Showing papers by "Marco Zennaro published in 2020"

LPWAN Technologies: Emerging Application Characteristics, Requirements, and Design Considerations

Abstract: Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. This paper focuses on defining a systematic and powerful approach of identifying the key characteristics of such applications, translating them into explicit requirements, and then deriving the associated design considerations. LPWANs are resource-constrained networks and are primarily characterized by long battery life operation, extended coverage, high capacity, and low device and deployment costs. These characteristics translate into a key set of requirements including M2M traffic management, massive capacity, energy efficiency, low power operations, extended coverage, security, and interworking. The set of corresponding design considerations is identified in terms of two categories, desired or expected ones and enhanced ones, which reflect the wide range of characteristics associated with LPWAN-based applications. Prominent design constructs include admission and user traffic management, interference management, energy saving modes of operation, lightweight media access control (MAC) protocols, accurate location identification, security coverage techniques, and flexible software re-configurability. Topological and architectural options for interconnecting LPWAN entities are discussed. The major proprietary and standards-based LPWAN technology solutions available in the marketplace are presented. These include Sigfox, LoRaWAN, Narrowband IoT (NB-IoT), and long term evolution (LTE)-M, among others. The relevance of upcoming cellular 5G technology and its complementary relationship with LPWAN technology are also discussed.

Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda.

Abstract: Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data.

A Low-Cost and Low-Power Messaging System Based on the LoRa Wireless Technology

Abstract: In this paper we describe a low-cost and low-power consumption messaging system based on LoRa technology. More that one billion people worldwide cannot access even the most basic connectivity services. For them even simple messaging services would be of great help, for example to farmers wishing to know the price of goods they want to sell or buy before deciding whether a possibly long, expensive and exhausting trip is undertaken. LoRa networks allow for very long wireless links that can connect villages and towns. This system falls in the category of community networks, where users build their own network where no commercial infrastructure is available. In addition to the simple messaging application, LoRa can be used to distribute sensor information to communities or to provide disaster alerts or meteorological data.

Introduction to low-power wide-area networks

Abstract: With the emergence of the Internet of things (IoT) and machine-to-machine (M2M) communications, massive growth in the sensor node deployment is expected soon. According to IHS Markit forecast, the number of connected IoT devices would grow to 125 billion by 2030. The exponential growth in IoT is impacting virtually all stages of industry and nearly all market areas. It is redefining the ways to design, manage, and maintain the networks, data, clouds, and connections. To support the requirements of new applications, an innovative paradigm called low-power wide-area networks (LPWAN) is evolved. The LPWAN is a class of wireless IoT communication standards and solutions with characteristics such as large coverage areas, low transmission data rates with small packet data sizes, and long battery life operation. The LPWAN technologies are being deployed and have shown enormous potential for the vast range of applications in IoT and M2M, especially in constrained environments. This chapter focuses on a general introduction to LPWANs, innovative applications and services, requirements, wireless access, and characteristics.

Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

Abstract: The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.

GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study

Abstract: Nowadays with the evolution of Internet of Things (IoT), building a network of sensors for measuring data from remote locations requires a good plan considering a lot of parameters including power consumption. A Lot of communication technologies such as WIFI, Bluetooth, Zigbee, Lora, Sigfox, and GSM/GPRS are being used based on the application and this application will have some requirements such as communication range, power consumption, and detail about data to be transmitted. In some places, especially the hilly area like Rwanda and where GSM connectivity is already covered, GSM/GPRS may be the best choice for IoT applications. Energy consumption is a big challenge in sensor nodes which are specially supplied by batteries as the lifetime of the node and network depends on the state of charge of the battery. In this paper, we are focusing on static sensor nodes communicating using the GPRS protocol. We acquired current consumption for the sensor node in different locations with their corresponding received signal quality and we tried to experimentally find a mathematical data-driven model for estimating the GSM/GPRS sensor node battery lifetime using the received signal strength indicator (RSSI). This research outcome will help to predict GPRS sensor node life, replacement intervals, and dynamic handover which will in turn provide uninterrupted data service. This model can be deployed in various remote WSN and IoT based applications like forests, volcano, etc. Our research has shown convincing results like when there is a reduction of −30 dBm in RSSI, the current consumption of the radio unit of the node will double.

