Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

[1]  Lei Liu,et al.  Particle swarm optimization algorithm: an overview , 2017, Soft Computing.

[2]  A. H. Fareen Farzana,et al.  Ant-based routing and QoS-effective data collection for mobile wireless sensor network , 2016, Wireless Networks.

[3]  Zhaolu Guo,et al.  Enhancing social emotional optimization algorithm using local search , 2016, Soft Computing.

[4]  Gang Wang,et al.  A novel bacterial foraging optimization algorithm for feature selection , 2017, Expert Syst. Appl..

[5]  Sankaranarayanan Swamynathan,et al.  Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network , 2017, Journal of Communications and Networks.

[6]  Jie Tian,et al.  Deployment and reallocation in mobile survivability-heterogeneous wireless sensor networks for barrier coverage , 2016, Ad Hoc Networks.

[7]  Mohammed Azmi Al-Betar,et al.  A survey on applications and variants of the cuckoo search algorithm , 2017, Appl. Soft Comput..

[8]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[9]  Jianqing Li,et al.  Optimization-Based Artificial Bee Colony Algorithm for Data Collection in Large-Scale Mobile Wireless Sensor Networks , 2016, J. Sensors.

[10]  Athanasios V. Vasilakos,et al.  Software-Defined Industrial Internet of Things in the Context of Industry 4.0 , 2016, IEEE Sensors Journal.

[11]  Ying Chen,et al.  Research on efficient-efficient routing protocol for WSNs based on improved artificial bee colony algorithm , 2017, IET Wirel. Sens. Syst..

[12]  Siobhán Clarke,et al.  Middleware for Internet of Things: A Survey , 2016, IEEE Internet of Things Journal.

[13]  Vili Podgorelec,et al.  A survey of genetic algorithms for solving multi depot vehicle routing problem , 2015, Appl. Soft Comput..

[14]  Li Chen,et al.  Image contrast enhancement using an artificial bee colony algorithm , 2018, Swarm Evol. Comput..

[15]  Victor C. M. Leung,et al.  Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integratedwith Mobile Cloud Computing , 2015, IEEE Transactions on Computers.

[16]  Ming Xu,et al.  An ACOA-AFSA Fusion Routing Algorithm for Underwater Wireless Sensor Network , 2012, Int. J. Distributed Sens. Networks.

[17]  D erviKARABO ˘ Ga,et al.  Artificial bee colony algorithm for dynamic deployment of wireless sensor networks , 2012 .

[18]  Cem Ersoy,et al.  Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink , 2015, IEEE Trans. Mob. Comput..

[19]  Mohammed Abo-Zahhad,et al.  Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[20]  Zhiyong Yu,et al.  An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications , 2019, Sensors.

[21]  Wuling Ren,et al.  A Localization Algorithm Based On SFLA and PSO for Wireless Sensor Network , 2013 .

[22]  Manoj Duhan,et al.  Bat Algorithm: A Survey of the State-of-the-Art , 2015, Appl. Artif. Intell..

[23]  A. Chitra,et al.  Reviewing the process of data fusion in wireless sensor network: a brief survey , 2015, Int. J. Wirel. Mob. Comput..

[24]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[25]  Rung Ching Chen,et al.  An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks , 2013, International Journal of Machine Learning and Cybernetics.

[26]  Jianqiao Yu,et al.  Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm , 2017, Neurocomputing.

[27]  Ramjee Prasad,et al.  Mobility and Heterogeneity Aware Cluster-Based Data Aggregation for Wireless Sensor Network , 2016, Wirel. Pers. Commun..

[28]  Dina S. Deif,et al.  An Ant Colony Optimization Approach for the Deployment of Reliable Wireless Sensor Networks , 2017, IEEE Access.

[29]  Marko Beko,et al.  Elephant Herding Optimization Algorithm for Wireless Sensor Network Localization Problem , 2018, DoCEIS.

[30]  Turgay Korkmaz,et al.  Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks , 2015, Comput. Commun..

[31]  Mohammed Abo-Zahhad,et al.  A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions , 2017, Wirel. Commun. Mob. Comput..

[32]  Sam Kwong,et al.  An Improved Artificial Bee Colony Algorithm With its Application , 2019, IEEE Transactions on Industrial Informatics.

[33]  Hui Wang,et al.  Firefly algorithm with random attraction , 2016, Int. J. Bio Inspired Comput..

