A novel approach of gateway selection and placement in cellular Wi-Fi system Presented By Rajesh Prasad

ABSTRACT 1.Establishing metro-scale “cellular Wi-Fi” network to support seamless Internet access in the urban area. 2.Find the minimum number of gateways and their optimal placement for a given network graph so as to minimize the network installation costs while maintaining reliability, flexibility, and an acceptable grade of service. 3.Develop a set of linear inequalities based on various constraints. 4.Solve the Integer Linear Programming(ILP) model by using lpsolve.

Introduction IEEE has one of the fastest adaptation rate seen in the history of technology. In the near future Access Points (APs) will be scattered over an entire city enabling people to use any mobile devices equipped with IEEE network interface card. The cellular Wi-Fi system is essentially a meshed infrastructure network, which is different from the existing cellular systems and ad hoc networks. Only a minimum number of APs called gateways in cellular Wi-Fi are connected to the wired backbone, while other APs connect to the gateways through single or multi-hop wireless links.

Related Work and our Contribution In most of the clustering techniques used in multihop wireless network, the cluster structure is controlled by hop distance. Graph Partitioning: The graph partitioning problem is NP- Complete. Partitioning software’s make best effort to partition the graph in k-parts, where each part has equal number of nodes. Most algorithm to select the location of gateways to interconnect a mobile data network with a fixed data network based on minimizing average packet delays. Different gateways have different bandwidth capacity, and each gateway can serve different number of APs. Our algorithm gives Good cluster

Conceptual Diagram Any AP with a connection to the backbone network (e.g. via DSL, cable modem, T1, T3 or OC1 line) is a candidate for gateway.

Network Graph

Constraints of an efficient gateway selection algorithm 1. Topology independent: The algorithm should be independent of network structure, with the ability of working efficiently for any given network topology. 2. Transparency: The algorithm should not impact the client and should be totally transparent to users. 3. Load balancing: It should distribute the network load as evenly as possible among the gateways. 4. Fault tolerance: It should prevent single point of failure.

Parameters of ILP model

Proposed ILP model

Explanation of ILP model (1) node ‘j’ is associated with node ‘i’, if node ‘i’ has been selected as the gateway and node ‘j’ is an AP

Explanation of ILP model (2) To make sure that the total traffic of all the APs selected by a gateway is not more than the maximum capacity of the gateway Once a node has been selected, it cannot be selected by another gateway.

Explanation of ILP model (3) Cost Function : Cost Function : We define the cost by considering both the establishment cost of the gateways and number of hops its associated APs are away from the gateway. Minimize the overall system cost:

Result of our algorithm

Result of greedy approach

Assumptions Population is uniformly distributed with a population density of 3371 per square mile by using the city of Houston, TX example. 25% of people use Wi-Fi devices and subscribe to cellular Wi-Fi service providers. Approximately 10 APs are needed per square mile so each AP will serve approximately 84 people. A normal user can withstand a delay of 10 seconds for his web page to be loaded and he roughly takes 30 seconds to absorb the content of the page. A normal user will not require more than 20 Kbps for normal web surfing, , citrix, messaging systems and the like. Gateway capacity varies from Mbps to Mbps.

Simulation Results The inequalities discussed in ILP model are solved by using lpsolve, a simplex-based code for linear and integer programming problems by Michel Berkelaar. The results obtained from lpsolve are compared with greedy algorithm for gateway selection. The results are discussed in the following slides

We assumed that 10% of the nodes in the network graph are the gateway candidates. Every mobile device sharing an AP can simultaneously connect at 20 Kbps. Number of nodes varies from 25 to 100.

The total number of node is 60 and each user generates data traffic at a rate of 25kbps.The percentage of candidate nodes varies from 5% to 20%.

The bandwidth usage by each user is varied from 20kbps to 45kbps. The total number of nodes is fixed at 60 and 10% of nodes are potential candidate for gateway.

Conclusions The proposed approach can effectively identify a minimum number of gateways at optimal locations in a cellular Wi-Fi network, resulting in significant lower cost and better clusters. The proposed ILP model considers not only the capacity of the gateways, but also economic aspects associated with installing them and minimum hop count of all the associated APs with a particular gateway. The algorithm provides a high degree of reliability, flexibility and scalability. It also prevents single point of failure.

Analogy

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