Indoor Sensor Research Articles

3-D Hybrid VLC-RF Indoor IoT Systems With Light Energy Harvesting

In this paper, a 3-D hybrid visible light communication (VLC)-radio frequency (RF) indoor Internet of Things system with spatially random terminals with one photodiode (e.g., indoor sensors: temperature sensors, humidity sensors, and indoor air quality sensors) is considered. Specifically, homogeneous Poisson point process is adopted to model to the distribution of the terminals, which means that the number of the terminals obeys Poisson distribution, and the positions of the terminals are uniformly distributed. VLC and RF communications are employed over downlink and uplink, respectively. Meanwhile, the terminals are designed to harvest the energy from the light emitted by the light-emitting diode over the downlink, which is used for the transmissions over the uplink. The light energy harvesting model is considered after introducing the line of sight propagation model for VLC. Then, the outage performance has been studied for the VLC downlink and non-orthogonal multiple access schemes over the RF uplink, respectively, by using stochastic geometry theory, while considering the randomness of the number of the terminals, and all terminals are spatially and randomly distributed in the 3-D room and all RF uplinks follow Rician fading. Finally, the approximated analytical expressions for the outage probability are derived and verified through Monte Carlo simulations.

SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUD WITH SLENET

Abstract. With the rapid development of new indoor sensors and acquisition techniques, the amount of indoor three dimensional (3D) point cloud models was significantly increased. However, these massive “blind” point clouds are difficult to satisfy the demand of many location-based indoor applications and GIS analysis. The robust semantic segmentation of 3D point clouds remains a challenge. In this paper, a segmentation with layout estimation network (SLENet)-based 2D–3D semantic transfer method is proposed for robust segmentation of image-based indoor 3D point clouds. Firstly, a SLENet is devised to simultaneously achieve the semantic labels and indoor spatial layout estimation from 2D images. A pixel labeling pool is then constructed to incorporate the visual graphical model to realize the efficient 2D–3D semantic transfer for 3D point clouds, which avoids the time-consuming pixel-wise label transfer and the reprojection error. Finally, a 3D-contextual refinement, which explores the extra-image consistency with 3D constraints is developed to suppress the labeling contradiction caused by multi-superpixel aggregation. The experiments were conducted on an open dataset (NYUDv2 indoor dataset) and a local dataset. In comparison with the state-of-the-art methods in terms of 2D semantic segmentation, SLENet can both learn discriminative enough features for inter-class segmentation while preserving clear boundaries for intra-class segmentation. Based on the excellence of SLENet, the final 3D semantic segmentation tested on the point cloud created from the local image dataset can reach a total accuracy of 89.97%, with the object semantics and indoor structural information both expressed.

A Pilot Study to Test the Feasibility of a Home Mobility Monitoring System in Community-Dwelling Older Adults.

Technology enables home-based personalized care through continuous, automated, real-time monitoring of a participant’s health condition and remote communication between health care providers and participants. Technology has been implemented in a variety of nursing practices. However, little is known about the use of home mobility monitoring systems in visiting nursing practice. Therefore, the current study tested the feasibility of a home mobility monitoring system as a supportive tool for monitoring daily activities in community-dwelling older adults. Daily mobility data were collected for 15 months via home-based mobility monitoring sensors among eight older adults living alone. Indoor sensor outputs were categorized into sleeping, indoor activities, and going out. Atypical patterns were identified with reference to baseline activity. Daily indoor activities were clearly differentiated by sensor outputs and discriminated atypical activity patterns. During the year of monitoring, a health-related issue was identified in a participant. Our findings indicate the feasibility of a home mobility monitoring system for remote, continuous, and automated assessment of a participant’s health-related mobility patterns. Such a system could be used as a supportive tool to detect and intervene in the case of problematic health issues.

MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics.

