continuously going on bymany researchers on MANETs: routing, congestion controltechniques, congestion adaptive techniques, load balancingin MANETs, and security. Many congestion adaptive mech-anisms have been proposed in the literature. Some of theimportant congestion adaptive techniques for MANETs havebeen considered here for the purpose of improvement ofthe work. Research in MANETs has been mainly focused ondesigning routing protocols to cope with dynamics of ad hocnetworks. There are several protocols in the literature thathave been specifically developed to cope with the limitationsimposed by ad hoc networking environments due to variousconstraints. In [5], a distance vector algorithm, ad hoc on-demand distance vector (AODV), was presented which ison-demand route acquisition system; nodes that do not lieon active paths neither maintain any routing informationnor participate in any periodic routing table exchanges. In[6], further improvements to the performance of dynamicsource routing (DSR) have been presented, for example, toallow scaling to very large networks and the addition ofnew features to the protocol, such as multicast routing andadaptive quality of service (QoS) reservations and resourcemanagement. In [7], an innovative approach, highly dynamicdestination-sequenced distance-vector routing (DSDV), hasbeen presented which models the mobile computers asrouters, which are cooperating to forward packets as neededto each other. This approach can be utilized at either thenetwork layer (layer 3) or below the network layer but stillabove the MAC layer software in layer 2. In [8], a newdistributed routing protocol, WRP, has been presented for apacket radio network, which works on the notion of second-to-last hop node to a destination. In [1, 2, 9], a routingwith congestion awareness and adaptivity inMANETs (CRP)has been presented. This protocol tries to prevent conges-tion from occurring in the first place and to be adaptiveshould congestion occur. Every node appearing on a routewarns its previous node when it is prone to be congested.The previous node uses a “bypass” route for bypassing thepotential congestion area to the first noncongested node onthe primary route. Traffic is split probabilistically over thesetwo routes, primary and bypass, thus effectively lesseningthe chance of congestion occurrence. In [10], an efficientcongestion adaptive routing protocol (ECARP) for MANETshas been proposed that outperforms all the other routingprotocols during heavy traffic loads. The ECARP is designedto ensure the high availability of alternative routes andreduce the rate of stale route. This can be achieved byincreasing the parameters of routing protocols (especiallyin AODV) that normally take more time for link recovery.

The number of packets in buffer has been used to determinethe congestion status of nodes. In [11], a congestion awarerouting protocol for mobile ad hoc networks (CARM) hasbeen proposed which employs the retransmission countweighted channel delay and buffer queuing delay, with pref-erence for less congested high throughput links to improvechannel utilization. Whenever streaming of multimediabased data such as video, audio, and text is performed trafficwill be more and network becomes congested in mobile adhoc networks. In [12], a congestion adaptive AODV (CA-AODV) routing protocol has been developed for streamingvideo in mobile ad hoc networks especially designed formultimedia applications. Since video data is very sensitivein delay and packet loss, the measurement of congestion hasbeen considered here depending on average packet deliverytime and packet delivery ratio. In [13], a congestion adaptiverouting mechanism has been presented which is appliedto reactive ad hoc routing protocol, denoted as congestionadaptive ad hoc on-demand distance vector routing protocol.The main characteristics of the mechanism are its support offinding alternate route, in case of congestion on the primaryroute, on the basis of status of the buffer size of the neighborand the status of the buffer size of the next node on theprimary route. This approach works in coordination withAODV. In [14], a hop-by-hop congestion aware routing pro-tocol (CARP) has been developedwhich employs a combinedweight value as a routing metric, based on the data rate,queuing delay, link quality, andMACoverhead in its standardcost function to account for the congestion level. The routewith minimum cost index is selected, which is based on thenodeweight of all the in-network nodes from the source nodeto the destination node.

