Dr. Kemal Akkaya E-mail: kemal@cs.siu

1. Department of Computer ScienceSouthern Illinois University CarbondaleMobile & Wireless ComputingRouting Protocols for Sensor NetworksHierarchical & Location-based and QoS Protocols Dr. Kemal Akkaya E-mail: kemal@cs.siu.edu Mobile & Wireless Computing 1

2. Hierarchical Protocols • When sensor density increases single tier networks cause • Sink overloading • Increased latency • Large energy consumption • Clustered Network allow coverage of large area of interest and additional load without degrading the performance • Hierarchical clustering schemes are the most suitable for wireless sensor networks • Uses Multi - hop communication within a cluster • Performs data aggregation and fusion on data to reduce number of transmitted messages to the sink • Maintain the energy reserves of nodes efficiently Mobile & Wireless Computing 2

3. Hierarchical Routing Mobile & Wireless Computing 3

4. LEACH • LEACH (Low Energy Adaptive Clustering Hierarchy) is the first hierarchical routing protocol for sensor networks • W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol for wireless sensor networks," in the Proceeding of the Hawaii International Conference System Sciences, Hawaii, January 2000. • Self-Organizing, adaptive clustering protocol • Even distribution of energy load among the sensors • Nodes organize themselves into clusters • Cluster-heads communicate data with the base station (sink) Mobile & Wireless Computing 4

5. LEACH • Dynamic cluster formation - Cluster-heads are not fixed • They rotate at each round randomly • Data-fusion at each cluster– reduces energy dissipation and enhances lifetime Dynamic Clustering Cluster-heads at time t Cluster-heads at time t + d Mobile & Wireless Computing 5

6. LEACH uses First Order Radio Model Transmit k-bit message a distance d using the radio model ETx-elec = Energy dissipated/bit at Transmitter ERx-elec = Energy dissipated/bit at Receiver Єamp = Amplification factor Energy equation at the Transmitter: Energy equation at the Receiver: Fig 1: First Order Radio Model Mobile & Wireless Computing 6

7. LEACH Algorithm • Algorithm is broken into rounds, and each rounds consists of following 4 phases: • Advertisement phase • Each node decides whether or not to become cluster-head • Advertises itself as cluster-head • Cluster Set-up phase • Each node decides to which cluster it belongs • Notification to the cluster-head • Schedule Creation • Cluster-head creates a TDMA schedule notifying each node when it can transmit • Data transmission • Each node send data during their allotted time Mobile & Wireless Computing 7

8. Simulation Results Direct: Direct Transmission to the Sink MTE: Minimum Transmission Energy Energy dissipation System Lifetime Mobile & Wireless Computing 8

9. Sensor Lifetimes • System life time after 1200 rounds Live nodes (circled) Dead nodes (dotted) Mobile & Wireless Computing 9

10. What about MTE & Direct Communication? • No of rounds: 180 • Alive (circles); Dead (dots) Direct Communication MTE Mobile & Wireless Computing 10

11. LEACH Summary • Factor of 7 reduction in energy dissipation as compared to Direct Communication • Uniform distribution of energy-usage in the network • Doubles the system lifetime compared to other methods • Nodes die essentially in random fashion, thus maintain the network coverage • Completely distributed, no network knowledge required • Problems: • Nodes use single-hop communication • Not good for large domains • Cluster-head change overhead Mobile & Wireless Computing 11

12. PEGASIS • Power Efficient GAthering in Sensor Information Systems • Improvement to LEACH • Form chains rather than clusters • S. Lindsey and C. S. Raghavendra, "PEGASIS: Power Efficient GAthering in Sensor Information Systems," in the Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2002. • Token-Passing Chain-Based • Considered Near-Optimal • Nodes die in random • Stationary Nodes and Sink • Every node have a global network map • Data Fusion • Greedy chain construction Mobile & Wireless Computing 12

13. Main Procedures • Greedy Algorithm Construct Chain –Start at a node far from sink and gather everyone neighbor by neighbor • Node i (mod N) is the leader in round i • Nodes passes token through the chain to leader from both sides • Each node fuse its data with the rest • Leader transmit to sink Mobile & Wireless Computing 13

14. PEGASIS - Illustration Mobile & Wireless Computing 14

15. Comparison Mobile & Wireless Computing 15

16. Summary • Outperforms LEACH by eliminating clustering overhead • Global Information assumed • Limited Scale: • Information travels many nodes • Excessive delay for far nodes • Assumes any node can communicate with sink • Hierarchical PEGASIS • Extension of PEGASIS • Decrease the delay for the packets during transmission to the base station • Simultaneous transmissions of data messages • Avoid collisions and possible signal interference • Signal Coding (e.g. CDMA) • Spatially separated nodes can transmit at the same time Mobile & Wireless Computing 16

17. Hierarchical PEGASIS Mobile & Wireless Computing 17

18. Location-based Protocols • If the locations of the sensor nodes are known, the routing protocols can use this information to reduce the latency and energy consumption of the sensor network. • Distance between two nodes is calculated using location information • Energy consumption can be estimated • Efficient energy utilization • Location of a node can be determined using • Global Positioning System (GPS) • Ultrasonic Systems using trilateration • Beacons • Although GPS is not envisioned for all types of sensor networks, it can still be used if stationary nodes with large amount of energy are allowed. • Location based protocols assume that each node knows its location in the network Mobile & Wireless Computing 18

19. GAF (Geographic Adaptive Fidelity) • GAF designed for both ad hoc and sensor networks • Y. Xu, J. Heidemann, and D. Estrin, "Geography-informed energy conservation for ad hoc routing," in the Proceedings of the 7 th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’01), Rome, Italy, July 2001. • Forms a virtual grid of the covered area • Each node associates itself with a point in the grid based on its location • Nodes associated with same point in grid are considered equivalent • Some nodes in an area are kept sleeping to conserve energy • Nodes change state from sleeping to active for load balancing Mobile & Wireless Computing 19

20. Routing in GAF Virtual Grid Sink Representative Node for the subregion Mobile & Wireless Computing 20

21. States in GAF • Nodes use GPS to associate itself to the grid • A node remains active for time Ta • Ta of a node in the grid is broadcasted to other equivalent nodes • The sleeping time of a node is adjusted depending on Ta • In the discovery state each node broadcasts discovery messages periodically (Td) • Handles mobility • Three States • Discovery: Determining neighbors • Active: Does routing • Sleep: Turn off radio Mobile & Wireless Computing 21

22. GAF Summary • Increase the lifetime of the network significantly • Works for MANETs as well • Handles mobility • Also considered to be hierarchical protocol • Each sub-region is a cluster • Representative node is the cluster-head • But does not perform any data aggregation • Not very scalable. As the network size increases distance to the sink increases • Overhead of forming the grid • Only the active nodes sense and report data. • Hence data accuracy is not very high. Mobile & Wireless Computing 22

23. Minimum Energy Communication Network (MECN) Connection A requires less energy than connection B because the power required to transmit between a pair of nodes increases as the nth power of the distance between them (n>=2). A B • L. Li and J.Y. Halpern, “Minimum-Energy Mobile Wireless Networks Revisited”. Proc. of IEEE Int. Conf. on Communications (ICC’01), Helsinki, Finland, June 2001. • Uses graph theory: • Each node knows its exact location • Network is represented by a graph G’, and it is assumed that the resulting graph is connected • A sub-graph G of G’ is computed. • G connects all nodes with minimum energy cost. Mobile & Wireless Computing 23

24. QoS Routing In WSN • QoS-aware protocols consider end-to-end delay requirements while setting up paths • End-to-end delay is the most common • Bandwidth • Video or image sensors • Real-time routing in • Disaster management • Fire detection • Tsunami alerts etc. • QoS in WSN is very challenging • Already have constraints such as bandwidth and energy • QoS routing will bring a lot of overhead • QoS in WSN is still in very early stages • May require redefinition of QoS for WSN Mobile & Wireless Computing 24

25. SPEED • A real-time routing protocol for WSN • T. He et al., “SPEED: A stateless protocol for real-time communication in sensor networks,” in the Proceedings of International Conference on Distributed Computing Systems, Providence, RI, 2003. • Each node maintains info about its neighbors and uses geographic forwarding to find the paths • Tries to ensure a certain speed for each packet in the network • Congestion avoidance Mobile & Wireless Computing 25

26. Energy-aware QoS Routing Protocol • K. Akkaya and M. Younis, "Energy-aware routing of time-constrained traffic in wireless sensor networks," in the International Journal of Communication Systems, Vol. 17(6), pp. 663-687, 2004. • Finds least cost and energy efficient paths that meet the end-to-end delay during connection • Energy reserve, transmission energy • WFQ (Weighted Fair Queuing) packet scheduling model used to support best-effort and real-time traffic • WFQ can provide upper delay bound • Used with constant data rate Mobile & Wireless Computing 26

27. Summary of Protocols for WSN Mobile & Wireless Computing 27