Pub Date : 2025-06-01Epub Date: 2025-05-23DOI: 10.1016/j.pmcj.2025.102065
Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey
Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study (N=26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency.
{"title":"Differentiating presence in virtual reality using physiological signals","authors":"Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey","doi":"10.1016/j.pmcj.2025.102065","DOIUrl":"10.1016/j.pmcj.2025.102065","url":null,"abstract":"<div><div>Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study (<em>N</em>=26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102065"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134125","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 : 2025-06-01Epub Date: 2025-05-22DOI: 10.1016/j.pmcj.2025.102064
Peng Tang, Yong Zhang
With frequent maritime activities, the number of overboard accidents at sea has increased, and rescue delays often lead to people being killed. Unmanned Aerial Vehicles (UAVs) have the advantages of fast localization and real-time monitoring in rescue, but the images taken by UAVs have many small targets, and the detection accuracy is insufficient; at the same time, target detection algorithms are difficult to be deployed due to the limitation of computational resources of UAVs. For this reason, this paper proposes a lightweight target detection model based on YOLOv8s improvement, LiteFlex-YOLO, which aims to improve the performance of target detection in UAVs sea rescue. Firstly, the small target sensing ability of the model is enhanced by introducing the P2 small target detection layer, secondly, replacing the C2f module with the lightweight C2fCIB module reduces the computational complexity to make the model more lightweight, furthermore, the feature extraction ability of the backbone is enhanced by using the ODConv (Omni-Dimensional Dynamic Convolution); Lastly, the attention mechanism of SimAM (Simple Attention Module) is introduced to enhance the attention of the key feature information. The final experimental results showed that, LiteFlex-YOLO achieves a [email protected] of 69.5% on the SeaDronesSee dataset, which is 18.2% improvement compared to YOLOv8s, and the model parameters are reduced to 71.2% of YOLOv8s. Moreover, compared with other SOTA algorithms, LiteFlex-YOLO performs excellently in small object detection accuracy, model lightweighting, and robustness.
{"title":"LiteFlex-YOLO:A lightweight small target detection network for maritime unmanned aerial vehicles","authors":"Peng Tang, Yong Zhang","doi":"10.1016/j.pmcj.2025.102064","DOIUrl":"10.1016/j.pmcj.2025.102064","url":null,"abstract":"<div><div>With frequent maritime activities, the number of overboard accidents at sea has increased, and rescue delays often lead to people being killed. Unmanned Aerial Vehicles (UAVs) have the advantages of fast localization and real-time monitoring in rescue, but the images taken by UAVs have many small targets, and the detection accuracy is insufficient; at the same time, target detection algorithms are difficult to be deployed due to the limitation of computational resources of UAVs. For this reason, this paper proposes a lightweight target detection model based on YOLOv8s improvement, LiteFlex-YOLO, which aims to improve the performance of target detection in UAVs sea rescue. Firstly, the small target sensing ability of the model is enhanced by introducing the P2 small target detection layer, secondly, replacing the C2f module with the lightweight C2fCIB module reduces the computational complexity to make the model more lightweight, furthermore, the feature extraction ability of the backbone is enhanced by using the ODConv (Omni-Dimensional Dynamic Convolution); Lastly, the attention mechanism of SimAM (Simple Attention Module) is introduced to enhance the attention of the key feature information. The final experimental results showed that, LiteFlex-YOLO achieves a [email protected] of 69.5% on the SeaDronesSee dataset, which is 18.2% improvement compared to YOLOv8s, and the model parameters are reduced to 71.2% of YOLOv8s. Moreover, compared with other SOTA algorithms, LiteFlex-YOLO performs excellently in small object detection accuracy, model lightweighting, and robustness.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102064"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125069","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 : 2025-06-01Epub Date: 2025-05-09DOI: 10.1016/j.pmcj.2025.102060
Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini
This paper empirically explores the resilience of the current Android ecosystem against stegomalware, which involves both Java/Kotlin and native code. To this aim, we rely on a methodology that goes beyond traditional approaches by hiding malicious Java code and extending it to encoding and dynamically loading native libraries at runtime. By merging app resources, steganography, and repackaging, the methodology seamlessly embeds malware samples into the assets of a host app, making detection significantly more challenging. We implemented the methodology in a tool, StegoPack, which allows the extraction and execution of the payload at runtime through reverse steganography. We used StegoPack to embed well-known DEX and native malware samples over 14 years into real Android host apps. We then challenged top-notch antivirus engines, which previously had high detection rates on the original malware, to detect the embedded samples. Our results reveal a significant reduction in the number of detections (up to zero in most cases), indicating that current detection techniques, while thorough in analyzing app code, largely disregard app assets, leading us to believe that steganographic adversaries are not even included in the adversary models of most deployed defensive analysis systems. Thus, we propose potential countermeasures for StegoPack to detect steganographic data in the app assets and the dynamic loader used to execute malware.
