Void Avoidance Scheme for Real-Time Data Dissemination in Irregular Wireless Sensor Networks

et al. 2021

Sensors

In wireless rechargeable sensor networks (WRSNs), a mobile charger (MC) moves around to compensate for sensor nodes’ energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term “network lifetime” is defined by the interval from when the network starts till the first target is not monitored by any sensor. The algorithm, named Fuzzy Q-charging, optimizes both the time and location in which the MC performs its charging tasks. Fuzzy Q-charging uses Fuzzy logic to determine the optimal charging-energy amounts for sensors. From that, we propose a method to find the optimal charging time at each charging location. Fuzzy Q-charging leverages Q-learning to determine the next charging location for maximizing the network lifetime. To this end, Q-charging prioritizes the sensor nodes following their roles and selects a suitable charging location where MC provides sufficient power for the prioritized sensors. We have extensively evaluated the effectiveness of Fuzzy Q-charging in comparison to the related works. The evaluation results show that Fuzzy Q-charging outperforms the others. First, Fuzzy Q-charging can guarantee an infinite lifetime in the WSRNs, which have a sufficient large sensor number or a commensurate target number. Second, in other cases, Fuzzy Q-charging can extend the time until the first target is not monitored by 6.8 times on average and 33.9 times in the best case, compared to existing algorithms.

Section: Fuzzy Q-charging Algorithmmentioning

confidence: 99%

“…This assumption is legitimate in geographic routing [34][35][36][37][38][39][40][41][42]. To determine the next node, the current node only needs to look up in its neighbor table and find the one with the smallest distance to the destination.…”

mentioning

confidence: 99%

An On-Demand Charging for Connected Target Coverage in WRSNs Using Fuzzy Logic and Q-Learning

et al. 2021

Sensors

“…The main idea is to create a forbidden area around every hole from which all the packets are kept to stay away from. Typically, forbidden area is a static region covering the hole and with simple shapes such as circle [29,30], ellipse [31], hexagon [32], convex hull [33,34] In [29], Yu et al, proposed a routing scheme wherein the forbidden area is a circle covering the hole. First, the nodes on the boundary of the holes are identified using Boundhole algorithm [24] and a virtual circle that exactly covers the hole is formed.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, to bypass the problems, the common idea is forming a forbidden area around each hole. As a result, there have been proposed forbidden areas with certain shapes such as circle [29,30], ellipse [31], hexagon [32]. Although the simplicity of shaped regions can reduce the control overhead, a significant enlargement on the routing path may occur.…”

Section: Introductionmentioning

confidence: 99%

A Path-Length Efficient, Low-Overhead, Load-Balanced Routing Protocol for Maximum Network Lifetime in Wireless Sensor Networks with Holes

2020

Sensors

In wireless sensor networks (WSNs) with holes, designing efficient routing protocols, which prolong the network lifetime, is one of the most critical issues. To this end, this paper proposes a new geographic routing protocol for WSNs named the load Balanced and constant Stretch protocol for bypassing Multiple Holes (i.e., BSMH). In contrast to the existing works in the literature, the design of BSMH simultaneously takes into account the three factors that impacted the network lifetime, including routing path length, control packet overhead, and load balance among the nodes. Moreover, BSMH aims at minimizing the routing path length and the control overhead, while maximizing the load balance. We theoretically prove the efficiency of BSMH and extensively evaluate BSMH against the state-of-the-art protocols. The evaluation results show that the proposed protocol outperforms the others in various investigated metrics, not only network lifetime, but also routing path stretch, load balance, and control overhead. Specifically, BSMH prolongs the network lifetime by 30 % compared to the existing protocols while guaranteeing that the routing path stretch is under 1.3 .

“…When a node wants to send a packet, it exploits the aware information to create a routing path that avoids all the holes [12], [13]. In [14]- [19], the authors proposed a variation of hole-awareness by introducing a forbidden area for routing packets around every hole, which is a static region with a simple shape such as circle [14], [15], ellipse [16], hexagon [17], or convex hull [18], [19]. The forbidden area information is then disseminated to nodes to establish hole awareness.…”

mentioning

confidence: 99%

QIH: An Efficient Q-Learning Inspired Hole-Bypassing Routing Protocol for WSNs

Dinh

et al. 2021

IEEE Access

This paper addresses the local minimum phenomenon, routing path enlargement, and load imbalance problems of geographic routing in wireless sensor networks (WSNs) with holes. These issues may degrade the network lifetime of WSNs since they cause a long detour path and a traffic concentration around the hole boundary. Aiming to solve these problems, in this work, we propose a novel geographic routing protocol for WSNs, namely, Q-learning Inspired Hole bypassing (QIH), which is lightweight and efficient. QIH's conceptual idea is to leverage Q-learning to estimate the distance from a node to the holes. QIH makes routing decisions following the nodes' residual energy, their estimated distance to the holes, and their distance to the destination. We first confirm the effectiveness of QIH by theoretical analysis. Then, we conduct extensive simulations of QIH in comparison to state-of-the-art protocols. The simulation results show that QIH outperforms the other protocols in terms of network lifetime, packet latency, and energy consumption.INDEX TERMS geographic routing, network lifetime, Q-learning, hole bypassing, energy efficiency. I. INTRODUCTION1 A wireless sensor network (WSN) has sensor nodes deployed 2 over a region of interest with various applications, such as disaster management or agriculture monitoring [1]-[4]. Each 4 sensor node senses its surrounding environment and sends 5 the data via multiple hops to a destination (i.e., the sink).6 Many WSN applications require full sensing coverage, where 7 the death of a single node may cause inefficacious network 8 operations. Moreover, the time from the beginning until the 9 first node's death (i.e., the earliest energy depletion) is de-10 fined as the network lifetime. Hence, one of the most critical 11 problems in WSNs is maximizing the network lifetime. A 12 sensor node typically consumes its energy for computation 13 and transmission tasks. The former energy amount is in-14 significant compared to the latter [5], [6]. Specifically, the 15 energy needed for transmitting a single bit is approximately 16 the same amount for processing a thousand operations [6].Therefore, an energy-efficient routing protocol is essential to 18 conserve energy.