scholarly journals A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1890
Author(s):  
Zhongliang Deng ◽  
Shihao Tang ◽  
Xiwen Deng ◽  
Lu Yin ◽  
Jingrong Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Fuzzy Comprehensive Evaluation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Fuzzy Evaluation ◽  
Node Density ◽  
Positioning Accuracy ◽  
Anchor Node

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable.

scholarly journals A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pingzhang Gou ◽  
Bo He ◽  
Zhaoyang Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Positioning Accuracy ◽  
Whale Optimization ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Better Than

With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

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

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

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.

scholarly journals Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Yinggao Yue ◽  
Hairong You ◽  
Shuxin Wang ◽  
Li Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Network Cost ◽  
Whale Optimization ◽  
Heterogeneous Wireless Sensor Networks

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

Cooperative localization and tracking in wireless sensor networks

2018 ◽  
Vol 32 (1) ◽  
pp. e3842 ◽  
Author(s):  
Kaouther Hehdly ◽  
Mohamed Laaraiedh ◽  
Fatma Abdelkefi ◽  
Mohamed Siala
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization And Tracking
Sign in / Sign up

Export Citation Format

Share Document