If This Then That Research Articles

Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules

Trigger-action platforms (TAPs) enable users without programming experience to personalize the behavior of Internet of Things applications and services through IF-THEN rules. Unfortunately, the arbitrary connection of smart devices and online services, even with simple rules, such as “IF the entrance Netatmo Wheather Station detects a temperature above 30 \({}^{\circ}C\) ( \(86^{\circ}F\) ) THEN open the shutters in the living room,” might expose users to potential security and privacy risks (e.g., the execution of the previous rule might provide an easy entry point for thieves, especially during the summer vacation period). The goal of our research is to make the users capable of understanding and mitigating the threats and risks associated with the execution of IF-THEN rules. To this end, we define a new challenging task, namely generating post hoc justifications of privacy and security risks associated with automation rules, and propose a novel natural language generation strategy based on hybrid prompt learning producing justifications in the form of real-life threat scenarios. The proposed strategy allows for prompt customization with task-specific information, providing contextual details enabling to grasp the nuances and subtleties of the domain language, resulting in more coherent justifications. The experiments conducted on the if-this-then-that (IFTTT) platform show that our method produces effective justifications, improving the explainability of discrete and hybrid prompting methods up to 27% in BLEURT score. The code of the software is publicly available on GitHub 1 .

Implementation of Smart Segregation Bins Using IoT

Swachh Bharat Abhiyan Clean India Mission and abbreviated as SBA or SBM for "Swachh Bharat Mission is a national campaign by the Government of India, covering 4,041 statutory cities and towns, to clean the streets, roads and infrastructure of the country. Waste segregation and recycling are effective ways of reducing trash. Segregation at the source is the key in solid waste management especially when we have limited economical resources. Segregation of waste and creating awareness of different types of waste is new boom. We divide solid waste into two categories Wet, and Dry waste. According to solid waste management rule, 2016 it is responsibility of generators to segregate waste into these three categories. The main objective of this project is to design a system using Arduino Mega for automatic segregating of waste at source and capable of cleaning. Moisture sensor categorize the waste based on its threshold value and Ultrasonic sensor estimates the distance and the status of the bin will be send through IFTTT (If this then that) for sending SMS. This bin can be used at places like offices, apartments, shopping malls etc.

IoT-Enabled Deep Learning Algorithm for Estimation of State-of-Charge of Lithium-ion Batteries

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.

An IoT-based Smart Home Application with Barrier-Free Stairs for Disabled/Elderly People

Home automation based on the Internet of Things (IoT) includes various components such as lighting, security, and remote control. Smart Home (SH) components should be varied and customised according to the user's specific needs. Therefore, in this study, a SH system needed by users with specials is designed and implemented on a model. The NodeMCU microcontroller with ESP8266 Wi-Fi module, Radio Frequency Identification (RFID) tags, temperature-humidity, motion detection, gas and moisture sensors and several actuators are used to build the system. Thanks to If This Then That (IFTTT), Google Assistant, and Blynk, the SH components can be managed remotely and with voice commands via a user-friendly Android-based mobile interface. The multiple control system and the ability to control the home components with different methods make this study comprehensive. In particular, the barrier-free stairs design has added innovation to the SH system for disabled people. Thus, the accessibility, security, and comfort requirements of disabled people are met, and their quality of life is improved to live independently.

Edge-Centric Programming for IoT Applications With Automatic Code Partitioning

IoT application development usually involves separate programming at the device side and server side. While separate programming style is sufficient for many simple applications, it is not suitable for many complex applications that involve complex interactions and intensive data processing. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EdgeProg</i> , an edge-centric programming approach to simplify IoT application programming, motivated by the increasing popularity of edge computing. With EdgeProg, users could write application logic in a centralized manner with an augmented If-This-Then-That (IFTTT) syntax and virtual sensor mechanism. The program can be processed at the edge server, which can automatically generate the actual application code and intelligently partition the code into device code and server code, for achieving the optimal latency. EdgeProg employs dynamic linking and loading to deploy the device code on a variety of IoT devices, which do not run any application-specific codes at the start. Results show that EdgeProg achieves an average reduction of 20.96%, 27.8% and 79.41% in terms of execution latency, energy consumption, and lines of code compared with state-of-the-art approaches.