[34]  Dong Yu,et al.  Optimal node deployment strategy for wireless sensor networks based on dynamic ant colony algorithm , 2016, Int. J. Embed. Syst..

[35]  Palvinder Singh Mann,et al.  Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks , 2019, Artificial Intelligence Review.

[36]  Ahmed Chiheb Ammari,et al.  An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem , 2015, Journal of Intelligent Manufacturing.

[37]  Yi Wang,et al.  A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem , 2017, Expert Syst. Appl..

[38]  Jiannong Cao,et al.  Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[39]  Albert Y. Zomaya,et al.  Rendezvous based routing protocol for wireless sensor networks with mobile sink , 2017, The Journal of Supercomputing.

[40]  Ling Wang,et al.  A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem , 2018, Swarm Evol. Comput..

[41]  Xiaoying Yang,et al.  An Improved DV-Hop Algorithm Based on Shuffled Frog Leaping Algorithm , 2015, Int. J. Online Eng..

[42]  F. Richard Yu,et al.  Green Machine-to-Machine Communications with Mobile Edge Computing and Wireless Network Virtualization , 2018, IEEE Communications Magazine.

[43]  Xiao Xue,et al.  Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition , 2016, Inf. Sci..

[44]  Feng Pan,et al.  Cluster-Based Routing for the Mobile Sink in Wireless Sensor Networks With Obstacles , 2016, IEEE Access.

[45]  Karim Faez,et al.  AMOF: adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks , 2016, Telecommun. Syst..

[46]  Mohsen Guizani,et al.  A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks , 2016, IEEE Communications Surveys & Tutorials.

[47]  Awais Ahmad,et al.  Data Transmission Scheme Using Mobile Sink in Static Wireless Sensor Network , 2015, J. Sensors.

[48]  Shengxiang Yang,et al.  Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems , 2017, IEEE Transactions on Cybernetics.

[49]  Parmeet Kaur,et al.  Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm , 2017, J. Parallel Distributed Comput..

[50]  Hany M. Hasanien,et al.  Shuffled Frog Leaping Algorithm for Photovoltaic Model Identification , 2015, IEEE Transactions on Sustainable Energy.

[51]  Suat Özdemir,et al.  Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization , 2016, The Journal of Supercomputing.

[52]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[53]  Tingzhang Liu,et al.  An Improved Artificial Fish Swarm Algorithm and Application , 2014 .

[54]  Can Berk Kalayci,et al.  A survey of swarm intelligence for portfolio optimization: Algorithms and applications , 2018, Swarm Evol. Comput..

[55]  Zhili Sun,et al.  Time Efficient Data Collection With Mobile Sink and vMIMO Technique in Wireless Sensor Networks , 2018, IEEE Systems Journal.

[56]  S. Sivakumar,et al.  Error Minimization in Localization of Wireless Sensor Networks using Fish Swarm Optimization Algorithm , 2017 .

[57]  Honggang Wang,et al.  Topology Control for Building a Large-Scale and Energy-Efficient Internet of Things , 2017, IEEE Wireless Communications.

[58]  Maurizio Rebaudengo,et al.  A Key Management Scheme for Mobile Wireless Sensor Networks , 2017 .

[59]  Rajeev Kumar,et al.  Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network , 2015, Wireless Networks.

[60]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[61]  Nima Jafari Navimipour,et al.  Deployment Strategies in the Wireless Sensor Networks: Systematic Literature Review, Classification, and Current Trends , 2016, Wireless Personal Communications.

[62]  Sachin Gajjar,et al.  FAMACROW: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks , 2016, Appl. Soft Comput..

[63]  Samad Najjar-Ghabel,et al.  Reliable data gathering in the Internet of Things using artificial bee colony , 2018, Turkish J. Electr. Eng. Comput. Sci..

[64]  Song Guo,et al.  Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective , 2016, IEEE Communications Standards.

[65]  Milan Tuba,et al.  Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks , 2019, Sensors.

[66]  Marko Beko,et al.  Wireless Sensor Network Localization Problem by Hybridized Moth Search Algorithm , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[67]  Sang-Ha Kim,et al.  Active data dissemination for mobile sink groups in wireless sensor networks , 2018, Ad Hoc Networks.

[68]  Rui Zhang,et al.  Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network , 2017, IEEE Wireless Communications Letters.

[69]  Dervis Karaboga,et al.  An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training , 2016, Appl. Soft Comput..

