A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation

Combining similarity functions and majority rules for multi-building, multi-floor, WiFi positioning

Fingerprint is one of the most widely used methods for locating devices in indoor wireless environments and we have witnessed the emergence of several positioning systems aimed for indoor environments based on this approach. However, additional efforts are required in order to improve the performance of these systems so that applications that are highly dependent on user location can provide better services to its users. In this work we discuss some improvements to the positioning accuracy of the fingerprint-based systems. Our algorithm ranks the information about the location in a hierarchical way by identifying the building, the floor, the room and the geometric position. The proposed fingerprint method uses a previously stored map of the signal strength at several positions and determines the position using similarity functions and majority rules. In particular, we compare different similarity functions to understand their impact on the accuracy of the positioning system. The experimental results confirm the possibility of correctly determining the building, the floor and the room where the persons or the objects are at with high rates, and with an average error around 3 meters. Moreover, detailed statistics about the errors are provided, showing that the average error metric, often used by many authors, hides many aspects on the system performance.This work was supported by the FEDER program through the COMPETE and the Portuguese Science and Technology Foundation (FCT), within the context of projects SUM – Sensing and Understanding human Motion dynamics (PTDC/EIA-EIA/113933/2009) and TICE.Mobilidade (COMPETE 13843)

Multi-agent location system in wireless networks

In this paper we propose a flexible Multi-Agent Architecture together with a methodology for indoor location which allows us to locate any mobile station (MS) such as a Laptop, Smartphone, Tablet or a robotic system in an indoor environment using wireless technology. Our technology is complementary to the GPS location finder as it allows us to locate a mobile system in a specific room on a specific floor using the Wi-Fi networks. The idea is that any MS will have an agent known at a Fuzzy Location Software Agent (FLSA) with a minimum capacity processing at its disposal which collects the power received at different Access Points distributed around the floor and establish its location on a plan of the floor of the building. In order to do so it will have to communicate with the Fuzzy Location Manager Software Agent (FLMSA). The FLMSAs are local agents that form part of the management infrastructure of the Wi-Fi network of the Organization. The FLMSA implements a location estimation methodology divided into three phases (measurement, calibration and estimation) for locating mobile stations (MS). Our solution is a fingerprint-based positioning system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of the network device manufacturer. In the measurement phase, our system collects received signal strength indicator (RSSI) measurements from multiple access points. In the calibration phase, our system uses these measurements in a normalization process to create a radio map, a database of RSS patterns. Unlike traditional radio map-based methods, our methodology normalizes RSS measurements collected at different locations on a floor. In the third phase, we use Fuzzy Controllers to locate an MS on the plan of the floor of a building. Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate an MS in a room or adjacent room

The Impact of Occupants’ Behaviours on Energy Consumption in Multi-Functional Spaces

Over the last 15 years, the estimation of energy consumption in buildings has become a critical process during various stages of building’s lifecycle due to growing global scientific and political pressure to respond to climate change. It has been widely acknowledged in the literature that there is a distinct performance gap between predicted and actual energy consumption of buildings which has attracted scholars across the world to investigate the sufficiency of software inputs and presumptions regarding how the buildings are actually used. Several studies have confirmed that occupant’s presence, in addition to, their interactions with building systems (such as: opening door and window, changing the thermostat set-point and using appliances), known as passive and active energy consumption behaviours, play significant roles in building’s energy consumption. However, the incorporation of occupants’ behaviours into the building energy performance analysis has been mostly overlooked. Most of the existing studies on the impacts of occupants on building energy consumption have focused on residential and office buildings. Therefore, there is a lack of knowledge about the impacts of occupants’ behaviours on energy consumption in public buildings such as: galleries, exhibitions, recreational facilities and institutional buildings. In such building occupants have limited access to building systems, and their energy consumption behaviours are limited to their presence and the production of metabolic heat (passive behaviour), in addition to, few activities such as: opening the entrance door. This research develops a conceptual framework to improve the accuracy of energy consumption assessment in multi-functional spaces at different stages of building’s lifecycle by integrating the impacts of occupants’ behaviours into building energy predictions to reduce the gap between actual and predicted energy consumption. In this quantitative research, a model simulation method is applied on multiple cases at different stages of the building lifecycle including design, construction and post-occupancy. The first two cases are multi-functional spaces of public buildings at the design and construction stages, which were studied to address the missing information and potential gaps in energy modelling and simulation. The study was then taken forward using case studies at the post-occupancy stage to integrate the realistic observed data into the building energy simulation tool. For each of the cases, energy simulation was run twice: first, using default values of the software, and second, using the collected data. The data collection included hourly observation of 38 zones in both cases at the post-occupancy stage for the duration of two weeks, in addition to, using available governmental and real-time statistics. The analysis of energy simulation results using default software values and collected data highlighted that lack of sufficient information regarding building working hours, space layout and function, occupancy density and schedules, the entrance door opening time and HVAC set-points may result significant performance gaps in energy consumption prediction of multi-functional spaces in institutional buildings and galleries. This study provides conceptual frameworks for the prospect energy modellers and researchers to obtain more accurate energy consumption predictions for multi-functional spaces of public buildings