Abstract — To carry multiple routing inorder to make mobile ad-hoc network more energy efficient and to performswapping of nodes in order to make network more reliable and balance the loadon to node ,here best fit function will be used in order to carry out swapping oftwo perfectly suitable nodes. System initially generates self-configuringnetwork, which contained mobile nodes without any fixed infrastructure. Aftergenerating the network, multipath source and destination is selected forsending the data. After that multipath is found for sending the data, shortestpath is found on the basis of energy of nodes and distance of nodes. After that energy consumption of each node iscalculated, the node which is in ideal state (node which is not in workingcondition or node with low energy) are swap by using the swapping algorithm tothe node with high energy and data is send to the destination node. Keywords—Wireless Sensor Networks, MANETs, Sensor Nodes, energy efficient, multipathrouting.
I. Introduction Wireless sensor networks (WSNs)have many uses in tracking and monitoring, where they have attracted moreattention in recent years. The applications of WSN can be classified intoindustrial, biomedical, environmental, military, agricultural, domestic, andcommercial fields. In the last few years, WSNs have received considerableattention in sports applications for monitoring athletes’ performance duringtraining sessions as well as in international competitions. Cycling is one ofthe sports that has recently attracted significant attention in this respect,and WSNs are widely used for monitoring the physiological and biomechanicalparameters of the athlete and bike, respectively, in order to assess cyclingperformance. Cycling performance can be monitored by using unobtrusive sensornodes; these nodes comprise different components such as a sensor, dataprocessor, transceiver module, and power unit.
A Mobile ad hoc network is a group of wireless mobile nodes in which nodes collaborate by forwarding packets for each otherand allow them to communicate outside the direct wireless range. These networksare fully distributed, and can work at any place without the help of any infrastructure 8. Ad-hoc network does not require any fixed network infrastructure such as base stations, and can be easily set up at low cost as needed. The routers, the participating nodes act as router, are free to move in network randomly and manage themselves arbitrarily; thus, the network’s wireless topology may change rapidly and unpredictably. Each of the mobile nodes is operated by a limited energy battery and usually it is impossible to recharge or replace the batteries in a remote area. Since wireless communications consume significant amount of battery power 1, this limited battery lifetime imposes a severe constraint onthe network performance.
Routing is a process of detecting various routes from source to destination nodes. All theroutes are calculated and then restored in a network. Routing tables are of two types: Static- Routing and Dynamic Routing. Static routing is a type of a network routing technique. Dynamic routing is anetworking technique that provides optimal data routing. The routing table is not affected by addition or deletions ofrouters in case of static routing but it is affected in dynamic routing. Due tochanging positions of nodes 3 andconnections, the energy and lifetime of network degrades.In this paper westudy about the related work done, in section II, the proposed approach modulesdescription, mathematical modeling, algorithm and experimental setup in sectionIII .
and at final we provide a conclusionin section IV. II. LITERATURE REVIEW In 1 highlights the energyconsumption in MANET by applying the fitness function technique to optimize theenergy consumption in ad hoc on demand multipath distance vector (AOMDV)routing protocol. The proposed protocol is called AOMDV with the fitnessfunction (FF-AOMDV). The fitness function is used to find the optimal path fromsource node to destination node to reduce the energy consumption in multipathrouting. The performance of the proposed FF-AOMDV protocol has been evaluatedby using network simulator version 2, where the performance was compared withAOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM)protocols, the two most popular protocols proposed in this area. Te comparisonwas evaluated based on energy consumption, throughput, packet delivery ratio,end-to-end delay, network lifetime and routing overhead ratio performancemetrics, varying the node speed, packet size, and simulation time. MANET (Mobile Ad-hoc Networks) isuseful in many practical scenarios since it provides multi-hop communicationwithout wired infrastructure.
However, there is a problem that thecommunication performance of a flow may be easily degraded by even a singlelocal congestion on the whole path. A solution for the problem is to use adetour path that avoids the local congestion. However, to this end, the detourpaths should not use the nodes in the congested area, which is in factrelatively large due to the nature of radio waves. In the current state of theart, we do not have such alternative-path computation algorithms. In thispaper, we propose an algorithm and a routing scheme to compute and utilizedetour paths adaptively according to the network traffic conditions. Throughevaluation, we show that the proposed scheme improve the communicationperformance by using the detour paths in practical network scenarios 2.
In 3 designed a new algorithmusing the combination of Ad-hoc on Demand Distance Vector (AODV) and Crosslayer design approach. It is referred as Congestion Control AODV (CCAODV)approach. It is used to avoid link break in MANET. Received signal strength isused as cross layer design parameter.