[70]  Karim Faez,et al.  Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[71]  Jian Xu,et al.  An Adaptive Fish Swarm-Based Mobile Coverage in WSNs , 2018, Wirel. Commun. Mob. Comput..

[72]  Emel Kizilkaya Aydogan,et al.  Balancing stochastic U-lines using particle swarm optimization , 2019, J. Intell. Manuf..

[73]  Zhetao Li,et al.  Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[74]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[75]  Om Prakash Verma,et al.  An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm , 2017, IEEE Transactions on Fuzzy Systems.

[76]  Mianxiong Dong,et al.  ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks , 2016, IEEE Transactions on Information Forensics and Security.

[77]  Yang Gao,et al.  Adequate is better: particle swarm optimization with limited-information , 2015, Appl. Math. Comput..

[78]  Mengjie Zhang,et al.  Pareto front feature selection based on artificial bee colony optimization , 2018, Inf. Sci..

[79]  Qingfu Zhang,et al.  Distributed evolutionary algorithms and their models: A survey of the state-of-the-art , 2015, Appl. Soft Comput..

[80]  Bamidele Adebisi,et al.  Dynamic clustering and management of mobile wireless sensor networks , 2017, Comput. Networks.

[81]  Bilal Alatas,et al.  Plant intelligence based metaheuristic optimization algorithms , 2017, Artificial Intelligence Review.

[82]  Robert Simon Sherratt,et al.  Energy-aware distributed routing algorithm to tolerate network failure in wireless sensor networks , 2017, Ad Hoc Networks.

[83]  Ameera Saleh Jaradat,et al.  Community Structure Detection Using Firefly Algorithm , 2018, Int. J. Appl. Metaheuristic Comput..

[84]  Okan K. Ersoy,et al.  Improvement and Application of Chicken Swarm Optimization for Constrained Optimization , 2019, IEEE Access.

[85]  Dieter Hogrefe,et al.  A Survey of Ant Colony Optimization Based Routing Protocols for Mobile Ad Hoc Networks , 2017, IEEE Access.

[86]  Wei Xiang,et al.  Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks , 2016, IEEE Transactions on Vehicular Technology.

[87]  D. Sridharan,et al.  Routing in mobile wireless sensor network: a survey , 2013, Telecommunication Systems.

[88]  Osama Moh'd Alia,et al.  Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm , 2017, Inf. Sci..

[89]  Vijander Singh,et al.  A novel nature-inspired algorithm for optimization: Squirrel search algorithm , 2019, Swarm Evol. Comput..

[90]  Kang Zhou,et al.  Application of hybrid artificial fish swarm algorithm based on similar fragments in VRP , 2018, International Symposium on Multispectral Image Processing and Pattern Recognition.

[91]  Marwan Al-Jemeli,et al.  An Energy Efficient Cross-Layer Network Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[92]  Maria Rita Palattella,et al.  Internet of Things in the 5G Era: Enablers, Architecture, and Business Models , 2016, IEEE Journal on Selected Areas in Communications.

[93]  Liang Feng,et al.  Gene Expression Programming: A Survey [Review Article] , 2017, IEEE Computational Intelligence Magazine.

[94]  Amir G. Aghdam,et al.  Distributed Deployment Algorithms for Coverage Improvement in a Network of Wireless Mobile Sensors: Relocation by Virtual Force , 2017, IEEE Transactions on Control of Network Systems.

[95]  K. Goutham Raju,et al.  Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in Wireless Sensor Networks , 2016 .

[96]  Raghavendra V. Kulkarni,et al.  Multistage localization in wireless sensor networks using artificial bee colony algorithm , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[97]  Sai Ji,et al.  Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks , 2017, The Journal of Supercomputing.

[98]  Li Cao,et al.  A Swarm Intelligence Algorithm for Routing Recovery Strategy in Wireless Sensor Networks With Mobile Sink , 2018, IEEE Access.

[99]  Xunli Fan,et al.  Shuffled Frog Leaping Algorithm based Unequal Clustering Strategy for Wireless Sensor Networks , 2015 .

[100]  Ping Xu,et al.  A Novel Coverage Holes Discovery Algorithm Based on Voronoi Diagram in Wireless Sensor Networks , 2016 .

[101]  M. Tuba,et al.  Static drone placement by elephant herding optimization algorithm , 2017, 2017 25th Telecommunication Forum (TELFOR).

[102]  Shaimaa Ahmed El-Said,et al.  Image quantization using improved artificial fish swarm algorithm , 2015, Soft Comput..

