DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Channel assignment for throughput optimization in multichannel multiradio wireless mesh networks using network coding
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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Channel assignment for throughput optimization in multichannel multiradio wireless mesh networks using network coding

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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

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DOTNET 2013 IEEE MOBILECOMPUTING PROJECT Channel assignment for throughput optimization in multichannel multiradio wireless mesh networks using network coding Document Transcript

  • 1. Channel Assignment for Throughput Optimization in Multichannel Multiradio Wireless Mesh Networks Using Network Coding ABSTRACT: Compared to single-hop networks such as WiFi, multihop infrastructure wireless mesh networks (WMNs) can potentially embrace the broadcast benefits of a wireless medium in a more flexible manner. Rather than being point-to-point, links in the WMNs may originate from a single node and reach more than one other node. Nodes located farther than a one-hop distance and overhearing such transmissions may opportunistically help relay packets for previous hops. This phenomenon is called opportunistic overhearing/ listening. With multiple radios, a node can also improve its capacity by transmitting over multiple radios simultaneously using orthogonal channels. Capitalizing on these potential advantages requires effective routing and efficient mapping of channels to radios (channel assignment (CA)). While efficient channel assignment can greatly reduce interference from nearby transmitters, effective routing can potentially relieve congestion on paths to the infrastructure. Routing requires that only packets pertaining to a particular connection be routed on a predetermined route. Random network coding (RNC) breaks this constraint by allowing nodes to randomly mix packets overheard so far before forwarding. A relay node thus only needs to know how many packets, and not which packets, it should send. We mathematically formulate the joint problem of random network coding, channel assignment, GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
  • 2. and broadcast link scheduling, taking into account opportunistic overhearing, the interference constraints, the coding constraints, the number of orthogonal channels, the number of radios per node, and fairness among unicast connections. We develop a suboptimal, auction-based solution for overall network throughput optimization. Performance evaluation results show that our algorithm can effectively exploit multiple radios and channels and can cope with fairness issues arising from auctions. Our algorithm also shows promising gains over traditional routing solutions in which various channel assignment strategies are used. EXISTING SYSTEM: The notion of network coding was first introduced and shown to be a promising strategy for improving network throughputs for multicast. Instead of just replication and forwarding, network coding allows intermediate nodes to algebraically combine packets before forwarding them to next hop neighbors established that such combining can be linear in order to achieve the maximum multicast capacity of a given network. The subsequently showed that random coefficients, rather than the deterministic ones, can also be used to achieve the same capacity. By doing such random combining, generally speaking, it does not matter what is received or lost at a destination, but it only matters that enough is received. We can think of routing as being a special case of network coding, where for each transmission there is only one packet to combine and coefficients for such combining are all ones. Although most promising results have been presented for multicast in the wired domain, the broadcast nature of a wireless medium turns out to be very useful for extracting the benefits of network coding for unicast as well. In general, a single wireless transmission is often received by more than one node. Nodes located farther than a one-hop distance may overhear transmissions and help relay packets for previous hops. DISADVANTAGES: The opportunistic overhearing/listening has been extensively studied in conjunction with interflow network coding. The Substantial unicast throughput gains over traditional routing for single-channel single radio WMNs have been reported through extensive experiments. Promising unicast throughput gains, when interflow network coding is used, have also been through experiments.
