Delayed offloading scheme for IoT tasks considering opportunistic fog computing environment

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

The implementation of artificial intelligence on very tiny chips plays an important role in the future of IoT. Typically, these chips do not conduct artificial intelligence operations locally. They mostly send collected data to a cloud service, where artificial intelligence is implemented for decision making. This leads to a time lag and significant dependency of the system on an internet connection, making it unsuitable for systems requiring immediate action. In a hydroponic system, there is an immediate need to control the speed of a pump to maintain pH level. However, there are many challenges in designing an intelligent system using low- powered chips with low computational power. Therefore, achieving high AI accuracy is very difficult for these devices. Additionally, tiny devices need to communicate with the user to execute IoT operations. To overcome these challenges, a hydroponic system was designed in this study to incorporate an ESP32 chip-based microcontroller with sensors and actuators attached to it, so that AI on edge and IoT tasks can be executed simultaneously. A dedicated android app was implemented to monitor and control the system remotely via IoT. Results show that the predicted pump speed just falls behind the expected speed by an average of 2.94%. The overall designed system was stable and reliable. Komatsuna plants were grown in a hydroponic system and the yield was compared with the plants grown in standard potting compost. The hydroponic system was monitored by the proposed method to produce a higher yield compared to the potting compost.

MEC and IoT are rapidly expanding technologies that offer numerous opportunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and latency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading computations task from IoT devices to MEC servers and efficiently allocating computing resources is a complex problem. IoT tasks may have specific requirements in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our approach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems.

The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE's key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.

Fog computing extends cloud capabilities toward the network edge, enabling low-latency Cyber-Physical-Social System (CPSS) services in domains such as smart cities and healthcare. However, multi-objective task scheduling in fog environments remains challenging due to conflicting goals minimizing execution time, resource costs, and energy consumption combined with the scalability limitations of classical evolutionary algorithms, which often converge slowly and produce poorly distributed Pareto fronts in large networks. To address these issues, this paper introduces FOG-QIEA, a quantum-inspired evolutionary algorithm designed for tri-objective fog scheduling. FOG-QIEA augments adaptive neighborhood mechanisms with quantum-inspired operators, including superposition-based population initialization, rotation-gate–driven updates, and measurement-guided selection, enabling faster and more diverse exploration of the solution space. The proposed model jointly optimizes total execution time, cost (including SLA violations), and energy efficiency while maintaining scalability across CPSS deployments with thousands of IoT tasks. Extensive simulations in iFogSim using realistic CPSS scenarios show that FOG-QIEA outperforms NSGA-II, MMPA-based approaches, and classical adaptive fog schedulers by 20–35% in convergence speed, 15–25% in energy reduction, and achieves significantly improved Pareto diversity. These results demonstrate the potential of FOG-QIEA as a sustainable and efficient scheduling framework, supporting future advancements toward quantum-hybrid optimization in fog and edge networks.

Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (<i>IoT</i>). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (<i>Multizone-DAG-Blockchain</i>) framework is proposed for the fog-based IoT environment. In this context, fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes, thus preserving the energy consumption of the IoT devices. Both IoT and fog nodes are initially authenticated using a non-cloneable physical function- based validation mechanism (<i>DPUF-VM</i>) in which multiple authentication certificates are verified in the blockchain. Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q- Curve algorithm. In the cloud, sensitive data is stored as ciphertext. Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes. The fog node first performs a redundancy analysis using the Jaccard Similarity (JS) measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network (<i>N2IN</i>) algorithm. A lightweight proof-of-authentication (<i>PoAh</i>) algorithm is presented and executed by the optimal consensus node selected by the bi- objective spiral optimization (<i>BoSo</i>) algorithm for transaction validation. The proposed work is modeled in Network Simulator 3.26 (ns-3.26), and the performance is evaluated in terms of energy consumption, storage cost, response time, and throughput.

Data scheduling and resource allocation in LEO satellite networks for IoT task offloading

Bandwidth-Deadline IoT Task Scheduling in Fog–Cloud Computing Environment Based on the Task Bandwidth

This research introduces a novel task classification and scheduling framework for IoT applications in heterogeneous cloud–fog computing environments, leveraging the Trees Social Relations (TSR) Optimization Algorithm. The proposed TSR-based scheduler is designed to minimize makespan, reduce energy consumption, and maximize throughput, addressing the challenges of dynamic workloads and resource heterogeneity. Extensive simulations conducted in the iFogSim environment using the real-world Tesla workload demonstrate that TSR achieves superior performance compared to traditional metaheuristics such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). Specifically, TSR reduces makespan by 21.6%, lowers energy consumption by 24.3%, and enhances throughput by 19.7%, thereby ensuring more efficient utilization of fog and cloud resources. These findings highlight the effectiveness of TSR in meeting the QoS requirements of IoT applications while promoting sustainable computing in fog– cloud ecosystems.

