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    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems
    (TECH SCIENCE PRESS, 871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052, 2023) Nain, Zulqar; Shahana, B.; Chaudhry, Shehzad Ashraf; Viswanathan, P.; Mekala, M. S.; Kim, Sung Won
    Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 ms of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.
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    REAS-TMIS: Resource-Efficient Authentication Scheme for Telecare Medical Information System
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2022) Tanveer, Muhammad; Khan, Abd Ullah; Alkhayyat, Ahmed; Chaudhry, Shehzad Ashraf; Bin Zikria, Yousaf; Kim, Sung Won
    The phenomenal growth of smartphones and wearable devices has begun crowd-sourcing applications for the Internet of Things (IoT). E-healthcare is considered the essential service for crowd-sourcing IoT applications that help remote access or storage medical server (MS) data to the authorized doctors, patients, nurses, etc., via the public Internet. As the public Internet is exposed to various security attacks, remote user authenticated key exchange (AKE) has become a pressing need for the secure and reliable use of these services. This paper proposes a new resource-efficient AKE scheme for telecare medical information systems, called REAS-TMIS. It uses authenticated encryption with associative data (AEAD) and a hash function. AEAD schemes are devised specifically for encrypted communication among resource-constricted IoT devices. These features of AEAD make REAS-TMIS resource-efficient. Moreover, REAS-TMIS dispenses with the elliptic curve point multiplication and chaotic map that are computationally expensive operations. In addition, REAS-TMIS renders the functionality of session key (SK) establishment for future encrypted communication between MS and users after validating the authenticity of the user. The security of SK is corroborated employing the well establish random oracle model. Moreover, Scyther-based security corroboration is implemented to show that REAS-TMIS is secure, and informal security analysis is executed to show the resiliency of REAS-TMIS against various security attacks. Besides, a thorough analysis shows that REAS-TMIS, while accomplishing the authentication phase, requires less computational, communication, and storage resources than the related authentication protocol.