Stephan Adler

Cooperative event detection in wireless sensor networks

Event detection in wireless sensor networks is a sophisticated method for processing sampled data directly on the sensor nodes, thereby reducing the need for multihop communication with the base station of the network. In contrast to application-agnostic compression or aggregation techniques, event detection pushes application-level knowledge into the network. In-network event detection - especially the distributed form involving multiple sensor nodes - has thus an exceptional potential to increase energy efficiency, thus prolonging the lifetime of the network. In this article, we summarize recently proposed system architectures and algorithms employed for event detection in wireless sensor networks. On the example of the AVS-Extrem platform, we illustrate how energy-efficient event detection can be implemented through a combination of custom hardware design and distributed event detection algorithms. We then continue to present a brief evaluation of the detection accuracy and the energy consumption that is achievable by current systems.

A survey of experimental evaluation in indoor localization research

During the last decade, research in indoor localization and navigation has focused on techniques, protocols, and algorithms. The first International Conference on Indoor Positioning and Indoor Navigation (IPIN) was held in 2010. Since then, this annual conference showed the progress of research and technology. The variations of evaluation methods are significant in this field: they range from none, to extensive simulations, and real-world experiments under non-lab conditions. We look at the articles published in the proceedings of IPIN by IEEE Xplore from 2010 to 2014, and analyze the development of evaluation methods. We categorized 183 randomly selected papers, in respect to five different aspects. Namely: (1) the underlying system/technology in use, (2) the evaluation method for the proposed technique, (3) the method of ground truth data gathering, (4) the applied metrics, and (5) whether the authors establish a baseline for their work.

Experimental evaluation of RF-based indoor localization algorithms under RF interference

In the current practice, the performance evaluation of RF-based indoor localization solutions is typically realized in non-standardized environments and following ad-hoc procedures, which hampers objective comparison and does not provide clear insight into their intrinsic properties. Many evaluation procedures also neglect important environmental factors like RF interference, diminishing the real-world value of the obtained results. Localization competitions, in which different solutions are evaluated along a set of standardized metrics under unified and representative conditions, can play an important role in mitigating these problems, but their organization is cost and labor intensive. In this paper we report on the design, execution and results from an online localization competition in which several different RF-based indoor localization algorithms have been evaluated with the help of a remotely accessible and automated testbed infrastructure, that reduces these overheads. The competing algorithms have been evaluated following a combination of precision, latency and sensitivity metrics, under four different benchmarking scenarios, resulting in 28 different benchmarking experiments. The obtained results provide strong indication that specific types of RF-interference noticeably degrade the localization performance.

The effects of human body shadowing in RF-based indoor localization

In radio frequency based indoor human localization systems with body mounted sensors, the human body can cause non-line-of-sight (NLOS) effects which might result in severe range estimation and localization errors. However, previous studies on the impact of the human body only conducted static experiments in controlled environments. We confirm known effects and conduct real-world experiments in a typical indoor human tracking scenario using 2.4 GHz time of flight (TOF) range measurements. We analyze the effect on the raw measurements and on the localization results using the localization algorithms Centroid, NLLS, MD-Min-Max, and Geo-n. The experiment design is focused on incident management, where an infrastructure might only be installed in front of the building. We show that these effects have considerable impact on the localization accuracy of the person.

Deployment and evaluation of a fully applicable distributed event detection system in Wireless Sensor Networks

Versatility and real world applicability are key features of Wireless Sensor Networks (WSNs). In order to achieve these benefits we have to face the challenges of high practical relevance during application. We deploy and evaluate the concrete example of a fence monitoring task to reveal how our distributed event detection system is able to perform under real application conditions.The challenge is to bring both opposing aspects-high event detection accuracy and long service life-into one applicable ubiquitous system. If sensor nodes compose parts of events cooperatively, a comprehensive event assessment with low energy demands is possible. We propose a classifier based distributed event detection system consisting of two frameworks. The evaluation framework delivers a classification model and enables the theoretical evaluation of a given training set. The distributed event detection framework subsequently applies the classification model to assess, filter and classify events within the network. We evaluate our system by training and detecting events with our WSN composed of 49 nodes which are integrated in construction site fence elements.We compare four different data application scenarios with varying data processing concepts and varying network sizes to analyze the resulting communication load as well as the system lifetime. We compare the results of our evaluation framework with the results of our application to show that the evaluation framework reflects the real world deployment results in a credible way. We show the full applicability of our approach by comparing the resulting increased event detection accuracy against our previous work. Compared to other information fusion scenarios, our distributed event detection system reduces energy consumption beyond a communication distance of two hops which yields a prolonged lifetime of the network while additionally achieving an improved event detection accuracy.

