Wireless Networked Sensors Routing Challenges Mikhail Nesterenko In this presentation I used the material from a presentation by David Culler, USB Kwong-Don Kang, SUNY, Binghamton

24/18/2007 WSN Reading List Deepak Ganesan, Bhaskar Krishnamachari, Alec Woo, David Culler, Deborah Estrin and Stephen Wicker, Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks, UCLA Computer Science Technical Report UCLA/CSD-TR A. Woo and D. Culler. Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks. In Proc. of the 1st ACM Conf. on Embedded Networked Sensor Systems (SenSys), pp Los Angeles, Nov

34/18/2007 WSN Outline empirical measurements of low-power radio performance  radio neighborhood  link quality estimation  issues with simple routing mintroute  link quality estimation  neighborhood selection  routing metrics  simulation results  experimental results

44/18/2007 WSN Radio Neighborhood radio neighborhood is not clearly defined  reception is probabilistic  not isotropic reception rate is low  “good” link drops 1 out of 4 packets (cf. ethernet drops 1 out of 10K)  changes with time!

54/18/2007 WSN Link Quality three regions based on reception  nearly perfect  unpredictable  nearly none one the fringes some links are asymmetric  more than 75% in one direction  less than 25% in the other what to do with them?  detect and ignore?  embrace?

64/18/2007 WSN Flood-Based Routing Issues simple flood-based routing is imperfect has  stragglers  backward links  dense clusters

74/18/2007 WSN Other Issues large variation in affinity  asymmetric links  long, stable high quality links  short bad ones varies with traffic load  collisions  distant nodes raise noise floor  reduce SNR for nearer ones many poor “neighbors” good ones mostly near, some far

84/18/2007 WSN Outline empirical measurements of low-power radio performance  radio neighborhood  link quality estimation  issues with simple routing mintroute  link quality estimation  neighborhood selection  routing metrics  simulation results  experimental results

94/18/2007 WSN Link Estimation Individual nodes estimate link quality by observing packet success and loss events Use the estimated link quality as the cost metric for routing Good estimator should:  React quickly to potentially large changes in link quality  Stable  Small memory footprint  Simple, lightweight computation

104/18/2007 WSN WMEWMA Snooping  Track the sequence numbers of the packets from each source to infer losses Window mean with EWMA  WMEWMA(t, a) = (#packets received in t) / max(#packets expected in t, packets received in t)  t, a: tuning parameters  t: #message opportunities  Take average in a window  Take EWMA of the average

114/18/2007 WSN WMEWA (t =30, a =0.6) simulation of empirical trace in stable setting

124/18/2007 WSN Neighborhood Management Neighborhood table  Record information about nodes from which it receives packets  MAC address, routing cost, parent address, child flag, reception (inbound) link quality, send (outbound) link quality, link estimator data structures  Propagate back to the neighbors as the outbound rather than inbound link quality is needed for cost-based routing  The receiving node may update its own table based on the received information possibly indicating topology changes  Distance-vector based routing How does a node determine which nodes it should keep in the table?  Keep a sufficient number of good neighbors in the table  Similar to cache management

134/18/2007 WSN Management Policies Insertion  Heard from a non-resident source  Adaptive down-sampling technique  Probability of insertion = N/T = neighbor table size / #distinct neighbors  At most N messages can be inserted for every T messages Eviction  FIFO, Least-Recently Heard, CLOCK, Frequency

144/18/2007 WSN #Good neighbors maintainable (table size 40) Frequency Algorithm  Keep a frequency count for each entry in the table  Reinforce a node by incrementing its count  A new node will be inserted if there is an entry with a zero count  Otherwise, decrement the count of all entries and drop the new candidate good node – 75% link accuracy

154/18/2007 WSN Background Link-State and Distance-Vector Routing Link state routing algorithm (ex: DSDV)  assume knowledge of the network topology and all link costs  apply Dijkstra’s algorithm to find the shortest path from one source to all the other nodes  Implemented via link state broadcast  memory intensive, has issues with information update Distance vector routing (ex: AODV)  each node propagates cumulative distance estimator (ex: min # hops) to all neighbors  neighbors update their metric and propagate further  has “counting to infinity” problem  countered by poisoned reverse or split horizon

