A novel distributed algorithm for complete targets coverage in energy harvesting wireless sensor networks

Abstract

Abstract-A fundamental problem in energy harvesting Wireless Sensor Networks (WSNs) is to maximize coverage, whereby the goal is to capture events of interest that occur in one or more target areas. To this end, this paper addresses the problem of maximizing network lifetime whilst ensuring all targets are monitored continuously by at least one sensor node. Specifically, we will address the Distributed Maximum Lifetime Coverage with Energy Harvesting (DMLC-EH) problem. The objective is to determine a distributed algorithm that allows sensor nodes to form a minimal set cover using local information whilst minimizing missed recharging opportunities. We propose an eligibility test that ensures the sensor nodes with higher energy volunteer to monitor targets. After that, we propose a Maximum Energy Protection (MEP) protocol that places an on-duty node with low energy to sleep while maintaining complete targets coverage. Our results show MEP increases network lifetime by 30% and has 10% less redundancy as compared to two similar algorithms developed for finite battery WSNs.

A Novel Multi-Targets Cycle Control Coverage Preservation Protocol in Wireless Sensor Network

“…In order to verify the validity and feasibility further, we utilize MATLAB 7 as the simulation platform, simulate MTCPP, [10], [11], [16] and [17], and show the performance comparison under different evaluation system. Simulation parameters are listed in Table 3.…”

On complete targets coverage and connectivity in energy harvesting wireless sensor networks

“…If s 3 forwards data through s 2 , say path P 1 3 , the increased energy consumption rate of s 1 is E f R; i.e., S(P 1 3 , s 1 ) = {s 2 } and δ(s 2 , C t ) = δ(s 1 , C t ) = 1 in Equ. (8) and (9). However, if s 3 forwards data through s 4 , then this rate at s 1 is 2E f R. This is because s 4 is not in C t and δ(s 4 , C t ) = 0 in Equ.…”

“…Critically, these solutions do not consider recharging opportunities. In the context of energy harvesting WSNs, only a handful of works, e.g., [2] [9], have considered the complete targets coverage problem. This so called Maximum Lifetime Coverage with Energy Harvesting node (MLCEH) problem, however, does not consider network connectivity to the sink; i.e., scheduling the wake-up time of sensor nodes in order to forward sensed data back to the sink.…”

On New Approaches of Maximum Weighted Target Coverage and Sensor Connectivity: Hardness and Approximation

“…In some cases, it is hard for people to deploy sensors manually, and therefore, random deployment [19], [20] can be used to construct a wireless sensor network. Because random deployment cannot ascertain the sensors' locations before deployment, the problem of scheduling sensors to be activated to form a wireless sensor network and covering targets such that the network lifetime is extended has received a great deal of attention [21], [22], [23]. In [21], a distributed algorithm is proposed to alternatively activate sensors to form a minimal set cover for covering all targets such that the network lifetime is maximized in energy-harvesting wireless sensor networks.…”