Search

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called "MFO-SDN-EC", Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm's superiority over several current approaches in terms of assessment measures. [ABSTRACT FROM AUTHOR]

Cloud federation is the place where the cloud service providers could supply their resource deficiency from other members and offer their extra resources to other members of the federation in case of necessity. From the viewpoint of maximum use of resources, resource pricing is one of the main challenges in cloud computing which affects the utilization of resources and is one of the methods of resource management. As far as pricing is effective on the service providers' profit, the appropriate pricing method will create proper profit for the providers in the federation and lead to optimum use of resources. In addition, the welfare of service providers will also increase, and the Quality of Services (QoS) in the federation will be enhanced. In the present study, first, we provide a method based on linear programming for the distribution of requests between members of the federation; then inspired by the concepts of macroeconomic, we explain a model for the evaluation of cloud service providers and provide a meta-heuristic algorithm for service pricing. The proposed algorithm utilizes the results of the evaluation to offer prices to the service providers and provides the best price based on the results of the evaluation to the cloud service providers to maximize their profit. In addition, the proposed algorithm manages the number of shared resources of providers in proportionate to the requests and price. Finally, a set of tests will be performed on the introduced system. [ABSTRACT FROM AUTHOR]

With increasing growth in IoT, the number of devices connected to the Internet is constantly growing. Moreover, the increase in the volume of data and their transmission through the Internet of Things, as well as the existence of inadequate bandwidth, limits cloud-based storage and data processing. Both fog and cloud computing provide the storage space, application, and data for users; however, fog is more proximate to the end user with wider geographical distribution. When bringing the computing resources closer to the required location in the fog environment, the efficiency of the system increases, and the distance at which data must be transmitted decreases. On the other hand, implementing IoT applications and satisfying the requests of end users in fog computing will create new challenges in resource allocation and dynamic resource provisioning. The flexible and usually automatic mechanisms require the determination of required virtual resources to minimize the resource consumption and service level agreement (SLA). In this paper, we introduce a framework for increasing resource management efficiency in the IoT ecosystem based on deep reinforcement learning (DRL). The proposed deep neural network (DNN) method for estimating value functions improves adaptability to different oscillating conditions, learns past sensible strategies, and as a self-learning adaptive system by replicating interactions with the fog environment. The DRL algorithm finds the best destination for implementing IoT services to compromise between minimizing average power consumption, minimizing average service latency, reducing costs, and balancing resource allocation. Finally, through simulations, we show that under different loading rates, the policy used compared to other comparable solutions is to increase utilization and reduce the rate of delay, while ensuring an acceptable level of service quality. [ABSTRACT FROM AUTHOR]

