Mobile agent itinerary planning for WSN data fusion: considering multiple sinks and heterogeneous networks

Summary Mobile agent (MA)-based middleware has been thoroughly investigated in the past few years as a means to address the efficiency, scalability, and reliability issues of data fusion applications on wireless sensor networks. Deriving an efficient itinerary for each MA to follow is of high importance, because itineraries determine to a large extent the overall performance of data fusion tasks. In this article, we present a novel algorithmic approach for efficient itinerary planning of MA objects undertaking data fusion tasks. We adopt a method based on iterated local search to construct the itineraries (ie, visiting sequences of source nodes) assigned to multiple traveling MAs. We apply alternative optimization criteria which aim either at minimizing the overall energy expenditure over all derived MA itineraries or prolonging the network lifetime. Furthermore, we propose algorithmic solutions for 2 realistic settings which have not been investigated in the past: firstly, the employment of multiple sinks that share the responsibility of MA-based data fusion tasks across the sensor field, and secondly, the consideration of heterogeneous sensor networks comprising nodes powerful enough to host the runtime environment required to execute MA code as well as “ordinary” nodes which lack these resources. Simulation tests verify the performance gain attained by our algorithmic methods against alternative itinerary planning approaches which involve multiple MAs. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Victor C. M. Leung,et al.  Mobile Agent-Based Directed Diffusion in Wireless Sensor Networks , 2007, EURASIP J. Adv. Signal Process..

[2]  Victor C. M. Leung,et al.  Balanced Itinerary Planning for Multiple Mobile Agents in Wireless Sensor Networks , 2010, ADHOCNETS.

[3]  Damianos Gavalas Mobile agent platform design optimisations for minimising network overhead and latency in agent migrations , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[4]  Damianos Gavalas,et al.  Mobile software agents for network monitoring and performance management , 2001 .

[5]  Xiaofei Wang,et al.  Multiple mobile agents' itinerary planning in wireless sensor networks: survey and evaluation , 2011, IET Commun..

[6]  Charalampos Konstantopoulos,et al.  An Iterated Local Search Approach for Multiple Itinerary Planning in Mobile Agent-Based Sensor Fusion , 2015, 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

[7]  Davinder S. Saini,et al.  Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing , 2015, J. Sensors.

[8]  S. Sitharama Iyengar,et al.  On computing mobile agent routes for data fusion in distributed sensor networks , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Helena Ramalhinho Dias Lourenço,et al.  Iterated Local Search , 2001, Handbook of Metaheuristics.

[10]  Hsiao-Hwa Chen,et al.  Energy-Spectrum Efficiency Tradeoff for Video Streaming over Mobile Ad Hoc Networks , 2013, IEEE Journal on Selected Areas in Communications.

[11]  Damianos Gavalas,et al.  Mobile software agents for decentralised network and systems management , 2001, Microprocess. Microsystems.

[12]  Charalampos Konstantopoulos,et al.  Mobile Agent Middleware for Autonomic Data Fusion in Wireless Sensor Networks , 2009, Autonomic Computing and Networking.

[13]  Liang Zhou,et al.  Mobile Device-to-Device Video Distribution , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[14]  Zhe Wei,et al.  Study on Data Fusion Techniques in Wireless Sensor Networks , 2016 .

[15]  Hairong Qi,et al.  Mobile-agent-based collaborative sensor fusion , 2008, Inf. Fusion.

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

[17]  Yacine Challal,et al.  Energy efficiency in wireless sensor networks: A top-down survey , 2014, Comput. Networks.

[18]  Wei Hong,et al.  Energy-Efficient Mobile Agent Communications for Maximizing Lifetime of Wireless Sensor Networks , 2016 .

[19]  Victor C. M. Leung,et al.  Multi-Agent Itinerary Planning for Wireless Sensor Networks , 2009, QSHINE.

[20]  Min Chen,et al.  Itinerary Planning for Energy-Efficient Agent Communications in Wireless Sensor Networks , 2011, IEEE Transactions on Vehicular Technology.

[21]  Charalampos Konstantopoulos,et al.  An approach for near-optimal distributed data fusion in wireless sensor networks , 2010, Wirel. Networks.

[22]  Charalampos Konstantopoulos,et al.  Benchmarking mobile agent itinerary planning algorithms for data aggregation on WSNs , 2014, 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN).

[23]  Junfeng Wang,et al.  EMIP: energy-efficient itinerary planning for multiple mobile agents in wireless sensor network , 2016, Telecommun. Syst..

[24]  Deborah Estrin,et al.  Efficient and practical query scoping in sensor networks , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[25]  Sarbani Roy,et al.  Multiple-sink placement strategies in wireless sensor networks , 2013, 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS).

[26]  Krishna M. Sivalingam,et al.  A Survey of Energy Efficient Network Protocols for Wireless Networks , 2001, Wirel. Networks.

[27]  Laurence T. Yang,et al.  Mobile agent-based energy-aware and user-centric data collection in wireless sensor networks , 2014, Comput. Networks.

[28]  Charalampos Konstantopoulos,et al.  Effective Determination of Mobile Agent Itineraries for Data Aggregation on Sensor Networks , 2010, IEEE Transactions on Knowledge and Data Engineering.

[29]  L. R. Esau,et al.  On Teleprocessing System Design Part II: A Method for Approximating the Optimal Network , 1966, IBM Syst. J..

[30]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[31]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.