Due to interference in the channels of the paths,multipath increases the end-to-end delay and does not workwell in highly congested networks. In [15], a congestion awaremultipath dynamic source routing protocol (CAWMP-DSR)has been proposed for maximum number of node disjointpaths using multipath DSR. A set of disjoint multipathsis generated and handles the problem of end-to-end delayusing the correlation factor measurement and as a result end-to-end delay improved and overhead as well. In [16], originalDSR protocol has been modified to define the occurrenceof congestion by monitoring and reporting multipleresource utilization thresholds as QoS attributes and usesmultipath routing and load balancing during the periods ofcongestion to improve QoS in MANETs for CBRmultimediaapplications. In this proposed protocol, the battery leveland queue length are used as the key resource utilizationparameters. In [17] authors have discussed protocols for all-to-all dissemination in ad hoc wireless networks. They haveevaluated the performance of the GOSSIP3 disseminationprotocol under varying network loads and have concludedthat MAC layer congestion awareness is important forimproving application-level efficiency. In [18], a congestionaware routing protocol for mobile ad hoc networks (CARM)was proposed, which employs the retransmission countweighted channel delay and buffer queuing delay, withpreference for less congested high throughput links toimprove channel utilization. In [4], congestion adoptive


load-aware routing protocol, DLAR, defined the networkload of amobile node as the number of packets in its interfacequeue. In [3] authors developed congestion adaptive AODV(CA-AODV) routing protocol for streaming video in mobilead hoc networks that provides alternate noncongested pathif node becomes congested. In [19], a hop-by-hop congestionaware routing mechanism was proposed; however, it wasdirected toward congestion adaptivity only. In [20], a workload based algorithm was proposed, which considered theworkload of the path and the network for finding the routewith less congestion, and was appropriate for low load only.In [21] an efficient congestion adaptive routing protocol(ECARP) is proposed for MANETs designed to ensure thehigh availability of alternative routes and reduce the rateof stale routes and also reduce the rate of broken routeremoval process by increasing the parameters of the routingprotocols (especially in AODV) such as active route time-out, route reply wait time, reverse route life, TTL start,TTL increment, TTL threshold, and delete period thatnormally take more time for link recovery. In [22] amultipath routing with load balancing was proposed but didnot consider the variable congestion status of the network. In[23], congestion aware routing methods have been studiedand it was found that none of the algorithms has takenboth parameters into consideration in the same protocol,which has prompted us to find solution by consideringload balancing as well as congestion adaptive scheme in theproposed method.

3. Congestion and CongestionAdaptive Routings

In wireless ad hoc network the congestion is the cause of con-cern and needs to be rectified for having better performanceof the network. To know the basics of these terms, it has beenrevisited in short below.

3.1. Congestion in MANETs. In mobile ad hoc network,congestion is a global issue, involving the behavior of all thehosts, all the routers, the store-and-forward processingwithinthe routers, and so forth, which occurs with limited sources.Congestion is a situation in which too many packets arepresent in (a part of) the subnet and performance degrades.Congestion results from applications sending more packetsthan the network devices (i.e., router and switches) canaccommodate, thus causing the buffers on such devices tofill up and possibly overflow. Traditionally, congestion occurswhen the total volume of traffic offered to the networkor part of the network exceeds the resource availability.Congestion typically manifests itself in excessive end-to-enddelay and packet drops due to buffer overflow. There area variety of conditions that can contribute to congestionand they include but are not limited to traffic volume, theunderlying network architecture, and the specification ofdevices in the network (e.g., buffer space, transmission rate,processing power, etc.). Network congestion can severelydeteriorate network throughput. Congestion not only leadsto packet losses and bandwidth degradation but also wastes

time and energy on congestion recovery. If no appropriatecongestion control is performed this can lead to a congestioncollapse of the network, where almost no data is successfullydelivered.

Congestion control is necessary in avoiding congestionand/or improving performance after congestion. Congestioncontrol schemes are usually composed of three components:congestion detection, congestion feedback, and sending-ratecontrol. Practically, congestion detection can be processed inintermediate nodes or receivers. The criteria for congestiondetection vary with protocols. Congestion can be determinedby checking queues length. It can also be indirectly detectedby monitoring the trend of throughput or response time.Chief metrics for monitoring the congestion are the per-centage of all packets discarded for lack of buffer space, theaverage queues lengths, and the number of packets that timedout and are retransmitted, the average packet delay, and thestandard deviation of packet delay. Congestion control is amethod used for monitoring the process of regulating thetotal amount of data entering the network so as to keeptraffic levels at an acceptable value. Various techniques havebeen developed in an attempt to minimize congestion incommunication networks. In addition to increasing capacityand data compression, they include protocols for informingtransmitting devices about the current levels of networkcongestion and reroute or delay their transmission accordingto congestion levels. When the input traffic rate exceedsthe capacity of the output lines, the routers are too slow toperformbookkeeping tasks (queuing buffers, updating tables,etc.) and the router’s buffer is too limited and congestionoccurs.

3.2. Congestion Adaptive routings in MANETs. The routingprotocols have been classified according to their basic ini-tiation and counter mechanism. Besides the classificationof routing protocols based on the network structure, thereis another dimension for categorizing routing protocols:congestion adaptive routing versus congestion unadaptiverouting. The routing protocols in which the congestion isreduced after it has occurred are congestion unadaptive, andall the congestion control routings belong to this group andthe routings in which the chances of congestion occurrenceare minimized are congestion adaptive. Congestion adaptiverouting tries to prevent congestion from occurring in the firstplace, rather than dealing with it reactively. In congestionadaptive routing, the route is adaptively changeable based onthe congestion status of the network. Every node appearingon a route warns its previous node when prone to be con-gested.The previous node uses a “bypass” route for bypassingthe potential congestion area to the first noncongested nodeon the primary route. Traffic is split probabilistically overthese routes, thus effectively lessening the chance of conges-tion occurrence. If a node is aware of a potential congestionahead, it finds a bypass that will be used in case the congestionactually occurs or is about to occur. Part of the incomingtraffic will be sent on the bypass, making the traffic comingto the potentially congested node less. The congestion maybe avoided as a result.


3.3. Load Balancing in MANETs. Load balancing can bedefined as a methodology to distribute or divide the trafficload evenly across two or more network nodes in order tomediate the communication and also to achieve redundancyin case that one of the links fails. Load balancing can beoptimal resource utilization, increased throughput, and lesseroverhead. The load can also be unequally distributed overmultiple links by manipulating the path cost involved. Inmobile ad hoc networks, balancing the load can evenlydistribute the traffic over the network and prevent earlyexpiration of overloaded nodes due to excessive power con-sumption in forwarding packets. It can also allow an appro-priate usage of the available network resources. The existingad hoc routing protocols do not have a mechanism to conveythe load information to the neighbors and cannot evenlydistribute the load in the network. On-demand routingprotocols such as AODV initiate the route discovery only ifthe current topology changes and the current routes are notavailable. In high mobility situations where the topology ishighly dynamic, existing links may break quickly. It may besafe to assume that in such scenarios the on-demand routingprotocol like AODV and DSR can achieve load balancingeffect automatically by searching for new routes and usingdifferent intermediate nodes to forward traffic.

Whereas, in the scenarios where the same intermediatenodes are used for longer period of time, the on-demandbehavior may create bottlenecks and cause network degra-dation due to the congestion and lead to long delays, inaddition, the cachingmechanism inmost on-demand routingprotocols for intermediate nodes to reply from cache cancause concentration of load on certain nodes. It had beenshown that the increase in traffic load degrades the networkperformance in MANETs. In other words, if the topologychanges are minimal then this behavior results in the sameroutes being used for a longer period of time which in turnincreases the traffic concentration on specific intermediatenodes. The early expiration of nodes can cause an increasein the control packets and the transmission power of othernodes to compensate the loss. Furthermore, it can result innetwork degradation and even an early expiration of theentire ad hoc network. Besides, using the same node forrouting traffic for a longer duration may result in an unevenusage of the available network resources, like bandwidth. Anetwork is less reliable if the load among network nodes isnot well balanced.

