Computer Science Review最新文献

{"title":"Non-square grids: A new trend in imaging and modeling?","authors":"Paola Magillo","doi":"10.1016/j.cosrev.2024.100695","DOIUrl":"10.1016/j.cosrev.2024.100695","url":null,"abstract":"<div><div>The raster format of images and data is commonly intended as a synonymous of a square grid. Indeed, the square is not the only shape that can tessellate the plane. Other grids are well-known, and recently they have moved out of the fields of art and mathematics, and have started being of interest for technological applications. After introducing the main types of non-square grids, this paper presents experiences of practical uses of non-square grids, especially the hexagonal one, in various fields, including digital imaging, geographic systems, and their applications in sciences like medicine, environmental monitoring, etc. We conclude with considerations on the state of the art and perspectives for the future. In our opinion, the research is mature enough to prefigure a broader diffusion of some non-square grids, especially the hexagonal one.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100695"},"PeriodicalIF":13.3,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review on current issues and advancements of Internet of Things in precision agriculture","authors":"S. Dhanasekar","doi":"10.1016/j.cosrev.2024.100694","DOIUrl":"10.1016/j.cosrev.2024.100694","url":null,"abstract":"<div><div>The Internet of Things (IoT) is the basis of smart agriculture technology since it connects all aspects of intelligent systems in other industries and agricultural applications. The current farming methods are sufficient to supply adequate food in the future due to the fast-expanding global population. Smart farming aims to increase farm output and efficiency by leveraging state-of-the-art information technologies. The present study of IoT in agriculture was discussed in this review paper by studying significant literature, new techniques, protocols, challenges, issues, and potential paths for IoT-based smart farming. The soil-free technique connected to the hydroponic and aeroponic methods, known as soilless cultivation, is an alternative technology that can adapt well to such circumstances. The aeroponics method offers more advantages regarding faster plant development, increased productivity, and better nutrient absorption. Moreover, several methods and their measures used in aeroponics system using IoT devices were discussed.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"55 ","pages":"Article 100694"},"PeriodicalIF":13.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review on Software-Defined Networking (SDN) and DDoS attacks: Ecosystem, taxonomy, traffic engineering, challenges and research directions","authors":"Amandeep Kaur ,&nbsp;C. Rama Krishna ,&nbsp;Nilesh Vishwasrao Patil","doi":"10.1016/j.cosrev.2024.100692","DOIUrl":"10.1016/j.cosrev.2024.100692","url":null,"abstract":"<div><div>Software Defined network (SDN) represents a sophisticated networking approach that separates the control logic from the data plane. This separation results in a loosely coupled architecture between the control and data planes, enhancing flexibility in managing and transforming network configurations. Additionally, SDN provides a centralized management model through the SDN controller, simplifying network administration. Despite these advantages, SDN has its security challenges. Issues such as topology spoofing, bandwidth exhaustion, flow table updates, and Distributed Denial of Service (DDoS) attacks are prevalent. Among these, DDoS attacks pose a significant threat to the SDN infrastructure. Understanding SDN’s comprehensive ecosystem and functionality is crucial for mitigating SDN vulnerabilities that may attract DDoS attacks. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we present: (i) A comprehensive SDN environment ecosystem with analysis of each class, (ii) A DDoS attacks taxonomy for the SDN environment with characterization of each class, (iii) Critically analyzed existing statistical, machine and deep learning-based DDoS attacks detection approaches for the SDN environment, (iv) Systematically characterize and compare existing open-source Distributed Processing Frameworks (DPF) for traffic engineering in the SDN environment, (v) Security challenges associated with the SDN environment, (vi) Summarize publically available DDoS attack datasets, (vii) Highlight open issues and future research directions for protecting the SDN environment from DDoS attacks.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"55 ","pages":"Article 100692"},"PeriodicalIF":13.3,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From accuracy to approximation: A survey on approximate homomorphic encryption and its applications","authors":"Weinan Liu ,&nbsp;Lin You ,&nbsp;Yunfei Shao ,&nbsp;Xinyi Shen ,&nbsp;Gengran Hu ,&nbsp;Jiawen Shi ,&nbsp;Shuhong Gao","doi":"10.1016/j.cosrev.2024.100689","DOIUrl":"10.1016/j.cosrev.2024.100689","url":null,"abstract":"<div><div>Due to the increasing popularity of application scenarios such as cloud computing, and the growing concern of users about the security and privacy of their data, information security and privacy protection technologies are facing new challenges. Consequently, Homomorphic Encryption (HE) technology has been developed. HE technology has evolved from Partially Homomorphic Encryption (PHE) to fully homomorphic encryption, from theory to practice Recently, the surge in machine learning applications has brought new challenges to HE technology. These applications require floating-point arithmetic numbers while adapting to a certain degree of precision loss, which has brought approximate homomorphic encryption (AHE) technology into the forefront of information security research. This work examines the development trajectory from PHE to AHE, and summarizes and analyzes the research works related to AHE from the three aspects: bootstrapping, improved precision, and variants. In addition, this work shows three different application scenarios of AHE from low level to high level, and combs the related works to present the readers with the latest application trend of AHE schemes.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"55 ","pages":"Article 100689"},"PeriodicalIF":13.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image processing and artificial intelligence for apple detection and localization: A comprehensive review","authors":"Afshin Azizi ,&nbsp;Zhao Zhang ,&nbsp;Wanjia Hua ,&nbsp;Meiwei Li ,&nbsp;C. Igathinathane ,&nbsp;Liling Yang ,&nbsp;Yiannis Ampatzidis ,&nbsp;Mahdi Ghasemi-Varnamkhasti ,&nbsp;Radi ,&nbsp;Man Zhang ,&nbsp;Han Li","doi":"10.1016/j.cosrev.2024.100690","DOIUrl":"10.1016/j.cosrev.2024.100690","url":null,"abstract":"<div><div>This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection and localization such as occlusions, varying illumination conditions, and clustered apples are highlighted, as well as the impact of environmental factors such as light changes on the performance of these algorithms. Potential future research perspectives are identified through a comprehensive literature analysis. These include combining cutting-edge deep learning and multi-vision and multi-modal sensors to potentially apply them in real-time for apple harvesting robots. Additionally, utilizing 3D vision for a thorough analysis of complex and dynamic orchard environments, and precise determination of fruit locations using point cloud data and depth information are presented. The outcome of this review paper will assist researchers and engineers in the development of advanced detection and localization mechanisms for infield apples. The anticipated result is the facilitation of progress toward commercial apple harvest robots.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100690"},"PeriodicalIF":13.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review on security aspects of fog computing environment: Challenges, solutions and future directions","authors":"Navjeet Kaur","doi":"10.1016/j.cosrev.2024.100688","DOIUrl":"10.1016/j.cosrev.2024.100688","url":null,"abstract":"<div><div>The dynamic and decentralized architecture of fog computing, which extends cloud computing closer to the edge of the network, offers benefits such as reduced latency and enhanced bandwidth. However, the existing fog architecture introduces unique security challenges due to the large number of distributed fog nodes, often deployed in diverse and resource-constrained environments. Further, the proximity of fog computing nodes to end-users and the open, distributed nature of the architecture make fog environments particularly vulnerable to unauthorized access and various types of cyberattacks. Therefore, in order to address these challenges, the study presented a detailed systematic review that aims to analyze existing security technologies in fog computing environments, identify current security gaps, and propose future research directions. The comprehensive literature review uses quality databases, focusing on articles published within the last four years, i.e. from 2020 to 2024. Further, the review followed a systematic methodology with clear inclusion and exclusion criteria to ensure relevance and quality with respect to security in fog computing. Consequently, key research questions are also formulated and answered for addressing various security concerns, such as architectural security, IoT integration vulnerabilities, and dynamic security management. Finally, the detailed review summarizes the key findings through MTGIR analysis to give valuable insights on the existing security framework of fog computing systems. The result analysis further revealed that 16% of the research is focusing on blockchain and elliptic curve cryptography, alongside the utilization of artificial intelligence and machine learning, which is around 13.2%, specifically for dynamic threat detection. Furthermore, there are few technologies which require attention are federated learning, secure key management, and secure communication mechanisms, as these technologies are less considered in literature, i.e. around 3% only. Finally, the analysis underscored the necessity for real-time security monitoring and adaptive threat response to manage the dynamic nature of fog computing environments effectively.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100688"},"PeriodicalIF":13.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of deep learning techniques for detecting and recognizing objects in complex environments","authors":"Ashish Kumar Dogra ,&nbsp;Vipal Sharma ,&nbsp;Harsh Sohal","doi":"10.1016/j.cosrev.2024.100686","DOIUrl":"10.1016/j.cosrev.2024.100686","url":null,"abstract":"<div><div>Object detection has been used extensively in daily life, and in computer vision, this sub-field is highly significant and challenging. The field of object detection has been transformed by deep learning. Deep learning-based methods have shown to be remarkably effective at identifying and localizing objects in images and video streams when it comes to object detection. Deep learning algorithms can precisely locate and localize objects inside photos and videos because of their capacity to learn complex and nonlinear patterns in data. Deep learning models may also be trained on big datasets with minimal human intervention, allowing them to rapidly improve their performance. This makes deep learning models useful for applications such as self-driving cars, recognizing faces, and healthcare diagnosis. The purpose of this study was to gain an in-depth understanding of the primary state of development for the object detection pipeline in complex environments. Initially, this study describes the benchmark datasets and analyzes the typical detection model, and then, the paper systematic approach covers both one-stage and two-stage detectors, giving a thorough overview of object detection techniques in complex environments. We also discuss the new and traditional applications of object detection. In the end, the study reviews how well various topologies perform over a range of parameters. The study has covered a total of 119 articles, of which 27% are related to one-stage detectors, 26% to two-stage detectors, 24% to supporting data related to deep learning, 14% to survey articles, 8% to the datasets covered in the study, and the remaining 1% to the book chapters.