GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

Low-Cost Traffic Sensing System Based on LoRaWAN for Urban Areas

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a largescale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lComment: 7 pages, accepted to Emerging Topics in Wireless (EmergingWireless) in CoNEXT 202

Low-cost traffic sensing system based on LoRaWAN for urban areas

The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a large-scale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lessons learned from such a deployment for traffic sensing.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database

A fog based approach for hazards differentiation in an IIoT scenario

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Industrial control systems (ICS) are applied in many critical infrastructures. Reducing reconfiguration time after hazard leads to safety improvement, so it is one of the most important objectives in these systems. Hazards can be due to the “system failure” or “cyber-attacks” factors. One of the procedures that can reduce the reconfiguration time is determining as soon as possible the cause of hazards based on the above mentioned factors. Differentiation of attack from failure without redundant data in addition to data from the system sensors is not possible. With advent of the IoT as IIoT, a condition is developed to provide the required redundant data; however, by increasing the number of IIoT devices within a factory, the generated data volume becomes too large. In this paper we describe a fog-based approach applied in a factory to deal with such increasing complexity. We compare the proposed method with a traditional cloud-based solution. According to the results, the proposed method leads to a reduction of 60% lost time in the recovery reconfiguration step of the system.Peer ReviewedPostprint (author's final draft

An IIoT based ICS to improve safety through fast and accurate hazard detection and differentiation

Safety and security of Industrial Control Systems (ICS) applied in many critical infrastructures is essential. In these systems, hazards can be due either to system failure or cyber-attacks factors. Accurate hazard detection and reducing reconfiguration time after hazard is one of the most important objectives in these systems. One of the procedures that can reduce the reconfiguration time is determining the cause of hazards and, based on the aforementioned factors, adopting the best commands in reconfiguration time. However, it is difficult to differentiate between different types of hazard because their effects on the system can be similar. With the advent of IoT into ICS, known as IIoT, it has become possible to differentiate the hazards through the adoption of data from different IIoT sensors in the environment. In this article, we propose a risk management approach that identifies hazards based on the physical nature of these systems with the support from the IIoT. The identified hazards fall into four categories: stealthy attack, random attack, transient failure, and permanent failure. Then, the reconfiguration process is run based on the proposed differentiation, which provides a better performance and reconfiguration time. In the experimental section, a fluid storage system is simulated, showing 97% correct differentiation of hazards and reducing in 60% the lost time in the system recovery reconfiguration.This work is supported by the Spanish Ministry of Economy and Competitiveness and by the European Regional Development Fund under Contract RTI2018-094532-B-I00 (MINECO/FEDER).Peer ReviewedPostprint (published version

Improvement of RSSI-Based LoRaWAN localization using Edge-AI

Localization is an essential element of the Internet of Things (IoT) leading to meaningful data and more effective services. Long-Range Wide Area Network (LoRaWAN) is a low-power communications protocol specifically designed for the IoT ecosystem. In this protocol, the RF signals used to communicate between IoT end devices and a LoRaWAN gateway (GW) can be used for communication and localization simultaneously, using distinct approaches, such as Received Signal Strength Indicator (RSSI) or Time Difference of Arrival (TDoA). Typically, in a LoRaWAN network, different GWs are deployed in a wide area at distinct locations, contributing to different error sources as they experience a specific network geometry and particular environmental effects. Therefore, to improve the location estimation accuracy, the weather effect on each GW can be learned and evaluated separately to improve RSSI-based distance and location estimation. This work proposes an RSSI-based LoRaWAN location estimation method based on Edge-AI techniques, namely an Artificial Neural Network (ANN) that will be running at each GW to learn and reduce weather effects on estimated distance. Results have shown that the proposed method can effectively improve the RSSIbased distance estimation accuracy between 6% and 49%, and therefore reduce the impact of the environmental changes in different GWs. This leads to a location estimation improvement of approximately 101 m.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/