[103]  Alagan Anpalagan,et al.  Wireless Sensor Network Optimization: Multi-Objective Paradigm , 2015, Sensors.

[104]  Milan Tuba,et al.  Bare Bones Fireworks Algorithm for Feature Selection and SVM Optimization , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[105]  Wei Zhao,et al.  Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks , 2014, TheScientificWorldJournal.

[106]  Yumin Chen,et al.  Finding rough set reducts with fish swarm algorithm , 2015, Knowl. Based Syst..

[107]  Gaige Wang,et al.  Dynamic Deployment of Wireless Sensor Networks by Biogeography Based Optimization Algorithm , 2012, J. Sens. Actuator Networks.

[108]  Shengyao Wang,et al.  An Estimation of Distribution Algorithm-Based Memetic Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[109]  Taku Yamazaki,et al.  Aco-inspired energy-aware routing algorithm for wireless sensor networks , 2019 .

[110]  Dusit Niyato,et al.  A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization , 2018, Applied Soft Computing.

[111]  Eadala Sarath Yadav,et al.  A Review on the Different Types of Internet of Things (IoT) , 2019 .

[112]  Robert Simon Sherratt,et al.  Mobile sink based fault diagnosis scheme for wireless sensor networks , 2016, J. Syst. Softw..

[113]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[114]  Suraj Sharma,et al.  Proactive data routing using controlled mobility of a mobile sink in Wireless Sensor Networks , 2018, Comput. Electr. Eng..

[115]  Dervis Karaboga,et al.  A survey on the applications of artificial bee colony in signal, image, and video processing , 2015, Signal, Image and Video Processing.

[116]  Fadi Al-Turjman,et al.  Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of Things , 2017, IEEE Sensors Journal.

[117]  Xin Yang,et al.  Energy Efficient Cross-Layer Transmission Model for Mobile Wireless Sensor Networks , 2017, Mob. Inf. Syst..

[118]  Subhash Chander Sharma,et al.  Analysis and Optimization of Energy of Sensor Node Using ACO in Wireless Sensor Network , 2015 .

[119]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[120]  Muhammad Faheem,et al.  MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things , 2017, Future Gener. Comput. Syst..

[121]  Ponnuthurai N. Suganthan,et al.  Population topologies for particle swarm optimization and differential evolution , 2017, Swarm Evol. Comput..

[122]  Wei Guo,et al.  Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection , 2015, Sensors.

[123]  Yanqin Zhu,et al.  Network Coding-Based Real-Time Retransmission Scheme in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[124]  Seyed Mohammad Mirjalili,et al.  Designing evolutionary feedforward neural networks using social spider optimization algorithm , 2015, Neural Computing and Applications.

[125]  Naixue Xiong,et al.  A PSO-Optimized Minimum Spanning Tree-Based Topology Control Scheme for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[126]  Sishaj P. Simon,et al.  Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony , 2015, IEEE Transactions on Sustainable Energy.

[127]  Dinesh Kumar,et al.  EACO and FABC to multi-path data transmission in wireless sensor networks , 2017, IET Commun..

[128]  Faisal Karim Shaikh,et al.  Energy harvesting in wireless sensor networks: A comprehensive review , 2016 .

[129]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[130]  Xia Li,et al.  A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows , 2015, Inf. Sci..

[131]  Damodar Reddy Edla,et al.  An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs , 2017, IEEE Sensors Journal.

[132]  Swagatam Das,et al.  Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network , 2015, Inf. Sci..

[133]  Arun Kumar Sangaiah,et al.  A Robust Time Synchronization Scheme for Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[134]  Hwee Pink Tan,et al.  Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.

[135]  Jong Hyuk Park,et al.  An improved ant colony optimization-based approach with mobile sink for wireless sensor networks , 2017, The Journal of Supercomputing.

[136]  Weiping Zhang,et al.  Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems , 2015, Journal of Intelligent Manufacturing.

[137]  Ping He,et al.  A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy, challenges, and future directions , 2018, Inf. Fusion.

[138]  Hua Han,et al.  An endocrine cooperative particle swarm optimization algorithm for routing recovery problem of wireless sensor networks with multiple mobile sinks , 2015, Inf. Sci..

[139]  Milan Tuba,et al.  Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems , 2014, Neurocomputing.

[140]  Qingsong Xu,et al.  Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm , 2015, Natural Computing.

