IET Smart Cities最新文献

{"title":"An Energy-Conscious, Regulation-Ready and Formally Verified Security Protocol for Low-Power Medical and Safety Devices","authors":"Sidra Anwar,&nbsp;Mouaz Hendi,&nbsp;Jonathan Anderson","doi":"10.1049/smc2.70028","DOIUrl":"https://doi.org/10.1049/smc2.70028","url":null,"abstract":"<p>This paper presents a formally verified, lightweight security protocol for energy-constrained IoT devices operating in regulated environments, with a primary focus on wearable medical systems. The protocol is designed to meet emerging cybersecurity requirements from HIPAA, GDPR and Health Canada and combines Ed25519-based mutual authentication, ephemeral Curve25519 key exchange and ChaCha20-Poly1305 authenticated encryption to provide secure, in-transit protection across heterogeneous nodes with minimal computational and energy overhead. Formal verification using ProVerif and Scyther establishes resilience against key compromise, replay and session confusion, with guarantees of end-to-end authentication, forward secrecy, nonce freshness and key integrity in multi-node settings. Practical feasibility is evaluated through a dual-phase methodology. A Python-based simulation framework examines runtime behaviour under adversarial and fault-prone conditions, enabling analysis of implementation-level state transitions and recovery behaviour beyond symbolic models. The protocol is then deployed on an STM32L431-based ECG wearable relayed through a mobile gateway to a cloud server, representing a CPU-bound short-range IoT platform. Energy profiling using Nordic PPK2 shows a daily protocol overhead of approximately 7.91 mWh, under 16% of the system energy budget, supporting over 30 days of secure operation per charge. Complementary evaluation on a cellular IoT platform with hardware-backed security further demonstrates that regulation-ready secure communication is feasible across heterogeneous low-power IoT deployments without compromising runtime efficiency.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of YOLO Architectures for Automated Detection of Buried Manhole Covers in GPR Radargrams","authors":"Jantana Panyavaraporn,&nbsp;Sitthiphat Eua-apiwatch","doi":"10.1049/smc2.70027","DOIUrl":"https://doi.org/10.1049/smc2.70027","url":null,"abstract":"<p>Urban paving and resurfacing operations frequently bury utility access points, complicating infrastructure maintenance and emergency response. Although ground-penetrating radar (GPR) can be used to locate buried manholes, manual radargram interpretation requires specialised expertise and is time-consuming and subjective. This paper introduces a systematic comparative evaluation of three YOLO (You Only Look Once) variants of different architectural generations, including YOLOv5s (anchor-based), YOLOv8s (anchor-free with decoupled heads), and YOLOv11s (transformer-enhanced). We trained and validated these models using 9-fold cross-validation on 54 controlled radargrams obtained at 1000 MHz under systematically varied conditions: three surface types (asphalt, concrete, sand), six burial depths (0.05–0.75 m), and three compaction levels (C0, C5, C10). The results show that YOLOv5s showed the best performance under sparse data conditions, with accuracy = 0.7865, precision = 0.8198, recall = 0.7533, [email protected] = 0.7950 and the lowest cross-fold variability (RMSE = 0.0725). YOLOv8s achieved higher [email protected]:0.95 (0.3711), indicating good generalisation on strict IoU thresholds, but YOLOv11s showed high instability (RMSE = 0.2200), indicating overfitting. Paired t-tests showed no statistically significant difference between architectures at <i>α</i> = 0.05, but practical stability differences that are deployment-relevant were prominent. Detection accuracy degraded systematically at high compaction (C10) and at greater burial depths, consistent with electromagnetic attenuation in dense media. YOLOv5s also offered the shortest inference time, which supports near-real-time deployment scenarios. A prototype of the interface based on the LINE is used to demonstrate the technical feasibility of field workflows. These results suggest that the lighter YOLO architecture provides better stability and efficiency for GPR-based utility detection when training data is limited. This can guide the model selection for infrastructure inspection applications.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148085783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TorreviejaSensing: A Social Sensing Approach for Real-Time Sustainable Tourism Monitoring in Coastal Cities","authors":"José Giner Pérez de Lucia,&nbsp;Julio Fernández Pedauyé,&nbsp;Marco A. Celdrán Bernabeu,&nbsp;José Norberto Mazón,&nbsp;José M. Cecilia","doi":"10.1049/smc2.70015","DOIUrl":"https://doi.org/10.1049/smc2.70015","url":null,"abstract":"<p>Tourism places increasing pressure on coastal cities, where seasonal congestion, fragmented urban development and changing citizen expectations challenge the sustainability of services and governance. To address these issues, we present TorreviejaSensing, a social sensing framework that transforms user-generated content into actionable evidence for policy and planning. The system combines multilingual natural language processing over X (Twitter) posts and Google Maps reviews, a policy-aware mapping of social signals to municipal strategic axes and a retrieval-augmented generation (RAG) chatbot that provides transparent evidence-grounded answers to policy queries. Applied to Torrevieja (Spain), the framework enables real-time tracking of mobility and cleanliness complaints, early detection of seasonal pain points and alignment of digital discourse with institutional strategies. A mixed-methods evaluation—including manual annotation of NLP outputs, baseline comparison and a pilot human assessment of chatbot responses—demonstrates both robustness and practical utility. Findings reveal strong seasonal dynamics, recurrent topic clusters consistent with known governance challenges and improved transparency of the RAG assistant compared to keyword-only approaches. Beyond the Torrevieja case, the framework is low-cost, transferable and provides a replicable model for integrating citizen discourse into sustainable urban tourism governance.