Pub Date : 2025-08-17DOI: 10.1016/j.pmcj.2025.102107
Armir Bujari , Mirko Franco , Claudio E. Palazzi , Davide Quaglio , Anna Maria Vegni
Pervasive and mobile computing can play a crucial role in the prevention, detection and management of natural and human-caused disasters. In this context, the Internet of Vehicles (IoV) is particularly noteworthy due to its recent technological advancements and increasing prevalence. In fact, IoV can be leveraged to improve various applications, including those aimed at reducing the millions of fatalities that occur every year. The effectiveness of these applications often relies on the rapid dissemination of emergency messages through position-based forwarding protocols, which can unfortunately be vulnerable to adversarial attacks. Without loss of generality, we focus on the specific case study of road safety to provide a realistic example and discuss two potential attacks based on fake position claims that malicious nodes could easily execute to compromise the performance of the position-based forwarding protocol. We also propose and analyze a validation system based on machine learning (ML) techniques designed to detect malicious nodes, discard false information, and protect against these attacks.
{"title":"Position claim verification for emergency message propagation in Vehicular Ad-Hoc Networks","authors":"Armir Bujari , Mirko Franco , Claudio E. Palazzi , Davide Quaglio , Anna Maria Vegni","doi":"10.1016/j.pmcj.2025.102107","DOIUrl":"10.1016/j.pmcj.2025.102107","url":null,"abstract":"<div><div>Pervasive and mobile computing can play a crucial role in the prevention, detection and management of natural and human-caused disasters. In this context, the Internet of Vehicles (IoV) is particularly noteworthy due to its recent technological advancements and increasing prevalence. In fact, IoV can be leveraged to improve various applications, including those aimed at reducing the millions of fatalities that occur every year. The effectiveness of these applications often relies on the rapid dissemination of emergency messages through position-based forwarding protocols, which can unfortunately be vulnerable to adversarial attacks. Without loss of generality, we focus on the specific case study of road safety to provide a realistic example and discuss two potential attacks based on fake position claims that malicious nodes could easily execute to compromise the performance of the position-based forwarding protocol. We also propose and analyze a validation system based on machine learning (ML) techniques designed to detect malicious nodes, discard false information, and protect against these attacks.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102107"},"PeriodicalIF":3.5,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-24DOI: 10.1016/j.pmcj.2025.102093
Aya Sakhri , Moufida Maimour , Noureddine Doghmane , Eric Rondeau , Saliha Harize
The alarming decline in animal populations, particularly birds, due to environmental degradation necessitates close monitoring of endangered migratory waterbirds in their natural habitats. This can be accomplished through the continuous capture and transmission for population estimation, habitat analysis, and various relevant studies. This paper introduces a three-tier IoMT (Internet of Multimedia Things) deployed along the Edge-Cloud continuum for automated bird monitoring systems aimed at safeguarding endangered waterbird populations. At the edge level, Wireless Multimedia Sensor Networks (WMSN) are used to periodically capture and transmit images to a central collection station (fog level). Challenges such as limited bandwidth and power in Low-Power and Lossy Networks (LLNs) are addressed through local audio identification of endangered bird calls, which activates cameras only for target birds. This significantly reduces data transmission and conserves energy. To tackle ambient noise issues in audio recognition, especially in complex environments such as wetlands, an appropriate noise reduction technique is employed to augment our automatic bird call recognition system. This paper details an energy-efficient approach addressing LLNs’ challenges and incorporates robust noise reduction techniques to improve local audio recognition. The research includes a thorough analysis of potential technical solutions prior to implementation, establishing a critical phase in the system development.
