Mobile ad hoc networks (MANETs) have provided the opportunity to establish a temporary mobile network with no fixed centre or support. However, it is a fact that the security of routing systems is greatly affected by the inherent features of MANETs. Ad hoc on-demand distance vector (AODV) is susceptible to attacks such as black hole, grey hole, and wormhole attacks, despite the fact that it is the most prevalent routing protocol in MANETs. This study tries to offer solutions to these issues by enhancing the security and efficiency of the routing process in MANETs through the formulation of the Reinforcement Learning (RL)–Artificial Bee Colony (ABC)–AODV protocol, which is a combination of RL and ABC Algorithm in AODV protocol. According to the network's behaviour, the ABC algorithm selects the optimal path from a list of choices based on a number of factors. The suggested RL–ABC–AODV technique also contains dynamic and adaptive aspects in which the Q-learning and the network's current state are used to adjust parameters in real-time. The RL–ABC–AODV protocol achieved a packet delivery ratio of 99.45%, an end-to-end delay of 0.02 s, a throughput of 250.6 Mbps, and a routing overhead of 0.8, significantly outperforming existing methods.

American scientific publishing group

The Vehicular Internet of Things (VIoT) is a novel idea in the field of connected transportation systems that defines a new paradigm. Nevertheless, even the most modern and complex system will require additional and more powerful layers to protect the conversation from interception and the data from leakage. The centralized models have problems like trust problems and that there might be vulnerabilities and that is why there are attempts to integrate decentralization in operation. The first challenge in the VIoT networks is that the security and openness of such a connection and data transfer are still not well developed. Another issue is the security and dependability of the interaction between the vehicles and the infrastructure, although this issue is magnified by the size of the VIoT network. This research is a blockchain and game theory base research that uses Multi-Agent Reinforcement Learning (MARL) to improve the security and efficiency of the VIoT ecosystem. The technology of blockchain gives a distributed ledger where data cannot be altered or erased. Moreover, the MARL architecture allows for the realisation of better decisions for each of the members of the network. To this, the set that was made up of the smart contracts, Vehicle Units (VUs) and the decentralized servers that form the proposed architecture would be added to allow for the right flow and processing of the data. The blockchain’s decentralized nature provides a guarantee for all secure, immutable data transfers and transparent transactions throughout the network of the VIoT. MARL enables agents to learn and acquire the best strategies as they pass through time, which leads to secure and effective communication among entities. Besides, the implementation of lightweight cryptography techniques and strategic selections according to game theory help to protect and improve the performance of the security system of the VIoT ecosystem.

The model mentioned in the study introduces a new Puzzle Optimization Algorithm-Based Fault Tolerant Scheduling (POAB-FTS) model specifically designed for the cloud computing setting. This pinpoints the significant challenge of achieving reliability, availability, and performance in resource scheduling in the context of failure cases, which is addressed by this novel technique. The POAB-FTS methodology integrates optimization using a game theory approach to perform actions that reduce execution time and failure probability while using a fitness function to provide better decision-making. This work entails an assessment of the main reasons behind task and hardware failures such as lack of resources, hardware defects, and suboptimal implementation. The model covers both active and passive fault tolerance approaches to workload balancing, migration before failure, and migration after failure points. Cooking schedules derived from the POAB-FTS technique are compared against the MAXMIN, ACO, and GTO-FTASS algorithms to present the makespan, failure ratios, and failure slowdowns—giving a comprehensive comparison of the method. As shown in this paper, the POAB-FTS framework can improve the system’s fault-tolerance and adapt resource allocation based on the actual demand thereby stressing its capacity to act as a scalable and cost-efficient solution for the improvement of cloud computing infrastructures. On this contribution, a sound and optimal cloud resource management is made possible.