RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation

The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users' daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users' needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users' and nodes' positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

Positioning by Multicell Fingerprinting in Urban NB-IoT Networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation

A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the L0 known problem and then extends its application to the case of L0 unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms.

LoPATraN: Low Power Asset Tracking by Means of Narrow Band IoT (NB-IoT) Technology

The narrowband Internet-of-Things (NB-IoT) communication standard is gaining momentum within the big picture of the Internet-of-Things (IoT) owing to its capabilities of ensuring pervasive and wide coverage while limiting power consumption. Therefore, it turns out to be a valuable enabling technology within a considerable number of applications. Apart from traditional remote monitoring and data acquisition purposes where comparable Low Power Wide Area Network (LPWAN) facilities have ruled for years, NB-IoT can potentially carve out space within specific alcoves in which low latency, low power, high data-rates and ubiquitous coverage are fundamentals requirements. Long term asset tracking definitely falls within such niches, and in particular NB-IoT can become a valuable alternative to be exploited by both replacing the conventional Global Position System (GPS) system, or supporting it. To this end, this paper proposes an innovative tracking system prototype for asset shipping which relies on two enabling technologies: GPS and NB-IoT. While position transmission is always put into effect via NB-IoT, it can be fetched by resorting to both GPS (like a standard tracker) or NB-IoT (thus establishing a GPS-less method). As a result, two localization techniques are arranged: the former one is preciser but energy hungrier, while the latter one is coarser but more low power. Such working principles were successfully tested on the field by means of two road tests in as much itineraries. Tests results are in agreement with the expectations underlying the two working principles since the finer one provides a more accurate tracking. In addition, a consumption analysis was also performed aiming at assessing the prototype lifetime. Finally, tests pursuing the assessment of the tracking error were carried out underling the fact that it strongly depends on the geographic deployment of NB-IoT towers.

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.