{"title":"An energy-efficient IoMT three-tier architecture for continuous monitoring of endangered bird species","authors":"Aya Sakhri , Moufida Maimour , Noureddine Doghmane , Eric Rondeau , Saliha Harize","doi":"10.1016/j.pmcj.2025.102093","DOIUrl":"10.1016/j.pmcj.2025.102093","url":null,"abstract":"<div><div>The alarming decline in animal populations, particularly birds, due to environmental degradation necessitates close monitoring of endangered migratory waterbirds in their natural habitats. This can be accomplished through the continuous capture and transmission for population estimation, habitat analysis, and various relevant studies. This paper introduces a three-tier IoMT (Internet of Multimedia Things) deployed along the Edge-Cloud continuum for automated bird monitoring systems aimed at safeguarding endangered waterbird populations. At the edge level, Wireless Multimedia Sensor Networks (WMSN) are used to periodically capture and transmit images to a central collection station (fog level). Challenges such as limited bandwidth and power in Low-Power and Lossy Networks (LLNs) are addressed through local audio identification of endangered bird calls, which activates cameras only for target birds. This significantly reduces data transmission and conserves energy. To tackle ambient noise issues in audio recognition, especially in complex environments such as wetlands, an appropriate noise reduction technique is employed to augment our automatic bird call recognition system. This paper details an energy-efficient approach addressing LLNs’ challenges and incorporates robust noise reduction techniques to improve local audio recognition. The research includes a thorough analysis of potential technical solutions prior to implementation, establishing a critical phase in the system development.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102093"},"PeriodicalIF":3.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1016/j.pmcj.2025.102091
Yang Han , Tasiu Muazu , Omaji Samuel , Shiyu Miao
Machine learning algorithms are powerful tools for analyzing data with several observations approximately equal to the number of predictors. However, the privacy of data owners may be revealed during the processes of analysis and mining in a distributed scenario. Today, federated learning is employed as the best paradigm for collaborative model training without disclosing the privacy of the data owners. Unfortunately, efficient client selection and incentive mechanisms need to provide for encouraging data sharing and analysis for constrained and non-constrained devices. Furthermore, trust in the system must be considered. To this end, this study proposes a federated blockchain-based incentive and selection mechanism for a federated learning system. Clients are selected using support vector machines (SVM), while the accuracy of SVM is improved by recursive feature elimination (RFE). A real-time incentive is provided to clients for collaborative learning using deep Q reinforcement learning, and an optimal incentive allocation policy is derived using the Markov decision process (MDP) framework. For miners’ selection, a proof of utility consensus is proposed using a sixteen-round addition game. Extensive simulations are conducted to evaluate the efficiency of the proposed system model. The performance of the proposed system is determined by its optimal statistical utility, system utility, and client utility, respectively. From the experimental results, the proposed SVM-RFE model outperform the existing algorithms. Additionally, security analysis is performed, which shows that the proposed system is safe against background knowledge attacks.
{"title":"A federated learning-based selection and incentive system using blockchain technology","authors":"Yang Han , Tasiu Muazu , Omaji Samuel , Shiyu Miao","doi":"10.1016/j.pmcj.2025.102091","DOIUrl":"10.1016/j.pmcj.2025.102091","url":null,"abstract":"<div><div>Machine learning algorithms are powerful tools for analyzing data with several observations approximately equal to the number of predictors. However, the privacy of data owners may be revealed during the processes of analysis and mining in a distributed scenario. Today, federated learning is employed as the best paradigm for collaborative model training without disclosing the privacy of the data owners. Unfortunately, efficient client selection and incentive mechanisms need to provide for encouraging data sharing and analysis for constrained and non-constrained devices. Furthermore, trust in the system must be considered. To this end, this study proposes a federated blockchain-based incentive and selection mechanism for a federated learning system. Clients are selected using support vector machines (SVM), while the accuracy of SVM is improved by recursive feature elimination (RFE). A real-time incentive is provided to clients for collaborative learning using deep Q reinforcement learning, and an optimal incentive allocation policy is derived using the Markov decision process (MDP) framework. For miners’ selection, a proof of utility consensus is proposed using a sixteen-round addition game. Extensive simulations are conducted to evaluate the efficiency of the proposed system model. The performance of the proposed system is determined by its optimal statistical utility, system utility, and client utility, respectively. From the experimental results, the proposed SVM-RFE model outperform the existing algorithms. Additionally, security analysis is performed, which shows that the proposed system is safe against background knowledge attacks.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102091"},"PeriodicalIF":3.5,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-16DOI: 10.1016/j.pmcj.2025.102092
Chao Lin, Zhanyong Mei, Linlong Mao, Zijie Mei
Wearable devices for gait information sensing provide a reliable and robust solution for identity recognition. However, in real-world applications, gait recognition systems based on these sensing devices should adapt to diverse walking activities, tackle the challenge of limited individual data, and continuously update to recognize both old and new users. In this study, we propose a framework based on hybrid prototype enhancement to address the challenge of few-shot class-incremental gait recognition in multi-activity scenarios (FC-GRMA). Firstly, hybrid prototypes are generated by introducing auxiliary activity labels, which are more generalizable than ordinary prototypes; secondly, the prototypes are adjusted by a selective prototype enhancement module, which improves the representative and discriminative abilities of the prototypes. Finally, validation on the public dataset USC-HAD and the self-built dataset CDUT-AG shows that our proposed framework performs best in solving the FC-GRMA problem. In particular, we also discuss the effect of different numbers of activities on the model performance, and the results show that our framework effectively addresses the issue of catastrophic forgetting in multi-activity scenarios. The source code is available at https://github.com/lc321/fc-grma.git.
{"title":"Enhanced hybrid prototype for few-shot class-incremental gait recognition in multi-activity scenarios using wearable sensors","authors":"Chao Lin, Zhanyong Mei, Linlong Mao, Zijie Mei","doi":"10.1016/j.pmcj.2025.102092","DOIUrl":"10.1016/j.pmcj.2025.102092","url":null,"abstract":"<div><div>Wearable devices for gait information sensing provide a reliable and robust solution for identity recognition. However, in real-world applications, gait recognition systems based on these sensing devices should adapt to diverse walking activities, tackle the challenge of limited individual data, and continuously update to recognize both old and new users. In this study, we propose a framework based on hybrid prototype enhancement to address the challenge of few-shot class-incremental gait recognition in multi-activity scenarios (<em>FC-GRMA</em>). Firstly, hybrid prototypes are generated by introducing auxiliary activity labels, which are more generalizable than ordinary prototypes; secondly, the prototypes are adjusted by a selective prototype enhancement module, which improves the representative and discriminative abilities of the prototypes. Finally, validation on the public dataset USC-HAD and the self-built dataset CDUT-AG shows that our proposed framework performs best in solving the <em>FC-GRMA</em> problem. In particular, we also discuss the effect of different numbers of activities on the model performance, and the results show that our framework effectively addresses the issue of catastrophic forgetting in multi-activity scenarios. The source code is available at <span><span>https://github.com/lc321/fc-grma.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102092"},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-23DOI: 10.1016/j.pmcj.2025.102080
Juan M. Núñez V., Sebastián López Flórez, Juan M. Corchado, Fernando De la Prieta
Climate change represents a critical threat to global food security, affecting agricultural production and exacerbating the food crisis projected by the FAO for 2050. Soil recovery and the adoption of sustainable agricultural practices, such as organic farming, are essential to address this challenge. Smart organic farming improves soil quality, crop productivity, and water retention capacity. In this context, vermiculture, which utilizes Eisenia Foetida (red worms), plays a fundamental role. This article highlights how humus production through vermiculture has been significantly optimized through an Edge AIoT platform that integrates an agricultural recommendation system based on bio-inspired algorithms, an LSTM network for predicting humus and worm populations, and a control system to regulate variables such as temperature, humidity, and pH. The results show an increase in humus production from 37.58% to 87.88% and in the worm population from 35.5% to 83%. Vermicompost, obtained through the non-thermophilic biodegradation of organic waste by worms, acts as a crucial biofertilizer that sustainably increases crop yields and helps farmers adapt to environmental stresses, contributing to the Sustainable Development Goals (SDGs). Finally, seven experiments were conducted in which the Edge AIoT-based agricultural recommendation platform optimized the vermicomposting process, improving efficiency and productivity in humus production. This technological approach not only mitigates the impact of climate change but also supports the recovery of degraded soils and promotes sustainable agricultural practices essential for ensuring future food security.