  • 3. PROPOSED SYSTEM: The explicit modeling of opportunistic listening may increase the computational complexity of the formulated optimization problem; we ask if the throughput gains are worth the computational efforts when compared with a traditional routing approach. The answer is positive. Also, it turns out that our resulting optimization framework is general enough to embrace multipath routing as a special case. We only deal with the distributed version of intraflow random network coding (RNC). The choice of interflow coding2 is desirable here because it tends to incur fixed and less overhead than interflow coding. For interflow network coding, packets are coded and decoded hop by hop. Each wireless mesh router with access point functionality serves as an ingress or egress for the aggregate traffic associated with the mobile/wireless clients in its coverage area. Such traffic is routed to and from the wired infrastructure via multiple wireless hops formed by the wireless mesh routers some of which also function as gateways to the wired infrastructure. Each wireless router may be equipped with multiple wireless interfaces (radios) each of which operates on an orthogonal channel. Each node in our system can algebraically combine incoming packets according to the random linear network coding (RNC) scheme before forwarding the resulting combined packets to other nodes via its broadcast link. We assume that wireless nodes are in promiscuous mode and that all wireless transmissions are in broadcast mode. Those wireless nodes that hear such transmissions may engage in packet forwarding. It is also assumed that our system operates synchronously in a time-slotted mode. ADVANTAGES: Those wireless nodes that hear such transmissions may engage in packet forwarding. It is also assumed that our system operates synchronously in a time-slotted mode. Each node in our system can algebraically combine incoming packets according to the random linear network coding (RNC) scheme before forwarding the resulting combined packets to other nodes via its broadcast link. We assume that wireless nodes are in promiscuous mode and that all wireless transmissions are in broadcast mode
  • 4. They need to use a common channel and stay within the communication range of one another. Even if they cannot directly communicate, any two nodes may however interfere with each other’s communication given that they use the same channel and stay within the interference range of each other. HARDWARE & SOFTWARE REQUIREMENTS: HARDWARE REQUIREMENT:  Processor - Pentium –IV  Speed - 1.1 GHz  RAM - 256 MB (min)  Hard Disk - 20 GB  Floppy Drive - 1.44 MB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA SOFTWARE REQUIREMENTS:  Operating System : Windows XP  Front End : Visual Studio .Net 2008  Scripts : C# Script.
  • 5. MODULES: MULTICHANNEL MULTIRADIO WMN CODING SUB GRAPH REFINING CHANNEL ASSIGNMENT IMPACT OF MULTIPLE RADIOS ADDRESSING THE FAIRNESS ISSUE COMPARISON WITH ROUTING MODULE DESCRIPTION: MULTICHANNEL MULTIRADIO WMN: One element we need in the formulation of the problem is a flow network which represents all possible routes that traffic can flow, without exceeding link capacities, in a multiradio WMN when utilizing all the orthogonal channels. We explain in this section how the flow network for the WMN IH constructed. One can visualize a flow network as a 3D multilayered hypergraph5 in which each layer represents a copy, except for source node s, of the WMN IH on each channel. We original WMN can be transformed into a multilayered hyper graph when two channels are available. CODING SUB GRAPH: The distributed random network coding, each node only needs to know how many, and not which, packets it should forward or inject into its hyperarcs/broadcast links. In the network coding literature, the term coding subgraph is used to specify the frequencies and locations of such packet injection. Let us describe a coding subgraph on the multilayered hypergraph. REFINING CHANNEL ASSIGNMENT: The broadcast links of the multilayered hyper graph H. Such refinement must also ensure that, after the refinement, the new traffic scaling factor n for all connections deviates from the best possible as slightly as
  • 6. possible. The final solution is therefore suboptimal to the upper bound solution output by LP1. We propose a two-step method to achieve this goal. ADDRESSING THE FAIRNESS ISSUE: The same number of hyper arcs in this evaluation, we let the number of connections and the number of radios per node be respectively. We show the results for five settings with the varying number of channels available. Each data point is averaged over 10 random topologies. We see that fairness increases significantly as the number of available channels increases. For example, with a normalized demand of 1, we see that when moving from 2 to 6 channels fairness increases from 0.33 to 0.94, a 66 percent increase. IMPACT OF MULTIPLE RADIOS: This evaluation, we refer to the upper bound throughput as the solution output from LP1 which does not necessarily yield a feasible channel assignment but provides upper bounds on flows and coding sub graphs on the multilayered hyper graph. Each connection has a normalized traffic demand of 1. The number of channels is fixed. Each data point is averaged over 10 random topologies. We show the results in Fig. 5a for three settings with the number of radios per node varying uniformly. For both upper bound and RNC-AucCA solutions, as expected, the average throughput decreases as the number of connections increases. As the number of radios uniformly increases, the throughput generally increases. We see that our algorithm can effectively exploit the number of radios available. COMPARISON WITH ROUTING: The traditional routing schemes in which various channel assignment strategies are employed. We fix the number of radios per node and the number of connections at 2 and 6, respectively. We plot the results three settings with the varying number of connections. Each connection has a normalized traffic demand of 1. Each data point is averaged over 10 topologies. First of all, we see that as the number of connections increases the throughputs obtained by the entire schemes drop. On average, RNC-AucCA outperforms the single-path routing solution with naive channel assignment by 40 percent in terms of throughput. This is due to the fact that, in contrast to SP-RandCA our RNC-AucCA system can fully exploit the broadcast nature of wireless channels by allowing packets to be transmitted on multiple alternative paths.
