Ant colony optimization algorithm for lifetime maximization in wireless sensor network with mobile sink

In wireless sensor networks (WSNs), sensors near the sink can be burdened with a large amount of traffic, because they have to transmit data generated by themselves and those far away from the sink. Hence the sensors near the sink would deplete their energy much faster than the others, which results in a short network lifetime. Using mobile sink is an effective way to tackle this issue. This paper explores the problem of determining the optimal movements of the mobile sink to maximize the network lifetime. A novel ant colony optimization algorithm (ACO), namely the ACO-MSS, is developed to solve the problem. The proposed ACO-MSS takes advantage of the global search ability of ACO and adopts effective heuristic information to find a near globally optimal solution. Multiple practical factors such as the forbidden regions and the maximum moving distance of the sink are taken into account to facilitate the real applications. The proposed ACO-MSS is validated by a series of simulations on WSNs with different characteristics. The simulation results demonstrate the effectiveness of the proposed algorithms.

[1]  Jun Zhang,et al.  An Intelligent Testing System Embedded With an Ant-Colony-Optimization-Based Test Composition Method , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Mihaela Cardei,et al.  Improved sensor network lifetime with multiple mobile sinks , 2009, Pervasive Mob. Comput..

[3]  Emanuel Melachrinoudis,et al.  Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[4]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[5]  Jun Zhang,et al.  Energy-efficient local wake-up scheduling in wireless sensor networks , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[6]  Jun Zhang,et al.  Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms , 2007, IEEE Transactions on Evolutionary Computation.

[7]  Ye Xia,et al.  Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications , 2010, IEEE Transactions on Mobile Computing.

[8]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Limin Sun,et al.  HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks , 2007, EURASIP J. Wirel. Commun. Netw..

[10]  Jun Zhang,et al.  A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems , 2010, IEEE Transactions on Evolutionary Computation.

[11]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[12]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[13]  Emanuel Melachrinoudis,et al.  A New MILP Formulation and Distributed Protocols for Wireless Sensor Networks Lifetime Maximization , 2006, 2006 IEEE International Conference on Communications.

[14]  Leonidas Georgiadis,et al.  A Distributed Algorithm for Maximum Lifetime Routing in Sensor Networks with Mobile Sink , 2006, IEEE Transactions on Wireless Communications.

[15]  Jun Zhang,et al.  An Ant Colony Optimization Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Jun Zhang,et al.  Optimizing Discounted Cash Flows in Project Scheduling—An Ant Colony Optimization Approach , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[17]  Joongseok Park,et al.  Maximum Lifetime Routing In Wireless Sensor Networks ∗ , 2005 .

[18]  Jun Zhang,et al.  Protein folding in hydrophobic-polar lattice model: a flexible ant-colony optimization approach. , 2008, Protein and peptide letters.

[19]  Ioannis Papadimitriou,et al.  Maximum Lifetime Routing to Mobile Sink in Wireless Sensor Networks , 2005 .

[20]  Yiwei Thomas Hou,et al.  Theoretical Results on Base Station Movement Problem for Sensor Network , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[21]  Hyung Seok Kim,et al.  Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks , 2003, SenSys '03.

[22]  Jun Zhang,et al.  Implementation of an Ant Colony Optimization technique for job shop scheduling problem , 2006 .

[23]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[24]  Jun Zhang,et al.  An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem , 2010, IEEE Transactions on Intelligent Transportation Systems.

[25]  Jun Zhang,et al.  Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks , 2010, IEEE Transactions on Evolutionary Computation.

[26]  Yi Sun,et al.  Moving Schemes for Mobile Sinks in Wireless Sensor Networks , 2007, 2007 IEEE International Performance, Computing, and Communications Conference.

[27]  D. Puccinelli,et al.  Wireless sensor networks: applications and challenges of ubiquitous sensing , 2005, IEEE Circuits and Systems Magazine.

[28]  H.S.-H. Chung,et al.  Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design , 2009, IEEE Transactions on Power Electronics.

[29]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Marco Dorigo Ant colony optimization , 2004, Scholarpedia.

[31]  Jun Zhang,et al.  Evolutionary Computation Meets Machine Learning: A Survey , 2011, IEEE Computational Intelligence Magazine.

[32]  Waylon Brunette,et al.  Data MULEs: modeling a three-tier architecture for sparse sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[33]  Martin Haenggi Opportunities and Challenges in Wireless Sensor Networks , 2004, Handbook of Sensor Networks.

[34]  Jun Zhang,et al.  Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).