Allocating Sensor Network Resources Using an Auction-Based Protocol

Trevathan, Jarrod; Hamilton, Lee; Read, Wayne

doi:10.4067/s0718-18762016000200005

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…We are working on an initiative to create an affordable, remote aquatic environmental monitoring platform [28][29][30][31][32]. Our work thus far has shown that such an approach is indeed possible with current technologies.…”

Section: Introductionmentioning

confidence: 99%

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Trevathan

Read

Schmidtke³

2020

Sensors

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Turbidity is a key environmental parameter that is used in the determination of water quality. The turbidity of a water body gives an indication of how much suspended sediment is present, which directly impacts the clarity of the water (i.e., whether it is cloudy or clear). Various commercial nephelometric and optical approaches and products exist for electronically measuring turbidity. However, most of these approaches are unsuitable or not viable for collecting data remotely. This paper investigates ways for incorporating a turbidity sensor into an existing remote aquatic environmental monitoring platform that delivers data in near real-time (i.e., 15-min intervals). First, we examine whether an off-the-shelf turbidity sensor can be modified to provide remote and accurate turbidity measurements. Next, we present an inexpensive design for a practical light attenuation turbidity sensor. We outline the sensor’s design rationale and how various technical and physical constraints were overcome. The turbidity sensor is calibrated against a commercial turbidimeter using a Formazin standard. Results indicate that the sensor readings are indicative of actual changes in turbidity, and a calibration curve for the sensor could be attained. The turbidity sensor was trialled in different types of water bodies over nine months to determine the system’s robustness and responsiveness to the environment.

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Section: Introductionmentioning

confidence: 99%

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Trevathan

Read

Schmidtke³

2020

Sensors

Self Cite

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“…The development of a non-cooperative auction theory algorithm was presented in [131] to allocate the network bandwidth/time-slot resources among the devices in a wireless sensor network system. In the study, bandwidth represents the commodity that the sensor devices in the network are bidding for, and the BS allocated bandwidth to the highest bidders among the devices.…”

Section: ) Examples Of Non-cooperative Games Solutionsmentioning

confidence: 99%

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Olatinwo

Joubert

2022

IEEE Access

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In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in the internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Consequently, the resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of artificial techniques, such as optimization approaches and game theory approaches, for resource management are common while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.

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“…A priority-based method is proposed by [27] to allocate network resources, maintaining fairness between the devices' communication. Although this proposal acts centrally and does not address traffic differentiation, the BS operates an auction-driven online selection scheme to define priority access considering characteristics such as cost, precision, location, and amount of data collected.…”

Section: Related Workmentioning

confidence: 99%

“…This section summarises the most relevant research works, addressing different issues: cluster scheduling [ 14 , 15 ], configuration of communication structures [ 10 , 11 , 16 , 17 , 18 , 19 ], data-load-based congestion control [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], and environmental monitoring network solutions for event-driven applications [ 28 , 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Reconfiguration of Cluster-Tree Wireless Sensor Networks to Handle Communication Overloads in Disaster-Related Situations

Lino

Leão

Soares

et al. 2020

Sensors

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The development of flexible and efficient communication mechanisms is of paramount importance within the context of the Internet of Things (IoT) paradigm. IoT has been used for industrial, commercial, and residential applications, and the IEEE 802.15.4/ZigBee standard is one of the most suitable protocols for this purpose. This protocol is now frequently used to implement large-scale Wireless Sensor Networks (WSNs). In industrial settings, it is becoming increasingly common to deploy cluster-tree WSNs, a complex IEEE 802.15.4/ZigBee-based peer-to-peer network topology, to monitor and control critical processes such as those related to oil or gas, mining, or certain specific chemicals. The remote monitoring of critical events for hazards or disaster detection in large areas is a challenging issue, since the occurrence of events in the monitored environment may severely stress the regular operation of the network. This paper proposes the Dynamic REconfiguration mechanism of cluster-Tree WSNs (DyRET), which is able to dynamically reconfigure large-scale IEEE 802.15.4 cluster-tree WSNs, and to assign communication resources to the overloaded branches of the tree based on the accumulated network load generated by each of the sensor nodes. A complete simulation assessment demonstrates the proposed mechanism’s efficiency, and the results show that it can guarantee the required quality of service level for the dynamic reconfiguration of cluster-tree networks.

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Allocating Sensor Network Resources Using an Auction-Based Protocol

Cited by 5 publications

References 24 publications

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Towards the Development of an Affordable and Practical Light Attenuation Turbidity Sensor for Remote Near Real-Time Aquatic Monitoring

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Dynamic Reconfiguration of Cluster-Tree Wireless Sensor Networks to Handle Communication Overloads in Disaster-Related Situations

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