scholarly journals A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3450
Author(s):  
Muhammad Diyan ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Smart Home ◽  
Scheduling Algorithm ◽  
Smart Homes ◽  
Human Interaction ◽  
Household Appliances ◽  
Slack Time ◽  
Home User

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

scholarly journals Analysis and Real-Time Implementation of IoT in Autism

2021 ◽  
Author(s):  
Muhammad Javaid Afzal ◽  
Muhammad Waseem Ashraf ◽  
Shahzadi Tayyaba ◽  
Farah Javaid
Keyword(s):  
Social Skills ◽  
Social Behavior ◽  
Real Time ◽  
Visual Information ◽  
Smart Home ◽  
Smart Homes ◽  
Graphical Analysis ◽  
Peer To Peer ◽  
Aba Therapy ◽  
Special Children

Abstract The present study comprises the importance and use of IoT, smart home, peer-to-peer, and some other different technologies for the betterment of mankind. Artificial intelligence and machine learning are becoming more and more popular in IoT-based smart homes and other techs. The IoT framework has been used to provide a predictable, concrete, and self-paced learning environment and encourages excellent visual information processing. These techs are also useful for special children with autism. They need smart homes, peer-to-peer networking, robot therapy, and wearable techs. In this paper, the authors presented a unique FLC simulation for the behavior of a child with moderate autism. This simulation shows that how IoT, smart home, peer-to-peer technology, ABA therapy, robot therapy, and wearable techs can bring comforts in the life of a child with autism. All of them have long lasted impacts in producing social skills in an ASD person. The 3D graphical figures presented the graphical analysis of a child’s social behavior. The simulation presented 50% betterment change in the social behavior of children and this can be increased up to 87% by using intensive therapies. It is also verified by Mamdani’s method. A real-time implementation of a boy with autism has shown significant improvements in his social skills.

scholarly journals Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach

2021 ◽  
Vol 5 (6) ◽  
pp. 840-854
Author(s):  
Jesmeen M. Z. H. ◽  
J. Hossen ◽  
Azlan Bin Abd. Aziz
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Unsupervised Learning ◽  
Real Time ◽  
Smart Home ◽  
Time Series Data ◽  
Economic Cost ◽  
Feature Reduction ◽  
Series Data ◽  
Learning Models

Recent years have seen significant growth in the adoption of smart home devices. It involves a Smart Home System for better visualisation and analysis with time series. However, there are a few challenges faced by the system developers, such as data quality or data anomaly issues. These anomalies can be due to technical or non-technical faults. It is essential to detect the non-technical fault as it might incur economic cost. In this study, the main objective is to overcome the challenge of training learning models in the case of an unlabelled dataset. Another important consideration is to train the model to be able to discriminate abnormal consumption from seasonal-based consumption. This paper proposes a system using unsupervised learning for Time-Series data in the smart home environment. Initially, the model collected data from the real-time scenario. Following seasonal-based features are generated from the time-domain, followed by feature reduction technique PCA to 2-dimension data. This data then passed through four known unsupervised learning models and was evaluated using the Excess Mass and Mass-Volume method. The results concluded that LOF tends to outperform in the case of detecting anomalies in electricity consumption. The proposed model was further evaluated by benchmark anomaly dataset, and it was also proved that the system could work with the different fields containing time-series data. The model will cluster data into anomalies and not. The developed anomaly detector will detect all anomalies as soon as possible, triggering real alarms in real-time for time-series data's energy consumption. It has the capability to adapt to changing values automatically. Doi: 10.28991/esj-2021-01314 Full Text: PDF

scholarly journals Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Tran Anh Khoa ◽  
Le Mai Bao Nhu ◽  
Hoang Hai Son ◽  
Nguyen Minh Trong ◽  
Cao Hoang Phuc ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Internet Of Things ◽  
Real Time ◽  
Mobile Networks ◽  
Smart Home ◽  
Smart Cities ◽  
Smart Homes ◽  
Authentication Mechanism

