Low-cost IoT Research Articles

LeaD: Learn to Decode Vibration-based Communication for Intelligent Internet of Things

In this article, we propose, LeaD , a new vibration-based communication protocol to Lea rn the unique patterns of vibration to D ecode the short messages transmitted to smart IoT devices. Unlike the existing vibration-based communication protocols that decode the short messages symbol-wise, either in binary or multi-ary, the message recipient in LeaD receives vibration signals corresponding to bits-groups. Each group consists of multiple symbols sent in a burst and the receiver decodes the group of symbols as a whole via machine learning-based approach. The fundamental behind LeaD is different combinations of symbols (1 s or 0 s) in a group will produce unique and reproducible patterns of vibration. Therefore, decoding in vibration-based communication can be modeled as a pattern classification problem. We design and implement a number of different machine learning models as the core engine of the decoding algorithm of LeaD to learn and recognize the vibration patterns. Through the intensive evaluations on large amount of datasets collected, the Convolutional Neural Network (CNN)-based model achieves the highest accuracy of decoding (i.e., lowest error rate), which is up to 97% at relatively high bits rate of 40 bits/s. While its competing vibration-based communication protocols can only achieve transmission rate of 10 bits/s and 20 bits/s with similar decoding accuracy. Furthermore, we evaluate its performance under different challenging practical settings and the results show that LeaD with CNN engine is robust to poses, distances (within valid range), and types of devices, therefore, a CNN model can be generally trained beforehand and widely applicable for different IoT devices under different circumstances. Finally, we implement LeaD on both off-the-shelf smartphone and smart watch to measure the detailed resources consumption on smart devices. The computation time and energy consumption of its different components show that LeaD is lightweight and can run in situ on low-cost smart IoT devices, e.g., smartwatches, without accumulated delay and introduces only marginal system overhead.

Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

According to a United Nations report, the world population will increase from 7 billion to 9 billion by 2050. Further, the water stress level is more than 70% in 22 countries while in another 31 countries it is between 25% and 70%. More than 2 billion people live in these 53 countries which are all underdeveloped. Water use has increased by 1% per year since the 1980s, so global demand is expected to rise by 30% by 2050. Thus, efficient water grid management is imperative to ensure there is sufficient water for the future. Information and Communication Technology (ICT) can be used to create smart water grids to optimize water distribution, reduce waste and leakage, and resolve quality and overuse issues. In this work, a low cost, real-time, reliable and sustainable IoT based solution called SmartTubewell is proposed for smart water grid management. It is composed of two components, a sensor node installed at tube wells and an application layer on Amazon Web Services (AWS) for data analysis, storage and processing. The sensor node is based on a Raspberry Pi with integrated current and voltage sensors and a local database. The sensor data is transmitted to AWS using a cellular (GPRS) network. A comparison between the proposed system and SCADA is presented which shows that SmartTubewell has a much lower cost. A field test with multiple tube wells in Peshawar, Pakistan indicates that this is a suitable solution for developing countries.

Preliminary Assessment of Air Pollution Quality Levels of Lagos, Nigeria

The low-cost sensors and IoT have come to the rescue due to the high cost and operational complexity of equipment and methodologies in environmental monitoring. They are relatively inexpensive and reliable. It is on this assumption that we have decided to use the World Air Quality satellite data supplied by air matters.com. This study is a 40-day preliminary work in which air quality [Air Quality Index (AQI), PM2.5, PM10, NO2, CO, SO2, and O3] and meteorological (temperature, humidity, and wind speed) parameters were monitored. The data collected were for five locations in Lagos State, Nigeria (Ojodu, Opebi, Ikeja, Maryland, and Eti-Osa). The data obtained were subjected to basic statistical analyses. The findings showed that the Opebi had the highest mean value of PM2.5 (69.28 μg/m3), PM10 (107.38 μg/m3), and CO (1392 μg/m3). The mean values of O3 are as follows: 32.52, 38.7, 36.2, 37.85, and 36.13 μg/m3 for Ikeja, Maryland, Opebi, Ojodu, and Eti-Osa, respectively. Opebi had the highest value (3179 μg/m3), followed by Eti-Osa (2978 μg/m3), and the lowest value in Maryland (1943 μg/m3) among the CO reference locations. AQI of all locations presented the levels of contamination as 'Unhealthy for Vulnerable Groups'. The pollutants were much higher than the World Health Organization (WHO) guidelines. There were relationships between the parameters monitored and meteorological influences, and the effects of natural and man-made activities may be the sources of the elevated pollutants throughout the locations.

A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application.

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.

