Adel Ahmed

The Industrial Internet of Things (IIoT) provides internet connectivity for instruments, digital machines, and any other manufactured object to enable intelligent industrial operations to achieve high productivity. Securing communications between IIoT devices remains a critical and challenging issue due to the resource-constrained and processing capabilities of sensing devices. Moreover, the traditional group shared key might implement complex mathematical operations that are not suitable for the limited recourse capability of the IIoT device. Furthermore, the standard Diffie–Hellman (DH) and elliptic curve Diffie–Hellman (ECDH), which are the most suited for tiny devices, only work between a pair of IIoT devices, while they are not designed to work among a group of IIoT devices. This paper proposes an authenticated group shared key (AGSK) mechanism that allows a set of industrial objects to establish a common session key over the IIoT. The proposed AGSK utilizes the combiner for the hash function and digital signature, which is implemented in IIoT devices. Additionally, the random oracle model has been used to prove the security of AGSK, while the IIoT adversary model has been used to analyze the AGSK countermeasures against cyberattacks. The results of the performance evaluation showed that the efficiency of the AGSK was reduced by 41.3% for CPU computation time, 45.7% for storage cost, and 40% less power consumption compared to the baseline group key management algorithms.

Vehicles serve as mobile nodes in a high-mobility MANET technique known as the vehicular ad hoc network (VANET), which is used in urban and rural areas as well as on highways. The VANET, based on 5G (5G-VANET), provides advanced facilities to the driving of vehicles such as reliable communication, less end-to-end latency, a higher data rate transmission, reasonable cost, and assured quality of experience (QoE) for delivered services. However, the crucial challenge with these recent technologies is to design a real-time multimedia traffic shaping that maintains smooth connectivity under the unpredictable change of channel capacity and data rate due to handover for rapid vehicle mobility among roadside units. This research proposes a smart real-time multimedia traffic shaping to control the amount and the rate of the traffic sent to the 5G-VANET based on distributed reinforcement learning (RMDRL). The proposed mechanism selects the accurate decisions of coding parameters such as quantization parameters, group of pictures, and frame rate that are used to manipulate the required traffic shaping of the multimedia stream on the 5G-VANET. Furthermore, the impact of the aforementioned three coding parameters has been comprehensively studied using five video clips to achieve the optimal traffic rate value for real-time multimedia streaming on 5G communication. The proposed algorithm outperforms the baseline traffic shaping in terms of peak-signal-to-noise-ratio (PSNR) and end-to-end frame delay. This research will open new comfortable facilities for vehicle manufacturing to enhance the data communication system on the 5G-VANET.