With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, Delay-Sensitive and Compute-Intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of user experience, we can offload DSCI-type workloads to Mobile Edge Computing (MEC) servers for processing. However, offloading massive amounts of tasks will incur higher energy consumption, which is a severe test for the limited battery capacity of devices. In addition, the delay caused by frequent communication between IIoT devices and MEC cannot be ignored. In this paper, we first formulate the stochastic computation offloading problem to minimize long-term energy consumption. Then, we construct a virtual queue using perturbed Lyapunov optimization techniques to transform the problem of guaranteeing task deadlines into a stable control problem for the virtual queue. Based on this a novel Delay-Aware Energy-Efficient (DAEE) online offloading algorithm is proposed, which can adaptively offload more tasks when the network quality is good. Meanwhile, it delays transmission in the case of poor connectivity but ensures that the deadline is not violated. Moreover, we theoretically demonstrated that DAEE can enable the system to achieve an energy-delay tradeoff, and analyzed the feasibility of constructing virtual queues to assist the actual queue offloading tasks. Finally, simulation results show that DAEE performs well in minimizing energy consumption and maintaining low latency, especially for DSCI-type tasks.

Recently, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance is minimized, termed the mobile sensor deployment (MSD) problem, has received a great deal of attention. However, the complexity of the MSD problem remains unknown because no exact proof has been provided before. In this paper, we show that not only the MSD problem, but also its special case, termed the target coverage problem, are NP-hard.

The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing’s potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.