Energy-aware distributed fence surveillance for wireless sensor networks

Fences are used all over the world to protect areas against unauthorized access. While most fences meet these requirements by building a physical and psychological barrier against intruders, this is not sufficient for areas of particular interest like restricted areas of an airport or construction sites with expensive goods. To detect intruders we integrate wireless sensor nodes into a fence. We fulfill the requirements for real-world energy-awareness by using active sensors with configurable logic, power saving modes like WOR and power down modes. The sensor nodes are capable of differentiating between several events, with the appliance of a distributed classification algorithm. We present an energy-aware platform for a distributed fence surveillance system in a wireless sensor network. We report on our experience in creating a platform that is concerning energy and performance demands and specifically tailored for the purpose of fence surveillance including fully integrated housing.

Device-free indoor localisation using radio tomography imaging in 800/900 MHz band

Radio tomographic imaging (RTI) can be used as a method for device free localisation of persons in rooms. By measuring the signal strength of all links of a network of sensor nodes, one can estimate the position of an attenuating object with reasonable precision. The sub-GHz is shown to be suitable for an implementation. Such a design is more energy efficient than a 2.4 GHz implementation. We adapt, evaluate, and improve the device-free localization method of Wilson and Patwari to indoor environments using the 800/900 MHz band. The advantage of using 800/900 MHz is reduced reflections compared to 2.4 GHz. At the same time, the signal is attenuated less by objects in its path. Thus, the methods of Wilson and Patwari needed to be refined and parameters needed to be adapted. We evaluate some combinations of the most common choices of norms to perform a Tikhonov regularisation. The first difference gradient operator with H1 norm works best. We equipped a 5 m×5 m room with 20 wireless sensor nodes. We evaluated the influence of the distance to walls and the height of nodes. The radio tomographic image are post-processed by filters to make likely positions of objects more apparent. Additional parameters values suggested by Wilson and Patwari could not be used for the new frequency and hardware. For example, the ellipse excess path length has been experimentally determined to be close to 20 cm instead of 2 cm. In our experiments-up, we achieve an a maximum average localisation error below of 78 cm. With this work, we have reproduced the results of Wilson and Patwari, adapted it to a different frequency in the 800/900 MHz band and developed improvements to the original algorithms.

Path loss and multipath effects in a real world indoor localization scenario

This paper presents results of a large real world experimental setup of an indoor localization system. We used a time-of-flight based radio range measurement system to collect a large body of ranging data between a mobile reference system and multiple anchor nodes with a fixed and known position. For our experiment the reference system moved autonomously through an office building while collecting ranging data. We used this data to analyze the impact of environmental parameters on the ranging accuracy. We saw effects which are not predicted by the standard channel models for multiple scenarios and discuss these effects in detail.

Experimental evaluation of indoor localization algorithms

In Radio Frequency (RF)-based indoor localization scenarios, localization algorithms are needed to alleviate the impact of non-line-of-sight and multipath effects on the measurements and thereby estimate the true position precisely. Several resilient lateration algorithms have been proposed in the last couple of years which claim to minimize these effects. However, most of these algorithms were only evaluated using simulations or small static testbeds. We conducted an experiment using 25 anchor nodes and a mobile node installed on top a robotic reference system to collect ranging values. The robot has a localization error of 6.5cm which is an order lower than our range measurement errors. We use this robot to collect range measurements and ground truth positions along a densely grid with approx. 10 cm spacing. The experiment was carried out in a hallway of our office-like building. We collected data on approx. 300 m2. First, we examine the influence of the anchor placement and anchor density on the ranging errors we see. Then, we evaluate and analyze the robustness of localization algorithms on our measured data to decide which one works best for a constellation of anchor placement and building. Our results show, that there are significant differences between the simulations published for lateration algorithms and actual experiments in real-world indoor localization scenarios. As we show in this paper, the distance measurement error distribution has a large influence on these algorithms.

Virtual testbed for indoor localization

We present a novel, easy to use virtual testbed for the evaluation of localization algorithms. Our testbed enables researchers to easily run tests on a huge body of real world range-based indoor localization data. The data consists of a dense grid of reference points belonging to one or multiple maps. Each point consists of a ground truth value and an arbitrary number of ranging values. Each ranging value belongs to a certain anchor node on a fixed position. The reference data is gathered by a robot which carries (arbitrary) localization devices. The robot stores its location as a ground truth value and simultaneously uses the localization device to measure the distance to a set of anchors in range. The ground truth value is gathered by an optical reference system which is applied to the robot. It is possible to define paths through a map using a web interface. Our system uses our experimental gathered reference points to deliver a dataset of ranging values for the current path. Therefore the researcher can run a virtual experiment by himself and can adjust several parameters. Our system enables other researchers to run reproducible experiments on real word data. The expensive and complex deployment of a dedicated infrastructure and experimental setup can be avoided as well as the error-prone task of modelling a localization system and running a simulation. Our system will be open to the research community and will help to develop a better understanding of the field of range based indoor localization.