Effective task placement algorithms in cloud-fog networks are required due to the substantial rise in data generated by the proliferation of Internet of Things (IoT) devices. This research introduces a unique Spider Wasp Optimization (SWO)-based method for IoT task allocation that improves performance and resource usage in cloud-fog situations. The SWO method is utilized to dynamically distribute IoT workloads between cloud and fog nodes, guaranteeing optimal load distribution and low latency. It is inspired by the hunting and nesting habits of spider wasps. To evaluate the performance of the suggested method, to carry out extensive simulations and compared them with conventional optimization methods. Our findings show that SWO greatly increases network resource management, decreases total execution time, and increases task placement efficiency. This work demonstrates how bio-inspired algorithms, such as SWO, can be used to tackle challenging IoT task placement problems in cloudfog networks, opening the door to more resilient and scalable IoT ecosystems.

Edge computing allows IoT tasks to be processed by devices with passive processing capacity at the network’s edge and near IoT devices instead of being sent to cloud servers. However, 5G‐enabled architectures such as Fog Radio Access Network (F‐RAN) use smart devices to bring the delay down to even a few milliseconds. This is important, especially in latency‐sensitive applications such as online digital games. However, a trade‐off must be made between the delay and energy consumption. If too many tasks are processed locally on edge devices or fog servers, energy consumption increases because mobile devices such as smartphones and tablets have limited energy charges. This paper proposes a Deep Reinforcement Learning (DRL) method for offloading optimization. In designing states, we consider all three critical components of memory consumption, the number of CPU cycles, and network mode. This makes the modeling aware of the workload of the tasks. As a result, the model matches the requirements of the real world. For each mobile device that submits a task to the system, we consider a reward. It includes the total delay of tasks and energy consumption. The output of our DRL model specifies to which edge/fog/cloud device each task should be offloaded. The results show that the DRL technique produces less resource waste than RL when the number of tasks is very high. In addition, DRL consumes 30% less network resources than the FIFO method. As a result, DRL provides a better trade‐off between offloading and local execution than other methods.

Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

Efficient scheduling of dependent IoT tasks in Edge Computing is challenging due to heterogeneous resources, strict latency requirements, and inter-task precedence constraints. We model task dependencies as a Directed Acyclic Graph (DAG), which turns the scheduling problem into a combinatorial optimization challenge with an exponentially ample search space. To address this, we propose a Task-Dependency Aware Generative Flow Network (TD-GFlowNet) that incrementally constructs valid schedules while learning to sample them in proportion to a multi-objective utility function combining latency, energy consumption, and deadline penalties. Unlike traditional reinforcement learning methods that optimize a single trajectory at a time, TD-GFlowNet efficiently explores diverse scheduling solutions guided by the DAG structure. We benchmark our method against policy-gradient RL, Greedy, and Random baselines under physics-based edge settings. Experimental results show that TD-GFlowNet achieves superior latency-deadline trade-offs compared to RL, while being more robust to server heterogeneity than Greedy policies, thus establishing a generative paradigm for combinatorial optimization in edge task scheduling. Our contribution is not only the adoption of GFlowNets but their DAG-feasible, two-head factorization (task/server) with a physics-grounded multi-objective utility.

This paper presents a conception of designing wireless sensor networks in mesh topology that perform their IoT tasks applying popular WiFi standards. Cheap IoT modules involve compromise between reliability and the price. Phenomena that occur in real wireless sensor network depends on many factors that are sometimes not well defined. Statistical analysis of the packet delays and failure rates for different scenario paths in our experimental network helps to identify anomaly nodes.

In this paper, an energy efficient IoT virtualization framework with peer-to-peer (P2P) networking and edge processing is proposed. In this network, the IoT task processing requests are served by peers. The peers in our work are represented by IoT objects and relays that host virtual machines (VMs). We have considered three scenarios to investigate the saving in power consumption and the system capabilities in terms of task processing. The first scenario is a ‘relays only’ scenario, where the task requests are processed using relays only. The second scenario is an ‘objects only’ scenario, where the task requests are processed using the IoT objects only. The last scenario is a hybrid scenario, where the task requests are processed using both IoT objects and VMs. We have developed a mixed integer linear programming (MILP) model to maximize the number of processing tasks served by the system, and minimize the total power consumed by the IoT network. Based on the MILP model principles, we developed an energy efficient virtualized IoT P2P networks heuristic (EEVIPN). Our results show that the hybrid scenario serves up to 77% (57% on average) processing task requests, but with higher energy consumption compared to the other scenarios. The relays only scenario serve 74% (57% on average) of the processing task requests with 8% saving in power consumption compared to the hybrid scenario. In contrast, 28% (22% on average) of task requests can be handled by the objects only scenario with up to 62% power saving compared to the hybrid scenario.