Smart Home Simulator Research Articles

IMPLEMENTATION OF A SMART HOME BASED ON INTERNET OF THINGS USING CISCO PACKET TRACER

A smart home is a home that uses information and communication technology, especially the Internet of Things (IoT), to increase comfort, efficiency, security and energy management. The main idea of ??a smart home is to connect various electronic devices and systems in the house so that they can interact with each other and with the occupants of the house automatically or through centralized control. Users can control devices in the home remotely via mobile devices or computers. This allows them to regulate the temperature, check home security, or control equipment even when they are not at home. Smart homes are often equipped with sophisticated security systems, including security cameras, motion sensors, and alarms. Residents can monitor and control this system via their smart devices. Smart homes can be a practical and innovative solution to improve the quality of life and optimize home management. However, it is important to consider the security and privacy issues as well as the costs associated with using this technology. Using this smart home really helps users to carry out activities calmly outside the home because it can monitor the electrical conditions at home. The device used to create a smart home simulation is using Cisco Packet Tracer.

SocIoTy: Practical Cryptography in Smart Home Contexts

Smartphones form an important source of trust in modern computing. But, while their mobility is convenient, smartphones can be stolen or seized, allowing an adversary to impersonate the user in their digital life: accessing the user's services and decrypting their sensitive files. With this in mind, we build SocIoTy, which leverages a user's existing IoT devices to add a context-sensitive layer of security for non-expert users. Instead of assuming the existence of dedicated hardware, SocIoTy re-uses the devices of a user's smart home to provide cryptographic services, which we term at-home cryptography. We show that at-home cryptography can be built from simple cryptographic primitives, and that our SocIoTy solution is able to provide useful functionalities, like two-factor authentication (2FA) and secure file storage, while protecting against powerful adversaries in this setting. We implement and evaluate SocIoTy in real-world use cases and provide microbenchmarks for individual cryptographic operations on realistic models of IoT devices. We also provide full benchmarks of an end-to-end deployment on a simulated smart home, using a smartphone and 9 IoT devices to generate and display 2FA one-time passwords in less than 200 milliseconds. SocIoTy is able to provide strong, practical cryptography while binding its execution to the smart home itself, all without requiring additional hardware.

Relativistic Digital Twin: Bringing the IoT to the future

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.

Leveraging Semantic Technologies for Collaborative Inference of Threatening IoT Dependencies

IoT Device Management (DM) refers to the remote administration of customer devices. In practice, DM is ensured by multiple actors such as operators or device manufacturers, each operating independently via their DM solution. These siloed DM solutions are limited in addressing IoT threats related to device dependencies, such as cascading failures, as these threats spread across devices managed by different DM actors, and their mitigation can no longer be performed without collaborative DM efforts. The first step toward collaborative mitigation of these threats is the identification of threatening dependency topology. However, this task is challenging, requiring the inference of dependencies from the data held by different actors. In this work, we propose a collaborative framework that infers the threatening topology of dependencies by accessing and aggregating data from legacy DM solutions. It combines the assets of Semantic Web standards and Digital Twin technology to capture on-demand the topology of dependencies, and it is designed to be used in business applications such as customer care to enhance customer Quality of Experience. We integrate our solution within the in-use Orange's Digital Twin platform Thing in the future and demonstrate its effectiveness by automatically inferring threatening dependencies in the two settings: a simulated smart home scenario managed by ground-truth DM solutions, such as Orange's implementation of the USP Controller and Samsung's SmartThings Platform , and a realistic smart home called DOMUS testbed.

Helping Users Debug Trigger-Action Programs

Trigger-action programming (TAP) empowers a wide array of users to automate Internet of Things (IoT) devices. However, it can be challenging for users to create completely correct trigger-action programs (TAPs) on the first try, necessitating debugging. While TAP has received substantial research attention, TAP debugging has not. In this paper, we present the first empirical study of users' end-to-end TAP debugging process, focusing on obstacles users face in debugging TAPs and how well users ultimately fix incorrect automations. To enable this study, we added TAP capabilities to an existing 3-D smart home simulator. Thirty remote participants spent a total of 84 hours debugging TAPs using this simulator. Without additional support, participants were often unable to fix buggy TAPs due to a series of obstacles we document. However, we also found that two novel tools we developed helped participants overcome many of these obstacles and more successfully debug TAPs. These tools collect either implicit or explicit feedback from users about automations that should or should not have happened in the past, using a SAT-solving-based algorithm we developed to automatically modify the TAPs to account for this feedback.