  • 7. FLOW CHART:
  • 8. CONCLUSION: We have studied the joint problem of channel assignment and broadcast link scheduling for an infrastructure- based network-coded multichannel multiradio wireless mesh network. The main objective is to optimize throughputs for multiple unicast connections in a fair manner. We have first formulated the problem as a linear program that finds the optimal coding subgraphs for all connections. Based on the solution to this linear program, we have proposed a centralized, auction-based algorithm that computes a feasible channel assignment for all nodes. We have demonstrated through simulations that our proposed Solute on can effectively exploit the increasing number of channels and radios. Our solution also shows promising gains over several traditional routing schemes in which various channel assignment strategies are employed. Through simulation, we also show that unfairness arising from auctions can also be managed by properly tuning a batch size of packets and utilizing more channels. The proposed framework can be extended to the scenarios where delay is a major concern in the network. Development of distributed solutions for channel assignment is an interesting avenue for future research. REFERENCES: [1] I.F. Akyildiz and X. Wang, “Wireless Mesh Networks: A Survey,” Computer Networks, vol. 47, pp. 445- 487, 2005. [2] M. Alicherry, R. Bhatia, and L.E. Li, “Joint Channel Assignment and Routing for Throughput Optimization in Multiradio Wireless Mesh Networks,” IEEE J. Selected Areas in Comm., vol. 42, no. 11, pp. 1960-1971, Nov. 2006. [3] A. Raniwala, K. Gopalan, and T.-C. Chiueh, “Centralized Channel Assignment and Routing Algorithms for Multi-Channel Wireless Mesh Networks,” ACM Mobile Computing and Comm. Rev., vol. 8, no. 2, pp. 50-65, 2004. [4] M. Kodialam and T. Nandagopal, “Characterizing the Capacity Region in Multi-Radio Multi-Channel Wireless Mesh Networks,” Proc. ACM MobiCom, pp. 73-87, 2005.
  • 9. [5] A.P. Subramanian, H. Gupta, S.R. Das, and J. Cao, “Minimum Interference Channel Assignment in Multiradio Wireless Mesh Networks,” IEEE Trans. Mobile Computing, vol. 7, no. 12, pp. 1459- 1473, Dec. 2008. [6] S. Avallone and I.F. Akyildiz, “A Channel Assignment Algorithm for Multi-Radio Wireless Mesh Networks,” Computer Comm., vol. 31, no. 7, pp. 1343-1353, 2008. [7] A. Raniwala and T. Chiueh, “Architecture and Algorithms for an IEEE 802.11-Based Multi-Channel Wireless Mesh Network,” Proc. IEEE INFOCOM, pp. 2223-2234, 2005. [8] A. Dhananjay, H. Zhang, J. Li, and L. Subramanian, “Practical, Distributed Channel Assignment and Routing in Dual-Radio Mesh Networks,” ACM SIGCOMM Computer Comm. Rev., vol. 39, no. 4, pp. 99-110, 2009.