Abstract

     Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.

Introduction

     A smart home is connected to the Internet, allowing users to manage a variety of smart gadgets, each of which serves an important purpose in the home for the user and their family. The IoT is the foundation of an intelligent home network, connecting disparate intelligent devices such as smartphones, smart computers, and wearable devices. Citizens’ lives can be made easier and safer by making their homes more open and secure. The smart home provides useful resources such as monitoring habits and even safety tests, which have compelled consumers and system developers to conduct extensive research.

     Blockchain-type systems and unified “cloud-like” computing networks can be used to solve these problems. Blockchain was developed in 2008 by Satoshi Nakamoto and includes a time-stamped set of malicious evidence documentation managed by a network of autonomous networks [1]. Blockchain architecture consists of a series of blocks linked together by simple cryptography. The three main concepts underlying the operation of blockchain technologies are inflexibility, decentralization, and transparency. The three roles have been remarkably effective, exposing them to a wide range of digital currency technologies, such as the functionality of mobile vehicles, mobile phones, and embedded systems. While the blockchain platform is secure and anonymous, there are some issues with its current implementation. For example, Sybil attacks by generations of false identities to manipulate the community have become more complex.

Conclusions

     In this study, the idea of a smart contract in blockchain technology is employed to validate the user’s identity for accessibility to centralized smart home services. The most significant benefit of this research is the demonstration of how easy it is to receive facilities and how secure the resources are. There is no need to have redundant authentication because no other third-party users can access smart home systems, even if another user tries to access an already used resource. Intrusion detection in smart homes, especially in the context of assessment and prediction, remains a key concern. In the meantime, recent advances in the blockchain and machine learning sectors have demonstrated tremendous promise to accomplish these aims. Discussing the need for an efficient approach, this study provided a compact and efficient mechanism for intrusion prevention. An RTS-DELM approach was developed, and also data fusion techniques were presented to optimize multi-sensor networks. Numerous measures were used to assess the feasibility of the proposal. The consistency of RTS-DELM findings showed that the proposed method is more successful than others. The suggested RTS-DELM solution obtained an exceptionally high rate of success, showing 95.28% accuracy. The findings obtained are encouraging, and we will continue to investigate more applications for the device through the deployment of more datasets and varying frameworks.

Abstract

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Abstract:

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

1. Introduction

The Internet of Things (IoT) is envisioned as a network of billions of devices that can sense, communicate and share data which can then be analyzed to unlock a wealth of intelligence useful for planning, management and decision making [1]. IoT promises huge benefits for a variety of domains such as healthcare, manufacturing, agriculture, telecommunication and transportation. Despite the popularity of the IoT concept and its promised benefits, its adoption has been significantly slower than expected [2,3]. Some of the major reasons behind this include: (1) security, privacy, policy and trust issues [3–5]; (2) organisational inertia, long capital cycles and shortage of specialist workforce needed to successfully implement IoT [2,5]; and (3) lack of convincing use cases with clear return on investment (ROI) in some sectors.

COVID-19 has impacted all walks of life so much that we may never return to the old normal. This pandemic is proving to be a catalyst for digital transformation because COVID19 has created or expanded applications and use cases of digital technologies [6–8]. It has also forced governments, organisations and individuals to change/adapt their priorities, their views on societal/ethical issues, and the way they operate. In many cases, this has addressed or mitigated many of the above mentioned reasons behind the slower-thanexpected adoption of IoT across many verticals. For example, governments have invested huge amounts of resources in IoT and other technologies to combat COVID-19. Lifestyle changes brought about by COVID-19 such as work/study from home have also provided new use cases of IoT with clear ROI such as remote asset control, workforce tracking and remote employee collaboration [9]. Consequently, many organisations have increased investment in IoT and the pace of their IoT projects [10,11]. The fight against COVID-19 has resulted in a less strict stance on privacy issues, higher trust in technology and fasttracked approval procedures. This is also paving the way for accelerated adoption of IoT in various verticals. Furthermore, regulatory changes such as stricter cleaning and tracking requirements for businesses are also accelerating the IoT adoption in smart buildings.

