scholarly journals A Two-Stage Wireless Sensor Grey Wolf Optimization Node Location Algorithm Based on K-Value Collinearity

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yinghui Meng ◽  
Qianying Zhi ◽  
Qiuwen Zhang ◽  
Erlin Lin
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Two Stage ◽  
Essential Information ◽  
K Value ◽  
Initial Location

In the practical application of WSN (wireless sensor network), location information of the sensor nodes has become one of the essential information pieces in the whole network. At present, some localization algorithms use intelligent optimization algorithm to optimize the node group directly. Although the overall localization error is reduced, the location deviation of individual unknown nodes will be larger, and the large number of iterations will cause a large energy consumption of nodes. Aiming at the above problems, this paper comes up with a two-stage WSN localization algorithm based on the degree of K-value collinearity (DC-K) and improved grey wolf optimization. The first stage is aiming at the defects of the existing collinearity algorithm, putting forward the concept of DC-K, according to the K-value to carry out the initial location in the first stage. The second stage is using the improved grey wolf optimization algorithm to optimize the location results which were obtained in the first stage, so as to get more accurate location results. The experimental results display that this localization algorithm with a better localization accuracy has high robustness and has fewer iterations in the optimization process, which greatly reduces the energy consumption of nodes.

scholarly journals A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Li Cao ◽  
Yinggao Yue ◽  
Yong Cai ◽  
Bo Hang
Keyword(s):  
Wireless Sensor Networks ◽  
Simulated Annealing ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Coverage Optimization ◽  
Grey Wolf Algorithm

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

scholarly journals Bio-inspired clustering scheme for Internet of Drones application in industrial wireless sensor network

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988990
Author(s):  
Farooq Aftab ◽  
Ali Khan ◽  
Zhongshan Zhang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Ant Colony Optimization ◽  
Sensor Network ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Route Selection ◽  
Grey Wolf ◽  
Grey Wolf Optimization

Recent technological improvements have revolutionized the wireless sensor network–based industrial sector with the emergence of Internet of Things. Internet of Drones, a branch of Internet of Things, is used for the communication among drones. As drones are mobile in nature, they cause frequent topological changes. This changing topology causes scalability, stability, and route selection issues in Internet of Drones. To handle these issues, we propose a bio-inspired clustering scheme using dragonfly algorithm for cluster formation and management. In this article, we propose cluster head election based on the connectivity with the base station along with the fitness function which consists of residual energy and position of the drones. Furthermore, for route selection we propose an optimal path selection based on the residual energy and position of drone for efficient communication. The proposed scheme shows better results as compared to other bio-inspired clustering algorithms on the basis of evaluation benchmarks such as cluster building time, network energy consumption, cluster lifetime, and probability of successful delivery. The results indicate that the proposed scheme has improved 60% and 38% with respect to ant colony optimization and grey wolf optimization, respectively, in terms of average cluster building time while average energy consumption has improved 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively.

scholarly journals Meta-heuristic-based offloading task optimization in mobile edge computing

2021 ◽  
Vol 17 (6) ◽  
pp. 155014772110230
Author(s):  
Aamir Abbas ◽  
Ali Raza ◽  
Farhan Aadil ◽  
Muazzam Maqsood
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Response Time ◽  
Mobile Devices ◽  
Optimization Algorithm ◽  
Edge Computing ◽  
Optimal Selection ◽  
Mobile Edge Computing ◽  
Grey Wolf ◽  
Grey Wolf Optimization

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

scholarly journals Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

2021 ◽  
Vol 13 (9) ◽  
pp. 4689
Author(s):  
Wei Qin ◽  
Linhong Wang ◽  
Yuhan Liu ◽  
Cheng Xu
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Support Vector Machine Regression ◽  
Electric Bus ◽  
Proposed Model ◽  
The Mean

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

scholarly journals Grey Wolf Optimization algorithm with Discrete Hopfield Neural Network for 3 Satisfiability analysis

2021 ◽  
Vol 1821 (1) ◽  
pp. 012038
Author(s):  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hopfield Neural Network ◽  
Grey Wolf ◽  
Grey Wolf Optimization
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