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Energy consumption mitigation Routing Protocols for Large-Scale Wireless Sensor Networks                                  ...
station. Compared to direct communication, cluster-           Clustering Protocol )BCDCP, the CHs are connected bybased me...
When a source needs to send data packets, it utilizes the     broadcasts data packets to all nodes within its coalitionuni...
path po such that C(po) is minimized. The routingproblem formulated above is a shortest path routingproblem on the new dir...
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Energy consumption mitigation routing protocols for large wsn's


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Energy consumption mitigation routing protocols for large wsn's

  1. 1. Energy consumption mitigation Routing Protocols for Large-Scale Wireless Sensor Networks Anil Kumar H1 ,Manjunath CR2 , Dr Nagaraj GS3 1,2 Dept of CSE,SET,Jain University, 3Prof,Dept of CSE,RVCE,VTU,, nagarajgs@yahoo.comAbstract: With the advances in micro-electronics, II-Energy consumption mitigation-basedwireless sensor devices have been made much smaller category:and more integrated, and large-scale wireless sensor The routing protocols in this class aim tonetworks (WSNs) based the cooperation among the mitigate the energy consumption. They exploit varioussignificant amount of nodes have become a hot topic. means to achieve this target, such as dynamic event“Large-scale” means mainly large area or high density of clustering, multi-hop communication, cooperativea network. Accordingly the routing protocols must scale communication and so on. These methods can consumewell to the network scope extension and node density the energy appropriately and avoid wasted energy [1].increases. A sensor node is normally energy-limited andcannot be recharged, and thus its energy consumption has III - Data Gathering algorithm based on Mobilea quite significant effect on the scalability of the Agent (DGMA)[3]protocol. In a hierarchical routing protocol, all the nodes In terms of energy consumption reduction andare divided into several groups with different assignment network end-to-end delay decrease, a Data Gatheringlevels. The nodes within the high level are responsible algorithm based on Mobile Agent (DGMA) is proposedfor data aggregation and management work, and the low for the cluster-based wireless sensor network. where anlevel nodes for sensing their surroundings and collecting emergent event occurs is clustered dynamically based oninformation. With focus on the hierarchical structure, in the event severity, by which the scale and lifetime ofthis paper we provide an insight into Energy clusters are determined. In each cluster a mobile agent isconsumption mitigation routing protocols designed utilized to traverse every member node to collect sensedspecifically for large-scale WSNs. data. In the higher level of the network, a virtual cluster According to the different objectives, the protocols are is formed among the cluster heads and the base station,generally classified based on different criteria such as and multi-hop communication is adopted for sensed datacontrol overhead reduction, energy consumption delivery to the base station (BS).mitigation and energy balance. This paper focuses on the In DGMA, all the sensor nodes are in “restraining” statestudy of energy consumption mitigation to show how to and they are activated only when some emergent eventmitigate the energy consumption. occurs. Then the nodes having monitored the event are clustered. After the event intension gets reduced, theKeywords: large-scale wireless sensor networks, routing clustered nodes will change to a “restraining” state forprotocol. the sake of energy consumption reduction. In the cluster, the tree structure is used to save energy instead of single hop communication between the sensor nodes and theI- Introduction cluster head. After the cluster construction is complete, a WSN is widely considered as one of the most route for the mobile agent, which is equipped on theimportant technologies for the twenty-first century. A cluster head, is used to traverse all the member nodes forWSN typically consists of a large number of low-cost, collecting the sensed event data. This process is startedlow-power, and multifunctional wireless sensor nodes, up by the cluster head and repeated at every clusterwith sensing, wireless communications and computation member by broadcasting a request packet, andcapabilities . These sensor nodes communicate over short anticipating a reply from its each neighbor for gettingdistance via a wireless medium and collaborate to residual energy, path loss, and event intensionaccomplish a common task, for example, environment information of the neighbor. To deliver the sensed data tomonitoring, military surveillance, and industrial process the final destination (here the base station) in the highercontrol.In many WSN applications, the deployment of1 level of the network a virtual cluster is formed whereinsensor nodes is performed in an ad hoc fashion without the base station acts as the cluster head. As in the localcareful planning and engineering. Once