Internet Of Things Sensor Devices Research Articles

Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control.

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.

Multi-modal IoT-based medical data processing for disease diagnosis using Heuristic-derived deep learning

Nowadays, the quick development of the Internet of Things (IoT) has changed our lifestyle. However, disease diagnosis is a difficult task owing to managing an enormous volume of data. Thus, in this research task, the latest IoT-assisted disease detection model for healthcare applications is developed. Initially, the patient data are taken from the IoT sensor devices in image, text, and signal data. The gathered patient data is further forward to the pre-processing phase. Accordingly, the images are gathered from patients’ data using IoT devices and forward to the feature extraction process. Feature extraction is done based on the IoT-collected images using VGG16 and Inception network. Moreover, the signals are garnered from the patient’s data and fed to the decomposition procedure. It is done by Empirical Mode Decomposition (EMD) technique. Then, the optimal feature selection of data, images, and signals is carried out by a hybrid algorithm termed Mating Probability-based Hybrid Strider Glowworm Swarm Optimization (MP-HSGSO). Finally, the Radial Basis Recurrent Neural Network (RBRNN) using MP-HSGSO is suggested to get the detected outcomes. Here, various brain diseases have been detected to classify the disease. This designed RBRNN method is utilized for efficient treatment, and also it provides the appropriate decision-making through processing various kinds of multimodal data. Throughout the simulation outcomes, the accuracy and precision of the offered method achieve 97 % and 96 %. Consequently, the simulation outcome of the offered method is proved that the designed model using a deep learning model shows enriched performance in clinical trial applications.

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

DATA SECURITY IN HEALTHCARE USING IOT

While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data don't seem to be focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this project, the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks related to unsecured data transmission, especially between IoT sensor devices and network routers, then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified employing a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end to-end security solution of healthcare data transmission in IoT. This project also provides further research avenues on IoT sensor driven security.

Missing Data Imputation in the Internet of Things Sensor Networks

The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted with electronics, sensors, and network connectivity. IoT sensor networks have become integral aspects of environmental monitoring systems. However, data collected from IoT sensor devices are usually incomplete due to various reasons such as sensor failures, drifts, network faults and various other operational issues. The presence of incomplete or missing values can substantially affect the calibration of on-field environmental sensors. The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors. To achieve this, we propose an efficient and robust imputation technique based on k-means clustering that is capable of selecting the best imputation technique for missing data imputation. We then evaluate the accuracy of our proposed technique against other techniques and test their effect on various calibration processes for data collected from on-field low-cost environmental sensors in urban air pollution monitoring stations. To test the efficiency of the imputation techniques, we simulated missing data rates at 10–40% and also considered missing values occurring over consecutive periods of time (1 day, 1 week and 1 month). Overall, our proposed BFMVI model recorded the best imputation accuracy (0.011758 RMSE for 10% missing data and 0.169418 RMSE at 40% missing data) compared to the other techniques (kNearest-Neighbour (kNN), Regression Imputation (RI), Expectation Maximization (EM) and MissForest techniques) when evaluated using different performance indicators. Moreover, the results show a trade-off between imputation accuracy and computational complexity with benchmark techniques showing a low computational complexity at the expense of accuracy when compared with our proposed technique.

Data Traffic Reduction with Compressed Sensing in an AIoT System

To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a YOLOv5 deep learning function for object detection, and an IoT server. By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device, we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices. In addition, we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall, precision, mAP50, and mAP, and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate. Consequently, if proper compressed sensing rates are chosen, the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