Cloud-based Voice Home automation System Based on Internet of Things

The rapid development of automation industries and technologies has shown incredible prospects for transforming our homes into a smart home automation system, which are more secure than a simple home. This paper proposes a home application based on voice and text called the Automated Control and Monitoring System (ASCM). This application can be utilized by both normal and vision-impaired people by using with a mobile phone.&#x0D; The application allows users to send voice commands through Google Assistant installed on Android to control the appliances. They can also have complete monitoring by logging onto the ThingSpeak dashboard, which displays a device status indicator and sends alert messages in the event of danger. The NodeMCU development board based on the ESP8266 and a combination of sensors, modules and actuators were used to create a "control system" for home appliances based on the Internet of Things (IoT). This work mitigates the challenges that faced the design of smart homes based on (IoT). The control and monitoring system is using voice without the need to use programming languages. As well as the automation process is not only on / off but also querying the status of any device sensors. In addition, this application dispenses with IFTTT(If-This-Then-that) services to send alert messages because their number is limited during the month, compared to the alerts provided by the Blynk app, which is unlimited. Mobile phones and robotic homes will make life easier and more enjoyable for the elderly and differently abled people who are unable to manually turn the items ON/OFF.

Nonnegative Matrix Factorization Based Heterogeneous Graph Embedding Method for Trigger-Action Programming in IoT

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.

What IoT devices and applications should be connected? Predicting user behaviors of IoT services with node2vec embedding

User-created automation applets to connect IoT devices and applications have become popular and widely available. Exploring those applets enables us to grasp the patterns of how users are utilizing and maximizing the power of connection by themselves, which can deliver practical implications for IoT service design. This study builds an IoT application network with the data of the IFTTT(if this then that) platform which is the most popular platform for self-automation of IoT services. The trigger-action relationships of the IFTTT applets currently activated are collected and used to construct an IoT application network whose nodes are IoT service channels, and links represent their connections. The constructed IoT network is then embedded by the node2vec technique, an algorithmic framework for representational learning of nodes in networks. Clustering the embedded nodes produces the four clusters of IoT usage patterns: Smart Home, Activity Tracking, Information Digest, and Lifelogging & Sharing. We also predict the IoT application network using node2vec-based link prediction with several machine learning classifiers to identify promising connections between IoT applications. Feasible service scenarios are then generated from predicted links between IoT applications. The findings and the proposed approach can offer IoT service providers practical implications for enhancing user experiences and developing new services.

If This Context Then That Concern: Exploring users’ concerns with IFTTT applets

Abstract End users are increasingly using trigger-action platforms like If-This-Then-That (IFTTT) to create applets to connect smart-home devices and services. However, there are inherent implicit risks in using such applets—even non-malicious ones—as sensitive information may leak through their use in certain contexts (e.g., where the device is located, who can observe the resultant action). This work aims to understand to what extent end users can assess this implicit risk. More importantly we explore whether usage context makes a difference in end-users’ perception of such risks. Our work complements prior work that has identified the impact of usage context on expert evaluation of risks in IFTTT by focusing the impact of usage context on end-users’ risk perception. Through a Mechanical Turk survey of 386 participants on 49 smart-home IFTTT applets, we found that participants have a nuanced view of contextual factors and that different values for contextual factors impact end-users’ risk perception differently. Further, our findings show that nudging the participants to think about different usage contexts led them to think deeper about the associated risks and raise their concern scores.

Smart Assistive Shoes with Internet of Things Implementation for Visually Impaired People

Visually impaired people usually have a lot of difficulties involved in interacting with their environment. Physical movement is a major challenge for them, because it can be tricky to make a distinction about where they are and how they can move from one place to another. In this project, smart assistive shoes with Internet of Things (IoT) implementation is designed. These shoes are equipped with ultrasonic sensors and vibration motors that can warn users about obstacles. Next, the IoT system is implemented using Adafruit IO and If This, Then That (IFTTT) to transfer data between Google Assistant and buzzer for shoes position finder purposes. NodeMCU allows the buzzer on shoes to be controlled by the Internet using its WiFi module which is connected to the mobile phone hotspots. As a result, shoes with an obstacle detection system which can detect obstacles within 20 cm distance and shoes position finder using Google Assistant are designed. In conclusion, hopefully these shoes will become one of the alternatives to aid the independent movement of the visually impaired people in the future.

A Novel Voice Controlled Robotic Vehicle For Smart City Applications

There are a lot of researchers working towards enhancing the connection between humans and robot. In this article, we have developed a robotic vehicle which can be controlled by voice commands. The control system of the robot movement will respond to the voice of the commanding person. A Texas board, MSP432P401R is connected to TI-RSL (Texas Instruments-Robotic System Learning) kit and used together with Wi-Fi module, CC3100 booster pack interfaced to an android application IFTTT (If This Then That) and Blynk to transmit the voice commands. The proposed system is designed based on microcontroller (MSP432P401R) which is connected to smart android phone through Wi-Fi module for receiving voice commands. The voice commands are converted to text by an android app and sends necessary data to the controller for robot movement. The voice commands are processed in real-time, using an online cloud server called Blynk. After receiving each command from the controller, the TI-RSL kit responds and moves in the desired direction. Moreover, we have also used camera module to watch the live streaming and direct the robotic vehicle without facing any obstacle. The camera is interfaced with Raspberry Pi for live streaming. The purpose of our article is to use this robotic vehicle in high toxic areas, where human cannot reach but human voice can.