4. Protocol Description

The wireless ad hoc networks have two types of routingprotocols. The protocols considered here are mostly fromreactive category as these have been trusted for wirelessad hoc networks for higher traffic scenarios. In reactiverouting protocols, the node does not attempt to continuouslydetermine the routes within the network topology; instead, aroute is searched when it is required and saves bandwidth ofthe channel. Example of such protocols is ad hoc on-demanddistance vector routing protocol (AODV).

4.1. AODV (Ad Hoc On-Demand Distance Vector Routing)Protocol. AODV builds routes using a route request/routereply query cycle. When a source node desires to transmitdata to a destination it searches for route to reach targetnode. As it does not have a route, it broadcasts a routerequest (RREQ) packet across the network. Nodes receivingthis packet update their information for the source node andset up backwards pointers to the source node in the routetables. In addition to the source node’s IP address, currentsequence number, and broadcast ID, the RREQ also containsthemost recent sequence number for the destination ofwhichthe source node is aware. A node receiving the RREQ maysend a route reply (RREP) either if it is the destination or ifit has a route to the destination with corresponding sequencenumber greater than or equal to that contained in the RREQ.If this is the case, it unicasts a RREP back to the source.Otherwise, it rebroadcasts the RREQ and nodes keep track oftheRREQ’s source IP address and broadcast ID. If they receivea RREQ which they have already processed, they discard theRREQ and do not forward it. As RREP propagates back tothe source, node sets up forward pointers to the destination.Once the source node receives the RREP, it may begin toforward data packets to the destination. If the source laterreceives a RREP containing a greater sequence number orcontains the same sequence numberwith a smaller hop count,it may update its routing information for that destination andbegin using the better route. As long as the route remainsactive, it will continue to bemaintained. A route is consideredactive as long as there are data packets periodically travellingfrom the source to the destination along that path. Oncethe source stops sending data packets, the links will timeout and eventually be deleted from the intermediate noderouting tables. If a link break occurs while the route is active,the node upstream of the break propagates a route error(RERR) message to the source node to inform it of thenow unreachable destination(s). After receiving the RERR,if the source node still desires the route, it can reinitiateroute discovery. Various techniques have been developed inattempt to minimize congestion in communication networkslike congestion adaptive routing (CRP) which tries to preventcongestion from occurring in the first place and be adaptiveshould congestion occur.

4.2. CRP (Congestion Adaptive Routing Protocol). This pro-tocol tries to prevent congestion from occurring in the firstplace and to be adaptive should congestion occur. CRP [9] isa congestion adaptive unicast routing protocol for MANETs.Every node appearing on a route warns its previous nodewhen prone to be congested. The previous node uses a“bypass” route for bypassing the potential congestion areato the first noncongested node on the primary route. Trafficis split probabilistically over these two routes, primary andbypass, thus effectively lessening the chance of congestionoccurrence. CRP is on-demand and consists of the followingcomponents:

(1) congestion monitoring,

(2) primary route discovery,


(3) bypass discovery,

(4) traffic splitting and congestion adaptivity,

(5) multipath minimization, and

(6) failure recovery.

