Pub Date : 2025-02-01Epub Date: 2025-01-16DOI: 10.1016/j.pmcj.2025.102012
Riya Samanta , Soumya K. Ghosh , Sajal K. Das
Crowdsourcing platforms face critical challenges in task assignment and workforce retention, particularly in aligning complex, skill-intensive tasks with crowd-worker willingness and potential while ensuring workforce diversity and balanced composition. This study introduces the Skill-Aligned Task Assignment and Potential-Aware Workforce Composition (SATA-PAW) framework to address these challenges. The proposed framework formulates the Task Assignment with Workforce Composition Balance (TACOMB) problem as a multi-constraint optimization task, aiming to maximize net utility under task budget constraints while promoting balanced workforce composition. SATA-PAW integrates two novel algorithms, Skill-Aligned Task Assignment (SATA), which optimizes task-worker matching by considering skills, willingness, and budget constraints, and Potential-Aware Workforce Composition (PAW), which leverages satisfaction score and latent potential to retain skilled workers and improve workforce diversity. Experimental evaluations on real-world (UpWork) and synthetic datasets demonstrate SATA-PAW’s superiority over five state-of-the-art methods. The results highlight SATA-PAW’s ability to integrate human-centric factors with efficient optimization, setting a new benchmark for skill-oriented task assignment and balanced workforce composition in crowdsourcing systems.
众包平台在任务分配和员工保留方面面临着严峻的挑战，特别是在确保劳动力多样性和平衡构成的同时，如何将复杂的、技能密集型的任务与众包工人的意愿和潜力结合起来。本研究引入了技能对齐任务分配和潜力感知劳动力构成（SATA-PAW）框架来解决这些挑战。该框架将劳动力构成平衡任务分配（Task Assignment with Workforce Composition Balance， TACOMB）问题表述为一个多约束优化任务，目的是在任务预算约束下实现净效用最大化，同时促进劳动力构成平衡。SATA-PAW集成了两种新颖的算法，一种是技能对齐任务分配（SATA）算法，它通过考虑技能、意愿和预算约束来优化任务与工人的匹配；另一种是潜力感知劳动力构成（PAW）算法，它利用满意度评分和潜在潜力来留住熟练工人，并提高劳动力多样性。在真实世界（UpWork）和合成数据集上的实验评估表明，SATA-PAW优于五种最先进的方法。结果表明，SATA-PAW能够将以人为中心的因素与高效优化相结合，为众包系统中以技能为导向的任务分配和平衡的劳动力构成设定了新的基准。
{"title":"Enhancing crowdsourcing through skill and willingness-aligned task assignment with workforce composition balance","authors":"Riya Samanta , Soumya K. Ghosh , Sajal K. Das","doi":"10.1016/j.pmcj.2025.102012","DOIUrl":"10.1016/j.pmcj.2025.102012","url":null,"abstract":"<div><div>Crowdsourcing platforms face critical challenges in task assignment and workforce retention, particularly in aligning complex, skill-intensive tasks with crowd-worker willingness and potential while ensuring workforce diversity and balanced composition. This study introduces the Skill-Aligned Task Assignment and Potential-Aware Workforce Composition (SATA-PAW) framework to address these challenges. The proposed framework formulates the Task Assignment with Workforce Composition Balance (TACOMB) problem as a multi-constraint optimization task, aiming to maximize net utility under task budget constraints while promoting balanced workforce composition. SATA-PAW integrates two novel algorithms, Skill-Aligned Task Assignment (SATA), which optimizes task-worker matching by considering skills, willingness, and budget constraints, and Potential-Aware Workforce Composition (PAW), which leverages satisfaction score and latent potential to retain skilled workers and improve workforce diversity. Experimental evaluations on real-world (UpWork) and synthetic datasets demonstrate SATA-PAW’s superiority over five state-of-the-art methods. The results highlight SATA-PAW’s ability to integrate human-centric factors with efficient optimization, setting a new benchmark for skill-oriented task assignment and balanced workforce composition in crowdsourcing systems.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"107 ","pages":"Article 102012"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155954","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}
Cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) enhances spectral decision-making precision but introduces vulnerabilities to malicious secondary user (SU) attacks. This paper proposes a decentralized trust and reputation management (TRM) framework to address these vulnerabilities, emphasizing the need to mitigate risks associated with centralized systems. Inspired by blockchain technology, we present a distributed TRM method for CSS in CRNs, significantly reducing the impact of malicious attacks. Our approach leverages a Proof of Trust (PoT) system to enhance the integrity of CSS, thereby improving the accuracy of spectral decision-making while reducing false positives and false negatives. In this system, SUs’ trust scores are dynamically updated based on their sensing reports, and they will collaboratively participate in new blocks' formation using the trust scores. Simulation results validate the effectiveness of the proposed method, indicating its potential to enhance security and reliability in CRNs.
