Wireless Ad Hoc and Sensor Networks

Predictive Congestion Control for Wireless Sensor Networks 449[ t1, t2]in which flow f is backlogged throughout the interval, Wf ( t1, t2)isgiven asf 1 2 f, l 2 1 f,l n 1 2∑ n ∈ QmaxW ( t , t ) ≥ ( t −t ) − l ( t , t )φ φ λ − φ ψ( λ) λ( , ) −(9.35)PROOF The proof follows in similar lines to ad hoc wireless networks(Regatte and Jagannathan 2004).THEOREM 9.3.8 (THROUGHPUT AND FAIRNESS GUARANTEE)Assume the hypothesis given in Theorem 9.6 and Theorem 9.7. If the backoffintervals are selected as according to Equation 9.17 and packet weights areupdated by using Equation 9.33, then the proposed scheme will deliver throughputwith a bounded error and ensure fairness even in the presence of congestion.PROOF Follows from the proofs of Theorem 9.3.6 and Theorem 9.3.7.f,lt t l1 2maxf9.4 SimulationThe proposed scheme was implemented in NS-2 simulator and evaluatedagainst DPC (Zawodniok and Jagannathan 2004) and the 802.11 protocolsas well as CODA. Information for backoff calculation is available locally.Hence, only the rate information has to be fed back to upstream nodesand is incorporated into the MAC frames. The calculations of the rate andbackoff intervals are performed periodically at every 0.5 sec.The parameters that were used include a 2-Mbps channel with path loss,shadowing, and Rayleigh fading with the AODV routing protocol. Thequeue limit is set to 50 with the packet size taken as 512 bytes. The SFQalgorithm (Bennett and Zhang 1996) was used to ensure fairness amongthe flows passing at a given node. It uses the assigned weights to schedulepackets for transmission, and the weights are not updated. The proposedscheme is compared with others. The CODA scheme has been implementedin NS-2 by carefully following the description in Wan et al. (2003). Additionally,performance of the scheme in case of variations in outgoing flowrate was assessed using MATLAB. Next, the results are discussed.Example 9.4.1: Tree Topology ResultsThe tree topology, typical of a sensor network, is used for the simulationsin which the sources represent cluster heads, and the sink is a base stationcollecting the sensor data. Traffic accumulates near the destination node