Energy Efficiency in Wireless Sensor Networks

TL;DR

This paper introduces an Energy Driven Architecture (EDA) for wireless sensor networks, aiming to minimize energy consumption, optimize network lifetime, and develop an energy map for efficient application design.

Contribution

It proposes a new constituent-based architecture and a comprehensive energy model for WSNs, along with an energy-efficient topology management algorithm.

Findings

Energy model accurately predicts energy consumption per packet.

Topology algorithm reduces energy use compared to existing methods.

Parameter reduction maintains model accuracy with fewer variables.

Abstract

Unlike most of the current research that focuses on a single aspect of WSNs, we present an Energy Driven Architecture (EDA) as a new architecture for minimising the total energy consumption of WSNs. EDA as a constituent-based architecture is used to deploy WSNs according to energy dissipation through their constituents. This view of overall energy consumption in WSNs can be applied to optimising and balancing energy consumption and increasing the network lifetime. Refer back to the architecture, we introduce a single overall model and propose a feasible formulation to express the overall energy consumption of a generic wireless sensor network application in terms of its energy constituents. The formulation offers a concrete expression for evaluating the performance of WSN application, optimising its constituents operations, and designing more energy-efficient applications. The ultimate aim is to produce an energy map architecture of a generic WSN application that comprises essential and definable energy constituents and the relationships between these constituents to explore strategies for minimising the overall energy consumption of the application. Later, parameters affecting energy in WSNs are extracted. The dependency between these parameters and the average energy consumption of an application is then investigated. A few statistical tools are applied for parameter reduction followed by random forest regression to model energy consumption per delivered packet with and without parameter reduction to determine the reduction in accuracy due to reduction. Finally, an energy-efficient dynamic topology management algorithm is proposed based on the EDA model and the prevalent parameters. The performance of the new topology management algorithm, which employs Dijkstra to find energy-efficient lowest cost paths among nodes, is compared to similar topology management algorithms.