When the number of packets coming to a node exceedsits carrying capacity, the node becomes congested and startslosing packets. A variety of metrics can be used for a nodeto monitor congestion status. The major techniques amongthese are the percentage of all packets discarded for lackof buffer space, the average queue length, the number ofpackets timed out and retransmitted, the average packetdelay, and the standard deviation of packet delay. In all ofthese cases, the rising numbers indicate growing congestion.To determine the congestion status, a node is said to be green(i.e., far from congested), yellow (i.e., likely congested), orred (very likely or already congested). A node𝑁 periodicallybroadcasts a UDT (update) packet, which contains node’scongestion status and a set of tuples [destination R, nextgreen node G, and distance to green node m]. When a node𝑁 receives a UDT packet from its next primary node 𝑁nextregarding destination 𝑅, 𝑁 will be aware of the congestionstatus of 𝑁next. A node 𝑁 receives a UDT packet from itsnext primary node𝑁next (regarding a destination 𝑅). If𝑁nextis yellow or red, congestion is likely ahead if data packetscontinue to be forwarded on link𝑁 to𝑁next. Since CRP triesto prevent congestion from occurring in the first place, 𝑁starts to discover a bypass route toward node 𝐺—the nextgreen node of 𝑁 known from the UDT packet. The bypasssearch is similar to primary route search. After searching thebypass route, the traffic is split between primary and bypassroutes with equal probability, hence effectively reducing thecongestion status at the next primary node. To adapt withcongestion due to network dynamics, the probability 𝑝 ismodified periodically based on congestion status of the nextprimary node and the bypass route. The congestion status ofbypass route is the accumulative status of every bypass route.To keep the protocol overhead small, CRP tries to minimizethe use of multiple paths. CRP does not allow a node to usemore than one bypass. Therefore, the bypass route discoveryis only initiated by a node if no bypass currently exists atthis node. The protocol overhead for using bypass is alsoreduced because of short bypass lengths. A bypass is removedwhen the congestion is totally resolved, and CRP does notincur heavy overhead due to maintaining bypass paths. Thebypass maintenance cost is further reduced because a bypassis typically short and a primary node can only create atmost one bypass. The recovery of a link breakage is realizedgracefully and quickly by making use of the existing bypasspaths.

These protocols generally help in managing the conges-tion but are not capable of considering congestion state of thenetwork and adapt to the change in traffic and find solutionby adopting the new state and do away with the congestion toenhance the performance of the network.

5. Proposed Load Balanced CongestionAdaptive Routing (LBCAR) Protocol

In a process to find the solution for avoiding the congestionin the network by adapting to the instant changes in thecongestion state a new algorithm has been proposed in thispaper.The proposed congestion adaptive algorithm is capableof countering congestion in the network and is referredto as load balanced congestion adaptive routing (LBCAR)algorithm. In this protocol each node maintains a record ofthe latest traffic load estimations at each of its neighbors ina table called the neighborhood table. This table is used tokeep the load information of local neighbors at each node.Neighbors that receive this packet update the correspondingneighbor’s load information in their neighborhood tables.LBCAR is a new load balanced congestion adaptive techniqueproposed to reduce congestion and to maximize the networkoperational lifetime.The metric traffic load density is used todetermine the congestion status of the route and link cost isused to determine the lifetime of the route. The route withlow traffic load intensity and maximum lifetime is selectedfor packet transmission and this algorithm practically limitsthe idealized maximum number of packets transmittablethrough the route having weakest node with minimumlifetime and high traffic load intensity.

Node 𝑥𝑖samples the interface queue length in the MAC

layer periodically. 𝑞𝑖(𝑗) is the 𝑗th sample value, and 𝑁 is the

sampling time over a period of time, and then the traffic loadof node 𝑥

𝑖is defined as follows:

Traffic Load (𝑖) ={∑

𝑁

𝑗=1𝑞𝑖(𝑗)}

𝑁

.

(5)

The total length of interface queue of node 𝑥𝑖in theMAC

layer is 𝑞max(𝑖); then the traffic load intensity function of node𝑥𝑖is defined as follows:

Traffic Load Intensity (𝑖) = Traffic Load (𝑖)𝑞max (𝑖)

. (6)

The link cost for a link (𝑖, 𝑗) contains two parameters:a node specific parameter, that is, 𝑃

𝑖, and a link specific

parameter, that is, 𝐸𝑖,𝑗, and is defined as

𝐿𝑖,𝑗=

𝑃𝑖

𝐸𝑖,𝑗

, (7)

where 𝑃𝑖is the residual battery energy of the node and 𝐸

𝑖,𝑗is

the energy spent in one or more retransmissions necessary inthe face of link error. 𝐸

𝑖,𝑗is measured as

𝐸𝑖,𝑗=

𝑒𝑖,𝑗

{(1 − 𝑝)

𝑛

}

, (8)

where 𝑒𝑖,𝑗is the energy involved in a single packet transmis-

sion, 𝑛 is the number of hops in retransmission, and 𝑝 is thepacket error probability.

