Low-cost IoT Research Articles

A Secure Blockchain Platform for Supporting AI-Enabled IoT Applications at the Edge Layer

In this study, a new blockchain protocol and a novel architecture that integrate the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, and blockchain were designed, developed, and validated. This new architecture has the ability to monitor the environment, collect data, analyze it, process it using an AI-expert engine, provide predictions and actionable outcomes, and finally share it on a public blockchain platform. For the use-case implementation, the pandemic caused by the wide and rapid spread of the novel coronavirus COVID-19 was used to test and evaluate the proposed system. Recently, various authors traced the spread of viruses in sewage water and studied how it can be used as a tracking system. Early warning notifications can allow governments and organizations to take appropriate actions at the earliest stages possible. The system was validated experimentally using 14 Raspberry Pis, and the results and analyses proved that the system is able to utilize low-cost and low-power flexible IoT hardware at the processing layer to detect COVID-19 and predict its spread using the AI engine, with an accuracy of 95%, and share the outcome over the blockchain platform. This is accomplished when the platform is secured by the honesty-based distributed proof of authority (HDPoA) and without any substantial impact on the devices’ power sources, as there was only a power consumption increase of 7% when the Raspberry Pi was used for blockchain mining and 14% when used to produce an AI prediction.

Low cost IoT based Heart Rate Monitoring system using node MCU and detection of early heart attack

In this project, an IOT-based system has been implemented which can monitor heartbeat from the output given by a hardware system. This 'Hardware System' consists of a Node MCU and Pulse Sensor. An 'Alert System' remains added, which gets executed if heartbeat goes below or abovethe permissible level. Through a 'Mobile Phone Application', Doctor receives the alert message and Doctor can access the heartbeat data of the patient from any location. Doctors and Nurses at hospital will be able to monitor the heart rate of the patient by virtue of Real time Monitoring System.'. Heartbeat data and other personal details of patient remain stored in cloud and can be utilized for future studies on health condition of the patient.

Design and Implementation of a Low-cost IoT Node for Data Processing, Case Study: Smart Agriculture

The majority of IoT nodes work within specific scenarios and can be configured in different ways. This paper seeks to design and implement a low-cost Internet of Things node for research applications to make it suitable for a wider variety of scenarios. The design is divided into hardware board and mobile application. With Bluetooth, the mobile application can connect to the node, and the node can collect data, store this data in the ThingSpeak database, control some connected devices, and check if the connected devices are on or off. The node was designed and tested for research purposes using smart agriculture as the case study. The system detects temperature, humidity, and soil moisture using node sensors, enabling data collection and interpretation by smartphone and web application. There are many challenges associated with the collected data preparation, analysis, visualization, and prediction using the Softmax function for optimal future management. Python was utilized to apply necessary data analysis techniques. The system saves time and makes farming more convenient as it uses few resources in terms of hardware and cost.

Development of Low-Cost IoT based data acquisition system for real-time monitoring of volcanoes seismicity

This paper describes how to develop a low-cost data acquisition (DAQ) system for real-time remote monitoring of volcano seismicity. The DAQ system is developed based on IoT platform, consists of several module devices both hardware and software, and installed in two places called seismic-stations (SS) and observation-post (OP). In the implementation, the SS is placed on the body of the volcano meanwhile the OP is usually placed at a certain distance from the SS with more adequate electrical and communication facilities. In this design, the SS consists of seismic sensors and a digitizer, whereas the OP consists of a computer server and ADSL modem. Communication between SS and OP conducted by using a 5 GHz Wi-Fi telemetry system. At the OP, the data is processed to produce the form of digital seismograms, hypocentres, arrival times, and signal magnitudes in SAC or mini seed file formats by Earthworm software. Database server for cloud access is created using Winston Wave Server (WWS) and MySQL Data Base Management Systems (DBMS). The seismic data can be accessed in real time remotely by users using the SWARM application via website access.

Energy-Saving Solutions for Cellular Internet of Things–A Survey

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

Design and Deployment of Vehicular Internet of Things for Smart City Applications

A novel computing paradigm, called the Internet of things emerged a few years ago. IoT is materialized by connecting both real and digital worlds together. The deployment of IoT in vehicular networks has introduced a new vehicular communication technology-themed vehicular internet of things (V-IoT). With the introduction of miniaturized sensors and actuators, V-IoT has demonstrated the ability to improve the level of urban transport systems through the development and deployment of low-cost but powerful technologies which seamlessly upgrade the level of smart transportation in urban environments. In this research article, we have presented the features of V-IoT that encompass both the benefits and potential challenges of the technology. Low-cost IoT prototypes have been built and tested for numerous functions in vehicular environments. The monitored parameters include air, road conditions such as traffics flow sizes, air quality, weather parameters, and signal status in terms of Received signal strength indicator, and Signal noise ratio for both road and intra-vehicular environments. Devices are implemented at every IoT architectural layer and tested on a web-based IoT front-end application using different protocols like LoRaWAN. Two LoRa sensors have been deployed in the public bus to monitor some of the mentioned parameters on a real-time basis and historical data could be retrieved through the developed web-based dashboard. Simplistic algorithms are implemented for both real-time and historical data demonstration.

