Systematic literature survey: applications of LoRa communication

LoRa is a communication scheme that is part of the low power wide are network (LPWAN) technology using ISM bands. It has seen extensive documentation and use in research and industry due to its long coverage ranges of up-to 20Km or more with less than 14dB transmit power. Moreover, some applications report theoretical battery lives of upto 10years for field deployed modules utilising the scheme in WSN applications. Additionally, the scheme is very resilient to losses from noise, as well bursts of interference through its FEC. Our objective is to systematically review the empirical evidence of the use-cases of LoRa in rural landscapes, metrics and the relevant validation schemes. In addition the research is evaluated based on (i) mathematical function of the scheme (bandwidth use, spreading factor, symbol rate, chip rate and nominal bit rate) (ii) use-cases (iii) test-beds, metrics of evaluation and (iv) validation methods. A systematic literature review of published, refereed primary studies on LoRa applications was conducted. Using articles from 2010-2019. We identified 21 relevant primary studies. These reported a range of different assessments of LoRa. 10 out of 21 reported on novel use cases. As an actionable conclusion, the authors conclude that more work is needed in terms of field testing, as no articles could be found on performance/deployment in Botswana or South Africa despite the existence of LoRa networks in both countries. Thus researchers in the region can research propagation models performance, energy efficiency of the scheme and MAC layer as well as the channel access challenges for the region

Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management

These days, the oil industrial industry is leaning toward employing smart field improvements to streamline various activities in the midstream area. Oil transportation over large distances via pipelines has a cheap cost and high efficiency in this sector. If pipelines are not properly maintained, they may fail, potentially causing catastrophic, long-term, and irreversible consequences on both natural and human conditions. Low power wide area networks (LPWANs) are without a doubt one of the domains that cause the most from industrial fields when it comes to realizing the vision of the internet of things (IoT). Long-range (LoRa) is an emerging LPWAN technology that is particularly useful for transmitting data over long distances. The goal of this work is to offer a methodology for managing oil pipelines over long distances utilizing the LoRa communication protocol and the installation of sensor nodes and LoRa gateways along the pipeline. We also used the optimized network engineering tools (OPNET) simulator to examine various simulation findings of LoRa performance

Implementing Efficient and Multi-Hop Image Acquisition In Remote Monitoring IoT systems using LoRa Technology

Remote sensing or monitoring through the deployment of wireless sensor networks (WSNs) is considered an economical and convenient manner in which to collect information without cumbersome human intervention. Unfortunately, due to challenging deployment conditions, such as large geographic area, and lack of electricity and network infrastructure, designing such wireless sensor networks for large-scale farms or forests is difficult and expensive. Many WSN-appropriate wireless technologies, such as Wi-Fi, Bluetooth, Zigbee and 6LoWPAN, have been widely adopted in remote sensing. The performance of these technologies, however, is not sufficient for use across large areas. Generally, as the geographical scope expands, more devices need to be employed to expand network coverage, so the number and cost of devices in wireless sensor networks will increase dramatically. Besides, this type of deployment usually not only has a high probability of failure and high transmission costs, but also imposes additional overhead on system management and maintenance. LoRa is an emerging physical layer standard for long range wireless communication. By utilizing chirp spread spectrum modulation, LoRa features a long communication range and broad signal coverage. At the same time, LoRa also has low power consumption. Thus, LoRa outperforms similar technologies in terms of hardware cost, power consumption and radio coverage. It is also considered to be one of the promising solutions for the future of the Internet of Things (IoT). As the research and development of LoRa are still in its early stages, it lacks sufficient support for multi-packet transport and complex deployment topologies. Therefore, LoRa is not able to further expand its network coverage and efficiently support big data transfers like other conventional technologies. Besides, due to the smaller payload and data rate in LoRa physical design, it is more challenging to implement these features in LoRa. These shortcomings limit the potential for LoRa to be used in more productive application scenarios. This thesis addresses the problem of multi-packet and multi-hop transmission using LoRa by proposing two novel protocols, namely Multi-Packet LoRa (MPLR) and Multi-Hop LoRa (MHLR). LoRa's ability to transmit large messages is first evaluated in this thesis, and then the protocols are well designed and implemented to enrich LoRa's possibilities in image transmission applications and multi-hop topologies. MPLR introduces a reliable transport mechanism for multi-packet sensory data, making its network not limited to the transmission of small sensor data only. In collaboration with a data channel reservation technique, MPLR is able to greatly mitigate data collisions caused by the increased transmission time in laboratory experiments. MHLR realizes efficient routing in LoRa multi-hop transmission by utilizing the power of machine learning. The results of both indoor and outdoor experiments show that the machine learning based routing is effective in wireless sensor networks