Evaluating the performance of NRENs in deploying IoT in Africa: the case for TTN

Abstract: The growth of the Internet worldwide has been fuelled by the development of the “National Research and Education Networks” (NRENs), i.e., networks of academic and educational institutions. In Africa the establishment of NRENs is more recent. In this paper we analyse the readiness of African NRENs to be part of “The Things Network” (TTN), a network of IoT gateways that has fostered the growth of IoT in Europe by adopting a community network model. We analyse RTT and packet loss toward the nearest TTN network server, in African countries where RIPE Atlas (RIPE - “Reseaux IP Europeens”, French for “European IP Networks”) probes are hosted both in academic and commercial networks. Our conclusion is that NRENs and commercial ISPs are on an equal foot in hosting TTN gateways in most countries we considered.

FUDGE: a frugal edge node for advanced IoT solutions in contexts with limited resources

Abstract: The growing connection between the Internet of Things (IoT) and Artificial Intelligence (AI) poses many challenges that require novel approaches and even a rethinking of the entire communication and processing architecture to meet new requirements for latency, reliability, power consumption and resource usage. Edge computing is a promising approach to meet these challenges that can also be beneficial in delivering advanced AI-based IoT solutions in areas where connectivity is scarce and resources are generally limited.In this paper, we introduce an edge/fog generic architecture to allow the adoption of edge solutions in IoT deployments in poorly connected and resource limited scenarios. To this end, we integrate, using microservices, an MQTT based system that can collect ingress data, handle their persistency, and coordinate data integration with the cloud using a specific service called aggregator. The edge stations have a dedicated channel with the aggregator based on LoRa to enable long-range transmissions with low power consumption. Some details of the implementation aspects are described along with some preliminary results. Initial testing of the architecture indicates that it is flexible and robust enough to become an alternative for the deployment of advanced IoT services in resource-constrained contexts.

A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring

Abstract: Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.

TV white spaces for low-power wide-area networks

Abstract: With digital TV switchover and using cognitive radio technology, a spectrum of unoccupied TV channels, called TV white spaces (TVWS), can be used for different applications. Radio signals in these bands travel farther and penetrate obstacles more effectively than those in cellular and 2.4 GHz band owing to the lower frequency. With the growth in wireless services and higher data rate applications, the demand for spectrum is also increasing. Studies show that such licensed spectrum is underutilized. This chapter covers the study on TVWS, its architecture, and protocols, TVWS over low-power wide-area networks, along with its applications and future challenges and opportunities.

Adding voice messages to a low-cost long-range data messaging system

Abstract: Remote locations in rural areas can benefit from any system that would provide some form of connectivity. In a previous work we described a low cost architecture that, thanks to the use of the LoRa technology, allowed long links using low energy and a cheap infrastructure. In this work we extend those results by adding the possibility to include generic external sources of data using an MQTT based interface. More specifically, we describe a voice messaging system that allows users that cannot read or write to send voice based messages. We describe how the system was integrated into the existing platform and present some performance results. We consider that these results are promising and provide a tool that can offer a useful service at a low cost.

Hardware and software platforms for low-power wide-area networks

Abstract: Low-power wide-area network (LPWAN) has become one of the prominent solutions for long-range and lower-power Internet of things and machine-to-machine applications. LPWANs enable communication with a large number of low-complex and low-cost remote nodes and objects with very low-power consumption, extending the lifetime of battery-operated nodes to months and years. It has a wide range of emerging applications in industry, smart cities, environment monitoring, agriculture, health care, and many other fields. There are several candidates for LPWAN technologies, and the selection of appropriate technology for a given application is a challenging task. This chapter presents the study of various LPWAN hardware and software platforms available in the market. It also describes various open-source tools available for simulations and research.

