Modeling location for pervasive environments

The representation of spaces, locations and the entities they contain is of great importance to location aware systems and pervasive computing scenarios. There has been an active research community in developing many diverse models of location, resulting in significant progress in the area. Various types of location model have evolved through experiment and experience however there still remains many challenges to be met by the research community. This paper aims to highlight previous trends in location modeling, discuss the research challenges ahead and to outline the initial design of a location model for the Strathclyde Context Infrastructure [?]

Opportunities and challenges for location aware computing in the construction industry

This paper describes the opportunities for location aware computing to enhance information capture and use within the construction industry. The construction industry is characterized as being slow to take up innovative mobile ICT, despite the highly mobile workforce who must collaborate with a range of on and off-site personnel, and make use of large volumes of information. Based on fieldwork and workshop activities within COMIT (a large-scale mobile IT project within the construction industry), the information used within two key business processes – health and safety audits, and site design problem resolution – is outlined, and the opportunities for support by location aware computing discussed. Some potential challenges are also identified, as is the need to understand how to provide real value (as opposed to just information) to the end user

Zenith: Utility-Aware Resource Allocation for Edge Computing

In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model

Context-aware mobile applications design: implications and challeges for a new indusy

Context-aware computing is slowly becoming the new mobile paradigm in which applications can discover and use information “out and about”. Typical sources of knowledge about context are the device’s location, data about the environment at large, the mobile device’s prior activity log and even the user’s biometrics. The mobile industry agrees that this paradigm improves the appeal and value of applications by personalising and adapting them to the context in which they run. However, capturing contextual information and processing it to enhance or create a new application is a daunting task: it involves scattered systems and infrastructures and an increasingly wide array of heterogeneous data, architectures and technological tools. In this paper, we explore and analyse existing mobile context-aware applications and the proposed frameworks that enable them. The paper aims to clarify the echnological choices behind context-aware mobile applications and the challenges that still remain ahead for this area to fulfil the promises it offers

A model for context awareness for mobile applications using multiple-input sources

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications

SpaceSemantics: an architecture for modeling environments

The notion of modeling location is fundamental to location awareness in ubiquitous computing environments. The investigation of models and the integration with the myriad of location sensing technologies makes for a challenging discipline. Despite notable development of location models, we believe that many challenges remain unresolved. Complexity and scalability, diverse environments coupled with various sensors and managing the privacy and security of sensitive information are open issues. In this paper we discuss our previous experience combining location sensing with mobile agents and how the lessons learnt have lead to the conception of SpaceSemantics, an open architecture for modeling environments