A Method for Identifying eHealth Applications Using Side-Channel Information

Vergütz, Andressa; Medeiros, Iago; Rosário, Denis; Cerqueira, Eduardo; Santos, Aldri; Nogueira, Michele

doi:10.1109/globecom38437.2019.9013503

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…Hence, it identifies the applications and configures the network on-the-fly. PRIMUS receives as input the application identified in a previous work [ 21 ], assisting our framework proposed in [ 20 ]. The next subsections detail the network model, the smart healthcare application requirements, and the PRIMUS architecture.…”

Section: The Primus Architecturementioning

confidence: 99%

“…Each device contains applications running on it. PRIMUS receives as input the application identification from our previous work [ 21 ]. In the core network, devices involve base stations, routers, switches, and others.…”

Section: The Primus Architecturementioning

confidence: 99%

“…Hence, PRIMUS needs to monitor the network traffic to establish and control the slice to support the requirements, especially the requirements of s-health. The identification and monitoring of s-health applications follow our previous work [ 20 , 21 ]. Based on the knowledge about the application context, PRIMUS creates the slices routes and adapts resources through NFVs.…”

Section: The Primus Architecturementioning

confidence: 99%

“…Thus, the slice-aware context step gets information about the application context and its requirements. As aforementioned, our previous work [ 21 ] identifies the application that is generating network traffic. Hence, through announce control messages, PRIMUS informs the requirements demanded by each application (we detail control messages in Table 3 from Resource Management Module).…”

Section: The Primus Architecturementioning

confidence: 99%

“…It adapts the network resources by creating and selecting dynamic routes for s-health traffic. The creation of these routes has metrics the flow latency and packet loss rate, being based on the application traffic identification, proposed in the previous work [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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An Architecture for the Performance Management of Smart Healthcare Applications

Vergütz

Prates

Schwengber

et al. 2020

Sensors

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The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G–6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.

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Section: The Primus Architecturementioning

confidence: 99%

Section: The Primus Architecturementioning

confidence: 99%

Section: The Primus Architecturementioning

confidence: 99%

Section: The Primus Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Architecture for the Performance Management of Smart Healthcare Applications

Vergütz

Prates

Schwengber

et al. 2020

Sensors

Self Cite

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A Survey on Long-Range Wide-Area Network Technology Optimizations

et al. 2021

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Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN's performance. We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future.

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