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep CNN Models for Weather Monitoring in Smart Agriculture Within Smart Cities","authors":"Maria Tariq,&nbsp;Asghar Ali Shah,&nbsp;Sagheer Abbas,&nbsp;Muhammad Adnan Khan","doi":"10.1049/smc2.70026","DOIUrl":"https://doi.org/10.1049/smc2.70026","url":null,"abstract":"<p>Weather monitoring in agriculture is complex, as it requires predicting future atmospheric states that directly affect farming activities. In smart cities, farmers rely on accurate, up-to-date weather information to make informed decisions. Increasing climate variability has made temperature prediction more challenging than ever. Deep learning has recently emerged as a powerful approach for weather forecasting due to its superior performance over conventional methods and its ability to extract and classify features within a single architecture. This study explores the use of deep Convolutional Neural Network (CNN) models, specifically Visual Geometry Group 16 (VGG16) and MobileNet, with transfer learning for intelligent weather monitoring. The combination of MobileNet and VGG16 leverages transfer learning for accuracy-driven and efficient operations. MobileNet is optimised for mobile and edge devices by delivering high-performance results with lower computational and energy costs, while the deep architecture of VGG16 effectively identifies complex visual features across multiple tasks. Trained on a dataset of weather images, the proposed models accurately detect and classify different weather conditions, enabling farmers to make improved field decisions. Experimental results demonstrate strong performance, with VGG16 achieving 96.95% accuracy and MobileNet achieving 96.19% accuracy.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text Chunking in Human-AI Teaming of Document Classification for Urban Systems Management Using Large Language Models","authors":"Joshua Rodriguez,&nbsp;Om Sanan,&nbsp;Guillermo Vizarreta-Luna,&nbsp;Steven A. Conrad","doi":"10.1049/smc2.70023","DOIUrl":"10.1049/smc2.70023","url":null,"abstract":"<p>Urban systems management requires analysing complex textual documentation that necessitates coding and analysis to set requirements and evaluate built environment performance. This study contributes to the research of applying large language models (LLMs) to qualitative coding activities, aiming to reduce resources while maintaining comparable reliability performing like humans. Qualitative coding faces challenges such as resource limitations, bias and consistency between evaluators. We report the application of LLMs to deductively code 10 case documents for 17 common characteristics for the management of urban systems. We utilise whole-text analysis and text-chunk analysis to compare the processing of LLMs with human coding efforts using three OpenAI models. Results indicate that LLMs may perform similarly to human coders when initialised with specific deductive coding contexts. GPT-4o, o1-mini and GPT-4o-mini showed significant agreement with human raters when using a chunking method. The application of GPT-4o and GPT-4o-mini as additional raters with humans showed statistically significant agreement, indicating that the analysis of textual documents benefits from LLMs addition. The novel contribution of this paper is the domain-specific evaluation of a chunk-based prompting approach for deductive coding in urban systems management, validating human–AI teaming performance.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148096833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Traffic Routing for Air Quality Enhancement During Urban Environmental Crises","authors":"José D. Padrón,&nbsp;Carlos T. Calafate,&nbsp;Juan-Carlos Cano,&nbsp;Pietro Manzoni","doi":"10.1049/smc2.70025","DOIUrl":"10.1049/smc2.70025","url":null,"abstract":"<p>This study introduces an air quality-aware traffic re-routing scheme designed to minimise vehicle emissions in highly polluted urban areas during environmental crises. Tested in two scenarios, a building fire and a smog episode near a train station, the scheme employs an ‘emission sensitivity factor’ (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation> ${Delta }$</annotation>\u0000 </semantics></math>) to adjust re-routing costs based on normalised air quality index (AQI) data and vehicle emission profiles. Results indicate that selecting an optimal <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation> ${Delta }$</annotation>\u0000 </semantics></math> can reduce <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mtext>NO</mtext>\u0000 <mi>x</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${text{NO}}_{x}$</annotation>\u0000 </semantics></math> concentrations by 15% and improve AQI by 10% in targeted areas without adversely affecting traffic performance. Key traffic metrics such as average vehicle duration, waiting time, and speed remained largely unchanged (under 2% variation), demonstrating a balance between traffic efficiency and air quality improvement. The findings highlight the potential for integrating dynamic AQI data into urban traffic management systems, offering valuable insights for urban planners and policymakers aiming to reduce pollution exposure while maintaining optimal traffic flow.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"8 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}