Home Area Network Research Articles

Transforming smart homes via P2P energy trading using robust forecasting and scheduling framework

With the advent of smart grids, advanced information infrastructures, advanced metering facilities, bidirectional exchange of information, and battery storage home area networks have all transformed the electricity consumption and energy efficiency. There is a significant shift in the energy management structure from the traditional centralized infrastructure to the flexible demand side driven cyber-physical power systems with clean energy and energy storage system. These changes have significantly evolved the home energy management (HEM) space. Consequently, stakeholders must define their responsibilities, create efficient regulatory frameworks, and test out novel commercial strategies. P2P energy trading appears to be a feasible solution in these circumstances, allowing users to trade electricity with one another before becoming completely reliant on the utility. P2P energy trading offers a more stable platform for energy trade by facilitating the exchange of energy between prosumers and consumers. This research proposes a novel demand and generation prediction techniques of P2P HEMS for optimal energy using the Multi-Objective Optimization model. An enhanced Wild Horse Optimization technique was first used to summarize historical records' qualities. Then, the Bi-LSTM is used to predict the demand and generation values. Furthermore, a Grasshopper optimization (GHO) approach is employed to fine-tune the model's hyperparameters. The P2P HEMS demand and generation prediction framework is offered with a probabilistic and fault evaluation that upholds load flow balance between need and supply for continuous operations. It results in an intelligent community transforming cities into smart ones, opening new avenues for scientific research in terms of technological developments.

Prediction of Load Demand in Home Area Network Using LSTM

Load forecasting is one of the most important tools for the energy management system in the modern world. Forecasting power consumption and load demand calculation became an interesting topic for the stakeholders in the electricity market. Decision-making in purchasing and generating electric power, load switching, and demand side management are dependent on load forecasting. This research work focuses on the prediction of power consumption in a Home Area Network (HAN) using time series forecasting methods like Long short-term memory (LSTM) neural Networks. Our goal was to design a model that could precisely forecast the electrical load required in a Home Area Network. Utility companies and homeowners can utilize this model to better plan and control their power usage. It can assist in lowering the likelihood of power outages and enhancing the effectiveness of the electrical system. The Root Mean Squared Error (RMSE) is used as the performance measure in our work. The dataset considered is from the UCI repository with 350400 rows and 4 parameters such as active power, and 3 submetering values, to achieve realistic data for training the model used the encoder-decoder LSTM model for load prediction and achieved an RMSE value of 23 and an MAE value of 18.6

Wi‐Fi 6‐based home area network for demand response in smart grid

SummaryThe past decade has brought prolific developments to power systems that range from large‐scale renewable integration to digital twins. Modern power systems consist of highly variable generation clusters. Hence, monitoring and prediction of power generation has become a vital part of today's power systems. On the other hand, detailed real‐time information of power usage is also a key parameter for the control algorithms used for the demand side management. With this information flow, power systems become smart grids which offer improved service and enable many advanced operations. In this regard, the home area network (HAN), which collects the power consumption/generation of household devices, plays a key role. So far, a number of technologies such as ZigBee, Z‐Wave, and Power Line Communication (PLC) have been considered for the HAN. Yet, widely used wireless local area network (WLAN) technology, Wi‐Fi, was not identified as a prominent candidate due to the limitations in its latency and reliability. However, with the introduction of the IEEE 802.11ax standard, Wi‐Fi 6 was developed, and it demonstrates guaranteed latency and reliability for its users. Thus, with its widespread deployment in households, Wi‐Fi is becoming a natural candidate for HAN. In this article, we present a comprehensive HAN protocol based on Wi‐Fi 6 which can achieve guaranteed latency and reliability for HAN users. The results show that when an efficient resource allocation mechanism is in place, using Wi‐Fi for HAN does not adversely affect the other Wi‐Fi users in the normal WLAN.

