Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

[1]  Parul Kansal,et al.  Data collection maximization of EH-WSN using mobile sink , 2017, 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT).

[2]  Ramesh Kumar,et al.  Hybrid Sink Repositioning Mechanism for Wireless Sensor Network , 2019, International Journal of Research in Advent Technology.

[3]  Xuxun Liu,et al.  An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[5]  A. Rajasekaran,et al.  Cluster-Based Wireless Sensor Networks Using Ant Colony Optimization , 2018, ICICI-2018.

[6]  Nicolas Vuillerme,et al.  Real-Time Obstacle Detection System in Indoor Environment for the Visually Impaired Using Microsoft Kinect Sensor , 2016, J. Sensors.

[7]  Arun Kumar Sangaiah,et al.  Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment , 2019, Sensors.

[8]  Ahmed M. Khedr,et al.  Distributed trajectory design for data gathering using mobile sink in wireless sensor networks , 2018, AEU - International Journal of Electronics and Communications.

[9]  Martyn Amos,et al.  Enhancing data parallelism for Ant Colony Optimization on GPUs , 2013, J. Parallel Distributed Comput..

[10]  Cem Ersoy,et al.  Distributed Mobile Sink Routing for Wireless Sensor Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[11]  Fazel Naghdy,et al.  An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks , 2014, IEEE Transactions on Vehicular Technology.

[12]  Prasanta K. Jana,et al.  Energy efficient path selection for mobile sink and data gathering in wireless sensor networks , 2017 .

[13]  D. PraveenKumar,et al.  ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints , 2018, Appl. Soft Comput..

[14]  Ammar Hawbani,et al.  Heuristic data dissemination for mobile sink networks , 2020, Wirel. Networks.

[15]  Walid Osamy,et al.  ADSDA: Adaptive Distributed Service Discovery Algorithm for Internet of Things Based Mobile Wireless Sensor Networks , 2019, IEEE Sensors Journal.

[16]  Liu Yang,et al.  A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks , 2016, Wirel. Networks.

[17]  Adel M. Alimi,et al.  Solving the Traveling Salesman Problem Using Ant Colony Metaheuristic, A Review , 2016, HIS.

[18]  Tuhina Samanta,et al.  Mobile sink based data collection for energy efficient coordination in wireless sensor network using cooperative game model , 2018, Telecommun. Syst..

[19]  Bellal Ahmed Bhuiyan An Overview of Game Theory and Some Applications , 2018, Philosophy and Progress.

[20]  Dinesh Dash,et al.  Time-sensitive data collection with path-constrained mobile sink in WSN , 2017, 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).

[21]  Marc Gravel,et al.  Parallel Ant Colony Optimization on Graphics Processing Units , 2013, J. Parallel Distributed Comput..

[22]  Miriam Carlos-Mancilla,et al.  Wireless Sensor Networks Formation: Approaches and Techniques , 2016, J. Sensors.

[23]  Wei Liu,et al.  Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks , 2019, Sensors.

[24]  Bharat Bhushan,et al.  $$E^{2} SR^{2}$$E2SR2: An acknowledgement-based mobile sink routing protocol with rechargeable sensors for wireless sensor networks , 2019, Wirel. Networks.

[25]  N Karthikeyan,et al.  Pair-based sink relocation and route adjustment in mobile sink WSN integrated IoT , 2020, IET Commun..

[26]  R. Vijayashree,et al.  Energy efficient data collection with multiple mobile sink using artificial bee colony algorithm in large-scale WSN , 2019, Automatika.

[27]  BellaltaBoris,et al.  Game theory for energy efficiency in Wireless Sensor Networks , 2015 .

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

[29]  Vivek Deshpande,et al.  Performance Analysis of Wireless Sensor Network by Varying Reporting Rate , 2016 .

[30]  Naixue Xiong,et al.  Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs , 2016, Sensors.

[31]  Walid Osamy,et al.  IBLEACH: intra-balanced LEACH protocol for wireless sensor networks , 2014, Wireless Networks.

