Range Communication Technology Research Articles

Distributed Embedded System for Air Quality Monitoring based on Long Range (LoRa) Technology

This study addresses the persistent challenge of urban air quality deterioration through the introduction of the Distributed Embedded System (DES). In response to limitations associated with conventional air quality measurement methods, the DES system offers a cost-effective and portable alternative for real-time monitoring. The study focuses on implementing a low-cost distributed system and developing effective communication protocols between sensor nodes. Equipped with Metal Oxide Semiconductor (MOS) sensors for pollutant gases, optical sensors for particulate matter, and meteorological sensors, the system strategically deploys multiple nodes within a 4 km range of urban areas. Real-time AQI and pollution severity have been measured for various locations. Implementation of Long Range (LoRa) communication technology allows the sensor nodes to efficiently transmit data to a central base station. Observations of received signal strength and signal quality indicate reliable and effective communication. To validate accuracy and reliability, acquired DES system data undergoes comparative analysis with data from a government-established meteorological station, revealing a strong correlation. This innovative approach presents a promising solution for widespread, continuous, and cost-effective real-time air quality monitoring in urban areas. The DES system addresses key challenges associated with air pollution, offering a portable and accessible tool that could revolutionize urban air quality management.

Remote Low-Cost Web-Based Battery Monitoring System and Control Using LoRa Communication Technology

Batteries are a complex electrochemical device that exhibit non-linearity and stochastic behavior which rely upon the operational conditions and environmental factors, making battery monitoring a vital feature throughout its application. This paper introduces a novel web-based battery monitoring and control system that utilizes Long Range (LoRa) communication technology, an integral part of the Internet of Things (IoT). The system is implemented with the ESP32 microcontroller, with an emphasis on affordability in broader applications. The system provides comprehensive real-time online data by integrating a combination of multiple sensors. The proposed system seeks to address the limitations of existing communication technology by utilizing the benefits of LoRa, a technology that facilitates effective long-range, low-energy communication which makes it particularly well-suited for real-time monitoring applications. In addition, a control operation enables users to regulate crucial aspects of batteries, such as their charging and discharging. The research conducted a meticulous experimental evaluation of the proposed system at different operations, and the results successfully aligned with the main objective and aims of the research. The proposed system successfully enables real-time remote monitoring and user control, long-term data visualization through data logging, and assessment of battery conditions. Data logging was introduced to enhance the utilization of future battery evaluation, such as State-of-Charge (SOC), State-of-Health (SOH) and Remaining Useful Life (RUL). As a result, the developed system makes it suitable for many applications requiring effective energy storage solutions, such as renewable energy and Electric Vehicle (EV) applications.

Emergency evacuation based on long range communication technology

The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building structures. In this study, we develop an Intelligent Emergency Evacuation System (IEES) based on the Long Range (LoRa) communication technology and the Evacuation Decision Optimization Model (EDOM). The IEES comprises three parts, i.e., multi-sensor Fire Monitoring System (FMS), Central Control System (CCS), and Wireless Evacuation Indication System (WEIS). The LoRa communication technology is used for data transmission among the three parts of the IEES to assist large scale evacuation. The EDOM is developed to support the CCS based on the improved adaptive ant colony algorithm. To facilitate the evacuation decision optimization, the initial node database in the evacuation spatial network is established intelligently on the Building Information Modeling (BIM) platform. An on-site application in a subway station with three underground floors is conducted. The results demonstrate that the IEES can guide people in evacuating from hazardous locations according to the state of the fire. Moreover, the phenomena of crowd retention and congestion at a few certain evacuation exits are considerably reduced to achieve global optimization of the evacuation path and accomplish the objective of intelligent emergency evacuation. Furthermore, WEIS can maintain normal operation and have good visibility in the fire environment of heat radiation and thick smoke. The IEES can realize dual wireless indication of vision and sound perception, and its response time is slightly affected by the building structure and transmission distance. The electromagnetic signal of electrical equipment and the operational state of vehicles in and out of the subway station causes the response time to a relatively larger lag. The response time of the IEES is delayed up to 14 ms. Nonetheless, it fulfills the requirements of response time of evacuation indication signs stated in the Technical Standard for Fire Emergency Lighting and Evacuate Indicating System.

Prototype of a System for Tracking Transit Service Based on IoV, ITS, and Machine Learning

The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control.

