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The internet of things (IoT) is a collection of systems connected to an online network consisting of things. Routing Protocol for Low-Power and Lossy Networks (RPL) is a proactive routing protocol for wireless networks based on distance vectors. Several methods have been proposed for improving RPL protocol security, suffering from lack of accuracy, the authenticity of intrusion detection, and lack of scalability. Therefore, in this research, an intrusion detection system based on deep cellular learning automata and semantic hierarchy is developed to increase RPL protocol security. Semantic hierarchy is used to transform attack features into significant values, and deep cellular learning automata are employed to increase the security of the RPL protocol. Here five datasets related to attacks, including Darknet, "Version Number", "NSL-KDD", "Botnet", and Distributed Denial of Service (DDoS), have been used. Comparing the proposed results on five datasets indicates that the proposed method outperforms its counterparts. Also, the proposed model has been tested on Blackhole, NID, and BoT-IoT datasets based on ANN and CNN's Deep Neural Network. The results of penetration detection accuracy of the proposed method on Blackhole datasets, NID, and BoT-IoT were 99.65%, 99.71%, and 93.75%, respectively, which improved by averages of 0.42% compared to ANN and 0.55% compared to CNN methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, we are witnessing an enormous burst of data due to the ever-increasing number of Internet of Things (IoT) devices. The traditional cloud computing paradigm has failed to scale; to be specific, its latency and bandwidth utilization are remarkably increased and consequently, Quality of Service (QoS) is decreased. On the other hand, the data management scope in fog computing require much more considerations in terms of performance and scalability. This is because of deploying IoT applications over fog nodes considering their resource-limited and heterogeneity. However, to the best of our knowledge, there is not any literature review that systematically categorizes these issues. In this paper, we have presented a classification of data replica placement approaches considering four main categories: framework-based, graph-based, heuristic-based, and meta-heuristic-based algorithms. To sum up, the primary contribution of this study is as follows: studying articles on data replica placement in fog computing, as well as presenting their strengths and weaknesses, providing a comprehensive systematic review of current approaches and categorizing them comprehensively, discussing research challenges, and future works to improve computing and evaluation mechanisms in the fog computing environment. This paper generally provides a classification, briefly explains the reviewed techniques, and then compares these methods in the end. [ABSTRACT FROM AUTHOR]

Over the past few years, Internet of Things (IoT) applications have grown rapidly. The data‐intensive IoT applications that take advantage of cloud servers for computations and data storage will result in higher latency and other network traffic in the Internet core. IoT applications are characterized by their sensitivity to latency. As an example, delays will result in irreparable damage in the medical and healthcare industries. Cloud servers are no longer necessary because cloud computing utilizes fog nodes that are closer to users. Nodes with different hardware capabilities pose a significant challenge since they differ significantly in latency and traffic reduction. This article presented a metaheuristic‐based method using the non‐dominated sorting genetic algorithm II for data‐intensive IoT applications in fog infrastructure. Besides, we provide a new automatic method for managing data replica transmissions, including deploying them in a fog cloud environment. The proposed solution was evaluated in the iFogSim simulator and compared with two other data replica placement methods in different scenarios. The results showed a decrease in latency and cost for data access and an increase in data availability. [ABSTRACT FROM AUTHOR]

The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More fog resources than needed leads to "Over-Provisioning", and fewer fog resources leads to the "Under-provisioning" issue. To this end, an effective deep learning-based resource auto-scaling mechanism has been proposed to manage the number of resources needed to handle dynamic workloads in a fog environment. The simulation results indicated that the proposed solution reduces cost, network usage, and delay violation and increases CPU utilization compared with existing resource auto-scaling mechanisms. [ABSTRACT FROM AUTHOR]

With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the challenging tasks in resource management scope to be considered. In this work, we propose a learning-based resource provisioning approach for managing time-varying workloads of IoT applications in the fog network. Our proposed approach utilises the nonlinear autoregressive (NAR) neural network as prediction method and hidden Markov model (HMM) as a decision-maker to identify scaling decisions to provision the fog resources for serving of workloads of IoT applications. The effectiveness of our proposed solution is evaluated using extension experiments under real-world datasets, and the obtained results from iFogSim toolkit demonstrated that it yields a reduction of the delay and cost and improves resource energy consumption compared with existing baseline mechanisms. [ABSTRACT FROM AUTHOR]

Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero. [ABSTRACT FROM AUTHOR]

