Resource allocation mechanisms and approaches on the Internet of Things

Ghanbari, Zahra; Navimipour, Nima Jafari; Hosseinzadeh, Mehdi; Darwesh, Aso Mohammad

doi:10.1007/s10586-019-02910-8

Cited by 80 publications

(45 citation statements)

References 104 publications

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“…Literature [15] established an efficient IoT mechanism based on cooperative communication and mobile cloud computing, which minimizes system energy consumption under the constraints of transmission power, mainly by using a joint algorithm of iterative computing and resource allocation to reduce computational complexity. Taking into account the quality of service (QoS) requirements of the IoT system, literature [16] established a resource allocation mechanism based on service level agreements and utilization perception to evaluate and compare different indicators to reduce operating costs and power consumption. Improve system performance.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Mukherjee

Goswami

Khan

et al. 2021

IEEE Internet Things J.

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The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). The Industrial equipment in 6G contains a large number of wireless sensors, which collect a large amount of data by sensing the surrounding environment, but the data is not always useful. The emergence of data mining has undoubtedly found a breakthrough point for extracting effective information from massive data. In the pursuit of lower latency, edge computing has also begun to develop. Eventually, 6G can make intelligent decisions in real-time and realize automated equipment operations. However, with the application of various technologies, the energy consumption of the system has increased, but the energy carried by the sensor is still limited. This paper addresses the energy consumption problem with a system model of industrial wireless sensor networks based on a multi-agent system (MAS) for industrial 6G applications. The method uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, we analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Mukherjee

Goswami

Khan

et al. 2021

IEEE Internet Things J.

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“…Resource allocation is one of the important subjects in the IoT, and the goals are load balancing and minimizing operational cost and power consumption. A systematic literature review (SLR) is provided in the study [19], in which the resources allocation methods in the IoT and used algorithms are investigated.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

Chiang

Hou

2020

Symmetry

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With the rapid development of the Internet of Things (IoT) technology, diversified applications deploy extensive sensors to monitor objects, such as PM2.5 air quality monitoring. The sensors transmit data to the server periodically and continuously. However, a single server cannot provide efficient services for the ever-growing IoT devices and the data they generate. This study bases on the concept of symmetry of architecture and quantities in system design and explores the load balancing issue to improve performance. This study uses the Linux Virtual Server (LVS) and virtualization technology to deploy a virtual machine (VM) cluster. It consists of a front-end server, also a load balancer, to dispatch requests, and several back-end servers to provide services. These receive data from sensors and provide Web services for browsing real-time sensor data. The Hadoop Distributed File System (HDFS) and HBase are used to store the massive amount of received sensor data. Because load-balancing is critical for resource utilization, this study also proposes a new load distribution algorithm for VM-based server clusters that simultaneously provide multiple services, such as sensor services and Web service. It considers the aggregate load of all back-end servers on the same physical server that provides multiple services. It also considers the difference between physical machines and VMs. Algorithms such as those for LVS, which do not consider these factors, can cause load imbalance between physical servers. The experimental results demonstrate that the proposed system is fault tolerant, highly scalable, and offers high availability and high performance.

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“…The literature corpus after application of all filters includes 31 articles. The contents of these studies are further analyzed to identify the main focus of the studies, which are:Patient monitoring: [38,39,40,41,42];Device integration and data interoperability: [43,44,45,46,47,48,49];Smart hospitals: [50,51,52];Operating Room monitoring (Intelligent OR): [18,19,20,21,22,23,24,25,26,27,28];QoS-Aware Middlewares: [53,54,55,56,57]. …”

Section: Related Workmentioning

confidence: 99%

“…The solution is composed by a controller nodes that monitor and manage OpenFlow switches to configure its queue priority. Finally, Ghanbari et al [57] present a systematic literature review focusing resource allocation in IoT. The authors demonstrate the different resource allocation strategies in the IoT field showing they belief that in the future self-adaption might become a trend for IoT systems.…”

Section: Related Workmentioning

confidence: 99%

On Providing Multi-Level Quality of Service for Operating Rooms of the Future

Rodrigues

Righi

Costa

et al. 2019

Sensors

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The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

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Resource allocation mechanisms and approaches on the Internet of Things

Cited by 80 publications

References 104 publications

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

On Providing Multi-Level Quality of Service for Operating Rooms of the Future

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