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings

In modern smart buildings, the electricity consumption of a building is monitored every time and costs differently at each time slot of a day. Smart buildings are also equipped with indoor sensors that can track the movement of human beings. In this paper, we propose a new elevator control system (ECS) that utilizes two kinds of context information in smart buildings: (1) human movements estimated by indoor sensors and (2) dynamic changes of electricity price. In particular, indoor sensors recognize elevator passengers before they press the elevator call buttons, and smart meters inform the dynamically changing price of the electricity to ECS. By using this information, our ECS aims at minimizing both the electricity cost and the waiting time of passengers. As this is a complex optimization problem, we use an evolutionary computation technique based on genetic algorithms (GA). We inject a learning module into the control unit of ECS, which monitors the change of the electricity price and the passengers’ traffic detected by sensors. Experimental results with the simulator we developed show that our ECS outperforms the scheduling configuration that does not consider sensor information or electricity price changes.

Environmental Influences on Health Outcomes-Integrating Real-Time Exposure Measures from Homes into Longitudinal Cohort Studies: Lessons from the Field

In North America, adults and children of all ages spend much of their time indoors with the home where a majority of time is passed. Exposure to respirable particulate matter (PM) of varying sizes has been linked to cardiovascular and respiratory problems. Exposures to PM2.5 contribute to childhood respiratory ailments such as asthma. Utah collaborators with pediatric cohorts aligned with the National Institutes of Health (NIH) ECHO program and NIH PRISMS program conducted pilot projects aimed at using innovative, real-time, low-cost air quality sensors to provide spatio-temporal records of particulate matter (PM) exposures. The projects evaluated participant acceptance, feasibility, and efficacy in building exposure profiles using instrumentation, extant data, models and health information integrated with a single informatics platform. The PM sensors are small and feature secure Wi-Fi and data storage capability. More than 30 participant homes had one outdoor and two indoor sensors capturing time stamped PM counts every minute which were aggregated to daily data for initial analyses. Participants were supportive of using their personal Wi-Fi for sensor data transmission. From the PRISMS project (n=6), mixed modeling showed the daily estimated marginal mean level of small (PM2.5) and large (PM10) particle counts was lower indoors than outdoors (p < 0.001). Among 6 pediatric asthmatic participants reporting daily asthma treatment usage via eAsthma Tracker, indoor PM counts were not related to treatment usage (p > 0.05). Higher levels of PM2.5 outdoors were related to increases in asthma treatment (p < 0.001) while outdoor PM10 were not related to treatment usage (p = 0.52) in this small sample. These pilot projects demonstrated the feasibility of gathering environmental exposure data from low-cost sensors at a very fine level of resolution, and ability to integrate and analyze data across disparate data sources using the Utah PRISMS developed infrastructure.

LINKING 3D BUILDING MODELS, MAPS AND ENERGY-RELATED DATA IN A WEB-BASED VISUALIZATION SYSTEM

Abstract. In a transformation process to become a climate-neutral city campus, universities have to deal with the sustainable concept. Since “human factor” plays a significant role in the transformation process, providing easy access to environmental data to influence building occupants’ behavior is essential. By utilizing energy-related data without spatial attribute and existing building geospatial data, data visualization in a web browser can be established for both 2D and 3D platforms. Our implementation presents a visualization of indoor sensor measurement data, where the same geospatial data can be used for both 2D and 3D visualizations even though the 3D platform needs an adjustment. Our approach results in a monitoring tool prototype based on visualization of indoor sensors measurement data, which can be accessed easily in a web browser by all building occupants.

Development of Fuzzy Logic Control for Indoor Lighting Using LEDs Group

This paper presents the design and the development of an indoor lighting control based on fuzzy logic controller (FLC). The objective of this study is to demonstrate how the FLC can optimize the lighting based on indoor and outdoor lighting environments. The FLC system uses two inputs with Light Dependent Resistors (LDR) as a sensor at indoor and outdoor lighting environments. The output is Light Emitting Diodes (LEDs) to provide lighting at the room automatically. The Pulse Width Modulation (PWM) is used to adjust the LEDs lighting in the room. FLC has successfully demonstrated performance to control the output of LEDs as needed. If the LDR 1 input as indoor sensor shows dim, it will automatically turn on brightly. The MSE values for simulation and experiment of LDR 1 and LDR 2 were 34.42 and 30.11 respectively. The results of FLC performance in the simulation work are further validated by an experimental work. Experimental results show similarities compared to the simulation results.