THE EFFECTS OF ENVIRONMENT-INDUCED RELAXATION ON COGNITIVE FORAGING UNDER STRESS IN MIDLIFE AND BEYOND

Abstract We defined enrichment seeking as the capacity for adults to engage in the novel and intellectually challenging activities for exploring diverse experience. Although enrichment seeking is associated with cognitive resilience in older adulthood, the tendency for older adults to adopt explorative behavior decreases with age in concert with decline in executive control. The goal of our study was to examine to what extent older adults can adjust their cognitive foraging performance under stress by inducing relaxation through the task environment. We used cognitive foraging games (including word search puzzle games) to understand how older adults optimize their search performance by the tradeoff between exploration (seeking new information) and exploitation (staying at old information). A modified Trier paradigm, where game performance was observed by a neurologist via video conferencing, induced stress; relaxation was manipulated by turning on an electric fireplace in the study room. The study followed a 2 (relaxation) X 2 (stress) within-subjects experimental design. Sixty-one adults (34 middle-age; 27 older adults; 54% women) played games on a tablet under stress / no stress conditions with and without the fireplace in a simulated smart home environment. The interaction effect of relaxation and stress on cognitive foraging performance was significant, indicating that with the relaxation elements in the environment (i.e., a fireplace), older adults are likely to have better cognitive foraging performance under stress. The association can be in part explained by individual differences in cognitive abilities and strategies. Implications on facilitating enrichment seeking through environmental factors are discussed.

Enhancing component-specific trust with consumer automated systems through humanness design

Consumer automation is a suitable venue for studying the efficacy of untested humanness design methods for promoting specific trust in multi-component systems. Subjective (trust, self-confidence) and behavioural (use, manual override) measures were recorded as 82 participants interacted with a four-component automation-bearing system in a simulated smart home task for two experimental blocks. During the first block all components were perfectly reliable (100%). During the second block, one component became unreliable (60%). Participants interacted with a system containing either a single or four simulated voice assistants. In the single-assistant condition, the unreliable component resulted in trust changes for every component. In the four-assistant condition, trust decreased for only the unreliable component. Across agent-number conditions, use decreased between blocks for only the unreliable component. Self-confidence and overrides exhibited ceiling and floor effects, respectively. Our findings provide the first evidence of effectively using humanness design to enhance component-specific trust in consumer systems. Practitioner summary: Participants interacted with simulated smart-home multi-component systems that contained one or four voiced assistants. In the single-voice condition, one component’s decreasing reliability coincided with trust changes for all components. In the four-voice condition, trust decreased for only the decreasingly reliable component. The number of voices did not influence use strategies. Abbreviations: ACC: adaptive cruise control; CST: component-specific trust; SWT: system-wide trust; UAV: unmanned aerial vehicle; CPRS: complacency potential rating scale; MANOVA: multivariate analysis of variance

STARE: Augmented Reality Data Visualization for Explainable Decision Support in Smart Environments

The Internet of Things (IoT) provides unprecedented opportunities for the access to and conflation of a myriad of heterogeneous data to support real-time decision-making within smart environments. Augmented Reality (AR) is on cusp of becoming mainstream and will allow for the ubiquitous visualization of IoT derived data. Such visualization will simultaneously permit the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cognitive and visual binding</i> of information to the physical object(s) to which they pertain. Important questions exist as to how one can efficiently filter, prioritize, determine relevance and adjudicate on individual information needs in support of real-time decision making. To this end, this paper proposes a novel AR decision support framework ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STARE</i> ) to support immediate decisions within a smart environment by augmenting the user’s focal objects with assemblies of semantically relevant IoT data and corresponding suggestions. In order to evaluate this technique, a remote user study was undertaken within a simulated smart home environment. The evaluation results demonstrate that the proposed Semantic Augmented Reality decision support framework leads to a reduction in information overloading and enhanced effectiveness, both in terms of IoT data interpretation and decision support.