[141]  Milan Tuba,et al.  Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[142]  Milan Tuba,et al.  Multilevel image thresholding by fireworks algorithm , 2015, 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA).

[143]  Weiren Shi,et al.  Mechanism of Immune System Based Multipath Fault Tolerant Routing Algorithm for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[144]  Parham Moradi,et al.  Relevance-redundancy feature selection based on ant colony optimization , 2015, Pattern Recognit..

[145]  Milan Tuba,et al.  Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm , 2019, J. Sens. Actuator Networks.

[146]  Shirshu Varma,et al.  Energy Efficient Data Aggregation in Mobile Agent Based Wireless Sensor Network , 2016, Wirel. Pers. Commun..

[147]  Dinghui Wu,et al.  Convergence Analysis and Improvement of the Chicken Swarm Optimization Algorithm , 2016, IEEE Access.

[148]  Dilip Kumar,et al.  Particle Swarm Optimization-Based Unequal and Fault Tolerant Clustering Protocol for Wireless Sensor Networks , 2018, IEEE Sensors Journal.

[149]  Jau-Yang Chang,et al.  An Efficient Tree-Based Power Saving Scheme for Wireless Sensor Networks With Mobile Sink , 2016, IEEE Sensors Journal.

[150]  Adamu Murtala Zungeru,et al.  Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors , 2018, IEEE Systems Journal.

[151]  Lin Zhang,et al.  A Nonmodel Dual-Tree Wavelet Thresholding for Image Denoising Through Noise Variance Optimization Based on Improved Chaotic Drosophila Algorithm , 2017, Int. J. Pattern Recognit. Artif. Intell..

[152]  Bin Li,et al.  Particle swarm optimization based clustering algorithm with mobile sink for WSNs , 2017, Future Gener. Comput. Syst..

[153]  Halil Yetgin,et al.  A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks , 2017, IEEE Communications Surveys & Tutorials.

[154]  Arun Kumar Sangaiah,et al.  A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs , 2019, Sensors.

[155]  P. Sengottuvelan,et al.  BAFSA: Breeding Artificial Fish Swarm Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks , 2017, Wirel. Pers. Commun..

[156]  Yang Li,et al.  Research on gateway deployment of WMN based on maximum coupling subgraph and PSO algorithm , 2017, Soft Comput..

[157]  Jia Zhao,et al.  Improved Shuffled Frog Leaping Algorithm And Its Application In Node Localization Of Wireless Sensor Network , 2012, Intell. Autom. Soft Comput..

[158]  Juergen Jasperneite,et al.  The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0 , 2017, IEEE Industrial Electronics Magazine.

[159]  M. Shamim Hossain,et al.  Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring , 2016, Comput. Networks.

[160]  Hairong Qi,et al.  Cost-effective barrier coverage formation in heterogeneous wireless sensor networks , 2017, Ad Hoc Networks.

[161]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[162]  Jiujun Cheng,et al.  Ant colony optimization with clustering for solving the dynamic location routing problem , 2016, Appl. Math. Comput..

[163]  Xuxun Liu,et al.  Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review , 2015, IEEE Sensors Journal.

[164]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[165]  Jun Zhang,et al.  Adaptive Multimodal Continuous Ant Colony Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[166]  Ruchuan Wang,et al.  Node Localization Based on Improved PSO and Mobile Nodes for Environmental Monitoring WSNs , 2018, Int. J. Wirel. Inf. Networks.

[167]  Song Han,et al.  Industrial Internet of Things: Challenges, Opportunities, and Directions , 2018, IEEE Transactions on Industrial Informatics.

[168]  Sebastián Ventura,et al.  A classification module for genetic programming algorithms in JCLEC , 2015, J. Mach. Learn. Res..

[169]  Jianneng Cao,et al.  Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks , 2016, Sensors.

[170]  S. Deb,et al.  Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[171]  Qingsong Xu,et al.  Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis , 2015, Neural Computing and Applications.

[172]  K. S Umadevi,et al.  Node Deployment Using Virtual Force with Particle Swarm Optimization in WSN , 2018 .

[173]  Sohrab Effati,et al.  On Maximizing the Lifetime of Wireless Sensor Networks in Event-Driven Applications With Mobile Sinks , 2015, IEEE Transactions on Vehicular Technology.

[174]  Hui Sun,et al.  Multi-strategy artificial bee colony based on multiple population for coverage optimisation , 2018, Int. J. Wirel. Mob. Comput..

