Smart Environments Research Articles

Blockchain For Edge Computing in Smart Environments: Use Cases, Issues, and Challenges

Blockchain For Edge Computing in Smart Environments: Use Cases, Issues, and Challenges

HARWE: A multi-modal large-scale dataset for context-aware human activity recognition in smart working environments

In recent years, deep neural networks (DNNs) have provided high performances for various tasks, such as human activity recognition (HAR), in view of their end-to-end training process between the input data and output labels. However, the performances of the DNNs are highly dependent on the availability of large-scale data for their training processes. In this paper, we propose a novel dataset for the task of HAR, in which the labels are specified for the working environments (WE). Our proposed dataset, namely HARWE, considers multiple signal modalities, including visual signal, audio signal, inertial sensor signals, and biological signals, that are acquired using four different electronic devices. Furthermore, our HARWE dataset is acquired from a large number of participants while considering the realistic disturbances that can occur in the wild. Our HARWE data is context-driven, which means there exist a number of labels in it that even though they are correlated with each other, they have contextual differences. A deep conventional multi-modal neural network provides an accuracy of 99.06% and 68.60%, for the cases of the easy and difficult settings of our dataset, respectively, which indicates its applicability for the task of human activity recognition.

Гуманистическая адаптация проекта «Умный город»: эстетический аспект

In the implementation of the Smart City project, one of the significant challenges lies in the complex interaction between digital technologies and new forms of sociality, aimed at creating a comfortable living environment for humans. The concept of sustainable development, which is a priority goal of modern urbanization, implies special attention to the environmental parameters of city life. New trends in the inclusion of a natural component in the fabric of urban life are developing in line with the phenomenon of aestheticization as a characteristic feature of modern culture. The tendency to give aesthetic sound to everyday phenomena is aimed at the sensory-emotional sphere as the basis of aesthetic sensibility. Embracing the principle of naturalness in shaping nature, even if this simplicity is achieved through complex technological solutions and adheres to archetypal artistic images, provides a stable guideline for assessing the appropriateness and harmony of the environment. The study delves into the reflective aspect of perception in urban environments, emphasizing the development of emotional intelligence and the ability to engage with the sensory-rich “smart environment”. It discusses the importance of flexible communication with digital devices, facilitated by an enhanced realm of sensuousness and emotional connectivity. By embracing humanistic principles and aesthetic sensibilities, the Smart City project can create more livable, sustainable, and emotionally engaging urban environments.

Initiation Of Smart Village Development In Lerep Tourism Village

Lerep Village, West Ungaran District, Semarang Regency is a tourist village that is widely known regionally and nationally.in the post-covid 19 pandemic era, the development of the Lerep tourism village has begun to rise. Along with the PPKM policy, this Tourism Village must be able to utilize information and communication technology (ICT) in order to be able to promote tourism objects to the wider community. The existence of the Smart Village concept is expected to be able to encourage the development of the Lerep Tourism Village, without eliminating local wisdom.Implementation of smart village through an approach that includes smart government, smart community, smart living, smart economy, smart environment, and smart mobility.The problem is the readiness and ability of the community and officials have not fully implemented the concept.The purpose of this research is to identify and analyze Smart Village initiatives in the management of the Lerep Tourism Village. The research was conducted in Lerep Village, West Ungaran District, Semarang Regency. with rationalistic qualitative deductive methods, descriptive analysis techniques. Conclusion Lerep Village already has an initiation of its smart village elements by not abandoning the local wisdom system at every village event. Keywords: initiation, smart, village, tourism

On pipelines, readiness and annotative labour: Political geographies of AI and data infrastructures in Africa

Data infrastructures are expanding rapidly across African societies, renewing the promise of modernisation, and providing a massive data resource to the dominant tech powers of the world. Google's private undersea cable, named Equiano, landed in west Africa with the intention of igniting data services, smart environments, investment opportunities and jobs. Non-governmental and multilateral agencies are busy supporting African governments in building regulatory frameworks that aim to ‘ready’ countries for the 4th industrial revolution. Young Africans labour in remodelled shipping containers to annotate data and train algorithms. While future promises pan out in wide-angled, utopian visions, this paper sets out an approach to understand African contexts as sites of heterogeneous experience of data infrastructures that are historically and politically contingent. The paper explores the spatial politics of extraction, surveillance, and exploitation in three examples of the Equiano pipeline, the Artificial Intelligence (AI) Readiness Index, and the AI annotative labour force. We challenge the homogenising discourses of global tech companies and transnational governance institutions in accounting for the geographical histories of colonialism and its afterlives in African societies. We further call for empirical studies that examine the granular multiplicities of data, providing nuanced understandings of AI in and from Africa.