deployed, the cluster, a multi-hop communication is adopted. Thesensor nodes must be able to autonomously organize current cluster head will select the node which is thethemselves into a wireless communication network. closest to the base station in the neighboring nodes as itsSensor nodes are battery-powered and are expected to next hop. If the distance from all neighbor nodes to theoperate without attendance for a relatively long period of base station is longer than that from the node itself, thetime. In most cases it is very difficult and even node will communicate with the base station directly.impossible to change or recharge batteries for the sensor When the number of the sensor nodes increases, thenodes. When the energy of a sensor reaches a certain energy consumption in DGMA increases more slowly.threshold, the sensor will become faulty and will not be Furthermore, the dynamic cluster formation featureable to function properly, which will have a major impact further reduces the energy consumption. The use of aon the network performance [1, 2]. mobile agent reduces energy consumption, but extends The routing protocols for large scale WSNs can the delay for the cluster head to collect all the sensed datacategorized as control from all the member nodes. The chain-like route delivery overhead reduction-based, of data by the cluster head makes the node closest to the energy consumption mitigation-based and base station overloaded and destroys the reliability. Energy balance-based. Cluster-based wireless sensor network saves energy by reducing the number of nodes communicating with base
  2. 2. station. Compared to direct communication, cluster- Clustering Protocol )BCDCP, the CHs are connected bybased method has a remarkable improving in energy- a tree instead of a club and the BS functions as theefficient. manager of the whole network, so BCDCP is moreDGMA includes dynamic clustering and Data Gathering energy-efficient than LEACH. DMSTRP improvesBased on Mobile Agent for Emergent Event Monitoring BCDCP further by connecting nodes in clusters by MSTs. In each cluster, all the nodes including the CH areDynamic Clustering connected by a MST and then the CH acts as the leadera) Dynamic Clustering Based on Event Severity to collect data from the nodes on the tree. On the higher Degree: level, all the CHs connected by another MST cooperateAfter wireless sensor network is deployed into the to route data towards the BS. The data fusion process ismonitoring environment, all nodes will be set to handled during the packet transmission along the tree“restraining” state rather than clustered. And they’re route.activated just when some emergent event occurs. Then Obviously, DMSTRP consumes energy more efficientlythe nodes will be clustered. The scale and lifetime of the than LEACH and BCDCP, because the averageclusters lie on the event severity degree. After the transmission distance between nodes is reduced throughstimulating intension is reduced, those activated nodes the multi-hop intra-cluster and inter-clusterwill change to “restraining” state over again. The cluster- communications, and thus the energy dissipation oftree structure is used to save energy , with multi-hop transmitting data is potentially reduced. Furthermore, duerather than single hop from the member nodes to the to the reasonable schedule, the transmission collision iscluster head. alleviated and DMSTRP can achieve shorter delay compared with LEACH and BCDCP. But the transmission schedule creates more overhead.b) The Construction of Virtual Cluster: Generally,single-hop communication is taken between the cluster V - Hierarchical Geographic Multicast Routingheads and the base station in spite of long distance, in (HGMR).[5]which those cluster heads away from the base station HGMR aims at enhancing data forwarding efficiency andalways have a weak lifetime because of more energy increasing the scalability to a large-scale network.consumption led by long-distance. A multi-hop virtual HGMR seamlessly incorporates the key design conceptscluster is formed with base station as the cluster head. of the Geographic Multicast Routing (GMR) andThe path from the cluster head to base station can be Hierarchical Rendezvous Point Multicast (HRPM)searched as follows. The cluster heads always select the protocols, and optimizes the two routing protocols in thenode which is the closest to base station in the neighbor wireless sensor network environment. HGMR starts withnodes as its next hop. If the distance from all neighbor a hierarchical decomposition of a multicast group intonodes to base station is longer than that from the node subgroup of manageable size using HRPM’s key conceptitself to base station, the node will communicate with of mobile geographic hashing. Within each subgroup,base station directly. HGMR uses GMR’s local multicast scheme to forward a data packet along multiple branches of the multicast treeData Gathering Based on Mobile Agent for Emergent in one transmission. In HGMR, the multicast group isEvent Monitoring divided into subgroups using the mobile geographica) Dynamic Route Planning of Mobile Agent: hashing idea: the deployment area is recursivelyFor an emergent