Conceptualising flood warning system for connected vehicles

Floods are a common natural hazard in Malaysia during the monsoon season. It affects millions of people each year that leads to severe deaths and infrastructure destruction. In recent time, flood warning system (FWS) has been a notable topic but it has not been extensively implemented in Malaysia. In this study, we developed a FWS that can interface with connected vehicles in order to provide alerts to drivers while also sending warnings to end users. This type of FWS enables vehicles to connect with one another within a particular radius to broadcast flood information via long range (LoRa) communication technology. When the water level rises over a certain point, the system sends a warning to drivers indicated through a mobile application. Drivers have the option to take alternative route, reducing the likelihood of damage when driving into or near a flooded area. The developed application demonstrates that the warning was able to be instantly displayed to the driver if there is a significant increase in water level. Experimental evidence shows that the driver was able to receive the water level alert and a visual interpretation of the immediate area affected by flood through the application.

Application and growth of long-range communications technology in vehicular communications

<p><span lang="EN-US">Long range communications technology (LoRa) has been widely used in a variety of applications and researched in different domains to exploit its full potential. Its openness makes it ideal for a variety of internet of things (IoT) installations which further allows opportunities for viable solutions in vehicular communications. Hence, a bibliometric analysis was performed to distinguish the application and growth of the technology specifically in vehicular communications. The scoping review processes from Arksey and O’Malley was applied to guide the review process. The selected scholarly works adhered to the PRISMA-Sc framework where 385 articles from two main electronic databases, i.e., Scopus and Science Direct which discussed LoRa in vehicular communications contexts were assessed. This study aims to: i) examine how LoRa’s research has grown from year 2010 to 2021 among the scholars; ii) determine key areas discussed in LoRa’s vehicular communications research. Findings from 70 studies in the final analysis indicated that LoRa has been widely studied based on application, theoretical or protocol and performance. However, it has not been widely explored in vehicular context. Hence, our findings support the global research community in this context.</span></p>

Fleet management and control system for developing countries implemented with Intelligent Transportation Systems (ITS) services

Fleet Management Control Systems (FMCS) monitor vehicles in real-time and help assessing schedule compliance. Transit services in developing countries must improve in aspects such as information about routes, schedules and users; and compliance with traffic regulations, to achieve an increase in their demand. Although a FMCS can deal with these issues, currently implemented systems have some limitations related to communications technology, associated costs, interoperability and standardization. This paper includes the following: 1) The Intelligent Transportation System (ITS) architecture designed for the FMCS; 2) The development of a FMCS prototype for developing countries, considering the proposed architecture, using the Long Range communication technology and Intelligent Transportation Systems services; 3) A validation and testing of the FMCS prototype, using transit vehicles in a city. The developed prototype was successfully tested. The ideal operating parameters of the FMCS in terms of communications technology were identified in one of the four routes used in the tests. The results in such route were validated with additional tests, using the same parameters, and reaching similar operating values. A data set with the information of the performed tests was generated. With the results obtained in the FMCS, a better control of the transit service can be achieved, helping to improve upon the problems identified for developing countries (especially in medium-sized cities) related to route and schedule compliance, speeding, safety, standardization and helpful information for users. Additionally, service operation costs are reduced by using cutting-edge communications technology and promoting interoperability with other mobility services created for these cities.

On the Error Performance of LoRa-Enabled Aerial Networks Over Shadowed Rician Fading Channels

UAVs can be used as aerial relays to provide communication services in remote uncovered areas or dense environments with occasional high capacity demands. However, due to the low power of Internet-of-Things (IoT) devices, UAV-based IoT applications, such as precision agriculture and environment monitoring, may experience high shadowing or equipment failure, which degrades the communications’ quality between IoT devices and their gateways. To tackle this issue, we consider the long range (LoRa) communication technology. Specifically, we investigate the performance of LoRA-enabled aerial communications, where a LoRa gateway communicates with a distant IoT device through the assistance of an amplify-and-forward (AF) aerial relay. Under the assumption of shadowed Rician fading channels, we characterize at first the end-to-end LoRa communication link. Then, we derive an exact symbol error rate expression for the underlying system model. Finally, numerical results are presented to corroborate the efficacy of our derived expressions and provide valuable insights into the error performance of LoRa-enabled aerial networks.

Accelerometer-based elderly fall detection system using edge artificial intelligence architecture

Falls have long been one of the most serious threats to elderly people's health. Detecting falls in real-time can reduce the time the elderly remains on the floor after a fall, hence avoiding fall-related medical conditions. Recently, the fall detection problem has been extensively researched. However, the fall detection systems that use a traditional internet of things (IoT) architecture have some limitations such as latency, high power consumption, and poor performance in areas with unstable internet. This paper intends to show the efficacy of detecting falls in a resource-constrained microcontroller at the edge of the network using a wearable accelerometer. Since the hardware resources of microcontrollers are limited, a lightweight fall detection deep learning model was developed to be deployed on a microcontroller with only a few kilobytes of memory. The microcontroller was installed in a low-power wide-area network based on long range (LoRa) communication technology. Through comparative testing of different lightweight neural networks and traditional machine learning algorithms, the convolutional neural network (CNN) has been shown to be the most suited, with 95.55% accuracy. The CNN model reached inference times lower than 37.84 ms with 61.084 kilobytes storage requirements, which implies the capability to detect fall event in real-time in low-power microcontrollers.

An IoT Measurement System Based on LoRaWAN for Additive Manufacturing.

The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible.

LoRaChainCare: An IoT Architecture Integrating Blockchain and LoRa Network for Personal Health Care Data Monitoring

Over the past several years, the adoption of HealthCare Monitoring Systems (HCS) in health centers and organizations like hospitals or eldery homes growth significantly. The adoption of such systems is revolutionized by a propelling advancements in IoT and Blockchain technologies. Owing to technological advancement in IoT sensors market, innovations in HCS to monitor patients health status have motivated many countries to strength their efforts to support their citizens with such care delivery systems under the directives of a physician who has access to patient’s data. Nevertheless, secure data sharing is a principal patient’s concern to be comfort to use such systems. Current HCS are not able to provide reassuring security policies. For that, one of our focus in this work, is to provide security countermeasures, likewise cost-efficient solution for HCS by integrating storage model based on Blockchain and Interplanetary File Systems (IPFS). Blockchain technology is an emerging solution in pharmaceutical industry and starts to take place for HCS and allows HealthCare providers to track connected devices and control access to shared data, hence protecting patients’ privacy. Furthermore, the addition of Edge and Fog computing has improved HCS to react in real-time and enhance their reliability. A variety of communication protocols can connect sensor devices to edge/Fog layer and the best choice will depend upon connectivity requirements: range, bandwidth, power, interoperability, security, and reliability. Instead, systems efficiency would decline and hurt if communication protocol is inconsistent. LoRa (Long Range) communications technology is emerging as the leader among Low-Power Wide-Area Networks (LPWANs) entering the IoT domain benefiting from many features such as long-range distances and low power consumption. This work proposes LoRaChainCare, an architecture model for HCS which combines the technologies Blockchain, Fog/Edge computing, and the LoRa communication protocol. A real implementation of LoRaChainCare system is presented and evaluated in terms of cost, run time and power consumption.

INTELLIGENT AQUACULTURE ENVIRONMENT MONITORING SYSTEM BASED ON LORA COMMUNICATION TECHNOLOGY

In view of the nonlinearity and large time delay characteristics of the aquaculture, this paper proposes an environment monitoring system based on low long range (LoRa) communication technology. The system integrates sensor nodes, dissolved oxygen regulation node, LoRa communication network and personal computer (PC) platform to realize real-time monitoring, storage and data sharing. The environmental parameters were processed by programmable logic controller (PLC) intelligent controller, in which, the dissolved oxygen was adjusted with the variable universe fuzzy PID algorithm. The system was tested in a fish pond with an area of 110*120 square meters. The results show that the system can obtain temperature, pH and dissolved oxygen in real time and it has the advantages of high measurement accuracy, stable and reliable data transmission, and can satisfy the needs of aquaculture intelligent management.

RF-Powered Low-Energy Sensor Nodes for Predictive Maintenance in Electromagnetically Harsh Industrial Environments.

This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRP) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%.

Experimental Analysis of IoT Networks Based on LoRa/LoRaWAN under Indoor and Outdoor Environments: Performance and Limitations

Nowadays, Internet of Things (IoT) has multiple applications in different fields. This concept allows physical devices to connect to the internet in order to establish a strong infrastructure that facilitates many device control and monitoring tasks. Low Power Wide Area (LPWA) communication protocols become widely used for IoT networks because of their low power consumption and the broad range communication. LPWA enables devices to transmit small amounts of data in a long distance. Among LPWA protocols, LoRa technology gained a lot of interest recently from the research community and many companies. LoRa is a long range and low power wireless communication technology regulated by the LoRaWAN standard. It can be o good candidate to deploy node network where long distance and extended battery life is required. A LoRaWAN architecture is deployed in a star-of-stars topology and based on a systematic evaluation of a long-term operation of the network monitoring. This works describes experimental results of testing LoRa in indoor and outdoor environments to understand how it works, evaluate its performance, and limitations. As expected, results show that LoRa performs better outdoor. It is also interesting to note that elevating the gateway in order to have a free line of sight with the IoT node, or close to it, increases the signal quality received by the end-node devices, and consequently, longer distances can be achieved.

Long Range Communication Technology for Weather Buoy

Long Range Communication Technology for Weather Buoy

LoRa Traffic Generator Based on Software Defined Radio Technology for LoRa Modulation Orthogonality Analysis: Empirical and Experimental Evaluation.