{"title":"Edge AIoT-based agricultural recommendation platform to improve humus productivity in vermicomposting processes","authors":"Juan M. Núñez V., Sebastián López Flórez, Juan M. Corchado, Fernando De la Prieta","doi":"10.1016/j.pmcj.2025.102080","DOIUrl":"10.1016/j.pmcj.2025.102080","url":null,"abstract":"<div><div>Climate change represents a critical threat to global food security, affecting agricultural production and exacerbating the food crisis projected by the FAO for 2050. Soil recovery and the adoption of sustainable agricultural practices, such as organic farming, are essential to address this challenge. Smart organic farming improves soil quality, crop productivity, and water retention capacity. In this context, vermiculture, which utilizes Eisenia Foetida (red worms), plays a fundamental role. This article highlights how humus production through vermiculture has been significantly optimized through an Edge AIoT platform that integrates an agricultural recommendation system based on bio-inspired algorithms, an LSTM network for predicting humus and worm populations, and a control system to regulate variables such as temperature, humidity, and pH. The results show an increase in humus production from 37.58% to 87.88% and in the worm population from 35.5% to 83%. Vermicompost, obtained through the non-thermophilic biodegradation of organic waste by worms, acts as a crucial biofertilizer that sustainably increases crop yields and helps farmers adapt to environmental stresses, contributing to the Sustainable Development Goals (SDGs). Finally, seven experiments were conducted in which the Edge AIoT-based agricultural recommendation platform optimized the vermicomposting process, improving efficiency and productivity in humus production. This technological approach not only mitigates the impact of climate change but also supports the recovery of degraded soils and promotes sustainable agricultural practices essential for ensuring future food security.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102080"},"PeriodicalIF":3.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-16DOI: 10.1016/j.pmcj.2025.102082
Samuel Pélissier , Abhishek Kumar Mishra , Mathieu Cunche , Vincent Roca , Didier Donsez
LoRaWAN is a leading IoT technology worldwide, increasingly integrated into pervasive computing environments through a growing number of sensors in various industrial and consumer applications. Although its security vulnerabilities have been extensively explored in the recent literature, its ties to human activities warrant further privacy research. Existing device identification and activity inference attacks are only effective with a stable identifier. We find that the identifiers in LoRaWAN exhibit high variability, and more than half of the devices use them for less than a week. For the first time in the literature, we explore the feasibility of device fingerprinting in LoRaWAN, allowing long-term device linkage, i.e. associating various identifiers of the same device. We introduce a novel holistic fingerprint representation utilizing multiple domains, namely content, timing, and radio information, and present a machine learning-based solution for linking identifiers. Through a large-scale experimental evaluation based on real-world datasets containing up to 41 million messages, we study multiple scenarios, including an attacker with limited resources. We reach 0.98 linkage accuracy, underscoring the need for privacy-preserving measures. We showcase countermeasures including payload padding, random delays, and radio signal modulation, and conclude by assessing their impact on our fingerprinting solution.