Wireless Sensor Network as a Mesh: Vision and Challenges

Nowadays, network technologies are developing very rapidly. The growing volume of transmitted information (video, data, VoIP, etc.), the physical growth of networks, and inter-network traffic are forcing manufacturers to produce more powerful and “smart” devices that use new methods of transferring and sorting data. Such connected smart devices (IoT) are used in intelligently controlled traffic for self-driving vehicles in Vehicle Adhoc Networks (VANET), in electricity and water in smart cities, and in-home automation in smart homes. These types of connected Internet of Things (IoT) devices are used to leverage different types of network structures. Such IoT sensor devices can be deployed as a wireless sensor network (WSN) in a mesh topology. Both WSNs and Wireless Mesh Networks (WMNs) are easy to organize as well as to deploy. In this case, there are many reasons for combining these different types of networks. In particular, the detailed sensory capabilities of sensor networks may be improved by increasing bandwidth, reliability and power consumption in the mesh topology. However, there are currently only a handful of studies devoting to integrate these two different types of networks. In addition, there is no systematic review of existing interconnection methods. That is why in this article we explore the existing methods of these two networks and provide an analytical basis for their relationship. We introduce the definition of WSN and WMN and then look at some case studies. Afterward, we present several challenges and opportunities in the area of combined Wireless Mesh Sensor Network (WMSN) followed by a discussion on this interconnection through literature review and hope that this document will attract the attention of the community and inspire further research in this direction.

Evaluation Method of Mesh Protocol over ESP32 and ESP8266

Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.

A full‐stack model proposal to remotely help the development of IoT sensor devices

AbstractThe teaching and development of the necessary skills to efficiently implement and develop Internet of Things (IoT) sensor devices is particularly challenging. Traditionally, the development of these skills is achieved with the help of several laboratorial facilities. Here, a full‐stack model to help in this process is presented. This model includes the utilization of a set of tools that can be used to remotely access the available resources, thus creating an “online lab.” This “online lab” is of particular interest and usefulness in the current pandemic context and is of particular interest not only for students to have access to real hardware devices, thus developing their skills and knowledge, but also to professional teams collaborating in the development of IoT sensor devices.

A Novel TRNG Based on Traditional ADC Nonlinear Effect and Chaotic Map for IoT Security and Anticollision

In the rapidly developing Internet of Things (IoT) applications, how to achieve rapid identification of massive devices and secure the communication of wireless data based on low cost and low power consumption is the key problem to be solved urgently. This paper proposes a novel true random number generator (TRNG) based on ADC nonlinear effect and chaotic map, which can be implemented by traditional processors with built-in ADCs, such as MCU, DSP, ARM, and FPGA. The processor controls the ADC to sample the changing input signal to obtain the digital signal DADC and then extracts some bits of DADC to generate the true random number (TRN). At the same time, after a delay based on DADC, the next time ADC sampling is carried out, and the cycle continues until the processor stops generating the TRN. Due to the nonlinear effect of ADC, the DADC obtained from each sampling is stochastic, and the changing input signal will sharply change the delay time, thus changing the sampling interval (called random interval sampling). As the input signal changes, DADC with strong randomness is obtained. The whole operation of the TRNG resembles a chaotic map, and this method also eliminates the pseudorandom property of chaotic map by combining the variable input signal (including noise) with the nonlinear effect of ADC. The simulation and actual test data are verified by NIST, and the verification results show that the random numbers generated by the proposed method have strong randomness and can be used to implement TRNG. The proposed TRNG has the advantages of low cost, low power consumption, and strong compatibility, and the rate of generating true random number is more than 1.6 Mbps (determined by ADC sampling rate and processor frequency), which is very suitable for IoT sensor devices for security encryption algorithms and anticollision.

An IoT based Tele-Health WBAN Model for Elderly People – A Review

The Tele-Health WBAN (Wireless Body Area Network) Model for patients required more attention especially old age people’s healthcare services in Low-cost Internet of Things (IoT) Devices. The advancements in telemedicine have increased drastically towards wearable sensor devices and mobile phone-based applications in the last few years. The study presents the integration of IoT and wearable sensor devices in the Tele - Health system developed for tracking heart patients among the elderly people and also to prevent them from stroke. In order to meet the demand for old age people healthcare services, it is very much essential to provide assistance in cardiac disease diagnosis and suggest medication in their home with comfortable environment. Hence, they can avoid frequent visit to hospitals and long stays.

Spectrum Based Power Management for Congested IoT Networks.

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

Deployment of internet of things-based cloudlet-cloud for surveillance operations

This research proposes the design of internet of things (IoT) camera/toxic gas sensors for the surveillance of a nation’s borders. Also, a wearable radio frequency identification (RFID) tag with built-in body-temperature/heartbeat sensors, for monitoring the health status and locations of military personnel while on border patrol duty or in battlefield combats. Mobile micro-controllers are deployed to gather sensed data retrieved from the sensors/RFID tags and transmitted to a cloudlet situated at the command control center, located 200 meters away from the sensor devices. Consequently, sensed data are dispatch to the cloud data center when there is a need for offline data mining or analysis. The distinguishing feature of our proposed system from previous researches is that the health status and locations of troops (soldiers) are monitored while they are in border patrol duty or in battlefield combats. Also, the introduction of cloudlet services closer to the IoT sensor devices for collection of sensed data. This way, the sensed data or information gathered at the cloudlet will aid timely information retrieval that will speed up intelligence gathering for strategic military operations, especially in critical situations. This is an innovative attempt to apply IoT-enabled cloudlet-based cloud computing to support military operations.

A Time Granular Analysis of Software Defined Wireless Mesh Based IoT (SDWM-IoT) Network Traffic Using Supervised Learning

The ceaseless increase in the number of the wireless Internet of Things (IoT) devices has resulted in the need of different traffic engineering techniques to manage the massive network traffic. Wireless Mesh Networks (WMNs) are an important constituent part of the wireless IoT networks, and are helpful to route the IoT networks’ traffic over long distances. The WMN devices are powerful in comparison to the IoT sensor devices, and are suitable to run the traffic engineering algorithms. To further improve the performance of the WMNs, Software Defined Networking can be used. Its unique features like global visibility, agility, etc., guarantee the optimal network management. As granularity plays an important role in data analysis and none of the existing works has discussed a time granularity based network data analysis, this work tries to offer a time granular analysis of Software Defined Wireless Mesh based IoT (SDWM-IoT) network’s traffic using supervised learning approaches. A time granular analysis helps to explore the functional traits of the data at the Coarse, Medium, and Fine granularity levels. This assists in divulging and understanding the hidden characteristics and behaviour of the SDWM-IoT network’s data based on varying time granularity, respectively. Some well known supervised learning algorithms are used to offer an in-depth analysis of the traffic, and to draw the relevant conclusions. Different variants of Decision Tree, Support Vector Machine and K-Nearest Neighbour (KNN) are used to analyze the traffic and achieve a reliable accuracy rate of more than 90%. Among all the variants, fine-KNN produces the best accuracy for most of the traffic classes with a rate of more than 98%. In addition to this, a tenfold cross-validation technique is also used to prevent the the chances of over-fitting.

IoT Sensor Initiated Healthcare Data Security

While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data are not focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this paper, we studied how the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, especially between IoT sensor devices and network routers, and then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that the packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified using a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end-to-end security solution of healthcare data transmission in IoT. This paper also opens up further research avenues on IoT sensor driven security.

Information processing in Internet of Things using big data analytics

With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering discipline. These applications and services could vigorously respond to the surroundings transformation and users’ preference. Developing a scalable system for data analysis, processing and mining of enormous real world based datasets has turned into one of the demanding problems that faces both system research scholars and data management research scholars. Employing big data analytics with IoT technologies is one of the ways for handling the timely analyzing information (i.e., data, events) streams. In this paper, we propose an integrated approach that coalesce IoT systems with big data tools into a holistic platform for real-time and continuous data monitoring and processing. We propose Fog assisted IoT based Smart and real time healthcare information processing (SRHIP) system in which large amounts of data generated by IoT sensor devices are offloaded at Fog cloud form data analytics and processing with minimum delay. The processed data is then transferred to a centralized cloud system for further analysis and storage. In this work, we introduce a Fog-assisted model with big data environment for data analytic of real time data with remote monitoring and discuss our plan for evaluating its efficacy in terms of several performance metrics such as transmission cost, storage cost, accuracy, specificity, sensitivity and F-measure. The proposed SRHIP system needs less transmission cost of 40.10% in comparison to SPPDA, 100% fewer bytes are compromised in comparison to GCEDA. Our proposed system data size reduction of 60% reduction due to proposed compression scheme in comparison to other benchmark strategies that offer 40% of reduction.

