IMPROVING THROUGHPUT AND FAIRNESS OF CONVERGE CAST IN VEHICULAR NETWORKS
ABSTRACT
Delivering data from source vehicles to infrastructures, or convergecast, is a fundamental operation in vehicular networks.However, the network capacity of vehicular network is always limited because of scarce inter-vehicle contacts. Thus, throughput maximization of convergecast in vehicular networks is of great importance. The unique characteristics of vehicular networks, however,present great challenges including frequent connection unavailability and opportunistic contacts. We propose an approach called Converge Code for improving the convergecast throughput in vehicular networks, which employs random linear coding for packetdelivery. A vehicle randomly combines all received coded data and forwards it to any contacted vehicles. Through extensive empiricalstudy based on the two large datasets of real GPS traces, we make the key observation that significant throughput gain can beachieved by using network coding but a serious fairness issue arises. In this paper, we study the problem of maximizing the throughputof convergecast in vehicular networks at the same time enhancing the fairness a different source nodes. We first formulate theproblem of allocating inter-vehicle contacts as lexicographical max-min multi-source flow problem, and then develop an efficientapproximation algorithm with_-approximation guarantee. Simulations based on real vehicular GPS traces have been performed andresults show that the throughput is improved by 74%-110% while the lexicographical max-min fairness is achieved.
PROPOSED SYSTEM:
The paper has made the following main intellectualcontributions.• This is the first work, to the best of our knowledge,to study the problem of maximizing the throughputof convergecast in vehicular networks, at the sametime enhancing the fairness a different sourcenodes• Through extensive empirical study based on thelarge-scale datasets of real GPS traces, we have madetwo key observations. On the one hand, there isa significant throughput gain by utilizing networkcoding. On the other hand, the fairness a differentsource nodes may be poor if a simple allocationscheme of inter-vehicle contact opportunities isadopted.• We formulate the problem of allocating inter-vehiclecontacts as a lexicographical max-min multi-sourceflow problem and solve it by developing an efficientapproximation algorithm with_-approximationguarantee.• Simulations based on real vehicular GPS traces havebeen performed and results show that the throughputis improved by 74%-110% while the lexicographicalmax-min fairness is achieved.
EXISTING SYSTEM:
A few convergecast approaches have been proposed forwireless networks. In, the authors focus on data aggregationin wireless sensor networks. The main idea isto differentiate the quality of data collected from differentsensor nodes to balance their energy consumption. In,the authors propose a scheduling algorithm for the treetopology to minimize the probability that the sum-queueof the system (i.e., the total backlog of all flows) exceedsa large threshold. However, they only consider stationarynetworks which are not applicable to vehicular networkwhere the topology is highly dynamic. In , Wang et al.,theoretically analyze convergecast in Mobile Ad hoc Networks(MANETs), in which the capacity and delay boundsof convergecast in MANETs are given.A number of routing algorithms have been proposed torouting data packets from vehicles to the infrastructure orAccess Points (APs). In, two algorithms are proposedfor data delivery in vehicular networks. Their goal is tobound the delay of delivery. VADD is an algorithmusing statistical vehicle trajectory data to optimize the datadelivery rate and delay. In TSF, Jaehoon et al., proposea routing protocol which chooses a target location based onthe vehicle trajectory to forward data packets. GPSR is a stateless routing algorithm which always chooses thenext hop closer to the destination, which can also be used toforward data packets from vehicles to roadside APs.
CONCLUSION AND FUTURE WORK
In this paper we have presented an approach calledConvergeCode for improving both throughput and fairnessof convergecast in vehicular networks. It employsrandom linear coding for data delivery, by which a vehiclerandomly combines all received codes and forwards it toany contacted vehicles. Through empirical study based ontwo large datasets of real vehicle GPS traces, we observethat significant throughput gain can be achieved by usingnetwork coding but a serious fairness issue also raises. Weformulate the problem of allocating inter-vehicle contactsas a lexicographical max-min multi-source network flowproblem. Then, an efficient algorithm based on the bisectionmethod is developed. The strength of the algorithm is that itis _-approximate to the optimum. Trace-driven simulationshave been performed and results show that the throughputis significantly improved and the lexicographical max-minfairness is achieved.The future work will be carried out in two aspects.First, considering the demands for video streaming andmultimedia services are growing, ConvergeCode can beextended to support data transfer from a central server tomobile vehicles. Second, we will develop a prototype andimplement the proposed ConvergeCode in the prototype.Based on experiments on this prototype, we shall furtherextend our approach for more practical application in thereal world.
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