Instrumenting Wireless Sensor Networks — A survey on the metrics that matter

Yuan, Dingwen; Kanhere, Salil S.; Hollick, Matthias

doi:10.1016/j.pmcj.2016.10.001

Cited by 53 publications

(18 citation statements)

References 63 publications

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“…The fairness index indicates whether the delivery ratio in different SFs is balanced. Specifically, it is calculated in terms of the Jain's fairness index [69] as follows:…”

Section: Summary Of Resultsmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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The author of this dissertation contributed to the development of the heuristic algorithms, the implementation of the simulator, the evaluation through simulation, the analysis of results, and writing the article.Publication IV empirically evaluates the impact of edge computing on emerging interactive applications and services. At the time of writing, there was no clear consensus on what specifically constitutes the edge of the network in edge computing. Thus, we first survey the state of the art in edge computing architectures and determine different deployment options for edge devices. Next, we experimentally evaluate edge computing when available at different physical locations and with different virtualization technologies. Specifically, we characterize the end-to-end latency (including

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“…The fairness index indicates whether the delivery ratio in different SFs is balanced. Specifically, it is calculated in terms of the Jain's fairness index [69] as follows:…”

Section: Summary Of Resultsmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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“…Even though only rare studies focused on the energy consumption of medication adherence monitoring systems, this is still central in this context as it can severely affect the performance and efficiency of the system [149,156]. This can be imagined by taking wearable systems powered by non-rechargeable batteries as an example.…”

Section: Energy Consumption and Lifetimementioning

confidence: 99%

A Review of Medication Adherence Monitoring Technologies

Aldeer

Javanmard

Martin

2018

ASI

112

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Medication non-adherence is a prevalent, complex problem. Failure to follow medication schedules may lead to major health complications, including death. Proper medication adherence is thus required in order to gain the greatest possible drug benefit during a patient's treatment. Interventions have been proven to improve medication adherence if deviations are detected. This review focuses on recent advances in the field of technology-based medication adherence approaches and pays particular attention to their technical monitoring aspects. The taxonomy space of this review spans multiple techniques including sensor systems, proximity sensing, vision systems, and combinations of these. As each technique has unique advantages and limitations, this work describes their trade-offs in accuracy, energy consumption, acceptability and user's comfort, and user authentication.

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“…These diagnostic tools may differ in the type of sniffer nodes, in the storage approach, and in the way the gathered information is transmitted. Specifically, some of the existing solutions use sink sniffer nodes to collect traffic data from the network; others use extra networks of sniffers deployed with the main WSN; others use sniffer nodes that collect data and store it in memory; and, lastly, the most expensive in terms of network installation, send network-related information by wired technologies (e.g., SNIF [ 55 ], SNTS [ 56 ], PDA [ 57 ], LiveNet [ 58 ] and L-SNMS [ 59 ]).…”

Section: Wsn Diagnostic Toolsmentioning

confidence: 99%

Industrial IoT Monitoring: Technologies and Architecture Proposal

Raposo

Rodrigues

Sinche

et al. 2018

Sensors

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Dependability and standardization are essential to the adoption of Wireless Sensor Networks (WSN) in industrial applications. Standards such as ZigBee, WirelessHART, ISA100.11a and WIA-PA are, nowadays, at the basis of the main process-automation technologies. However, despite the success of these standards, management of WSNs is still an open topic, which clearly is an obstacle to dependability. Existing diagnostic tools are mostly application- or problem-specific, and do not support standard-based multi-network monitoring. This paper proposes a WSN monitoring architecture for process-automation technologies that addresses the mentioned limitations. Specifically, the architecture has low impact on sensor node resources, uses network metrics already available in industrial standards, and takes advantage of widely used management standards to share the monitoring information. The proposed architecture was validated through prototyping, and the obtained performance results are presented and discussed in the final part of the paper. In addition to proposing a monitoring architecture, the paper provides an in-depth insight into metrics, techniques, management protocols, and standards applicable to industrial WSNs.

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Instrumenting Wireless Sensor Networks — A survey on the metrics that matter

Cited by 53 publications

References 63 publications

Efficient placement of edge computing devices for vehicular applications in smart cities

Efficient placement of edge computing devices for vehicular applications in smart cities

A Review of Medication Adherence Monitoring Technologies

Industrial IoT Monitoring: Technologies and Architecture Proposal

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