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Abstract

In an energy harvesting sensor actor network, a node recharges its battery from harvestable sources, such as solar, wind and vibrations. Sustainability of the network till next recharge time is one of the most important challenges in harvesting sensor networks. In this paper, a fuzzy based adaptive duty cycling algorithm has been proposed to achieve the network sustainability in harvesting sensor actor networks. In this work, current residual energy, predicted harvesting energy (for a futuristic time slot) and predicted residual energy parameters are considered as fuzzy input variables to estimate duty cycle for a sensor node. In this work, a harvesting model has been adopted to predict the harvesting energy. In this paper implement the mobile access coordinated wireless sensor network (MC-WSN) a novel energy efficient scheme for time-sensitive applications. In conventional sensor networks with mobile access points (SENMA), the mobile access points (MAs) traverse the network to collect information directly from individual sensors. In SENMA, the mobile access points (MAs) traverse the network to collect the sensing information directly from the sensor nodes. SENMA model improved the energy efficiency of the individual sensor nodes over ad-hoc networks by relieving sensors from complex and energy-consuming routing functions. A major limitation with SENMA is that a transmission is made only if an MA visits the corresponding source node and thus, data transmission is largely limited by the physical speed of the MAs and the length of their trajectory, resulting in low throughput and large delay. The main limitation of these existing approaches is that data transmission depends on the physical speed of the access point, which is not desirable for time-sensitive applications. This thesis proposed a three-layer framework is proposed for mobile data transmission in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as DDU. The objective is to achieve good scalability, and low data transmission latency. At the sensor layer, a distributed load balanced clustering algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, the scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. The paper simulations are conducted to evaluate the effectiveness of the proposed scheme. The results show that when each cluster has at most two cluster heads, the scheme achieves over more energy saving per node and more energy saving on cluster heads comparing with data collection through multihop relay to the static data sink.

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How to Cite
Vaishnavi R, Vidhyavarshini P, Venkateshprasath M, & M.Umamaheshwari. (2019). Improved adaptive duty cycling model using senma base LBC-DDU algorithm for wireless sensor networks . International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 1211–1219. Retrieved from https://ijiarec.com/ijiarec/article/view/1111