{"title":"Efficiently linking LoRaWAN identifiers through multi-domain fingerprinting","authors":"Samuel Pélissier , Abhishek Kumar Mishra , Mathieu Cunche , Vincent Roca , Didier Donsez","doi":"10.1016/j.pmcj.2025.102082","DOIUrl":"10.1016/j.pmcj.2025.102082","url":null,"abstract":"<div><div>LoRaWAN is a leading IoT technology worldwide, increasingly integrated into pervasive computing environments through a growing number of sensors in various industrial and consumer applications. Although its security vulnerabilities have been extensively explored in the recent literature, its ties to human activities warrant further privacy research. Existing device identification and activity inference attacks are only effective with a stable identifier. We find that the identifiers in LoRaWAN exhibit high variability, and more than half of the devices use them for less than a week. For the first time in the literature, we explore the feasibility of device fingerprinting in LoRaWAN, allowing long-term device linkage, i.e. associating various identifiers of the same device. We introduce a novel holistic fingerprint representation utilizing multiple domains, namely content, timing, and radio information, and present a machine learning-based solution for linking identifiers. Through a large-scale experimental evaluation based on real-world datasets containing up to 41 million messages, we study multiple scenarios, including an attacker with limited resources. We reach 0.98 linkage accuracy, underscoring the need for privacy-preserving measures. We showcase countermeasures including payload padding, random delays, and radio signal modulation, and conclude by assessing their impact on our fingerprinting solution.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102082"},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-16DOI: 10.1016/j.pmcj.2025.102083
Elif Bozkaya-Aras
Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.
{"title":"Digital twin-enabled age of information-aware scheduling for Industrial IoT edge networks","authors":"Elif Bozkaya-Aras","doi":"10.1016/j.pmcj.2025.102083","DOIUrl":"10.1016/j.pmcj.2025.102083","url":null,"abstract":"<div><div>Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102083"},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Vehicles has evolved significantly with the integration of intelligent technologies, transforming vehicular networks by enhancing communication, resource management, and decision-making at the network’s edge. With the increasing complexity of vehicular environments and data demands, efficient caching mechanisms have become essential to ensure seamless service delivery and optimized resource usage. In this paper, we present LF-MARLEC, a Leader Follower Multi-Agent Reinforcement Learning solution for Edge Caching within the Internet of Vehicles. Our approach introduces a hierarchical distribution of action importance, enabling more effective decision-making at the network edge. Extensive experiments, conducted using widely adopted simulation tools such as SUMO and Veins, demonstrate that our approach substantially enhances caching performance and overall system efficiency. Specifically, our approach achieves nearly 9% reduction in content distribution delay and over 11% improvement in cache hit rate compared to state-of-the-art methods, thereby enhancing the effectiveness of intelligent edge caching in Internet of Vehicles environments. The source code is publicly available at: https://github.com/amine9008/RL-EDGE-CACHING.
{"title":"An optimized Multi Agent Reinforcement Learning solution for edge caching in the Internet of Vehicles","authors":"Mohamed Amine Ghamri, Badis Djamaa, Mohamed Akrem Benatia, Redouane Bellahmer","doi":"10.1016/j.pmcj.2025.102081","DOIUrl":"10.1016/j.pmcj.2025.102081","url":null,"abstract":"<div><div>The Internet of Vehicles has evolved significantly with the integration of intelligent technologies, transforming vehicular networks by enhancing communication, resource management, and decision-making at the network’s edge. With the increasing complexity of vehicular environments and data demands, efficient caching mechanisms have become essential to ensure seamless service delivery and optimized resource usage. In this paper, we present LF-MARLEC, a Leader Follower Multi-Agent Reinforcement Learning solution for Edge Caching within the Internet of Vehicles. Our approach introduces a hierarchical distribution of action importance, enabling more effective decision-making at the network edge. Extensive experiments, conducted using widely adopted simulation tools such as SUMO and Veins, demonstrate that our approach substantially enhances caching performance and overall system efficiency. Specifically, our approach achieves nearly 9% reduction in content distribution delay and over 11% improvement in cache hit rate compared to state-of-the-art methods, thereby enhancing the effectiveness of intelligent edge caching in Internet of Vehicles environments. The source code is publicly available at: <span><span>https://github.com/amine9008/RL-EDGE-CACHING</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102081"},"PeriodicalIF":3.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-09DOI: 10.1016/j.pmcj.2025.102078
Mahdi Nikooghadam, Hamid Reza Shahriari
The concept of crowdsourcing uses shared intelligence to solve complex tasks through group collaboration. Crowdsourcing involves gathering information and opinions from participants who submit their data, or solutions, over the Internet using a specific program. Given that the communication environment for crowdsourcing platforms is the Internet, there is a significant opportunity for attackers to compromise the confidentiality and integrity of information and violate participants’ privacy. Despite the great benefits of crowdsourcing, concerns about security and privacy are growing and require attention. Unfortunately based on our knowledge, the schemes presented to preserve security and privacy in crowdsourcing are susceptible to security and privacy attack and have a high computational and communication overhead. Therefore, they are not appropriate for crowdsourcing environments. This paper presents an ultra-lightweight authentication and key establishment protocol based on hash functions. This protocol meets all security requirements, is invulnerable to known attacks, and imposes a very low network overhead. The security of the proposed scheme has been formally proved, depicting the resistance of the proposed scheme to different types of possible attacks. In addition, the robustness of the proposed scheme against potential attacks has been proven through Scyther’s automatic software validation tool. The performance evaluation ultimately demonstrated that the proposed protocol incurs significantly reduced computational and communication costs compared to previous schemes and is very suitable for the crowdsourcing environment.
