Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Road Traffic Congestion Analysis Via Connected Vehicles

La congestion routière est un état particulier de mobilité où les temps de déplacement augmentent et de plus en plus de temps est passé dans le véhicule. En plus d’être une expérience très stressante pour les conducteurs, la congestion a également un impact négatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercées sur les autorités afin qu’elles prennent des mesures décisives pour améliorer le flot du trafic sur le réseau routier. En améliorant le flot, la congestion est réduite et la durée totale de déplacement des véhicules est réduite. D’une part, la congestion routière peut être récurrente, faisant référence à la congestion qui se produit régulièrement. La congestion non récurrente (NRC), quant à elle, dans un réseau urbain, est principalement causée par des incidents, des zones de construction, des événements spéciaux ou des conditions météorologiques défavorables. Les opérateurs d’infrastructure surveillent le trafic sur le réseau mais sont contraints à utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas être mesuré partout car il n’est pas réaliste de déployer des équipements sophistiqués pour assurer la collecte précise des données de trafic et la détection en temps réel des événements partout sur le réseau routier. Alors certains emplacements où le flot de trafic doit être amélioré ne sont pas surveillés car ces emplacements varient beaucoup. D’un autre côté, de nombreuses études sur la congestion routière ont été consacrées aux autoroutes plutôt qu’aux régions urbaines, qui sont pourtant beaucoup plus susceptibles d’être surveillées par les autorités de la circulation. De plus, les systèmes actuels de collecte de données de trafic n’incluent pas la possibilité d’enregistrer des informations détaillées sur les événements qui surviennent sur la route, tels que les collisions, les conditions météorologiques défavorables, etc. Aussi, les études proposées dans la littérature ne font que détecter la congestion ; mais ce n’est pas suffisant, nous devrions être en mesure de mieux caractériser l’événement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilité de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriées pour atténuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion

Context Aware Pre-Crash System for Vehicular ad hoc Networks Using Dynamic Bayesian Model

Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people’s lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems’ safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people’s lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network

Enhancing service quality and reliability in intelligent traffic system

Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.Doctor of Philosoph

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Markov decision processes with applications in wireless sensor networks: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier

TalkyCars: A Distributed Software Platform for Cooperative Perception among Connected Autonomous Vehicles based on Cellular-V2X Communication

Autonomous vehicles are required to operate among highly mixed traffic during their early market-introduction phase, solely relying on local sensory with limited range. Exhaustively comprehending and navigating complex urban environments is potentially not feasible with sufficient reliability using the aforesaid approach. Addressing this challenge, intelligent vehicles can virtually increase their perception range beyond their line of sight by utilizing Vehicle-to-Everything (V2X) communication with surrounding traffic participants to perform cooperative perception. Since existing solutions face a variety of limitations, including lack of comprehensiveness, universality and scalability, this thesis aims to conceptualize, implement and evaluate an end-to-end cooperative perception system using novel techniques. A comprehensive yet extensible modeling approach for dynamic traffic scenes is proposed first, which is based on probabilistic entity-relationship models, accounts for uncertain environments and combines low-level attributes with high-level relational- and semantic knowledge in a generic way. Second, the design of a holistic, distributed software architecture based on edge computing principles is proposed as a foundation for multi-vehicle high-level sensor fusion. In contrast to most existing approaches, the presented solution is designed to rely on Cellular-V2X communication in 5G networks and employs geographically distributed fusion nodes as part of a client-server configuration. A modular proof-of-concept implementation is evaluated in different simulated scenarios to assess the system\u27s performance both qualitatively and quantitatively. Experimental results show that the proposed system scales adequately to meet certain minimum requirements and yields an average improvement in overall perception quality of approximately 27 %