Dynamic fog node placement optimization using adaptive dynamic pufferfish optimization for real-time IoT networks.

This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio, coverage ratio and sensing error. Data optimization was performed based on the availability of cloud resource, sensor energy, data flow volume and the centroid of each state. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process. This strategy significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods. Different benchmark problems are used to validate the proposed algorithm and the simulation results are compared with state-of-the-art dynamic multiobjective optimization algorithms (DMOAs). The results show that our approach is capable of improving the computational speed by two orders of magnitude while achieving a better quality of solutions than existing methods.

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified.

The Internet of Things (IoT) technology has revolutionized the world where anything is smartly connected and is accessible. The IoT makes use of cloud computing for processing and storing huge amounts of data. In some way, the concept of fog computing has emerged between cloud and IoT devices to address the issue of latency. When a fog node exchanges data for completing a particular task, there are many security and privacy risks. For example, offloading data to a rogue fog node might result in an illegal gathering or modification of users' private data. In this paper, we rely on trust to detect and detach bad fog nodes. We use a Mamdani fuzzy method and we consider a hospital scenario with many fog servers. The aim is to identify the malicious fog node. Metrics such as latency and distance are used in evaluating the trustworthiness of each fog server. The main contribution of this study is identifying how fuzzy logic configuration could alter the trust value of fog nodes. The experimental results show that our method detects the bad fog device and establishes its trustworthiness in the given scenario.

As the most notably emerging wave of Internet deployments, Internet of Things (IoTs) requires mobility support, location awareness and low latency. Fog Computing, also termed edge computing, is a promising solution for IoTs by extending the Cloud Computing paradigm to the edge of Internet. But how to locate fog nodes' sites and determine the scale of each fog node is a main challenge of Fog Computing systems, especially for time sensitive Fog Computing systems. In this paper, we try to deal with this problem by proposing an improved Fast Search and Find of Density Peaks-based fog node location strategy to locate the fog nodes' sites and determine the resources for each located fog node. To this end, we firstly formulate the fog node location of Fog Computing systems as a clustering problem with multi-constraints. Then we propose an improved Fast Search and Find of Density Peaks-based fog node location algorithm, which introduces the time sensitive feature of IoT applications and improves the Fast Search and Find of Density Peaks clustering algorithm to make this clustering algorithm more robustness and adaptability. The experiment results show that our fog node location strategy not also can avoid the NP-hard problem of the traditional server placement strategies, but also has low time complexity.

Blockchain, an emerging decentralized but trusted system, has been applied in many applications, such as the Internet of Things (IoT), supply chains, and smart grid. However, due to the large amount of computing and storage resources blockchain typically demands, its wide deployment is faced with the sustainability issue. To resolve this issue, a viable solution is to empower the IoT system with fog computing that can offload the computation-demanding tasks. Due to varieties of mining tasks and heterogeneous resource capabilities at fog nodes (FNs), it is not an easy task to schedule mining tasks and manages resource allocation among FNs of conflicting interests and independent IoT devices in a distributed manner. In this article, under the framework of matching theory, we design a distributed matching mechanism to maximize the social welfare of resource-restricted FNs while guaranteeing various mining requirements of FNs. Besides, we also provide formal proof regarding the convergence and computational complexity of a distributed matching algorithm (DMA). Finally, we verify that DMA not only improves the social welfare of FNs but also reduces the mining latency compared with the existing algorithms through extensive simulations.

Network optimizations in the Internet of Things: A review

A communicable illness is one that spreads from person to person by a number of means, including contact with blood and body fluids, inhaling an airborne virus, or being bitten by an insect. Internet of Things (IoT)-oriented medical technologies have proliferated in prior decades, and many more applications depend on wearable sensors for versatility. Modern research has examined the use of software-defined networking (SDN)-oriented fog framework to create intelligent health care services. To choose the best fog nodes within the fog layers, however, to anticipate communicable disease detection.To solve this issue, the SDN-enabled Fog Node Selection for Early Detection of Communicable Diseases (FogCom) is created in this study. The FogCom picks the optimal fog nodes using game theory’s Subgame Perfect Nash Equilibrium (SPNE) method. The suggested method also takes into account the three-layered CNN methodology for detecting communicable illnesses. Efficiency analysis shows that the suggested FogCom outperforms earlier baseline techniques.

The ultimate goal of the Internet of Things (IoT) is to provide ubiquitous services. To achieve this goal, many challenges remain to be addressed. Inspired from the cooperative mechanisms between multiple systems in the human being, this paper proposes a bio-inspired self-learning coevolutionary algorithm (BSCA) for dynamic multiobjective optimization of IoT services to reduce energy consumption and service time. BSCA consists of three layers. The first layer is composed of multiple subpopulations evolving cooperatively to obtain diverse Pareto fronts. Based on the solutions obtained by the first layer, the second layer aims to further increase the diversity of solutions. The third layer refines the solutions found in the second layer by adopting an adaptive gradient refinement search strategy and a dynamic optimization method to cope with changing concurrent multiple service requests, thereby effectively improving the accuracy of solutions. Experiments on agricultural IoT services in the presence of dynamic requests under different distributions are performed based on two service-providing strategies, i.e., single service and collaborative service. The simulation results demonstrate that BSCA performs better than four existing algorithms on IoT services, in particular for high-dimensional problems.

Medical Internet of Things (MIoT) offers innovative solutions to a healthier life, making radical changes in people's lives. Healthcare providers are enabled to continuously and remotely monitor their patients for many medial issues outside hospitals and healthcare providers' offices. MIoT systems and applications lead to increase availability, accessibility, quality and cost-effectiveness of healthcare services. On the other hand, MIoT devices generate a large amount of diverse real-time data, which is highly sensitive. Thus, securing medical data is an essential requirement when developing MIoT architectures. However, the MIoT architectures being developed in the literature have many security issues. To address the challenge of data security in MIoT, the integration of fog computing and MIoT is studied as an emerging and appropriate solution. By data security, it means that medial data is stored in fog nodes and transferred to the cloud in a secure manner to prevent any unauthorized access. In this paper, we propose a design for a secure fog-cloud based architecture for MIoT.

Future Internet of Things (IoT) will be a connected network of sensors enabling applications such as industrial automation and autonomous driving. To manage such a large number of applications, efficient computing techniques using fog nodes will be required. A major challenge in such IoT networks is to manage the resource allocation of fog computing nodes considering security and system efficiency. A secure selection of fog nodes will be needed for forwarding the tasks without interception by the eavesdropper and minimizing the task delay. However, challenges such as the secure selection of fog nodes for forwarding the tasks without interception by the eavesdropper and minimizing the task delay are critical in IoT-based fog computing. In this paper, an efficient technique is proposed that solves the formulated problem of allocation of the tasks to the fog node resources using a stable matching algorithm. The proposed technique develops preference profiles for both IoT and fog nodes based on factors such as delay and secrecy rate. Finally, Gale–Shapley matching is used for task offloading. Detailed simulation results show that the performance of the proposed technique is significantly higher than the recent techniques in the literature.