CMIP: Clone Mobile-Agent Itinerary Planning Approach for Enhancing Event-to-Sink Throughput in Wireless Sensor Networks

In order to mitigate the problem of data congestion, increased latency, and high-energy consumption in wireless sensor networks, mobile agent (MA) has been proven to be a viable alternative to the traditional client-server data gathering model. MA has the ability to migrate among network nodes based on an assigned itinerary, which can be formed via single itinerary planning (SIP) or multiple itinerary planning (MIP). MIP-based data gathering approach solves problems associated with SIP in terms of task duration, energy consumption, and reliability. However, the majority of the existing MIP approaches focus only on reducing energy consumption and task duration, while the event-to-sink throughput has not been considered. In this paper, a clone mobile-agent itinerary planning approach (CMIP) is proposed to reduce task duration while improving the event-to-sink throughput in real-time applications, especially when the MA is assigned to visit a large number of source nodes. Simulation results show that the CMIP approach outperforms both central location-based MIP (CL-MIP) and greatest information in greatest memory-based MIP (GIGM-MIP) in terms of reducing task duration by about 56% and 16%, respectively. Furthermore, CMIP improves the event-to-sink throughput by about 93% and 22% as compared to both CL-MIP and GIGM-MIP approaches, respectively.

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

[2]  Behzad Moshiri,et al.  CLONE -BASED MOBILE AGENT ITINERARY PLANNING USING SEPARATE TREES FOR DATA FUSION IN WSN S , 2012 .

[3]  Saeid Pourroostaei Ardakani,et al.  Wireless Sensor Network Routing Protocols for Data Aggregation , 2014 .

[4]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

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

[6]  Charalampos Konstantopoulos,et al.  CBID: A Scalable Method for Distributed Data Aggregation in WSNs , 2010, Int. J. Distributed Sens. Networks.

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

[8]  Abderrahim Beni Hssane,et al.  Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks , 2018, EURASIP J. Wirel. Commun. Netw..

[9]  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.

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

[11]  Subramaniam Shamala,et al.  Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: A review paper , 2017, Int. J. Distributed Sens. Networks.

[12]  Hairong Qi,et al.  Mobile-agent-based collaborative signal and information processing in sensor networks , 2003, Proc. IEEE.

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

[14]  Victor C. M. Leung,et al.  Applications and design issues for mobile agents in wireless sensor networks , 2007, IEEE Wireless Communications.

[15]  Hairong Qi,et al.  Optimal Itinerary Analysis for Mobile Agents in Ad Hoc Wireless Sensor Networks , 2001 .

[16]  Χαράλαμπος Κωνσταντόπουλος,et al.  Deriving efficient mobile agent routes in wireless sensor networks with NOID algorithm , 2015 .

[17]  Victor C. M. Leung,et al.  Energy-Efficient Itinerary Planning for Mobile Agents in Wireless Sensor Networks , 2009, 2009 IEEE International Conference on Communications.

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

[19]  Multi Mobile Agent Itinerary for Wireless Sensor Networks , .

[20]  Khalid Bouragba,et al.  Determination of Itinerary Planning for Multiple Agents in Wireless Sensor Networks , 2018, Int. J. Commun. Networks Inf. Secur..

[21]  Joel J. P. C. Rodrigues,et al.  Real-time data management on wireless sensor networks: A survey , 2012, J. Netw. Comput. Appl..

[22]  Okba Kazar,et al.  A new Itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption , 2015, Int. J. Commun. Networks Inf. Secur..

[23]  Manoj Misra,et al.  Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks , 2014, J. Netw. Comput. Appl..

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

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

[26]  Charalampos Konstantopoulos,et al.  Energy-efficient multiple itinerary planning for mobile agents-based data aggregation in WSNs , 2016, Telecommun. Syst..

[27]  Wei Cai,et al.  A Genetic Algorithm Approach to Multi-Agent Itinerary Planning in Wireless Sensor Networks , 2011, Mob. Networks Appl..