Channel Assignment and Congestion Control in Mesh Networks that are Multi-Radio
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Outline
Channel Assignment and Congestion Control in Mesh Networks that are Multi-Radio
1.1 Abstract
Introduction
2.1Thesis Statement
Analysis of the problem of channel assignment and congestion and its solution
Description and analysis of Channel Assignment
Methods of solving channel congestion
Use of the Iterative approach in solving the problem of channel assignment and congestion control.
Findings of the research
Results obtained from using the MRMC-CC approach
Results obtained from using the ABL05 approach
Conclusion
Importance of carrying out the research
Advantage of solving the problem of Wireless Congestion Control
Work cited list
Channel Assignment and Congestion Control in Mesh Networks that are Multi-Radio
Abstract
Exchange of information is made easy through timely and rapid internet. The latter is a platform which provides servers and clients with the much needed disparate of information. Furthermore, according to the rules of communication, various protocols have to be related closely. This is for the benefit of ensuring that there is efficient and maximum transfer of data on the internet. Some vital elements of wireless congestion control are fairness and network utilization. Through the use of the Iterative approach channel assignments and yield rates obtain fair objectives. This is what this paper seeks to find and establish as being true.
Introduction
In many orthogonal channels that have multiple radios, the deployment of mesh networks is taking place. This is meant to ensure that high speeds are achieved. There is also a chance that interference mitigation will occur. Many flexibilities have not been explored and this is observed by the architectures that exist. Channels and radios are meant to be allocated fairly in order to control congestion and to maintain fairness. The reason for engaging in this research is that the problem is extremely complex. Furthermore, joint optimization of traffic allocations and radio channel assignments has to occur. The latter is different from the solutions, which exist for single radio.
This paper seeks to find a solution on the problem of channel assignment and congestion control. Also, it seeks to follow an approach that focuses on decomposition and iterativenss.
Analysis of the problem of channel assignment and congestion and its solution
Channel and rate assignment is a problem that needs to be solved through joint optimization. The nature of channel assignments is extremely discrete and thus, the need to study it. Studying an assignment that is fixed channel and the sub problem of congestion control will ensure a solution is found (Bertsekas, 2003. 23). The problem is referred to as being non-linear programming and it is vital in many aspects. The various multiple radios and converged solution accounts are derived from updates that are rate adapted. It also deals with channel-dependent interference and traffic distribution. The latter is possible by ensuring that traffic distribution is flows through radio paths that have been constructed. Moreover, time slots and channels have to be shared in order for traffic to occur naturally. This is possible through the feasibility conditions that exist in their different appropriate sets.
Wireless networks are gaining popularity fast, and the result is that there is congestion and heavy utilization. Bottlenecks for performance are measured by the wireless networks that are heavy utilized. The robust operation of wireless networks is understood best by the various networks used in communication. Network performance is also optimized by having the required understanding. Channel utilization is another method that can effectively solve the problem of congestion control (Wang & Palaniswami & Low, 2003.119).
Congestion control is an issue that needs to be addressed through channel utilization. In order for the network capacity to be distributed spatially, channel assignments have to be empowered by multiple radios. It is obvious that the different channel and radio exploitation transmissions are advantageous. Distribution of traffic should be conducted in a method that focuses on direction. Unfortunately, the latter still needs to be proven by researchers in this field. At every intermediate node, there is traffic that is incoming and it is referred to as total. The various links should all accept the incoming links that are split. The different radios will also accommodate the traffic that is incoming. Furthermore, the outgoing and incoming traffic is distributed as per source. The latter is also possible through conjunction of fairness requirements and source rates. The range of transmission should be within the distance of the flows. In turn, it will be possible to account for the impact achieved through induced interference.
Induced interference is present in every node, and a radio to radio path has to be constructed. It is referred to as the outer‘s’ of the node to node. ‘S’ is referred to as the source, and it is adjacent to the route known as ‘e’. The nodes have ends as well as common channels where a path has to be created. In route ‘e’ there are links, which are subsequent, and they are positioned between the nodes. The previous links are the main reason why the paths are created (Lin & Shroff, 2004). This means that the link, which exists, is known as radio to radio. The procedure of path incremental construction makes sure that all other links are interrelated to it. It extends further until the route of node-to –node is linked. This means that every source‘s’ has paths. The two sub problems are formulated through the introduction of a formulation. Node paths for assignments that are fixed channel are achieved through traffic allocation (informit.com, 2011.1). This means that the sub problem of congestion control takes place. The other sub problem is associated with assignments that are combinatorial channel discrete. The problem of optimization is a solution to congestion control derived from mapping. In turn, the radio paths are distributed further into traffic. Information on channel congestion is provided by finding the solution. It addresses the sub problem of channel assignment. The procedure that follows in interactive whereby, utilization of the overall network is successfully guaranteed. The approach used in this research is beneficial in achieving fairness and utilization of networks.
Individual rates are equal to the radio-to-radio links, and they all follow a crossing network link. The aggregate link portion is affected by the individual rates. The various radio paths are routed with the incoming traffic. The relationships that exist between the networks are in the form of generic contention (Pahlavan & Krishnamurthy, 2009.78). Channel assignment is given to source rates and radio paths, to control the sub problem of congestion control. Resources that are highly congested will no longer have traffic due to channel assignment. The option would be to make sure that the congested areas have added bandwidth. Information obtained from congestion control solves the sub problem that is heuristic. In turn, network utility will be successful and guaranteed.
Algorithms are used in channel assignment and congestion control and they are successful and guaranteed methods. The latter functions based on radio paths and assignments that are fixed channel. This procedure is of extremely low intensity thus, usage of algebraic operations that are comprehensible. On the other hand, algorithms used in channel assignments have low complexity. The search occurs in cliques that are extremely congested. Channel modifications that are favorable are selected through conditions that are eligible. Verification and manipulation of algebraic equations leads to solving the sub problem.
