Ad-Hoc and Sensor Networks Routing

1. Ad-Hoc and Sensor Networks Routing CS 598 IG Bach Bui Juan Jose Jaramillo

2. A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks Elizabeth M. Royer and Chai-Keong Toh

3. Approaches • Table-Driven Routing Protocols • Using tables to maintain consistent, up-to-date routing information from each node to every other node in the network • Source-Initiated On-Demand Routing • Paths discovered and established by sources when needed

4. j x i Destination-Sequenced Distance-Vector Routing • Distance-Vector Routing • Based on a distributed version of the Bellman-Ford algorithm • Node i records for each destination x a set of distance vectors . Each vector is the distance from i to x through a neighbor j • When i wants to send data to x, i forwards data to j if

5. Destination-Sequenced Distance-Vector Routing • Problem in Distance-Vector Routing • Short-lived and long-lived loop because of stale information • DSDV solve the problem by using Destination-Sequence number • Each node records information of every destination (1) Address of the destination (2) Number of hops to destination (3) Next hop (4) Sequence number originated from destination 50 C A 1 1 B

6. Node Gateway Cluster head Cluster-head Gateway Switch Routing Protocol • Hierarchical structure • Distributed Clustering algorithm: • Least Cluster Change: reduce clustering overhead

7. Node Gateway Cluster head Cluster-head Gateway Switch Routing Protocol • Routing algorithm • Using DSDV as a basis • Cluster member table + DSDV Routing table • The benefit: A framework to implement channel access, bandwidth allocation

8. Wireless Routing Protocol • Aiming to beat other protocols in term of link-failure recovery and routing loop elimination • Each node in the network maintains four tables • Distance table • Routing table • Link-cost table • Message retransmission list table

9. Wireless Routing Protocol • Key difference • The use of second-to-last hop information • And reliable message transmission • Like DSDV, periodically broadcast routing information: include distance to destination + second-to-last hop information

10. Source-Initiated On-Demand Routing • Ad Hoc On-Demand Distance Vector Routing (AODV)

11. Source-Initiated On-Demand Routing • Dynamic Source Routing (DSR)

12. Source-Initiated On-Demand Routing • Temporally Ordered Routing Algorithm (TORA)

13. Source-Initiated On-Demand Routing • Associativity-Based Routing (ABR) • Signal Stability Routing

14. Table Driven vs. On-Demand Routing

15. Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks C. Intanagonwiwat, R. Govindan and D. Estrin

16. Sensor Network Routing • Challenges for Sensor Network Routing • Large scale network • Unattended or adversary area • Battery power Scalable Robust Energy efficiency • Requirements • Scalable • Energy efficiency • Robust

17. Is there rabbit in area X? Area X Update me the number of rabbits at each 5s when found? An Envisioned Application

18. Design 1 • Sensors in area X send raw sensing data to the base node at every 5s • The base node processes raw data Question: Is it scalable? No. Is it energy efficiency? No. Area X I am receiving raw data at every 5s and processing it

19. Design 2 • Sensors in area X send event information to the base node when the event occurs Question: Is it scalable? Yes. Is it energy efficiency? Yes. Event occurs! We will update you at each 5s Area X

20. What we learnt from the example? • What to send is as important as How to send • The need of in-network processing • Communication paradigm shift: Address-centric (where to send) Data centric (what to send)

21. Protocol Stages • Directed diffusion solves the problem by exploiting the application-specific nature of sensor networks. • Protocol Stages • Interest propagation • Users subscribe the interest event • Data propagation • Sensor nodes publish information when event occurs • Reinforcement • Users choose a better “deliver” to draw information

22. Interest Naming • To do in-network processing sensor nodes need to “understand” the data. • Interest descriptions are named by a list of attribute-value pairs (application-specific) • Animal tracking task type = four-legged animal // name of the task interval = 1s // send back events every 1s rect = [-100,100, 200, 400] // from sensor within rectangle • Response is similar type = four-legged animal // name of the task instance = elephant // instance of this type location = [125,220] // node location intensity = 0.6 // signal amplitude measure

23. Interest propagation • Interests are periodically broadcast into the network from a base node (sink). • Nodes receiving an interest forward it if the interest is new • Nodes needs a cache of interests • Heuristic routing algorithm can be employed • Interests are distinguished by value of some attributes not by the sink type = four-legged animal interval = 1s rect = [-100,200,200,400] timestamp = 01:20:40 expiresAt = 01:30:40 Sink

