Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

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Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant Colony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. Keywords: Optimal Coverage, Bio-inspired Algorithm, Lion Optimization, WSNs.

Nature-Inspired Algorithms for Wireless Sensor Networks: A Comprehensive Survey

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator Networks

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Purpose




This paper aims to propose an optimized overview of firefly algorithm (FA) over physical-natural impression of fireflies and its application in mobile robot navigation under the natural intelligence mechanism.




Design/methodology/approach




The brightness and luminosity are the decision variables in proposed study. The paper achieves the two major goals of robot navigation; first, the optimum path generation and, second, as an obstacle avoidance by co-in-centric sphere-based geometrical technique. This technique comprises the optimum path decision to objective function and constraints to paths and obstacles as the function of algebraic-geometry co-relation. Co-in-centric sphere is the proposed technique to correlate the constraints.




Findings




It is found that the present FA based on concentric sphere is suitable for efficient navigation of mobile robots at the level of optimum significance when compared with other approaches.




Originality/value




The paper introduces a novel approach to implement the FA for unknown and uncertain environment.

On firefly algorithm: optimization and application in mobile robot navigation

Graphical abstractDisplay Omitted HighlightsSensor node localization is an unconstrained optimization problem that falls under NP-hard class of problems.To minimize the localization error in WSN certain meta-heuristic approaches were applied on the results of mobile anchor positioning.RMSE based localization error is reduced significantly using FOA-MAP meta-heuristic approach.Proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches. Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, Bat Optimization Algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly Optimization Algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches.

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Node localization in wireless sensor networks (WSNs) is one of the most important primary requisite that needs to be resolved efficiently as it plays a significant role in many applications namely environmental monitoring, routing and target tracking which is location dependent. Localization is defined as finding the physical co-ordinates of a group of sensor nodes. Localization is classified as an unconstrained optimization problem. Localization protocols are broadly classified as range-based and range-free protocols. The range based protocols employ distance or angle estimation techniques, hardware. The range-free techniques depend on the contents of received messages to support coarse grained accuracy. In this paper, a range-free localization method known as Mobile Anchor Positioning Mobile Anchor & Neighbor (MAP-M&N) is used to calculate the location of sensor nodes. Mobile Anchor equipped with Global Positioning System (GPS), broadcasts its coordinates to the sensor nodes as it moves through the network. As the sensor nodes collect enough beacons, they are able to calculate their locations. MAP-M&N with Fish Swarm Optimization Algorithm (MAP-M&N with FSO) is the proposed metaheuristic approach to calculate the location of sensor nodes with minimal error. Root Mean Square Error (RMSE) is used as the performance metric to compare between the two approaches namely, MAP-M&N and MAP-M&N with FSO. Simulation results reveal that MAP-M&N with FSO algorithm is effective to bring down the localization error to a bigger level when compared to using only MAP-M&N algorithm. General Terms Localization in Wireless Sensor Networks.

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Error Minimization in Localization of Wireless Sensor Networks using Fish Swarm Optimization Algorithm

The important tasks in a wireless sensor network such as routing, target tracking are highly dependent on the location of a sensor node. Hence localization becomes an essential criterion in wireless sensor networks. Higher the localization accuracy better is the performance of the sensor network as a whole. Traditional mathematical algorithms can be used for localization. But these algorithms do not give very high localization accuracy. Genetic algorithm is proven to be effective in searching a solution space and hence can be modeled for the localization problem in Wireless Sensor Network (WSN). The strategy used in this paper for localization uses two phases. The first phase uses a traditional range free localization algorithm based on Mobile anchor to estimate the location of a sensor node roughly. The second phase is a post optimization phase that uses Genetic algorithm which increases the accuracy of localization. General Terms Localization in Wireless Sensor Networks.

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Error Minimization in Localization of Wireless Sensor Networks using Genetic Algorithm

Localization is considered as one of the most significant research issues in Wireless Sensor Network (WSN). The objective of localization is to determine the physical co-ordinates of sensor nodes distributed over the sensing field. Location information plays a vital role for coverage, deployment of sensor nodes, routing and target tracking applications. Initially, the localization of sensor nodes can be performed by Mobile Anchor Positioning (MAP), a range-free localization method. To further enhance the location accuracy obtained by MAP, we propose three algorithms, viz. Differential Evolution with MAP (DE-MAP), Ant Colony Optimization with MAP (ACO-MAP) and Simulated Annealing-Differential Evolution with MAP (SA-DE-MAP). The scope of this work is to compare the performance of these three algorithms. Root Mean Square Error (RMSE) has been used as the metrics for comparing the performance. Simulation result demonstrates that out of the proposed algorithms, SA-DE-MAP algorithm achieves better performance in minimizing the localization error when compared to DE-MAP and ACO-MAP algorithms.

http://nopr.niscair.res.in/bitstream/123456789/34180/1/JSIR%2075%285%29%20289-295.pdf

Performance Evaluation of Hybrid Evolutionary Algorithms in Minimizing Localization Error for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a kind of ad-hoc networks where the nodes in the network have sensors on board and can sense different phenomena around the sensors deployed in the field. WSNs became very popular due to its diverse nature of applications including Cyber-Physical Systems (CPS), Precision Agriculture, Disaster relief & Rescue operation, Object Tracking in terrestrial environment, Health care application to monitor the physical parameters of a human, space application etc. Most applications use the location information of a sensor node as an inherent characteristic. Location information is mandatory in order to identify in which spatial coordinate the sensor data originates. Broadly, the localization techniques are classified as: range based and range free methods. Hence study of localization techniques for Wireless Sensor Networks is highly significant today for different kind of applications. This paper gives an overview on survey of localization techniques in Wireless Sensor Networks and its current significance.

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S. Sivakumar

Papers

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Error Minimization in Localization of Wireless Sensor Networks using Fish Swarm Optimization Algorithm

Error Minimization in Localization of Wireless Sensor Networks using Genetic Algorithm

Performance Evaluation of Hybrid Evolutionary Algorithms in Minimizing Localization Error for Wireless Sensor Networks

A survey on localization techniques in wireless sensor networks