{"title":"Would you mind hiding my malware? Building malicious Android apps with StegoPack","authors":"Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini","doi":"10.1016/j.pmcj.2025.102060","DOIUrl":"10.1016/j.pmcj.2025.102060","url":null,"abstract":"<div><div>This paper empirically explores the resilience of the current Android ecosystem against stegomalware, which involves both Java/Kotlin and native code. To this aim, we rely on a methodology that goes beyond traditional approaches by hiding malicious Java code and extending it to encoding and dynamically loading native libraries at runtime. By merging app resources, steganography, and repackaging, the methodology seamlessly embeds malware samples into the assets of a host app, making detection significantly more challenging. We implemented the methodology in a tool, StegoPack, which allows the extraction and execution of the payload at runtime through reverse steganography. We used StegoPack to embed well-known DEX and native malware samples over 14 years into real Android host apps. We then challenged top-notch antivirus engines, which previously had high detection rates on the original malware, to detect the embedded samples. Our results reveal a significant reduction in the number of detections (up to zero in most cases), indicating that current detection techniques, while thorough in analyzing app code, largely disregard app assets, leading us to believe that steganographic adversaries are not even included in the adversary models of most deployed defensive analysis systems. Thus, we propose potential countermeasures for StegoPack to detect steganographic data in the app assets and the dynamic loader used to execute malware.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102060"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068520","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 : 2025-06-01Epub Date: 2025-05-28DOI: 10.1016/j.pmcj.2025.102066
Agnaldo de Souza Batista , Aldri Luiz dos Santos
Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for various services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAV location-sharing service that employs opportunistic approaches to deliver UAVs’ location. FlySafe takes into account the freshness of UAVs’ location to maintain their spatial awareness. Further, it counts on the age of the UAV’s location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m.
{"title":"Resilient UAVs location sharing service based on information freshness and opportunistic deliveries","authors":"Agnaldo de Souza Batista , Aldri Luiz dos Santos","doi":"10.1016/j.pmcj.2025.102066","DOIUrl":"10.1016/j.pmcj.2025.102066","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for various services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAV location-sharing service that employs opportunistic approaches to deliver UAVs’ location. FlySafe takes into account the freshness of UAVs’ location to maintain their spatial awareness. Further, it counts on the age of the UAV’s location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102066"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184361","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 : 2025-05-01Epub Date: 2025-04-04DOI: 10.1016/j.pmcj.2025.102044
Fahrurrozi Rahman, Martin Schiemer, Andrea Rosales Sanabria, Juan Ye
Human activity recognition (HAR) is a key enabler for many applications in healthcare, factory automation, and smart home. It detects and predicts human behaviours or daily activities via a range of wearable sensors or ambient sensors embedded in an environment. As more and more HAR applications are deployed in the real-world environments, there is a pressing need for the ability of continually and incrementally learning new activities over time without retraining the HAR model. Recently, various continual learning techniques have been applied to HAR; however, most of them commit to a large architecture, which might not suit to devices that deploy HAR models. In addition, these techniques often require to deploy the same large architecture on the devices and cannot customise the architecture for different requirements. To tackle this challenge, we present a dynamic mixture-of-experts approach, which grows an expert for each new task and allows flexible composition of experts to suit individual needs of applications. We have empirically evaluated our technique on 4 third-party, publicly available datasets and compared with 11 state-of-the-art continual learning techniques. Our results demonstrate that our technique can achieve better or comparable performance but with much less parameter spaces and training time.
