Low Power Wide Area Networks Research Articles

Low-Power Wide-Area Networks: Comparison of LoRaWAN and NB-IoT Performance

Low-power wide-area networks (LPWANs) have become an important enabler for the Internet of Things (IoT) connectivity. Application domains, such as smart cities, smart agriculture, intelligent logistics, and transportation, require communication technologies that combine long transmission ranges and energy efficiency. Recent and future trends make the long-range wide-area network (LoRaWAN) and narrowband-IoT (NB-IoT) the most prospective drivers of the IoT business. In this article, after discussing the main features of the two technologies, we carry out a fair quantitative comparison between the two, investigating different performance indicators, in order to guide designers in the selection of the most appropriate technology, depending on the application requirements.

Modeling LoRa Communications in Estuaries for IoT Environmental Monitoring Systems

Low-power wide-area networks (LPWANs) are extending beyond the conventional terrestrial domain. Coastal zones, rivers, and wetlands, among others, are nowadays common deployment settings for Internet-of-Things nodes where communication technologies such as Long Range (LoRa) are becoming popular. In this article, we investigate large-scale fading dynamics of LoRa line-of-sight (LoS) links deployed over an estuary with characteristic intertidal zones, considering both shore-to-shore (S2S) and shore-to-vessel (S2V) communications. We propose a novel methodology for path-loss prediction which captures: 1) spatial; 2) temporal; and 3) physical features of the RF signal interaction with the environmental dynamics, integrating those features into the two-ray propagation model. To this purpose, we resort to precise hydrodynamic modeling of the estuary, including the specific terrain profile (bathymetry) at the reflection point. These aspects are key to accounting for a reflecting surface of varying altitude and permittivity as a function of the tide. Experimental measurements using LoRa devices operating in the 868-MHz band show major trends in the received signal power in agreement with the methodology’s predictions.

FLoRa: Sequential fuzzy extractor based physical layer key generation for LPWAN

The security of Low-Power Wide-Area Network (LPWAN) mainly relies on encryption for ensuring packet integrity and confidentiality. Unfortunately, the latest LPWAN specifications refrain from specifying how to distribute keys for encryption. In this paper, we tackle this problem via physical layer security, which exploits the channel characteristics to generate secret keys at the physical layer. In order to generate consistent keys from noisy feature sources and to achieve high reconciliation success rate, we propose FLoRa, a physical layer key generation system for LPWAN based on sequential fuzzy extractor. An adaptive multi-bit quantization algorithm is first proposed to generate the initial key, which accelerates the bit generation rate at the start-up procedure. We then design a novel fuzzy extractor by sequentially slicing the initial key, which improves the reconciliation success rate, as well as reduces the key reconciliation time. We implement FLoRa in a LoRaWAN based network prototype and evaluate it by conducting extensive indoor and outdoor experiments. Experimental results reveal that FLoRa is capable of generating consistent secret keys with high key generation performance in both static and dynamic network environments.

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

Blast source TDOA localization with time synchronization estimation based on spatial overpressure-monitoring network

To implement blast source localization precisely by the lowest number of sensor nodes, we propose a time delay of arrival (TDOA) localization method with time synchronization estimation algorithm based on spatial overpressure sensing system (SOSS) using low-power wide-area network (LPWAN) in this paper. The proposed SOSS consisting of an internal spatial wireless sub-array (ISWS) and external wireless planar sub-arrays (EWPS) is to combine the angle with distance to assess the blast source location by the least average error approximation algorithm. The ISWS is used to calculate the incoming angle of blast source towards the central point of the SOSS, while the EWPS calculates the distance from the central of the SOSS to the blast source. We have also proved that the time synchronization algorithm has an obvious impact on the blast range estimation error calculation. The results in the proposed method show that the localization distance and angle error of blast source is within 1 m and 1°in 2D coordinates, respectively. This paper is conducted to provide new insight into blast source localization algorithm and explore how time synchronization of WSN affects localization precision.Index Terms: Blast wave, Overpressure, Time synchronization, LPWAN, WSN.

