Edge computing is a viable paradigm for supporting the Industrial Internet of Things deployment by shifting computationally demanding tasks from resource-constrained devices to powerful edge servers. In this study, mobile edge computing (MEC) services are provided for multiple ground mobile nodes (MNs) through a time-division multiple access protocol using the unmanned aerial vehicle (UAV)-enabled edge servers. Remotely controlled UAVs can serve as MEC servers due to their adaptability and flexibility. However, the current MEC approaches have proven ineffective in situations where the number of MNs rapidly increases, or network resources are sparsely distributed. Furthermore, suitable accessibility across wireless networks via MNs with an acceptable quality of service is a fundamental problem for conventional UAV-assisted communications. To tackle this issue, we present an optimized computation resource allocation model using cooperative evolutionary computation to solve the joint optimization problem of queue-based computation offloading and adaptive computing resource allocation. The developed method ensures the task computation delay of all MNs within a time block, optimizes the sum of MN's accessibility rates, and reduces the energy consumption of the UAV and MNs while meeting task computation restrictions. Moreover, we propose a multilayer data flow processing system to make full use of the computational capability across the system. The top layer of the system contains the cloud centre, the middle layer contains the UAV-assisted MEC (U-MEC) servers, and the bottom layer contains the mobile devices. Our numerical analysis and simulation results prove that the proposed scheme outperforms conventional techniques such as equal offloading time allocation and straight-line flight.

Edge computing improves the Internet of Vehicles (IoV) by offloading heavy computations from in-vehicle devices to high-capacity edge servers, typically roadside units (RSUs), to ensure rapid response times for intensive and latency-sensitive tasks. However, maintaining Quality of Service (QoS) remains challenging in dense urban settings and remote areas with limited infrastructure. To address this, we propose an software-defined networking (SDN)-driven model for uncrewed aerial vehicle (UAV)-assisted vehicular edge computing (VEC), integrating RSUs and UAVs to provide computing services and gather global network data via an SDN controller. UAVs serve as adaptable platforms for mobile-edge computing (MEC), filling gaps left by traditional MEC frameworks in areas with high vehicle density or sparse network resources. An optimal offloading mechanism, designed to minimize the Age of Information (AoI) while balancing energy consumption and rental costs, is implemented through a soft actor-critic (SAC)-based algorithm that jointly optimizes UAV trajectory, user association, and offloading decisions. Experimental results demonstrate the model’s superior performance, achieving up to 87.2% energy savings in energy-limited settings and a 50% reduction in time-sensitive scenarios, consistently outperforming traditional strategies across various task sizes.

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

Today, there is a crucial need for precise monitoring and prediction of energy consumption at the building level using the latest technologies including Internet of Things (IoT) and data analytics to determine and enhance energy usage. Data-driven models could be used for energy consumption prediction. However, due to high non-linearity between the inputs and outputs of energy consumption prediction models, these models need improvement in terms of accuracy and robustness. Therefore, this work aims to predict energy usage for the optimum outline of building-extensive energy distribution strategies based on a lightweight IoT monitoring framework. To calculate accurate energy consumption, an enhanced hybrid model was developed based on Auto-Regressive Integrated Moving Average (ARIMA) and Imperialist Competitive Algorithm (ICA). The parameters of the ARIMA model were optimized by adapting the ICA technique that improved fitting accuracy while preventing over-fitting on the acquired data. Then, Exponentially Weighted Moving Average (EWMA) was applied to monitor the predicted values. The proposed AIK-EWMA hybrid model was assessed based on the actual power consumption data and validated using mathematical tests. As compared to previous works, the findings revealed that the hybrid model could accurately predict power consumption for green building automation applications.

In vehicular ad-hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, a big storage and high computation platform is needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with resource allocation problem assisted by cloud computation. By integration of EC and software defined networking (SDN), this study provides a new software defined networking edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multi-agent RL-based approach using experience reply. The proposed algorithm stores the users’ communication information and the network tracks’ state in real-time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study.

In real-time medical monitoring systems, given the significance of medical data and disease symptoms, a secure and always-on connection with the medical centre over the public channels is essential. To this end, an edge-enabled In- ternet of Medical Things (IoMT) scheme is designed to improve flexibility and scalability of the network and provide seamless connectivity with minimum latency. The entities involved in such network are vulnerable to various attacks and can potentially be compromised. To address this issue, an authentication scheme comprised of digital signature and Authenticated Key Exchange (AKE) protocol is proposed which guarantees only authorized entities get access to the services available in the medical system. Moreover, to fulfill the privacy-preserving, each entity is mapped to a different pseudo-identity. The non-mathematical and performance analysis show that the proposed scheme is robust against various attacks such as impersonation and replay attacks.