Towards Reliable Wireless Sensor and Actuator Networks

1. Towards Reliable Wireless Sensor and Actuator Networks Presentation at 8th Scandinavian Workshop on Wireless Adhoc Networks May 7-8, 2008 Johannesberg Estate Prof. Riku Jäntti Department of Communications and Networking (Comnet)

2. Contents • Intelligent monitoring systems • System requirements • Challenges • Wireless sensor networking solutions • State of art • Open issues & current research • Tools for overall system design • PiccSim • Examples: • Building automation: Identification of bottlenecks • Robotics: Routing • Conclusions

3. Wireless automation today: A journey towards Reliable Wireless automation • Wireless Networked control systems are real-time computing and control systems over wireless networks. • Embedded systems where the different devices (sensors, controllers and actuators) communicate seamlessly using wireless technology • Connection of field devices through a field bus requires a lot of network planning, wiring and troubleshooting as a result, for many automation systems the cost is in “all in the wires” • Wireless vision: autonomic communications and computing gets rid of the human-in-the-loop by making the systems self-configuring, self-healing, self-optimizing and self-protecting

4. Requirements • We aim at developing an intelligent system: • The events in the environment need to be detected and acted upon in real-time (sensing, timeliness), • The information must be seamlessly delivered from the environment to the decision making units and users (communications), • The decisions need to be taken in real-time (information fusion and control), • Actions are to be made in order to affect the environment (actuation), • Reconfiguration of the used devices must be easy and fast (tasks, networking, algorithms), • The system should be able to monitor its performance and adapt if necessary (fault detection, self-healing), and • Security, safety and application management need to be inbuilt features of such systems, not add-ons. • The system should be energy efficienti such that battery operation time is maximized • The system should be based on open standards (IEEE802.15.4, 6LOWPAN,ROLL,ISA SP100,…)

5. Statistical fades can cover multiple IEEE802.15.4 channels In large networks, there might not be a single channel that provides full connectivity Air interface is vulnerable to interference and jamming. E.g. WiFi is efficient jammer for IEEE802.15.4 Network capacity is limited by interference Physical layer challenges Ward et al. “Improved quality of service in 802.15.4 wireless mesh networks”, In Internationalworkshop on wireless and industrial automation, San Francisco, 2005. M. Petrova et al, “Performance study of IEEE 802.15.4 using measurements and simulations”, IEEE WCNC 2006.

6. State of Art in WSANs ISA SP100 and WirelessHART

7. ISA SP100 and WirelessHART IEEE802.15.4 radio on 2.4 GHz ISM band Frequency hopping over all 16 channels Conflict free FDMA/TDMA MAC End-to-end security End-to-end reliable transport Centralized control Network Manager NM Security Manager WirelessHART All data routed through Gateway/access point Supports HART protocol for communication with field devices ISA SP100 Direct paths betwen nodes 6LoWPAN IPv6 over sensor networks (mesh under) Supports multiple fieldbus protocols State of Art in WSANs

8. Both ISA SP100 and WirelessHART use Time Synchronized Mesh Protocol (TSMP) Frequency hoppin over 16 channels Conflic-free MAC based on FDMA/TDMA Multi-path transmission (Graph routing with two link disjoint paths) Centralized control of network Dust Network has reported carrier class reliability 99.9995% over 26 days 44 nodes 33 packets/15 min 1kbit/s access point State of Art in WSANs

9. Limitations of the state of art solutions Centralized network and security managers Single point of failure (although redundancy possible) Does not support autonomous applications Network / frequency planning Does not scale well (network manager is very complex entity) Setting up network is slow. This can be problem if the network needs to re-establish itself after crash. (Self-healing) Frequency hopping (with blacklisting) Diversity technique Interference is not avoided automatically There can still be co-existence problems (e.g. deployment in a city area / office buildings where high density WLAN occupy most of the channels.) Network topology is inflexible Sensor data is often correlated and redundant. Unfortunately, the end-to-end security does not allow data aggregation and data fusion in the intermediate nodes. No support for multicasting State of Art in WSANs

10. Nordite WISA Project Quality of service Increase robustness Degrease jitter Requirements for current control algorithms Data fusion PID Controller tuning New control algorithms Increase jitter margin and tolerance to errors Wireless automation systems Coexistence protocols Multi-path routing (mesh) Synchronization Performance of current wireless networks

11. “Cognitive radio” sensor network Adapt to the interference situation rather than average it Find white spaces in the spectrum Decentralized operation with self-organization and self-healing would enable autonomous systems Flexible topologies Store-process-and-forward to allow data aggregation and data fusion which reduces the need to transmit data. This would require new security solutions (packet level authentication?) Co-design of communications and data fusion and control Intelligent data fusion and control methods can tolerate certain packet loss and jitter and thus relax the requirements for communications Jitter and asynchronous sampling are challenges for control engineers, but tools for handling them are under development. Communication protocols, data fusion and control schemes can have complex interactions not always foreseen by the designers. This calls for tools for system design and testing. The system should support reconfiguration Software updates without shutting down the operation Adaptation to changes in the environment e.g. by changing the sampling intervals or data fusion techniques Research problems

12. WISA Phase I & II Cross-layer design

13. Strategic framework (Shankar Sastry, University of California Berkeley, 2006). WISA-II Workpackages Project organization Algorithms Protocols Tools Theory Schemes Testbed

14. There is a lack of design tools that are able to deal with integrated communication and control systems The performance of data fusion and control schemes depend on the network and the network performance depend on the traffic generated by these schemes =>Co-design is needed Development tools for co-design Distributed systems Communication Systems Control Systems Components composition Layer composition Plant & controller model PiccSIM

15. Platform for (wireless) communications and applications co-simulation Integrates MATLAB computation environment algorithm development, control design and analysis tools, simulation of dynamical systems etc. with Ns-2 network simulator de-facto standard network simulator in research Implementation of e.g. data fusion and control algorithms and routing protocols on the same simulation tool -> simulation of networked control systems (NCS) Automatic code generation capabilities provide ease of use Allows for testing various algorithms (e.g. speaker identification, coordination of robots) with a realistic communications model Platform for integrated control and communications simulation PiccSIM

16. PiccSIM Architecture

17. PiccSIM Example:Building Automation – Identification of bottlenecks • Testing of wireless temperature and ventilation control • 40 ZigBee nodes • Shadowing from blueprint

18. Physical Models: Heat balance in rooms (PID control) CO2 concentration in rooms (relay control) Event driven signals, lighting (on/off) Communication models IEEE802.15.4 radio Indoor propagation modeling PiccSIM Example:Building Automation – Identification of bottlenecks

19. Sensor Motes equipped with Ultra sound receivers and a radio module forms a Grid network A mobile Node (Trolley/Robot) emits Periodic Ultrasound pulse Sensor Motes estimate the distance to the Mobile using Distance information is forwarded to the Controller, where Position estimation is done Controller estimates the position using 3-D Position Sensing scheme, where the Differences in the Time-of-Flights from a Wave Source to Various Receivers [Ajay]. Finally controller sends Control (Action) Message to the Mobile nodes. PiccSIM Example:Robotics - Multipath routing Sensors->Controller Controller->Mobile Node

20. LMNR (Localized Multiple next hop routing) Set up multiple routes Next hop is locally decided based on load, interference, and link availability => Increase robustness against link faults (decrease the need for rerouting in case of failures) PiccSIM Example:Robotics - Multipath routing AODV AOMDV LMNR S. Nethi, C. Gao and  R Jäntti, “Localized Multiple Next-hop Routing Protocol”, in Proc. 7th international conference on ITS telecommunication (ITST 2007), Paris, France, June 5-8, 2007

21. PiccSIM Example:Robotics - Multipath routing • LMNR (Localized Multiple next hop routing) • Intermediate nodes are given the liberty to choose from multiple Next-hop to the destination, • Basically, intermediate nodes choose from possible next hop node depending upon cost function of the respective node. • Finally, Update cost, using Periodic hello messages. • In order to achieve load balancing, the cost function should somehow reflect the traffic volumes carried by the nodes on a route Cn Cost associated with node n Rn Number of entries in the routing table of node n Dtr Life-time of routing table entry r ter Expire time of route entry r t Current time

22. NS2 Simulation results Simulation time: 250 s Number of nodes: 50 Number of traffic flows: 10 Packet size: 512 bytes PiccSIM Example:Robotics - Multipath routing Performance improves, but what would this mean from the application point of view?

23. PiccSIM Example:Robotics - Multipath routing • Control of mobile node over wireless network • Singlepath vs. multipath

24. PiccSIM Example:Robotics - Multipath routing Packet delivery fraction and Avg. end-to-end delay Outage time and Error estimate

25. Robot squad Leader controls positions of other nodes Multihop scenario Routing is very important Communication constrained control Dropped packets degrade control performance Halt when several packets dropped Rerouting when link breaks PiccSIM Example:Robotics – Communications & Control

26. Control options PID controller Tuned stable for specified jitter Slightly conservative Kalman filter + PID KF estimates current process state No jitter margin needed Tight tuning Networking options AODV Single path routing LMNR Multi path routing Priorization of traffic based on number of hops PiccSIM Example:Robotics – Communications & Control

27. PiccSIM Example:Robotics – Communications & Control • Control cost • Average distance from reference trajectory, rk • Only when communication No cost if break (large dominating cost) • Costs:

28. PiccSIM Example:Robotics – Communications & Control • Packet delay for singlepath routing as a function of hops. 2. Average delay as function of number of total hops, with different priorizations. Multipath Vs Single path

29. Concluding remarks • Current WSAN solutions can achieve very high reliability, but they are • Inflexible • Complex to implement and thus do not scale well • The use of WSAN in autonomous systems would require • decentralized communication and data fusion solutions • re-configurability This calls for cognitive networking • Co-design of communications, data fusion and control can help relax the reliability requirements and thus achieve • better scalability • energy efficiency • Store-process-and-forward data transmission could be utilized to • reduce the network load • improve estimator/controller performance but they would require new transport and data security solutions

30. Any Questions ?