The findings of this research are thus interpreted by various methods that are accepted all over the world. The approach used is known as MRMC-CC, and it focuses on essential aspects of the research. This means that the multi channel and multi radio wireless mesh networks are computed in two approaches (Greenspan & Klerer, 2008. 60). The other approach is known as ABL05, and it ensures that the pairs of source destinations are computed. The latter uses the paths referred to as node to node and are involved in linear programming. The source rate that is obtained gives a maximum yield and ensures fairness is maintained. The demand vector is made to be in proportion with the weight that has been allocated.
The above approach used in this research uses formulation that is utility based. The latter ensures that fairness is maintained all the time. The path of the node to node used is single and this is due to the source destination. Congestion of networks is controlled by networks that engage in robust operations. The latter is especially beneficial when dealing with wireless networks, which are heavily congested. Rates of data, which are low, are used in frameworks for transmission. In turn, good put, and network throughput, are decreased in the network. Frame loss is achieved as a result of the nodes that allow transmission of rates of data that is low. The distances between the frames can be increased further through a signal to noise ratio. Rates of data, which are high, should be consumed when congestion is present. The implemented multirates adaption scheme functions in a unique way. The impact on work performance of the wireless congested control is high (Xu &Tian & Ansari, 2005.220). The other option, which exists, is that power transmission can be controlled by the clients. The latter ensures that high data rates are effectively utilized through transmission and data frames.
In the different scenarios, which have been witnessed, the mesh network finds that there is under utilization when the ABL05 is used. When compared to the MRMC-CC, the latter is much low as compared to the minimum rate, which is required. Furthermore, there are two reasons, which bring about under utilization. Source rates are forced upon by fairness constraints, and this occurs even in topologies that are non regular. ABL05 constraints, which are linked based, are conservative and thus, different from the ones in MRMC-CC. There is a belief that the links are interrelated in many ways. Topolog, which is based on a grid, seems to favor the ABL05 approach due to the constraints, which are involved. Spatial reuse is exploited by the paths of the node to node that are multiple (Stewart, 2000.1). Constraints that are linked based and those related to fairness are under utilized by the ABL05. The average rates of ABL05 are lower than those of MRMC-CCM, as the latter has higher yields. If the network has added resources, a difference between the two approaches increases. Network resources are utilized by the proportional fairness that exists and in a manner that is fair. If the problem of wireless communication congestion is not solved, performance will reduce drastically. Also, there is a risk that the wireless networks will degrade and become worthless. Losses might occur to the people who use wireless technologies, and especially those who use computerized systems.
Conclusion
In conclusion, the problem of wireless congestion should be addressed, as it benefits many of its users. Furthermore, it will be easy not to misjudge or misinterpret congestion of networks. Bandwidth and jitter ratio is achieved through the information feedback, which is obtained. A control mechanism for congestion control should exist in order to improve the system. The other areas of congestion control, which need to be researched, are on whether a permanent solution can be found to solve congestion control. This means that existing wireless networks will no longer have to affect various computerized systems. The community has a role to play in this issue, as it can advocate for more wireless communication networks to benefit it. Furthermore, society should use wireless communication networks that do not cause congestion. In turn, there will be an improvement in wireless communication congestion. Thus, channel assignment and congestion control are indeed problems that can be solved through proper and relevant research.
Work cited
Bertsekas, D. Nonlinear Programming. New York: Athena Scientific, 2nd edition, 2003. Print.
Xu, K. &Tian, Y. & Ansari, N. “ HYPERLINK “http://dx.doi.org/10.1016/j.comnet.2004.07.006” Improving TCP performance in integrated wireless communications networks,” Computer Networks, 47, 2, (2005): pp. 219–237.
Greenspan, A. & Klerer, M. Et al. “ HYPERLINK “http://dx.doi.org/10.1109/MCOM.2008.4557043” IEEE 802.20: Mobile broadband wireless access for the twenty-first century,” IEEE Communications Magazine, 46, 7, (2008): pp. 56–63.
Stewart, R. “Stream Control Transmission Protocol.” Internet Engineering Task Force (IETF), RFC 2960, 2000.
Lin, X. & Ness, Shroff. Utility maximization for communication networks with multi-path routing. IEEE Transactions on Networking, 51,5, (2006): pp 766–781.
Giannoulis, T & Salonidis, E. & Knightly, W. Congestion control and channel assignment algorithms in multi-radio multi-channel wireless mesh networks. Extended version available at http://www.ece.rice.edu/∼ag4934/agiannou/mrmc-cc-extended.pdf.
HYPERLINK “http://www.informit.com/articles/printerfriendly.aspx?p=98132” “Getting to Know Wireless Networks and Technology”. informit.com. HYPERLINK “http://www.informit.com/articles/printerfriendly.aspx?p=98132” http://www.informit.com/articles/printerfriendly.aspx?p=98132. Retrieved 2011-11-23.
Cantieni, G. Et al. Performance Analysis under Finite Load and Improvements for Multirate 802.11. Elsevier Computer Communications Journal, 28, 10, (2005) : pp 1095.1109.
Lin, X. & Shroff, B. Joint rate control and scheduling in multihop wireless networks. In Proc. Control and Decision Conference (CDC), Atlantis, Paradise Island, Bahamas, December 2004.
Wang, W. & Palaniswami, M. & Low, S. Optimal flow control and routing in multi-path networks. Performance Evaluation, 52, 2-3, (2003):119–132.
Pahlavan, Kaveh. & Krishnamurthy, Prashant. Networking Fundamentals – Wide, Local and Personal Area Communications. New York: Wiley, 2009.Print.
