Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data

A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance

Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources andheavy environmental impacts. As a result of these concerns, a trend of green and smart cities has beenincreasing. To respond to this increasing trend of smart cities with buildings every time more complex,in this paper we have proposed a new method to solve energy inefficiencies detection problem in smartbuildings. This solution is based on a rule-based system developed through data mining techniques andapplying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is alsoproposed to detect anomalies. The data mining system is developed through the knowledge extracted bya full set of building sensors. So, the results of this process provide a set of rules that are used as a partof a decision support system for the optimisation of energy consumption and the detection of anomaliesin smart buildings.Comisión Europea FP7-28522

A Comparative Analysis of IEEE 802.15.4 Adapters for Wireless Range Finding

ZigBee wireless networks have become increasingly prevalent over the past decade. Based on the IEEE 802.15.4 low data rate wireless standard, ZigBee offers low-cost mesh connectivity in hospitals, refineries, building automation, and critical infrastructure. This thesis explores two ZigBee Received Signal Strength Indicator (RSSI)-based rangefinding tool sets used for assessing wireless network security: Z-Ranger and Zbfind. Z-Ranger is a new tool set developed herein for the Microchip Zena Wireless Adapter that offers configurable distance estimating parameters and a RSSI resolution of 256 values. Zbfind is an application developed for the Atmel RZUSBstick with no configurable distance estimating parameters and a RSSI resolution of 29 values. The two tool sets are evaluated while rangefinding four low-rate wireless devices indoors and two devices outdoors. Mean error is calculated at each of the 35 collection points and a 99% confidence interval and p-Test are used to identify statistically significant deviations between the two tool sets