Multi-Language Real Time Home Appliances Controlling System using Google Assistance

Smart home system is one of the key domains in applications of Internet of things (IoT). This is attracting attention from large tech companies. The fundamental reason for this undertaking is to control home gadgets with voice. There are numerous gadgets available accessible to do that, however, making our own with low cost and friendlier is main aim of proposed system. Our framework utilizes Adafruit account which is a cloud based free IoT web worker used to make virtual switches. Then it is connecting to IFTTT (IF THIS THEN THAT) site shortened as which is utilized to make if else restrictive explanations. The voice orders for Google collaborator have been added through IFTTT site. As the client provides orders to the Google collaborator, Home Appliances like Bulb, Fan and Motor and so on, can be controlled likewise. The gadget associated with the particular hand-off can be turned on or OFF according to the client’s solicitation to the Google Assistant. Our proposed system uses microcontroller Nodemcu (ESP8266).

Design and Implementation of a Smart Home Security System Using Voice Command and Internet of Things

A smart home security system consists of various sensors and recorders to automatically provide data on the conditions of a house. Home electronic equipment may be controlled from distant places using the internet of things technology and speech recognition. This research aims to develop a smart home security system by monitoring fire hazards and theft. It helps also control electronic equipment using voice commands that is useful especially for the disabled. The system design consists of hardware, software, and application design. Hardware design uses ESP8266, Arduino Mega 2560, MicroSD Card module, VC0706 serial camera, DHT22, magnetic door switch, PIR HC SR501, and Google Assistant device. Software design uses Arduino IDE for programming Arduino Mega and ESP8266. Applications used in the design are Adafruit.io, Thingspeak, and IFTTT (If This Then That). Voice commands control home electronic devices (lights), while fire and theft are monitored through the use of sensors and cameras. The system test shows that voice command can control lights on and off at an accuracy of 88%. Temperature and humidity sensors acquire data and send them to Thingspeak application for online fire monitoring. Sensors to detect intruders in the form of door switch and PIR work well and automatically activate cameras that capture objects to store in a MicroSD card.

Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks.

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.

A3ID: An Automatic and Interpretable Implicit Interference Detection Method for Smart Home via Knowledge Graph

The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger–action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have considered explicit interference problems related to multiple rules targeting a single actuator, whereas implicit interference (interference across different actuators) detection is still challenging and not yet well studied owing to the effort-intensive and time-consuming annotation work of obtaining device information. The lack of knowledge about devices is a critical reason that affects the accuracy and efficiency in implicit interference detection. In this article, we propose A3ID, an automatic detection method for implicit interference based on knowledge graphs. Using natural language processing (NLP) techniques and a lexical database, A3ID can extract knowledge of devices from a knowledge graph, including functionality, effect, and scope. Then, it analyzes and detects interferences among the different devices semantically in three steps, without human intervention. Furthermore, it provides user-friendly explanations in a well-designed structure to specify possible reasons for the implicit interference problems. Our experiment on 11 859 IFTTT-style rules shows that A3ID outperforms state-of-the-art methods by more than 33% in the F1-score for the detection of implicit interference. Moreover, evaluations on an extended data set for devices from ConceptNet (a knowledge graph) and five smart home systems suggest that A3ID also has favorable performance with other devices not limited to the smart home domain.

Google Assistant Controlled Home Automation

World’s interest for power had become 85% somewhere in the range of 2010 and 2017 this expansion is more than the present all out utilization of power in India, USA, Japan, Australia consolidated. We can’t diminish the power development rate however we can decrease the measure of power squandered every year by killing our home machines when not being used. This venture shows a plan and model of Home Automation framework that will utilize ESP8266 Wi-Fi module as a system supplier in associating with different machines. The proposed framework has two principle segments. The primary fundamental part is Arduino, which controls and oversees contribution of Wi-Fi module. The other principle segment is Wi-Fi module through Wi-Fi module a web server can be added to the module which will help in controlling of gadgets over Internet. One server can oversee numerous equipment interface modules as long as it exists on Wi-Fi arrange inclusion. It bolsters a wide scope of home mechanization gadgets like power the board segments, and security parts. We need to make this automation framework incorporated and misleadingly astute. The framework is executed utilizing common family apparatuses Natural language voice directions are given to the Google Assistant and with the assistance of IFTTT (If This Then That) application and the Blynk application the directions are decoded and after that sent to the microcontroller, the microcontroller thusly controls the transfers associated with it as required, turning the gadget associated with the separate hand-off On or OFF according to the clients demand to the Google Assistant. The microcontroller utilized is Node MCU (ESP8266) and the correspondence between the microcontroller and the application is built up by means of Wi-Fi.

