Intelligent task off-loading and resource allocation for IOE devices in smart environment

The Internet of Everything (IoE) enables the smart environment to monitor multiple geographically distributed IoE nodes. There has been an enormous increase in information flow and communication data due to the rapid rise in the number of IoE devices and the development of cutting-edge technologies, such as the rollout of the Sixth Generation (6G) network. This thesis aims to investigate the problem of optimally allocating computational resources for a stringent smart architecture and establish solutions by efficiently off-loading tasks locally before transferring them to the cloud environment. The focus is on building a conceptual design to establish efficient task off-loading and Resource Allocation (RA) architecture for a smart environment. Firstly, a novel conceptual design called a conventional model for task off-loading and RA is presented. A solution for such a stringent network design is typically extended by presenting a conceptual cloud architecture based on key components such as RA, scheduling and task off-loading for IoE devices. The IoE devices utilise a scheduler to access resources from nearby higher-resourced IoE devices for their task computation. The scheduler allocates incoming requests according to the availability of resources within a cluster of devices or to other devices in nearby clusters. The thesis proposes scheduling algorithms for efficient task allocation and dissemination to find an optimal technique for task off-loading in futuristic networks.
The thesis then presents the design of a state-of-the-art intelligence-based RA model for IoE edge networks. The intelligence in the scheduling algorithm is used to improve proper scheduling decisions that can be made using intelligence behaviour. The focus of the intelligent scheduling algorithm is within the IoE cluster. Theoretical analysis of the proposed intelligence-based algorithm using different techniques helped calculate the allocated task’s success and failure probabilities. The performance of the proposed algorithm is evaluated algorithm provides early detection of any devices that may be acting maliciously, preventing
them from causing any damage to the network. The novel secure and intelligent RA algorithm also establishes the mechanism to secure the task so that the actual device receives the information as intended by the originating device. The Encryption/decryption of tasks at originating and receiving devices respectively is incorporated. An overall secure and intelligent task allocation mechanism maximises the Quality of Service (QoS) and ensures optimal service is provided during each allocation period using real-life IoE network parameters.
The thesis considers a secure resource allocator in order to secure the overall intelligent RA system. The design of the proposed secure and intelligent RA model and scheduling algorithm provides early detection of any devices that may be acting maliciously, preventing them from causing any damage to the network. The novel secure and intelligent RA algorithm also establishes the mechanism to secure the task so that the actual device receives the information as intended by the originating device. The Encryption/decryption of tasks at originating and receiving devices respectively is incorporated. An overall secure and intelligent task allocation mechanism maximises the Quality of Service (QoS) and ensures optimal service is provided during each allocation period.