24. Cache Interest 1 Interest 2 … Interest n Interest Cache • Type, interval, rect: • distinguishing attributes • Timestamp: • being updated when receiving a new instance of the same interest • Gradients (data rate, duration): • up to one per neighbor • node A, rate 1s, duration 10s; • node B, rate 1s, duration 10s; • node C, rate 1s, duration 10s; A B D Sink 1 C

25. Data Propagation • If nodes in interest area finds a target • It searches for a matching interest entry then tasks sensor to work with highest requested data rate. • Then send to requesting neighbors events at highest rate type = four-legged animal instance = 1s location = [125,150] intensity = 0.6 A B D C

26. Data Propagation • Nodes receiving a data message forward it if the data is (1) of interest and (2) is new • (1) use interest cache • (2) use data cache • Data cache record most recent data for each interest • Nodes forward data at different rates depending on requesting neighbors

27. Reinforcement • Purpose: allowing a base node to choose a better path • drawing higher quality data at higher rate • for example: paths from which an event is received first or is low delay • The sink sends to preferred neighbors the original interest with a higher rate type = four-legged animal interval = 10ms rect = [-100,200,200,400] timestamp = 01:20:40 expiresAt = 01:30:40 Sink

28. Reinforcement • Nodes receiving reinforcement may reinforce upstream nodes • Use data cache to choose which nodes to reinforce type = four-legged animal interval = 10ms rect = [-100,200,200,400] timestamp = 01:20:40 expiresAt = 01:30:40 Sink

29. Locating and Bypassing Routing Holes in Sensor Networks Qing Fang, Jie Gao and Leonidas J. Guibas Stanford University

30. Motivation • Greedy algorithms have been proposed to route on sensor networks, but the problem is the “local minimum phenomenon”, where packets can get stuck. • Using greedy algorithms together with perimeter routing solves problem, at the expense of having planar graphs at every node.

31. Greedy algorithm • Example: apply the greedy algorithm to the traveling salesman problem: “at each stage visit the closest unvisited city to the current one”. 13,509 U.S. cities with populations of more than 500 people connected optimally using methods developed by CRPC researchers

32. Local minimum phenomenon on Greedy algorithm • Problem: send packet from A to F using Greedy algorithm  packet will get stuck at B! Wireless link among neighboring nodes F A B E C D

33. Weak stuck node • Node p is a weak stuck node if there exists a node b outside p’s transmission range (of radius 1) so none of the 1-hop neighbors of p is closer to b (black node) than p itself.

34. Voronoi diagram and Delaunay triangulation Restricted Delaunay Graph Finding the holes • Lemma: In the Delaunay triangulation, if all the edges adjacent to a node p are no longer than 1, then p is not a weak stuck node. • Definition: a hole is a face in the Restricted Dealunay Graph with at least 4 vertices. • Theorem: All the weak stuck nodes must be on the boundaries of holes.

35. Strong stuck node • Node p is a strong stuck node if there exists a location q outside p’s transmission range in 2 so that none of the 1-hop neighbors of is closer to q than p itself. The set of such points q is called a black region.

36. The TENT rule • Black region only exists if the intersection O of bisectors and is outside p’s transmission range  angle upv has to be greater than .

37. BOUNDHOLE – the finding hole algorithm • Starting from a strong stuck node p, whose stuck angle is given by spt1, do the following: • From t1, search in a counterclockwise direction for the first node not inside the shaded area. • Continue procedure until you go back to p and hole is enclosed. • If there is an edge intersection, get rid of the loop.

38. Protocol • A “messenger packet” is originated at strong stuck node v and it follows the path dictated by BOUNDHOLE. • To avoid multiple nodes trying to identify same hole, the messenger packet carries an ID and nodes will only forward packets whose ID is less than the last messenger packet sent. • The stuck node that originated the packet claims itself the hole leader, generates a random ID for the hole, and informs other nodes in the boundary by sending refresh packets every Tr seconds.

39. Handling node failures/topology changes • If a node fails, it can create a hole or modify the boundary of a hole  send a heart-beat to all the 1-hop neighbors. If heart-beat fails to appear three consecutive times, assume node has failed. Thus, run TENT rule and if node is a stuck node, then run BOUNDHOLE to find boundary of hole. • In case the failed node was part of an edge boundary, run BOUNDHOLE immediately without running first the TENT rule. • If topology changes (e.g., a new node is added), node runs TENT rule to check if it still is a strong stuck node: if so, it runs BOUNDHOLE again, if not, then it “retires” as a stuck node.

40. Applications • Routing. • Identifying regions of interest (e.g., isothermal contours). • Supporting path migration.