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

PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

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