Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

Abstract

Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated.

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

“…This is because the energy insufficiency of sensor nodes limits their operational life [39], and whenever the residual energy of a sensor node is exhausted, this node becomes inactive thus causing coverage holes. Consequently, the use of schemes aiming at the achievement of power control [40], data aggregation [41,42], energy efficiency [43][44][45], energy balancing [46][47][48], data compression and restoration [49][50][51], congestion avoidance, and congestion control [52][53][54][55][56] should be also considered. Likewise, the preservation of network connectivity is an issue of crucial importance for WSNs in order to be operational and thus has to be pursued [57][58][59][60].…”

Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review

“…One possibility for aggregating the data in a network is via a mobile node [ 120 ], which collects the data from the normal nodes and transmits them to the base station (BS). The so-called mobility driven schemes present an efficient technique for minimizing the energy consumption by avoiding multi-hop communication and reducing the overload for the nodes closer to the base station, which typically occurs in other routing protocols [ 121 ]. In this energy saving technique, the mobile node needs to have a high energy budget, but the consumed energy in normal nodes is uniformly distributed.…”

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

“…Among them, noteworthy examples of Internet of Things Wireless Sensor Networks (IoT -WSNs), monitoring platforms and recommender systems are reported in the following (Table 1), also exploiting the use of machine learning (ML) in IoT systems [8]. Importantly, the applicability of the WSNs mainly depends on the lifetime of the sensor nodes, and, for this reason, it is important to design this type of system while bearing in mind this crucial aspect and selecting the more convenient energy efficient routing protocol [9][10][11]. Other important design parameters are [10,12,13]: (i) the limited storing and computational resources of each sensing nodes, (ii) the costs (i.e., cheap sensors are prone to failure, while expensive sensors need good housing and cannot be used for dense deployments), (iii) the position of each sensing node, which cannot be predetermined and depends on the accessibility of the point where the node should be placed, (iv) the sensing nodes' deployment (to collect the needed data, to have the required coverage and connectivity, to extend the network lifetime, and to minimize energy consumption), and (v) the minimum number of time slots required to aggregate data along the edges of a data-gathering tree spanning all the nodes in a WSN (a.k.a., minimum aggregation delay), if the gathered data are aggregated before the transmission to the control center.…”