It is considered that the number of neighboring nodesof 𝑥𝑖is 𝑛 and all the traffic load intensity functions are


YesNo

BroadcastRREQ

pi ≥ 0.2 androute ≥ 2?

Yes

No

Node has datato send

Forward data

correspondingforwardingprobability

RebroadcastRREQ

Received RREPpackets

Wait forRREP

Calculatelink cost

Calculate trafficload intensity

Calculate packetforwarding

probability pi

Is neighboringnode active?

Are

packets as per

Figure 2: Flowchart of LBCAR processing.

known. These 𝑛 values are sorted in the ascending order andget a sequence number named as seq(𝑚)

𝑖{1 ≤ seq(𝑚)

𝑖≤

𝑛} corresponding to the traffic load intensity (𝑖) of 𝑥𝑖. The

forwarding probability of the data for the node 𝑥𝑖is given by

the following expression:

𝑝𝑖= 1 − {

seq (𝑚)𝑖

𝑛𝐿𝑖,𝑗

} , (9)

where 𝑝𝑖is related to the existing traffic load of neighboring

nodes.According to the network load the calculation at node is

done for link cost using formulae above. In this way, traffic issplit according to the traffic load intensity and link cost. Theoverloaded nodes are protected by using the nodes of lightertraffic load to establish the route, so as to balance the networkload, lessen the congestion of the network, improve the datatransmission efficiency, and maximize the network lifetime.Flowchart of this algorithm is shown in Figure 2.

The flowchart is clearly indicative according to the linkcost and other parameters being considered for load balanc-ing and congestion adaptivity in the network.

6. Simulation Parameters

6.1. Simulation Setup. The simulations of network have beencarried out using Qualnet 5.2. The simulation parameters aregiven in Table 1.

6.2. Performance Metrics. In this paper, the performancemetrics such as packet delivery ratio, average end-to-enddelay, and normalized routing overhead were calculated andevaluated for AODV, CRP, and LBCAR.

6.2.1. Packet Delivery Ratio. Packet delivery ratio is the ratioof the number of data packets successfully received at thedestinations to the number of data packets generated by thesources.


Table 1

Parameters ValuesNode placement strategy Random

Propagation model Two-ray ground radiopropagation model

Environment size 1500m × 1500mNumber of nodes 100Transmitter range 250mBandwidth 1MbpsSimulation time 300 s

Traffic type Constant bit rate(CBR)

Number of CBR sources 20Packet size 512 byteNumber of packets transmitted by sources 100Maximum speed 20m/s

Mobility model Random way pointmodel

Pause time 10 s

Packet rate 1, 5, 10, 20, 30,40 packets/s

6.2.2. Average End-to-End Delay. The average end-to-enddelay is a measure of average time taken to transmit eachpacket of data from the source to the destination.Higher end-to-end delay is an indication of network congestion.

6.2.3. Normalized Routing Overhead. The ratio of the amountin bytes of control packets transmitted to the amount in bytesof data received.

According to the performance parameters and theselected variations for the proposed algorithm the simulationhas been undertaken and results are recorded according tothe set parameters.

7. Simulation Results

In this paper, the results have been presented by takingaverage of over 10 runs of each simulation setting. LBCARhas been compared with AODV and CRP.