Early Steps in Automated Behavior Mapping via Indoor Sensors

Behavior mapping (BM) is a spatial data collection technique in which the locational and behavioral information of a user is noted on a plan layout of the studied environment. Among many indoor positioning technologies, we chose Wi-Fi, BLE beacon and ultra-wide band (UWB) sensor technologies for their popularity and investigated their applicability in BM. We tested three technologies for error ranges and found an average error of 1.39 m for Wi-Fi in a 36 m2 test area (6 m × 6 m), 0.86 m for the BLE beacon in a 37.44 m2 test area (9.6 m × 3.9 m) and 0.24 m for ultra-wide band sensors in a 36 m2 test area (6 m × 6 m). We simulated the applicability of these error ranges for real-time locations by using a behavioral dataset collected from an active learning classroom. We used two UWB tags simultaneously by incorporating a custom-designed ceiling system in a new 39.76 m2 test area (7.35 m × 5.41 m). We considered 26 observation points and collected data for 180 s for each point (total 4680) with an average error of 0.2072 m for 23 points inside the test area. Finally, we demonstrated the use of ultra-wide band sensor technology for BM.

Indoor Environment in Residential Prefabricated Buildings

The contribution presents results of the experimental measurement of indoor air quality in residential prefabricated buildings. People spend about 90% of their life in the indoor environment of buildings. Hygrothermal parameters and indoor air quality are the essential component that define the quality of the indoor environment. The results of case study characterize the quality of the indoor environment of the ordinary occupants in housing unit of residential prefabricated building. A current problem of revitalized prefabricated buildings is inadequate air exchange and related thereto to poor indoor air quality. The experimental measurements were carried out just before and at the beginning of the heating season (from 1st October to 30th November 2016). Heating season was launched in the middle of experimental measurement. The wireless indoor sensor Elgato Eve Room was used for measurements. The obtained values of indoor air temperature [°C], relative humidity [%] and indoor air quality [ppm] are describe and analysis in this study. The results of the study indicate that the values of temperature and indoor air quality meet optimal levels during the experiment with nuances. The mean air temperature in the indoor environment is 22.43 °C. The temperature of the indoor environment is held at the optimum level (18-24 °C) for 94.50% time of the experimental measurements. In addition, the indoor air quality in the context of the content of harmful volatile organic compounds (VOCs) has been excellent for almost 91% time of the total experiment. However, the values of relative humidity were less than the optimum value nearly 40% of the total observed time. The mean 10-minutes values of relative humidity during the heating season is about 10% lower than the mean 10-minutes relative humidity before the heating season.

Impact of heatwave on a megacity: an observational analysis of New York City during July 2016

More than half of the world’s current population resides in urban areas, and cities account for roughly three-quarters of the total greenhouse gas emissions. Current and future trends in urbanization will have significant impacts on global climate. However, our collective understanding of the climate of urban areas remains deficient, which is mainly related to significant knowledge gaps in observations. The New York City Summer Heat Campaign was initiated to address some of these critical knowledge gaps. As part of the campaign the urban boundary layer over New York City was continuously monitored during July 2016, a period that witnessed three heatwave events. Surface weather stations and indoor sensors were also used to characterize the urban heat island intensity. Our results reveal that during the month, the urban heat island intensity was nearly twice compared to the decadal average. During the heatwave episodes, urban heat island intensities as high as 10 °C were observed. The thermal profiles indicate elevated temperatures in much of the boundary layer between 800–2500 m during the heatwave episodes. The profiles indicate a complex thermal structure and high intra-city variability. Thermal internal boundary layer was observed in neighborhoods populated by tall buildings. Overall the high-pressure system during the heatwave episodes acted as a thermal block and much of the heat generated in the urban surface layer remained within the boundary layer, thereby amplifying the near surface air temperature.