[175]  Junqing Li,et al.  Optimal chiller loading by improved artificial fish swarm algorithm for energy saving , 2019, Math. Comput. Simul..

[176]  Nabil Alrajeh,et al.  Error Correcting Codes in Wireless Sensor Networks: An Energy Perspective , 2015 .

[177]  Kyung-Ah Shim,et al.  A Secure Data Aggregation Scheme Based on Appropriate Cryptographic Primitives in Heterogeneous Wireless Sensor Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[178]  B. Kaarthick,et al.  An Efficient Cluster-Tree Based Data Collection Scheme for Large Mobile Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[179]  Trong-The Nguyen,et al.  A Compact Articial Bee Colony Optimization for Topology Control Scheme in Wireless Sensor Networks , 2015, J. Inf. Hiding Multim. Signal Process..

[180]  Jian Zhang,et al.  Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks , 2017, Int. J. Sens. Networks.

[181]  Shaoxing Zhejiang,et al.  Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm , 2015 .

[182]  Jing J. Liang,et al.  A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems , 2018, Swarm Evol. Comput..

[183]  Huan Zhao,et al.  Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink , 2015, Appl. Soft Comput..

[184]  Michael Devetsikiotis,et al.  Blockchains and Smart Contracts for the Internet of Things , 2016, IEEE Access.

[185]  Brian Keegan,et al.  Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs , 2018, Sensors.

[186]  Marjan Kuchaki Rafsanjani,et al.  Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm , 2018, Appl. Soft Comput..

[187]  Dario Pacciarelli,et al.  Ant colony optimization for the real-time train routing selection problem , 2016 .

[188]  Rosdiadee Nordin,et al.  Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling , 2016, IEEE Sensors Journal.

[189]  Wen-Tsai Sung,et al.  A distributed energy monitoring network system based on data fusion via improved PSO , 2014 .

[190]  Victor C. M. Leung,et al.  Energy Efficient Cooperative Computing in Mobile Wireless Sensor Networks , 2018, IEEE Transactions on Cloud Computing.

[191]  Ngoc-Tu Nguyen,et al.  On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees , 2016, Comput. Networks.

[192]  Azzedine Boukerche,et al.  Underwater Wireless Sensor Networks , 2018, ACM Comput. Surv..

[193]  Md. Akhtaruzzaman Adnan,et al.  Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey , 2013, Sensors.

[194]  Abdul Hanan Abdullah,et al.  VGDRA: A Virtual Grid-Based Dynamic Routes Adjustment Scheme for Mobile Sink-Based Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[195]  Jun Zhang,et al.  Genetic Learning Particle Swarm Optimization , 2016, IEEE Transactions on Cybernetics.

[196]  Sajjad Mohsin,et al.  Adaptive image denoising using cuckoo algorithm , 2016, Soft Comput..

[197]  Milan Tuba,et al.  Convolutional Neural Network Architecture Design by the Tree Growth Algorithm Framework , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[198]  Arshdeep Bahga,et al.  Blockchain Platform for Industrial Internet of Things , 2016 .

[199]  Taher Niknam,et al.  Optimal energy management of smart renewable micro-grids in the reconfigurable systems using adaptive harmony search algorithm , 2016, Int. J. Bio Inspired Comput..

[200]  Parham Moradi,et al.  Gene selection for microarray data classification using a novel ant colony optimization , 2015, Neurocomputing.

[201]  Marko Beko,et al.  Monarch butterfly optimization algorithm for localization in wireless sensor networks , 2018, 2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA).

[202]  Fei Tao,et al.  IIHub: An Industrial Internet-of-Things Hub Toward Smart Manufacturing Based on Cyber-Physical System , 2018, IEEE Transactions on Industrial Informatics.

[203]  Yu Gu,et al.  The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[204]  Farid Nouioua,et al.  Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems , 2019, Comput. Ind. Eng..

[205]  Zheng Yao,et al.  An efficient distributed routing protocol for wireless sensor networks with mobile sinks , 2015, Int. J. Commun. Syst..

[206]  Vivek K. Patel,et al.  A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO) , 2016, Inf. Sci..

[207]  S. Anandamurugan,et al.  Antipredator Adaptation Shuffled Frog Leap Algorithm to Improve Network Life Time in Wireless Sensor Network , 2017, Wirel. Pers. Commun..

[208]  Deming Lei,et al.  A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption , 2017, Int. J. Prod. Res..

[209]  Tingzhang Liu,et al.  A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm , 2016, Neurocomputing.