164/18/2007 WSN Cost-based routing Key ideas  Minimize the cost that is abstract measure of distance  Could be #hops, #retransmissions, etc.  Minimize # retransmissions: A longer path with fewer #retransmission could be better than a shorter path with more retransmissions! Distance-vector based approach implemented by the parent selection component  Periodically run parent selection to identify one of the neighbors for routing  May also locally broadcast a route message including parent address, estimated routing cost to the sink, and a list of reception link estimations of neighbors  A receiving node may update the neighbor table based on the received info or drop it  Flag a child in the table to avoid a cycle  When a cycle is detected trigger parent selection without the current parent

174/18/2007 WSN Routing Framework

184/18/2007 WSN Underlying Issues Parent selection  If connectivity to the current parent is lost, a node disjoins from the tree, and sets its routing cost to infinity  Reselect a parent Rate of parent change  Periodic: Parent selection for every route update msg from neighbors incurs a domino effect of route changes Parent snooping  For example, quickly learn routing info Cycles  Monitor forwarding traffic and snoop on the parent address in each neighbor’s msg -> Identify child nodes and don’t consider them as potential parents

194/18/2007 WSN Underlying Issues Duplicate packet elimination  Use sender address & sequence number Queue management  Give priority to originating traffic assuming originating data rate is lower than forwarding rate  General fair queuing is not considered in this paper Relation to link estimation  Link failure detection based on a fixed number of consecutive xmission failures can be ineffective over semi-lossy links  Link quality estimation can be a better judgment of link failure  Bidirectional link estimations can avoid routing over asymmetric links  Stability and agility of link estimators can significantly affect routing  Final tuning must be done while observing its effect on routing performance

204/18/2007 WSN Cost metric MT (Minimum Transmission) metric:  Expected number of transmissions along the path  For each link, MT cost is estimated by 1/(Forward link quality) * 1/(Backward link quality)  Inherently non-linear  For MT, a substantial noise margin should be used in parent select to enhance stability Reliability  Another cost metric  Product of link qualities along the path  Not explored in this paper

214/18/2007 WSN Performance Evaluation: Tested Routing Algorithms Minimum Transmission (MT)  Use the expected #transmissions as the cost metric  Use a new path if the new cost is lesser by a noise margin MTTM  Assume a neighbor table can maintain only 20 entries Broadcast  Root periodically floods the network  A node chooses a parent that forwards the flooded msg to itself first in each epoch  Use the reverse path to reach the root

224/18/2007 WSN Performance Evaluation: Tested Routing Algorithms Shortest Path  Conventional distance-vector approach  Each node picks a minimum hop-count neighbor as the parent and set its own hop-count to one greater than its parent  Two variations for performance analysis  SP: A node is a neighbor if a packet is received from it  SP(t): A node is a neighbor if its link quality exceeds the threshold t o t = 70%: only consider the links in the effective region o t = 40%: also consider good links in the transitional region

234/18/2007 WSN Packet level simulations Built a discrete time, event-driven simulator in Matlab Network of 400 nodes: 20 * 20 grid with 8 feet spacing Sink is placed at a corner to maximize the network depth

244/18/2007 WSN Packet level simulation Hop DistributionPath reliability over distance

254/18/2007 WSN Packet level simulation

264/18/2007 WSN Empirical study of a sensor field Evaluate SP(40%), SP(70%), MT 50 Berkeley motes inside a building 5 * 10 grid w/ 8 foot spacing  90% link quality in 8 feet 3 inches above the ground sink in the middle of short edge of the grid measurements at night to avoid pedestrian traffic

274/18/2007 WSN Link Quality of MT - Vary around 70% - SP(70) may suffer Hop Distribution - SP(70) failed to construct a routing tree - MT congested: Triple the data origination and route update ra te

284/18/2007 WSN E2E success rate Stability

294/18/2007 WSN Irregular Indoor Network 30 nodes scattered around an indoor office of 1000ft 2 E2E Success Rate Link Estimation of a node to its neighbors over time

304/18/2007 WSN Conclusions Link quality estimation and neighborhood management are essential to reliable routing  WMEWMA is a simple, memory efficient estimator that reacts quickly yet relatively stable MT (Minimum Transmissions) is an effective metric for cost-based routing The combinations of these techniques can yield high end-to-end success rates