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings’ energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

1. Introduction

Buildings such as residential, education, office, healthcare, and industrial are emerging as critical consumers in energy consumption. Energy consumption for buildings represents 30–45% of global energy use [1–3], with a larger part of the energy used by the building subsystems, which consist of cooling and heating systems; safety, water, lighting, and similarly combined subsystems. In this context, efforts at this time are focused on the fulfillment of the requirements for energy-efficient in buildings, by guaranteeing the operative needs with the base conceivable energy cost and environmentally friendly [4]. In many developing and developed countries, energy efficiency is viewed as the best mechanism to address and defeat ever-rising energy needs [5]. In any case, advancing the energy efficiency of these subsystems is very testing since they typically have to comply with complex working requirements, dynamic energy necessity, and comfort needs [6].

1- Introduction

2- Smart-building model

3- Smart-buildings community framework

4- Case study and discussion

5- Conclusion

References

Abstract

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manage the daily energy exchanges within a community of Smart-Buildings, in the presence of local Renewable Energy Sources (RES). The proposed algorithm taps into the flexibility of the participants to let them decide of a day-ahead community power profile, and subsequently ensures the forecast tracking during the next day. In practice, the algorithm is fully decentralized by the Blockchain technology, that enables a trusted communication medium among the participants and enforces autonomous monitoring and billing via Smart-Contracts. With such an energy management framework, participating Smart-Buildings can together aim at a common objective, such as carbon-free resources usage or aggregated grid services, without depending on a centralized aggregator/utility. Simulations on realistic Swiss building models demonstrate that nearly all the renewable production resources could be harnessed locally through the presented framework, compared to selfish individual optimization. Under a quadratic cost of grid electricity, the considered community profile could dramatically be flattened, hence avoiding costly peaks at the grid interface. A scalability analysis shows that, considering the current public Ethereum Blockchain, the framework could handle a community size of up to 100 Smart-Buildings.

Introduction

The increasing world demand in electricity is envisioned to be entirely supplied by sustainable Renewable Energy Source (RES) in order to counteract the global climate change. However, intrinsic volatility and uncontrollably of decentralized RES production pose severe challenges to the current electrical grid, that must ensure stability at any time. Progressively, the centralized grid sees a change in paradigms, transitioning from a system dispatching a production portfolio following the electrical demand to a Smart-Grid that handles a portfolio of controllable demand to match an uncontrollable supply [1]. To assist this transition, Smart-Buildings have recently emerged as a solution to leverage the flexibility offered by the various entities commonly found in buildings. Equipped with the right hardware and Information and Communication Technology (ICT) they can provide active Demand Response (DR) to the electrical grid [2,3]. DR regroups a set of mechanisms divided into incentive and price-based programs, that specify various signals to be exchanged between the grid and the consumers in order to shape the power profile of the latter. Many works have tackled the problem at individual building level, demonstrating their capability to adapt their power consumption to grid signals while ensuring occupant comfort [4–7]. Beyond individual building optimization, there is a need of handling the problem at the community level. By doing so, local resources such as Photovoltaic (PV) production can optimally be harnessed while reducing the overall peak power demand. Many optimization frameworks have emerged to collectively manage the energy of multiple users [8–12]. Nevertheless, these solutions require a central agent that collects user information to subsequently dispatch optimal set points to each of them. Even though generic simplified models can be used to represent buildings flexibility [9,12], centralized solutions still face issues of privacy, single point of failure, scalability, and market entry of small prosumers.

The construction of smart cities in our country has received extensive attention. Under the situation that smart cities are vigorously promoted nowadays, compared with traditional construction and operation and maintenance methods, building information model (BIM) technology is more suitable to serve as an important foundation for intelligent management in the whole process of construction projects. BIM is an abbreviation for building information model. BIM relies on a variety of digital technologies, which can be used to realize information modeling of urban buildings and infrastructure. The efficiency of information exchange in the process of intelligence construction ensures the integrity and accuracy of information data exchange and maintains the consistency of information data exchange. Data and information have objectivity, applicability, transferability, and sharing. Geographic data is a digital representation of various geographical features and phenomena and their relationships. BIM is a digital representation of physical and functional characteristics of a facility. It can It is used as a shared knowledge resource for facility information. It becomes a reliable basis for facility life-cycle decision-making. Input BP neural network, and then learn and train by BP neural network.