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100686"},"PeriodicalIF":13.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intervention scenarios and robot capabilities for support, guidance and health monitoring for the elderly","authors":"Saja Aldawsari,&nbsp;Yi-Ping Phoebe Chen","doi":"10.1016/j.cosrev.2024.100687","DOIUrl":"10.1016/j.cosrev.2024.100687","url":null,"abstract":"<div><div>Demographic change in the world is a reality, and as a result, the number of elderly people is growing in both developed and developing countries, posing several social and economic issues. Most elderly people choose to stay alone at home rather than living with their families who can take care of them. Robots have the potential to revolutionize elderly care by providing aid, companionship, and monitoring services. The objective of this study is to present a comprehensive review which summarizes the cutting-edge works in adapting robotic applications to improve the quality of life for the elderly. We compare paradigms thoroughly and methodically in terms of support, guidance, health monitoring, and usability. We then summarize the current achievements while acknowledging their limitations before presenting perspectives on highly promising future work.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100687"},"PeriodicalIF":13.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience of deep learning applications: A systematic literature review of analysis and hardening techniques","authors":"Cristiana Bolchini,&nbsp;Luca Cassano,&nbsp;Antonio Miele","doi":"10.1016/j.cosrev.2024.100682","DOIUrl":"10.1016/j.cosrev.2024.100682","url":null,"abstract":"<div><div>Machine Learning (ML) is currently being exploited in numerous applications, being one of the most effective Artificial Intelligence (AI) technologies used in diverse fields, such as vision, autonomous systems, and the like. The trend motivated a significant amount of contributions to the analysis and design of ML applications against faults affecting the underlying hardware. The authors investigate the existing body of knowledge on Deep Learning (among ML techniques) resilience against hardware faults systematically through a thoughtful review in which the strengths and weaknesses of this literature stream are presented clearly and then future avenues of research are set out. The review reports 85 scientific articles published between January 2019 and March 2024, after carefully analysing 222 contributions (from an initial screening of eligible 244 publications). The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities, based on several parameters, starting from the main scope of the work, the adopted fault and error models, to their reproducibility. This framework allows for a comparison of the different solutions and the identification of possible synergies. Furthermore, suggestions concerning the future direction of research are proposed in the form of open challenges to be addressed.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100682"},"PeriodicalIF":13.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven cluster-based routing protocols in WSNs: A survey of fuzzy heuristics, metaheuristics, and machine learning models","authors":"Mohammad Shokouhifar ,&nbsp;Fakhrosadat Fanian ,&nbsp;Marjan Kuchaki Rafsanjani ,&nbsp;Mehdi Hosseinzadeh ,&nbsp;Seyedali Mirjalili","doi":"10.1016/j.cosrev.2024.100684","DOIUrl":"10.1016/j.cosrev.2024.100684","url":null,"abstract":"<div><div>Cluster-based routing techniques have become a key solution for managing data flow in Wireless Sensor Networks (WSNs), which often struggle with limited resources and dynamic network conditions. With the growing need for efficient data management in these networks, it is more important than ever to understand and enhance these techniques. This survey evaluates recent cluster-based routing protocols released from 2021 to 2024, focusing on the AI-driven approaches in WSNs including fuzzy heuristics, metaheuristics, and machine learning models, along with their combinations. Each approach is evaluated through a deep analysis of solution-based and network configuration-based factors. Solution-based parameters include performance mode, selection strategies, optimization objectives, modeling techniques, and key factors affecting the overall effectiveness of each approach. Additionally, network configuration analysis deals with the type of topology, communication architecture, network scale, performance metrics, and simulators used. This comprehensive analysis unveils valuable insights into the capabilities and limitations of each method. By identifying shortcomings and highlighting areas for improvement, this survey aims to guide future research towards the development of more efficient cluster-based routing techniques for WSNs. These methods, incorporating intelligent performance characteristics, will be well-equipped to address the ever-growing demands of the intelligent era.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100684"},"PeriodicalIF":13.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}