Node Localization in Wireless Sensor Networks Based on Quantum Annealing Algorithm and Edge Computing

Edge computing is a distributed computing paradigm in which computation is largely or completely performed on distributed device nodes. It can be applied in node localization in wireless sensor networks. Classical simulated annealing (SA) and genetic algorithm (GA) are widely used in non-ranging localization of wireless sensor network nodes (WSNN). However, they both have many problems, including 1) easy to fall into local optimum, difficult to achieve global optimum and low localization accuracy; 2) complicated calculation and high energy consumption, etc. In this study, a localization method for WSNN based on quantum annealing (QA) algorithm was proposed by using quantum tunneling effect, in which the energy barrier can be quickly penetrated from local optimum to global optimum, so that the calculation is simplified and the computation speed is increased. The simulation results demonstrated that the proposed algorithm improves the precision and reduces the energy consumption compared with the traditional GA and the classical SA algorithm, making it have wider application prospects.

[1]  Laurence T. Yang,et al.  A Big Data-as-a-Service Framework: State-of-the-Art and Perspectives , 2018, IEEE Transactions on Big Data.

[2]  Ranjit Kumar Upadhyay,et al.  Detecting malicious chaotic signals in wireless sensor network , 2018 .

[3]  Javier Del Ser,et al.  A novel grouping harmony search algorithm for the multiple-type access node location problem , 2012, Expert Syst. Appl..

[4]  Aníbal Ollero,et al.  Efficient integration of RSSI for tracking using Wireless Camera Networks , 2017, Inf. Fusion.

[5]  L. Javier García-Villalba,et al.  Software Defined Networks in Wireless Sensor Architectures , 2018, Entropy.

[6]  Manuel Ricardo,et al.  The self-configuration of nodes using RSSI in a dense wireless sensor network , 2016, Telecommun. Syst..

[7]  Rongbo Zhu,et al.  Node Location Privacy Protection Based on Differentially Private Grids in Industrial Wireless Sensor Networks , 2018, Sensors.

[8]  Andriyan Bayu Suksmono Finding a Hadamard Matrix by Simulated Quantum Annealing , 2018, Entropy.

[9]  Krishnan Murugan,et al.  False Data Elimination in Heterogeneous Wireless Sensor Networks Using Location-Based Selection of Aggregator Nodes , 2014 .

[10]  S. Knysh,et al.  Quantum Annealing via Environment-Mediated Quantum Diffusion. , 2015, Physical review letters.

[11]  Laurence T. Yang,et al.  A Distributed HOSVD Method With Its Incremental Computation for Big Data in Cyber-Physical-Social Systems , 2018, IEEE Transactions on Computational Social Systems.

[12]  Xin Feng,et al.  A new range-free algorithm based on hop correction of RSSI , 2018, J. Intell. Fuzzy Syst..

[13]  Renato Machado,et al.  Improved solution for node location multilateration algorithms in wireless sensor networks , 2016 .

[14]  Jing-Shyang Yen,et al.  Enhanced Gaussian mixture model of RSSI purification for indoor positioning , 2017, J. Syst. Archit..

[15]  Mohammad Reza Meybodi,et al.  A two-objective memetic approach for the node localization problem in wireless sensor networks , 2016, Genetic Programming and Evolvable Machines.

[16]  Laurence T. Yang,et al.  A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data , 2019, IEEE Transactions on Sustainable Computing.

[17]  Yingjun Zhang,et al.  Location Algorithm for Nodes of Ship-Borne Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[18]  Laurence T. Yang,et al.  A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems , 2020, IEEE Transactions on Big Data.

[19]  Fatos Xhafa,et al.  A genetic algorithm-based system for wireless mesh networks: analysis of system data considering different routing protocols and architectures , 2016, Soft Comput..

[20]  Laurence T. Yang,et al.  Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City Services , 2018, IEEE Transactions on Sustainable Computing.

[21]  Yi Pan,et al.  Edge Computing for the Internet of Things , 2018, IEEE Netw..

[22]  Alice Buffi,et al.  RSSI Measurements for RFID Tag Classification in Smart Storage Systems , 2018, IEEE Transactions on Instrumentation and Measurement.