Fixed chain-based wireless sensor network for intelligent transportation systems

Wireless Sensor Networks (WSNs) are distributed and interconnected wirelessly sensors that are used in a variety of fields of our daily life, such as the manufacturing, utility operations and traffic monitoring. Many WSN applications come with some technical weaknesses and issues, especially when they are used in Intelligent Transportation Systems (ITS). For ITS applications that use a fixed chain topology which contains road studs deployed at ground level, there are some challenges related to radio propagation, energy constraints and the Media Access Control (MAC) protocol. This thesis develops a ground level radio propagation model for communication between road studs, and energy efficiency metrics to manage the resources to overcome the energy constraints, as well as a MAC protocol compatible with chain topology and ground level communication. For the challenges of the physical layer, this thesis investigates the use of a WSN for communicating between road-based nodes. These nodes are situated at ground level, and two-way wireless communication is required between the nodes and from the nodes to a roadside control unit. Field measurements have been carried out to examine the propagation close to the ground to determine the maximum distance between road-based nodes as a function of the antenna height. The results show that for a frequency of 2.4 GHz, a range of up to 8m is achievable with 2mW equivalent isotropically radiated power (EIRP). An empirical near-ground level radio propagation model has been derived, and the predicted results from this model are shown to match closely to the measured results. Since wireless sensor networks have power constraints, green energy efficiency metrics have been proposed for low-power wireless sensors operating at ground level. A numerical analysis is carried out to investigate the utilisation of the green energy efficiency metrics for ground level communication in wireless sensor networks. The proposed metrics have been developed to calculate the optimal sensor deployment, antenna height and energy efficiency level for the near ground wireless sensor. As an application of the proposed metrics, the relationship between the energy efficiency and the spacing between the wireless sensor nodes has been studied. The results provide guidance for energy efficient deployment of near ground level wireless sensors. To manage the communication between large numbers of nodes deployed on a chain topology, this research presents a time division multiple access (TDMA) MAC protocol that is specifically designed for applications requiring periodic sensing of the sensor field. Numerical analysis has been conducted to investigate the optimum transmission scheduling based on the signal-to-interference-plus-noise-ratio (SINR) for ground level propagation model applied on wireless chain topology. The optimised transmission schedule considers the SINR value to enable simultaneous transmission from multiple nodes. The most significant advantages of this approach are reduced delay and improved Packet Received Ratio (PRR). Simulation is performed to evaluate the proposed protocol for intelligent transport system applications. The simulation results validate the MAC protocol for a fixed chain topology compared with similar protocols

Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2) consideration of imbalanced classification in the experiments. Submitted to IEEE Journal for possible publicatio

Routing Diverse Evacuees with Cognitive Packets

This paper explores the idea of smart building evacuation when evacuees can belong to different categories with respect to their ability to move and their health conditions. This leads to new algorithms that use the Cognitive Packet Network concept to tailor different quality of service needs to different evacuees. These ideas are implemented in a simulated environment and evaluated with regard to their effectiveness.Comment: 7 pages, 7 figure