E-mail: kemal@cs.siu.edu Department of Computer Science Southern Illinois University Carbondale Mobile & Wireless Computing Routing Protocols for Sensor Networks Hierarchical & Location-based and QoS Protocols Dr. Kemal Akkaya E-mail: kemal@cs.siu.edu Mobile & Wireless Computing 1

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

Hierarchical Routing Mobile & Wireless Computing 3

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

Cluster-heads at time t Cluster-heads at time t + d 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

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

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

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

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

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

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

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

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

PEGASIS - Illustration Mobile & Wireless Computing 14

Comparison Mobile & Wireless Computing 15

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

Hierarchical PEGASIS Mobile & Wireless Computing 17

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

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

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

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

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

Minimum Energy Communication Network (MECN) 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. A B 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). Mobile & Wireless Computing 23

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

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

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

Summary of Protocols for WSN Mobile & Wireless Computing 27