Appliance Control with IOT-Arduino of Voice Command Detection for Mobility Impaired People

People with physical mobility disabilities experience limited ability to perform daily routine. Integration between home appliances and Internet of Thing (IoT) can be used to support these people. The technology allows tasks to be performed much more easily and enable access to applications and services that would be a challenge with existing digital user interfaces. However, the developed system that is commercially available is costly and equipped with highly specific interfaces which may require considerable technical competency to conduct system interfacing. The purpose of this study is to develop a low cost IOT system for voice-enabled lamp control by utilizing Google Assistant that is available in commercial Android based mobile devices, allowing user to operate lighting appliances without moving. Personal assistance using Google platform embedded with Arduino microcontroller, ESP8266-based NodeMCU development board and integrated Blynk application allow for remote monitoring capability. The system is remotely controlled from a tablet or smartphone to assist disabled people with restricted mobility, in particular those with lower limb disabilities. The system had been tested and demonstrated that the lamp control can be easily accessed by voice command that is then validated using voice pitch analyzer. Test result indicated that capability of the integrated voice control system is verified up to a maximum distance of 24.5 meter, making it a potential implementation in any customized smart home or room system for people with mobile disability.

Low-Cost IoT Enabled Embedded System for Measurement of Environmental Pollutants

In today’s world, with ever-changing pollutants and their concentrations, the designing of low-cost meteorological systems is unavoidable for assessing environmental parameters. Wireless instrumentation is an effective way of measuring the physical quantities as it can measure and transmit the data to the targeted location at high speed. In the present work, an IoT-enabled embedded system was developed to measure the concentration of carbon dioxide, ozone, and the presence of smoke. The ARM microcontroller reads the sensor data and processes the information to calculate the pollutant parameters. The measured data is displayed on the LCD, mobile phone, and a computer simultaneously using wireless technology. With Embedded C, the Keil compiler was used to develop the interfacing software for the designed system. Portability, user-friendliness, and reliability are the significant advantages of the device compared with the conventional systems, and it can be widely used as an inexpensive solution for the monitoring of environmental conditions.

Validation of an inertial-based contact and swing time algorithm for running analysis from a foot mounted IoT enabled wearable.

Running gait assessment for shoe type recommendation to avoid injury often takes place within commercial premises. That is not representative of a natural running environment and may influence normal/usual running characteristics. Typically, assessments are costly and performed by an untrained biomechanist or physiotherapist. Thus, use of a low-cost assessment of running gait to recommend shoe type is warranted. Indeed, the recent impact of COVID has heightened the need for a shift toward remote assessment in general due to social-distancing guidelines and restriction of movement to bespoke assessment facilities. Mymo is a Bluetooth-enabled, inertial measurement unit (IMU) wearable worn on the foot. The wearable transmits inertial data via a smartphone application to the Cloud, where algorithms work to recommend a running shoe based upon the users/runner's pronation and foot-strike location/pattern. Here, an additional algorithm is presented to quantify ground contact time and swing/flight time within the Mymo platform to further inform the assessment of a runner's gait. A large cohort of healthy adult and adolescents (n=203, 91M:112F) were recruited to run on a treadmill while wearing the Mymo wearable. Validity of the inertial-based algorithm to quantify ground contact time was established through manual labelling of reference standard ground truth video data, with a presented accuracy between 96.6-98.7% across the two classes with respect to each foot.Clinical Relevance-This establishes the validity of a ground contact and swing times for runner with a low-cost IoT wearable.

VentQsys: Low-cost open IoT system for CO_2 monitoring in classrooms

In educational context, a source of nuisance for students is carbon dioxide (CO_2) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide (CO_2), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring

The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data.

IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing.

The Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)-based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low-cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.

Distributed Sensing with Low-Cost Mobile Sensors Toward a Sustainable IoT

Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of the COVID.19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT.

A SENSOR NETWORK BASED ON IOT AND ISTSOS TO ANALYSE THE CATCH BASIN ENVIRONMENT IN THE CONTEXT OF THE AE. ALBOPICTUS EXPANSION IN SWITZERLAND

Abstract. The continuous expansion of invasive Asian tiger mosquito, Aedes albopictus, combined to its ability to transmit arboviruses (e.g. dengue, chikungunya) is raising major public health concern in Europe. In Switzerland, the mosquito is firmly established in most urban areas of the Canton of Ticino, south of the Alps, and there is a real risk that it will colonize also urban areas north of the Alps in the next years. The spatial distribution and colonization of new areas by Ae. albopictus depends on several environmental parameters, such as winter and summer temperatures, and precipitation patterns. A key factor for Ae. albopictus to establish at higher latitudes is the capability to develop cold-tolerant overwintering diapausing eggs under specific environmental conditions. Weather-driven abundance models are used to map the areas of potential distribution and to predict temporal dynamics of Ae. albopictus and the transmission potential of arboviruses. This contribution presents the designed system that integrates low-cost and on-line IoT sensors to monitor temperature, humidity and light with istSOS an OGC Sensor Observation Service server implementation with a user friendly interface and rich feature collection to easily manage this sensor network and distribute data in a standard way (www.istsos.org).