The digital revolution has changed the way we implement and use connected devices and systems by offering Internet communication capabilities to simple objects around us. The growth of information technologies, together with the concept of the Internet of Things (IoT), exponentially amplified the connectivity capabilities of devices. Up to this moment, the Long Range (LoRa) communication technology has been regarded as the perfect candidate, created to solve the issues of the IoT concept, such as scalability and the possibility of integrating a large number of sensors. The goal of this paper is to present an analysis of the communication collisions that occur through the evaluation of performance level in various scenarios for the LoRa technology. The first part addresses an empirical evaluation and the second part presents the development and validation of a LoRa traffic generator. The findings suggest that even if the packet payload increases, the communication resistance to interferences is not drastically affected, as one may expect. These results are analyzed by using a novel Software Defined Radio (SDR) technology LoRa traffic generator, that ensures a high-performance level in terms of generating a large LoRa traffic volume. Despite the use of orthogonal variable spreading factor technique, within the same communication channel, the collisions between LoRa packets may dramatically decrease the communication performance level.

Using Reinforcement Learning With Partial Vehicle Detection for Intelligent Traffic Signal Control

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.

The extent of reliability for vehicle-to-vehicle communication in safety critical applications: an experimental study

Vehicle-to-vehicle (V2V) communication using Dedicated Short Range Communications (DSRC) technology promises to help drastically reduce vehicle collisions. DSRC allows vehicles in a highly mobile and complex network to send and receive safety messages with more reliability and lower latency compared with other wireless technologies used for automotive communications. However, there are many factors that could cause a safety-critical automotive application to become unreliable due to communication failures. While the reliability of V2V communication has been a subject of study by several researchers, there are still open questions regarding how the placement of the DSRC devices (inside or outside the host vehicle), the vehicle’s interior elements and the differences in altitude can affect the V2V communications. This article provides experimental testing data and analyses in order to quantify the impacts of relative vehicle speeds, altitude differences between vehicles, and interior obstacles on V2V communication range and reliability for opposite traffic, in both city and highway environments. We discuss how these results can adversely affect the design parameters of safety critical applications by considering the V2V application “Safe Pass Advisory” on two-lane rural highways.

Balancing Power and Rate Control for Improved Congestion Control in Cellular V2X Communication Environments

The Society of Automotive Engineers (SAE) J2945/1 standard prescribes a wireless channel congestion control algorithm for periodic Basic Safety Messages (BSMs) in vehicular safety communication. The algorithm is designed to work with the Dedicated Short Range Communication (DSRC) technology as the radio interface, and utilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are supposed to work in a complementary manner. An equivalent standard is being made for the cellular vehicle-to-everything (C-V2X) communication environment, and it is expected that the J2945/1 congestion control algorithm will be reused. Unfortunately, we find that when transplanted to the C-V2X communication environment, the J2945/1 algorithm exhibits a poor balance between the two control elements under congestion. Specifically, the Tx rate control is pushed to its limit much earlier than the Tx power control. We believe that this is due to the new relation formed between the respective inputs to the control elements, channel busy ratio (CBR) and the vehicle density (VD), induced by the different radio technology used in the C-V2X communication. The consequence of the unbalanced control dynamics is the degraded update delay of neighboring vehicles' kinematics due to the over-exerted rate control, which potentially undermines the driving safety. In this paper, we propose to adjust the rate control function to react to congestion in a more relaxed manner so that the power control contributes more actively to the overall congestion control. With the re-balancing between the control elements, we show that the update delay improves, where larger improvements are obtained in closer, more safety-critical distances from the BSM transmitter. We believe that in future efforts to define the standard congestion control algorithm for the C-V2X environment, the balanced control aspect as explored in this paper should be considered in the design phase.

Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture

Precision agriculture is a suitable solution to these challenges such as shortage of food, deterioration of soil properties and water scarcity. The developments of modern information technologies and wireless communication technologies are the foundations for the realization of precision agriculture. This paper attempts to find suitable, feasible and practical wireless communication technologies for precision agriculture by analyzing the agricultural application scenarios and experimental tests. Three kinds of Wireless Sensor Networks (WSN) architecture, which is based on narrowband internet of things (NB-IoT), Long Range (LoRa) and ZigBee wireless communication technologies respectively, are presented for precision agriculture applications. The feasibility of three WSN architectures is verified by corresponding tests. By measuring the normal communication time, the power consumption of three wireless communication technologies is compared. Field tests and comprehensive analysis show that ZigBee is a better choice for monitoring facility agriculture, while LoRa and NB-IoT were identified as two suitable wireless communication technologies for field agriculture scenarios.