General Overview and Proof of Concept of a Smart Home Energy Management System Architecture

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

Self-reconfigurable partial mesh using ZigBee control board towards fail-proof and reliable home area networking

This paper deals with the development of customized home area networking (HAN) with a higher degree of reliability so that a robust communication network can be provided to smart home owners. The proposed HAN is built with the multi-gateway-based partial mesh networking topology using state-of-the-art ZigBee control boards (ZCBs) where each individual appliance within the home can be monitored and controlled remotely. The gateway is actually used to coordinate amongst the In-home display (IHD), ZCBs, smart meter, and remote server-based home energy management software or even with the mobile Apps in such a way that the best possible energy usage can be made. In order to enhance the reliability of this proposed HAN, the active and pseudo gateway concept is introduced in which under the failure of the active gateway (AG), pseudo gateway (PG) takes control, and the HAN gets reconfigured automatically. Malfunctioning or failure of any ZCB or even gateway node is detected during route discovery with ad-hoc on-demand distance vector (AODV) routing. The reliability of the proposed system is estimated using possible path models and region models considering all of its running conditions. The proposed system is simulated and a prototype laboratory setup with 15 and 10 numbers of ZCBs is built for experimental verification. Various failure conditions are experimented with to demonstrate the alternate routes and activation of PG as AG. The availability of an alternate route and/or gateway makes this proposed HAN a fail-proof and reliable one and this is reflected from the relative values obtained from the simulated and experimental results.

Novel hybrid chaotic map-based secure data transmission between smart meter and HAN devices

ABSTRACT Home area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks.

Seamless Connections: Harnessing Machine Learning for MAC Optimization in Home Area Networks

The latest technologies and communication protocols are arousing a keen interest in automation, in which the field of home area networks is the most prominent area to work upon toward solving the issues and challenges faced by wireless home area networks regarding adaptability, reliability, cost, throughput, efficiency, and scalability. However, managing the immense number of communication devices on the premises of a smart home is a challenging task. Moreover, the Internet of Things (IoT) is an emerging global trend with billions of smart devices to be connected in the near future resulting in a huge amount of diversified data. The continuous expansion of the IoT network causes complications and vulnerabilities due to its dynamic nature and heterogeneous traffic. In the applications of IoT, the wireless sensor network (WSN) plays a major role, and to take benefits from WSN, medium access control (MAC) is the primary protocol to optimize, which helps in allocating resources to a huge number of devices in the smart home environment. Furthermore, artificial intelligence is highly demanded to enhance the efficiency of existing systems and IoT applications. Therefore, the purpose of this research paper is to achieve an optimized medium access control protocol through machine learning. The machine learning classifier, e.g., random forest (RF) and linear regression model, is adopted for predicting the features of home area networks. The proposed technique is helpful and could overcome the demerits of existing protocols in relation to scalability, throughput, access delay, and reliability and help in achieving an autonomous home area network (HAN).

Performance Evaluation of Communication Infrastructure for Peer-to-Peer Energy Trading in Community Microgrids

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

An Enhanced Channel Estimation for IEEE 802.15.4 OFDM Receiver in High-Speed Mobile IoT Communication Systems

Currently, orthogonal frequency division multiplexing (OFDM), standardized in IEEE 802.15.4, has attracted attention as a transmission scheme for higher data rates, long-distance transmissions, and mobile communications for the expansion of IoT applications as well as for legacy IoT applications, such as smart metering systems and home-area networks. In particular, receiving schemes using IEEE 802.15.4-based OFDM have been actively examined to realize mobile communication. Previous studies used the long training field (LTF) and pilot subcarriers for channel estimation of multipath fading, achieving mobile communication with data rates of 100 kbps or above at moving speeds of up to 40 km/h in an urban environment. In a previous study, channel estimates were obtained by repeating channel estimates from the LTF and pilot subcarriers in the time domain and by interpolating the estimates in the frequency domain. This study proposes a receiving scheme for IEEE 802.15.4 OFDM aimed at high-speed mobile IoT communication systems. In the time axis direction, the proposed scheme obtains channel estimates in the same manner as previous schemes. However, in the frequency axis, the proposed scheme obtains the channel estimates by copying estimates of neighboring subcarriers according to the coherence bandwidth of the multipath fading channel to update the channel estimates frequently. Computer simulation results show that the proposed scheme including delay spread estimation can achieve mobile communication with a data rate of 100 kbps at moving speed of 200 km/h in the urban environment.

