Optimal Hop Lengths to Ensure Minimum Energy Consumption in Wireless Sensor Networks

Optimal Hop Lengths to Ensure Minimum Energy Consumption in Wireless Sensor Networks

Mekkaoui Kheireddine, Rahmoune Abdellatif
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJTD.2018100101
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In wireless sensor networks, nodes have a low computing capacity, a small antenna and a very limited energy source; thereby batteries are considered as a critical resource and should be used efficiently. On the other hand, the antennas are the biggest consumers of energy, therefore, and their use must be very efficient to minimize energy consumption. In a dense WSN, each node may route messages to destination nodes either through short-hops or long-hops, by using a short or a long radio range. Thus, the hop length optimization can save energy. In this article, the authors propose a theorem to optimize the hop lengths and a routing algorithm to improve the WSN power consumption. The theorem establishes a simple condition to ensure the optimal hop lengths which guarantees the minimum energy consumption. And the proposed algorithm based on that condition is used to find the optimal routing path. The simulation results are obtained by applying the condition and the algorithm on WSNs and reveals a high performance regarding WSNs energy consumption and network lifetime.
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For the last two decades, technological developments allowed the creation of miniaturized devices, called sensors, equipped with a small processor, a reduced memory, a small antenna and a very limited energy source (Meijer, 2014; Soloman, 2009). These sensors, also known as mote, are usually deployed with large scale for the monitoring/detection of different events in the environment (temperature, pressure, humidity, acoustic, etc.), thus forming a new type of networks, called wireless sensor networks (WSNs) (Akyildiz, 2010).

In the last years, Wireless sensor networks have been used in many applications like military, surveillance, biomedical health monitoring, home applications, disaster management, forest fire detection, seismic detection, habitat monitoring, inventory tracking, animal tracking, hazardous environment sensing and smart spaces, general engineering and commercial applications (Simon et al., 2004; Castillo-Effer, Quintela, Moreno, Jordan, & Westhoff, 2004; Rappaport, 2002; Lorincz et al., 2004; Wu, Chen, Shi, Xiao, & Xu, 2012), Indeed, Business 2.0 lists sensors as one of six technologies that will change the world (Ilyas & Mahgoub, 2004), and Technology Review at MIT and Global future identify WSNs as one of the 10 emerging technologies that will change human’s life (Kheireddine & Abdellatif, 2015).

A wireless sensor network consists of a large number of sensor nodes which can reach up to 1 million nodes, often, randomly deployed by an airplane, a car or rocket launcher in hostiles areas (volcano, seabed, etc.), where human intervention is impossible (Xue, Zhang, Yan, Wang, & Li, 2013).

In the most cases, it’s an impossible task to replace or to recharge the nodes batteries because of the nature of the monitoring areas (Akyildiz, 2010; Ilyas & Mahgoub, 2004); therefore, each node should have strict limitations in the usage of its energy source. Thereby, the nodes batteries in a WSN are considered as scarce resources and should be used efficiently (Chakraborty, Mitra, & Naskar, 2011).

In a typical WSN, a sensor node consumes energy from its battery in sensing, processing, sending and receiving data. Several studies showed that the most energy-consuming task is sending/receiving data which uses the radio module that provide wireless communications (Akyildiz, 2010; Ilyas & Mahgoub, 2004; Kheireddine, & Abdellatif, & Gialuigi, 2015); indeed, the energy consumed, by a node, when transmitting 1 bit of data by its antenna on the wireless channel is equivalent to the energy required to execute thousands of instructions by its CPU (Yick, Mukherjee, & Ghosal, 2008). Therefore, the communication protocol used in a WSN affects largely the energy consumption and the network's lifetime (Rachamalla & Kancharla, 2015; Singh & Sharma, 2016).

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