ESmart Home: AI and IoT Integration for Energy Efficient Solar Powered Homes

The eSmart Home project aims to revolutionize sustainable living by integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies into solar-powered home automation systems. Using Arduino as the core microcontroller platform, the project focuses on enhancing energy management and optimizing solar power utilization. By leveraging real-time monitoring and automated control of electrical appliances, the system minimizes energy waste and lowers utility bills. Advanced AI algorithms enable predictive maintenance and adaptive energy allocation based on usage patterns, ensuring efficient operation and proactive problem-solving. Additionally, the eSmart Home project emphasizes user-friendliness, offering homeowners intuitive interfaces to interact with and control their smart home environment. The system includes various sensors for monitoring temperature, humidity, light, motion, current, and voltage, combined with actuators for appliance control. Communication modules facilitate seamless connectivity, while AI modules process data for intelligent decision-making. By integrating these technologies, the eSmart Home not only enhances energy efficiency but also provides a scalable and adaptive solution for modern smart homes.

Enhancing Najran’s sustainable smart city development in the face of urbanization challenges in Saudi- Arabia

ABSTRACT This research offers valuable perspectives on the nexus between urbanization and the smart environment within the Najran city, Saudi Arabian context. Najran City, with its diverse districts and evolving urban landscape, is at the forefront of adopting advanced technologies and participatory governance models to create a resilient, environmentally conscious, and sustainable urban environment, likewise positioning itself as a model of smart urban development but is also fostering an innovation-driven community equipped to address the difficulties and exploit the prospects given by the 21st century. The study systematically analyzes the multifaceted factors of smart city variables in Saudi Arabia and their direct impact on urban sustainability through extensive quantitative investigations. This study reveals the positive correlations between smart city attributes, on the attainment of urban sustainability. In order words, the smart city’s attributes such as IoT (Internet of Things) devices, smart infrastructure, and data-driven solutions are linked to the creation of a resilient urban environment in the face of urbanization and sustainability challenges. On the other hand, the study noted that the integration of smart technologies might lead to unintended consequences in urban planning. For example, an overemphasis on technology may overshadow human-centric approaches, which might potentially affect the community well-being and social cohesion. Study’s insights provide policymakers, urban planners, and players with an in-depth knowledge of the pathways to harness smart city attributes for sustainable Najran urban development. The implications of this research extend beyond the borders of Saudi Arabia, serving as a valuable reference for regions worldwide grappling with similar urbanization and sustainability challenges in the 21st century. By harnessing the potential of smart cities, Saudi Arabia and other nations can pave the way for greener, more resilient urban futures.

Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting

Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

Artificial Intelligence in Smart Cities—Applications, Barriers, and Future Directions: A Review

As urbanization continues to pose new challenges for cities around the world, the concept of smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this transformation. This paper presents a literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people. The analysis covers publications from 2021 to 2024 available on Scopus. This paper examines the application of AI in each area and identifies barriers, advances, and future directions. The authors set the following goals of the analysis: (1) to identify solutions and applications using artificial intelligence in smart cities; (2) to identify the barriers to implementation of artificial intelligence in smart cities; and (3) to explore directions of the usage of artificial intelligence in smart cities.

Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method

Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.

Al‐based energy aware parent selection mechanism to enhance security and energy efficiency for smart homes in Internet of Things

AbstractThe growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments.

How Effective is the Law in Supporting the Smart Environment Program in Smart City Surakarta?

Abstract Implementation of the Smart Environment program in the City of Surakarta requires effective regulations to realize advanced and quality city development. However, there are still several environmental problems that need to be addressed. This research uses an empirical juridical approach with data collected through literature study, interviews and questionnaires. The research results show that the implementation of Smart Environment provides benefits to a sustainable environment and strengthens Surakarta’s status as an innovative and environmentally friendly city. The effectiveness of the law has made a positive contribution in achieving sustainable development goals in Surakarta. However, several factors do not support the realization of a smart environment, including the lack of green open space and the understanding of the people of Surakarta about smart environments.

What I Don’t Like about You?: A Systematic Review of Impeding Aspects for the Usage of Conversational Agents

Abstract The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human–computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA’s technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs.

Wireless Sensor Network for Smart Education: Design, Architecture, and Implementation

Abstract: This paper delineates the design, architecture, and implementation of a Wireless Sensor Network (WSN) tailored for smart education environments. As educational institutions increasingly integrate technology to enhance pedagogical practices and operational efficiency, the deployment of WSNs presents substantial advantages. The study initiates with an examination of fundamental design considerations, encompassing energy efficiency, scalability, data security, and user experience. It proposes a comprehensive architecture that includes sensor nodes, communication protocols, network topology, base stations, and cloud integration to facilitate effective data collection and processing. Key implementation strategies are detailed, including sensor node deployment, communication protocol configuration, cloud server setup, and user training. The paper underscores the suitability of routing protocols such as LEACH, AODV, and Directed Diffusion for educational applications, highlighting their efficacy in achieving energy-efficient and reliable data transmission. This research elucidates the potential of WSNs to foster interactive, adaptive, and resource-optimized educational environments, providing valuable insights into their practical deployment and benefits in contemporary educational settings.