AI-powered virtual assistants nudging occupants for energy saving: proactive smart speakers for HVAC control

ABSTRACT Virtual assistants powered by Artificial Intelligence (AI) and integrated into the smart home ecosystems facilitate human–building interactions. We have envisioned that the proactive virtual assistant capabilities could be designed to encourage energy conservation behaviours by relying on their nudging effect through conversational interactions, autonomous actuation and guiding users’ decision-making. To this end, we investigated how proactive virtual assistants, in a simulated smart home ecosystem, influence occupants to take energy-saving, adaptive actions for Heating, Ventilation and Air Conditioning (HVAC) operations and how participants’ personal characteristics affect their responses. Through an interactive online experiment, we collected data from 307 participants from diverse backgrounds across the United States. It was found that proactive communications with follow-up conversations can significantly increase the likelihood of accepting virtual assistance recommendations. This improvement was reflected in an increased number of participants (by 16%) who accepted energy-saving suggestions by comparing initial versus final responses during proactive conversations. Characterizing groups of participants based on their personal features and individual differences showed that user experience (with ∼30% increase), pro-environmental values/beliefs (with ∼24% to 35% increase) and forgiving thermal preferences (with ∼12% increase) had a significant influence on participants’ stated likelihood to accept virtual assistants’ recommendations and their evaluation of the general concept of proactive communication from virtual assistants.

Improve Security in Smart Cities Based on IoT, Solve Cyber Electronic Attacks with Technology by using Packet Tracer

Smart cities are expected to significantly improve people's quality of life, promote sustainable development, and enhance the efficiency of operations. With the implementation of many smart devices, c problems have become a serious challenge that needs strong treatments, especially the cyber-attack, which most countries suffer from it. My study focuses on the security of smart city systems, which include equipment like air conditioning, alarm systems, lighting, and doors. Some of the difficulties that arise daily may be found in the garage. This research aims to come up with a simulation of smart devices that can be and reduce cyber attach. Use of Cisco Packet tracer Features Simulated smart home and c devices are monitored. Simulation results show that smart objects can be connected to the home portal and objects can be successfullymonitored which leads to the idea of real-life implementation and see. In my research make manysolutions for attachingissues,which was great, and apply some wirelessprotocol.

Context-based Reasoning through Fuzzy Logic for Edge Intelligence

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

Context-Rich Privacy Leakage Analysis Through Inferring Apps in Smart Home IoT

Emerging Internet of Things (IoT) systems leverage connected devices to enable intelligent and automated functionalities. Despite the benefits, there exist privacy risks of network traffic, which have been studied by the previous research. However, with the current privacy inference remaining at the event-level, potential privacy risks are underestimated, which, as our study shows, can be much higher than previously reported through app-level traffic analysis. A key observation of our research is that IoT event-triggered traffic is generated by apps, which often adopt an if-trigger-then-action (trigger-action) programming paradigm. We utilize this feature to develop fingerprints to differentiate running apps and learn context-rich privacy-sensitive information from apps. In this article, we present a privacy leakage analysis called ALTA to infer running apps in smart home IoT environments. First, ALTA identifies app fingerprints through static analysis and extracts sensitive information from app descriptions and input prompts. Then, through dynamic traffic profiling, it learns traffic fingerprints of apps. Finally, ALTA matches the fingerprints of app and traffic, and thus is able to pinpoint which app is running from IoT traffic at runtime. To demonstrate the feasibility of our approach, we analyze 254 SmartThings applications via program and natural language processing (NLP) analysis. We also perform the app inference evaluation on 31 apps executed in a simulated smart home. The results suggest that ALTA can effectively infer running apps from IoT traffic and learn context-rich information (e.g., health conditions, daily routines, and user activities) from apps with high accuracy.

Intelligent recognition of dance training movements based on machine learning and embedded system

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.

Gesture recognition smart home

Different from the traditional smart home control method, this article uses the PAJ7620U2 chip gesture recognition to add to the smart home system.PAJ7620U2 gesture recognition can recognize 9 different gestures. Based on this performance, a simulated smart home system based on gesture recognition is designed. It contains home intelligent electronic photo frames, audio and video entertainment modules, intelligent security monitoring, ZigBee wireless communication environment detection, and light control. The experimental results show that the system is simple to operate and powerful, and is integrated into the traditional smart home control method without gaps.

