Abstract-Load imbalance among hot spot nodes causes network congestion and earliest energy depletion of nodes in wireless sensor networks. This increases the probability of disconnecting or partitioning the network and premature death of entire network. The inefficiency in the WSN is more attributed to load imbalance or unbiased traffic. In this paper, an optimized congestion aware (OCAEE-LB) energy efficient traffic load balancing scheme for routing in WSN is proposed. The scheme utilizes the neglected information during route discovery process and considers a composite routing metric to determine congested status of a node and to enforce the traffic load balancing. The proposed scheme is simulated using ns-2 and the results demonstrate that the proposed mechanism performs better than the existing AODV-LB algorithm of various performance metrics such as, packet delivery ratio, throughput, routing overhead, endto-end delay, load distribution and energy consumption.

Abstract-Traffic in wireless sensor networks (WSN) exhibits a many-to-one pattern in which multiple source nodes send sensing data to a single sink node. Since bandwidth, processor and memory are highly constrained in WSN, packet loss is common when a great deal of traffic rushes to sink. The system must provide differentiated service to individual traffic classes. In this paper, a pre-emptive multiple queue based congestion control mechanism is proposed. To detect congestion and to provide QoS for high priority traffic multiple buffers are used. Using this mechanism, high system utilization, reduced packet waiting time, and reduced packet drop probability are achieved. An analytical model is developed to predict the performance of the proposed mechanism by calculating the performance measures including system throughput, drop probability of packets, and mean queue length. By comparing analytical and simulation results the effectiveness and accuracy of the model is demonstrated. Markovian process is used to develop the analytical model and ns-2 for evaluating the performance of the mechanism.

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In resource constrained wireless sensor networks, congestion control is an extremely important issue that need to be addressed. The individual capacities of the channels are exceeded by the bulk traffic and creates adverse effects on the performance of the network. Therefore, to resolve the congestion problems in wireless sensor network the challenge lies in developing more sophisticated routing techniques which are able to fairly deliver the data between source and destination with minimum consumption of energy and reduced congestion. In the recent times, various swarm intelligence based routing approaches are proposed that aided in congestion detection and control mechanisms. Most of them are found to be with lower convergence rate. Therefore, a nature inspired hierarchical routing technique which aims to reduce congestion and energy consumption with network longevity and faster convergence rate is proposed. In this technique, a static partition of the target area based on node density is done to optimize energy efficiency. Firefly behavior based routing is modeled to select the optimal path for data transmission. This approach is concerned with exploiting global behavioral patterns emerging from local interactions. The proposed technique aims to minimize congestion by applying network load balance. network lifetime, energy consumption and pathlength. This ideally makes the proposed algorithm suitable for diverse WSN applications.

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G ri d an d U ti li ty C o m p u ti n g

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