{"title":"Lightweight secure key establishment to create a secure channel between entities in a crowdsourcing environment","authors":"Mahdi Nikooghadam, Hamid Reza Shahriari","doi":"10.1016/j.pmcj.2025.102078","DOIUrl":"10.1016/j.pmcj.2025.102078","url":null,"abstract":"<div><div>The concept of crowdsourcing uses shared intelligence to solve complex tasks through group collaboration. Crowdsourcing involves gathering information and opinions from participants who submit their data, or solutions, over the Internet using a specific program. Given that the communication environment for crowdsourcing platforms is the Internet, there is a significant opportunity for attackers to compromise the confidentiality and integrity of information and violate participants’ privacy. Despite the great benefits of crowdsourcing, concerns about security and privacy are growing and require attention. Unfortunately based on our knowledge, the schemes presented to preserve security and privacy in crowdsourcing are susceptible to security and privacy attack and have a high computational and communication overhead. Therefore, they are not appropriate for crowdsourcing environments. This paper presents an ultra-lightweight authentication and key establishment protocol based on hash functions. This protocol meets all security requirements, is invulnerable to known attacks, and imposes a very low network overhead. The security of the proposed scheme has been formally proved, depicting the resistance of the proposed scheme to different types of possible attacks. In addition, the robustness of the proposed scheme against potential attacks has been proven through Scyther’s automatic software validation tool. The performance evaluation ultimately demonstrated that the proposed protocol incurs significantly reduced computational and communication costs compared to previous schemes and is very suitable for the crowdsourcing environment.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102078"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-05DOI: 10.1016/j.pmcj.2025.102077
Liliana Martirano , Lucio La Cava , Andrea Tagarelli
Social media have widely been recognized as a valuable proxy for investigating users’ opinions by echoing virtual venues where individuals engage in daily discussions on a wide range of topics. Among them, climate change is gaining momentum due to its large-scale impact, tangible consequences for society, and enduring nature. In this work, we investigate the social debate surrounding climate emergency, aiming to uncover the fundamental patterns that underlie the climate debate, thus providing valuable support for strategic and operational decision-making. To this purpose, we leverage Graph Mining and NLP techniques to analyze a large corpus of tweets spanning seven years pertaining to the Conference of the Parties (COP), the leading global forum for multilateral discussion on climate-related matters, based on our proposed framework, named NATMAC, which consists of three main modules designed to perform network analysis, topic modeling and affective computing tasks. Our contribution in this work is manifold: (i) we provide insights into the key social actors involved in the climate debate and their relationships, (ii) we unveil the main topics discussed during COPs within the social landscape, (iii) we assess the evolution of users’ sentiment and emotions across time, and (iv) we identify users’ communities based on multiple dimensions. Furthermore, our proposed approach exhibits the potential to scale up to other emergency issues, highlighting its versatility and potential for broader use in analyzing and understanding the increasingly debated emergent phenomena.
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