Information Agents for Pervasive Sensor Networks

In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental parameters will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and the correlations and delays that exist between them. We validate our approach using data collected from a network of weather sensors located on the south coast of England

Overview of a Cyber-enabled Wireless Monitoring System for the Protection and Management of Critical Infrastructure Systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84821/1/OverviewSHMTech.pd

Multidisciplinary approach for developing a new robotic system for domiciliary assistance to elderly people

This paper aims to show the effectiveness of a (inter / multi)disciplinary team, based on the technology developers, elderly care organizations, and designers, in developing the ASTRO robotic system for domiciliary assistance to elderly people. The main issues presented in this work concern the improvement of robot’s behavior by means of a smart sensor network able to share information with the robot for localization and navigation, and the design of the robot’s appearance and functionalities by means of a substantial analysis of users’ requirements and attitude to robotic technology to improve acceptability and usability

"I'd want to burn the data or at least nobble the numbers": Towards data-mediated building management for comfort and energy use

In this paper, we explore the role of pervasive environmental sensor data in workplace building management. Current interactions between management and workplace occupants are limited by the gap between experiences of (dis)comfort (i.e. individual preferences and perceptions) and the rigid objectivity of organisational policies and procedures such as static setpoint temperatures for indoor spaces. Our hypothesis is that pervasive sensor data that captures the indoor climate can provide an effective platform from which to more successfully communicate about comfort and energy use. Through a qualitative study with building managers and occupants, we show that while data does not necessarily resolve these tensions, it provides an engaging forum for a more inclusive building management process, and we outline directions for taking a more conversational approach in the design of comfort and energy-use interventions for the workplace

An Architecture for IoT-Enabled Smart Transportation Security System: A Geospatial Approach

Internet of Things (IoT) in urban transportation systems have been ubiquitously embedded into a variety of devices and transport entities. The IoT-enabled smart transportation system (STS) has thus gained growing tractions amongst scholars and practitioners. However, several IoT challenges in relation to cyber–physical security are exposed due to the heterogeneity, complexity and decentralisation of the IoT network. There also exist geospatial security concerns with respect to the embeddings of 5G networks into public infrastructures that are interconnected with the transport system via IoT. To address these concerns, this article aims to apply geospatial modelling approach to propose a smart transportation security systems (STSSs). It is modelled and simulated by undertaking an experimental study in the city of Beijing, China. The simulation outcome of the proposed architecture is expected to offer a strategic guide for strategic security management of urban smart transportation

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Real-Time Context-Aware Computing with Applications in Civil Infrastructure Systems.

This dissertation contributes a structured understanding of the fundamental processes involved in developing context-aware computing applications for the civil infrastructure industry. The civil infrastructure industry is characterized by mobile human and machine agents actively engaged in real-time decision-making tasks in a dynamic and unstructured workspace environment. This distinguishes context-aware computing from other computing technologies in three aspects: 1) it has the ability to perceive, interpret, and adapt to the agent’s evolving workspace; 2) It streamlines project data and presents the agent with information pertinent to its context, thus eliminating the agent’s tasks to accomplish the same; 3) By leveraging contextual information, it supplements decision-making tasks in real-time. This research has successfully investigated technical approaches to address fundamental aspects of introducing context-aware applications to civil engineering, including: the ubiquitous localization of mobile agents in dynamic, unstructured environments; abstraction of the spatial-context and identifying the objects of interest to the agent; and the suitability of using standard models to manage and organize data for context-aware computing applications. A computational framework for designing context-aware applications to support real-time decision-making has also been implemented. The framework allows researchers and other end users to leverage currently available context-sensing technology to design and implement innovative solutions to domain specific problems. The researched methods have been validated through several experiments conducted at the University of Michigan, the National Institute of Standards and Technology, and the Michigan Department of Transportation. These experiments have resulted in the implementation of several applications – to support real-life decision-making tasks – that not only serve to illustrate the usefulness of the framework, but also have significant social and economic implications. Among these applications are the controlled drilling system that warns drilling personnel when the drill bit tip is about to strike rebar or utility lines, thus helping preserve the structural integrity of concrete decks and preventing utility strike accidents; an automated fault detection system that diagnoses faulty components of an underperforming HVAC distribution network; and an innovative bridge inspection solution that supports condition assessment decision-making, thus introducing objectivity to visual condition assessment by providing concurrence with the Structural Health Monitoring data.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99816/1/akulaman_1.pd