zCap: a zero configuration adaptive paging and mobility management mechanism

Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards

Innovation in Mobile Learning: A European Perspective

In the evolving landscape of mobile learning, European researchers have conducted significant mobile learning projects, representing a distinct perspective on mobile learning research and development. Our paper aims to explore how these projects have arisen, showing the driving forces of European innovation in mobile learning. We propose context as a central construct in mobile learning and examine theories of learning for the mobile world, based on physical, technological, conceptual, social and temporal mobility. We also examine the impacts of mobile learning research on educational practices and the implications for policy. Throughout, we identify lessons learnt from European experiences to date

Location and resource management for quality of service provisioning in wireless/mobile networks

Wireless communication has been seen unprecedented growth in recent years. As the wireless network migrates from 2G to 2.5G and 3G, more and more high-bandwidth services have to be provided to wireless users. However, existing radio resources are limited, thus quality-of-service (QoS) provisioning is extremely important for high performance networKing In this dissertation, we focus on two problems crucial for QoS provisioning in wireless networks. They are location and resource management. Our research is aimed to develop efficient location management and resource allocation techniques to provide qualitative services in the future generations of wireless/mobile networks. First, the hybrid location update method (HLU) is proposed based on both the moving distance and the moving direction of mobile terminals. The signaling cost for location management is analyzed using a 2D Markov walk model. The results of numerical studies for different mobility patterns show that the HLU scheme outperforms the methods employing either moving distance or moving direction. Next, a new dynamic location management scheme with personalized location areas is developed. It takes into account terminal\u27s mobility characteristics in different locations of the service area. The location area is designed for each individual mobile user such that the location management cost is minimized. The cost is calculated based on a continuous-time Markov chain. Simulation results acknowledge a lower cost of the proposed scheme compared to that of some known techniques. Our research on the resource management considers the dynamic allocation strategy in the integrated voice/data wireless networks. We propose two new channel de-allocation schemes, i.e., de-allocation for data packet (DASP) and de-allocation for both voice call and data packet (DASVP). We then combine the proposed de-allocation methods with channel re-allocation, and evaluate the performance of the schemes using an analytic model. The results indicate the necessity of adapting to QoS requirements on both voice call and data packet. Finally, a new QoS-based dynamic resource allocation scheme is proposed which differentiates the new and handoff voice calls. The scheme combines channel reservation, channel de-allocation/re-allocation for voice call and packet queue to adapt to QoS requirements by adjusting the number of reserved channels and packet queue size. The superiority of the propose scheme in meeting the QoS requirements over existing techniques is proved by the experimental studies

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201