Data centre adaptive numerical temperature models

Research results are presented regarding the online derivation of temperature state space models for air cooled data centre rooms. Using exclusively real time temperature measurements from indoor sensors, filter algorithms are programmed for the numerical computation of the parameters of discrete time varying state space models. These control oriented models are adaptive and can predict the temperature distribution across data centre rooms where air-conditioning units are used to compensate heat loads generated by the computing equipment. The research has been conducted for the European Union project GENiC and the adopted approach has been tested and validated on a real data centre facility.

Indoor cognitive radio operation within the broadcast TV protection contour

Although the broadcast television (TV) spectrum is currently open for unlicensed operation in the USA, a considerably large geographic area still remains excluded from the unlicensed operation due to potential interference to the licensed users. However, it might be possible to reuse primary spectrum within the protection contour if the frequency reuse occurs inside a building that shields radio signals and reduces interference to the primary system. Interference to outdoor licensed users from the indoor operations can be minimized if the unlicensed users adjust their transmit power according to their locations in the building. This paper presents an analysis and effectiveness evaluation of a novel cognitive radio (CR) system which enables CRs to access the licensed spectrum inside a building in the area within the protection contour. The system utilizes an indoor sensor network for (i) interference sensing, (ii) CR transmit power control, to limit the interference to the outdoor primary receiving antennas. Power control model of the indoor system has been developed to estimate safe transmit power for the indoor users. Two cases have been considered; single-user single-sensor (single indoor user and single sensor), and multi-user multi-sensor. Based on the power control model, a power control algorithm has been developed and its effectiveness is assessed through simulations. The algorithm is effective in realistic propagation scenarios, e.g. when internal partition walls and multipath fading are present. The outage probabilities in these propagation scenarios are found and the procedure of determining the transmit powers for CRs is presented.

Dynamic Deployment of Wireless Multimedia Sensor Network Nodes Based on Balanced Energy Consumption

The quality of data sensing, storage, and interaction depends on node deployment, so the optimization of node deployment becomes the key mission to response network congestion. This paper studies the indoor sensor network nodes deployment and simulates the corresponding sensor network environment, in order to solve the congestion of wireless multimedia sensor network based on balanced energy. We introduce the balanced energy consumption of hybrid nodes deployment strategy to maximize use of network resources. The two-dimensional plane nodes are redeployed and further extended to three-dimensional space on the basis of energy equilibrium. The whole experiment states clearly that intelligent node deployment strategy use wireless multimedia sensor network node resources to effectively extend the network life cycle.

Energy-efficient vertical transportation with sensor information in smart green buildings

In modern smart green buildings, sensors can detect various physical status of a building such as temperature, humidity, motion, and light, which can be used for smart living services. This paper presents an energy-efficient vertical transportation by making use of indoor sensor technologies. Specifically, sensors detect elevator users before they push the call button, and then inform to the elevator control system through building networks. By using this information, our system generates a reservation call and controls the moving time and direction of each elevator efficiently. Simulation experiments with a variety of traffic situations show that our elevator control system exhibits significantly better performance than the conventional system that does not use sensor information with respect to passengers’ waiting time and energy consumption.

Measurements Data-Based Location Determination for Characterizing Indoor Signal Propagations of the Shipboard Sensor Nodes

It is a crucial factor to determine the locations of the sensor nodes accurately and promptly. It affects the stability of connections among sensors significantly since the routes between signal source and its destination can be established within a minimum time when they are required. To this end it can be achieved through the measurement of the bi-directional channel qualities among the nodes after the locations of the sensor nodes are temporarily placed. For the 1st stage the piconet-type sensor networks are considered to measure the channel qualities between a specific node and the cluster node of the master node. After completing the 1st stage measurement the nodes needing the relay nodes are selected and the measurement action enters into the 2nd stage. In the 2nd stage the measurement between a candidate relay node and the node with a relay node is initiated. After the 2nd stage the relay nodes connecting to the cluster node are determined and the information delivery paths between a node and the cluster head are established. The feasibility of the proposed scheme has been checked by gathering and analyzing the measurement results in the realistic environment to show that the measurement procedure can be applied in the wireless indoor shipboard networks. The indoor propagation characteristics measurement and location determination scheme based on signature sequences can be reliably applied to the shipboard indoor sensor networks.