Introduction

The development potential of smart city refers to a kind of goal formed by various development factors under the support of smart city information infrastructure, which aims at promoting economic growth, social management, public service and residents’ living and working in peace and contentment [1]. Building a smart city is conducive to further promoting the in-depth integration of industrialization, informatization, urbanization and agricultural modernization, accelerating the construction of a “two-oriented” society, intelligently advancing the process of new urbanization, and realizing scientific urban governance [2]. The related work of construction engineering can obtain the required information from this building information model to express the interpretation of geographic data of the relationship between geographic features and geographic phenomena, thus geographic information has three major features: spatial features, temporal features and attribute features [3]. Management and mechanism innovation has become the core concept that it must follow. Among them, technological innovation provides strong support for management and mechanism innovation. It can guide the corresponding work and feedback the corresponding work information to the model [4-6]. The continuous incentives and the existence of resources indicate that the organization’s sensitivity to these forces will increase and the adoption rate of new technologies will increase. From this conclusion, we can summarize the rate of diffusion of BIM technology in the industry in a smart city environment and fully reflect the wisdom of BIM technology. Requirements for building operation phase [7].

The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‚ boost economic efficiency‚ increase the user’s satisfaction by choosing between electricity sellers and buyers‚ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‚ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‚ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.

Introduction

The applications of hybrid energy systems have been studied with the focus on smart homes (SHs) and their optimal management in various studies. In (Yin, et al., 2017)‚ a hybrid DC Microgrid (MG) (based on power supply from photovoltaic (PV) cells) was investigated. This research shows that if the smart grid (SG) is in the off-grid state‚ the diesel generator with the electrochemical storage can be a better choice as a backup source. On the other hand‚ diesel generator cannot supply a stable power at the start-up. In (Arabul, et al., 2017)‚ the smart home loads are supplied by fuel cells (FCs), wind and solar energy resources. The method used in this research which is balancing and optimization of energy production and consumption using the Energy Management Algorithm (EMA), has been investigated. This method enhances the efficiency of the whole system improves the penetration of renewable energy sources‚ and increases the reliability of the system compared to conventional load shifting methods. In (Pilloni, et al., 2018)‚ the energy management method based on renewable energies in MG is presented., The energy sharing between SHs considering renewable energy participation is analysed in (Celik, et al., 2018). (Wu, et al., 2018)‚ adopt a method for energy management in SHs which is discussed considering renewable energy systems.

1- Introduction

2- System modelling

3- Proposed control framework for GISBs

4- Results and discussions

5- Conclusions

References

Abstract

Grid-interactive smart buildings with thermostatically-controlled loads can be modeled as virtual energy storage systems with dissipation, which have great potentials for providing grid ancillary services such as frequency support. In this paper, a new distributed aggregation control method is proposed for multiple grid-interactive smart buildings in one frequency control area (e.g. a residential community) to provide fast frequency support. The proposed method is based on the distributed sliding mode control via a leader-follower communication scheme. A leader control is designed to provide power and comfort/energy level references for the smart building aggregator based on the area frequency deviation, while references are tracked by each smart building using the proposed distributed sliding mode control. The stability of the proposed control method for grid-interactive smart buildings is proved by the Lyapunov method. With the proposed method, the external characteristics of the aggregated smart buildings will have good power tracking and energy recovery capability, which can effectively improve the system frequency response. In the aggregator, fair and efficient power and comfort/energy level sharing are achieved among all participating grid-interactive smart buildings. The proposed control scheme is tested on a three-area power system considering both system contingency and normal operation scenarios.

Background and motivation

In the last decade, the penetration of renewable energy resources into modern power systems is increasing rapidly. For example, in Singapore, the projected photovoltaics (PVs) generation will be increased to 350 MWp by 2020 [1]. However, high penetrations of renewable energy sources (RESs) such as solar PV and wind power have reduced power system inertia by replacing the synchronous machines [2], which can significantly challenge the system frequency regulation [3], control schemes [4] and transient stability [5]. In the meantime, fast and stochastic power variations of RESs will also lead to rapid frequency fluctuations [6]. It is well known that the power system frequency is an indicator of real-time power balancing between generation and consumption. So the frequency regulation becomes quite important in order to maintain a stable and efficient operation of the power systems. The severe frequency deviations can lead to load/generation trip-off, which jeopardize the stability and security of power systems as well as decline energy efficiency. A possible way to deal with the negative impact of renewable penetration is to increase the spinning reverses from power plants. However, involving more spinning reserves will bring additional fuel consumption, which is not an economic and energy efficient way to handle this problem.

