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Big Data Oriented Energy Aware Routing for Wireless Sensor Networks - 2018

Research Area:  Wireless Sensor Networks


Wireless Sensor Networks (WSN), require energy efficient routing protocols to address their limited node energy issue. Many protocols attempt to provide the least energy cost path to perform data routing. But, this routing solution can lead to fast node energy depletion and eventual network disconnection, if more number of packets are routed. To overcome this issue, energy aware routing protocol [1] was proposed, which achieved efficient routing data load distribution by selecting multiple low cost paths and involving these paths for data packet routing. Currently, many WSN are generating huge volumes of data/Big Data, and energy aware routing protocol is not sufficient to achieve the required load distribution for Big Data routing. In this work, energy aware routing protocol [1] is extended to address Big Data issue. Since, many Big Data applications require Quality of Service (QoS), priority levels are assigned to differentiate WSN applications. The most critical applications are provided with the best QoS. More number of nodes is involved in data packet routing compared to energy aware routing protocol [1], so that, load distribution effectiveness increase. The nodes which have richer resources to satisfy application QoS constraints and require less energy costs for data packet transmission are frequently selected through the aid of a novel probability mass function. This proposed technique is implemented in Network Simulator 3. The empirical results demonstrate orders of magnitude load distribution effectiveness and slightly increased total energy consumption of the proposed routing technique when compared to least energy cost routing protocol.

Author(s) Name:  J. Reshma, T. Satish Kumar, B. A. Vani and S. Sakthivel

Journal name:  Mobile Networks and Applications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11036-018-1042-y

Volume Information:   volume 24, pages 298–306 (2019)