Timestamp free clock synchronization in a master-slave network, i.e., synchronization where no timestamps are exchanged between the nodes, is considered. For highly accurate synchronization, a novel discrete-valued clock model is introduced. It is based on the observation that clocks are discrete counters in digital wireless radios. Considering this model, it is shown that the round-trip time (RTT) measurements follow specific pulse or step shaped functions. The estimated parameters of these RTT functions are used to determine the clock parameters (clock skew and phase) and the propagation delay. Numerical analysis illustrate that when RTT measurements are collected using discrete-valued clocks, the proposed estimation schemes outperform estimators derived from the continuous clock model, which is used in the state-of-the-art methods. Moreover, the presented scheme performs similar to recently presented discrete-valued clock approaches with more stringent hardware assumption. The correctness of the proposed models is validated through hardware experiments.

Phase-of-arrival-based localization systems for passive ultrahigh frequency (UHF) radio-frequency identification (RFID) transponders must typically be calibrated to account for unknown phase offset values. Usually, reference measurements are performed using known tag positions. However, if the measurement scenario changes or other types of transponders are used, the system must be recalibrated. This paper introduces an automatic blind calibration method based on a Viterbi algorithm that requires no reference measurements. With this approach, the phase offset values are determined autonomously based on the measurement data set without any prior knowledge of the transponder positions, which are also estimated. To prove the calibration approach, a multichannel system demonstrator was used comprising a commercial off-the-shelf RFID reader, conventional passive UHF RFID tags, eight transceiver front ends, baseband hardware, and signal processing. Measurements were carried out in a 2-D indoor office environment, which was defined to be 3.5 m x 2.5 m. Compared with the common exhaustive calibration method using reference measurements at known tag positions, the automatic blind Viterbi-based approach offers similar results with a root-mean-square deviation of the phase offset values of 0.33 rad, which corresponds to an electrical length of 18 mm. Regarding the localization, the experiments showed accurate results with a root-mean-square error of 56 mm and a median absolute error of 21 mm.

The digital world offers ample availability of data, both historic and real-time. While this capability has the potential for better decision making, the contrary can be the case for a human actuator. Information overflow causes mental overload rather than empowerment of choice. In the context of traditional supermarket shopping, for example, customers are exposed to unstructured and complex product information including ingredients, nutrition facts, product labels, and more. Processing all this information in the context of multiple sustainability aspects requires expert knowledge. On the other hand, the rise of digitalization and the Internet of Things can be used to assist and empower customers during this shopping process. However, an integrated solution is required to provide a high grade of usability and crucial complexity reduction for customers. Therefore, we outline an IoT decision support system that assists customers on the sales floor and enables better decision making according to personal preferences and sustainable consumption. It integrates an indoor localization system, a product information database, and a ranking system considering the individual shopping preferences, where the latter is specified by the customer within an interactive smartphone application. The discussed IoT decision support system was deployed and tested in two retail stores. Its interaction with non-expert test participants was observed over months, and the results are summarized in this contribution.

The need for accurate positioning and tracking of mobile sensor nodes arises in many applications. To compute position estimates from raw measurements in positioning systems, Bayesian filtering techniques such as Kalman, histogram, or particle filters are frequently employed. A major disadvantage of these techniques is the fact that they compute only a single position estimate in every timestep, and therefore do not utilize the available information to full extent. The work presented in this paper improves over this current state of the art by instead estimating full node trajectories, and therefore, the most plausible sequence of positions in every timestep. We present two distinct algorithms—for a discrete and for a continuous state-space—and highlight the particular advantages of each variant. Achievable results are shown by simulation and measurement to be more accurate than the traditional Bayesian filtering approach. The most prominent advantage of much more accurate total track length estimation is particularly emphasized.

Self-positioning of smartphones in indoor environments offers a wide variety of applications. Anyway, in harsh environments, the achievable accuracies using received signal strength indicator measurement data are comparably low. However, restrictions due to geometry allow more accurate estimates of smartphone positions and trajectories. Based on received signal strength data from Bluetooth low energy beacons and Gaussian assumptions, an application of a discrete-state hidden Markov model – taking the geometry into account – in combination with dynamic model parameter estimation, leads to a significant improvement of error statistics.

Position estimation in localization and tracking is often approached with Bayesian filtering. In combination with Gaussian assumptions about state distributions this approach results in efficient algorithms such as the Kalman filter. Indoors, however, the location state space is naturally constrained by walls and other obstacles that cannot be taken into account with Kalman or other Gaussian filters. More general Hidden Markov Models (HMMs) on the other hand do not make restrictive assumptions and thus allow for extensive explicit modelling. In particular for indoor positioning, we show how to inject geometric apriori knowledge, i.e. a given floor plan, into the parameters of a discrete-state HMM. In contrast to classical position estimation, we use an adapted Viterbi-algorithm for decoding the maximum aposteriori (MAP) trajectory estimate based on the geometry-aware HMM. Results and advantages of the presented method are discussed for the localization of a mobile sensor node in a simulated measurement setup.