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

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

Sensor network coverage and data aggregation problem: solutions toward the maximum lifetime

In the coverage problem, an optimal solution is proposed for the maximum lifetime sensor scheduling problem, which could find the upper bound of a sensor network\u27s lifetime. This research reveals the relationship between the degree of redundancy in sensor deployment and achievable extension on network lifetime, which can be a useful guide for practical sensor network design --Introduction, page 4

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

Comparative Study of New and Traditional Estimators of a New Lifetime Model

In this article, we have studied the behavior of estimators of parameter of a new lifetime model, suggested by Maurya et al. (2016), obtained by using methods of moments, maximum likelihood, maximum product spacing, least squares, weighted least squares, percentile, Cramer-von-Mises, Anderson-Darling and Right-tailed Anderson-Darling. Comparison of the estimators has been done on the basis of their mean square errors, biases, absolute and maximum absolute differences between empirical and estimated distribution function and a newly proposed criterion. We have also obtained the asymptomatic confidence interval and associated coverage probability for the parameter

Average Case Network Lifetime on an Interval with Adjustable Sensing Ranges

Given n sensors on an interval, each of which is equipped with an adjustable sensing radius and a unit battery charge that drains in inverse linear proportion to its radius, what schedule will maximize the lifetime of a network that covers the entire interval? Trivially, any reasonable algorithm is at least a 2-approximation for this Sensor Strip Cover problem, so we focus on developing an efficient algorithm that maximizes the expected network lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an expected network lifetime within 12 % of the theoretical maximum. Most of the algorithms that we consider come from a particular family of RoundRobin coverage, in which sensors take turns covering predefined areas until their battery runs out