{"title":"Continual learning in sensor-based human activity recognition with dynamic mixture of experts","authors":"Fahrurrozi Rahman, Martin Schiemer, Andrea Rosales Sanabria, Juan Ye","doi":"10.1016/j.pmcj.2025.102044","DOIUrl":"10.1016/j.pmcj.2025.102044","url":null,"abstract":"<div><div>Human activity recognition (HAR) is a key enabler for many applications in healthcare, factory automation, and smart home. It detects and predicts human behaviours or daily activities via a range of wearable sensors or ambient sensors embedded in an environment. As more and more HAR applications are deployed in the real-world environments, there is a pressing need for the ability of continually and incrementally learning new activities over time without retraining the HAR model. Recently, various continual learning techniques have been applied to HAR; however, most of them commit to a large architecture, which might not suit to devices that deploy HAR models. In addition, these techniques often require to deploy the same large architecture on the devices and cannot customise the architecture for different requirements. To tackle this challenge, we present a dynamic mixture-of-experts approach, which grows an expert for each new task and allows flexible composition of experts to suit individual needs of applications. We have empirically evaluated our technique on 4 third-party, publicly available datasets and compared with 11 state-of-the-art continual learning techniques. Our results demonstrate that our technique can achieve better or comparable performance but with much less parameter spaces and training time.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102044"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-05-01Epub Date: 2025-04-25DOI: 10.1016/j.pmcj.2025.102059
Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey
The advances in sensing and computing methodologies have allowed ubiquitous Cyber–Physical Systems (CPS) which have enabled intelligent monitoring and management of crop plants, leading to Smart Agriculture. Yet, the computational constraints of the edge-computing devices have been a roadblock for utilization of complex processing algorithms for real-time applications like leaf-disease detection, were immediate and highly accurate results are of paramount importance. To address this, we propose EdgePlantNet, a Lightweight Edge-Aware CPS for Plant Disease Detection using Enhanced Attention CNNs. It comprises a novel dual-branched Convolutional Neural Network (CNN) architecture that incorporates an improved multi-layered perceptron based spatial attention mechanism (MLP-ATCNN). The MLP-ATCNN is fed with both the original leaf image and its segmented copy, allowing it to simultaneously focus on the leaf image at two scales namely, the diseased regions, and the overall leaf. This allows it to learn robust discriminatory features corresponding to different diseases, even when trained with much lower samples of data. We validate the performance of the EdgePlantNet on two popular and diverse datasets that are the PlantVillage and the BPLD dataset. The novelty of our proposed CPS much lower computational complexity and high disease detection accuracy as compared to other state-of-the-art methods. We also implement the EdgePlantNet on a resource constraint IoT edge device, demonstrating its efficiency for mobile computing.