1- Introduction

2- Related works

3- ICA-BEMS architecture and features

4- The solution scheduling

5- ICA-BEMS implementation

6- Conclusions

References

Abstract

Context-awareness is an important research area. Many energy efficiency applications of ubiquitous computing need to access some related contexts to provide the best and adequate energy saving services at the right time and at the right place. One of the big challenges is the difficulty and the complexity to make systems identify and understand situations in the building business, especially in complex situations to provide adequate energy-saving services for each of which. This issue is divided into two parts: On one hand, the smart building and its environment must be modeled in a way that provides context-awareness. On the other hand is the how to effectively exploit this context-awareness to reduce energy consumption and increase the user comfort. In this paper, we propose an intelligent context-awareness Building Energy Management System (ICA-BEMS). ICA-BEMS uses hybrid energy-saving techniques based on a Smart Context-Awareness Management (Smart-CAM). Smart-CAM uses smart building ontology to organize smart building knowledge and utilizes a new context-awareness mechanism to provide contextual information. The former exploits the contextual information in reasoning to reduce the building energy consumption, promoting users behavioral change and maximizing the user’s comfort to bring about a better energy efficiency policy. To evaluate our system, we have developed a smart building simulator (Open-SBS) which simulates smart building comportments and human behaviors. We have created various scenarios of daily life in the smart building simulator. We have tested our ICA-BEMS effect on energy consumption having a positive result. Energy consumption has been decreased by 40% of total energy consumption.

Introduction

In the ubiquitous computing environment, every day technology produces new devices, services, and systems to make our daily life easier and comfortable. The building is one of the most important places in people’s everyday life. 90% of people spend most of their time in buildings [1]. Smart building is an important research area of ubiquitous computing. Knowing that global primary energy demand will grow by 31% between 2012 and 2035 and energyrelated CO2 emissions increase by 18.1% [2]. The commercial and residential building accounts for about 20% of the world’s total energy consumption [3]. The energy demand in this sector grows faster than other areas throughout some projections [3]. Energy efficiency has significant gains like saving money and good impact on the environment. Therefore several works have been conducted to saving energy [4–8]. Such as the ones made by the major Information and Communication Technology (ICT) enterprises, as IBM, MONI, Control4, and Samsung who have created their own Building Management System (BMS). In our case, we are interested in BEMS (Building Energy Management System). There are two types of BEMS: Actives and Passives. The passive methods collect environment data, devices information and past energy use in a smart building. This information is used to provide new decisions to make the best saving energy policy in the future. These methods are easy to apply. Moreover, they are faster, cheaper and better for small buildings. Active methods operate at a level of intelligence and automation. BEMS have sensors, meters, and devices to control building in real-time to reduce energy consumption.

This article focuses on interactive and holistic smart home design for two-bedroom architectural pattern based on Internet of Things. Integrated system over home devices and information exchange among human, machine and things can be realized by connecting the home devices together with ZigBee agreement. Moreover, information on the environment, devices and the family members in the home can be transferred with the ZigBee network. The feasibility and interactivity of the proposed smart home design is demonstrated through experiment results.

INTRODUCTION

Internet of Things, a brand-new field of technology, realizes the connection between things and internet under agreement with the aid of information sensors such as radio frequency identification, infrared sensor, GPS and laser scanner to exchange information and communicate, thus positioning, tracking, monitoring and managing intelligently [1]. With sensors, the application of Internet of Things can achieve the intelligence in human-computer interaction. With the aid of Internet of Things, human-computer interaction as well as information exchange and communication among things can be realized[2]. Smart home embodies the specific application of the Internet of Things, where network communication, automation and control technology and artificial intelligence technology are integrated on the platform of homes[3]. Compared with common homes, the smart ones, in addition to their traditional functions of homes for living, are also equipped with all-round information interactivity and the advantage of saving energy with the combination of network communication, information domestic appliance and equipment automation in them. All the devices in the smart homes are connected through the Internet of Things, enabling varied functions and means such as information home appliances control, lighting system, telephone remote control, indoor and outdoor remote control, anti-theft alarm, environmental monitoring and heating and ventilation control and so on[4-7]. In-depth researches have been conducted on smart home in America, Germany and Singapore, leading others in the world. The concept of smart home originated in 1970s. In 1984, the first smart building came into being in America, marking the realization of the concept of smart home. Now, smart homes have been under batched construction in America and other countries. We Chinese got to know the concept of smart home at the beginning of 1990s and researches on the smart home system was conducted at the end of the 1990s. Now, the generation and popularization of Internet of Things help smart home turn to a new page. As wireless communication technology of Internet of Things takes place of complicated wiring technology and cheap sensors reduce the costs of smart homes greatly, development of smart homes has been promoted in China[8].

