Mobile Data Gathering with Space-Division Multiple Access in Wireless Sensor Networks

Recent years have witnessed a surge of interest in efficient data gathering schemes in wireless sensor networks (WSNs). In this paper, we address this important issue in WSNs by adopting mobility and space-division multiple access (SDMA) technique to optimize system performance. Specifically, a mobile data collector, for convenience, called SenCar in this paper, is deployed in a WSN. It works like a mobile base station and polls each sensor while traversing its transmission range. Each sensor directly sends data to the SenCar without any relay so that the lifetime of sensors can be prolonged. We also consider applying SDMA technique to data gathering by equipping the SenCar with two antennas. With SDMA, two distinct compatible sensors may successfully make concurrent data uploading to the SenCar. Intuitively, if the SenCar can always simultaneously communicate with two compatible sensors, data uploading time can be cut into half in the ideal case. We focus on the problem of minimizing the total time of a data gathering tour which consists of two parts: data uploading time and moving time. To better enjoy the benefit of SDMA, the SenCar may have to visit some specific locations where more sensors are compatible, which may adversely prolong the moving path. Hence, an optimum solution should be a tradeoff between the shortest moving path and full utilization of SDMA. We refer to this optimization problem as mobile data gathering problem with SDMA, or MDG-SDMA for short. We formalize the MDG-SDMA problem into an integer program (IP) and then propose three heuristic algorithms that provide practically good solutions to the problem. Our simulation results demonstrate that the proposed algorithms can greatly reduce the total data gathering time compared to the non-SDMA algorithm with only minimum overhead.

[1]  A. Manjeshwar,et al.  TEEN: a routing protocol for enhanced efficiency in wireless sensor networks , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[2]  Mani B. Srivastava,et al.  Mobile element scheduling for efficient data collection in wireless sensor networks with dynamic deadlines , 2004, 25th IEEE International Real-Time Systems Symposium.

[3]  Anna Scaglione,et al.  On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks , 2002, MobiCom '02.

[4]  J. Edmonds Paths, Trees, and Flowers , 1965, Canadian Journal of Mathematics.

[5]  Alex Pentland,et al.  DakNet: rethinking connectivity in developing nations , 2004, Computer.

[6]  Ossama Younis,et al.  Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach , 2004, IEEE INFOCOM 2004.

[7]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[8]  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..

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

[10]  Rajeev Shorey,et al.  Mobile, Wireless and Sensor Networks: Technology, Applications and Future Directions , 2005 .

[11]  Yuanyuan Yang,et al.  Enhancing Downlink Performance in Wireless Networks by Simultaneous Multiple Packet Transmission , 2006, IEEE Transactions on Computers.

[12]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[13]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[14]  Lili Qiu,et al.  Impact of Interference on Multi-Hop Wireless Network Performance , 2003, MobiCom '03.

[15]  Mingyan Liu,et al.  Data-gathering wireless sensor networks: organization and capacity , 2003, Comput. Networks.

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

[17]  David Tse,et al.  Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality , 2003, IEEE Trans. Inf. Theory.

[18]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[19]  Hui Liu,et al.  Uplink Channel Capacity of Space-Division-Multiple-Access Schemes , 1998, IEEE Trans. Inf. Theory.

[20]  Yuanyuan Yang,et al.  Energy-Efficient Multihop Polling in Clusters of Two-Layered Heterogeneous Sensor Networks , 2008, IEEE Transactions on Computers.

[21]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[22]  Kathryn Fraughnaugh,et al.  Introduction to graph theory , 1973, Mathematical Gazette.

[23]  Yuanyuan Yang,et al.  Energy efficient multi-hop polling in clusters of two-layered heterogeneous sensor networks , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

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

[25]  Mingyan Liu,et al.  On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data , 2003, IPSN.

[26]  Hesham El Gamal,et al.  On the scaling laws of dense wireless sensor networks: the data gathering channel , 2005, IEEE Transactions on Information Theory.

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