Mobile Crowdsensing Based Road Surface Monitoring Using Smartphone Vibration Sensor and Lorawan

Abstract: Road surface monitoring is a critical activity in road transport infrastructure management. In this paper, we present a mobile crowd-sensing based road surface monitoring using Smartphone sensors and a LoRaWAN network. Using the accelerometer and GPS sensors of the Smartphone, it's possible to measure vibration and where it happens, enabling the generation of reports of road conditions and anomalies. These reports can be transmitted by low-cost, low-power and secure communication links provided by the LoRaWAN network infrastructure thus saving the added cost of transmitting them over the cellular network. We focus on monitoring the asphalt road surface using a machine learning model classifying the vibration generated by vehicles as pothole, speed bump, damaged road or patched road. As proof of concept, we developed a mobile application with a built-in machine learning model to detect and classify road condition. To reduce the bandwidth consumption, the application reports only road condition classification instead of sending the raw vibration signal. The main objective is to reduce the burden of manual inspection and measurement while minimizing communication cost. Our approach was tested and evaluated by real-world experiments in a road segment.

[Invited] LoRaCTP: a LoRa based Content Transfer Protocol for sustainable edge computing

Abstract: In this paper we present a flexible protocol based on LoRa technology that allows for the transfer of “content” to large distances with very low energy. LoRaCTP provides all the necessary mechanisms to make LoRa reliable, by introducing a lightweight connection set-up and ideally allowing the sending of an as-long-as necessary data message. We designed this protocol as a communication support for edge based IoT solutions given its stability, low power usage and the possibility to cover long distances. We present the evaluation of the protocol with various sizes of data content and various distances to show its performance and reliability.

The Blindspot of Electromagnetic Interference Affecting Sensitive Medical Devices at Hospitals and Healthcare Facilities

Abstract: Electromagnetic interference (EMI) can affect a hospital's sensitive medical equipment when the EMI is powerful enough and close enough to cause partial or complete failure of the equipment or its sub systems. Particularly amidst a lack of EMI shielding, the closer in proximity the EMI is to susceptible medical equipment, the chance of interference grows and is more likely to occur. Failure of critical electronic medical equipment can be life-threatening and should be prevented whenever possible. Power recycling/re-calibration time can be a lengthy process, so EMI should be mitigated to the maximum extent possible. The adverse impact of EMI, as pertains to sensitive medical devices, may only be transient in some instances, but it may also have profound impact leading to patient injury. True EMI immunity can only be ascertained by careful measurement and testing, and this is usually not performed routinely as hospitals and healthcare facilities have rapidly expanded. Indeed, while alternate current (AC) powerline EMI filters may be commonly employed, oftentimes what is in plain sight is overlooked — that of “noise” from a direct current (DC) source from a nearby instrument transformer, disconnecting switch, and similar components. The National Institutes of Health (NIH) have reported that several medical-related devices have had operational issues due to radiofrequency interference (RFI), particularly as pertains to super low frequency (SLF) (in the frequency range of 30 Hz up to 300 Hz), such as for power grids.

On Time Synchronization of LoRaWAN Based IoT Devices for Enhanced Event Correlation

Abstract: Low power wide area network (LPWAN) technologies are becoming prominent in the era of the Internet of Things. In this context, LoRa long range technology emerges as a good solution thanks to its property of providing long-range along with low power although with limited bandwidth. In this paper, we focus on the use of a LoRa network for time synchronization among various nodes, to support message transmission and critical data sharing in a correct real-time manner. When providing a reliable collaborative service, clock synchronization is required, for example to apply AI solutions in IoT. In this paper, we evaluate different methodologies based on LoRa network time synchronization using commodity low cost hardware so that our solution could be adopted even in low operating expenditures or rural contexts.

LoRa Transmission Over Rayleigh Fading Channels in Presence of Interference

Abstract: Although LoRa (long range) technology is designed to tackle interference and power consumption issues, interference from other LoRa and non-LoRa networks limits its performance. In practice, LoRa performance also gets constrained by multipath fading. This paper presents the study of LoRa transmission over Rayleigh fading. We considered different scenarios of LoRa networks along with simultaneous parallel transmissions, change in transmission bandwidths, and Doppler shifts. Results demonstrate deviation in the BER because of multipath and interference. It is also evident that even for a change in transmission bandwidth or Doppler shift for Rayleigh fading channel, LoRa performance gets impacted.