D2D Group Key Agreement Scheme for Smart Devices in HANs

<p>The home area network (HAN) is one of the most widely researched areas in recent years. HANs integrate 5G/6G networks and artificial intelligence technology to provide data services for home users. The devices in HANs collect and transmit data relating to users’ daily activities for analysis by remote service providers. These data often contain a large number of users’ personal privacy. The disclosure of these data could have far-reaching consequences for the privacy of the individuals involved. Some researchers are dedicated to investigating the authentication of smart devices by the system. However, the increased frequency of interactions between devices and gateways, as well as between devices themselves, is a defining characteristic of HANs. In this paper, a device-to-gateway (D2G) authentication scheme is proposed. Based on the authentication result, a partial key is generated for smart devices and the gateway. Finally, a device-to-device (D2D) group key agreement scheme is presented. The security and efficiency of the proposed scheme are proved according to the analysis.</p> <p> </p>

Secure authentication protocol for home area network in smart grid-based smart cities

The Internet of Things (IoT) allows better solutions for managing challenges such as reliability and power quality in a smart grid environment. It also assists in adopting smart grid measures in smart city development. A smart meter in the smart grid environment must securely access services from a service provider via public channels. An adversary can pose several security vulnerabilities as the communication occurs through a public channel. Various researchers recently conducted several research types to determine the most challenging problems with the smart grid. The security features like integrity, authentication, and confidentiality are indispensable requirements of the smart grid environment. On the other hand, different objects like smart meters in the smart grid environment are resource-constrained, posing in designing lightweight security protocols. Hence, we propose a lightweight mutual authentication scheme to offer surveillance to smart meters in smart grid infrastructure. We analyze the suggested protocol against well-known security threats. Finally, we evaluate the performance of the protocol to illustrate its efficiency.

Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication.

Smart appliances' run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers' energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.

An embedded intrusion detection and prevention system for home area networks in advanced metering infrastructure

AbstractWith the widespread adoption of smart metres in the power sector, anomaly detection has become a critical tool for analysing customers' unusual consumption patterns and network traffic. Detecting anomalies in power consumption and communication is primarily a real‐time big data analytics issue regarding data mining along with a vast number of parallel streaming data from smart metres. In this study, an embedded Intrusion Detection and Prevention System (IDPS) is proposed as a Wifi‐based smart metre for Home Area Networks (HANs) in the Advanced Metering Infrastructure (AMI) network. So, the proposed system employs one machine learning model based on IDPS to guard the HAN network from various attacks that utilise the Message Queueing Telemetry Transport protocol between the smart metre and IoT sensors. Also, it uses two machine learning models to detect the abnormality in periodic and daily data metering respectively. So, multiple algorithms have been used to find the suitable algorithm for each of the three anomaly detection models. These models have been evaluated and tested using real data sets regarding resources usage and detection performance to demonstrate the efficiency and effectiveness of using machine learning algorithms in the built anomaly detection models. The experiments show that the anomaly detection models performed well for various abnormalities.

Privacy-Preserving Serverless Computing Using Federated Learning for Smart Grids

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

A Comprehensive Review on Wired and Wireless Communication Technologies and Challenges in Smart Residential Buildings

Background: The smart grid communication network is constructed with three tiers namely, Home Area Networks (HANs), Neighborhood Area Networks (NANs) and Wide Area Networks (WANs). These networks function with various communication protocols like table protocol, on-demand protocol, Wi-Fi, Zigbee, Z-Wave, Wi-MAX, GSM, LTE, Cognitive Radio Networks. The network interconnection is heterogonous and all appliances have to communicate through the IP gateways. A large amount of data is collected from various sensors placed in different locations. The analytics on large data- “big data” is essential because these data were used to organize and plan an efficient control and management of the smart home including secured data exchange in different sectors. Objective: This paper investigates broadly on data rate, channel bandwidth, power consumption, and a coverage range of both wired and wireless communication technologies used in residential buildings. Besides, a literature survey on optimization algorithms with various constraints to manage home appliances through scheduling is included. The paper also discusses the communication standards along with security and privacy requirements for smart metering networks. Conclusion: Discussion on IEEE standards for both wired and wireless communication protocols. Gives direction to identify the suitable communication technique through mathematical model for computing the communication channel bandwidth. Comparison of various optimization algorithms with multiple constraints in HEMS to achieve the minimum electricity cost and user comfort (with and without Renewable Energy Sources). From the investigation on both wired and wireless networks, the wireless communication networks (Zig-Bee & Wi-Fi) are mostly preferred to use in HAN because of more reliability and low cost. Zigbee is the most appropriate technology used for data transmission between the individual appliances and smart meters. Wi-Fi is a suitable technology for controlling and monitoring appliances because of its high data rate.

