LIFETIME IMPROVEMENT OF WIRELESS SENSOR NETWORK USING NETWORK CODING AND ADAPTIVE DUTY CYCLE
Journal Title: International Journal of Advances in Scientific Research and Engineering - Year 2015, Vol 1, Issue 2
Abstract
Wireless sensor networks (WSNs) are composed of very low cost, low power consuming devices with sensing, signal processing and wireless communication systems. Minimizing energy consumption and maximizing network lifetime are important issues in the design of protocols for sensor networks. Wireless sensor network consists of automatic sensors, which have a limited power battery. Nodes that are present near the centralized collecting point will be in demand of much power which limits the overall network life time. The active area near to the sink node creates a bottleneck zone because of the presence of large traffic- flow which causes minimization of the network lifetime in wireless sensor network. In this project, we introduce an adaptive duty cycle and encoding technique for energy consumption minimization in the bottleneck zone .An efficient technique of communication has been adopted in the bottleneck zone by using the combination of adaptive duty cycle and encoding technique. Energy efficiency of the bottleneck zone will get increased due to more volume of the data that would be transmitted to the sink with the same number of transmissions. Hence the lifetime of the wireless sensor network is increased. This work archive to the enhancement of the energy efficiency of the bottleneck zone which in turn leads to overall improvement of the network lifetime by considering an network coded adaptive duty cycle WSN. Linear Network coding does not simply relay on the packets of information that they receive, the sensor nodes of a network take several packets and combine all of them together for the transmission and apply it to the bottleneck zone. By applying the above techniques we finally achieve the increased overall lifetime of node. This proposed system investigates life time improvement approx 8% - 14%, and minimizing energy consumption.
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