Embedded federated learning over a LoRa mesh network

In on-device training of machine learning models on microcontrollers a neural network is trained on the device. A specific approach for collaborative on-device training is federated learning. In this paper, we propose embedded federated learning on microcontroller boards using the communication capacity of a LoRa mesh network. We apply a dual board design: The machine learning application that contains a neural network is trained for a keyword spotting task on the Arduino Portenta H7. For the networking of the federated learning process, the Portenta is connected to a TTGO LORA32 board that operates as a router within a LoRa mesh network. We experiment the federated learning application on the LoRa mesh network and analyze the network, system, and application level performance. The results from our experimentation suggest the feasibility of the proposed system and exemplify an implementation of a distributed application with re-trainable compute nodes, interconnected over LoRa, entirely deployed at the tiny edge.This work was supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA).Peer ReviewedPostprint (published version

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

An Alternative Internet-of-Things Solution Based on LoRa for PV Power Plants: Data Monitoring and Management

This paper proposes a wireless low-cost solution based on long-range (LoRa) technology able to communicate with remote PV power plants, covering long distances with minimum power consumption and maintenance. This solution includes a low-cost open-source technology at the sensor layer and a low-power wireless area network (LPWAN) at the communication layer, combining the advantages of long-range coverage and low power demand. Moreover, it offers an extensive monitoring system to exchange data in an Internet-of-Things (IoT) environment. A detailed description of the proposed system at the PV module level of integration is also included in the paper, as well as detailed information regarding LPWAN application to the PV power plant monitoring problem. In order to assess the suitability of the proposed solution, results collected in real PV installations connected to the grid are also included and discussed.This work was partially supported by the Spanish agreement (2017) between the Institute for Development of the Region of Murcia (INFO) and the Technological Center for Energy and Environment (CETENMA). The paper includes results of activities conducted under the Research Program for Groups of Scientific Excellence at Region of Murcia (Spain), the Seneca Foundation, and the Agency for Science and Technology of the Region of Murcia (Spain). This work was also supported by project AIM, Ref. TEC2016-76465-C2-1-R (AEI/FEDER, UE). The authors thank the staff of the Universidad Politécnica de Cartagena (Spain) for services and facilities provided

A COMPREHENSIVE REVIEW OF INTERNET OF THINGS WAVEFORMS FOR A DOD LOW EARTH ORBIT CUBESAT MESH NETWORK

The Department of Defense (DOD) requires the military to provide command and control during missions in locations where terrestrial communications infrastructure is unreliable or unavailable, which results in a high reliance on satellite communications (SATCOM). This is problematic because they use and consume more digital data in the operational environment. The DOD has several forms of data capable of meeting Internet of Things (IoT) transmission parameters that could be diversified onto an IoT network. This research assesses the potential for an IoT satellite constellation in Low Earth Orbit to provide an alternative, space-based communication platform to military units while offering increased overall SATCOM capacity and resiliency. This research explores alternative IoT waveforms and compatible transceivers in place of LoRaWAN for the NPS CENETIX Ortbial-1 CubeSat. The study uses a descriptive comparative research approach to simultaneously assess several variables. Five alternative waveforms—Sigfox, NB-IoT, LTE-M, Wi-sun, and Ingenu—are evaluated. NB-IoT, LTE-M, and Ingenu meet the threshold to be feasible alternatives to replace the LoRaWAN waveform in the Orbital-1 CubeSat. Six potential IoT transceivers are assessed as replacements. Two transceivers for the NB-IoT and LTE-M IoT waveforms and one transceiver from U-blox for the Ingenu waveform are assessed as compliant.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments

The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment.Spanish project RTI2018-093421-B-I0