Traditional data mining algorithms are mostly difficult to handle large data sets, but need to manage big data and discover hidden knowledge. Therefore, the combination of data mining algorithms and IoT technology is ...
详细信息
The large-scale development of internet of Things devices emerged a new computing environment called fog computing to reduce the makespan and cost spent on the cloud devices as a result of distant communication. Howev...
详细信息
ISBN:
(纸本)9781665436663
The large-scale development of internet of Things devices emerged a new computing environment called fog computing to reduce the makespan and cost spent on the cloud devices as a result of distant communication. However, unless the appropriate assignment of tasks is strictly allocated on an available resource of fog nodes, it results in wastage of resources and unachievable quality of service. In this paper, the balance of the most common conflicting objectives in task scheduling that is makespan and cost for the distributed fog-cloud environment is investigated. A novel hybrid squirrel search and invasive weed (HSSIW) algorithm is adapted to assign generated tasks from the internet of Things(IoT) devices at appropriate fog and cloud nodes so that reduction in cost and makespan is assured. The proposed algorithm has been compared with three related state-of-the algorithms such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), and squirrel search algorithm(SS). The experiment conducted on Cloudsim shows that the proposed algorithm reduces makespan 18% better than classic algorithms such as First Come First Serve(FCFS) and Short Job First(SJF) algorithms. Similarly, it has made a reduction in latency 4% better than GA and PSO with optimal cost.
Now a day, internet of Things (IoT) are being used in several areas such as vehicular systems, smart city, healthcare, and supply chain management. Sensor data is stored in the cloud in an IoT system. Therefore, there...
详细信息
Now a day, internet of Things (IoT) are being used in several areas such as vehicular systems, smart city, healthcare, and supply chain management. Sensor data is stored in the cloud in an IoT system. Therefore, there is always a security concern with this data. There are several techniques for maintaining security, but most of them work in a centralized manner. However, blockchain is an emerging technology that works in a distributed manner and is based on peer-to-peer computing. Security can be enhanced using blockchain in any system. Blockchain technology offers decentralised security and privacy. Sensors used in loT systems are resource constrained. Therefore, implementing blockchain in any loT system creates several research challenges. This paper provides a discussion on such performance issues and research challenges while integrating blockchain with loT. The paper also gives some insights to overcome such issues.
Edge computing has revolutionized distributed architectures, enabling workloads to be strategically positioned at the network’s edge, where data is generated and actions are initiated. This paradigm facilitates vario...
详细信息
internet of Vehicles (IoV) is a distributed network supporting V2X communications. Two potential technologies of V2X communications are dedicated short-range communication (DSRC) and cellular network technologies. DSR...
详细信息
Live Video Streaming (LVS) services are critical in supporting real-time applications in internet of Vehicles (IoV) by transmitting real-time generated video content from streaming server to mobile vehicles. However, ...
Live Video Streaming (LVS) services are critical in supporting real-time applications in internet of Vehicles (IoV) by transmitting real-time generated video content from streaming server to mobile vehicles. However, due to restricted spectrum resources and high mobility of vehicles, LVS suffers from notable performance degradation. Accordingly, our paper investigates the problem of LVS-IoV by synthesizing multicasting and Scalable Video Coding (SVC)-based encoding techniques with the goal of maximizing Quality of Experience (QoE). We resolve the LVS-IoV by decoupling it into three sub-problems: vehicle grouping, quality selection, and resource allocation. Firstly, we propose a K-means-based mechanism for vehicle grouping that considers the geographical distribution and dynamic channels of vehicles. Secondly, we develop the Value Decomposition Network for quality selection which obtains the optimal reward with fast convergence via global value function decomposition and centralized training followed by distributed execution. Lastly, we propose a sub-gradient algorithm to achieve optimal resource allocation within a few iteration steps. We build simulation model and perform extensive evaluation, which demonstrates its superiority compared to other competitive methods.
The internet of things (IoT) is a new technology that shapes the future of a world that is rapidly being invaded by smart devices connected to the internet. Such technology has a great role in developing the idea of a...
详细信息
In 2008, blockchain was proposed as the basic technology of Bitcoin, and it is essentially a decentralized database. This technology is widely used in financial industry, data storage, edge computing and other scenari...
详细信息
Software-defined networking (SDN) introduces a novel networking paradigm by decoupling control logic from forwarding operations, resulting in improved network resource management and administration efficiency. However...
详细信息
ISBN:
(数字)9798350375190
ISBN:
(纸本)9798350375206
Software-defined networking (SDN) introduces a novel networking paradigm by decoupling control logic from forwarding operations, resulting in improved network resource management and administration efficiency. However, centralized network design in SDN gives rise to significant security challenges, notably denial-of-service (DoS) attacks. Attackers exploit SDN’s lack of effective message-verification mechanisms to launch DoS attacks by falsifying source address information. In response, this study proposes an innovative approach to internet of Things (IoT) security leveraging software-based networks and collaborative learning. One key strategy involves partitioning the network domain into smaller subdomains, each with its own controller, facilitating the exchange of security rules among subdomains. However, this topology necessitates routing every packet through the subnet’s control node. To continually monitor network traffic data and detect attacks, each controller node employs an integrated learning model. This model combines statistical insights from data streams with artificial neural networks and the Extreme Gradient Boosting Algorithm to anticipate potential attacks. Additionally, the ICOA model selects relevant characteristics for analysis. Through comprehensive performance evaluation, including empirical values, the ensemble model demonstrates superior performance compared to existing approaches. Furthermore, the study compares various models and methodologies, affirming the efficacy of the proposed strategy in accurately identifying distributed Denial-of-Service (DDoS) attacks in SDNs, thereby presenting a groundbreaking approach to SDN security.
internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio...
详细信息
暂无评论