{"title":"EdgePlantNet: Lightweight edge-aware cyber–physical system for plant disease detection using enhanced attention CNNs","authors":"Mohammad Zeeshan , Maryam Shojaei Baghini , Ankur Pandey","doi":"10.1016/j.pmcj.2025.102059","DOIUrl":"10.1016/j.pmcj.2025.102059","url":null,"abstract":"<div><div>The advances in sensing and computing methodologies have allowed ubiquitous Cyber–Physical Systems (CPS) which have enabled intelligent monitoring and management of crop plants, leading to Smart Agriculture. Yet, the computational constraints of the edge-computing devices have been a roadblock for utilization of complex processing algorithms for real-time applications like leaf-disease detection, were immediate and highly accurate results are of paramount importance. To address this, we propose EdgePlantNet, a Lightweight Edge-Aware CPS for Plant Disease Detection using Enhanced Attention CNNs. It comprises a novel dual-branched Convolutional Neural Network (CNN) architecture that incorporates an improved multi-layered perceptron based spatial attention mechanism (MLP-ATCNN). The MLP-ATCNN is fed with both the original leaf image and its segmented copy, allowing it to simultaneously focus on the leaf image at two scales namely, the diseased regions, and the overall leaf. This allows it to learn robust discriminatory features corresponding to different diseases, even when trained with much lower samples of data. We validate the performance of the EdgePlantNet on two popular and diverse datasets that are the PlantVillage and the BPLD dataset. The novelty of our proposed CPS much lower computational complexity and high disease detection accuracy as compared to other state-of-the-art methods. We also implement the EdgePlantNet on a resource constraint IoT edge device, demonstrating its efficiency for mobile computing.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102059"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899007","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 : 2025-05-01Epub Date: 2025-04-08DOI: 10.1016/j.pmcj.2025.102045
Sara Lazzaro , Vincenzo De Angelis , Anna Maria Mandalari , Francesco Buccafurri
In the context of consumer Internet of Things (IoT) devices, the identification of vulnerabilities is becoming increasingly relevant. In this paper, we propose a scalable black-box assessment methodology for identifying authentication and reliability issues in IoT devices without the need for prior knowledge of device models or communication protocols. Our methodology consists of a suite of five black-box tests focusing on two specific aspects: authentication and reliability. One of these tests required the development of a tool, called REPLIOT, specifically aimed at discovering replay attacks on the local network. To the best of our knowledge, the development of such a tool is a significant contribution, as there was no similar tool previously available in the literature. We applied these tests to a testbed consisting of 51 consumer IoT devices. Our experiments reveal that 88% of the tested devices fail at least one of the proposed tests. Further manual investigation reveals severe implications of these results in terms of privacy, security, and reliability. Our findings underline a strong need to improve consumer IoT devices security practices to minimize these potential risks and protect smart home environments.
{"title":"A black-box assessment of authentication and reliability in consumer IoT devices","authors":"Sara Lazzaro , Vincenzo De Angelis , Anna Maria Mandalari , Francesco Buccafurri","doi":"10.1016/j.pmcj.2025.102045","DOIUrl":"10.1016/j.pmcj.2025.102045","url":null,"abstract":"<div><div>In the context of consumer Internet of Things (IoT) devices, the identification of vulnerabilities is becoming increasingly relevant. In this paper, we propose a scalable black-box assessment methodology for identifying authentication and reliability issues in IoT devices without the need for prior knowledge of device models or communication protocols. Our methodology consists of a suite of five black-box tests focusing on two specific aspects: authentication and reliability. One of these tests required the development of a tool, called REPLIOT, specifically aimed at discovering replay attacks on the local network. To the best of our knowledge, the development of such a tool is a significant contribution, as there was no similar tool previously available in the literature. We applied these tests to a testbed consisting of 51 consumer IoT devices. Our experiments reveal that 88% of the tested devices fail at least one of the proposed tests. Further manual investigation reveals severe implications of these results in terms of privacy, security, and reliability. Our findings underline a strong need to improve consumer IoT devices security practices to minimize these potential risks and protect smart home environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102045"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821505","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 : 2025-05-01Epub Date: 2025-04-07DOI: 10.1016/j.pmcj.2025.102049
Jay Kumar Jain , Dipti Chauhan
Wireless communication is pivotal in the modern era, enabling seamless connectivity across diverse applications. However, the increasing complexity and sophistication of cyber threats pose significant challenges to the security of wireless communication systems. This paper proposes an innovative approach to enhance wireless communication security through integrating artificial intelligence (AI) techniques. First, we construct the network using the Horizontal Partitioning Sierpinski Triangle to reduce the network's high traffic and perform the authentication process. After successful authentication, we perform the clustering process and Game Theory-Driven Clustering (GT-DC) allows nodes to strategically optimize energy utilization while forming clusters as rational entities in a cooperative game. Perform the beacon injection and detect the attacks using the Improved Random Forest (IRF) that signifies the accurate identification of cyber-attacks, IRF is improving the Bootstrap Sampling, Class Weights, and Anomaly Score Threshold. In Routing implement Improved Cache LEACH Protocol (ICLP) which discovers the effective routing establishing the Cache nodes (Cn), to obtain optimal routing by lowering latency, improving data access, enhancing data reliability, and reducing data redundancy. The proposed work is compared with evaluation metrics such as authentication time, throughput, attack detection rate, energy consumption, packet delivery rate, and delay.
{"title":"Optimized secure and energy-efficient approach for IoT-enabled wireless sensor networks","authors":"Jay Kumar Jain , Dipti Chauhan","doi":"10.1016/j.pmcj.2025.102049","DOIUrl":"10.1016/j.pmcj.2025.102049","url":null,"abstract":"<div><div>Wireless communication is pivotal in the modern era, enabling seamless connectivity across diverse applications. However, the increasing complexity and sophistication of cyber threats pose significant challenges to the security of wireless communication systems. This paper proposes an innovative approach to enhance wireless communication security through integrating artificial intelligence (AI) techniques. First, we construct the network using the Horizontal Partitioning Sierpinski Triangle to reduce the network's high traffic and perform the authentication process. After successful authentication, we perform the clustering process and Game Theory-Driven Clustering (GT-DC) allows nodes to strategically optimize energy utilization while forming clusters as rational entities in a cooperative game. Perform the beacon injection and detect the attacks using the Improved Random Forest (IRF) that signifies the accurate identification of cyber-attacks, IRF is improving the Bootstrap Sampling, Class Weights, and Anomaly Score Threshold. In Routing implement Improved Cache LEACH Protocol (ICLP) which discovers the effective routing establishing the Cache nodes (Cn), to obtain optimal routing by lowering latency, improving data access, enhancing data reliability, and reducing data redundancy. The proposed work is compared with evaluation metrics such as authentication time, throughput, attack detection rate, energy consumption, packet delivery rate, and delay.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102049"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829093","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 : 2025-05-01Epub Date: 2025-03-31DOI: 10.1016/j.pmcj.2025.102043
Bhabani Sankar Gouda , Trilochan Panigrahi , Sudhakar Das , Meenakshi Panda , Linga Reddy Cenkeramaddi
Sensor nodes in wireless sensor networks (WSNs) for several remote applications are deployed in harsh environments and are coupled with low-cost components. Because of these factors, sensor nodes are becoming faulty, resulting in serious data inaccuracy in the network if not diagnosed in a timely manner. The current approaches to centralized or distributed fault detection algorithms are based on statistical methods or machine learning algorithms. Statistical methods require more data to achieve the desired detection accuracy and may be impractical for sparse networks. Machine learning approaches are computationally demanding. We know that the mean and variance of data from a faulty node differ from those from a healthy node. As a result, simultaneous likelihood ratio statistics are proposed here to determine the sensor node’s fault status in WSNs. The proposed hybrid method, in which the faulty status of the node connected to the anchor node is diagnosed by the anchor node, assumes that the anchor node has statistics for all connected nodes. During the diagnosis time, the simultaneous likelihood ratio statistics (SLRS) are computed using the data received by the anchor node over a specific time period. The fault status is determined by comparing the likelihood ratio to a predetermined threshold based on the tolerance limit. The algorithm’s performance is determined and compared to state-of-the-art algorithms using real-time measured data from the literature.
{"title":"Distributed fault detection in sparse wireless sensor networks utilizing simultaneous likelihood ratio statistics","authors":"Bhabani Sankar Gouda , Trilochan Panigrahi , Sudhakar Das , Meenakshi Panda , Linga Reddy Cenkeramaddi","doi":"10.1016/j.pmcj.2025.102043","DOIUrl":"10.1016/j.pmcj.2025.102043","url":null,"abstract":"<div><div>Sensor nodes in wireless sensor networks (WSNs) for several remote applications are deployed in harsh environments and are coupled with low-cost components. Because of these factors, sensor nodes are becoming faulty, resulting in serious data inaccuracy in the network if not diagnosed in a timely manner. The current approaches to centralized or distributed fault detection algorithms are based on statistical methods or machine learning algorithms. Statistical methods require more data to achieve the desired detection accuracy and may be impractical for sparse networks. Machine learning approaches are computationally demanding. We know that the mean and variance of data from a faulty node differ from those from a healthy node. As a result, simultaneous likelihood ratio statistics are proposed here to determine the sensor node’s fault status in WSNs. The proposed hybrid method, in which the faulty status of the node connected to the anchor node is diagnosed by the anchor node, assumes that the anchor node has statistics for all connected nodes. During the diagnosis time, the simultaneous likelihood ratio statistics (SLRS) are computed using the data received by the anchor node over a specific time period. The fault status is determined by comparing the likelihood ratio to a predetermined threshold based on the tolerance limit. The algorithm’s performance is determined and compared to state-of-the-art algorithms using real-time measured data from the literature.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"110 ","pages":"Article 102043"},"PeriodicalIF":3.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
Pub Date : 2025-05-01Epub Date: 2025-04-21DOI: 10.1016/j.pmcj.2025.102048
Hooman Sarvghadi , Andreas Reinhardt , Esther A. Semmelhack
Technology has rapidly evolved over the course of the last decades, and drastically transformed our way of life. Robots are no longer just mechanical aides, but have become collaborators on many tasks. Wearable gadgets have become virtually ubiquitous due to their ability to collect data, monitor health parameters, and assist users in various day-to-day tasks. In recent years, there has been a surge in interest around the use of wearable technologies to collect human psychological parameters for human–robot collaboration. With the field of robotics advancing, there is a growing need for robots to interact with humans seamlessly. To achieve this seamless human–robot connection, robots must be able to interpret human emotions and react appropriately. While understanding human emotions and behavior is a complex task in itself, wearable sensor systems contribute valuable insights. This survey provides a comprehensive overview of wearable gadgets and technologies proposed for measuring five key human factors — trust, cognitive workload, stress, safety perception, and fatigue — within the scope of human–robot collaboration, based on the systematic review of papers published between 2015 and the end of 2024 in six major databases. Our analysis indicates that trust and cognitive workload have received greater attention from researchers in recent years, as compared to other human factors. The Empatica E4 wristband, Shimmer3 GSR+ and EPOC X EEG headset are among the most widely used wearable devices, capable of capturing essential physiological parameters widely used for human–robot collaboration, including electrodermal activity, heart rate variability, skin temperature, and electroencephalogram. Besides reviewing the potentials and capabilities of these gadgets, we highlight their shortcomings and offer directions for future research in this domain.
在过去的几十年里，科技迅速发展，彻底改变了我们的生活方式。机器人不再仅仅是机械助手，而是在许多任务中成为合作者。由于可穿戴设备能够收集数据、监测健康参数并协助用户完成各种日常任务，因此它们几乎无处不在。近年来，人们对使用可穿戴技术来收集人类心理参数以进行人机协作的兴趣激增。随着机器人领域的发展，人们对机器人与人类无缝互动的需求越来越大。为了实现这种无缝的人机连接，机器人必须能够理解人类的情感并做出适当的反应。虽然理解人类的情绪和行为本身就是一项复杂的任务，但可穿戴传感器系统提供了有价值的见解。本调查基于对2015年至2024年底在六个主要数据库中发表的论文的系统综述，全面概述了可穿戴设备和技术，这些设备和技术用于测量人机协作范围内的五个关键人为因素——信任、认知工作量、压力、安全感知和疲劳。我们的分析表明，与其他人为因素相比，信任和认知工作量近年来受到了研究人员的更多关注。Empatica E4腕带、Shimmer3 GSR+和EPOC X EEG耳机是应用最广泛的可穿戴设备，能够捕获广泛用于人机协作的基本生理参数，包括皮电活动、心率变率、皮肤温度和脑电图。除了回顾这些小工具的潜力和能力外，我们还指出了它们的不足之处，并提出了该领域未来的研究方向。
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