An energy-aware clustering method in the IoT using a swarm-based algorithm

Sadrishojaei, Mahyar; Navimipour, Nima Jafari; Reshadi, Midia; Hosseinzadeh, Mehdi; Ünal, Mehmet

doi:10.1007/s11276-021-02804-x

Cited by 29 publications

(22 citation statements)

References 65 publications

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“…24 Numerous issues have arisen in cloud infrastructures due to the enormous increase in the number of IoT devices. 25 Fog computing may be used to expand cloud computing's storage and processing capabilities by acting as a middle layer between consumer devices and the cloud. 26 It is a useful addition to cloud computing since it allows users to provide services and resources outside of the cloud, close to their end devices.…”

Section: Fog Computingmentioning

confidence: 99%

“…It is employed in a variety of settings, including smart manufacturing and automotive ad hoc networks 24 . Numerous issues have arisen in cloud infrastructures due to the enormous increase in the number of IoT devices 25 . Fog computing may be used to expand cloud computing's storage and processing capabilities by acting as a middle layer between consumer devices and the cloud 26 .…”

Section: Related Workmentioning

confidence: 99%

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A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

Wang

Arefzadeh

2022

Concurrency and Computation

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Summary The goal of the healthcare system is to offer a dependable and well‐organized solution for improving human's wellbeing. Examining a patient's history can assist clinicians in considering the patient's wants when building a healthcare system and providing service, resulting in increased patient satisfaction. Thus, healthcare is becoming a more competitive sector. Massive data volume, latency, response time, and security susceptibility are all difficulties resulting from this substantial increase in healthcare systems. As a famous distributed structure, fog computing might thus aid in the resolution of such problems. Processing parts are situated among end devices and cloud components in a fog computing infrastructure and run programs. This design is well suited to real‐time and low‐latency applications, like healthcare systems. Because task scheduling is an NP‐hard optimization issue in fog‐based medical healthcare systems, this work proposes a hybrid genetic algorithm and particle swarm optimization (GA‐PSO) strategy. A powerful single‐objective optimization technique is the GA‐PSO. Individuals in a novel generation are formed in GA‐PSO through mutation and crossover operations in GA‐PSO, which uses a redefined local optimization swarm. Hence, it may avoid local minimums and perform well in global searches. The study's goal in fog‐based medical healthcare systems is to lower the makespan and overall response time. The suggested technique is simulated in MATLAB and compared to the GA and PSO methods. The empirical findings confirmed the improved makespan, making the approach appropriate for medical and real‐time systems applications.

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Section: Fog Computingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

Wang

Arefzadeh

2022

Concurrency and Computation

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“…Internet of Things (IoT) is considered a network of networks that connects a variety of devices that can be static or dynamic [1][2] (see Figure 1). These devices also represent the resources of the IoT since they have memories, power sources, connections, etc.…”

Section: Overviewmentioning

confidence: 99%

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

MUNDHER¹,

HADEED²,

Alramli³

2022

MINAR Journal

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The advent of the Internet of Things (IoT) introduces a variety of challenges. One of the most frequent challenges is the consumption of resources (e.g., power and memory). The consumption of resources is considered an important aspect when it comes to the general performance of the system. Therefore, it is important to consider this kind of issue before designing such systems or applications. This research aims to assess the number of resources consumed when having hybrid objects (static and dynamic) in the Internet of Things (IoT). The objects considered in this work can be devices such as sensors, smartphones, or other sensing objects that can be used in exchanging data (resources). The settings of the experiments performed in this work vary including colorful parameters (i.e., object deployment, movement patterns, and routing protocols) and a combination of them. In this work, 4 groups of experiments are designed considering different parameters. The simulations are evaluated in terms of two metrics; the amount of data exchanged and covered areas. These two metrics are used as indicators to measure the consumption of resources. The findings showed that the Gaussian strategy in deploying the static and mobile nodes in the IoT can reduce the consumption of resources (e.g., memory and power) and cover more areas within the simulation environment regardless of the movement pattern and the routing protocols used.

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“…This situation is even worse in the IIoT environment since the main characteristic of IIoT is large equipment (for example, monitors and sensors) [3]. For this reason, several protocols, appliances, and security solutions have been deployed and developed to manage the continuously evolving security threat in IIoT [4]. As we know that this solution is inflexible, complex, hard to manage, and highly proprietary that sequentially makes the network more bloated and ossified.…”

Section: Introductionmentioning

confidence: 99%

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

2022

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Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.INDEX TERMS Industrial Internet of Things, clustering, bald eagle search algorithm, trust aware protocols, security.

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An energy-aware clustering method in the IoT using a swarm-based algorithm

Cited by 29 publications

References 65 publications

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

On the Consumption of Resources of the Hybrid Objects in the Internet of Things (Iot)

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

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