Problem Domains in Energy-Efficient and Load Balanced WSNs

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		RAJIV YADAV	--	1292	2022-05-23 14:55:47	\|
2	format correct	Conner Chen	-66 word(s)	1226	2022-05-24 02:55:49	\|

This entry is adapted from the peer-reviewed paper 10.3390/electronics11101545

Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. Wireless Sensor Networks (WSN) can detect, store, and transmit data in real-time. These tasks must be completed efficiently to avoid wasting the limited sensor battery life.

nature-inspired algorithms energy utilization WSNs

1. Introduction

Wireless Sensor Networks (WSN) can detect, store, and transmit data in real-time. These tasks must be completed efficiently to avoid wasting the limited sensor battery life. We cannot extend the sensor’s life by providing external or extra energy since most sensors are placed in difficult-to-reach locations. With a lot of work, the sensor node’s lifespan has been prolonged. In addition to the energy limitation, WSNs confront a variety of problems, including precise sensing and non-redundant information. There are three types of WSN significant issues: energy efficiency, security, and service quality (QoS). Many of these concerns are subject to trade-offs such as network lifetime for a better QoS. The same is true for the security parameters. Individually solving these problems has taken a considerable amount of time and effort. When dealing with these problems separately, there are several flaws. As a result, to create better WSNs, we must address all of these problems at the same time.

On the other hand, meta-heuristics methods are problem-independent. They can be utilized as a black box since they are non-adaptive and non-greedy. These algorithms frequently allow temporary deterioration of the solution to reach the global optima. Meta-heuristic or intelligent optimization algorithms are sometimes known as nature-inspired algorithms. The natural environment serves as inspiration for these algorithms. There are four types of nature-inspired/meta-heuristic algorithms: bio-inspired, physics-inspired, geography-inspired, and human-inspired. Biological systems are the source of the great majority of nature-inspired algorithms. As a result, bio-inspired algorithms (biology-inspired) comprise a large portion of nature-inspired algorithms as shown in Figure 1.

Figure 1. Classification of Nature-inspired Optimization Algorithm.

The goal of the optimization process is to discover the best solution to a given issue. The selection of an appropriate algorithm is critical for achieving this goal. However, certain issues are complicated, and finding all feasible solutions is challenging. Several meta-heuristic algorithms have been created in the literature to simulate the biological behavior of animal or insect groups by creating deterministic or random rules to be used in addressing various optimization issues.

Nature-inspired hybrid algorithms are designed to overcome different constraints in WSNs. Many researchers have implemented different meta-heuristic algorithms in the past to improve the lifetime, stability, and performance of the entire WSN. Hybridization techniques in optimization algorithms have helped in improving the network lifetime, stability period, throughput, number of dead nodes per iteration, and residual energy of the network. Sometimes, these bio-inspired algorithms evaluate incorrect solutions for some real-time applications. Convergence speed, multiple objective problems, dynamic problems, and local optima convergence are hot research problems nowadays. Hybridization of algorithms requires a large number of functions to be evaluated, resulting in more accuracy and improved performance of WSNs. Researchers have suggested the use of creating and optimizing a multi-objective function with a suitable mathematical function-based optimizer or hybridization technique to solve challenging, dynamic, and multi-objective problems in WSN.

2. Problem Domains in Energy-Efficient and Load Balanced WSNs

Individually resolving these concerns has taken a substantial amount of time and effort; hence, researchers have focused on addressing both of these challenges at the same time. The development of a multi-objective function followed by its optimization with an appropriate optimizer or algorithm is one such technique. The behavior of the algorithm, the kind of issue, the time restriction, resource availability, and required accuracy are also known to influence the algorithm’s selection. Figure 2 shows the various optimization problems in WSNs including clustering, routing, area coverage, sensor localization, and data aggregation techniques.

Figure 2. Problem domains found in the active research area of Wireless Sensor Network.

2.1. Energy Efficient Clustering and Routing in WSNs

Energy-efficient infrastructure is essential as sensors have a finite amount of energy. The bulk of sensor resources is used to transmit the detected data. As the transmission duration grows, the amount of energy required for data transmission increases exponentially. As a result, multi-hop communication is used in sensor data transfer. In WSNs, routing refers to the path traveled by data packets from the source node to the sink. In this, the sensors are first sorted into categories based on CH and Non-CH. The CH sensors are then chosen and collected from the non-CH sensors. This collected data are subsequently sent to the sink using the most efficient routing choices available. Owing to this process, it can be noticed that the selection of the CH is of high importance. The main issues in this domain are primarily the optimal routing path in each cycle, data maximization with increased network lifespan, and contact distance reduction.

2.2. Requirement of Sensor Localization in WSNs

Sensor localization is the process of estimating a sensor’s location in a network. There are two parts to it, i.e., distance measurement and location computation. To localize the other nodes in the WSNs, several localization methods are utilized to use the existing knowledge about distances and locations. Minimizing the localization error and improving the precision of the unknown node position are the two most difficult problems in this sector. The anchor or beacon node has a known position that may be determined via the Global Positioning System (GPS) or automatically pre-programmed before deployment of a WSN.

2.3. Requirement of Optimal Coverage in WSNs

Optimal Coverage is prime in the development of a WSN and has become a hot issue in this field. Finding a collection of sensors to cover a specified target region or all of the target points is referred to as coverage in a given target area of WSN. Optimal coverage entails using the fewest number of sensors to cover the whole region or all of the target sites. The geometry of the detecting zone is one of the most important aspects of a sensor’s coverage in WSNs. Due to topographical factors and solid buildings, the shape of the sensing zone is uneven and intricate in real life. The only difficulty in this area is to reduce the number of overlapping sensing patches with no detection void. The more overlapping regions there are, the more redundant information the sensors will detect, wasting more battery life. Optimizing the sensor node location, which is a single-objective optimization problem, is one way to remove redundancy. By including the other network elements, a single aim multi-objective WSN may could be made.

2.4. Requirement of Data Aggregation in WSNs

Data aggregation is the second strategy for decreasing redundant content detection and is also considered an energy-efficient approach in WSN. When sensors track a region, they capture local data and send them as fully processed or partially processed data to a data aggregation center. Based on the data collected, the data aggregation center makes a clear choice to extend the sensor lifespan by decreasing the sensing of overlap or common locations. There are four types of data aggregation strategies: tree-based, cluster-based, grid-based, and chain-based. The major concerns focus on addressing the challenge of optimum power allocation, identifying the least number of aggregation points while routing the data, and establishing consistency for wide-ranging and complicated WSNs.

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RAJIV YADAV

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Update Date: 24 May 2022

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