Topology Management In Sensor Networks

1. Topology Management In Sensor Networks

2. The Need for Topology Management • What is it? • The physical or logical interconnection pattern of a network • Topology schemes in wired networks: • Bus • Star • Ring • Why do we need different schemes in sensor networks? • location of sensors is not deterministic • resource constraints

3. The Need for Topology Management • Energy/Power consumption • Interference • Throughput • Connectivity

4. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Topology Control • Does not describe a new topology • Provides a mechanism to build certain topology • Distributed • No central control or central source of information • Asymmetric Links • Due to the presence of heterogeneous devices

5. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Objective • Reachability between any two nodes is guaranteed to be like initial topology • Nodal power consumption is minimized

6. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Model • Network of heterogeneous sensors (called nodes) • Nodes deployed in a two dimensional plane • Each node equipped with omni-directional antenna with adjustable transmission power • Nodes have different maximum power • Pi = Transmission Power of Node i • Pimax = Maximum Transmission Power of Node i • Pij = Transmission Power required for node i to reach j • Lij = Asymmetric link from i to j • G = (V, L) : directed graph of topology with max power • G is strongly connected

7. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Algorithm • Fully distributed with no synchronization required • Takes G as input and produces G’ where G’ has: • Same bi-directional reachability • Consumes minimum power • Phases • Establishing the vicinity topology • Deriving the minimum power vicinity tree • Propagation of transmission powers • Optimizations

8. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Establishing the vicinity topology • Node i broadcasts initialization request (IRQ) with Pimax • Vi is the set of nodes that receive the message {i.e. locationi, Pimax} • Each node j in Vi sends initialization reply (IRP) message {i.e. locationj, Pjmax} • If Pjmax > Pij , j can reach I with single hop Lji • Otherwise find a multi-hop path to reach i • Given the knowledge of location and max power of itself and all vicinity nodes, node i can determine the vicinity edges • Node i establishes a vicinity topology , Gi =(Vi, Ei)

9. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Deriving Minimum Power Vicinity Tree • Derive Minimum power path in Gi, to reach from a node i to node j using Dijkstra or Bellman-Ford algortihms based on sum of power consumption on that path. • Compute it for each node in Vi to obtain minimum-power vicinity tree, • Gis = (Vi, Eis)

10. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Propagation of transmission powers • Node i computes minimum power requirement for itself and others nodes in Vi • Node i sends a power request (PR) message to each node in Vi, describing the minimum power required for that node to reach farthest hop • A node j in Vi, receiving the PR message increases its power requirement if the requested power in PR is greater than current one • Otherwise, it discards the PR message

11. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Optimizations • Achieved via discarding PR messages when • A node already has run the algorithm to find its shortest vicinity tree • A node receives a PR message for a node in its vicinity • Example: A asks B for PBC while B already has PBD to reach node C Figure 1: A scenario of further optimized nodal transmission range

12. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Advantages: • Guarantees same bi-directional interconnection while reducing per node power consumption • Distributed algorithm: • No synchronization required • No central control node with network information • Easy to add/remove nodes from the network • Uses existing well known algorithms to obtain minimum power consumption • Works on network with asymmetric links, which seem more realistic • Assumption of asymmetric links, makes it possible to obtain minimum power path via multi-hop rather than using a single hop high power link

13. Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links [Liu+ 2003] • Disadvantages: • Computationally expensive to be run on network with mobile sensors • Overhead of sending IRQ, IRP and RP in a large network of sensors • Time to converge for the algorithm is large • Suggestions/Improvements/Future Work: • More details on how multi-hop paths will be discovered • Detailed example covering more complex scenarios

14. Optimizing Sensor Networks in the Energy-Latency-Density Design Space[Schurgers+ 2002] • Describes • a topology management technique that is power efficient • energy, Latency and Density trade-offs • Provides • a theoretical analysis of these techniques, including a mathematical formulation that can be used to design a network with required energy, latency and density configuration • a hybrid solution with existing topology management scheme (GAF) to provide energy saving of over two order of magnitude • The proposed new topology management scheme is called • STEM (Sparse Topology and Energy Management)

15. Optimizing Sensor Networks in the Energy-Latency-Density Design Space[Schurgers+ 2002] • Two states for sensor nodes: • Monitoring State • Transfer State • Most of the time a sensor remains in monitoring state (i.e. sensing environment) • When an event occurs, nodes come into transfer mode and transfer their data