Software defined networks and the Internet of Things (IoT) are two major and emerging developments in networking that have much in common and their survival depends on each other. Software Defined Networking (SDN) is one of the 5G enabling innovations that can help design complex, manageable, cost-effective and adaptable networks. On the other hand, Edge Computing (EC) will do automatic analytical computing on data from sensors, network switches, or other devices instead of waiting for data to be returned to a centralized data store. The IoT also requires a decentralized Internet, as the demand for real-time data analysis is growing and centralized processing systems are being overrun. So, it is necessary to provide a solution to encompass the advantages of EC, IoT and SDN simultaneously, as an integrated platform. In this research, after presenting a brief explanation about the key points of the SDN, IoT, EC and related concepts, and recent articles in this regard are investigated. This systematic literature review (SLR) study focuses on different frameworks and platforms that meet the mentioned requirements by considering the advantages of integrating EC, SDN, and IoT technologies. This platform provides centralized management of heterogeneous devices and architectures and supports the problem of resource limitations in IoT. The short output of this literature contains the following: (1) presenting a summary of review studies and research articles in this area which have been published from 2013 till 2021; (2) providing some key technical questions; (3) presenting some different technical classifications to categorizing the characteristics and features of EC and SDN in IoT; (4) discussing the key challenges; (5) Presenting future directions for research and open issues. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is an ecosystem that can improve the life quality of humans through smart services, thereby facilitating everyday tasks. Connecting to cloud and utilizing its services are now public and common, and the experts seek to find some ways to complete cloud computing to use it in IoT, which in next decades will make everything online. Fog computing, where the cloud computing expands to the edge of the network, is one way to achieve the objectives of delay reduction, immediate processing, and network congestion. Since IoT devices produce variations of workloads over time, IoT application services will experience traffic trace fluctuations. So knowing about the distribution of future workloads required to handle IoT workload while meeting the QoS constraint. As a result, in the context of fog computing, the main objective of resource management is dynamic resource provisioning such that it avoids the excess or dearth of provisioning. In the present work, we first propose a distributed computing framework for autonomic resource management in the context of fog computing. Then, we provide a customized version of a provisioning system for IoT services based on control MAPE‐k loop. The system makes use of a reinforcement learning technique as decision maker in planning phase and support vector regression technique in analysis phase. At the end, we conduct a family of simulation‐based experiments to assess the performance of our introduced system. The average delay, cost, and delay violation are decreased by 1.95%, 11%, and 5.1%, respectively, compared with existing solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the use of cloud-based services that are offered in software as a service (SaaS) models by SaaS providers, and irregular access of different users to these cloud services leads to fluctuations in the demand workload. It is difficult to determine the suitable amount of resources required to run cloud services in response to the varying workloads, and this may lead to undesirable states of over-provisioning and under-provisioning. In this paper, we address improvements to resource provisioning for cloud services by proposing an autonomic resource provisioning approach that is based on the concept of the control monitor-analyze-plan-execute (MAPE) loop, and we design a resource provisioning framework for cloud environments. The experimental results show that the proposed approach reduces the total cost by up to 35 %, the number of service level agreement (SLA) violations by up to 40 %, and increases the resource utilization by up to 25 % compared with the other approaches. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks (MANETs), the IEEE 802.11 CSMA/CA is deployed as the primary medium access control (MAC) layer protocol to schedule the access to the wireless medium. The IEEE 802.11 standard was designed with the assumption that nodes would never deviate from the protocol. However, MANET nodes may purposefully show misbehavior at the MAC layer to obtain more bandwidth or degrade the network performance and disrupt the network services. This paper reviews and classifies the most important strategies generating MAC layer misbehavior based on their objectives and operating principles. Then, it examines some of the recent proposed solutions and mechanisms for detecting and preventing MAC layer misbehavior. A comparison of the studied solutions is carried out using a set of critical evaluation metrics. Finally, the paper concludes with a brief summary of key ideas and a general direction that can provide a basis for future work. [ABSTRACT FROM AUTHOR]

In the IEEE 802.11 standard, network nodes experiencing collisions on the shared medium need a mechanism that can prevent collisions and improve the throughput. Furthermore, a backoff mechanism is used that uniformly selects a random period of time from the contention window (cw) that is dynamically controlled by the Binary Exponential Backoff (BEB) algorithm. Prior research has proved that the BEB scheme suffers from a fairness problem and low throughput, especially under high traffic load. In this paper, we present a new backoff control mechanism that is used with the IEEE 802.11 distributed coordination function (DCF). In particular, we propose a dynamic, deterministic contention window control (DDCWC) scheme, in which the backoff range is divided into several small backoff sub-ranges. In the proposed scheme, several network levels are introduced, based on an introduced channel state vector that keeps network history. After successful transmissions and collisions, network nodes change their cw based on their network levels. Our extensive simulation studies show that the DDCWC scheme outperforms four other well-known schemes: Multiplicative Increase and Linear Decrease, Double Increment Double Decrement, Exponential Increase Exponential Decrease, and Linear/Multiplicative Increase and Linear Decrease. Moreover, the proposed scheme, compared with the IEEE 802.11 DCF, gives 30.77% improvement in packet delivery ratio, 31.76% in delay, and 30.81% in throughput. [ABSTRACT FROM AUTHOR]