scholarly journals ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3576
Author(s):  
Vaibhav Kotiyal ◽  
Abhilash Singh ◽  
Sandeep Sharma ◽  
Jaiprakash Nagar ◽  
Cheng-Chi Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Information Source ◽  
Critical Role ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Wireless Sensor ◽  
Search Process

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

scholarly journals The Application of Improved Sparrow Search Algorithm in Sensor Networks Coverage Optimization of Bridge Monitoring

2020 ◽  
Author(s):  
Yueqi Peng ◽  
Yunqing Liu ◽  
Qi Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Initial Population ◽  
Bridge Monitoring ◽  
Speed Up ◽  
Point Set

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

scholarly journals Hybrid Bacteria Foraging Optimization with Cuckoo Search Algorithm for Optimal Cluster head Selection in Wireless Sensor Networks

2019 ◽  
Vol 9 (1S3) ◽  
pp. 87-92
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Search Algorithm ◽  
Cluster Head ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Energy Utilization ◽  
Wireless Sensor ◽  
Optimal Cluster

Wireless Sensor Networks (WSN) are operated on a battery source. The sensor nodes are required more energy for the communication and information processing and also reduced the network lifetime. An efficient clustering scheme is designed to reduce the energy utilization and prolonged the network lifetime. In this paper, a hybrid approach of Bacteria Foraging Optimization with Cuckoo Search algorithm is proposed in the LEACH-C algorithm. This proposed methodology improves the chemotaxis behavior of Bacteria Foraging Optimization by using the Cuckoo Search algorithm. The residual energy of the node and the minimum distance among the nodes are considered for selecting the optimal cluster head. The proposed methodology has been done in Network Simulator (NS-2) and the experimental results improve the network lifetime by increasing the alive nodes and minimize the energy consumption. The proposed methodology has provided 700 seconds of life duration to the nodes, which is significantly higher than the lifetime provided by any other conventional techniques.

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

Energy efficient fixed-cluster architecture for wireless sensor networks

2021 ◽  
Vol 40 (5) ◽  
pp. 8727-8740
Author(s):  
Rajvir Singh ◽  
C. Rama Krishna ◽  
Rajnish Sharma ◽  
Renu Vig
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Wireless Sensor ◽  
Efficient Operation ◽  
Cluster Architecture ◽  
Dynamic Cluster

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

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