A system for collecting and analyzing experience-sampling data.

Online and sensor technologies promise to transform many areas of psychological enquiry. However, collecting and analyzing such data are challenging. In this article, we introduce the unforgettable.me experience-sampling platform. Unforgettable.me includes an app that can collect image, Global Positioning System, accelerometry, and audio data in a continuous fashion and upload the data to a server. The data are then automatically augmented by using online databases to identify the address, type of location, and weather conditions, as well as provide street view imagery. In addition, machine-learning classifiers are run to identify aspects of the audio data such as voice and traffic. The augmented data are available to participants in the form of a keyword search interface, as well as via several visualization mechanisms. In addition, Unforgettable Research Services partners with If This Then That (IFTTT), and so can accumulate data from any of over 600 sources, including social media, wearables, and other devices. Through IFTTT, buttons can be added as icons to smartphones to allow participants to register mood conveniently, as well as behaviors and physiological states such as happiness, microaggressions, or illness. Furthermore, unforgettable.me incorporates a mechanism that allows researchers to run experiments and analyze data within an authenticated environment without viewing users' private data.

Design and Fabrication of Smart Home With Internet of Things Enabled Automation System

Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users' devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.

Remotely Access “My” Smart Home in Private: An Anti-Tracking Authentication and Key Agreement Scheme

Smart home is one of the key applications of the Internet of Things (IoT), which allows users to control the smart devices in their houses through the Internet. However, a smart home system also faces severe challenges in terms of privacy and confidentiality when users are allowed to remotely access it. Despite the recent research efforts on authentication schemes to improve the security aspects of a smart home, there are still unsolved problems. On the one hand, most of the existing schemes focus on secure authentication and communication via a trusted third party without taking its privacy leakage into consideration. On the other hand, many protocols enable the users to directly authenticate themselves to a large number of smart devices in the smart home network, which is often inefficient and inconvenient. To cope with these issues, we propose a smart home system model based on Internet services, like if this then that (IFTTT), and design an anti-tracking mutual authentication scheme with a key agreement element in it. Specifically, our scheme introduces an IFTTT home gateway as the control commands' executor and the security guard to allow a user to remotely access a smart home system privately. The proposed scheme employs the elliptic curves' cryptography (ECC) algorithm, nonces, XOR, and cryptographical hash functions to achieve mutual authentication with security features, such as anonymity and perfect forward security. The security analysis and performance comparison results demonstrate that the proposed scheme achieves secure and private authentication.

Systematically Ensuring the Confidence of Real-Time Home Automation IoT Systems

Recent advances and industry standards in Internet of Things (IoT) have accelerated the real-world adoption of connected devices. To manage this hybrid system of digital real-time devices and analog environments, the industry has pushed several popular home automation IoT (HA-IoT) frameworks, such as If-This-Then-That (IFTTT), Apple HomeKit, and Google Brillo. Typically, users author device interactions by specifying the triggering sensor event and the triggered device command. In this seemingly simple software system, two dominant factors govern the system confidence properties with respect to the physical world. First, IoT users are largely nonexperts who lack the comprehensive consideration regarding potential impact and joint effect with existing rules. Second, while the increasing complexity of IoT devices enables fine-grained control (e.g., heater temperature) of continuous real-time environments, even two simply connected devices can have a huge state space to explore. In fact, bugs that wrongfully control devices and home appliances can have ramifications on system correctness and even user physical safety. It is crucial to help users to make sure the system they created meets their expectation. In this article we introduce how techniques from hybrid automata can be practically applied to assist nonexpert IoT users in the confidence checking of such hybrid HA-IoT systems. We propose an automated framework for end-to-end programming assistance. We build and check the Linear Hybrid Automata (LHA) model of the system automatically. We also present a quantifier elimination-based method to analyze the counterexample found and synthesize fix suggestions. We implemented a platform, MenShen, based on this framework and proposed techniques. We conducted sets of real HA-IoT case studies with up to 46 devices and 65 rules. Empirical results show that MenShen can find violations and generate rule fix suggestions in only 10 seconds.