1. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 1 Wireless Sensor Networks Boleslaw Szymanski szymab@rpi.edu@rpi.edu Center for Pervasive Computing and Networking Rensselaer Polytechnic Institute

2. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 2 networkingdistributed computingreal time systemssimulationsmultimedia adaptive scalable run-time execution environments mobile software engineering scalable distributed real-time systems distributed system security next generation network management crisis & catastrophe management mobile asset management education assistive technologies for the disabled secure access & protection of assets integration, scalability and composability: fundamental contributing fields of computer science and engineering: applications: RPI’s Center on Pervasive Computing and Networking: Hierarchy of Challenges

3. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 3 Gap between Trends Drives System Design Changes relative cost-structures… Larger implications also for impact of IT on society

4. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 4 Pervasive Wireless Communications q Ever-growing communication capacity needs for broadband wireless and mobile accessing in multimedia- rich environment, everywhere and anytime q There is a worldwide recognition that traditional methods of radio resource usage reach their limit and are no longer optimal q New communications frontier need to be explored for future wireless and mobile environments

5. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 5 Sense-and-respond systems Sense-and-respond systems Background “Sentient” networks: Computer networks composed of embedded “nodes” with onboard sensing, computational, and communication capability used for autonomous environmental monitoring Military acoustic networks Air-defense radars DARPA-lead projects [SensIT] Past (80s-90s) Present (last 5 yrs.) Multi-modal devices Ad hoc comm. Small form factor Industrial apps. Future (next decade) Smart “dust” Low cost Disposable Consumer apps. Heterogeneous networks Pervasive Ubiquitous Actuators: Responsive services/ devices offering sensor or environmental control The “Embedded Internet!” System size Amount of decentralization Projected revenues!!!

6. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 6 Sensor Temp., light, humidity, chemicals, acoustics, vibration Computer 4 MHz Atmel ATmega 128L (equiv. to original ’82 IBM PC) C R S Radio 2.4 GHz IEEE 802.15.4, <100m TX range Base station Sense-and-respond systems Sense-and-respond systems Wireless sensor networks and applications Features q Offers macroscopic observation for real-time environmental/contextual interaction q Self-organizing, self-regulating, and self-repairing systems q Multi-hop or direct-connect configurations to base station(s) q Current state – extremely application-oriented!!!

7. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 7 Sensor Temp., light, humidity, chemicals, acoustics, vibration Computer 4 MHz Atmel ATmega 128L (equiv. to original ’82 IBM PC) Enemy intrusion detection Habitat monitoring Structural monitoring Home automation and safety Traffic control Supply chain management (RFID) Practical applications C R S Radio 2.4 GHz IEEE 802.15.4, <100m TX range Base station Sense-and-respond systems Sense-and-respond systems Wireless sensor networks and applications

8. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 8 Sense-and-respond systems Sense-and-respond systems Salient Challenges q Constrained resources q Limited CPU, battery, and storage q Premium communication costs q Ad hoc routing q Dynamic topology q Transient wireless links and devices q Collaborative information processing q Faulty sensors produce erroneous data q Tradeoffs between performance and resource utilization q Sensor and actuator interaction q Synchronization between independent and heterogeneous services q Many more (querying, tasking, security, pollution, etc.) Crossbow® MICAZ mote

9. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 9 ESCORT: Motivation q Wireless communication is a premium cost q Transient wireless links threaten application integrity q Experiments show that at least 20% of nodes exhibit at least 10% packet loss, and at least 10% of nodes exhibit more than 30% packet loss q Assuming an ARQ protocol is used, transmission cost increases as link quality worsens q Many proposed routing protocols q A protocol-independent method for enhancing energy-efficiency must be adopted q WSNs are envisioned to be highly redundant Function/ component Operating current (mA) Transmission (full power) 25 Reception 8 Radio (sleep) <1µA Sensor board (full power) 5 Sensor board (sleep) 5µA CPU (full power) 8 CPU (sleep) 8µA MICA2DOT series specs. [Crossbow]

10. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 10 ESCORT: Overview q Blue and orange nodes form communities which act as “virtual” nodes to the network layer q Orange nodes help coordinate community operation q Green nodes are shared neighbors of the community q Signal quality assessment, a combination of two separate metrics, is used to form clusters of redundant nodes Source Sink Communal node Shared neighbor Communication border Coordinator node

11. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 11 Lecture Hall Algorithm and the local leader election problem q Local leader election describes the problem of finding a node (leader) with the most desired property among a local group of nodes q Example desired properties may include distance- from-destination, energy, computational load, etc. q Can readily be applied to routing (selecting the next hop neighbor) q The traditional approach would require at least n messages and log(n) time q We require at most 3 messages in constant time using the “self-selection” algorithm

12. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 12 SSR: Overview q Forwarding tables are not used q Packets are forwarded based on a gradient metric – “hop count” q Packets are freely broadcast to all neighbors and “self-selection” is used to determine the forwarding node X-dim Y-dim Hop count Base station Simulated WSN Simplified SSR example Base station

13. Boleslaw Szymanski, RPI, Troy, NY Center for Pervasive Computing and Networking 13 The SSR algorithm: Conclusion Remarks q Low overhead for route maintenance and repair q Good performance with simulated device failures and transient (and asynchronous) links q Self-selection algorithm is well suited for application tuning Future work q Evaluate SSR using real wireless sensor network under various operational conditions q Explicitly extend SSR to exhibit energy-efficiency (via radio control) q Interact radio behavior with link behavior or self-selection results q Reduce amount of required synchronization