Dependability of Wireless Sensor Networks

As wireless sensor networks (WSNs) are becoming ever more prevalent, the runtime characteristics of these networks are becoming an increasing issue. Commonly, external sources of interference make WSNs behave in a different manner to that expected from within simplistic simulations, resulting in the need to use additional systems which monitor the state of the network. Despite dependability of WSNs being an increasingly important issue, there are still only a limited number of works within this specific field, with the majority of works focusing on ensuring that specific devices are operational, not the application as a whole. This work instead aims to look at the dependability of WSNs from an application-centric view, taking into account the possible ways in which the application may fail and using the application's requirements to focus on assuring dependability

FULLY AUTONOMOUS SELF-POWERED INTELLIGENT WIRELESS SENSOR FOR REAL-TIME TRAFFIC SURVEILLANCE IN SMART CITIES

Reliable, real-time traffic surveillance is an integral and crucial function of the 21st century intelligent transportation systems (ITS) network. This technology facilitates instantaneous decision-making, improves roadway efficiency, and maximizes existing transportation infrastructure capacity, making transportation systems safe, efficient, and more reliable. Given the rapidly approaching era of smart cities, the work detailed in this dissertation is timely in that it reports on the design, development, and implementation of a novel, fully-autonomous, self-powered intelligent wireless sensor for real-time traffic surveillance. Multi-disciplinary, innovative integration of state-of-the-art, ultra-low-power embedded systems, smart physical sensors, and the wireless sensor network—powered by intelligent algorithms—are the basis of the developed Intelligent Vehicle Counting and Classification Sensor (iVCCS) platform. The sensor combines an energy-harvesting subsystem to extract energy from multiple sources and enable sensor node self-powering aimed at potentially indefinite life. A wireless power receiver was also integrated to remotely charge the sensor’s primary battery. Reliable and computationally efficient intelligent algorithms for vehicle detection, speed and length estimation, vehicle classification, vehicle re-identification, travel-time estimation, time-synchronization, and drift compensation were fully developed, integrated, and evaluated. Several length-based vehicle classification schemes particular to the state of Oklahoma were developed, implemented, and evaluated using machine learning algorithms and probabilistic modeling of vehicle magnetic length. A feature extraction employing different techniques was developed to determine suitable and efficient features for magnetic signature-based vehicle re-identification. Additionally, two vehicle re-identification models based on matching vehicle magnetic signature from a single magnetometer were developed. Comprehensive system evaluation and extensive data analyses were performed to fine-tune and validate the sensor, ensuring reliable and robust operation. Several field studies were conducted under various scenarios and traffic conditions on a number of highways and urban roads and resulted in 99.98% detection accuracy, 97.4782% speed estimation accuracy, and 97.6951% classification rate when binning vehicles into four groups based on their magnetic length. Threshold-based, re-identification results revealed 65.25%~100% identification rate for a window of 25~500 vehicles. Voting-based, re-identification evaluation resulted in 90~100% identification rate for a window of 25~500 vehicles. The developed platform is portable and cost-effective. A single sensor node costs only $30 and can be installed for short-term use (e.g., work zone safety, traffic flow studies, roadway and bridge design, traffic management in atypical situations), as well as long-term use (e.g., collision avoidance at intersections, traffic monitoring) on highways, roadways, or roadside surfaces. The power consumption assessment showed that the sensor is operational for several years. The iVCCS platform is expected to significantly supplement other data collection methods used for traffic monitoring throughout the United States. The technology is poised to play a vital role in tomorrow’s smart cities

Group Activity Recognition Using Wearable Sensing Devices

Understanding behavior of groups in real time can help prevent tragedy in crowd emergencies. Wearable devices allow sensing of human behavior, but the infrastructure required to communicate data is often the first casualty in emergency situations. Peer-to-peer (P2P) methods for recognizing group behavior are necessary, but the behavior of the group cannot be observed at any single location. The contribution is the methods required for recognition of group behavior using only wearable devices

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes