Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels

Abstract

In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since the optimization problem is non-convex and difficult to solve directly, by considering the network with the relatively high signal-to-interference-plus-noise ratio (SINR), the non-convex optimization problem can be transformed into a convex optimization problem by convex approximation. We attain the properties of the optimal solution by Lagrange duality and solve the convex optimization problem by the CVX solver. The experimental results demonstrate that the total delay of the energy harvesting WSNs with interference channels is more than that in the orthogonal channel; the total network delay increases with the increasing data flow for the fixed energy arrival rate; and the energy transfer can help to decrease the total delay.

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“…In 2019, Jiao, et al Jiao et al (2019a) have introduced the di culty of capacity allocation within wireless sensor networks (WSN) of energy harvesting by interference channels. For xed information and energy topologies, the optimization issue was formulated while the ow of information leftovers stable on entire data connection and every sensor node collects energy during the interval; optimal solution properties are attained through Lagrange duality using CVX solver to solve the problem.…”