RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Propagation modelling and measurements in a populated indoor environment at 5.2 GHz

There are a number of significant radiowave propagation phenomena present in the populated indoor environment, including multipath fading and human body effects. The latter can be divided into shadowing and scattering caused by pedestrian movement, and antenna-body interaction with bodyworn or hand portable terminals [1]. Human occupants within indoor environments are not always stationary and their movement will lead to temporal channel variations that can strongly affect the quality of indoor wireless communication systems. Hence, populated environments remain a major challenge for wireless local area networks (WLAN) and other indoor communication systems. Therefore, it is important to develop an understanding of the potential and limitations of indoor radiowave propagation at key frequencies of interest, such as the 5.2 GHz band employed by commercial wireless LAN standards such as IEEE 802.11a and HiperLAN 2. Although several indoor wireless models have been proposed in the literature, these temporal variations have not yet been thoroughly investigated. Therefore, we have made an important contribution to the area by conducting a systematic study of the problem, including a propagation measurement campaign and statistical channel characterization of human body effects on line-of-sight indoor propagation at 5.2 GHz. Measurements were performed in the everyday environment of a 7.2 m wide University hallway to determine the statistical characteristics of the 5.2 GHz channel for a fixed, transverse line-of-sight (LOS) link perturbed by pedestrian movement. Data were acquired at hours of relatively high pedestrian activity, between 12.00 and 14.00. The location was chosen as a typical indoor wireless system environment that had sufficient channel variability to permit a valid statistical analysis. The paper compares the first and second order statistics of the empirical signals with the Gaussian-derived distributions commonly used in wireless communications. The analysis shows that, as the number of pedestrians within the measurement location increases, the Ricean K-factor that best fits the Cumulative Distribution Function (CDF) of the empirical data tends to decrease proportionally, ranging from K=7 with 1 pedestrian to K=0 with 4 pedestrians. These results are consistent with previous results obtained for controlled measurement scenarios using a fixed link at 5.2 GHz in [2], where the K factor reduced as the number of pedestrians within a controlled measurement area increased. Level crossing rate results were Rice distributed, considering a maximum Doppler frequency of 8.67 Hz. While average fade duration results were significantly higher than theoretically computed Rice and Rayleigh, due to the fades caused by pedestrians. A novel statistical model that accurately describes the 5.2 GHz channel in the considered indoor environment is proposed. For the first time, the received envelope CDF is explicitly described in terms of a quantitative measurement of pedestrian traffic within the indoor environment. The model provides an insight into the prediction of human body shadowing effects for indoor channels at 5.2 GHz

Relative and contextual contribution of different sources to the composition and abundance of indoor air bacteria in residences.

BackgroundThe study of the microbial communities in the built environment is of critical importance as humans spend the majority of their time indoors. While the microorganisms in living spaces, especially those in the air, can impact health and well-being, little is known of their identity and the processes that determine their assembly. We investigated the source-sink relationships of airborne bacteria in 29 homes in the San Francisco Bay Area. Samples taken in the sites expected to be source habitats for indoor air microbes were analyzed by 16S rRNA-based pyrosequencing and quantitative PCR. The community composition was related to the characteristics of the household collected at the time of sampling, including the number of residents and pets, activity levels, frequency of cooking and vacuum cleaning, extent of natural ventilation, and abundance and type of vegetation surrounding the building.ResultsIndoor air harbored a diverse bacterial community dominated by Diaphorobacter sp., Propionibacterium sp., Sphingomonas sp., and Alicyclobacillus sp. Source-sink analysis suggested that outdoor air was the primary source of indoor air microbes in most homes. Bacterial phylogenetic diversity and relative abundance in indoor air did not differ statistically from that in outdoor air. Moreover, the abundance of bacteria in outdoor air was positively correlated with that in indoor air, as would be expected if outdoor air was the main contributor to the bacterial community in indoor bioaerosols. The number of residents, presence of pets, and local tap water also influenced the diversity and size of indoor air microbes. The bacterial load in air increased with the number of residents, activity, and frequency of natural ventilation, and the proportion of bacteria putatively derived from skin increased with the number of residents. Vacuum cleaning increased the signature of pet- and floor-derived bacteria in indoor air, while the frequency of natural ventilation decreased the relative abundance of tap water-derived microorganisms in air.ConclusionsIndoor air in residences harbors a diverse bacterial community originating from both outdoor and indoor sources and is strongly influenced by household characteristics

Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Study of the applicability of cognitive path algorithms for 3D indoor navigation

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