Search

The Agricultural Internet of Things (AIOT) transforms food production by integrating IoT and IFTTT services in farming. Devices gather data on temperature, humidity, soil quality, and pests, enhancing monitoring accuracy. However, web services like IFTTT pose security risks, challenging agricultural operations’ integrity. A new study proposes a robust three-way authentication method for smart agriculture systems, rigorously evaluated using ROR model, BAN logic, and AVISPA tool. Computational tests with MIRACL Cryptographic SDK confirm efficiency. This research builds on previous work, ensuring AIOT-based farming’s security and integrity. Comparative analysis shows the proposed scheme’s computational efficiency, lower storage needs, and enhanced security over existing approaches. [ABSTRACT FROM AUTHOR]

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane’s architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community.

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. [ABSTRACT FROM AUTHOR]

The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s. [ABSTRACT FROM AUTHOR]

This research introduces an innovative smart cane architecture designed to empower visually impaired individuals. Integrating advanced sensors and social media connectivity, the smart cane enhances accessibility and encourages physical activity. Three meticulously developed algorithms ensure accurate step counting, swing detection, and proximity measurement. The smart cane's architecture comprises the platform, communications, sensors, calculation, and user interface layers, providing comprehensive assistance for visually impaired individuals. Hardware components include an audio–tactile interaction module, input command module, microphone integration, local storage, step count module, cloud integration, and rechargeable battery. Software v1.9.7 components include Facebook Chat API integration, Python Facebook API integration, fbchat library integration, and Speech Recognition library integration. Overall, the proposed smart cane offers a comprehensive solution to enhance mobility, accessibility, and social engagement for visually impaired individuals. This study represents a significant stride toward a more inclusive society, leveraging technology to create meaningful impact in the lives of those with visual impairments. By fostering socialization and independence, our smart cane not only improves mobility but also enhances the overall well-being of the visually impaired community. [ABSTRACT FROM AUTHOR]

Most of us tend to enjoy the ease of living by doing the bare minimum. The same applies while operating the devices at home by just a few touches or by using our voice in the preferred language. A smart home is an Internet of Things (IoT) platform that uses the internet to control the devices at our home, and this technology has grown enormously over the past few years encouraging new ideas. And with that thought, this system will be implementing Home Automation with Raspberry Pi and Google Assistant by controlling the appliances like lights, fans, air conditioners, temperature sensors, and more, in any preferred language. Platforms like If This Then That (IFTTT), Adafruit, Message Queueing Telemetry Transport (MQTT), and Raspberry Pi IO are used to connect the hardware with the software that is a common path for devices that are connected to the Relay module and the Google Assistant. The IFTTT platforms are easily available on our smartphones or a website that makes it easy for us to access different devices at different parts of the house or anywhere. Home automation minimizes the manual switching ON/OFF of the appliances whilst being controlled by the commands that are given by the users. This project builds an automation system that uses the range of Wifi or Bluetooth, which is easily accessible by the users to connect their devices and control them by voice through Google Assistant. This makes it easy for the users to access their devices wherever they are. Home automation comes as an advantage for older people and especially the physically disabled. The main objective of this proposed project is to provide a comfortable and a digitalized environment to use the day-today appliances with added security. [ABSTRACT FROM AUTHOR]

Summary: In this article we present an application of the Usage Control paradigm to a Smart Home infrastructure, based on a model extension and structured use of obligations. In the proposed extended model obligations are exploited to enforce two different access revocation time, namely revoke and suspend. This increases the policy expressiveness and enable to optimize the resource usage. Furthermore, obligations are exploited to send commands via IFTTT to different interconnected Smart Home devices, to impose safety‐relevant behaviors, or to act on policy attributes to implement a self‐healing paradigm for revoked sessions. The article is motivated by a parental control use case where deep learning is used in combination with Usage Control to regulate dynamically viewing rights of a smart‐TV and interactions with interconnected devices. Accuracy and performance experiments show the effectiveness and feasibility of the proposed work. [ABSTRACT FROM AUTHOR]

The unpredictable nature of epileptic seizures makes it challenging to detect and effectively treat this disorder. The seizures are random, and most epileptic patients experience dangerous physical symptoms during an attack that renders the patient uneasy when conducting their daily tasks. This paper focuses on the generalised type of epilepsy, namely ''Grand mal epilepsy Tonic-Clonic (GTC) seizure. The research aims to monitor symptoms of epileptic disease behaviour signals in humans and prevent it at its early stage of illness. To achieve this objective, we used the Electrocardiogram (ECG), Electromyography (EMG), accelerometer 3-axes for fall detection, and Dallas sensor for body temperature signals monitoring for updating the IoT system. The fuzzy logic algorithm that has been used to assess specified data set of diseased patients' parameters allows the classification into diverse types of seizures such as heart rate, body temperature, muscles spasm and falls. These are used as inputs to obtain the seizure type as an output which is then illustrated graphically on the dashboard of an IoT platform (Think-Speak), where abnormal conditions have been used to notify the medical personnel by sending an SMS message through ''If This Then That” (IFTTT) technology. A prototype of an epileptic monitoring system has been successfully built and tested. It has an average accuracy of 98.90%, 95.49%, 83.0%, and 87.21% for body temperature, heart rate monitoring, muscle spasm, and fall detection.

