Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

AN ADAPTIVE INFORMATION DISSEMINATION MODEL FOR VANET COMMUNICATION

Vehicular ad hoc networks (VANETs) have been envisioned to be useful in road safety and many commercial applications. The growing trend to provide communication among the vehicles on the road has provided the opportunities for developing a variety of applications for VANET. The unique characteristics of VANET bring about new research challenges

Flexible cross layer design for improved quality of service in MANETs

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityMobile Ad hoc Networks (MANETs) are becoming increasingly important because of their unique characteristics of connectivity. Several delay sensitive applications are starting to appear in these kinds of networks. Therefore, an issue in concern is to guarantee Quality of Service (QoS) in such constantly changing communication environment. The classical QoS aware solutions that have been used till now in the wired and infrastructure wireless networks are unable to achieve the necessary performance in the MANETs. The specialized protocols designed for multihop ad hoc networks offer basic connectivity with limited delay awareness and the mobility factor in the MANETs makes them even more unsuitable for use. Several protocols and solutions have been emerging in almost every layer in the protocol stack. The majority of the research efforts agree on the fact that in such dynamic environment in order to optimize the performance of the protocols, there is the need for additional information about the status of the network to be available. Hence, many cross layer design approaches appeared in the scene. Cross layer design has major advantages and the necessity to utilize such a design is definite. However, cross layer design conceals risks like architecture instability and design inflexibility. The aggressive use of cross layer design results in excessive increase of the cost of deployment and complicates both maintenance and upgrade of the network. The use of autonomous protocols like bio-inspired mechanisms and algorithms that are resilient on cross layer information unavailability, are able to reduce the dependence on cross layer design. In addition, properties like the prediction of the dynamic conditions and the adaptation to them are quite important characteristics. The design of a routing decision algorithm based on Bayesian Inference for the prediction of the path quality is proposed here. The accurate prediction capabilities and the efficient use of the plethora of cross layer information are presented. Furthermore, an adaptive mechanism based on the Genetic Algorithm (GA) is used to control the flow of the data in the transport layer. The aforementioned flow control mechanism inherits GA’s optimization capabilities without the need of knowing any details about the network conditions, thus, reducing the cross layer information dependence. Finally, is illustrated how Bayesian Inference can be used to suggest configuration parameter values to the other protocols in different layers in order to improve their performance.National Foundation of Scholarships of Greece(I.K.Y.

An efficient cluster-based service model for vehicular ad-hoc networks on motorways

Vehicular Ad-Hoc Networks (VANET) can, but not limited to provide users with useful traffic and environmental information services to improve travelling efficiency and road safety. The communications systems used in VANET include vehicle-to-vehicle communications (V2V) and vehicle-to-infrastructure communications (V2I). The transmission delay and the energy consumption cost for maintaining good-quality communications vary depending on the transmission distance and transmission power, especially on motorways where vehicles are moving at higher speeds. In addition, in modern transportation systems, electric vehicles are becoming more and more popular, which require a more efficient battery management, this also call for an efficient way of vehicular transmission. In this project, a cluster-based two-way data service model to provide real-time data services for vehicles on motorways is designed. The design promotes efficient cooperation between V2V and V2I, or namely V2X, with the objective of improving both service and energy performance for vehicular networks with traffic in the same direction. Clustering is an effective way of applying V2X in VANET systems, where the cluster head will take the main responsibility of exchanging data with Road Side Units (RSU) and other cluster members. The model includes local service data collection, data aggregation, and service data downloading. We use SUMO and OMNET++ to simulate the traffic scenarios and the network communications. Two different models (V2X and V2I) are compared to evaluate the performance of the proposed model under different flow speeds. From the results, we conclude that the cluster-based service model outperforms the non-clustered model in terms of service successful ratio, network throughput and energy consumption

Design and Performance Analysis of Urban Traffic Control Systems

This study aims to investigate the design and performance of different architectures for urban traffic control with consideration of variations and uncertainties in traffic flow. The architectures, which ranging from centralised, semi-centralised to decentralised, are applied to different road networks. Both macroscopic and microscopic flow models are developed and used to calculate the performance of the systems. The macroscopic model is capable of generating essential traffic dynamics, such as traffic queues’ spillover, formation and dissipation. The control systems’ are tested under varies traffic demand levels. The results suggest that the centralised systems generally can outperform the decentralised systems, and the most benefit gained in the centralised control comes from its setting of signal offsets. On the other hand, the microscopic flow model captures the movement of each individual vehicle and drivers' rerouting behaviour with respect to traffic conditions. The test results showed that the drivers' response to the traffic condition can help a decentralised system perform as well as a centralised system. This study brings a new insight into cooperative transport management, and contributes to the state-of-the-art of urban traffic system design

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph