Journal of Ambient Intelligence and Smart Environments - Volume Pre-press, issue Pre-press

Authors: Uysal, Serra | Sandıkkaya, Mehmet Tahir

Article Type: Research Article

Abstract: Connected and Automated Vehicles (CAVs) are rapidly evolving technology with great benefits such as reducing gas emissions and decreasing traffic congestion. They have the potential to change the traditional transportation industry due to their benefits. However, the implementation phase for CAVs decelerates with the uncertainties of legislation on privacy-preserving and public concerns. Perception of people needs to be understood beforehand. Main concern points like possible attacks and mitigation techniques, and privacy protection should be addressed. Certain regulation system should be implemented, and transportation habits should be considered. After thinking over those points, adaption of CAVs can be achieved more smoothly. …In this survey paper, we aim to shed light on the obstacles to the widespread use of CAVs by collecting existing literature and creating a sophisticated bouquet of the issues. Public perception, common attacks and mitigation techniques, privacy protection, regulations, and possible transportation habit shifts related to CAVs are examined. With the information gathered from this survey, manufacturers and policymakers can determine an influential pathway for the development of CAVs. Show more

Keywords: CAV, privacy, cyber security

DOI: 10.3233/AIS-230232

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Li, Xiaohui | Fan, Dongfang | Feng, Junjie | Lei, Yu | Cheng, Chao | Li, Xiangnan

Article Type: Review Article

Abstract: In the modern era of sports training, the synergy between motion capture and Virtual Reality (VR) offers an innovative approach to enhancing training precision. This systematic review delves into the application of motion capture within VR for sports training, highlighting its transformative potential. Through a comprehensive literature search, we examined the myriad applications, from physical conditioning enhancements to accelerated rehabilitation processes. Our findings underscore the capability of real-time feedback, immersive training environments, and tailored regimes that this fusion provides. However, despite its promise, challenges such as hardware constraints, data processing complexities, and interaction interface limitations persist. Future trajectories indicate an …increasing influence of AI and deep learning, promising more sophisticated hardware and a broader spectrum of applications, including niche sports disciplines. The review concludes with an emphasis on the wider societal implications, suggesting a shift towards a holistic athlete well-being approach. Show more

Keywords: Virtual reality, motion Capture, sports training, immersive experience, real-time feedback, injury prevention

DOI: 10.3233/AIS-230198

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-23, 2024

Authors: Tian, Yunzhi | Zhou, Qiang | Li, Wan

Article Type: Research Article

Abstract: To solve the problems of sleep disorders such as difficulty in falling asleep and insufficient sleep depth caused by uncomfortable indoor temperature, this paper proposes a deep reinforcement learning method based on deep Q-network (DQN) with human sleep electroencephalogram (EEG) as input to improve human sleep. Firstly, the EEG is subjected to a short-time Fourier transform to construct a time-frequency feature data set, which is used as input to DQN along with temperature. Secondly, the agent performs environmental interaction actions in each time step and returns a reward value. Finally, the optimal strategy for indoor temperature control is formulated by …the agent. The simulation results show that this method can dynamically adjust the indoor temperature to the optimal temperature for human sleep, and can alleviate sleep disorders, which has certain practical significance Show more

Keywords: Deep reinforcement learning (DRL), sleep improvement, air conditioning control, deep Q-network (DQN), electroencephalogram (EEG), deep learning

DOI: 10.3233/AIS-230294

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Giménez, José G. | González, Martín | Martínez-España, Raquel | Cecilia, José M. | López-Espín, José J.

Article Type: Research Article

Abstract: Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency in assessing shallow lakes has been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze remote sensing data to measure chlorophyll-a (Chl-a) concentration levels. Focused on a shallow coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour and compares Machine Learning and Deep Learning techniques to enhance efficiency and accuracy data of this satellite. Convolutional Neural Networks (CNNs) stand out as a robust choice, capable of delivering excellent results even in the presence …of anomalous events. Our findings demonstrate that the CNN-based approach directly utilizing satellite data yields promising results in monitoring shallow lakes, offering enhanced efficiency and robustness. This research contributes to optimizing remote sensing data to and produce a continuous information flow addressed to monitoring shallow aquatic ecosystems with potential environmental management and conservation applications. Show more

Keywords: Environmental intelligence, Satellite Remote Sensing, machine learning, artificial intelligence, IoT

DOI: 10.3233/AIS-230461

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Siryeh, Fatima Abu | Ibrahim, Abdullahi Abdu

Article Type: Research Article

Abstract: A system without any communication delays, called edge computing, has been introduced for nearer and faster services. The major concern in the edge computing scenario is its privacy risks. A user, as well as a cloud data preservation scheme, is the main aim of this paperwork. Test data is given by the user to access the cloud-based data processing framework. The training of the suitable model is carried out by utilizing the data stored in the cloud. The suggested model divides the entire model into two sections, namely, the untrusted cloud and the trusted edge. On the trusted edge side …the data is directly provided to the developed advanced deep learning model called the Atrous Convolution based Cascaded Deep Temporal Convolution Network (ACC-DTCN) for the data analysis process. However, instead of giving the whole data directly to the untrusted cloud side, the test data is protected on the cloud side by utilizing a hybrid encryption technique called the Optimal Hybrid Encryption Model (OHEM). Both Attribute-Based Encryption (ABE) and Homomorphic Encryption (HE) are utilized in the recommended OHEM scheme. The OHEM variables are tuned with the help of an advanced algorithm called the Enhanced Ladybug Beetle Optimization algorithm (ELBOA). The confidence score vector among the testing and training data is predicted by the implemented ACC-DTCN model by utilizing the encrypted data on the cloud side. The suggested privacy preservation scheme provides higher prediction accuracy and prevents interference attacks while contrasting it against conventional methods during extensive experimentations. Show more

Keywords: Internet of Things, cloud computing, edge computing, privacy and security of data, Atrous Convolution based Cascaded Deep Temporal Convolution Network, Optimal Hybrid Encryption Model, Enhanced Ladybug Beetle Optimization algorithm

DOI: 10.3233/AIS-230626

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-32, 2024

Authors: Khanjari, Saeed | Rahmani, Amir Masoud

Article Type: Research Article

Abstract: One of the challenges drivers face in today’s fast-paced urban world is the ability to access parking spaces quickly. The increase in the number of vehicles in cities, especially in urban areas, as well as the growth of network services in smart cities, has created the need to provide easy-to-use smart parking networks. However, the most critical challenge in building such a network is the secure storage of customer and service provider information. Using blockchain to store data securely requires powerful computing resources and high energy consumption. In this research, combining existing ideas in the field of smart parking and …blockchain, a solution will be proposed to use blockchain in dynamic nodes with poor computing resources, such as sensors used in smart parking. As one of the layers introduced in smart parking, we simulated the proposed algorithm in a wireless sensor network (WSN). In light of the findings of previous studies demonstrating the efficacy of Zigbee technology as a mesh topology in WSNs with low-cost infrastructure and resource requirements and our simulation results, we employed this technology to evaluate its performance in our proposed smart parking application. On the other hand, functions created with the Solidity language can use online payment services on such a secure network. Show more

Keywords: Security, Internet of Things, smart parking, smart cities, blockchain

DOI: 10.3233/AIS-230269

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-20, 2024

Authors: Muñoz, Andrés | Martínez-España, Raquel | Guerrero-Contreras, Gabriel | Balderas-Díaz, Sara | Arcas-Túnez, Francisco | Bueno-Crespo, Andrés

Article Type: Research Article

Abstract: This paper presents a novel Multi-DL Fuzzy Approach aimed at performing image recognition in the development of a real-time traffic alert system, addressing the problem of traffic congestion and related incidents. Traditional monitoring by road operators predominantly relies on fixed location cameras, yielding limited and sometimes ambiguous information. This study proposes leveraging Twitter (now known as ‘X’) as a more comprehensive data source alongside employing fuzzy techniques with Deep Learning (DL) neural networks such as CNN, VGG16, and Xception to analyze and classify traffic images. The innovative integration of these technologies augments the precision in categorizing varying traffic conditions, namely …fluid and dense traffic, accidents and fires. Thus, this proposal mitigates the ambiguities prevalent in traffic image interpretation, and reduces the dependency on static data sources. The proposed models showed improved results by combining information from the DL models, elevating accuracy from 84% in crisp classification to 90% utilizing fuzzy information. Show more

Keywords: Traffic monitoring, deep learning, fuzzy classification, alert system, image recognition, real-time system

DOI: 10.3233/AIS-230433

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Li, Hua-Yu | Chu, Edward T.-H.