Copyright of Multidiszciplináris Tudományok is the property of University of Miskolc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The combination of smart home platforms and automation apps introduce many conveniences to smart home users. However, this also brings the potential of privacy leakage. If a smart home platform is permitted to collect all the events of a user day and night, then the platform will learn the behavior patterns of this user before long. In this paper, we investigate how IFTTT, one of the most popular smart home platforms, has the capability of monitoring the daily life of a user in a variety of ways that are hardly noticeable. Moreover, we propose multiple ideas for mitigating privacy leakages, which all together form a “Filter-and-Fuzz” (F&F) process: first, it filters out events unneeded by the IFTTT platform. Then, it fuzzifies the values and frequencies of the remaining events. We evaluate the F&F process and the results show that the proposed solution makes the IFTTT unable to recognize any of the user's behavior patterns.

The worldwide outbreak of COVID-19 has significantly changed the mindset of the people and over the period they started practicing healthy lifestyle to contain the spread of the virus. Despite this, increase in the number of cases and death rates across the globe are major cause of concern. In addition to maintaining the healthy lifestyle, it is also essential to exploit the technological advancements in the field of Internet of Things (IoT) [1] in designing a costeffective wearable device which could possibly indicate the early stages of virus infection. In this work, a low cost IoT enabled wearable device is designed which generates alerts in case of any of the measured parameter goes out of the normal range besides sending notifications. [ABSTRACT FROM AUTHOR]

Smart Home technology is increasing dramatically. Most of the developers offer central control units and different actuators and sensors which make it lively to control home appliances remotely via the Internet of Things or other gadgets like smartphones, tabs, etc. IoT is an assembly of microdevices that exchanges information through a Unique Identifier (ID number) with digital devices such as mobiles, tabs and remotely controls mechanical appliances such as air conditioners, microwave ovens, etc., without the aid of any human. All of the tools and systems are connected to the Internet of Things platform via sensors and exchange information through the Internet. With the introduction of IoT, the workload of service labor will be to reduced drastically. IoT technology has wide applications in homes, offices, industries, healthcare, etc., and are continuing to grow. The current implementation of 5G in telecommunication will further expand the growth of IoT. This paper explores modern IoT devices used in home automation by using Raspberry Pi. [ABSTRACT FROM AUTHOR]

IFTTT is a neutral platform that offers easy and free ways to get all your apps and devices talking to each other. Millions of users worldwide have enabled more than 75 million Applets for over 600 services that already cooperate with the platform. Linden Tibbets, co-founder and CEO of IFTTT, explains that everything in the future will be a digital service. Connecting all these services is a tricky task and many companies are struggling with making these connections. Tibbets explains how the IFTTT platform tackles these interfaces and functions and how end users and companies can get more value from being able to connect just about everything with everything.

Humans have always been at the top of the animal kingdom. It's a privilege we are all grateful for, but wouldn't it be marvelous if we could substitute our chores for some leisure time? We can start by deploying small functional autonomous cars. Autonomous cars are the elementary steps of progress towards advancement in IoT and Artificial Intelligence. In this paper, we present the idea of a low-cost autonomous vehicle, which will be controlled by voice commands, given by the user. The user may be located at some remote location, but as long as he/she is connected to the Internet, the vehicle will follow voice instructions. The idea is implemented through NodeMCU ESP866, Adafruit, and IFTTT. The instructions are fed to the vehicle through Google Assistant. [ABSTRACT FROM AUTHOR]

Internet of Things technology is rapidly becoming a reality in many parts of our lives through various product services, and product development especially in the field of smart home is being actively carried out. Most controllers for controlling various smart home products use smart phone applications. However, smart phone applications are not suitable as smart home controllers, contrary to smart home services that emphasize intelligence and convenience. In order to provide intelligent smart home service, intuitive form of smart home controller is needed, which enables integrated control of smart home device. Therefore, this paper proposes a smart home remote control that can control the Internet devices and services of objects. The proposed smart home remote control provides an environment where users can build a smart home service through the IFTTT(If This Then That) automated service platform. [ABSTRACT FROM AUTHOR]

A small device with WiFi multi-sensing element is essential under a digital social century. This study aims to implement the hardware and the power of the algorithm with WiFi technologies. Notably, the multi-sensors have to reinforce around a home area and support to any requirement in the term of digital society. This work focus to care at home security- on going to the fire detection with applying several technologies based on social networks. Firstly, the multisensor calibration used the calibration time, and the self-calibration used the Finite Impulse Response (FIR) filter. Next, the Full-Scale Kalman Filter (FSKF) helps to fill data and estimate the accuracy data. After that, a fire detection mechanism used Fuzzy logic to detect and sent alert messages over the term of IF This Then That (IFTTT) process. The home event changes the WiFi multi-sensor data. They took the data range effect to the fire proportion inside the home. Furthermore, The OFF-Mode is to reduce the power consumption suddenly the WiFi module sends the sensor data to the Cloud. If had a critical point, sent an alert message to social networks. Finally, the WiFi multi-sensor node has to use more than one same detector that it will be high stability and high accuracy. [ABSTRACT FROM AUTHOR]

The internet of things technology (IoT) is growing very rapidly. IoT implementation has been conducted in several sectors. One of them is for aquaculture. For the traditional farmers, they face problems for monitoring water quality and the way to increase the quality of the water quickly and efficiently. This paper presents a real-time monitoring and controlling system for aquaculture based on If This Then That (IFTTT) model and cloud integration. This system was composed of smart sensor module which supports modularity, smart aeration system for controlling system, local network system, cloud computing system and client visualization data. In order to monitor the water condition, we collect the data from smart sensor module. Smart sensor module consists of sensor dissolved oxygen, potential of hydrogen, water temperature and water level. The components of smart aeration system are microcontroller NodeMCU v3, relay, power supply, and propeller that can produce oxygen. The system could set the IFTTT rules for the ideal water condition for the pond in any kinds of aquaculture based on its needs through the web and android application. The experimental result shows that use IFTTT model makes the aquaculture monitoring system more customizable, expandable and dynamic.