Thinking and Research on the Construction of Web Application Firewall in Smart Campus Environment

With the rapid development of information technology, the Internet has penetrated into various fields, especially the field of education. As an important carrier of education informatization, smart campus provides strong support for improving education quality and management level. However, the network security problems of smart campus also come up, especially the security problems of Web applications. Web Application Firewall, as an important network security technology, plays a vital role in the construction of smart campus, and is of great significance for guaranteeing the information security of smart campus. This paper mainly discusses how to effectively build a Web Application Firewall in a smart campus environment, including the design, configuration, and management of the firewall, in order to safeguard the security of the smart campus network.

A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm

A distributed machine learning technique called federated learning allows numerous Internet of Things (IoT) edge devices to work together to train a model without sharing their raw data. Internet of Vehicular Things (IoVT) are an important tool in smart cities for moving objects, such as knowing the traffic patterns, road conditions, vehicle behavior, etc. To enhance traffic management and optimize routes, federated learning, and IoT must work jointly, which may achieve sustainable development goals (SDG) in many ways. This research outlines a system for federated learning in vehicular networks in smart cities. The suggested architecture considers the difficulties presented by such situations’ restricted network connectivity, privacy issues, and security concerns. The framework employs a hybrid methodology integrating federated learning on a centralized server with local training on individual cars. The proposed framework is assessed based on a real-world dataset from a smart city through IoT devices. The findings demonstrate that the suggested method successfully increases model accuracy while preserving the confidentiality and security of the data. In this investigation, we incorporated the Federated Learning model, which can fetch all the information between arbitrary nodes and derive the Traffic, Fuel Cost, Safety, Parking Cost, and Transportation cost for a better routing approach. The suggested framework can be utilized to increase the effectiveness of the transportation system, decrease congestion in smart cities, and improve traffic management. We employ an improved genetic algorithm (iGA) with generation-dependent even mutation to tackle the emission in the smart environment.

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments.

Enhancing trash classification in smart cities using federated deep learning

Efficient Waste management plays a crucial role to ensure clean and green environment in the smart cities. This study investigates the critical role of efficient trash classification in achieving sustainable solid waste management within smart city environments. We conduct a comparative analysis of various trash classification methods utilizing deep learning models built on convolutional neural networks (CNNs). Leveraging the PyTorch open-source framework and the TrashBox dataset, we perform experiments involving ten unique deep neural network models. Our approach aims to maximize training accuracy. Through extensive experimentation, we observe the consistent superiority of the ResNext-101 model compared to others, achieving exceptional training, validation, and test accuracies. These findings illuminate the potential of CNN-based techniques in significantly advancing trash classification for optimized solid waste management within smart city initiatives. Lastly, this study presents a distributed framework based on federated learning that can be used to optimize the performance of a combination of CNN models for trash detection.

Enhancing security and privacy in smart agriculture: A novel homomorphic signcryption system

Technology driven Smart Agriculture is transforming modern day farming practices through digital tools and real-time data analysis. It monitors plant health in both soil and soilless cultivation, promoting environmentally responsible practices by utilizing integrated sensors and precision methods. Despite the significant potential benefits, challenges persist in ensuring data security in the smart agriculture frameworks. The existing research has explored various mechanisms to cater these issues, yet these solutions susceptible to known attacks i.e., Confidentiality, Authentication, Privacy preservation. Notably, the available schemes in open literature face performance deficiencies due to an overreliance on Rivest-Shamir-Adleman (RSA), bilinear pairing, and elliptic curve (EC). To comprehensively address these challenges, this study presents a novel approach via employing homomorphic Signcryption based on Hyper-Elliptic Curves (HEC). Our HEC based Signcryption technique enhances security with lower computational cost and communication overhead as compared to the existing techniques. Furthermore, our solution provides the best performance in terms of efficiency, addressing the privacy preservation issues with conformance to the (Confidentiality, Integrity, and Availability) CIA triad. For proof of concept and correctness, we have validated the results in Scyther tool, with the assurance that proposed scheme is correct, verified, and resistant against known set of attacks. The proposed scheme achieves a remarkable reduction in both computational and communication costs around 95.08 % and 89.28 % respectively, enhancing practicality and suitability for real-world implementation. Thus, making it a suitable candidate for employing in smart agriculture environments, encompassing both soil and soilless cultivation scenarios. This research contributes a robust, secure, cost effective, and efficient solution to the challenges posed by the evolving landscape of agriculture, paving the way for enhanced data protection and system resilience in smart agriculture systems.