**Introduction to Wireless Sensor Networks ** Routing in WSNs 28 February 2005

**Organizational** Class Website www.engineering.uiowa.edu/~ece195/2005/ Class Time Office Hours Midterm Exam Time: March 10, 2005

**Routing** • What is meant by “routing”? • Internet (TCP/IP) • Routing tables often large • Can be updated frequently • WSN • Frequent topology changes • Modest local storage • Expensive to update frequently • => Need local, stateless algorithms where nodes know only immediate neighbors

**Routing** • Consider the following • The fundamental difference between classical routing and routing for sensor networks is that the separation between address and content of packet no longer viable • What does it mean? • Network is a system, individual nodes come and go, information sensed by one node can be sensed by another close by • Data-centric view • Routing decision as based not on destination address, but rather on destination attributes and relation to attribute of packet content • Information providers and information seekers must be matched using data attributes and not (hard) network address

**Examples of Attributes** • Node location • But is this not just its address? • Get the rain data from the nodes at the Iowa City airport • Types of sensor connected to a node • Send a control packet to all nodes that have a light sensor connected to it • Certain range of values in certain type of sensed data • Get max, min temperature values in from the sensor network • Pull model • Network is queried similar to a database • Push model • Network can initiate flow of information based on events

**WSN Routing** • Geographic routing (more traditional view) • Greedy distance • Compass • Convex perimeter routing • Routing on a curve • Energy-minimizing broadcast • Attribute-based routing (data-centric view) • Directed diffusion • Rumor routing • Geographic hash tables

**Graphs**

**Greedy Distance and Compass Routing** • Greedy distance –pick the locally optimum (distance) neighbor • Compass routing – pick the locally optimum (angle) neighbor

**Problem With Greedy Distance** • Here both x’s neighbors are further than destination

**Side-Bar Maze Solver**

**not planar** planar Planar Graphs

**Planerization** Basic idea – keep connectivity between nodes Convex Polygon Concave Polygon

**Planarization Requirements for WSN** • WSNs: local planarization algorithms, where edge xy is introduced if a geometric region (witness region) around xy is free of other nodes. • Require accurate information about location of nodes

**y** x y x Planerization • Basic idea – keep connectivity between nodes • Relative Neighborhood Graph (RNG) • The edge xy is introduced if the intersection of circles centered at x and y with radius the distance d(x,y) is free of other nodes • Grabriel Graph • The edge xy is introduced if the diameter xy is free of other nodes • Key for WSN: RNG and Gabriel graphs can be found with distributed construction

**Examples** RNG Gabriel

**Convex Perimeter Routing** • Objective: route from s to d (assume planar graph) • Start in the face just beyond s along sd and walk around that face. Stop if d is reached. If the segment sd is about to be crossed, cross over to the next face along sd, and repeat

**Variations** • Non-convex routing adaptation • OFR – Other face routing

**Side-Bar Parametric Equations** • Circle • Non parametric: x2 + y2 = a2 • Parametric: x = a cos(t), y = a sin(t), t the parameter • Straight Line • Non parametric: y = mx+c • Parametric: line through point (a, b) parallel to vector (u, v) is given by (x, y) = (a, b) + t·(u, v), t the parameter • Given t one can compute x and y

**Routing on A Curve** • Specify a curve a packet should follow • Analytical description of a curve carried by the packet • Curves may correspond to natural features of the environment where the network is deployed • Can be implemented in a local greedy fashion that requires no global knowledge • Curve specified in parametric form C(t)=(x(t),y(t)) • t – time parameter – could be just relative time • Each node makes use of nodes trajectory information and neighbor positions to decide the next hop for the packet • Also called trajectory-based routing

**Optimal Path** • What do we mean by “optimal” • Minimum delay => fewest hops • Minimum Energy => frequent hops (why) • Formally, cost of a path • Where l(e) is the length of the edge in the graph • k is in range 1…5 • k = 0 => Hop length, measure delay • k = 1 => Euclidian path length • k > 1 => Capture energy of path, depending on attenuation model

**Review Questions** • Write a short (5 sentence) paragraph contrasting the needs and resources available in WSN as opposed to, say, the Internet. • Explain the statement “When routing a packet in a WSN, more hops increase delay, but the advantage is that it increases energy efficiency for the WSN as a whole” • Write a 6-7 sentence paragraph explaining the term “routing on a curve” • Write a paragraph explaining the term “convex perimeter routing” • True of False – a major disadvantage of perimeter routing in WSN is that path construction require knowledge of the global topology • With the aid of a figure, explain how a greedy forwarding strategy can result in a packet being stuck at a node in a WSN

**Review Questions** • Below is a connectivity graph for a WSN. (a) Planerize it using the RNG method. Planerize it using the Grabriel method. (figure goes here) • True or False – a problem with “Routing on a Curve” is that each nodes must know the location of all nodes along the routing path. • Write a short (5 sentence) paragraph explaining what Trajectory-Based Routing is.