TurboLoRa: enhancing LoRaWAN data rate via device synchronization

Abstract: Over the last few years we have witnessed an exponential growth in the adoption of LoRaWAN as LPWAN technology for IoT. While LoRaWAN offers many advantages, one of its limitations is the paltry data rate. Most IoT applications don't require a high throughput but there are some that would benefit from a higher data rate. In this paper, we present TurboLoRa, a system that combines the strengths of LoRaWAN while providing a higher data rate by synchronizing the transmission of multiple LoRaWAN devices. Our proposal allows to combine cheap devices making it a frugal solution to this kind of problems. We present some preliminary results obtained using a real prototype of TurboLoRa.

On the Use of LoRaWAN Technology for Independent Monitoring of Unreliable Campus WIFI Networks

Abstract: We designed and deployed a networking monitoring tool based on LoRaWAN for a campus network in Benin. Using affordable LoRaWAN devices and by placing the server in our network, we have been able to measure and to analyze network coverage and failures in real-time. We can receive SMS alerts when a WiFi access point does not work correctly. It is then an affordable and scalable solution for Campus networks with an unreliable cable network.

Enhancing Education in the Rural Community through Online Training

Abstract: Everybody irrespective of status, gender or geographical location is entitled to basic education. However, increasing African population is increasing the strain on existing resources and infrastructure even for education.Nigeria has the highest out of school population in the world as it is finding it hard to provide education to all its youths and adolescents. The available data show that 60% of Nigeria's out of school population are located in the rural Northern part of the country and 60% of them are girls. Investment in good and equal opportunity education is vital for national development and well-being.The research presented in this paper seeks to address the challenge of out of school youths and adolescents in Nigeria using innovative online learning solutions that would enhance youth and adolescent education in the rural communities and also among the girl child. It is expected that by implementing these solutions, Nigeria can improve its attainment towards some specific SDG goals.The paper also considers possible challenges that the Nation may face in sustaining the proposed solutions (online training) in the rural communities.

Detecting Powerline Noise with Low-Cost Noise Sensors for Power Outage Mitigation

Abstract: Relatively low-cost noise sensors can be utilized to detect for aberrant powerline noise, which is an early indicator and warning of potential power reliability and stability issues. By taking preemptive action, power outages may be avoided. Accordingly, the practicality as pertains to the utilization of low-cost noise sensors, segueing to scalability and extensibility, is examined. The strategic placement of sensors at key distribution poles has been examined previously. This paper examines how low-cost noise sensors provide an opportunity for comprehensive coverage and more likely detection of certain powerline noise aberrations at the "edge" against a set of compiled heuristics.

TROPPO LoRa: TROPospheric Personal Observatory using LoRa signals

Abstract: With the growth of LoRa deployments there are plenty of anecdotal reports of very long wireless links, well beyond the line of sight. Most reports suggest that these links are related to anomalous tropospheric propagation. We developed a platform to study tropospheric links based on TheThingsNetwork, a popular LoRaWAN-based infrastructure. We present some preliminary results and call for the IoT community to participate in this radio propagation experiment.

TROPPO LoRa: TROPospheric Personal Observatory using LoRa signals

Abstract: With the growth of LoRa deployments there are plenty of anecdotal reports of very long wireless links, well beyond the line of sight. Most reports suggest that these links are related to anomalous tropo-spheric propagation. We developed a platform to study tropospheric links based on TheThingsNetwork, a popular LoRaWAN-based infrastructure. We present some preliminary results and call for the IoT community to participate in this radio propagation experiment.

Integrating an MQTT Proxy in a LoRa-Based Messaging System for Generic Sensor Data Collection.

Abstract: In this paper we use a “frugal innovation approach” to propose an efficient and generic solution to provide support to the deployment of IoT system in rural areas. Our proposal includes an MQTT (Message Queuing Telemetry Transport) proxy to integrate generic low-cost and low-power sensor devices in a messaging system based on LoRa (Long Range) technology. MQTT allows these data to be provided to external “data lakes” so that they can be used for tasks such as reporting, visualization, advanced analytic, and machine learning. LoRa technology provides long wireless links that can be used to connect villages and towns.