Ken Murray

Call Admission and Handover in Heterogeneous Wireless Networks

Todays mobile communication networks support not just simple mobile voice and data services but also access to mobile Internet-based services with varying bandwidth and quality-of-service requirements. The work presented in this article addresses this issue by proposing a management architecture for mobile user roaming based on efficient call admission and handover control in heterogeneous wireless access networks. Our proposed architecture consists of databases that hold profile and policy information, a roaming controller with two main elements (namely, call admission control for new and handover traffic), and intersystem handover control

Policy based access management and handover control in heterogeneous wireless networks

The next generation of mobile networks is expected to utilise multiple radio access technologies, seamlessly integrated to form a heterogeneous wireless network. On the arrival of a call request, the network operator must assign an access network to the user. A call request can be due to a new call initiated within a cell, or to an intra-system or inter-system handover attempt. Selecting the most optimal network from those available and controlling network access is an important consideration for overall network stability and QoS provisioning. The paper proposes a policy based management system to control network access in a heterogeneous wireless network. A call admission control policy admits a new user based on the current load and service mix in each available network. We also present an adaptive handover capacity reservation scheme using neural networks to maximize resource usage while limiting the dropped call rate and number of unnecessary inter-system handover attempts as users move between cells within the same network.

Intelligent network access and inter-system handover control in heterogeneous wireless networks for smart space environments

The next generation of mobile networks supports not just the simple mobile connectivity but also the access in the evolving smart space environments. It is expected that these systems utilise multiple radio access technologies, seamlessly integrated to form a heterogeneous wireless access network, thus enabling total ubiquitous computing. On the arrival of a service request from a smart space user or device, the network operator must assign one of the available access networks. Selecting the network that has the highest probability of providing the best quality of service (QoS) for a particular service type is an important consideration for overall network stability and QoS provisioning. In this paper, we present the aspects of intelligent radio resource management based on policy based call admission control to select the best available network and fuzzy logic based inter-system handover control. The admission controller admits a new user based on the current load and service mix in each available network. The fuzzy logic controller focuses on inter-system handover initiation so as to maintain satisfactory QoS to the end user, while limiting excessive inter-system handover attempts.

The European Network of Excellence CRUISE Application Framework and Network Architecture for Wireless Sensor Networks

This paper presents the main application scenarios under investigation within the network of excellence CRUISE, which is funded in the 1ST 6th framework programme. First an application scenario framework is presented being used for the design process of wireless sensor networks (WSN). Using the framework WSN applications can be compared and contrasted in terms of user, systems requirements, and object interaction. Following the applications are mapped to the sensor network architecture applied in CRUISE and described in the paper. Finally the application scenarios are mapped into the network architecture to demonstrate its generic structure and applicability across multiple application domains.

Neural network based adaptive radio resource management for GSM and IS136 evolution

With the evolution toward 2.5G bringing a wide range of new services, it is expected that the tele-traffic demand on current GSM and IS136 networks will further increase. In this paper we propose a new pro-active resource allocation method of increasing cellular network capacity by introducing an adaptive radio resource management system into a typical GSM/IS136 network. Adaptation is performed by using neural networks (NNs) to predict each cells future resource demands and adjusting the available resources accordingly. Results are presented which exhibit less resource requirements than existing fixed channel allocation (FCA) networks and performance that is comparable to previously proposed dynamic resource allocation (DRA) schemes, but with the advantage of significantly less complexity and no additional network signaling load.

Building Networkable Smart and Cooperating Objects

Wireless networks and sensor technology have converged, and are being broadly applied in numerous areas. Amongst other things, pervasive computing highlights a specific aspect of this convergence, namely the integration of distributed sensing and wireless communications into everyday objects. The rationale that future computing systems should be unobtrusive and form a seamless part of our environment not only underpins this objective, it demands that the integration process be effective. It is challenging to design and build Smart Objects. It is more even challenging to seek to transform our everyday environments, and the objects in them, on a massive scale. This Chapter addresses the practical problem of building a networkable Smart Object that is expected to perform largely the same physical functions as its current ‘dumb’ equivalent, while ‘infusing’ its use-space with an intelligence that supports intuitive interaction, creativity and provides access to significant computing resources on demand. An approach, which is based upon hierarchical systems architectures, uses the construction of a smart table to investigate what is required in terms of ‘whole-smart-artifact design’ and (current and future) materials and also outlines issues relating to how (and when) the actual physical integration process should be implemented.