event monitoring scene, when some partitioned into equal-sized square sub-domains calledevent occurs, only those nodes in event area would be cells, where d is decomposition index depending on theactivated to cluster. The selection of the next hop for encoding overhead constraints, and each cell consists ofmobile agent not only bases on energy consumption and a manageably-sized subgroup of members. An Accesspath loss, but also the stimulated intension received by Point (AP) is responsible for all members in its cell, andthe nodes, in which the discrete emergent event is under APs are managed in turn by a Rendezvous Point (RP).consideration. The role of each AP or RP is mapped to some uniqueb) The Data Aggregation on Mobile Agent geographic location by a simple hash function. The nodeThe mobile agent consists of identification ID, route that is currently closest to that location then serves theinformation, data buffer and processing codes, in which role of AP/RP, and routing to the AP/RP is convenientlydata buffer mainly load the data distilled or fused data achieved by geographic routing. To join a hierarchicallyfrom sensor nodes decomposed multicast group, a node first hashes the multicast group identifier (GID) to obtain the hashedIV - Dynamic Minimal Spanning Tree Routing location of the RP via a hashed function and sends aProtocol (DMSTRP)[4] JOIN message to the RP, which is the same as in the flat DMSTRP is a cluster-based routing protocol, domain scenario. After receiving the value of the currentuses Minimal Spanning Tree (MSTs) to replace clubs to d of the hierarchy from the RP, the node utilizes the hashconnect the node in the clusters in two layers of the function with d and the node’s location to compute thenetwork: intra-cluster and inter-cluster. Because clubs are hashed location of the AP belonging to its cell. Note thatless effective than a spanning tree in connecting the computing the hashed location assumes that all nodesnodes if the network area is larger, DMSTRP is an know the approximate geographic boundaries of theelegant solution in larger network areas. network. After that the source builds an overly tree, the(Low Energy Adaptive Clustering Hierarchy)LEACH Source → APs tree, whose the vertices are active APs inchooses clubs as the basic topology of the network, as a topology graph; and an AP → Members overly tree isshown in Figure 1 and managing clubs does not need also built from the AP, considering each member as themulti-hops and thus makes the routing path simple. One vertex. 2 dstep further in (Base Station Controlled Dynamic
  3. 3. When a source needs to send data packets, it utilizes the broadcasts data packets to all nodes within its coalitionunicast-based forwarding strategy belonging to HRPM to and looks for the next stage coalition to forward thepropagate data packets to each AP along the Source → packet to. Once the next stage CH, denoted by CHk, wasAPs tree. In each cell, adjusting the value of d, the chosen, CHi coordinates the nodes within its coalition tonumber of members for which an AP is responsible does cooperatively forward the packet to CHk. This processnot increase too much. Therefore, GMR’s cost over continued until the data were forwarded to the destinationprogress optimizing the broadcast algorithm, which isused to select the next relay node at each hop, contributesto reduce the number of data transmissions whilemaintaining a low encoding overhead compared with theunicast communication. Sensor nodes running GMR usethe position of their neighbors to select the subgroupwhich is the best one to deliver the message towards thedestination, and the selected neighbors can reduce mostthe total route to destination. When no neighbor of thecurrent node can reduce the route to the destination, facerouting is used to circuitously search the path to thedestination. In HGMR, the geographic hashing algorithmmakes the membership management very simple withalmost zero cost. According to the number of the nodeswhich play the different roles, HGMR selects thetransmission methods for different hierarchies in reason,which makes the routing energy-efficient and scalable.However, the RP is in charge of too much missions in It focus on joint optimal clustering and cooperativeHGMR, which may bring the problem of rapid energy routing. Consider a cooperative sensor network, where aconsumption and make the entire network collapse. node with data would first multicast the packet to a subset of its neighbors, and then ask them to dynamicallyHGMR starts with a hierarchical decomposition of a form a coalition, and cooperatively transmit the packet tomulticast group into subgroups of manageable size (i.e. the next-hop destination. The corresponding energyencoding overhead) using HRPM’s key concept mobile consumption is the sum of the multicast cost and thegeographic hashing. Within each subgroup, HGMR uses cooperative transmission cost. Intuitively, when theGMR’s local multicast scheme to forward a data packet number of nodes in a coalition increases, the cooperativealong multiple branches of the multicast tree in one transmissiontransmission. Thus, HGMR can simultaneously achieve cost would decrease, but