A Hybrid Approach to Indoor Sensor Area Localization and Coverage

This study presents a hybrid approach to indoor object detection and tracking. A test area is partitioned into regions that are covered by sensors placed on the ceilings. The region location of an object is determined by these sensors using RSSI. The region determination is then followed by the exact localization of the object within the region using Building Information Modeling (BIM) and the 3D stereo image measurements. To determine the coverage ratio of a region for better placement of sensors, the 3D space is partitioned into 2D planes whose coverage points are determined by a newly developed software product called LOCOPac. Other than indoor localization, some simulation results of sensor distribution algorithms that are more suitable for outdoor applications will also be presented. Based on the experimental and simulation results, the proposed approach to indoor localization and LOCOPac has the potential to be used for real world applications.

RBGF: Recursively Bounded Grid-Based Filter for Indoor Position Tracking Using Wireless Networks

Numerical methods for recursive Bayesian estimation are widespread in position tracking of robotics. However, challenges arise to indoor network positioning due to the limited processing power and inaccurate ranging measurements of low-end network nodes. For efficient and robust indoor position tracking, we incorporate a recursive bound to a grid-based filter namely RBGF, which approximates the posterior of the target's position by a grid of weighted cells over a bounded state-space. The state-space (the set in which the state samples can take) is recursively confined based on both the previous estimation and current measurements, therefore, the grid cells converge to the true state and the effect of non-line-of-sight (NLOS) measurements is bounded. Experimental results by an indoor sensor test-bed demonstrate RBGF achieves the average and the worst-case of positioning errors about 1 meter and 3 meters, respectively on condition that the average ranging error is about 3 meters.

On deploying relays for connected indoor sensor networks

This paper considers the crucial problem of deploying wireless relays for achieving a connected wireless sensor network in indoor environments, an important aspect related to the management of the sensor network. Several algorithms have been proposed for ensuring full sensing coverage and network connectivity. These algorithms are not applicable to indoor environments because of the complexity of indoor environments, in which a radio signal can be dramatically degraded by obstacles such as walls. We first prove theoretically that the indoor relay placement problem is NP-hard. We then predict the radio coverage of a given relay deployment in indoor environments. We consider two practical scenarios; wire-connected relays and radio-connected relays. For the network with wire-connected relays, we propose an efficient greedy algorithm to compute the deployment locations of relays for achieving the required coverage percentage. This algorithm is proved to provide a H n factor approximation to the theoretical optimum, where H n = 1 + 1/2 + ⋯ + 1/n = ln(n) + 1, and n is the number of all grid points. In the network with radio-connected relays, relays have to be connected in an ad hoc mode. We then propose an algorithm based on the previous algorithm for ensuring the connectivity of relays. Experimental results demonstrate that the proposed algorithms achieve better performance than baseline algorithms.

Blind Signal Processing for Wireless Sensor Networks

In indoor sensor networks equalization algorithms based on the minimization of Euclidean distance (MED) for the distributions of constant modulus error (CME) have yielded superior performance in compensating for signal distortions induced from optical fiber links, wireless-links and for impulsive noise problems. One main drawback of MED-CME algorithms is a heavy computational burden hindering its implementation. In this paper, a recursive gradient estimation for weight updates of the MED-CME algorithm is proposed for reducing the operations <TEX>$O(N^2)$</TEX> of the conventional MED-CME to O(N) at each iteration time for N data-block size. From the simulation results of the proposed recursive method producing exactly the same results as the conventional method, the proposed estimation method can be considered to be a reliable candidate for implementation of efficient receivers in indoor sensor networks.