Biblio

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex tasks that are time-sensitive, in the presence of an adversary. The task is specified as a formula in metric interval temporal logic (MITL). The adversary is assumed to have the ability to tamper with the control input to the CPS and also manipulate timing information perceived by the CPS. In order to model the interaction between the CPS and the adversary, and also the effect of these two classes of attacks, we define an entity called a durational stochastic game (DSG). DSGs probabilistically capture transitions between states in the environment, and also the time taken for these transitions. With the policy of the defender represented as a finite state controller (FSC), we present a value-iteration based algorithm that computes an FSC that maximizes the probability of satisfying the MITL specification under the two classes of attacks. A numerical case-study on a signalized traffic network is presented to illustrate our results.

The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.

In this paper we consider connected and autonomous vehicles (CAV) in a traffic network as moving waves defined by their frequency and phase. This outlook allows us to develop a multi-layer decentralized control strategy that achieves the following desirable behaviors: (1) safe spacing between vehicles traveling down the same road, (2) coordinated safe crossing at intersections of conflicting flows, (3) smooth velocity profiles when traversing adjacent intersections. The approach consist of using the Kuramoto equation to synchronize the phase and frequency of agents in the network. The output of this layer serves as the reference trajectory for a back-stepping controller that interfaces the first-order dynamics of the phase-domain layer and the second order dynamics of the vehicle. We show the performance of the strategy for a single intersection and a small urban grid network. The literature has focused on solving the intersection coordination problem in both a centralized and decentralized manner. Some authors have even used the Kuramoto equation to achieve synchronization of traffic lights. Our proposed strategy falls in the rubric of a decentralized approach, but unlike previous work, it defines the vehicles as the oscillating agents, and leverages their inter-connectivity to achieve network-wide synchronization. In this way, it combines the benefits of coordinating the crossing of vehicles at individual intersections and synchronizing flow from adjacent junctions.

The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data.

As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.

Automobiles provide comfort and mobility to owners. While they make life more meaningful they also pose challenges and risks in their safety and security mechanisms. Some modern automobiles are equipped with anti-theft systems and enhanced safety measures to safeguard its drivers. But at times, these mechanisms for safety and secured operation of automobiles are insufficient due to various mechanisms used by intruders and car thieves to defeat them. Drunk drivers cause accidents on our roads and thus the need to safeguard the driver when he is intoxicated and render the car to be incapable of being driven. These issues merit an integrated approach to safety and security of automobiles. In the light of these challenges, an integrated microcontroller-based hardware and software system for safety and security of automobiles to be fixed into existing vehicle architecture, was designed, developed and deployed. The system submodules are: (1) Two-step ignition for automobiles, namely: (a) biometric ignition and (b) alcohol detection with engine control, (2) Global Positioning System (GPS) based vehicle tracking and (3) Multisensor-based fire detection using neuro-fuzzy logic. All submodules of the system were implemented using one microcontroller, the Arduino Mega 2560, as the central control unit. The microcontroller was programmed using C++11. The developed system performed quite well with the tests performed on it. Given the right conditions, the alcohol detection subsystem operated with a 92% efficiency. The biometric ignition subsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiency in locations registered with the neuro-fuzzy system. The vehicle tracking subsystem operated with an efficiency of 90%.