Summary: With the ever‐growing use of active internet devices, the Internet of Things (IoT) has achieved vast popularity at present. Authentication technology is essential for the success of integrating IoT and cloud computing. Recently, the adversary capabilities are enhanced, and the current authentication techniques are vulnerable in plenty of possible network attacks. This paper presents a secure and lightweight authentication protocol for IoT‐based cloud environment. The proposed scheme can withstand various attacks and provide secure mutual authentication and anonymity by utilizing secret parameters and biometric. Lightweight crypto modules are adopted to pursue the best energy efficiency. The AVISPA tool is used to evaluate the protocol's performance. The obtained results indicate that the proposed protocol can effectively resist all kinds of known vulnerabilities. [ABSTRACT FROM AUTHOR]

In recent years, Fog Computing (FC) is known as a good infrastructure for the Internet of Things (IoT). Using this architecture for the mobile applications in the IoT is named the Mobile Fog Computing (MFC). If we assume that an application includes some modules, thus, these modules can be sent to the Fog or Cloud layer because of the resource limitation or increased runtime at the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research is conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, when the modules arrive at gateway, then, a Hidden Markov model Auto-scaling Offloading (HMAO) finds the best destination to execute the module to create a compromise between the energy consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the energy consumption, execution cost, delay, and network resource usage shows that the proposed method on average is better than the local execution, First-Fit (FF), and Q-learning based method. [ABSTRACT FROM AUTHOR]

Today a lot of data sensed from the environment by the Internet of things applications. These data need to process with the lowest delay. Mobile devices (MDs) as ubiquitous tools are end devices in the network. These devices with limited resources cannot process all computations locally. Mobile edge computing (MEC) is a good architecture for processing computations in the network's edge. It solves the cloud challenges such as delay, energy, and cost. If MDs could not process the computations, they will offload tasks to the edge or cloud. Research shows that ignoring context information of application, requests, sensors, resources, and network tools cause to not complete the offloading method. In this article, we consider Bayesian learning automata (BLA) with considering context‐aware offloading in MEC with multiuser. BLA learns all states and actions in the network and helps us to improve the offloading algorithm. The contexts are collected using autonomous management as the monitor‐analysis‐plan‐execution loop in all offloading processes. The simulation results indicate that our method is superior to local computing and offload without considering context‐aware algorithms in some metrics such as energy consumption, execution cost, network usage, delay, and fairness. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT)-based applications and the era of 5G networks has led to an exponential increase in the amount of data required for processing the IoT services. The fog computing paradigm has emerged as a distributed computing solution for serving these applications using available fog nodes near the IoT devices. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) requirements that can be deployed on the fog nodes with different resource capabilities in the fog ecosystem, finding an efficient service placement plan is one of the challenging issues to be considered. In this paper, we propose an efficient IoT service placement solution based on the autonomic methodology for deploying IoT applications on the fog infrastructure. Our proposed solution monitors the QoS requirements of IoT services and capabilities of available fog nodes to determine an efficient service placement plan using the whale optimization algorithm (WOA) meta -heuristic technique. Besides, our evolutionary-based mechanism utilized the throughput and the energy consumption as objective functions for finding desirable IoT service placement plan while meeting the QoS requirements of each IoT service. Also, we develop an autonomous service placement framework according to a three-tier architecture of the fog ecosystem to show the interaction between the main components of the IoT device and fog layers for deploying IoT applications. The simulation results demonstrate that the proposed solution increases the resource usage and service acceptance ratio and reduces the service delay and the energy consumption compared with the other metaheuristic-based mechanisms. [ABSTRACT FROM AUTHOR]

• Designing an autonomous dynamic service provisioning manager (DSPM) • Proposing an efficient dynamic service provisioning approach using learning automata. • Validating the proposed solution in terms of delay, cost, and delay violation metrics. Recent advances in the Internet of Things (IoT) technology have contributed to growing the number of IoT applications in various scenarios, e.g., buildings, cities, healthcare, wearable devices, and businesses to change the way work and live. The fog computing model has appeared as an appropriate distributed platform to service at the edge of the network using the resource capacities to support and execute the real-time IoT applications. One of the most challenging issues in IoT application management is the dynamic service provisioning problem to fix changes in IoT application resource usage patterns. In this paper, we propose an efficient dynamic service provisioning mechanism using the learning automata technique to determine the service provisioning decisions to deploy or release the IoT applications over the heterogeneous and dynamic fog infrastructure. Besides, we design an autonomous dynamic service provisioning manager (DSPM) that follows a self-management control loop to provision the IoT applications on the fog infrastructure. The simulation results obtained using synthetic and real-world traffic traces show that our proposed algorithm effectively reduces service delay, cost and service delay violation compared to other algorithms. [ABSTRACT FROM AUTHOR]