Decentralized Adaptive Spectrum Learning in Wireless IoT Networks Based on Channel Quality Information

In the Internet of Things (IoT) context such as low power wide area network (LPWAN), it is essential to reduce the packet losses, e.g., to save energy. Decentralized artificial intelligence (AI) techniques have been proposed to combat radio collisions, but the approach here is extended to deal additionally with the channel propagation effects. In this article, a Quality of Channel Allocation (QoC-A) learning technique based on bandit algorithms is proposed in order to choose the transmission channel. This aims to reduce the effect of the propagation impairments between the radio channels while using the effective signal power (ESP) as a quality metric. In addition, a discounted QoC-A (DQoC-A) algorithm is proposed to adapt rapidly to any abrupt change in the channels’ conditions. An experimental campaign on a real IoT device is carried out to demonstrate the low complexity and efficiency of these proposed decentralized algorithms. In the given results, QoC-A outperforms the classical upper confidence bound (UCB) policy with a more accelerated learning process. On the other hand, the feasibility of using the DQoC-A in nonstationary scenarios is illustrated by its rapid convergence when abrupt changes in the channels’ conditions occur. At the end of the process, these proposed learning techniques give 4.1 and 2.4 times fewer packet losses than the traditional ones with a random channel assignment scheme, in the stationary and nonstationary scenarios, respectively.

Reducing Operational Expenses of LoRaWAN-Based Internet of Remote Things Applications.

LoRaWAN has become the most widely used low-power wide-area network technology to implement monitoring solutions based on the Internet of remote things (IoRT) paradigm. Typically, these solutions interconnect remote sensing areas and data processing infrastructure located in urban centers. The operation expenses of these solutions depend mainly on the traffic sent through the network backhaul, i.e., the link that connects the remote sensing area and the urban area where the data are usually processed and stored. This service is provided by telecommunication companies and represents the main operation cost of IoRT solutions. These expenses usually limit the affordability of IoRT-based systems in developing countries, and also in scenarios where the operational cost is an issue to address. This paper presents an extension to the LoRaWAN protocol, named Node-Aware-LoRaWAN (NA-LoRaWAN), that reduces the traffic in the backhaul, thus decreasing the operational expenses of IoRT-based systems. In order to evaluate the performance of NA-LoRaWAN, it was compared to a regular LoRaWAN implementation. Depending on the network scenario, the proposed extension reduced the traffic through the backhaul in the range of 12–34%. This extension opens several opportunities to use IoRT solutions in application domains with a low operational budget, e.g., precision agriculture, environmental monitoring and natural hazards’ early detection.

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation.

This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.

NELoRa

Low-Power Wide-Area Networks (LPWANs) has emerged as a promising mechanism to connect billions of low-cost Internet of Things (IoT) devices for wide-area data collection. Long Range (LoRa) [1] is a commercialized and widely deployed wireless technology that facilitates the establishment of LPWANs. As illustrated in Figure 1, a LoRaWAN consists of end nodes, gateways, a network server, and an application server. The collected sensory data (e.g., temperature, humidity) transmitted from the distributed end nodes are relayed by several gateways to the network server.

Over-the-Air Firmware Updates for Constrained NB-IoT Devices.

The Internet of Things (IoT) is being deployed to provide smart solutions for buildings, logistics, hospitals, and many more. It is growing with billions of connected devices. However, with such tremendous growth, maintenance and support are the hidden burdens. The devices deployed for IoT generally have a light microcontroller, low-power, low memory, and lightweight software. The software, which includes firmware and applications, can be managed remotely via a wireless connection. This improves flexibility, installation time, accessibility, effectiveness, and cost. The firmware can be updated constantly to remove known bugs and improve the functionality of the device. This work presents an approach to update firmware over-the-air (OTA) for constrained IoT devices. We used Narrowband IoT (NB-IoT) as the wireless communication standard to communicate between the managing server and devices. NB-IoT is one of the most promising low power wide area (LPWA) network protocols that supports more than 50k devices within a cell using a licensed spectrum. This work is a proof of concept demonstrating the usage of NB-IoT to update firmware for constrained devices. We also calculated the overall power consumption and latency for different sizes of the firmware.