Smart Grids (SGs) [1] are emerging as a very promising technology meant to cope with the energy efficiency and reliability issues by disseminating relevant information in a real-time mode among the different SG components. Such dissemination requires a reliable network that would connect the different SG components. Building constitute a central component is SGs. They are the dominant consumers of electrical energy, accounting for more than 40% of total electricity use in the US in 2016 (see table. 1) [2]; and it accounts for more than 70% in third-world countries where transportation and industry contributes with minimal electricity consumption shares. ICT is a key component in SGs. It is the SG component that is responsible for the “smartness” feature. The latter is leveraged via a real-time dissemination of information among the different SGs components, e.g., smart meters at the production site (i.e., renewable sources), sensors (measuring electricity consumption) at the consumer site, actuators (controlling electrical appliances by switching them On/Off), the control plane which decides on whether to injecting renewable into the local Grid or send it to a third party (e.g., providers gird or a storage site), etc. These components are Internet of Things (IoT) that source and sink different types of data and control signals. The interconnection of these IoT components needs a reliable network: the Advanced Metering Infrastructure (AMI) [3], and the disseminated data needs to be reliably stored using NoSQL approach.

The Internet of Things (IoT), as per the ITU Recommendation ITU-T Y.2060 [2], has been defined as: “a global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.” Among physical objects, buildings are playing a major role in this technological transition. Building function either as human habitats, for example domestic buildings, or for some specialized goals, for example storehouses, shops, industrial buildings, or schools. In some cases they can have both functions. There are multiple aspects about the operations of modernday buildings that will lead to future automation and optimization. It has been shown that the benefits of an improved energy management through automation are indeed significant [32]; also, security of buildings, human-friendliness and adaptation to preferences has a vast spectrum of improvement. Smart buildings, as well as smart cities that are supposed to contain them, are the target of much research today, and promise to dramatically improve our lives increasing sustainability and improving the environment. Traditionally, most building automation systems were made for specific applications and offered a few properties of openness and flexibility. However, with the fast maturation of a number of supporting technologies, the opportunity to change this status quo is rapidly growing. First, cheap sensing and perception technologies have become available for a wide range of examples: covering not only physical properties of the building and its spaces, such as temperature, light, and humidity, but also providing information about the presence, number, identities, activities, and even emotional states of the people inside a building or in its surrounding spaces [17].

In the new configuration for the Internet of Things (IoT), a revision on the traditional concept of the internet is essential. In the traditional version, the internet is an infrastructure which provides the terminals for end users, while within the Internet of Things it provides the interconnection of smart objects within a ubiquitous computing environment [1]. The internet infrastructure will play a vital role as the global platform to enable the communication capability of physical objects. The novelty will be enabled by embedding electronics into objects, making them smart while being integrated into the worldwide physical infrastructure. The term Internet of Things refers to this internet-based architecture which facilitates the exchange of services, information and data between billions of objects, mostly smart. It was first introduced by Kevin Ashton in 1998 and has obtained a lot of attention in the industry and academia [2]. In some texts, it is addressed as the Internet of Everything (IoE) to emphasize the ubiquitous usage of the internet-enabled objects. IoT provides the connection between all these objects to facilitate and make people’s lives more comfortable and efficient in all situations. Within this approach different aspects of both hardware and software solutions work together to realize the Internetof-Things paradigm. The IoT should be capable of connecting billions or trillions of heterogeneous devices through the internet, so there is a critical need for a flexible layered architecture. The IoT domain encloses a wide range of standardized or unstandardized technologies, software platforms and diverse applications. Therefore, a single reference architecture cannot be used as a layout for all possible concrete implementations. Though a reference model can be considered for IoT, most likely several reference architectures will coexist [3]. Here, we define the architecture as a framework in which the things, the people and the cloud services are combined to facilitate application tasks. Therefore, the reference model for the IoT can schematically be depicted as in Fig. 1.