7.1. Comparison of LBCAR with AODV. Figures 3–5 showpacket delivery ratio, average end-to-end delay, and nor-malized routing load with varying packet rates for AODVand LBCAR. Figure 3 shows packet delivery ratio for AODVand LBCAR with varying packet rates. This figure showsthat packet delivery ratio for LBCAR is greater than that ofAODV for all values of packet rates.When the traffic loadwashigh, AODV could not handle congestion.The reason, again,was the ability of LBCAR to adapt to network congestion.Figure 3 shows average end-to-end delay for AODV andLBCAR with varying packet rates. This figure shows thatLBCAR has nearly the same or somewhat smaller delay asthat of AODV. An interesting observation was that the delayvariation in LBCAR was less than that of AODV making

0

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80

1 5 10 20 30 40

Pack

et d

eliv

ery

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(%)

Packet rate (packet/s)

Packet delivery ratio versus packet rate

AODVLBCAR

Figure 3: Packet delivery ratio at different packet rates.

0

0.2

0.4

0.6

0.8

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1.2

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age e

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ay (s

)

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Average end-to-end delay versus packet rate

AODVLBCAR

Figure 4: Average end-to-end delay at different packet rates.

LBCAR more suitable for multimedia applications. HenceLBCAR outperforms AODV in terms of average end-to-enddelay. Figure 4 shows normalized routing load for AODVand LBCAR with varying packet rates. This figure showsthat packet delivery ratio for LBCAR is smaller than that ofAODV for all values of packet rates.Thus normalized routingoverheads have also decreased to an extent. Thus, out ofLBCAR and AODV, performance of LBCAR is slightly betterthan that of AODV in all respects.

7.2. Comparison of LBCAR with CRP. In this section theproposed protocol has been compared with CRP algorithm,as shown in Figures 6–8. Figure 6 shows packet deliveryratio for LBCAR and CRP with varying packet rates. Bothroutings give almost the same packet delivery ratio except athigh packet rates. Figure 7 shows average end-to-end delayfor LBCAR and CRP with varying packet rates. LBCARgives slightly less average delay than CRP. Therefore, LBCARoutperforms CRP in terms of average end-to-end delay.Figure 8 shows normalized routing overhead for LBCARand CRP with varying packet rates. LBCAR shows highernormalized routing overhead than CRP. Thus LBCAR is


0

1

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3

4

5

1 5 10 20 40

Nor

mal

ized

rout

ing

over

head

Packet rate (packets/s)

Normalized routing overhead versus packet rate

LBCARAODV

Figure 5: Normalized routing overhead at different packet rates.

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Pack

et d

eliv

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Packet delivery ratio versus packet rate

LBCARCRP

Figure 6: Packet delivery ratio at different packet rates.

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age e

nd-to

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ay (s

)


Average end-to-end delay versus packet rate

LBCARCRP

Figure 7: Average end-to-end delay at different packet rates.

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mal

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rout

ing

over

head


Normalized routing overhead versus packet rate

LBCARCRP

Figure 8: Normalized routing overhead at different packet rates.

outperformed in terms of packet delivery ratio and averagedelay.

8. Conclusion

In this paper, a load balanced congestion adaptive routing(LBCAR) has been proposed. The simulation has been doneas per parameter selected. The performance of the MANEThas been analyzed and compared with AODV in terms ofpacket delivery ratio, average end-to-end delay, and normal-ized routing overhead. LBCAR outperformed the AODV andreduced the congestion. LBCARhas also been comparedwiththe congestion adaptive routing (CRP) and it has been foundthat packet delivery ratio is almost the same in both routingprotocols. Average delay is slightly less in LBCAR comparedto CRP but normalized routing overhead of LBCAR is higherthan that of CRP.The property of LBCAR is its adaptability tocongestion. LBCAR enjoys fewer packet losses than routingprotocols that are not adaptive to congestion. This is becauseLBCAR tries to prevent congestion fromoccurring in the firstplace, rather than dealingwith it reactively.Thenoncongestedroute concept in the algorithm helps next node that may gocongested. If a node is aware of congestion ahead, it finds anoncongested route that will be used in case that congestionis about to occur.The part of incoming traffic is split and senton the noncongested route, making the traffic coming to thecongested node less.Thus congestion can be avoided andwithLBCAR the traffic load is more balanced, and the probabilityof packet loss is reduced. The results also show the scalabilityof the protocol having robustness for large network.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

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