Mobile Wireless Sensor Networks: Architects for Pervasive Computing

A mobile wireless sensor network owes its name to the presence of mobile sink or sensor nodes within the network. The advantages of mobile wireless sensor network over static wireless sensor network are better energy efficiency, improved coverage, enhanced target tracking and superior channel capacity. In this chapter we will present and discuss different classifications of mobile wireless sensor network as well as hierarchical multi-tiered architectures for such networks. This architecture makes basis for the future pervasive computing age. The importance of mobility in traditional wireless sensor network (WSN) is highlighted in this chapter along with the impact of mobility on different performance metrics in mobile WSN. A study of some of the possible application scenarios for pervasive computing involving mobile WSN is also presented. These application scenarios will be discussed in their implementation context. While discussing the possible applications, we will also study related technologies that appear promising to be integrated with mobile WSN in the ubiquitous computing. With an enormous growth in number of cellular subscribers, we therefore place the mobile phone as the key element in future ubiquitous wireless networks. With the powerful computing, communicating and storage capacities of these mobile devices, the network performance can benefit from the architecture in terms of scalability, energy efficiency and packet delay, etc. For mobile wireless sensor networks, there are basically two sensing modes, local sensing and remote sensing. By allowing and leveraging sink mobility and sink coordination, mobile WSN can achieve the goal of lower and balanced energy consumption with the following features:  Single-hop clustering. By allowing only single hop transmission between sensor and sink node, most previous multi hop relaying sensor nodes may become unnecessary. In fact, sensor nodes can enter sleep mode until the sink approaches. Therefore, the original energy budget for multi hop relaying can be saved.  Sink mobility and coordination. For a delay tolerant application, single mobile sink in fact equals virtually multiple static sinks at different positions. Multi-sink deployment can bring more uniform energy dissipation, therefore the possibility of energy hole will be reduced and network coverage will be improved. 1

[1]  Ashutosh Sabharwal,et al.  Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks , 2003, IPSN.

[2]  M. Shaw HOW TO REFERENCE , 2004 .

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

[4]  Lang Tong,et al.  Sensor networks with mobile agents , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[5]  Haiyun Luo,et al.  A two-tier data dissemination model for large-scale wireless sensor networks , 2002, MobiCom '02.

[6]  Rolland Vida,et al.  Adaptive sink mobility in event-driven multi-hop wireless sensor networks , 2006, InterSense '06.

[7]  Ellen W. Zegura,et al.  Message ferry route design for sparse ad hoc networks with mobile nodes , 2006, MobiHoc '06.

[8]  Zhen Liu,et al.  Capacity, delay and mobility in wireless ad-hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[9]  Milind Dawande,et al.  Energy efficient schemes for wireless sensor networks with multiple mobile base stations , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[10]  M. Weiser The Computer for the Twenty-First Century , 1991 .

[11]  Donald F. Towsley,et al.  Mobility improves coverage of sensor networks , 2005, MobiHoc '05.

[12]  Deborah Estrin,et al.  Intelligent fluid infrastructure for embedded networks , 2004, MobiSys '04.

[13]  Eylem Ekici,et al.  Mobility-based communication in wireless sensor networks , 2006, IEEE Communications Magazine.

[14]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

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

[16]  Jun Luo,et al.  MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks , 2006, DCOSS.

[17]  M. Younis,et al.  Energy-aware routing to a mobile gateway in wireless sensor networks , 2004, IEEE Global Telecommunications Conference Workshops, 2004. GlobeCom Workshops 2004..

[18]  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..

[19]  Canfeng Chen,et al.  MEMOSEN: multi-radio enabled mobile wireless sensor network , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[20]  Stephan Eidenbenz,et al.  Maneuverable relays to improve energy efficiency in sensor networks , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

[21]  Jian Ma,et al.  A moving strategy for mobile sinks in wireless sensor networks , 2006, 2006 2nd IEEE Workshop on Wireless Mesh Networks.

[22]  Ellen W. Zegura,et al.  Controlling the mobility of multiple data transport ferries in a delay-tolerant network , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

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

[24]  Bhaskar Krishnamachari,et al.  Learning-enforced time domain routing to mobile sinks in wireless sensor fields , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[25]  Deborah Estrin,et al.  Controllably mobile infrastructure for low energy embedded networks , 2006, IEEE Transactions on Mobile Computing.

[26]  Jian Ma,et al.  The hybrid mobile wireless sensor networks for data gathering , 2006, IWCMC '06.

[27]  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..

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

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

[30]  Gaetano Borriello,et al.  Exploiting Mobility for Energy Efficient Data Collection in Wireless Sensor Networks , 2006, Mob. Networks Appl..

[31]  Hanif D. Sherali,et al.  Prolonging sensor network lifetime with energy provisioning and relay node placement , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[32]  A SomasundaraA.,et al.  Mobile Element Scheduling for Efficient Data Collection in Wireless Sensor Networks with Dynamic Deadlines , 2004 .

[33]  Hongyi Wu,et al.  DFT-MSN: The Delay/Fault-Tolerant Mobile Sensor Network for Pervasive Information Gathering , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.