Network approaches in anomaly detection for disease conditions

Anomaly detection in time series or any ordered data sequence is described using a multilayered network-based algorithm. An anomaly in the biomedical context has an obvious diagnostic implication. The first step in this time-series-to-network (T2N) approach is to partition the signal intensity in q user-defined intervals where, q≪N, N being the number of discrete sampling points of the time series. This maps the time series into a multigraph GM, the state transition between different signal partitions serving as directed edges E of the graph GM(V,E), q being the cardinality of the vertex set V. Eventually, GM may have self-loops. Next, GM is split into a simple graph G=G(V,H),with edge-set H devoid of self-loops and three sets of parameters, namely, node strength index (NSI), interaction strength ratio (AISR), self-loop weights. The order of computational tasks reduces from O(N) to O(3q) with q≪N. Using such decomposition, we have performed beat classification of ECG data collected from MIT BIH repository. The network derived feature sets enables us to drastically compress the training set size. We next show how a simple neural MLP model with a single hidden layer with ∼100 neurons is able to learn to classify normal and abnormal single beats with good precision and recall scores. The results showcase the possibility of performing anomaly detection in bio-signals with an algorithm exhibiting low computational complexity, showing the promise of implementation on low cost IoT hardware devices integrated with modern digital data acquisition systems for bedside patient monitoring.

An Agricultural Irrigatıon Management System Based on the Internet of Things With MQTT Protocol

There are several factors upon which the yield of a crop depends. Water, external temperature, humidity, the fertility of the soil, etc. Among these, water is a vital and scarce resource, and thereby more human attention is needed. When and how to water the plants are the two important things to consider in agricultural irrigation! Modernizing agriculture with the help of smart technologies is a promising solution. This project presents an automated irrigation system for agriculture-based out of several low-cost sensors that monitor and maintain the soil moisture based on real-time data from the field with very low form factor compared to the existing systems. This automated system is a combination of low-cost hardware, IoT, and cloud server. The ESP8266 MCU installed in the field will collect all the sensor data from the crops and send it to the cloud server, thingspeak, for storage via Wi-Fi. The farmers can monitor the stored or real-time data on their mobile or in a web portal. They can also check the status of the pump at any time from anywhere. The amount of moisture required for a specific type of crop is predetermined and saved in the system thereby human presence can be decreased and can effectively make the system automated.

A Low-Cost IoT Sensors Network for Monitoring Three-Phase Induction Motor Mechanical Power Adopting an Indirect Measuring Method.

Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.

An Ultra-Lightweight Mutual Authentication Protocol Based on LPN Problem with Distance Fraud Resistant

RFID tags are one of the main enablers of the internet of things. All objects have to be equipped with an electronic product code such as RFID tags. Because of minimizing the price, RFID environments are resource-scarce, then designing ultra-lightweight authentication protocols is of great importance. Many ultra-lightweight authentication protocols such as HB family protocols are proposed. One of the most important threats against HB family protocols is a type of man-in-the-middle attack called GRS. Also, in the real world, IoT requires mutual authentication that traditional HB protocols do not support it. Besides, misconceptions about reader-tag distance could create problems in several applications especially in contactless systems such as access control and electronic payment systems, which could be damaged by distance-based frauds. In the present work, we have proposed a novel distance bounding protocol based on HB family protocols with four major characteristics: (1) it can resist terrorist, mafia, and distance fraud attacks; (2) it is a lightweight mutual authentication protocol capable of being used in low-cost IoT equipment such as NFC and RFID; (3) it employs a hard problem that be post-quantum resistant, and (4) it identifies and solves the weaknesses of HB protocols including GRS attacks. The proposed protocol has also been shown to be able to address the known weaknesses and attacks in distance frauds and HB family protocols.

New Practical Teaching Model Design for mutualized practical works based on low cost ICT, IoT and AI developed solutions

Recently, Information and Communication Technology (ICT) plays a crucial role in teaching-learning, particularly tracking, trainings, knowledge management and knowledge organization. The aim of this article is to propose a new practical teaching model design for practical works based on ICT, IoT and AI Tools. This new model will allow students to acquire skills and knowledges outside their field of training related to their course. The challenge of this teaching model is to teach more practical works by adopting the learning by doing approach, with a minimum cost effect.

3D Rapid-Prototyped 21-31-GHz Hollow SIWs for Low-Cost 5G IoT and Robotic Applications

This article presents, for the first time, new design and fabrication techniques for Hollow Substrate Integrated Waveguides (HSIWs), demonstrated in the nominal frequency from 21 to 31 GHz, for use in wireless communication applications such as 5G, IoT and robotics. The design and fabrication techniques introduced in this paper feature: 1) the use of low-cost rapid prototyping additive manufacturing based on polymer jetting (PJ), and 2) the use of commercially available through-substrate copper via transitions. In contrast to the conventional SIW designs and fabrications, this new approach does not rely on through-substrate via fabrication, hence avoiding some difficult manufacturing steps, such as through-substrate etching, via formation and via metallization, which are considered complex and expensive to implement. The 3D printed HSIWs in this article can achieve a propagation loss of lower than 1.56 Np/m (13.55 dB/m), which is considered one of the results with the lowest propagation loss achieved to date, when compared to the state-of-the-art.

