These authors contributed equally to this work. Background: Capsaicin-rich diets are common worldwide. Capsaicin has been shown to have favorable effects on various diseases including atherosclerosis, cardiovascular diseases, stroke, obesity, hyperte

Wireless Sensor Networks - An Introduction | InTechOpen, Published on: 2010-12-14. Authors: Qinghua Wang and Ilangko Balasingham

Low-data-rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio-frequency magnetic field and can be detected by observing the change in radio attenuation. This gives the basis for the radio tomographic imaging technology, which has recently been developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multiobject intrusion detection and localization is a challenging issue. This paper proposes an extended variational Bayesian Gaussian mixture model (VB-GMM) algorithm in treating this problem. The extended VB-GMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects and uses a modified version of the variational Bayesian approach for model estimation. Real-world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multiobject localization algorithm.

In a typical sensor network, nodes around the sink consume more energy than those further away. It is not unusual that limited energy resources available at the nodes around the sink become the bottleneck which confines the performance of the whole network. In this letter, we firstly present our considered bottleneck zone in a general sensor network scenario. Then, the effect of the bottleneck zone on network performance is investigated by deducing performance bounds imposed by the energy resources available inside the bottleneck zone. In this letter, both the performance bound in terms of network lifetime and the performance bound in terms of information collection are explored. Finally, the ways by which network deployment variables may affect the performance bounds are analyzed.

Flooding risk is a threat to many sea-level cities and residential areas in the world. In the city Kristianstad in southern Sweden, a large number of sensors of different types have been deployed to monitor rain and other weather conditions, water levels at sea and lakes, ground water levels, and water flows in the city’s storm-water and sewage systems. All the sensors are enabled by battery and wireless communication, and allow real-time data to be transferred and visualized on a cloud-based Internet of Things (IoT) portal. To better enable the system with capacity of foreseeing upcoming flooding threats and to allow early response from decision-makers, it is desired to build a real-time flood forecast system by utilizing the massive sensor data collected at the IoT portal and data from 3rd party weather forecast service. In this article, we have developed a smart flood forecast system using machine learning and artificial neural networks. The developed forecast system has successfully integrated data from multiple sources and can make accurate flood forecast at distributed locations for the coming days. After being successfully implemented as software product and integrated with the city’s IoT portal, our developed flood forecast system has significantly extended the basic monitoring functions of the city’s IoT infrastructure. This article presents the context of this work, the challenges that have been encountered during our development, our solutions and performance evaluation results. To the best of our knowledge, this is the first large-scale IoT-based real-time flood forecast system that has been enabled by artificial intelligence (AI) and deployed in real world.

Range estimation based on Radio Signal Strength (RSS) has been widely adopted by the indoor localization systems. Many existing works have been devoted to tackle the imprecise and unreliable RSS measurements caused by multipath fading. But there exist only a few works dealing with errors caused by non-line-of-sight (NLOS) radio propagation. In some circumstances, it is common for obstacles (e.g. human movements) to cause NLOS measurements, which could undermine the whole ranging process by introducing significant NLOS errors. In this letter, we propose to use a Gaussian Mixture Model (GMM) to model the distribution of a set of NLOS corrupted range estimations. In the GMM method, the distribution of LOS estimations and the distribution of NLOS estimations are represented by different Gaussian components. Consequently, the ranging quality is improved by employing soft exclusion of those Gaussian components associated with NLOS. An indoor field experiment has been performed to verify the proposed method.