Application modeling for performance evaluation on event-triggered wireless sensor networks

This paper presents an approach for event-triggered wireless sensor network (WSN) application modeling, aiming to evaluate the performance of WSN configurations with regards to metrics that are meaningful to specific application domains and respective end-users. It combines application, environment-generated workload and computing/communication infrastructure within a high-level modeling simulation framework, and includes modeling primitives to represent different kind of events based on different probabilities distributions. Such primitives help end-users to characterize their application workload to capture realistic scenarios. This characterization allows the performance evaluation of specific WSN configurations, including dynamic management techniques as load balancing. Extensive experimental work shows that the proposed approach is effective in verifying whether a given WSN configuration can fulfill non-functional application requirements, such as identifying the application behavior that can lead a WSN to a break point after which it cannot further maintain these requirements. Furthermore, through these experiments, we discuss the impact of different distribution probabilities to model temporal and spatial aspects of the workload on WSNs performance, considering the adoption of dynamic and decentralized load balancing approaches

Adaptive k-cast Scheduling for High-Reliability and Low-Latency in IEEE802.15.4-TSCH

International audienceThe Industrial Internet of Things tends now to emerge as a key paradigm to interconnect a collection of wireless devices. However , most industrial applications have strict requirements, especially concerning the reliability and the latency. IEEE802.15.4-TSCH represents currently a promising standard relying on a strict schedule of the transmissions to provide such guarantees. The standard ISA-100.11a-2011 has proposed the concept of duocast, where a pair of receivers are allocated to the same transmission opportunity to increase the reliability. In this paper, we generalize this approach to involve k different receivers, and we explore the impact of this technique on the performance of the network. We propose an algorithm assigning several receivers for each transmission to increase the probability that at least one device receives correctly the packet. By exploiting a multipath topology created by the routing layer, we are able to reduce the number of transmissions while still achieving the same reliability. We consequently increase the network capacity, and reduce significantly the jitter. Our simulation results highlight the relevance of this k-cast technique in TSCH for the Industrial Internet of Things

LoRa from the city to the mountains: exploration of hardware and environmental factors

LoRa technology is an increasingly popular option for applications that can exploit its low power and long range capabilities. While most efforts to date have studied its characteristics for smart city environments, we take LoRa outside the city limits, exploring how the environment affects its core communication properties. Specifically, we offer two novel parameter explorations to understand first how vegetation affects communication range and second how antennas change radio behavior. Our results provide insight into LoRa in non-urban environments, specifically showing that vegetation dramatically reduces the communication range and that the antenna selection can have a profound effec

Dissecting aortic aneurysm in a young woman

We present a case of thoracic aortic dissection in a 33-year-old woman. The diagnosis was difficult, and the right diagnosis was made 4 months after the initial clinical manifestation. Aortic dissection was caused by necrosis of the media combined with longstanding arterial hypertension. Surgical intervention was performed timely and was effective

Automated estimation of link quality for Lora: A remote sensing approach

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurementsÐa common alternativeÐare impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readily-available multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (&gt;90%) and spatial resolution (10×10m2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20ś40dBm of popular channel models.</p

Automated estimation of link quality for Lora: A remote sensing approach

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurementsÐa common alternativeÐare impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readily-available multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (&gt;90%) and spatial resolution (10×10m2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20ś40dBm of popular channel models.Embedded and Networked System