Contactless Multi-Sensor Solution for E-Treatment of Musculoskeletal Disorders

The treatment of Musculoskeletal Disorders requires physical exercises to be executed regularly and correctly. This paper presents a technological solution to support this therapy consisting of on-body sensors and a supporting platform that captures and forwards the data automatically. We implemented wireless communication and charging in the sensor module. The wireless functionality enhances patient's comfort during movements and allows for easy and hygienic maintenance. Validation in a real-life case has demonstrated the suitability of the solution to monitor treatment remotely. Furthermore, it was shown to be easy-to-use by non-technical experts. The sensor has an autonomy of 20 hours, which contributes to its user friendliness. We explain and share the designs that have been realized with low cost IoT technologies including Micro-Electro-Mechanical System sensors, low power wireless connectivity and microcontrollers. The contactless multi-sensor solution can be broadly adopted both in medical cabinets and for remote treatment. The latter is of specific interest when the patient is far from the therapist, encounters mobility problems, or when safe distancing is needed.

Internet of Things based Hazardous Gas leakage Detection System using Arduino UNO

Interjecting the unfortunate and adverse impacts of gas leakage, authors have proposed an IOT based method for real time gas leakage detection system that categorizes the content of the feedback as gas leakage and helps to sense the gas leakage from gas pipe on time. Many of the hotels and restaurants do not keep any security measures to detect gas leakage due to lack of enforcement of standards and pre-assumption that installing such precautionary systems will be more costly. Proposed IOT Based hazardous gas leakage detection system which catalogues and categorizes the content of the feedback as positive and negative and consequently indicates whether gas leakage is there or not in the gas pipe. It can extensively be used to detect fire breakouts in buildings, hotels, hospitals and industries or other public places. In this paper, the proposed gas detection system based on Arduino UNO, is used to detect the site from where the gas is leaking and accordingly disseminate messages to the coupled customers or its analogous users. Hence certainly, can be utilized to aqueduct the safety, nursing and proper maintenance of the end users. The proposed low cost IOT based project uses MQ6 gas sensor which can be calibrated to detect gas leakage levels based on ambience, also it comes out huge inexpensive when compared to its defensive system. MQ-2 Gas sensors are generally used to detect or measure presence of gases like LPG and butane. Once the proposed gas leakage system is installed, it generates an identifiable buzzing sound alert using buzzer after detecting the gas leakage. There is an alarm which automatically goes off when the sensor does not detect any utensils on the burner.

Usage of DTNs for low-cost IoT application in smart cities: performance evaluation of spray and wait routing protocol and its enhanced versions

Usage of DTNs for low-cost IoT application in smart cities: performance evaluation of spray and wait routing protocol and its enhanced versions

A comparative analysis of LSTM and ARIMA for enhanced real-time air pollutant levels forecasting using sensor fusion with ground station data

One of the most basic needs of any human being to survive is air. Unfortunately, this basic need is being polluted by many natural factors like volcanic eruptions, forest fires, and man-induced factors like transportation emission. Unpolluted air is now an ideal environment that can never be achieved. So, the pollution levels should be monitored continuously. However, monitoring the levels of pollution will not fix the environment. Forecasting these pollution levels can make the society more aware of the environment and help prepare safety measures. This research paper aims to forecast the air pollutant levels by comparing futuristic machine learning models, which are Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) performed on the ground station data from Central Pollution Control Board (CPCB), the data collected from a low-cost IoT hardware setup, and the fused data. The LSTM and ARIMA models have been used to forecast the air pollutant levels in the future. Further, the main novelty of this research is to show that the concept of sensor fusion increases the accuracy of the dataset. The outputs obtained after implementing LSTM and ARIMA models show more accurate results when compared with ground station data and the IoT data from sensors.

Smart Metering for Challenging Scenarios: A Low-Cost, Self-Powered and Non-Intrusive IoT Device.

