Mobile Wireless Sensor Network (mWSN) at Nokia Jian Ma Nokia Research Center, China Thanks also to Canfeng Chen 1 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Emerging applications of WSN Many critical issues facing science, government, and the society call for high-fidelity and real-time observations and manipulations of the physical world Within the next few years, distributed sensing and computing will be everywhere, i.e., homes, offices, factories, automobiles, shopping centers, super-markets, farms, forests, rivers, lakes, mobile phone, and even body. Some of the immediate commercial applications of WSN are Industrial automation (process control) Environmental (ecology system monitoring) Defense (unattended sensors, real-time monitoring) Utilities (automated meter reading), Weather prediction (temperature, humidity) Security (smart building, tracking, car theft detecting) Building automation (HVAC controllers). Disaster relief operations (earthquake, firefighting) Medical monitoring and instrumentation (remote sensing and health care) Intelligent transportation (unmanned driving) HVAC: Heating, ventilation, and air-conditioning 2 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

WSN System Model Emergence of Application-Driven (not Application-Specific) Common System Model 3 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Research Challenges of WSN Numerous unattended and resource-constrained sensors, deployed at high density in regions requiring surveillance and monitoring. Network topology is unknown due to unexpected node failures. Sensors are memory as well as energy constrained, and power consumption in WSN can be divided into three domains: Communication, Data Processing, and Sensing Tradeoff between energy and QoS Prolong network lifetime by sacrificing application requirements, such as delay, throughput, reliability, data fidelity,… Emerging new applications results diversity of small sensor networks Mobility and Heterogeneity are being re-considered Connectivity and Scalability Cluster and Overlay 4 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Why mobile WSN Connecting small world networks Convergence of diversity WSNs – global WSN services Pervasive / ubiquitous computing: user centric, Invisible, Transparent, Embedded, Mobile, Everywhere Popularity of multi-Radio mobile terminals Multi-mode mobile terminal such as WiFi-UMTS, WiMax/WiBro-UMTS, Bluetooth-UMTS, Zigbee-UMTS integrated mobile phones, PDAs, smart phones, etc Deployment of sensors in mobile phones and mobile commerce Various sensors in mobile phones E-Wallet, Smart Card, RFID, NFC Mobility-aware and location-based services GPS and Cellular Positioning for location-aware computing Need for customized services Profit opportunity of serving sudden impulses and customized needs of customers at various moments 5 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

mWSN Architecture 6 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Mobile networks /Internet Overlay structure Mobile networks /Internet 7 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Key features of MWSN: Heterogeneity and Mobility Heterogeneity: radio, computing, energy, type, … Radio link heterogeneity leads to hierarchy/overlay, more scalable Energy heterogeneity suggests routing optimization, not shortest-path-first Computing heterogeneity partitions whole network to different roles Actor/Actuator nodes with decision unit Provide greater functionality than sensor nodes: make decisions and perform actions Example: WSAN – feedback closed loop Mobile phones have multiple radios, and will become powerful computing devices besides communication devices Mobility: information mining on-the-fly One mobile sink equals virtually multiple sinks Example: Networked Info-Mechanical Systems (NIMS) Reduce path length, energy consumption, energy dissipation non-uniformity Enhance coverage, connectivity, and relocability Realize adaptive resource provision and fidelity-driven sampling Mobile phones can gather and process information on-the-fly, realizing interactions with ambient intelligence Static Sensor nodes spatially distributed Limited energy and sampling rate Simultaneous sampling (dense in time, but possibly sparse in space) Nodes with mobility Allows dense sampling (dense spatially, but possibly sparse in time) Goal is to have statistical information over entire study region with quality similar to high resolution sampling, without applying high resolution to entire region 8 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Summary Emerging of Multi-Radio mobile terminals builds gateway for people to access any WSN services Multi-Radio heterogeneity in wireless networks can be leveraged to connect small world networks Introduction of mobile sink can reduce path length and energy dissipation non-uniformity Sensor mobility can be exploited to compensate for the lack of sensors or sensor failures and improve network coverage mWSN enables innovative ubiquitous applications 9 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials

Thank you 10 © 2005 Nokia V1-Filename.ppt / yyyy-mm-dd / Initials