the multicast cost wouldenergy efficiency (through higher forwarding efficiency increase, and vice versa.utilizing multicast advantage) and scalability (throughlow overhead hierarchical decomposition). JOINT CLUSTERING AND MINIMUM ENERGY COOPERATIVE ROUTING includesVI - Joint Clustering and Optimal Cooperative a) Optimal Coalition Size: Consider a sensor network,Routing (JCOCR):[6] where each node has a strict power constraint Pmax. Data need to be routed from a source node S to a destination joint clustering and optimal cooperative routing, where node D. In each transmission, an intermediate nodeneighboring nodes dynamically form coalitions and would multicast the packet to a subset of its neighbors,cooperatively transmit packets to the next hop and ask the nodes in the subset to dynamically form adestination. The cooperative sensor network can be coalition and cooperatively transmit the packet to nextmodeled as an edge-weighted graph, based on which stage destination (point-to-multiple-point transmissionminimum energy cooperative routing is characterized by first, and then multiple-point-to-point transmission).using the standard shortest path algorithm.We study two During the routing process, the number of neighboringinteresting cases: 1) For the case where the delay can be nodes that participate in the cooperative transmission,expressed in terms of the number of hops, we use the bi- i.e., the size of the dynamic coalition, plays a key role.section method to find the maximum throughput routing; Note that the energy cost of each transmission is the sum2) For large scale networks where the end-to-end delay of the multicast cost and the cooperative cost. Intuitively,can be approximated as the product of the number of a larger coalition would reduce the cooperative cost, buthops and the average one-hop delay, we present a may require more multicast energy to reach nodes furtherpolynomial time algorithm to find the maximum away, whereas a smaller coalition would require lessthroughput routing. the energy efficient cooperative multicast energy but higher cooperative cost. Thusrouting can enhance the performance of WSNs motivated, we characterize the optimal coalition size tosignificantly. minimize the transmission cost.We have taken some initial steps to investigate b) Minimum Energy Cooperative Routing: The minimumdistributed cooperative geographic routing, building on energy cooperative routing problem (MECR) can benode cooperation and traditional geographic routing. As defined as follows. Definition : (MECR) The Instance isillustrated in Fig. 1, for a given source-destination pair, given by an edge-weighted directed graph G = (V, E,C, γ)the routing problem in a coalition-aided network was and a source destination pair S-D. Let p be a path in Gtreated as a multiplestage decision problem, where at and C(p) be the sum of the costs over the edges on p,stage i, the coalition head, denoted as CHi, first C(p) =∑e∈ p C(e). The Problem is to find the optimal
  4. 4. path po such that C(po) is minimized. The routingproblem formulated above is a shortest path routingproblem on the new directed graph G, and can be solvedby the well-known Dijkstra’s algorithm. The minimumenergy cooperative routing would achieve better energysaving, because of the following reasons. 1) It exploitsoptimal power allocation within each coalition to reducethe cooperative transmission cost. 2) It characterizes theoptimal coalition size to minimize the energy cost ofeach transmission. 3) It chooses the routing path basedon global information instead of local information.VII- CONCLUSIONAt present, routing in large-scale WSNs is a hot researchtopic with a limited but rapidly growing set of effortsbeing published. This paper is contribution to study on 4various routing protocols of Energy-ConsumptionMitigation in large-scale WSN’s. With the increasing functionalities available to awireless sensor node, more complicated tasks whichinvolve more energy consumption and network overheadmay be assigned to the sensor nodes. To increase energyefficiency and scalability of the network still remains achallenging research area.REFERENCES[1] Changle Li *, Hanxiao Zhang, Binbin Hao and Jiandong Li,“A Survey on Routing Protocols for Large-Scale WirelessSensor Networks”,[2] Al-Karaki, J.N.; Kamal, A.E., “ Routing techniques inwireless sensor networks: A survey”. IEEE Wirel. Commun.2004, 11, 6-28.[3] Lingyun Yuan, Xingchao Wang, “Study on Data GatheringAlgorithm Based on MobileAgent and WSN for EmergentEvent Monitoring”, Yunnan Normal University Kunming,China.[4] Guangyan Huang1, Xiaowei Li1, and Jing He2, “DynamicMinimal Spanning Tree Routing Protocol forLarge Wireless Sensor Networks” Advanced Test TechnologyLab., Institute of Computing Technology,Chinese Academy of Sciences, Beijing, China, 2006 IEEE[5] Dimitrios Koutsonikolas1, Saumitra Das1, Y. Charlie Hu1,and Ivan Stojmenovic2,3, “Hierarchical Geographic MulticastRouting forWireless Sensor Networks”, The University ofBirmingham, United Kingdom3SITE, University of Ottawa, Ontario, Canada, 2007 IEEE[6] Weiyan Ge and Junshan Zhang, Guoliang Xue, “JointClustering and Optimal Cooperative RoutingIn wireless sensor network”,IEEE 2008