Design of Smart Home System Based on ZigBee Technology and R&amp;amp;D for  Application

Gao, Lin; Wang, Zhixin; Zhou, Jianlong; Zhang, Chao

doi:10.4236/epe.2016.81002

Cited by 21 publications

(13 citation statements)

References 2 publications

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“…The flow of data stores and signal requests as well as the remote control of monitored electrical household appliances in the HEMS with the NILM in this paper is shown in Figure 2. ZigBee (the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard) [13] is arguably the most popular technology for creating wireless sensor networks, and it has been included in some of the latest commercial [14] and academic home automation cases, including water pump control in smart fish farms with efficient energy consumption [15][16][17]. Thanks to the ZigBee wireless communication network, the HG communicating with the monitored household appliances is able to perform remote direct as well as distributed load control for DSM/electrical energy management.…”

Section: Methodsmentioning

confidence: 99%

Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

Lin

2018

Processes

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Concerning electrical energy used in today’s modern society, electrical energy demands requested from downstream sectors in a smart grid are continuously increasing. One way to meet the electrical demands requested is to monitor and manage industrial, commercial, as well as residential electrical appliances efficiently in response to Demand Response (DR) programs for Demand-Side Management (DSM). Monitoring and managing electrical appliances that consume electrical energy in fields of interest can be realized through use of Energy Management Systems (EMS) with Non-Intrusive Load Monitoring (NILM). This paper presents an Internet of Things (IoT)-oriented Home EMS (HEMS). Also, a novel hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO)-integrated NILM approach is proposed and used to model and identify electrical appliances for DSM in the HEMS. ANN can be applied in NILM as a load identification task. Nevertheless, the performance of ANN used for load identification depends on three principal design factors: The network topology designed, the type of activation functions chosen, and the training algorithm adopted. As a result, PSO is conducted and used to incorporate meta-heuristics with ANN considering the three principal design factors relating to an ANN design. The HEMS with the novel hybrid ANN-PSO-integrated NILM proposed in this paper was deployed and evaluated in a realistic residential house environment. As the experimentation reported in this paper shows, the presented HEMS utilizing the proposed novel hybrid ANN-PSO-integrated NILM to model and identify monitored electrical appliances is feasible and workable, with an overall classification rate of 91.67% in load classification for DSM.

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Section: Methodsmentioning

confidence: 99%

Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

Lin

2018

Processes

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“…ZigBee was based on the IEEE 802.15.4 standard detail and is made by a set of companies that shape the ZigBee Alliance. Whereas other wireless standards are concerned with exchanging huge sums of information, ZigBee is built for devices that have littler throughput needs [32]. Besides, the other driving components are low cost, high reliability, higher security, less battery usage, simplicity and interoperability with other ZigBee devices [33].…”

Section: Existing Security Protocolsmentioning

confidence: 99%

A Review and Development Methodology of a LightWeight Security Model for IoT-based Smart Devices

Gurunathan

Mahmoud²

2020

IJACSA

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Internet of Things (IoT) turns into another time of the Internet, which contains connected smart objects over the Internet. IoT has numerous applications, for example, smart city, smart home, smart grid and healthcare. In common, the IoT system comprises of heterogeneous devices that deliver then trade endless sums of safety-critical information, also as privacysensitive information. Nevertheless, connected devices can give your business a genuine lift, yet anything that is connected to the Internet can be vulnerable to cyberattacks. Most present IoT arrangements rely upon centralized architecture by associating with cloud servers through the Internet. The public cloud is described as computing services publicized by third-party suppliers over the Internet, making them accessible to anybody who needs to use or buy them. This solution gives magnificent flexible calculation and information the executives capacities, as IoT systems are developing increasingly mind-boggling; nonetheless, despite everything, it faces different of security issues. One of the weaknesses is that your information moving in IoT devices by means of public cloud could be in danger, despite the fact that the hacker was not explicitly focusing on you and with the public cloud you have insignificant authority over how rapidly you can grow the cloud. In this case, a secured protocol in IoT is vital to ensure optimum security to the information being traded between connected devices. To overcome the limitation, in this paper, we conduct a comprehensive review on existing security protocols and propose a development methodology of a blockchain-based lightweight security model that provides end to end security. By utilizing lightweight, an authenticated client can get to the information of IoT sensors remotely. The presentation investigation shows that lightweight offers better security, less overheads, and low communication.

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“…In the NILM communicated with the HEMS, a standardized MySQL Connector/Open Database Connectivity (ODBC) driver and LabSQL Virtual Instruments using the ADO (ActiveX Data Objects) object collection and SQL (Structured Query Language) in LabVIEW TM are included. ZigBee (the IEEE 802.15.4 standard) [25] is arguably the most popular technology for creating wireless sensor networks, and it has been included in some of the latest commercial and academic home automation cases [26][27][28][29]. networks, and it has been included in some of the latest commercial and academic home automation cases [26][27][28][29].…”

Section: Hemsmentioning

confidence: 99%

A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning

Lin

2020

Sensors

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Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN’s performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.

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Design of Smart Home System Based on ZigBee Technology and R&D for Application

Cited by 21 publications

References 2 publications

Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

Electrical Energy Management Based on a Hybrid Artificial Neural Network-Particle Swarm Optimization-Integrated Two-Stage Non-Intrusive Load Monitoring Process in Smart Homes

A Review and Development Methodology of a LightWeight Security Model for IoT-based Smart Devices

A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning

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