Effect of data aggregation on the delay of demand-response communications within a Wi-Fi home area network with and without other traffic

Home area networks play an important role in the demand-response function of smart grids. It is responsible for responding to requests received from the grid by controlling the devices in the home in a predefined manner. Communication within a Home Area Network should be efficient in terms of both delay and energy. Delay matters since the devices need to respond to the request within the stipulated delay. Energy matters since thousands of Home Area Networks are likely to create a significant energy footprint on the global level. In order to reduce energy consumption, the number of communications needs to be reduced and data aggregation can achieve this goal. However, data aggregation introduces a prolonged delay and may thus render the system unfit for its purpose. Therefore, it is required to determine the variation of delay when data aggregation is performed at different levels. This paper presents algorithms for data aggregation and device clustering optimization. Finally, the delay distribution was studied in a simulation environment with one level of data aggregation. The results show that an existing Wi-Fi network can be used for Smart Grid communications with in-network data aggregation provided that there is a spare (unused) bandwidth of 3 Mbit/s in the network.

Improving Network-Based Anomaly Detection in Smart Home Environment.

The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%.

Enhanced Home Energy Management Scheme (EHEM) in Smart Grids

Wireless Sensor Networks (WSNs) have become one of the most important components that play a major role in home environment applications. It plays a major role in the creation and the development of smart home environments. Smart homes creates home area network (HAN) to be used in different applications including smart grids. In this paper, we propose an enhancement to in-Home Energy Management (iHEM) scheme, namely EHEM, to reduce energy consumption by shifting the residents’ demands to mid-peak or off-peak periods depending on the appliances priorities and delays. The proposed system handles challenging cases by using internal storage battery. The performance of the proposed system is compared against iHEM and the traditional iHEM scheme, based on the total cost of the power consumption. Obtained results show slight improvement over the existing iHEM scheme

Edge security in smart inverters: Physical invariants based approach

The endeavour towards making power distribution systems (PDSs) smarter has made the interdependence on communication network indispensable. Further, prospective high penetration of intermittent renewable energy sources in the form of distributed energy resources (DERs) has resulted in the necessity for smart controllers on such DERs. Inverters are employed for the purpose of DC to AC power conversion in the distribution network where the present standards require these inverters to be smart. In general, distributed energy resource management systems (DERMS) calculate and send set points/operating points to these smart inverters using protocols such as smart energy profile (SEP) 2.0. Given the nature of sites at which such DERs are installed i.e., home area networks with a pool of IoT(Internet-of-Things) devices, the opportunity for a malicious actor to sabotage the operation is typically higher than that for a transmission system. National Electric Sector Cyber-security Organization Resource (NESCOR) has described several failure scenarios and impact analyses in case of cyber attacks on DERs. One such failure scenario concerns attacks on real/reactive power control commands. In this paper, it is demonstrated that physical invariant based security on the edge devices, i.e. smart controllers deployed in DER inverters, is an effective approach to minimize the impact of cyber attacks targeting reactive power control in DER inverters. The proposed defense is generic and can also be extended to attacks on real-power control. The proposed defense is validated on a co-simulation platform (OpenDSS and MATLAB/SIMULINK).

A Novel Privacy Preserving Scheme for Smart Grid-Based Home Area Networks.

Despite the benefits of smart grids, concerns about security and privacy arise when a large number of heterogeneous devices communicate via a public network. A novel privacy-preserving method for smart grid-based home area networks (HAN) is proposed in this research. To aggregate data from diverse household appliances, the proposed approach uses homomorphic Paillier encryption, Chinese remainder theorem, and one-way hash function. The privacy in Internet of things (IoT)-enabled smart homes is one of the major concerns of the research community. In the proposed scheme, the sink node not only aggregates the data but also enables the early detection of false data injection and replay attacks. According to the security analysis, the proposed approach offers adequate security. The smart grid distributes power and facilitates a two-way communications channel that leads to transparency and developing trust.