Combining System Introspection with User-Provided Description to Support Configuration and Understanding of Pervasive systems

Pervasive computing systems such as smart spaces typically combine multiple embedded and/or mobile sensing, computing and interaction devices. A variety of distributed computing approaches are used to integrate these devices to support coordinated applications. This paper describes how simple user descriptions of (primarily) physical aspects of such a system can be combined with information from system introspection to make the system and its log recordings more understandable to potential users, as well as supporting easier configuration and monitoring, and allowing the expression of certain kinds of system behaviour that are otherwise hard to achieve

Applying an SOM Neural Network to Increase the Lifetime of Battery-Operated Wireless Sensor Networks

Wireless sensor networks have garnered significant attention in recent years. According to (The Mobile Internet, 2004), more than half a billion nodes will be shipped for wireless sensor applications in 2010, for an end user market worth at least $7 billion. Wireless sensor networks are one of the first real-world examples of pervasive computing, the notion that small, smart, computing and cheap sensing devices will eventually permeate the environment (Bulusu & Jha, 2005). The combination of distributed sensing, low power processors and wireless communication enables such technology to be used in a wide array of applications such as habitat monitoring and environment monitoring, military solutions, such as battlefield surveillance, and commercial applications, such as monitoring material fatigue and managing inventory.peer-reviewe

Towards low cost prototyping of mobile opportunistic disconnection tolerant networks and systems

Fast emerging mobile edge computing, mobile clouds, Internet of Things (IoT) and cyber physical systems require many novel realistic real time multi-layer algorithms for a wide range of domains, such as intelligent content provision and processing, smart transport, smart manufacturing systems and mobile end user applications. This paper proposes a low cost open source platform, MODiToNeS, which uses commodity hardware to support prototyping and testing of fully distributed multi-layer complex algorithms over real world (or pseudo real) traces. MODiToNeS platform is generic and comprises multiple interfaces that allow real time topology and mobility control, deployment and analysis of different self-organised and self-adaptive routing algorithms, real time content processing, and real time environment sensing with predictive analytics. Our platform also allows rich interactivity with the user. We show deployment and analysis of two vastly different complex networking systems: fault and disconnection aware smart manufacturing sensor network and cognitive privacy for personal clouds. We show that our platform design can integrate both contexts transparently and organically and allows a wide range of analysis

Monitoring Energy Consumption of Smartphones

With the rapid development of new and innovative applications for mobile devices like smartphones, advances in battery technology have not kept pace with rapidly growing energy demands. Thus energy consumption has become a more and more important issue of mobile devices. To meet the requirements of saving energy, it is critical to monitor and analyze the energy consumption of applications on smartphones. For this purpose, we develop a smart energy monitoring system called SEMO for smartphones using Android operating system. It can profile mobile applications with battery usage information, which is vital for both developers and users.Comment: The 1st International Workshop on Sensing, Networking, and Computing with Smartphones (PhoneCom), IEEE, Dalian, China, Oct 19-22, 201

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure