Continuous and low-cost broadcast authentication is a fundamental security service for distributed sensor networks. This paper presents a novel development of a continuous and low-overhead broadcast authentication protocol named enhanced Infinite timed-efficient stream-loss tolerant authentication (enhanced Inf-TESLA) protocol, based on the Inf-TESLA protocol, whose continuous authentication is limited to the duration of its keychains. The enhanced Inf-TESLA protocol satisfies important security properties, including lower communication and computational overhead; a continuous generation of keychains without the need to establish synchronization packets; scalability to a large network; and resistance to masquerading, modification, man-in-the-middle, and replay attacks. We also highlighted an unaddressed authentication issue in the last packets of the original TESLA protocol and proposed a corresponding solution. We performed a simulation analysis using JAVA and proved that, compared to the Inf-TESLA protocol, the enhanced Inf-TESLA protocol can continuously authenticate packets for the entire lifetime of the receiver. We also compared the enhanced Inf-TESLA protocol with the original TESLA protocol in terms of time complexity and critical authentication processes. The results revealed the superiority of the enhanced Inf-TESLA protocol over the original TESLA protocol in terms of the message authentication code (MAC) value generation time and packet authentication time, which we believe can significantly improve the lifetime and lower the energy expenditure of Internet of Things devices with limited power sources. INDEX TERMS Continuous authentication, Internet of Things, low overhead, TESLA protocol, time complexity I. INTRODUCTIONThe development of the Internet of Things (IoT) technology has enabled billions of devices around the world to be connected to the Internet to collect and share data, create a level of digital intelligence, and support real-time communication of data [1]. Majority of devices that contribute to the IoT are constrained devices that have access to user information and daily life changes, which makes them vulnerable to cybersecurity attacks. Counter actions include using IoT devices as entry points to access other parts of the network or as a bait to turn turn down the attacker's system down. Constrained devices, such as sensors or smart devices, have limited CPU, memory, and power resources, which restricts the use of security protocols in protecting the privacy of their transferred data [1], [2].The main challenges in securing broadcast communication are source and integrity authentication, verifying that the received data comes from a legitimate source and has not been altered en-route [3]. Furthermore,

Autonomous Vehicles (AVs) have advanced rapidly in recent years as they promise to be safe and minimize the burden coming from the driving task. AVs share the road with various categories of vehicles as Emergency Vehicles (EMVs) (e.g police and ambulance vehicles). When being approached by an active EMV, it is natural to expect all vehicles to cooperate with EMV, such that the EMV travel time is minimized. The decision-making block of an AV includes the responsibility of instructing the AV to change lanes, which is typically handled by the Lane Change Decision (LCD) model. A typical LCD model tends to overlook the presence of EMVs around, as they neglect the impact of the lane change on the EMV utility. To address this challenge , this paper proposes an Emergency Vehicle Aware LCD via utilizing Deep Reinforcement Learning . To our best knowledge, this is one of the pioneering works that propose a DRL solution for the problem, addressing important limitations that have been identified. The proposed solution was evaluated against a rule-based LCD known as MOBIL in terms of safety and level of cooperativeness with the EMV. Some key results found from the comparison between the proposed solution and MOBIL are (1) identical safety levels ,(2) proposed solution is takes far less time to give up the lane when being approached by an EMV, and (3) proposed solution never blocks the path of the EMV, whereas MOBIL occasionally block the path.

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Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.

The evolution of 5G and 6G networks has enhanced the ability of massive IoT devices to provide real-time monitoring and interaction with the surrounding environment. Despite recent advances, the necessary security services, such as immediate and continuous authentication, high scalability, and cybersecurity handling of IoT cannot be achieved in a single broadcast authentication protocol. This paper presents a new hybrid protocol called Hybrid Two-level µ-timed-efficient stream loss-tolerant authentication (Hybrid TLI-µTESLA) protocol, which maximizes the benefits of the previous TESLA protocol variants, including scalability support and immediate authentication of Multilevel-µTESLA protocol and continuous authentication with minimal computation overhead of enhanced Inf-TESLA protocol. The inclusion of three different keychains and checking criteria of the packets in the Hybrid TLI-µTESLA protocol enabled resistance against Masquerading, Modification, Man-in-the-Middle, Brute-force, and DoS attacks. A solution for the authentication problem in the first and last packets of the high-level and low-level keychains of the Multilevel-µTESLA protocol was also proposed. The simulation analysis was performed using Java, where we compared the Hybrid TLI-µTESLA protocol with other variants for time complexity and computation overhead at the sender and receiver sides. We also conducted a comparative analysis between two hash functions, SHA-2 and SHA-3, and assessed the feasibility of the proposed protocol in the forthcoming 6G technology. The results demonstrated the superiority of the proposed protocol over other variants in terms of immediate and continuous authentication, scalability, cybersecurity, lifetime, network performance, and compatibility with 5G and 6G IoT generations.