Fractional rider optimization algorithm for the optimal placement of the mobile sinks in wireless sensor networks

2020 ◽  
Author(s):  
Arikrishnaperumal Ramaswamy Aravind ◽  
Rekha Chakravarthi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Mobile Sinks

Adaptive Optimization for Optimal Mobile Sink Placement in Wireless Sensor Networks

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Arikrishnaperumal Aravind ◽  
Rekha Chakravarthi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Research Work ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Delivery Ratio ◽  
Minimal Distance ◽  
Mobile Sinks

In recent years, Wireless Sensor Networks (WSN) with mobile sinks has attracted much attention as the mobile sink roams over the sensing field and collects sensing data from sensor nodes. Mobile sinks are mounted on moving objects, such as people, vehicles, robots, and so on. However, optimal placement of the sink for the effective management of the WSN is the major challenge. Hence, an adaptive Fractional Rider Optimization Algorithm (adaptive-FROA) is developed for the optimal placement of mobile sink in WSN environment for effective routing. The adaptive FROA, which is the integration of the adaptive concept in the FROA, operates based on the fitness measure based on distance, delay, and energy measure of the nodes in the network. The main objective of the research work is to compute the energy and distance. The proposed method is analyzed based on the metrics, such as energy, throughput, distance, and lifetime of the network. The simulation results reveal that the proposed method acquired a minimal distance of 24.87m, maximal network energy of 94.54 J, maximal alive nodes of 77, maximal throughput of 94.42 bps, minimum delay of 0.00918s, and maximum Packet delivery ratio (PDR) of 87.98%, when compared with the existing methods.

A MAC Layer Optimization Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 35 (3) ◽  
pp. 529-539 ◽  
Author(s):  
Yun-Lu LIU ◽  
Ju-Hua PU ◽  
Wei-Wei FANG ◽  
Zhang XIONG
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Mac Layer

scholarly journals An Uneven Node Self-Deployment Optimization Algorithm for Maximized Coverage and Energy Balance in Underwater Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1368 ◽  
Author(s):  
Luoheng Yan ◽  
Yuyao He ◽  
Zhongmin Huangfu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Balance ◽  
Optimization Algorithm ◽  
Growth Ring ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Connected Network ◽  
Deployment Optimization

The underwater wireless sensor networks (UWSNs) have been applied in lots of fields such as environment monitoring, military surveillance, data collection, etc. Deployment of sensor nodes in 3D UWSNs is a crucial issue, however, it is a challenging problem due to the complex underwater environment. This paper proposes a growth ring style uneven node depth-adjustment self-deployment optimization algorithm (GRSUNDSOA) to improve the coverage and reliability of UWSNs, meanwhile, and to solve the problem of energy holes. In detail, a growth ring style-based scheme is proposed for constructing the connective tree structure of sensor nodes and a global optimal depth-adjustment algorithm with the goal of comprehensive optimization of both maximizing coverage utilization and energy balance is proposed. Initially, the nodes are scattered to the water surface to form a connected network on this 2D plane. Then, starting from sink node, a growth ring style increment strategy is presented to organize the common nodes as tree structures and each root of subtree is determined. Meanwhile, with the goal of global maximizing coverage utilization and energy balance, all nodes depths are computed iteratively. Finally, all the nodes dive to the computed position once and a 3D underwater connected network with non-uniform distribution and balanced energy is constructed. A series of simulation experiments are performed. The simulation results show that the coverage and reliability of UWSN are improved greatly under the condition of full connectivity and energy balance, and the issue of energy hole can be avoided effectively. Therefore, GRSUNDSOA can prolong the lifetime of UWSN significantly.

scholarly journals A Novel Bioinspired Multiobjective Optimization Algorithm for Designing Wireless Sensor Networks in the Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jun Huang ◽  
Liqian Xu ◽  
Cong-cong Xing ◽  
Qiang Duan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Chaotic Behavior ◽  
Ant Colony ◽  
Wireless Sensor ◽  
The Internet ◽  
The Internet Of Things

The design of wireless sensor networks (WSNs) in the Internet of Things (IoT) faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS). Unlike the ant colony optimization (ACO) algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant) to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.

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