IoT–smart contracts in data trusted exchange supplied chain based on block chain

Internet of Things (IoT) assumes a critical part in the advancement of different fields. The IoT data trusted exchange in recent year extend of uses influence an awesome request and increasing scale. In such a platform, exchange the data sets that they require and specialist organization can search. However, the enough trust as the third-party mediators for data exchange in centralized infrastructure cannot provide. This paper proposes a blockchain for IoT data trusted exchange based on decentralized solution. In particular, the fundamental standards of blockchain in verify manner, individuals can communicate with each other without a confided in mediator intermediary. Blockchain enable us to have a distributed, digital ledger. IoT (Internet of Things) sensor devices (zigbee) utilizing blockchain technology to assert public availability of temperature records, tracking location shipment, humidity, preventing damage, data immutability. The sensor devices looking the temperature, location, damage of each parcel during the shipment to completely guarantee directions. In blockchain all data is got moved from one position to another, where a smart contract assesses against the product attributes. Ethereum blockchain and smart contracts atlast it gets through knowledge a design to be copied and presents its decentralized distributed digital ledger, auditable, transparent, features visually.

IoT Analysis and Processing System using Sensed Data for Laboratory Safety Management

This study proposes and implements a real-time message analysis processing system using sensed data for safety management of a laboratory composed of Internet of Things (IoT) sensor devices. Existing IoT systems have difficulty storing data in a relational database system due to the different data formats between sensor devices, and they also have preprocessing overhead due to nonstandard communications protocols. Furthermore, they are centralized systems with unstable service continuity, and it is difficult to add new functions or extend the system because of the complex connections among sensor devices and services. Studies on real-time analysis and processing of large volumes of sensor data are also insufficient. To address these problems, the proposed system uses IoT sensor devices, based on standard communications protocols, and message queue brokers based on standard messaging protocols. The message queue brokers can facilitate adding sensor devices or services by delivering the sensor data from the gateway and central server to the services. Services can continue, even in the event of network trouble, because the services that had been provided from the central server are now provided through smart gateways. The central server can provide risk analysis and prevention services for laboratory safety management through Apache Spark, a real-time big data analysis and processing platform. Through the experiments in this study, the proposed real-time message analysis processing system for laboratory safety management was implemented. The message queue brokers between the standard Message Queuing Telemetry Transport (MQTT) protocol and the smart gateway receiving the data were built into sensor devices equipped with network and sensor modules. Exchange routing keys are also specified by topic and service for each sensor device in line with the laboratory safety management environment. Data transmission processing is handled by a federated connection of the gateway brokers and the central server brokers.

Device Identification Interoperability in Heterogeneous IoT Platforms.

With the continuous improvement of Internet of Things (IoT) technologies, various IoT platforms are under development. However, each IoT platform is developed based on its own device identification system. That is, it is challenging to identify each sensor device between heterogeneous IoT platforms owing to the resource request format (e.g., device identifier) varying between platforms. Moreover, despite the considerable research focusing on resource interoperability between heterogeneous IoT platforms, little attention is given to sensor device identification systems in diverse IoT platforms. In order to overcome this problem, the current work proposes an IoT device name system (DNS) architecture based on the comparative analysis of heterogeneous IoT platforms (i.e., oneM2M, GS1 ‘Oliot’, IBM ‘Watson IoT’, OCF ‘IoTivity’, FIWARE). The proposed IoT DNS analyzes and translates the identification system of the device and resource request format. In this process, resource requests between heterogeneous IoT platforms can be reconfigured appropriately for the resources and services requested by the user, and as a result, users can use heterogeneous IoT services. Furthermore, in order to illustrate the aim of the proposed architecture, the proposed IoT DNS is implemented and tested on a microcomputer. The experimental results show that a oneM2M-based device successfully performs a resource request to a Watson IoT and FIWARE sensor devices.

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data.

The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).