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Home / programming language / NS2 / Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks
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Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks

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SKU: PROJ9012 Categories: 2018-2019 Projects, Mobile Computing Projects, NS2 Tags: 2019 projects, academic projects, Clustered Wireless Sensor Network, Energy Efficiency, Energy Harvesting, Final Year Projects, Mobile Computing Projects, project center madurai, project center ramnad, Resource Allocation, Wireless Information and Power Transfer
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Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks

Abstract-In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising
solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) by trading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays under two scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power and power splitting ratio.

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