The CCAODV protocol creates strong andstable route by using signal strength of node. The signal strength mainlydepends on the parameters like transmission power of node and distance betweentwo nodes. The cross layer design approach is tested by using Ns 2.35 simulatorand compared with the AODV routing protocol.
Wireless Sensor technology is oneamong the fast emerging technologies in the current scenario and it has widerange of application also which has small sensors with minimum communicationaland computational power. Depending on the overhead of a node, the energyconsumption varies with each other. This leads to the non-uniform distributionof the energy which in turn degrades the performance of the whole network. SwapRate algorithm (SRA) is used for detecting the low level energy node. Inaddition, the nodes are detected even during the other network interruptions.In the recovery method, the node in its vicinity will detect the low levelenergy node position and it will update to sink node which in turn sends nearbynode that has good energy level to recover the node.
It will replace the nodeand finally the data transmission will be taking place without any obstacles toachieve the reliability in the network 4.In 5 a particle swarmoptimization (PSO)-based lifetime prediction algorithm for route recovery inMANET has been proposed. This technique predicts the lifetime of link and nodein the available bandwidth based on the parameters like relative mobility ofnodes and energy drain rate, etc. Using predictions, the parameters arefuzzified and fuzzy rules have been formed to decide on the node status. Thisinformation is made to exchange among all the nodes. Thus, the status of everynode is verified before data transmission. Even for a weak node, theperformance of a route recovery mechanism is made in such a way that correspondingroutes are diverted to the strong nodes.
With the aid of the simulated results,the minimization of data loss and communication overhead using PSO predictionhas been discussed in detail.. III. PROPOSEDAPPROACH A. ProposedSystem Overview Figure1:proposed System Architecture Detailed descriptions of the proposed system are as follows: 1. Network GenerationInitially random network isgenerated and node position in random network is not fixed. 2.
Select Source and DestinationNodeAfter the network creation, theselection of source node and destination node is done. 3. Find the PathDepending on the source node anddestination node generated, the multiple paths from source node to destinationnode are found. 4.
Search Shortest PathNext step is to search theshortest path among the multiple paths to send data. 5. Energy Value CalculationAfter finding shortest pathcalculate the energy of each node of shortest path, if node energy issufficient to transfer data then data is transferred from source todestination.
6. Swapping of NodeIf node energy is not sufficientto transfer data then it checks the neighboring paths node energy, if there issufficient energy to data transfer then swapping of node is performed. 7. Send DataAfter selecting the shortest pathwith energy efficient node then send the data from source node to destinationnode .
B. Algorithm Algorithm1: Proposed Algorithm 1. Deploymentof Random nodes in the network .2. Selectsource node S(n) and Destination node D(n).3. Findthe multiple paths from source to destination for sending data.4.
Dependon the hop(h) value find the shortest path from multiple path.5. Afterselecting the Shortest path, calculate the energy of each node, e(n1), e(n2).e(n3)….
e(n) in path.6. If e(n) ? Te(n) then Transferof data from source to destinations. Else Checks the neighboring paths node energye(n). If e(n)? Te(n) then Selectthe node for swapping.
Else Check another neighboring node. 7. After swapping the node get appropriatepath to send data from source to destination.C.
MathematicalModel Set Theory System S is represented as S=Dn,S(n),D(n), P, PS, 1. Deploy nodesDn = {D1, D2, …..,Dn}Dn is set of all deployed nodes.
2. Select source ad destinationS(n)= {S1}D(n)={D1} 3. Find Multiple path source todestinationP= {P1,P2,P3,P4……}Where P is a set of all Paths 4. Find shortest pathPS= {PS1, PS2, PS3….
.}Where PS is the shortest path 5. Calculate the energy inshortest path E = {E1, E2, E3…..
En}Where E is a set of all nodesenergy 6. Swapping the node N = {N1, N2, ….
,}Where N is a set of all swappingnodes 7. Data sending from clustermembers to cluster Head and from here to base stationF = {f1, f2, f3, ….fn}Where, F is a set of all datapackets transmitted. IV. RESULTS AND DISCUSSIONA.
Experimental SetupThe system isbuilt using Java framework on Windows platform. The Net beans IDE is used as adevelopment tool. The system doesn’t require any specific hardware to run; anystandard machine is capable of running the application. B. Expected ResultIn this section discussed theexperimental result of the proposed system. Following figure 2 shows the timeconsumption graph of the proposed system with the existing system. Comparison graph shows that the time requiredfor implementing the proposed system is less than the time required forimplementing the existing system. Fig.
2: Time Graph IV. conclusion and future scopeAnenergy efficient routing has been simulated by using node rotation conceptwhich helps in the uniform distribution of the energy throughout the network.The critical nodes will be disconnected from the data transmission and the neighbornodes that have energy level greater than threshold level will be elected asborder nodes after.
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