Smart homes are an element of developing smart cities. In recent years, countries around the world have spared no effort in promoting smart cities. Smart homes are an interesting technological advancement that can make people’s lives much more convenient. The development of smart homes involves multiple technological aspects, which include big data, mobile networks, cloud computing, Internet of Things, and even artificial intelligence. Digital information is the main component of signal control and flow in a smart home, while information security is another important aspect. In the event of equipment failure, the task of safeguarding the system’s information is of the utmost importance. Since smart homes are automatically controlled, the problem of mobile network security must be taken seriously. To address these issues, this paper focuses on information security, big data, mobile networks, cloud computing, and the Internet of Things. Security efficiency can be enhanced by using a Secure Hash Algorithm 256 (SHA-256), which is an authentication mechanism that, with the help of the user, can authenticate each interaction of a given device with a WebServer by using an encrypted username, password, and token. This framework could be used for an automated burglar alarm system, guest attendance monitoring, and light switches, all of which are easily integrated with any smart city base. In this way, IoT solutions can allow real-time monitoring and connection with central systems for automated burglar alarms. The monitoring framework is developed on the strength of the web application to obtain real-time display, storage, and warning functions for local or remote monitoring control. The monitoring system is stable and reliable when applying SHA-256.

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

2019 ◽  
Vol 118 (4) ◽  
pp. 160
Author(s):  
G. Madhumita ◽  
G. Rajini ◽  
B. Subisha
Keyword(s):  
Energy Consumption ◽  
Energy Harvesting ◽  
Real Time ◽  
Scheduling Algorithm ◽  
Earliest Deadline First ◽  
Real Time Systems ◽  
Tasks Scheduling ◽  
Time Systems ◽  
Earliest Deadline ◽  
Reducing Energy

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life.  Energy is a parameter by which the lifetime of system can be enhanced.  To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.  

Research on Communication Scheduling Algorithm for Smart Home in Internet of Things Under Cloud Computing

2019 ◽  
Vol 23 (1) ◽  
pp. 124-128 ◽  
Author(s):  
Jie Zhang ◽  
◽  
Mantao Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Objective Function ◽  
Smart Home ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Low Latency ◽  
Communication Scheduling

The current communication scheduling algorithm for smart home cannot realize low latency in scheduling effect with unreasonable control of communication throughput and large energy consumption. In this paper, a communication scheduling algorithm for smart home in Internet of Things under cloud computing based on particle swarm is proposed. According to the fact that the transmission bandwidth of any data flow is limited by the bandwidth of network card of sending end and receiving end, the bandwidth limits of network card of smart home communication server are used to predict the maximum practicable bandwidth of data flow. Firstly, the initial value of communication scheduling objective function of smart home and particle swarm is set, and the objective function is taken as the fitness function of particle. Then the current optimal solution of objective function is calculated through predicted value and objective function, current position and flight speed of particle should be updated until the iteration conditions are met. Finally, the optimal solution is output, the communication scheduling of smart home is thus realized. Experiments show that this algorithm can realize low latency with small energy consumption, and the throughput is relatively reasonable.

Smart Home Scheduling for Cost Reduction and Its Implementation on FPGA

2015 ◽  
Vol 24 (04) ◽  
pp. 1550044 ◽  
Author(s):  
Lin Liu ◽  
Xin Yang ◽  
Han Huang ◽  
Shiyan Hu
Keyword(s):  
Energy Consumption ◽  
Smart Home ◽  
Hybrid Electric Vehicle ◽  
Electricity Consumption ◽  
Area Network ◽  
Household Appliances ◽  
Monetary Cost ◽  
Central Controller ◽  
Home Area ◽  
Continuous Power

In a typical smart home scenario, various household appliances of a residential user such as washing machine and plug-in hybrid electric vehicle (PHEV) are connected via a home area network. Household appliances can be automatically scheduled by a central controller for satisfying the timing and energy constraints. It enables the reduction of monetary cost of electricity consumption and the peak energy load of the home system. On the other hand, the prevailing household appliances usually offer multiple discrete power levels. However, the existing smart home scheduling controllers can only handle the continuous power levels, while makes them unsuitable for prevailing household appliances. In this paper, a dynamic programming-based scheduling technique is proposed and implemented on FPGA to schedule household appliances with discrete power levels. It features a solution pruning technique, which can largely improve the time complexity. A case study which constitutes ten household appliances is performed. The experimental results demonstrate that our technique can reduce the monetary cost by 39.3% on weekday and 27.2% on weekend, respectively, comparing to the traditional scheduling. For energy consumption balancing, it shows that the peak to average ratio (PAR) of the energy consumption is reduced by 43.6% on weekday and 24.0% on weekend, respectively.

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