Architecture and Protocols for Service and Application Deployment in Resource Aware Ubiquitous Environments

Realizing the potential of pervasive computing will be predicated upon the availability of a flexible, mobility-aware infrastructure and the technologies to support seamless service management, provisioning and delivery. Despite the advances in routing and media access control technologies, little progress has been made towards large-scale deployment of services and applications in pervasive and ubiquitous environments. The lack of a fixed infrastructure, coupled with the time-varying characteristics of the underlying network topology, make service delivery challenging. The goal of this research is to address the fundamental design issues of a service infrastructure for ubiquitous environments and provide a comprehensive solution which is robust, scalable, secure and takes into consideration node mobility and resource constraints. We discuss the main functionalities of the proposed architecture, describe the algorithms for registration and discovery and present a power-aware location-driven message forwarding algorithm to enable node interaction in this architecture. We also provide security schemes to ensure user privacy in this architecture. The proposed architecture was evaluated through theuse of simulations. The results show that the service architecture is scalable and robust, even when node mobility is high. The comparative analysis shows that our message forwarding algorithm consistently outperforms contemporary location-driven algorithms. Furthermore, thisresearch work was implemented as a proof-of-concept implementation and tested on a real world scenario

Technical considerations towards mobile user QoE enhancement via Cloud interaction

This paper discusses technical considerations of a Cloud infrastructure which interacts with mobile devices in order to migrate part of the computational overhead from the mobile device to the Cloud. The aim of the interaction between the mobile device and the Cloud is the enhancement of parameters that affect the Quality of Experience (QoE) of the mobile end user through the offloading of computational aspects of demanding applications. This paper shows that mobile user’s QoE can be potentially enhanced by offloading computational tasks to the Cloud which incorporates a predictive context-aware mechanism to schedule delivery of content to the mobile end-user using a low-cost interaction model between the Cloud and the mobile user. With respect to the proposed enhancements, both the technical considerations of the cloud infrastructure are examined, as well as the interaction between the mobile device and the Cloud

Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

Office Occupancy Detection based on Power Meters and BLE Beaconing

Energy consumption for both residential and non-residential buildings is significant and has been increasing regularly. For non-residential buildings, asking the user to be directly involved in energy saving can be challenging as occupants (e.g., employees) are less aware of and affected by high energy bills compared to their domestic situation. Employees are less careful when leaving empty office spaces heated and illuminated, resulting in unnecessary energy consumption. This thesis focuses on finding solutions for solving energy waste in non-residential buildings by automatically detecting the presence, thus enabling energy-saving automation.To reduce energy consumption due to unnecessary use, precise and detailed user contexts play an important role. User contexts (e.g., occupancy and activity of users) provide grounds to buildings’ control and energy management systems for efficient lighting and HVAC actuation. We explore sensing systems that indicate occupancy. Namely, we extract occupancy from power consumption (i.e., power metering or sub-metering systems) and proximity location (i.e., mobile phones with beaconing systems). We investigate several strategies and machine learning algorithms to infer occupancy from these sources. We also study fusions at the decision-level and feature-level. The former allows sub-systems to infer local decisions and finally combines the outputs to form a final decision. The latter yields only decision after sensor readings have been combined. The approaches are tested in actual office environments populated by researchers and software developers. We finally discuss potential energy saving, user privacy, and portability to provide insight into how the proposed occupancy detection systems may impact building use and control

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial