A Hashing Scheme for Multi-channel Wireless Broadcast

The rapid development of wireless communication technology and battery-powered portable devices is making mobile information services increasingly popular. Since the bandwidth resource of wireless networks is scarce and the mobile devices have a limited battery capacity, any solution for information access must be devised in such a way that time and power consumption for the devices are minimized. Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. This paper proposes a hashing scheme for information access via wireless broadcast through multiple channels in which hash functions are used to index broadcast information across multiple channels. In this scheme, two different hash functions called Primary Hash Function (PHF) and Secondary Hash Function (SHF) are used, where PHF is used to determine the channel in which the desired data item is to be broadcasted and SHF is used to locate the data item within that channel. The proposed hashing scheme reduces both the access latency and tuning time and shortens the broadcast length. Moreover, Access Probabilities of data items and User Profiles that indicate the client behavior in the environment at any given time are considered in this system to construct an efficient broadcast schedule. This broadcast schedule is a non-flat data broadcast that further reduces the average access latency. Finally, Caching techniques are also implemented to further improve the access latency and tuning time

Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

<div><p>In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.</p></div

Simulation Metrics.

<p>Simulation Metrics.</p

Handover failure rate computation(a) Results with velocity 10 Km/h (b) Results with velocity 20 Km/h.

<p>Handover failure rate computation(a) Results with velocity 10 Km/h (b) Results with velocity 20 Km/h.</p

Delay performance between WIMAX and WiFi network with 10 km/h.

<p>Delay performance between WIMAX and WiFi network with 10 km/h.</p

Architecture of proposed system.

<p>Heterogeneous environment with zone gateway decision head.</p

Packet transfer computation (a)Results with mobile velocity 10 Km/hmobile (b)Results with velocity 20 km/h.

<p>Packet transfer computation (a)Results with mobile velocity 10 Km/hmobile (b)Results with velocity 20 km/h.</p

Decision Metrics for Various Traffic Classes.

<p>Decision Metrics for Various Traffic Classes.</p