Connected vehicles (CVs) have facilitated the development of intelligent transportation system that supports critical safety information sharing with minimum latency. However, CVs are vulnerable to different external and internal attacks. Though cryptographic techniques can mitigate external attacks, preventing internal attacks imposes challenges due to authorized but malicious entities. Thwarting internal attacks require identifying the trustworthiness of the participating vehicles. This paper proposes a trust management framework for CVs using interaction provenance that ensures privacy, considers both in-vehicle and vehicular network security incidents, and supports flexible security policies. For this purpose, we present an interaction provenance recording and trust management protocol. Different events are extracted from interaction provenance, and trustworthiness is calculated using fuzzy policies based on the events.

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

Connected autonomous vehicles (CAVs) have fostered the development of intelligent transportation systems that support critical safety information sharing with minimum latency and making driving decisions autonomously. However, the CAV environment is vulnerable to different external and internal attacks. Authorized but malicious entities which provide wrong information impose challenges in preventing internal attacks. An essential requirement for thwarting internal attacks is to identify the trustworthiness of the vehicles. This paper exploits interaction provenance to propose a trust management framework for CAVs that considers both in-vehicle and vehicular network security incidents, supports flexible security policies and ensures privacy. The framework contains an interaction provenance recording and trust management protocol that extracts events from interaction provenance and calculates trustworthiness using fuzzy policies based on the events. Simulation results show that the framework is effective and can be integrated with the CAV stack with minimal computation and communication overhead.

In the United States, an estimated 7,005 (crude rate 2.13) pedestrians were killed in traffic crashes in 2020, according to the Centers for Disease Control and Prevention (CDC). This statistic is currently increasing annually and research suggests that distraction by smartphones may be a primary reason for the increasing number of pedestrian injuries and deaths. Timely interruptions may alert inattentive pedestrians and prevent fatalities. To this end, we developed StreetBit, a Bluetooth beacon-based system that warns distracted pedestrians with a visual and/or audible interruption when they approach a potentially dangerous traffic intersection while distracted by their smartphones. We posit that by using StreetBit, we can educate distracted pedestrians and elicit behavioral change to reduce or remove smartphone-based distractions when they enter and cross roadways. To demonstrate the feasibility of StreetBit, we conducted a field study with 385 participants. Results show that the system demonstrates adequate feasibility and behavior change in response to the StreetBit program.

The Internet of Things (IoT) offers new services in the context of smart cities through digital devices embedded with sensing, computation, and communication capabilities. The IoT devices enhance the smart city vision by employing advanced communication and computation technologies for smart city administrations. The IoT-based smart city applications require many IoT devices and gateways to be deployed at different city points. Heterogeneous sensing devices, placing smart devices in a constrained or physically inaccessible area, and large urban areas to monitor together make IoT node deployment and sensing management tasks difficult, time-consuming, and expensive. Additionally, certain tasks may require smart devices to be deployed for a very short period of time to sense and report contextual information, making it economically infeasible to purchase the devices. In this regard, we propose a drone-based IoT as a Service (IoTaaS) framework that enables the dynamic provisioning or deployment of IoT devices using drones. IoTaaS allows IoT devices and gateways to be mounted on drones and provides a distributed cloud service by placing the IoT devices in an area according to the requirements specified by a user. We also provide an economic analysis for operating such drone-based IoT services. A proof-of-concept implementation of IoTaaS for smart agriculture and air pollution monitoring applications shows that IoTaaS can reduce setup costs and increase the usage of IoT devices.

The safety of distracted pedestrians presents a significant public health challenge in the United States and worldwide. An estimated 6,704 American pedestrians died and over 200,000 pedestrians were injured in traffic crashes in 2018, according to the Centers for Disease Control and Prevention (CDC) [1]. This number is increasing annually and many researchers posit that distraction by smartphones is a primary reason for the increasing number of pedestrian injuries and deaths. One strategy to prevent pedestrian injuries and death is to use intrusive interruptions that warn distracted pedestrians directly on their smartphones. To this end, we developed StreetBit, a Bluetooth beacon-based mobile application that alerts distracted pedestrians with a visual and/or audio interruption when they are distracted by their smartphones and are approaching a potentially-dangerous traffic intersection. In this paper, we present the background, architecture, and operations of the StreetBit Application.

Autonomous vehicles (AVs) rely on on-board sensors and computation capabilities to drive on the road with limited or no human intervention. However, autonomous driving decisions can go wrong for numerous reasons, leading to accidents on the road. The AVs lack a proper forensics investigation framework, which is essential for various reasons such as resolving insurance disputes, investigating attacks, compliance with autonomous driving safety guidelines, etc. To design robust and safe AVs, identifying the actual reason behind any incident involving the AV is crucial. Hence, it is essential to collect meaningful logs from different autonomous driving modules and store them in a secure and tamper-proof way. In this paper, we propose AVGuard, a forensic investigation framework that collects and stores the autonomous driving logs. The framework can generate and verify proofs to ensure the integrity of collected logs while preventing collusion attacks among multiple dishonest parties. The stored logs can be used later by investigators to identify the exact incident. Our proof-of-concept implementation shows that the framework can be integrated with autonomous driving modules efficiently without any significant overheads.