Predetermined path of mobile data gathering in wireless sensor networks based on network layout

Data gathering is among the issues constantly acquiring attention in the area of wireless sensor networks (WSNs). There is a consistent increase in the research directed on the gains of applying mobile elements (MEs) to collect data from sensors, especially those oriented to power issues. There are two prevailing strategies used to collect data in sensor networks. The first approach requires data packets to be serviced via multi-hop relay to reach the respective base station (BS). Thus, sensors will send their packets through other intermediate sensors. However, this strategy has proven to consume high and a substantial amount of energy due to the dependency on other nodes for transmission. The second approach encompasses a ME which serves as the core element for the searching of data. This ME will visit the transmission range of each sensor to upload its data before eventually returning to the BS to complete the data transmission. This approach has proven to reduce the energy consumption substantially as compared to the multi-hop strategy. However, it has a trade-off which is the increase of delay incurred and is constrained by the speed of ME. Furthermore, some sensors may lose their data due to overflow while waiting for the ME. In this paper, it is proposed that by strategically divisioning the area of data collection, the optimization of the ME can be elevated. These derived area divisions are focused on the determination of a common configuration range and the correlation with a redundant area within an identified area. Thus, within each of these divided areas, the multi-hop collection is deployed as a sub-set to the main collection. The ME will select a centroid point between two sub-polling points, subsequently selecting common turning points as the core of the basis of the tour path. Extensive discrete-event simulations have been developed to assess the performance of the proposed algorithm. The acquired results depicted through the performance metrics of tour length and latency have determined the superior performance of the proposed algorithm in comparison to the existing strategy. In addition, the proposed algorithm maintains the energy consumption within an acceptable level.

[1]  Alicja R. Rudnicka 2. Essential medical statistics (2nd edn). Betty R. Kirkwood and Jonathan A. C. Sterne, Blackwell Science, Oxford, 2003. No. of pages: 512. Price: £22.95. ISBN 0‐86542‐871‐9 , 2005 .

[2]  Miao Zhao Design and Optimization on Mobile Data Gathering in Wireless Sensor Networks , 2010 .

[3]  Krishna M. Sivalingam,et al.  Energy-efficient mobile data collection in Wireless Sensor Networks with delay reduction using wireless communication , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).

[4]  Neelakantan Pattathil Chandrasekharamenon,et al.  Connectivity analysis of one-dimensional vehicular ad hoc networks in fading channels , 2012, EURASIP Journal on Wireless Communications and Networking.

[5]  Limin Sun,et al.  DAR: An energy-balanced data-gathering scheme for wireless sensor networks , 2007, Comput. Commun..

[6]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[7]  Azzedine Boukerche,et al.  Mobile data collector strategy for delay-sensitive applications over wireless sensor networks , 2008, Comput. Commun..

[8]  Chao Wang,et al.  Data Collection in Wireless Sensor Networks by Utilizing Multiple Mobile Nodes , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[9]  Bezalel Gavish,et al.  Formulations and Algorithms for the Capacitated Minimal Directed Tree Problem , 1983, JACM.

[10]  Zhehan Ding,et al.  An improvement of energy efficient multi-hop time synchronization algorithm in wireless sensor network , 2010, 2010 IEEE International Conference on Wireless Communications, Networking and Information Security.

[11]  Elfed Lewis,et al.  A comparative review of wireless sensor network mote technologies , 2009, 2009 IEEE Sensors.

[12]  Sherali Zeadally,et al.  Balancing energy consumption with mobile agents in wireless sensor networks , 2012, Future Gener. Comput. Syst..

[13]  Edward J. Coyle,et al.  Spatio-temporal sampling rates and energy efficiency in wireless sensor networks , 2005, IEEE/ACM Transactions on Networking.

[14]  Khaled Almiani,et al.  Mobile Element Path Planning for Time-Constrained Data Gathering in Wireless Sensor Networks , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[15]  Tao Liu,et al.  Power-Efficient Clustering Routing Protocol Based on Applications in Wireless Sensor Network , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[16]  Mani B. Srivastava,et al.  Multiple Controlled Mobile Elements (Data Mules) for Data Collection in Sensor Networks , 2005, DCOSS.

[17]  Simon Bond,et al.  Essential Medical Statistics (2nd edn). Kirkwood BR, Sternc JAC. Malden, MA: Blackwell Publishing, 2003, pp. 288, $52.95 (PB). ISBN 0865428719. , 2004 .

[18]  Gaurav S. Sukhatme,et al.  Robomote: enabling mobility in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[19]  Yuanyuan Yang,et al.  Tour Planning for Mobile Data-Gathering Mechanisms in Wireless Sensor Networks , 2013, IEEE Transactions on Vehicular Technology.

[20]  Jang-Ping Sheu,et al.  Efficient path planning and data gathering protocols for the wireless sensor network , 2010, Comput. Commun..

[21]  Yuanyuan Yang,et al.  SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks , 2007, IEEE Trans. Parallel Distributed Syst..

[22]  Van-Duc Nguyen,et al.  Optimizing the operating time of wireless sensor network , 2012, EURASIP Journal on Wireless Communications and Networking.

[23]  Sajal K. Das,et al.  EBRP: Energy-Balanced Routing Protocol for Data Gathering in Wireless Sensor Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[24]  S Sujatha,et al.  Efficient Data Gathering With Mobile Collectors and Space-Division Multiple Access Technique in Wireless Sensor Networks , 2014 .

[25]  Prasanta K. Jana,et al.  An Energy efficient Load Balancing Algorithm for cluster-based wireless sensor networks , 2012, 2012 Annual IEEE India Conference (INDICON).

[26]  Guoliang Xing,et al.  Rendezvous design algorithms for wireless sensor networks with a mobile base station , 2008, MobiHoc '08.

[27]  Hui Peng,et al.  Tree-Adapting: An Adaptive Data Aggregation Method for Wireless Sensor Networks , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[28]  Yu-Chee Tseng,et al.  Data Gathering by Mobile Mules in a Spatially Separated Wireless Sensor Network , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[29]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[30]  Yuanyuan Yang,et al.  SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks , 2006, IEEE Transactions on Parallel and Distributed Systems.

[31]  C. Siva Ram Murthy,et al.  Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints , 2010, J. Parallel Distributed Comput..

[32]  Guoliang Xing,et al.  Efficient Rendezvous Algorithms for Mobility-Enabled Wireless Sensor Networks , 2012, IEEE Transactions on Mobile Computing.

[33]  Yuanyuan Yang,et al.  Bounded relay hop mobile data gathering in wireless sensor networks , 2009, 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems.

[34]  Nei Kato,et al.  A Novel Scheme for WSAN Sink Mobility Based on Clustering and Set Packing Techniques , 2011, IEEE Transactions on Automatic Control.

[35]  Waylon Brunette,et al.  Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks , 2003, Ad Hoc Networks.

[36]  Rabindra Bista,et al.  A New Energy-Balanced Data Aggregation Scheme in Wireless Sensor Networks , 2009, 2009 International Conference on Computational Science and Engineering.

[37]  Marimuthu Palaniswami,et al.  Spatio-temporal modelling-based drift-aware wireless sensor networks , 2011, IET Wirel. Sens. Syst..

[38]  Sunggu Lee,et al.  Optimal wake-up scheduling of data gathering trees for wireless sensor networks , 2012, J. Parallel Distributed Comput..

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

[40]  Guoliang Xing,et al.  Real-time Power-Aware Routing in Sensor Networks , 2006, 200614th IEEE International Workshop on Quality of Service.

[41]  Martin Vetterli,et al.  Proceedings of the 4th international symposium on Information processing in sensor networks , 2005 .

[42]  J. Sterne,et al.  Essential Medical Statistics , 2003 .