[32]  Ahmed M. Khedr,et al.  An information entropy based-clustering algorithm for heterogeneous wireless sensor networks , 2018, Wirel. Networks.

[33]  D. V. Ashoka,et al.  Validation of Multiple Mobile Elements Based Data Gathering Protocols for Dynamic and Static Scenarios in Wireless Sensor Networks , 2016 .

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

[35]  Basilis Mamalis Prolonging Network Lifetime in Wireless Sensor Networks with Path-Constrained Mobile Sink , 2019, ArXiv.

[36]  Ki-Il Kim,et al.  Wireless Sensor Networks for Big Data Systems , 2019, Sensors.

[37]  Moacir Godinho Filho,et al.  Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research , 2013, Eng. Appl. Artif. Intell..

[38]  Qingwei Liu,et al.  Energy-efficient clustering algorithm based on game theory for wireless sensor networks , 2017, Int. J. Distributed Sens. Networks.

[39]  Fumiyuki Adachi,et al.  Power Efficient Adaptive Network Coding in Wireless Sensor Networks , 2011, 2011 IEEE International Conference on Communications (ICC).

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

[41]  Ibrahim Kamel,et al.  Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things , 2017, Inf..

[42]  Yuanyuan Yang,et al.  Dellat: Delivery Latency Minimization in Wireless Sensor Networks with Mobile Sink , 2015, J. Parallel Distributed Comput..

[43]  Quan Wang,et al.  An Energy-Efficient Clustering Algorithm Combined Game Theory and Dual-Cluster-Head Mechanism for WSNs , 2019, IEEE Access.

[44]  Ahmed Khedr,et al.  SATC: A Simulated Annealing Based Tree Construction and Scheduling Algorithm for Minimizing Aggregation Time in Wireless Sensor Networks , 2019, Wireless Personal Communications.

[45]  Mahesh Kadam,et al.  Performance analysis of tree cluster based data gathering for WSNs , 2017, 2017 International Conference on Intelligent Computing and Control (I2C2).

[46]  Annu Ghotra Optimizing Inter Cluster Ant Colony Optimization Data Aggregation Algorithm with Rendezvous Nodes and Mobile Sink , 2017 .

[47]  Theo Tryfonas,et al.  Game theoretic approach towards energy-efficient task distribution in wireless sensor networks , 2015, 2015 IEEE SENSORS.

[48]  Muhamad Asvial,et al.  Optimization of Heterogeneous Sensor Networks with Clustering Mechanism Using Game Theory Algorithm , 2019, ICSIM 2019.

[49]  Roxanne Evering,et al.  An ant colony algorithm for the multi-compartment vehicle routing problem , 2014, Appl. Soft Comput..

[50]  Ahmed Aziz,et al.  Cluster-Tree Routing Based Entropy Scheme for Data Gathering in Wireless Sensor Networks , 2018, IEEE Access.

[51]  Jarong Chou,et al.  Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks , 2019, EURASIP J. Wirel. Commun. Netw..

[52]  Boris Bellalta,et al.  Game theory for energy efficiency in Wireless Sensor Networks: Latest trends , 2015, J. Netw. Comput. Appl..

[53]  Liu Yang,et al.  A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks , 2017, Sensors.

[54]  Adamu Murtala Zungeru,et al.  Communication protocols for wireless sensor networks: A survey and comparison , 2019, Heliyon.

[55]  Oussama Habachi,et al.  A Coalitional Game-Theoretic Framework for Cooperative Data Exchange Using Instantly Decodable Network Coding , 2019, IEEE Access.

[56]  Shashikala Tapaswi,et al.  A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink , 2020, Wirel. Networks.

[57]  Di Bai,et al.  Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks , 2016, Sensors.

[58]  Sangman Moh,et al.  Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey , 2019, Applied Sciences.

[59]  Thomas Hanne,et al.  Variation of ant colony optimization parameters for solving the travelling salesman problem , 2017, 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI).

[60]  Ossama Younis,et al.  Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks , 2018, IEEE Transactions on Industrial Informatics.