In this work, a novel current metering device was presented. This device was intended to bring current metering capabilities to a wide variety of scenarios: Developing countries, rural areas, or any situation with technological constraints. The device was designed to provide a straightforward installation with no intrusion in the electrical panels. This was achieved by applying energy harvesting techniques and wireless communication technology for data transmission. The device was able to exploit the magnetic field inducted around a wire carrying electricity as energy harvesting, thus acquiring the power it needed to work. Since very low power was harvested, an efficient treatment for the incoming power and a minimal power consumption system were essential. Although exploiting the magnetic fields inducted around a wire has been used for years, the combination of this technology for both energy harvesting and current metering in an end-user device was off-center. To work in a wide variety of scenarios, it used Sigfox for communications as this brought wide coverage and out-of-the-box functioning. The theoretical design of the device was validated by verification assessments for the joint performance of the individual parts compounding the device, including metering capabilities and wireless communication test-bench. Finally, the metering device was tested under three distinct real-world scenarios that demonstrated the viability of the system. Results show that, depending on the metering period and the average current value in the mains line, the device could work forever acquiring and sending electricity consumption data. Perpetual working was achieved with an average current of 3.1 A to meter every 15 min, and an average current of 5 A for a 5-min metering period.

Efficient Preamble Detection and Time-of-Arrival Estimation for Single-Tone Frequency Hopping Random Access in NB-IoT

The narrowband Internet-of-Things (NB-IoT) standard is a new cellular wireless technology, which has been introduced by the 3rd generation partnership project (3GPP) with the goal to connect massive low-cost, low-complexity and long-life IoT devices with extended coverage. In order to improve power efficiency, 3GPP proposed a new random access (RA) waveform for NB-IoT based on a single-tone frequency-hopping scheme. RA handles the first connection between user equipments (UEs) and the base station (BS). Through this, UEs can be identified and synchronized with the BS. In this context, receiver methods for the detection of the new waveform should satisfy the requirements on the successful user detection as well as the timing synchronization accuracy. This is not a trivial task, especially in the presence of radio impairments like carrier frequency offset (CFO) which constitutes one of the main radio impairments besides the noise. In order to tackle this problem, we propose a new receiver method for NB-IoT physical RA channel (NPRACH). The method is designed to eliminate perfectly the CFO without any additional computational complexity and supports all NPRACH preamble formats. The associated performance has been evaluated under 3GPP conditions. We observe a very high performance compared both to 3GPP requirements and to the existing state-of-the-art methods in terms of detection accuracy and complexity.

A Low-Cost Real-Time IOT Enabled Data Acquisition System for Monitoring of PV System

ABSTRACT The performance degradation due to environmental factors represents a major shortcoming to the solar PV system making them untrustworthy for dessert or remote plants. Real-Time monitoring systems are extensively required in the remote PV system for gathering all parameters for estimating system performance and optimization purposes. Many existing studies present wired data acquisition systems, using LABVIEW license software, which are costly and not easily accessible. The wired data acquisition systems are accessible only near the PV systems. There are some wireless data acquisition systems, but they depend on license-based software and cloud services. There are very few wireless data acquisition systems using open-access software, which are monitored and recorded fewer parameters at a very high cost. The existing data acquisition systems are either continuously connected to supply or manually controlled, require the manpower to operate the system near the remote PV plant. These issues can be eliminated by using the proposed internet of things (IoT) based data acquisition system (DAQ). The proposed data acquisition system is based on open-access software and cloud service. In this paper, we presented all the design steps of the proposed low-cost data acquisition system systematically, also tested the performance of the proposed IoT enabled data acquisition system (DAQ) under harsh environment conditions over 28 days. Real-time test results revealed that the suggested data acquisition system is appropriate, reliable, cost-effective, and suitable for harsh outdoor conditions for monitoring and gathering operational information of the PV system to assess its performance. The information updated by the proposed DAQ system can be seen from anyplace. The proposed data acquisition system can be controlled (ON/OFF) by using the Wi-Fi-enabled switch according to the requirement. In this way, we can not only save energy by 58% but also save the life of sensors. It has been observed that the output power of the PV module is reduced by 13% within 28 days without adopting any cleaning technique. In this paper, we also discussed the comparison between the proposed and some exiting methodology of the data acquisition systems.

Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard Monitorization

Population growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under different environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment.

Two‐way oxide rupture scheme for PUF implementation in low‐cost IoT systems

A two-way oxide rupture scheme in a transistor is proposed for area-efficient PUF implementation for low-cost IoT systems. Compared to a conventional one-way oxide rupture scheme, which requires a 4T unit cell structure, the proposed scheme allows a 2T unit cell structure, which enables up to 70% area reduction. A test chip is fabricated in 180 nm CMOS process to evaluate the effectiveness of the proposed scheme, confirming good randomness and uniqueness with small area overhead.

Monitoring of Patient Critical Health Parameters by using a Low-Cost IoT based System

Monitoring of Patient Critical Health Parameters by using a Low-Cost IoT based System