Performance evaluation of random access in narrow band Internet of Things

Low power wide area network (LPWAN) is one of the enabling technologies for Internet of Things (IoT). 3GPP has introduced a new standard called Narrow Band Internet of Things (NB-IoT) for LPWAN. This paper develops an analytical model to estimate the throughput of User Equipments (UEs) in NB-IoT system. We carry out Markov chain analysis of our model under steady state conditions by considering different Coverage Enhancement (CE) levels, number of preamble transmission attempts, and size of the back-off window as state variables. The Markov chain model is used to derive the transmission and the success probabilities of random access procedure for UEs in different CE levels. Further, we derive the throughput of the packets transmitted by the UEs in different CE levels. The analytical model is validated against simulation for a different set of combinations of parameters. We show that the throughput of NB-IoT critically depends on the allocation of the number of sub-carriers to different CE levels.

IoTMapper: A Metrics Aggregation System Architecture in Support of Smart City Solutions.

Smart cities are, nowadays, an unavoidable and growing reality, supported on software platforms that support city management, through the processing and presentation of a large number of data, obtained from sensors used throughout the cities. Low-power wide area networks (LPWAN) leverage the sensorization process; however, urban landscape, in turn, induces a high probability of change in the propagation conditions of the LPWAN network, thus requiring active monitoring solutions for assessing the city LPWAN network condition. Currently existing solutions usually consider the existence of only one type of LPWAN network to be monitored. In this paper, an architecture for aggregation of metrics from heterogeneous LPWAN networks is presented. The architecture, named IoTMapper, combines purpose build components with existing components from the FIWARE and Apache Kafka ecosystems. Implementation details for the LPWAN networks are abstracted by adapters so that new networks may be easily added. The validation was carried out using real data collected for long-range wide-area network (LoRaWAN) in Lisbon, and a simulated data set extrapolated from the collected data. The results indicate that the presented architecture is a viable solution for metrics aggregation that may be expanded to support multiple networks. However, some of the considered FIWARE components present performance bottlenecks that may hinder the scaling of the architecture while processing new message arrivals.

Adding Redundancy to LoRaWAN for Emergency Communications at the Factory Floor

Wireless communications may be the key solution to satisfy mobility, flexibility, and scalability requirements of the Internet of Things. Particularly, the low power wide area networks, operating in unlicensed bands, are promising because they permit both private and public backends. Unfortunately, their target applications are situations where sporadic transmissions are tolerated. They could not fit scenarios in which high reliability and short round trip time are required. In this article, the LoRaWAN solution is considered for managing emergency and alarm communications. Reliability and timeliness limits have been overcome by jointly using a message replication scheme with variable payload length together with low-cost repeaters. Experiments have been carried out in both fixed and low-speed mobility scenarios (with an interfering traffic of 95 message/h, generated by regular LoRaWAN colocated network). Results, in the former case, show that the success probability of confirmed messages is 100%, the 99.6% of the available data are delivered to the backend, and the average delay decreases by 150 ms. When mobility is considered (e.g., for an automatic guided vehicle use case) results confirm significant improvements for both the transaction success ratio and the data extraction rate that significantly increase up to 95% and 87%, respectively. On the contrary, the overall average delay is not always reduced.

ZCNET: Achieving High Capacity in Low Power Wide Area Networks

In this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves significantly higher network capacity than existing solutions, such as LoRa, Sigfox, and RPMA. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations, and the results confirm that ZCNET achieves significant gains over LoRa, Sigfox, and RPMA. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications.

An Evaluation of Self-Built Low-Power Wide-Area Network Based on LoRa

<p>With the rapid development of applications in Internet of Things (IoT). Low power consumption and wide area is one of the solutions for the development of information transmission. Therefore, Low-Power Wide-Area Network (LPWAN) technology had led to extensive applications and discussions. The Long Range (LoRa) has the characteristics of self-built network and programmable control of communication parameters besides the above features of LPWAN technologies. The LoRa has more flexible application capabilities compared with the LPWAN technology that requires infrastructure provided by Internet Service Providers (ISP). Therefore, this paper describes the evaluation method from the LoRa parameters and the experiment of long-distance transmission. According to the experimental results, the actual effective transmission distance of the lightweight LoRa module from the packet loss rate is 621 meters. Moreover, the phenomena that different types of antennas connected to the transmitter affect the direction of the device are discussed.</p> <p> </p>

Community Internet of Things as Mobile Infrastructure: Methodological Challenges and Opportunities

From smart devices to homes to cities, Internet of Things (IoT) technologies have become embedded within everyday objects on a global scale. We understand IoT technologies as a form of infrastructure that bridges the gaps between offline spaces and online networks as they track, transmit, and construct digital data from and of the physical world. We examine the social construction of IoT network technologies through their technological design and corporate discourses. In this article, we explore the methodological challenges and opportunities of studying IoT as an emerging network technology. We draw on a case study of a low-power wide-area network (LPWAN), a cost-effective radio frequency network that is designed to connect sensors across long distances. Reflecting on our semi-structured interviews with LPWAN users and advocates, participant observation at conferences about LPWAN, as well as a community-based LPWAN project, we examine the intersections of methods and practices as related to space, data, and infrastructures. We identify three key methodological obstacles involved in studying the social construction of networked technologies that straddle physical and digital environments. These include (a) transcending the invisibility and abstraction of network infrastructures, (b) managing practical and conceptual boundaries to sample key cases and participants, and (c) negotiating competing technospatial imaginaries between participants and researchers. Through our reflection, we demonstrate that these challenges also serve as generative methodological opportunities, extending existing tools to study the ways data connects online and offline spaces.

LoRaWAN Networks for Smart Applications in Rural Settings

The Internet of Things is becoming a key enabler for a wide range of novel applications. For example, Smart applications in rural environments can help optimizing agricultural resources. Given rural environment characteristics, the low power wide area network solution LoRaWAN is a perfect candidate, providing wide coverage of several kilometres, with low power consumption and robust communication. Nevertheless, rural scenarios introduce varying conditions that may affect during communication. This article characterizes applications and recommend configurations in rural settings and support using LoRaWAN networks, after validation with a network deployment on different areas that cover foreseen applications and environmental conditions.

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex.

The automation of water leakage detection and monitoring systems has recently been made possible by the Internet of Things (IoT). However, the high cost is an obstacle when applying a network over a large area. The Low-Power Wide-Area Network (LPWAN) was created specifically to address long-range IoT applications. The Long-Range Wide-Area Network (LoRaWAN) is one of the most common LPWANs. In this study, a method for monitoring and detecting water leakage in a housing complex was tested using LoRaWAN. Water leakage was detected using a low-pressure system model comprising a water meter, presser sensor, and smart valve within a LoRa node. This study investigates the use of LoRaWAN for water monitoring and leakage detection by implementing a comprehensive case study to identify LoRaWAN’s feasibility, reliability, and scalability for water monitoring and leakage detection in simulated scenarios. The housing complex varied in size and number of nodes. The LoRaWAN was evaluated by the FloRa simulator package through the Objective Modular Network Testbed (OMNeT++) platform. The results indicated that it was an efficient means of water monitoring and leakage detection in housing complexes.

Fog Computing-Based 5G LPWAN Anomaly Detection for Smart Cities

Known for excellent convenience and abundant facilities, smart cities offer CCTV, delivery robots, security robots, and so on to its residents. Along with the collaboration of IoT (Internet Of Things), the innovation of smart city has gained immense attraction at present. Besides, the risks and challenging in the field of telecommunication still persists as the implemented wireless networks results in traffic and anomaly behaviour. Such issues become critical in case of large-scale infrastructure networks like WSN’s. As such circumstances, to perform efficient health and environment monitoring, the need for a next generation networked system raises. As the traditional anomaly detection schemes doesn’t work out for delay-sensitive environments due to increased latency, we propose a scalable, hybrid spatiotemporal anomaly detection approach that can effectively detect potential anomalies in the network. With the use of real-time stream processing, and other methodologies like Software-Defined Networking (SDN), a Fog Computing-based 5G low-power Wide Area Network (LPWAN) solution is developed and tested on a Antwerp’s City of Things testbed. The proposed approach is found to be beneficial when deployed in a real network environment with nearly 1800 sensor nodes.

Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by $\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.