In modern mobile edge computing (MEC) systems, effectively integrating service caching, task offloading, and service delivery is crucial for ensuring high network quality and enhanced user experience. In this article, we introduce a novel joint optimization framework that employs Uncrewed aerial vehicles (UAVs) as dynamic edge computing nodes, collaborating with service collection units (SCUs) that aggregate service requests from Internet of Things (IoT) devices. In the proposed system, UAVs equipped with small-capacity edge servers can process computational tasks locally or forward them to a base station (BS) via the backhaul when necessary. Both user layers (UEs) and UAVs dynamically cache programs using an adaptive algorithm based on task frequency and program weight, which maximizes cache hit rates and expedites service execution. A joint optimization problem is formulated to minimize the overall end-to-end latency through coordinated task offloading, CPU allocation, caching, and UAV scheduling, subject to energy, storage, and mobility constraints. Our resource-aware task offloading and UAV trajectory planning further enable efficient service responsiveness and minimize reliance on the backhaul link. The overall objective is to reduce maximum task latency and backhaul congestion across the network. Extensive simulation results show that our framework significantly outperforms standard baselines, including random caching, most caching, and calculation delay and energy consumption multiobjective optimization problem (CDECMOP), achieving up to$18.5\%$lower energy consumption and$23.6\%$lower latency across diverse network scenarios. These findings underscore the effectiveness of the proposed UE-UAV cooperative caching and computation strategy for scalable, energy-efficient, and low-latency UAV-assisted MEC systems.

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones.

The integration of emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and blockchain is transforming the landscape of modern healthcare. These technologies enable real-time monitoring, data-driven diagnostics, personalized treatment, and remote patient care, leading to more efficient and accessible healthcare delivery. This paper presents a comprehensive review of smart and advanced healthcare solutions driven by these technologies, exploring their architecture, applications, benefits, and limitations. It categorizes healthcare use cases into critical domains, including wearable health devices, intelligent diagnostics, telemedicine, and emergency response systems. Furthermore, the paper critically examines key challenges such as interoperability, energy efficiency, data quality, security, and ethical concerns. To address these issues, it discusses current solutions and highlights future research directions essential for scalable and sustainable healthcare innovation.

Industry 5.0 represents a significant advancement from Industry 4.0, emphasizing improved cooperation between humans and machines, eco-friendly practices, and customized solutions in manufacturing. In contrast to earlier research on Industry 4.0, this survey provides a thorough examination of the function of Machine Learning (ML) in Industry 5.0 by examining the ways in which various learning paradigms facilitate sustainable production, human-machine cooperation, and customized industrial solutions. We first examine cutting-edge learning approaches, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning methodologies, and their role in advancing Industry 5.0 objectives. Next, we emphasizes the incorporation of ML techniques to improve manufacturing productivity and optimize decision-making processes. Even with advancements, Industry 5.0 may suffer several challenges and limitations, such as intelligent decision-making, security, data privacy, etc. To address the challenges of Industry 5.0, it is crucial to comprehend its protocols, which this survey examines alongside relevant technologies. Therefore our survey also explores the primary challenges encountered when incorporating ML into Industry 5.0, such as integrating data, ensuring interoperability, and maintaining sustainability. Furthermore, the study elaborates on how these techniques drives substantial progress in various emerging applications by enhancing operational procedures and enabling innovative capabilities. Finally, we discuss potential research routes to overcome these hurdles and achieve the practical implementation of Industry 5.0.

Unmanned Aerial Vehicles (UAVs) have become increasingly pivotal in various Internet of Things (IoT) implementations due to their dynamic mobility and adaptability. This article examines the deployment of a rotary-wing UAV to efficiently manage data retrieval from dispersed IoT nodes within a UAV-assisted IoT framework. To support energy-efficient data collection, we incorporate uplink Non-Orthogonal Multiple Access (NOMA), enabling multiple IoT nodes to transmit data simultaneously. The core objectives of our research include improving the UAV navigational path, reducing energy consumption, reducing mission duration, and improving overall data collection efficiency. However, these goals present considerable difficulties, as conventional optimization techniques often fall short when faced with unpredictable time slots, resulting in a vast range of decision variables and the complexity of non-convex functions. We approach these challenges by framing the optimization tasks within a Markov Decision Process (MDP), employing the Deep Deterministic Policy Gradient (DDPG) algorithm to develop adaptive, robust UAV control strategies. This methodology facilitates dynamic adjustment of UAV operations, ensuring effective data collection and management. Numerical analysis validates the effectiveness of our method, showcasing significant improvements over existing relevant works.

Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing surveys or tutorials have addressed the importance of ML- or DL-based techniques for LoRaWAN. This article provides a comprehensive survey of current LoRaWAN challenges and recent solutions, particularly using DL and ML algorithms. The primary objective of this survey is to stimulate further research efforts to enhance the performance of LoRa networks and facilitate their practical deployments. We begin by emphasizing the characteristics of LoRaWAN compared to other LPWAN technologies and then examine the technical specifications of LoRaWAN that have been released so far, as well as the current research trends. Furthermore, we discuss an overview of the most utilized DL and ML algorithms for overcoming LoRaWAN challenges. We also present an interoperable reference architecture for LoRaWAN and validate its effectiveness using a wide range of applications. Additionally, we shed light on several evolving challenges of LoRa and LoRaWAN for the future digital network, along with possible solutions. Finally, we conclude our discussion by briefly summarizing our work.

Unmanned Aerial Vehicles (UAVs) have become vital tools for data collection in Internet of Things (IoT) networks, enabling efficient monitoring and information acquisition across various domains. However, UAV-assisted IoT networks often face significant challenges such as high data loss, latency, and resource inefficiency due to inadequate buffer management and dynamic service time (DST) allocation for IoT nodes. Existing approaches frequently overlook critical factors such as IoT nodes' energy levels, UAVs' energy constraints, and the dynamic data generation rates of IoT devices. To address these challenges, this article introduces a novel strategy for dynamically optimizing UAV trajectories by integrating real-time data from ground nodes and UAV energy levels. Given the DST and buffer constraints (BC) of IoT devices, optimizing UAV trajectories for data collection is a complex problem. We propose an optimization framework that strategically plans UAV trajectories to minimize service time at designated Rendezvous Points (RPs) and bypasses RPs when necessary to reduce the overall trajectory path, taking into account buffer status and dynamic service time adjustments. To efficiently solve this optimization problem, we employ a meta-heuristic technique known as Path Cheapest Arc with Guided Local Search (PCA-GLS) for UAV route planning within predefined time windows. Extensive simulations demonstrate the effectiveness of our proposed solution in optimizing UAV trajectories and improving data collection performance compared to existing algorithms such as BA-UAV, ACO-MS, and NSGA-II.

The Internet of Things (IoT) has been increasingly applied in various applications in recent years. In IoT, many tasks are performed for a load operation, such as creating a cluster, preserving convergence/connectivity issues, etc. However, the energy consumption rate is also high due to more traffic in dense networks. Generally, a traditional IoT node's battery power capacity is limited due to a short-range cycle. To address the energy shortage problem, researchers have tackled it through the Solar Powered (SP) energy harvesting technique. This method provides abundant energy to the IoT nodes at a lower cost. One issue arises from the target coverage area, which requires that each target must have at least one node for continuous monitoring in a given area. To address these issues, we have designed an effective solution called the Efficient Scheduling Target Coverage (ESTC) algorithm. This approach consists of various cover sets that work in an interleaving way. If only some node sets need to be active to satisfy coverage constraints, then there is no need to activate all sets simultaneously. ESTC provides robust coverage awareness with a perpetual network lifetime using scheduling techniques. Furthermore, the proposed work also promotes a green IoT network.

Unmanned Aerial Vehicles (UAVs) are like modern weather surveyors, flying through the skies and collecting valuable atmospheric data with their instrumentation. However, collecting accurate timing data involves a delicate balance between energy conservation and high-speed operation across a variety of computing devices. In order to address this issue, we created a unique Federated Learning computer system designed for the development of weather forecasting using data collected from UAVs. This dynamic variation now reduces operational completion instances through outlier detection and a softmax weight allocation. In the long run, the weather forecast’s overall performance and effectiveness have been greatly improved. A splendid innovation in our framework is creating Federated Weighted Average (FedWAvg) set of rules, specifically designed to deal with delays due to outliers or inaccuracies at some stage in the discussion. FedWAvg allows quick convergence without compromising statistical accuracy, increasing the trustworthiness of weather forecasting in real-world conditions. Putting those advancements together, we have made significant improvements to make weather forecasting more reliable and useful, for the people and the businesses that rely on weather forecasting to get better rewards.

Unmanned Aerial Vehicles (UAVs) play a vital role in modern Internet of Things (IoT) ecosystems by providing services like task offloading. Although simultaneous execution can be done through resource optimization and offloading, it is crucial to choose which task to be executed where considering a trade-off between energy consumption and execution delay. Considering these constraints, in this work, we propose a Federated Learning (FL) aided framework for multi-device task offloading in UAV-enabled edge networks while reducing energy consumption and task execution delay. The proposed approach involves a two-step process to execute tasks on various computing devices. In the first step, a task's priority is determined, considering factors like delay deadlines (maximum allowed delay) and resource requirements which include memory, storage and CPU instructions. In the second step, we leverage FL to dynamically calculate energy and delay of the task for different paradigms. This approach aims to maintain a balance between minimizing energy consumption by the UAV and reducing task execution delay. The effectiveness of the proposed framework is evaluated through experiments in terms of energy efficiency and end-to-end execution delay of the UAVs.

The technological advancements in Unmanned Aerial Vehicles (UAVs) have brought significant changes in various domains including surveillance, agriculture and disaster rescue. The convergence of Machine Learning (ML) and UAV networks contributes significantly to their automation and decision-making capabilities. Traditional ML techniques are centralized, i.e., they face many issues such as privacy due to data sharing, scalability and single-point failure. In this work, we propose a hierarchical decentralized framework for Federated Learning (FL) that addresses all the aforementioned issues. The proposed framework, Cluster Based Pseudo Hierarchical Decentralized Federated Learning (PHDFL), is tailored to UAV networks for learning where the learning and aggregation tasks are distributed among different UAVs in the network. This also introduces the concept of pseudo-hierarchy as all the UAVs are at the same level due to Decentralized Federated Learning (DFL) but the learning happens in a hierarchical manner where the network is divided into clusters and each cluster has a cluster head which in then communicates with other cluster heads. The effectiveness of the proposed framework is evaluated through experiments in terms of learning time, energy consumed and convergence of the model.

In recent times, Unmanned Aerial Vehicles (UAVs) have played a significant role in various fields like agriculture, defense, environmental monitoring, and many more. By minimizing the latency, energy consumption and improving the quality of services (QoS) while offloading the tasks, UAVs have played an outstanding role in the Internet of Things (IoT). Despite having a huge advantage, as the UAVs have limited computation, they cannot handle all the tasks that require intensive computation. To tackle the above problem, we have adopted the Deep Reinforcement Learning (DRL) technique in the UAV network, which helps in handling computationally expensive tasks by sharing the workload among the UAVs. The DRL-based strategy helps in reducing latency and energy consumption while maximizing the resource utilization of UAVs. Experiments have shown the significance of the adopted DRL strategy in reducing energy consumption by at least 16% compared to traditional algorithms.

Over the last few decades, Internet of Things (IoT) has become the spotlight area of research within the Industries and Academics. Primarily, IoT devices are characterized by small and nonscalable resources, including low processing capabilities, less internal memory, and short battery life. However, IoT applications demand extensive storage and faster response to ensure seamless and interoperable communication. Hence, Edge Computing and data/task offloading among the edge or cloud servers become promising, while also posing critical research challenges for edge-enabled large-scale IoT ecosystems. Several research activities have addressed the difficulties of determining an efficient and scalable data offloading strategy utilizing edge and cloud computing-supported technologies. This article focuses on the state-of-the-art edge IoT data offloading techniques, and optimization models in the heterogeneous IoT environment. We examine how edge and cloud-supported technologies can handle delay-sensitive IoT applications efficiently. Moreover, we introduce an IoT-based healthcare use case scenario to explain edge data execution and resource provisioning in IoT networks. Finally, we discuss several challenging issues and possible solutions to establish interoperable communication and computation for IoT applications.

Industry 5.0 aims to elevate industrial operations, businesses, and revolution to new heights by promoting sustainable, resilient, and human-centric practices. The popularity of Industry 5.0 is reflected in the increasing demand for real-time and near-edge processing in most latency-critical Industrial Internet of Things (IIoT) applications. However, designing an efficient task priority assignment strategy and accordingly executing tasks within the stipulated deadline is complex and challenging. Therefore, in this work, we design a novel Multi-device Edge Service Provisioning (MESP) framework for optimizing delay in Industry 5.0. At first, the MESP strategy classifies edge executable tasks using multi-nomial probability theory. Then, we prove that multi-device service demand at the edge devices is an NP-Hard problem, which requires approximate algorithms for finding near-optimal solutions. To follow this, we propose a game-theoretic approach where multiple IIoT devices request various services simultaneously while maximizing their mutual satisfaction. We also examine the structural property of the proposed game and show how this property helps in achieving the equilibrium point of the proposed game with finite improvement steps. Experimental analysis shows that MESP reduces computational overhead and end-to-end execution delay by 20-30% compared to standard algorithms.