16. Optimizing Sensor Networks in the Energy-Latency-Density Design Space[Schurgers+ 2002] • Issues: • Nodes must listen periodically for call to duty (i.e transfer) • But if they poll periodically on the same frequency, it will collide with other data transfer • Solution: • Use two radios, one for polling while the other for data transfer • STEM-B (Beacon Approach): • Initiator sends a stream of beacon packets to poll a target with initiator and target MAC addresses • Target node sends acknowledgement on receiving the packet • Target node turns its transfer radio on

17. Optimizing Sensor Networks in the Energy-Latency-Density Design Space[Schurgers+ 2002] • STEM-T (Tone Approach) • Initiator sends a wake up tone • Every node receiving that tone starts its data transfer radio • No need to send acknowledgement • Every node in the neighborhood of initiator wakes up • STEM/GAF Hybrid • GAF proposes a scheme in which a sensor network is divided in a grid • One node in a region has its radio on, others have it turned off • Nodes alternate the responsibility of being the active node • GAF uses network density to conserve energy • Assuming the active node to be the virtual node, STEM can be used on the virtual node to manage whole network

18. Optimizing Sensor Networks in the Energy-Latency-Density Design Space[Schurgers+ 2002] Advantages: • Highly efficient in environments where events are rare • Flexible in term of design trade-off for energy, latency and density • Transition from monitoring state to transfer state is easily achieved • No synchronization required • Can be use with other topology management schemes like GAF Disadvantages: • Continuous polling consumes energy • Not suitable for highly reactive environments • Requires extra radio on sensor nodes • Suggestions/Improvements/Future Work: • Analysis of STEM with clustered networks

19. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • In ASCENT, the nodes coordinate to exploit the redundancy provided by high density to extend the overall system lifetime • Nodes achieve self-configuration to establish a topology that provides communication and sensing coverage under energy constraints • Each node examines its connectivity and adapts its participation in the multi-hop network topology based on the operating region • The node • Signals when it detects high message loss, requesting additional nodes to join the network to continue relaying messages • Reduces its duty cycle if high messages losses are detected due to collisions • Probes local communication environment and only joins to the multi-hop routing infrastructure if it is useful

20. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • Sensor nodes do local processing to reduce communication and energy costs • Challenges arises from the increased level of dynamics(systems and environmental) • One of the most important challenge arises from energy constraints imposed by unattended systems • These systems must be long-lived and operate without manual intervention • They need to self-configure and adapt to environmental dynamics and some terrain conditions may result regions with non-uniform communication density • These issues can be addressed by deploying redundant nodes and designing algorithms to use redundancy to extend the system lifetime • Scaling challenges are associated with spatial coverage and robustness • Central vs. Distributed • When energy is constraint and environment is dynamic, distributed approaches are preferable and practical

21. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • Scalable wireless sensor networks require to avoid large amounts of data being transmitted over long distances • ASCENT applies well-known techniques from MAC layer protocols to the problem of distributed topology formation • Imagine a habitat monitoring sensor network that is deployed in remote forest • The deployed systems must confer with the following conditions • Ad-hoc deployment • Energy constraints • Unattended operation under dynamics • If we use too few nodes initially: • the distance between neighboring nodes will be too far • packet loss rate may increase • energy required to transmit over larger distances may be prohibitive

22. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • If we use all deployed nodes simultaneously: • system will expand unnecessary energy • nodes interfere with each other by congesting the channel • ASCENT does not use localized distributed algorithm to find a single solution • Adaptive self-configuration using localized is suited to problem spaces which have a vast number of possible solutions (in this case, large solution spaces means dense node deployment) • ASCENT has the following two assumptions: • Carrier Sense Multiple Access (CSMA) MAC protocol • Possibilities for resource contention when too many neighboring nodes participate in the multi-hop network • Reacts when links have high packet loss • Does not detect or repair network partitions and assumes that there is enough node density to connect the entire region

23. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • Two essential contributions of ASCENT design are: • Adaptive techniques that allow applications to configure the topology based on the needs while saving energy to extend network lifetime. The techniques do not assume a specific model or fairness, degree of connectivity, or capacity required • Self-configuring techniques that react to operating conditions are measured locally. It does not assume any specific radio propagation model, geographical distribution of nodes, or routing mechanisms used • ASCENT Design • Adaptively elects active nodes from all the nodes • Active nodes stay awake always and participate in routing while the other nodes remain passive and periodically checks if they should become active

