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.

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.

Abstract Driven by the vision of real‐time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve. These challenges are dealing with solving complex optimization problems including scheduling and processing real‐time applications. Nature‐inspired meta‐heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP‐complete problems, from computationally tractable approximate solutions to real‐time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

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.

Wireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) that forward collected data to the sink. The node’s location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. WSN scenarios, such as tracking, smart cities, and agriculture applications, require location details to accomplish the objective. Assuming a 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line-of-sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node’s location is known prior and can limit the receiving signal in LOS, single, or twice reflection. We start to exploit the sensor node’s geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.

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.

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.