Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

Cyber-Physical Systems (CPSs) are used in various important application areas. Networking of several CPSs, the internal networking of CPSs components as well as the interconnection with other systems, is of major importance with respect to scientific, engineering and technical considerations; yet, it is also very challenging. In this paper we describe an architecture and methods which can be used for various networked CPSs. To base the system design and approaches on realistic requirements and devise suitable methods, we use tightly cooperating, mobile robots as application area. This is an example of challenging CPSs which put high demands on the networking methods. Nevertheless, the considerations are applicable to other CPSs as well. In order to enable networked mobile robots to perform individual and cooperative tasks, real-time support and network operations such as merge, split, and synchronize among clusters of such robots are needed. Further, management functions have to be provided which enable independent, but concurrent clusters to allocate and share scarce network resources. In the context of this paper, network resources are considered in a broad sense (e.g., time slots, frequency channels, codes) and assigned by a novel scheduling algorithm. Thus, a schedule means not only a sequence of time slots, but it takes all mentioned dimensions into account.

Multipath propagation introduces particular challenges for ranging schemes that exploit radio wave propagation. We investigate the impact of multipath propagation on phase-based ranging in the 2.4 GHz band. We show that a phase-based ranging system might report the length of a reflected signal path instead of that of the direct path. In our experiments, we identified three different measurement outcomes, depending on physical properties of the radio link between devices. Our findings show that multipath propagation can lead to severe measurement errors. To mitigate the effects, we propose a novel algorithm to compute the distance from measured phase angles. Our algorithm reduces measurement errors by 51 % compared to a state-of-the-art algorithm in a scenario with strong multipath effects.

Many localization approaches need distance measurements to known locations as input. We present the Complex-valued Distance Estimation algorithm to compute the distance between two radio transceivers from phase measurements. Our proposed algorithm reliably computes the distance from a sampled phase response via Fast Fourier Transform. We compare the new approach with two other state-of-the-art algorithms by evaluating distance errors of real-world measurements in different scenarios. Our algorithm achieves high accuracy and precision with a mean error of 14.9 cm and a standard deviation of 10.4 cm. Further, we evaluate the ability of all algorithms to detect outliers with large errors in their results. We optimize our approach to reduce computation complexity via interpolation without affecting accuracy or precision.

Being able to determine the location of a node is of great advantage in many IoT and WSN applications. For example, in health care scenarios or for autonomous configuration of IoT setups this information can be useful. One of the key challenges in localization is to estimate the distance between nodes. Most present indoor localization systems require additional hardware for this estimation which is costly in terms of money and energy consumption. To overcome this disadvantage, we developed a system which is able to perform distance measurements without adding any extra hardware and costs. It is based on phase measurements performed by the IEEE 802.15.4 transceiver chip that is normally solely used to realize communication. In the evaluation we investigate the performance of our system in different real world environments that are typical for IoT and WSN setups.

In modern industry, robots and other highly automated machines are one of the most important parts in production. For future development in production techniques these machines will work in more flexible setups. To enable such setups wireless communication needs to fulfill strict real time requirements. In this paper we present a tool to verify whether a communication method fulfills the requirements. Furthermore, it helps during development of this communication by providing debug information. All data is provided to Wireshark, an easy to extend, open source protocol analyzer.

Modern cars are already able to park in parking spaces adjacent to streets. Soon autonomous cars will be able to navigate themselves through parking garages to find a parking lot assigned to them. Indoor localization is essential to qualify cars and parking garages to perform this operation. Currently the reliability of many indoor localization schemes suffers from non-line-of-sight propagation paths. We present a position estimation algorithm for indoor localization systems based on phase measurements of electromagnetic signals. Our algorithm is designed to detect and exclude measurements originating from these non-line-of-sight paths to reduce their harmful influence on the localization.