Industry 5.0 is the next stage in industrial evolution, collaborating between human ingenuity and intelligent technologies to provide manufacturing solutions. Integrating modern technology like Artificial Intelligence (AI), robotics, and the Internet of Things (IoT) into manufacturing and production processes characterizes Industry 5.0. On the other hand, edge computing provides real-time data processing and analysis at the networks edge, closer to the data source and a vital component of Industry 5.0. Edge computing enables Industry 5.0 to access and communicate information about their industrial sectors using more accessible, standard hardware and software resources. However, no recent survey papers have examined the importance of edge computing in Industry 5.0. This study aims to fill that gap by presenting a survey on the importance of edge computing in Industry 5.0 and discussing a variety of technologies that could be used to implement and support this new industrial paradigm. First, we outline an overview and fundamentals of edge computing in Industry 5.0 architecture. Then objectives of Industry 5.0 are summarized to address various research challenges, including privacy, human-robot co-working, sustainability, and robust networks. Afterwards, this paper provides an extensive overview of emerging technologies for Industry 5.0, such as collaborative robots, AI, Digital Twins, and many more. In addition, this survey highlights various open research challenges and potential solutions that should be addressed further to achieve Industry 5.0.

Nowadays, by integrating the Industrial Internet of Things (IIoT) with fog networks, companies can efficiently manage the increasing data traffic and enhance the capabilities of sensing devices. However, control of critical IIoT applications has become difficult because of the increasing demand for technology during the Industry 4.0 revolution and the use of fog computing. To address this issue, we introduce an efficient resource provisioning strategy called Fair Scheduling and Computation Co-offloading (FSCC) for executing the maximum number of tasks within the corresponding deadline while achieving network stability. Initially, we formulate the task scheduling problem as a stochastic problem and devise a novel optimization framework by exploiting the Lyapunov optimization technique. A two-phase task offloading strategy is also proposed to efficiently offload scheduled tasks to suitable computing devices in fog networks. The proposedFSCCstrategy combines devices’ current state information and a collaborative fog-cloud infrastructure for controlling network parameters and utilizing available fog resources. Experimental results demonstrate that the proposed strategy improves 15-20% end-to-end delay and deadline satisfaction over existing methods.

With the evolutionary development of the latency-sensitive industrial Internet-of-Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this article, we introduce a new deep reinforcement learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Furthermore, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. The experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

Energy consumption for large amounts of delay-sensitive applications brings serious challenges with the continuous development and diversity of Industrial Internet of Things (IIoT) applications in fog networks. In addition, conventional cloud technology cannot adhere to the delay requirement of sensitive IIoT applications due to long-distance data travel. To address this bottleneck, we design a novel energy–delay optimization framework called transmission scheduling and computation offloading (TSCO), while maintaining energy and delay constraints in the fog environment. To achieve this objective, we first present a heuristic-based transmission scheduling strategy to transfer IIoT-generated tasks based on their importance. Moreover, we also introduce a graph-based task-offloading strategy using constrained-restricted mixed linear programming to handle high traffic in rush-hour scenarios. Extensive simulation results illustrate that the proposedTSCOapproach significantly optimizes energy consumption and delay up to 12%–17% during computation and communication over the traditional baseline algorithms.

Considering the increasing number of communicable disease cases such as COVID-19 worldwide, the early detection of the disease can prevent and limit the outbreak. Besides that, the PCR test kits are not available in most parts of the world, and there is genuine concern about their performance and reliability. To overcome this, in this paper, we develop a novel edge-centric healthcare framework integrating with wearable sensors and advanced machine learning (ML) model for timely decisions with minimum delay. Through wearable sensors, a set of features have been collected that are further preprocessed for preparing a useful dataset. However, due to limited resource capacity, analyzing the features in resource-constrained edge devices is challenging. Motivated by this, we introduce an advanced ML technique for data analysis at edge networks, namely Deep Transfer Learning (DTL). DTL transfers the knowledge from the well-trained model to a new lightweight ML model that can support the resource-constraint nature of distributed edge devices. We consider a benchmark COVID-19 dataset for validation purposes, consisting of 11 features and 2 Million sensor data. The extensive simulation results demonstrate the efficiency of the proposed DTL technique over the existing ones and achieve 99.8% accuracy while diseases prediction.

Fog computing is one of the promising technology that could reduce the execution cost and energy consumption of smart industrial Internet of Things (IIoT) devices via a strategy called offloading. However, designing an intelligent offloading strategy for large-scale industrial applications becomes challenging. To address this issue, in this article, we design a novel fog federation, a computation offloading framework for industrial networks called cost-efficient computation offloading (CeCO), where a master fog controller regulates the network and distributes the IIoT data among the fog devices. In particular, we design our cost optimization function as the sum of weighted energy-delay cost of IIoT devices while reaching several constraints. To determine this optimization problem, we first design a frequency control mechanism for the IIoT devices. Then, we introduce a controller-based device adaptation strategy and a policy-based reinforcement learning technique for efficiently controlling emergency-based service demands and accordingly route them toward the fog devices following the shortest path. Experimental results demonstrate the effectiveness of theCeCOstrategy then the baseline algorithms while maintaining the same and even better cost utilization and performance maximization upto 13%–18% for industrial applications.

Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%-20% service delay and 20%-25% of service cost over the standalone fog and cloud frameworks.