Maciej Nikodem

Historically, the first indoor localization method using Bluetooth Low Energy (BLE) relied on received signal strength indicator (RSSI) measurements. This simple and cost-effective method suffers from limited accuracy due to its high susceptibility to environmental factors. The introduction of angle-of-arrival (AoA) functionality in BLE v5.1 significantly improved accuracy by enabling signal phase measurements. AoA exploits the phase difference of the received signal across spatially distributed antennas to estimate the direction of arrival and then pinpoint the location using triangulation. In contrast, multi-carrier phase difference (MCPD) leverages the phase difference of the signal at a single antenna but across different frequencies. The phase difference is used to estimate the distance, which allows the location to be determined using multilateration. MCPD is a recent addition to the BLE standard known as channel sounding, but unfortunately, real-world evaluations of this method are scarce. This paper addresses this gap by experimentally analyzing the performance of MCPD in an indoor environment. We conduct comprehensive experiments in a 100m ² office space, comparing the accuracy of MCPD with RSSI and AoA-based approaches in the same area. The results show that the MCPD technique yields similar localization accuracy to the AoA method, with a mean localization error of 0.98m and 1.26 m, respectively, while avoiding the need for complex antenna arrays. Additionally, MCPD significantly outperforms RSSI-based methods, which have a mean localization error of 3.83 m.

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared with the traditional timestamp approach. We demonstrate that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared to the traditional timestamp approach. We demonstrated that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

The log sequence is often referred to as a language in automated log analysis. The natural consequence of this is that the log sequence should have a structure consisting of words and sentences. However, the word definitions in the log sequence are not uniform in the literature. The first approach splits line-by-line, and the second retrieves word-like structures from the log sequence. The main challenge in the second approach is the measurement of results. There are approaches for constructing unsupervised metrics; however, we found them to be inconsistent. Other methods rely on manually prepared golden standards; however, a benchmark for golden segmentation is not available for any set of logs. To overcome this problem, we created a benchmark of preprocessed log event IDs gathered from the open-source CloudStack log and commercial Nokia software execution. We created a gold segmentation standard with the help of a human expert, and made it publicly available. We then tested known unsupervised segmentation methods used for log sequence segmentation and adapted the Nested Pitman-Yor Language Model. We found that the results of log segmentation performed by these methods vary significantly between the natural language domain and the log domain. VotingExperts achieved the best F-score, recording 97.3% for CloudStack and 44.1% for Nokia logs. The results are related to the uni-gram entropy of the log sequence, which differs across software platforms.

The use of automated methods for log analysis is unavoidable in any large company; therefore, it has attracted attention from engineers and researchers. As a result, the number of articles in the field grows yearly and new approaches are frequently proposed. Unfortunately, published research works only sometimes meet the needs of engineers wishing to apply the methods in real-life systems. A common issue is that the method’s benefits often do not compensate for the effort required for its implementation and maintenance. Therefore, engineers must understand the pros and cons of full-scale applications, including the implementation details and the required effort. This work provides a comprehensive review of automated log analysis methods and aims to offer a guide for software engineers who fix integration and production failures. The article categorizes and provides an overview of existing methods and assesses their implementation and maintenance costs, as well as the feasibility of the methods. The article also identifies and describes the shortcomings of existing methods, including concept drift, which is not addressed with sufficient attention, as well as the lack of online benchmarks and the interpretation of the log sequence as a language, without an in-depth analysis of its properties. Despite growing efforts to provide feasible and widely adopted solutions, many reference implementations are unavailable. Consequently, the time and computation complexities differ between various implementations of the same approaches, making the results of research work difficult to replicate in real-life scenarios.

Bluetooth Low Energy (BLE) is a ubiquitous low-power communication technology used in many applications including location-based services. Typically, in BLE localization, the beacons transmit advertisement messages while moving devices infer their location from radio signal strength measurements. Previous work has noted that three radio channels used for advertisement transmission exhibit different propagation conditions including frequency dependent shadowing and multipath. Because most of the consumer electronic devices report only the measured signal strength value without the number of a channel in which it was measured, the variance of the measurements increases and adversely affects the accuracy of localization. Information on the channel used can improve the localization accuracy, however, the existing approaches are limited to three advertisement channels, ignoring the remaining 37 channels available in BLE communication. This article analyses the impact of channel diversity on the accuracy of BLE-based indoor localization. In contrast to previous work, we use signal strength measurements from all 40 channels and show that channel diversity can significantly improve the localization accuracy. Experiments conducted in 100m2 office area show that using signal strength measurements in 40 channels improves the average localization accuracy by approximately 50% and 20% compared to the use of 3 channels without and with information on the channel used, respectively. Overhead of the proposed method can be reduced through careful selection of the radio channels used in measurements.We propose a channel selection method which allows to significantly improve the localization accuracy using measurements collected from between 10 and 15 channels.

Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.