Article Type: Research Article

Abstract: With the continuous growth of the global population and the concentration of people in urban areas, the development of high-rise buildings has thrived, making elevator waiting time a significant concern. However, traditional elevators are unable to know passengers’ destination floors and waiting times in advance. Consequently, passengers often experience longer waiting times. With the flourishing advancement of the Internet of Things (IoT) and edge computing, it has become feasible to know the demands of passengers in advance and how long they have been waiting. In this work, we propose SmartRide, an intelligent elevator system that provides reservation, check-in, boarding, and …scheduling features. SmartRide allows users to reserve elevators in advance through a mobile app from anywhere. Artificial intelligence (AI) image recognition technology is employed for efficient check-in and boarding confirmation. Additionally, a novel scheduling algorithm is proposed to minimize the average waiting time by leveraging the reservation information. Experimental results demonstrate that SmartRide can accurately manage check-ins and boarding confirmations. SmartRide can also reduce waiting times by at least 30% compared to traditional elevator scheduling using FCFS (First Come First Served), thereby optimizing elevator operational efficiency and improving user experience. Show more

Keywords: Elevators, artificial intelligence, facial recognition, scheduling

DOI: 10.3233/AIS-230330

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-27, 2024

Authors: Cergolj, Vincent | Stankoski, Simon | Pirc, Matija | Luštrek, Mitja

Article Type: Research Article

Abstract: Adequate hydration is important for one’s health, but many people do not consume sufficient fluids. By constantly monitoring fluid intake, we gain information that can be extremely useful in dealing with unhealthy drinking habits. This paper deals with the problem of developing a machine learning method for drinking detection, intended for use on an edge device, with a specific focus on power consumption. The proposed approach is based on data from inertial sensors built into a practical, non-invasive wrist-worn device that monitors wrist movement throughout the day and automatically detects drinking events. It ensures low energy consumption by triggering the …machine learning only when the probability of drinking is high, as well as by other energy saving measures. To develop and validate our methods, we collected data from 19 participants, which resulted in 135 hours of data, of which 2 hours and 30 minutes correspond to drinking activities. The algorithm was thoroughly assessed through both offline testing and by running the algorithm directly on the wristband in real life. During the offline evaluation, we obtained a precision of 94.5 %, a recall of 84.9 %, and an F1 score of 89.4 %. Testing in real life demonstrated a precision of 74.5 % and a recall of 89.9 %. Additionally, the energy efficiency analysis showed that our proposed technique for triggering the drinking detection method reduced the battery power consumption during the periods of inactivity by a factor of 5.8 compared to continuously monitoring for drinking events. Show more

Keywords: Activity recognition, drinking detection, machine learning, microcontroller, inertial sensors

DOI: 10.3233/AIS-230524

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-20, 2024

Authors: Bechoo, Chandrashekhar | Mungur, Avinash

Article Type: Research Article

Abstract: Due to the continuous growth of Internet and online applications, network traffic classification is not only becoming one of the most crucial disciplines in network management but is also becoming quintessential for providing advanced tasks such as Quality of Service and network security. Moreover, even though many studies have been undertaken in recent years, real-time encrypted traffic classification continues to be an important challenge in the field of network traffic classification. Therefore, in this paper a real-time network traffic classification system is proposed together with five new models. The real-time classification system classified each incoming real-time packets into appropriate classes …of interest and the five new models make use of a cost-sensitive learning strategy to deal with the unbalanced data problem during the training phase. The proposed models, which are called Cost-Sensitive Long-Short Term Memory (CSLSTM), Cost-Sensitive Gated Recurrent Unit (CSGRU), Cost-Sensitive Convolution Neural Network (CSCNN), CSNN with LSTM and CSCNN with GRU, can handle both traffic categorization and application identification. These proposed models were compared with prominent methods in this field and the proposed CSCNN was observed to outperform the researched deep learning models by at least 4% to 16% in correctly classifying packets from the ISCX VPN-nonVPN dataset. Show more

Keywords: Convolutional neural networks, cost-sensitive learning, network traffic classification, real-time network classification system, recurrent neural networks

DOI: 10.3233/AIS-230620

Citation: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-31, 2024