24. Help Messages Data Message Passive Neighbor Active Neighbor Source Sink ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT Design • Initially, only some nodes are active while other are passively listening to packets but not transmitting • When source starts transmitting data packets towards the sink, the sink gets high message loss from the source due to limited radio range, called communication hole • The receiver gets high packet loss due to poor connectivity with the sender Figure 2(a): Communication Hole

25. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT Design • Sink start sending help messages to neighbors that are in listen-only mode, called passive neighbors, to join the network • When a neighbor receive a help message, it decides to join the network or not • If the node joins, it becomes an active neighbor and signals the existence of a new active neighbor to other passive neighbors by sending a neighbor announcement message • It continues until the number of active nodes stabilizes on a certain value and the cycle stops

26. Neighbor Announcements Messages Data Message Sink Source Sink Source ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT Design • When the process is completed, the newly joined nodes participate in the data delivery process from source to sink more reliably • The process will be repeated in the case of network event (e.g., node failure) or environmental effect (e.g., new obstacle) causes message loss Figure 2(b-c): Self-configuration transition and final state

27. Test Active after Tt neighbors > NT (high ID for ties); or loss > loss T0 • neighbors < NT and • loss > LT • loss < LT & help Sleep after Tp Passive after Ts ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] ASCENT State Transactions NT: neighbor threshold LT: loss threshold T?: state timer values (p: passive, s: sleep, t: test) DL: Data loss rate

28. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT State Transactions • Initially, a random timer turns on the nodes to avoid synchronization • Node initializes to test state: • Sends data and routing control messages • Sets up a timer, Tt and sends neighbor announcement messages • Moves into passive state if the conditions are met before Tt expires • When Tt expires, it enters to active state • The reasoning behind the test state is to probe the network to decide whether the addition of a new node would improve connectivity • On entering the passive state, node: • Sets up a timer Tp and when Tp expires, it enters to sleep state • If before Tp expires, it enters to test state only if the conditions are met • Nodes in passive state can hear all packets transmitted, but no routing or data packets are forwarded in this state since this is listen-only state

29. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT State Transactions • The reasoning behind the passive state is to gather information about the state of the network without causing interference with other nodes • Nodes in passive and test states update the number of active neighbors and data loss rates • In passive states, the nodes still consume energy since the radio is on • The nodes in sleep state turns the radio off, sets up timer Ts and goes to sleep • When Ts expires, the nodes moves into passive state • A node in the active state continuous to forward data and routing packets until it runs out of energy

30. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT Parameters Tuning • ASCENT has many parameters and the choices are left to the applications such as a particular application may trade energy savings for greater sensing coverage • Neighbor Threshold (NT): • Determines the average connectivity if the network • Tradeoff between energy consumed and/or level of interference (packet loss) vs. desired sensing coverage • Loss Threshold (LT): • Determines the maximum amount of data loss an application can tolerate before requesting help to improve network connectivity • This value is highly application dependent

31. ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies [Cerpa+ 2002] • ASCENT Parameters Tuning • Test timer (Tt), Passive timer (Tp), Sleep timer (Ts): • Determines the maximum time a node remains in test, passive, sleep states • Tradeoff between power consumption vs. decision quality

32. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] • The primary design constraints of the sensor network algorithms and protocols are: energy-efficiency, scalability and localization • The improved energy efficiency can be achieved by designing protocols and algorithms with cross-layer approach, i.e., considering interactions between different layers of the communication process such that overall energy consumption is minimized • A scalable algorithm performs well in a large network • The scalability for an algorithm is related to that of localization: in a scalable algorithm each node exchanges information only with its neighbors (localized information exchange) in a very large wireless network

33. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] • This paper considers the interaction between topology control and energy efficient geographical routing • The question to answer is: “How extensive should be the Local Knowledge of the global topology in each sensor node, so that an energy efficient geographical routing can be guaranteed?” • This question is related to the degree of localization of the routing scheme • If each sensor node have the complete knowledge of the topology, it could compute the “global” optimal next hop to minimize the energy consumption • However, the knowledge of complete topology information has an associated cost, i.e., energy used to exchange the signaling traffic

34. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] • An analytical framework is developed to capture the tradeoff between the topology information cost, which increases with the Knowledge Range of each node, and the communication cost, which decreases when the knowledge becomes more complete • This analytical framework is then applied to different position based forwarding schemes and demonstrated by using Monte Carlo simulations that a limited knowledge is sufficient to make energy efficient routing decisions • A “neighbor” for a certain sensor node is another node which falls into its topology Knowledge Range, denoted as KR

35. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] • The contributions of this work are: • Introduction of a novel analytical framework to evaluate the energy consumption of geographical routing algorithms for sensor networks • Integer Linear Programming (ILP) formulation of the topology Knowledge Range optimization problem • Detailed comparison of the leading existing forwarding schemes [Takagi+ 1984, Hou+ , Finn 1987, Kranakis+ 1999, Nelson+ 1984] and introduced a new scheme called Partial Topology Knowledge Forwarding (PTKF) • Introduction of PRobe-bAsed Distributed protocol for knowledge rAnge adjustment (PRADA) for the on-line solution of the problem that allows nodes to select near-optimal Knowledge Ranges in a distributed way

36. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] Advantages: • No need for knowing the global topology of the network • PRADA can be run independently in the nodes, thus the nodes do not require time synchronization • Demonstrates a limited amount of topology knowledge is sufficient in order for energy conserving routing protocols to be implemented • The nodes periodically update their knowledge range, thus the algorithm could be implemented in sensor networks where the nodes are mobile • Draws a fine line between topology information cost and communication cost

37. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] Disadvantages: • No mentioning about the sensitivity towards location error of their proposed protocol • For a pair of source-destination path, the most optimal path is always chosen; however, this would lead to a starvation of some of the nodes that would not get any traffic • The performance evaluation of protocol does not consider the lower layers, such as MAC

38. Optimal Local Topology for Energy Efficient Geographical Routing in Sensor Networks[Melodia+ 2004] • Suggestions/Improvements/Future Work: • Extending the optimization objectives to include not only power but also battery level of each node (thus improving network lifetime) • Implementing the proposed routing protocol within a simulator which considers routing and MAC layer together to draw a more convincible conclusion

39. References [Cerpa+ 2002] A. Cerpa and D. Estrin, ASCENT: Adaptive Self-Configuring Sensor Networks Topologies, Proceedings of the Twenty First International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002), New York, NY, USA, June 23-27 2002. [Finn 1987] G.G. Finn, Routing and Addressing Problems in Large Metropolitan-Scale Internetworks, ISI res. rep ISU/RR- 87-180, Mar. 1987. [Hou+ ] T.C. Hou and V.O.K. Li, Transmission Range Control in multihop packet radio networks, IEEE Transactions on Communications, Vol. 34, No.1, pp. 38-44. [Kranakis+ 1999] E. Kranakis, H. Singh, and J. Urrutia, Compass routing on geometric networks, Proceedings of the 11th Canadian Conference on Computational Geometry, Vancouver, Canada, August 1999. [Liu+ 2003] J. Liu and B. Li, Distributed Topology Control in Wireless Sensor Networks with Asymmetric Links, GLOBECOM 2003. [Melodia+ 2004] T. Melodia, D. Pompili, and I.F. Akyildiz, Optimal Topology Knowledge for Energy Efficient Geographical Routing in Sensor Networks, Proceedings of the Twenty First International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2004), Hong Kong, P.R., China, March 2004. [Nelson+ 1984] R. Nelson and L. Kleinrock, The spatial capacity of a slotted ALOHA multihop packet radio network with capture, IEEE Transactions on Communications, Vol. 32, No.6, pp. 684-694, 1984.

40. References [Takagi+ 1984] H. Takagi and L. Kleinrock, Optimal Transmission Ranges for Randomly Distributed Packet Radio Terminals, IEEE Transactions on Communications, Vol. 32, No.3, pp. 246-57, 1984. [Schurgers+ 2002] C. Schurgers, V. Tsiatsis, S. Ganeriwal, and M.B, Srivastava, Optimizing Sensor Networks in the Energy-Latency-Density Design Space, IEEE Transactions on Mobile Computing, Vol. 1, No.1, pp. 70-80, January-March 2002.