Developing a Smart Home Solution Based on Personalized Intelligent Interior Units to Promote Activity and Customized Healthcare for Aging Society

The world’s population is aging at an unprecedented pace. Aging society is not only a severe crisis in the developed world, but also a rigorous challenge in emerging economies. Many age-related diseases are fostered by the lack of physical, cognitive, and social activities. Increasing the activity level has many benefits for the elderly and can improve their independence. Based on the analysis of the state-of-the-art and shortcomings of the current smart home solutions, the EU-funded research project REACH aims to develop a service system that will turn clinical and care environments into personalized modular sensing, prevention, and intervention systems that encourage the elderly to become healthy through various activities. To achieve that goal, the research team developed a series of smart furniture (PI2Us) that materialize the research concepts and functionality seamlessly into the various environments. Specifically, the development process and the functionality testing results of the PI2Us are reported in detail. In addition, a simulated smart home solution concept (TRACK) integrating key assistive technologies developed is proposed to create a comprehensive interior living and care environment for elderly users. Due to its modularity and flexibility, the partial or entire solution can be easily adapted and rapidly deployed in different use case settings in Europe, which allows the project consortium to execute different testing activities. In the next phase, the research team will focus on enhancing the usability and performance of the prototypes in order to eventually push them to the market. In conclusion, this research provides an innovative smart home solution for the industry to mitigate the impact of aging society.

An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner

With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

Design and implementation of elderly assistant & custody system based on Kinect and Gizwits

Since most of the traditional elderly assistant system cannot achieve remote control and send immediate notifications to their families when falling of the elderly occurs, a somatosensory interactive and remote controllable elderly assistant & custody system is designed and realized based on Kinect and Gizwits in this paper. Old people’s falling can be detected via the depth images and skeleton information captured by Kinect sensor and notified to their family members through web service. Remote control of the simulated smart home appliance through the mobile APP can be achieved via Data Points set in Gizwits cloud platform. Because of the remote control of the domestic appliance and accurate identification of falling-down action, the presented system can provide the elderly people with great convenience and ensure their living safety.

Hybrid simulation system for testing artificial intelligence algorithms used in smart homes

Smart home automation systems allow the user to control and track lighting, security, ventilation and temperature systems more easily. While these systems provided easy control of the house depending on the commands only given by the user in the early period, today with artificial intelligence algorithms developed for energy saving, home security, control etc., these systems have begun to perform many operations autonomously. Many researchers and R&D companies work in this field and develop new algorithms every day to create new products. Artificial intelligence algorithms must be continuously tested and improved under different conditions in order to function properly and perform the desired function. Performing the algorithm tests in real environments involves many problems and reduces the efficiency in terms of studies. In similar situations in smart homes and different disciplines, algorithm development and improvement studies are performed in simulated environments. However, it may not be possible to include all the variables of real life in simulation environments. Considering all these situations, in this study, a hybrid smart home simulation that runs real and virtual smart homes together is developed to test the artificial intelligence algorithms developed for use in smart homes. A real smart home system is first developed and installed in a room for hybrid simulation. Then, a simulated house is developed with the desired number of rooms, desired number of smart home components with different tasks for this house, virtual individuals who use this house, and weekly life scenarios for these individuals. The room in which the real smart home system works is converted into a room in the virtual house by changing the simulation codes. Likewise, a real person who uses the real smart home becomes an inhabitant living in a virtual home. In addition, virtual inhabitants are created to use the real smart home system. Thus, the real and the virtual smart home and real and virtual inhabitants live together in a hybrid simulation. Finally, the hybrid simulation is operated smoothly under different conditions for two months with different artificial intelligence algorithms.

SIMADL: Simulated Activities of Daily Living Dataset

With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

Living preference modeling of smart homes for different target groups

The smart home concept has been around for more than 30 years. It is nevertheless fair to say that it has not taken off commercially on a wider basis so far, in spite of many optimistic forecasts over the years. One of the main reasons for this, aiming to be addressed in this paper, is that the smart home developments still have not properly matched with the needs and preferences of people in real life. In this paper, we investigate how smart homes will be used in daily life of different target groups. Accordingly, a virtual experiment is applied in which nearly 254 respondents are involved. Through multiple tasks, respondents explore a 3D interactive simulated smart home and report their one day living in it for a weekday and a weekend. A Bayesian Belief Network (BBN) is used to estimate and formulate the relationships of the variables that directly and indirectly influence users' living preferences. Modeled estimations are given for the living preferences of 5 target groups ranging from an individual, who has a free lifestyle and low workload to a dual income family, who has a busy lifestyle and high workload at home. Finally, conclusions are accomplished by analyzing variations of the estimated living preferences among these 5 target groups.