An enhanced symmetric-key based 5G-AKA protocol

5G technology is called to support the next generation of wireless communications and realize the “Internet of Everything” through its mMTC (massive Machine-Type-Communications) service. The recently standardized 5G-AKA protocol is intended to deal with security and privacy issues detected in earlier generations. Nevertheless, several 5G-AKA shortcomings have been reported, including a possibly excessive computational complexity for many IoT devices. To address these, a promising lightweight 2-pass authentication and key agreement (AKA) protocol for 5G mobile communications has recently been proposed by Braeken. Compared to the 5G-AKA protocol, this does not require the use of public key encryption. This paper analyzes the security claims of Braeken’s protocol and shows that it does not provide full unlinkability, but only session unlinkability, and is (still) subject to Linkability of AKA Failure Messages (LFM) attacks. We propose solutions to such problems and prove that symmetric-key based protocols cannot offer higher privacy protection levels without compromising availability. We then describe an enhanced version of this protocol that addresses these vulnerabilities and supports forward secrecy, which is a desirable feature for low-cost IoT devices.

DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs

The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increase energy efficiency -- requiring explicit DMA-based memory transfers between different levels of the memory hierarchy. Mapping modern DNNs on these systems requires aggressive topology-dependent tiling and double-buffering. In this work, we propose DORY (Deployment Oriented to memoRY) - an automatic tool to deploy DNNs on low cost MCUs with typically less than 1MB of on-chip SRAM memory. DORY abstracts tiling as a Constraint Programming (CP) problem: it maximizes L1 memory utilization under the topological constraints imposed by each DNN layer. Then, it generates ANSI C code to orchestrate off- and on-chip transfers and computation phases. Furthermore, to maximize speed, DORY augments the CP formulation with heuristics promoting performance-effective tile sizes. As a case study for DORY, we target GreenWaves Technologies GAP8, one of the most advanced parallel ultra-low power MCU-class devices on the market. On this device, DORY achieves up to 2.5x better MAC/cycle than the GreenWaves proprietary software solution and 18.1x better than the state-of-the-art result on an STM32-F746 MCU on single layers. Using our tool, GAP-8 can perform end-to-end inference of a 1.0-MobileNet-128 network consuming just 63 pJ/MAC on average @ 4.3 fps - 15.4x better than an STM32-F746. We release all our developments - the DORY framework, the optimized backend kernels, and the related heuristics - as open-source software.

IOTFlood: hardware and software platform using internet of things to monitor floods in real time

Floods are responsible for a high number of human and material losses every year. Monitoring of river levels is usually performed with radar and pre-configured sensors. However, a major flood can occur quickly. This justifies the implementation of a real-time monitoring system. This work presents a hardware and software platform that uses Internet of Things (IoTFlood) to generate flood alerts to agencies responsible for monitoring by sending automatic messages about the situation of rivers. Research design involved laboratory and field scenarios, simulating floods using mockups, and later tested on the Mundaú River, state of Alagoas, Brazil, where flooding episodes have already occurred. As a result, a low-cost, modular and scalable IoT platform was achieved, where sensor data can be accessed through a web interface or smartphone, without the need for existing infrastructure at the site where the IOTFlood solution was installed using affordable hardware, open source software and free online services for the viewing of collected data.

IoT Based Smart Home Automation and Security Using Labview and NodeMCU

The main objective of this paper is to develop a smart home using NodeMCU with the help of LabVIEW software. As technology is advancing, surroundings are also getting smarter. As a part of this smart development, modern houses are shifting from conventional switches to centralized control systems. The conventional system makes it difficult for the elderly and physically challenged to operate. So, this home automation system provides the best modern solution. In this home automation system, we use LabVIEW connected to NodeMCU which is a low-cost open-source IoT platform that helps in controlling lights, fans and to detect temperature, gas. LabVIEW is engineering software that provides rapid access to hardware and data insights. It makes the operation easy and helps us avoid bulk connections. Creating this type of integrated advanced technology helps we move faster into a new modern world. Home automation avail oneself of LabVIEW is tested and compared with existing standard ways. The planned system contains an easy structure, economically value effective, and versatile scaling up for a correct automatic home future.

Development and Performance Evaluation of a Plant Phenotyping Platform Using Low-cost IoT Devices

Development and Performance Evaluation of a Plant Phenotyping Platform Using Low-cost IoT Devices

A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices

Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83 μA during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution—SPWAS’21—is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices.