Julian K. Buhagiar

Exploiting Adaptive Window Techniques to Reduce TCP Congestion in Mobile Peer Networks

Exploitation of mobile peer networking in providing data and multimedia services has to-date been limited for a number of reasons, these include difficulties in addressing different ubiquitous radio access networks, cost per bandwidth, as well as increased TCP congestion due to reduced radio resources. A new mobile peer networking algorithm involving adaptive window techniques based on Linear-Quadratic-Gaussian (LQG) control is proposed. Results show that the proposed technique presents a remarkable network performance improvement over standard algorithms.

The application of discrete time methods to position estimation in WMANs

Recent development in wireless ad-hoc metropolitan area networks (WMANs) has increased the demand for a new service portfolio based on client location. Potential services include location sensitive billing, push-activated social-networking, location-based experiences, and applications exploiting least-cost routing. This paper proposes a discrete time solution for a scenario in which a client application measures and collects data from the indoor and outdoor radio environments and uploads the compressed bit-stream to a de-centralised server. The location of the mobile station is then estimated, within acceptable accuracies, at this server using a neural network approach.

Exploiting Traffic Sampling Techniques to Optimize Energy Efficiency in Mobile Peer Networks

Wireless infrastructures have seen a drastic increase in energy requirements as technology shifted towards higher frequencies in an attempt to increase bandwidth. Driven by the increase in demand from an always increasing subscriber base, large cities are also demanding more base stations and access points to guarantee adequate quality of service. Mobile peer networking is a possible solution for power-efficient communication, since transmission over short distances demands less power. A novel algorithm based on peer nodal hierarchies, traffic mapping and neural networks is proposed. Results show that this technique presents a remarkable power efficiency improvement over standard peer-to-peer networks.

An Evaluation of Neural Network Architecture Performance in Wireless Geo-Location

Wireless geo-location applications require robust algorithms that are capable of locating and/or tracking wireless users requesting the service. To this effect, the performance of three neural network architectures has been evaluated through simulation to determine the optimal performance algorithm that can be applied to these new applications, such as location based-services (LBS). The results indicate that neural networks having self-organizing characteristics quickly learn to adapt to the rapid changing radio environment as opposed to other architectures which take much longer. Typical figures indicate that this family of neural networks reaches performance advantages of 45% and above when compared to other neural families making then the ideal candidates for such applications.

Cellular network coverage optimization through the application of self-organizing neural networks

Site cluster coverage optimization is crucial in improving the quality of service offered by a cellular system. An optimization technique using a two-layered self-organizing neural network is presented. The kernel maps the deterministic patterns obtained through actual traffic data and produces a performance relationship with regard to cluster size. Optimum configuration for a given cellular cluster can thus be determined as the kernel changes hardware parameters with the aim of enhancing coverage. The algorithms resulting tuning parameters can later be applied to the cellular networks equipment. Simulation results have demonstrated the efficacy of the kernel in improving cellular network coverage.

Optimizing Multicast Protocols to Reduce Energy Dissipation in Mobile Peer Networks

Wireless infrastructures have seen a drastic increase in energy requirements as technology shifted towards higher frequencies in an attempt to increase bandwidth. This is further accentuated by increasingly demanding real-time services such as video streaming. To cater for these new services and still guarantee an adequate quality of service, wireless network operators in large cities have to install more base stations and access points, hence increasing power consumption. Mobile peer networking is a possible solution for power-efficient wireless communication, since it reduces infrastructural requirements and peer transmission over short distances demands less power. A novel algorithm based on multicast protocols, traffic sampling, and state switching is proposed. Results show that this technique presents a remarkable power efficiency improvement over standard peer-to-peer networks.

Exploiting traffic sampling techniques for mobile peer networking

Solutions that provide practical mobile peer networking have to date been ineffective for a number of reasons; these include challenges to address ubiquitous radio access networks, cost per bandwidth, as well as the spatial-temporal availability of each peer node within the mobile network. A new peer networking algorithm involving peer nodal hierarchies, traffic mapping and neural networks is proposed. Results show that the technique presents a remarkable network performance improvement over standard peer-to-peer networks. Comparisons with alternative peer topologies also show that the proposed solution exhibits higher performance in peer applications, suggesting better usage of the limitations present in mobile networks.

The application of discrete time methods to position estimation in indoor wireless networks

In the recent upsurge of ad-hoc wireless local area networks (WLANs) there has been an increased demand for a new services portfolio based on position. This has been accentuated due to the rise of 802.11/16 enabled chipsets inside mobile handsets. These services include location sensitive billing, profile selection based on specific indoor positions, and new location based applications such as automated utilities distribution based on user density and location, all of which depend on accurate indoor geolocation. This paper proposes a solution for such a scenario whereby a client application measures and collects data from the radio environment and uploads the compressed bit-stream to a de-centralised server which identifies the location of the handset and configures the corresponding application according to the requested service, be it client initiated or server initiated