{"title":"Blockchain-Inspired Trust Management in Cognitive Radio Networks with Cooperative Spectrum Sensing","authors":"Mahsa Mahvash , Neda Moghim , Mojtaba Mahdavi , Mahdieh Amiri , Sachin Shetty","doi":"10.1016/j.pmcj.2024.101999","DOIUrl":"10.1016/j.pmcj.2024.101999","url":null,"abstract":"<div><div>Cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) enhances spectral decision-making precision but introduces vulnerabilities to malicious secondary user (SU) attacks. This paper proposes a decentralized trust and reputation management (TRM) framework to address these vulnerabilities, emphasizing the need to mitigate risks associated with centralized systems. Inspired by blockchain technology, we present a distributed TRM method for CSS in CRNs, significantly reducing the impact of malicious attacks. Our approach leverages a Proof of Trust (PoT) system to enhance the integrity of CSS, thereby improving the accuracy of spectral decision-making while reducing false positives and false negatives. In this system, SUs’ trust scores are dynamically updated based on their sensing reports, and they will collaboratively participate in new blocks' formation using the trust scores. Simulation results validate the effectiveness of the proposed method, indicating its potential to enhance security and reliability in CRNs.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"106 ","pages":"Article 101999"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722139","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 : 2025-01-01Epub Date: 2024-11-23DOI: 10.1016/j.pmcj.2024.101998
Ahmed Fahim Mostafa , Mohamed Abdel-Kader , Yasser Gadallah
Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.
{"title":"Three-dimensional spectrum coverage gap map construction in cellular networks: A non-linear estimation approach","authors":"Ahmed Fahim Mostafa , Mohamed Abdel-Kader , Yasser Gadallah","doi":"10.1016/j.pmcj.2024.101998","DOIUrl":"10.1016/j.pmcj.2024.101998","url":null,"abstract":"<div><div>Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"106 ","pages":"Article 101998"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758973","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 : 2025-01-01Epub Date: 2024-11-28DOI: 10.1016/j.pmcj.2024.101997
Giacomo Longo , Alessandro Cantelli-Forti , Enrico Russo , Francesco Lupia , Martin Strohmeier , Andrea Pugliese
Accurately determining the number of people affected by emergencies is essential for deploying effective response measures during disasters. Traditional solutions like cellular and Wi-Fi networks are often rendered ineffective during such emergencies due to widespread infrastructure damage or non-functional connectivity, prompting the exploration of more resilient methods. This paper proposes a novel solution utilizing Bluetooth Low Energy (BLE) technology and decentralized networks composed entirely of mobile and wearable devices to count individuals autonomously without reliance on external communication equipment or specialized personnel. This count leverages uncoordinated relayed communication among devices within these networks, enabling us to extend our counting capabilities well beyond the direct range of rescuers. A formally evaluated, experimentally validated, and privacy-preserving counting algorithm that demonstrates rapid convergence and high accuracy even in large-scale scenarios is employed.
{"title":"Collective victim counting in post-disaster response: A distributed, power-efficient algorithm via BLE spontaneous networks","authors":"Giacomo Longo , Alessandro Cantelli-Forti , Enrico Russo , Francesco Lupia , Martin Strohmeier , Andrea Pugliese","doi":"10.1016/j.pmcj.2024.101997","DOIUrl":"10.1016/j.pmcj.2024.101997","url":null,"abstract":"<div><div>Accurately determining the number of people affected by emergencies is essential for deploying effective response measures during disasters. Traditional solutions like cellular and Wi-Fi networks are often rendered ineffective during such emergencies due to widespread infrastructure damage or non-functional connectivity, prompting the exploration of more resilient methods. This paper proposes a novel solution utilizing Bluetooth Low Energy (BLE) technology and decentralized networks composed entirely of mobile and wearable devices to count individuals autonomously without reliance on external communication equipment or specialized personnel. This count leverages uncoordinated relayed communication among devices within these networks, enabling us to extend our counting capabilities well beyond the direct range of rescuers. A formally evaluated, experimentally validated, and privacy-preserving counting algorithm that demonstrates rapid convergence and high accuracy even in large-scale scenarios is employed.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"106 ","pages":"Article 101997"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757430","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 : 2024-12-01Epub Date: 2024-10-10DOI: 10.1016/j.pmcj.2024.101993
Youjia Han, Huibin Wang, Yueheng Li, Lili Zhang
Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.
{"title":"Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks","authors":"Youjia Han, Huibin Wang, Yueheng Li, Lili Zhang","doi":"10.1016/j.pmcj.2024.101993","DOIUrl":"10.1016/j.pmcj.2024.101993","url":null,"abstract":"<div><div>Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101993"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531086","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 : 2024-12-01Epub Date: 2024-10-16DOI: 10.1016/j.pmcj.2024.101995
Tingxuan Fu , Sijia Hao , Qiming Chen , Zihan Yan , Huawei Liu , Amin Rezaeipanah
The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.
{"title":"An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation","authors":"Tingxuan Fu , Sijia Hao , Qiming Chen , Zihan Yan , Huawei Liu , Amin Rezaeipanah","doi":"10.1016/j.pmcj.2024.101995","DOIUrl":"10.1016/j.pmcj.2024.101995","url":null,"abstract":"<div><div>The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101995"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531088","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 : 2024-12-01Epub Date: 2024-09-26DOI: 10.1016/j.pmcj.2024.101992
Zahra Aghaee , Afsaneh Fatemi , Peyman Arebi
In recent years, one type of complex network called the Social Internet of Things (SIoT) has attracted the attention of researchers. Controllability is one of the important problems in complex networks and it has essential applications in social, biological, and technical networks. Applying this problem can also play an important role in the control of social smart cities, but it has not yet been defined as a specific problem on SIoT, and no solution has been provided for it. This paper addresses the controllability problem of the temporal SIoT network. In this regard, first, a definition for the temporal SIoT network is provided. Then, the unique relationships of this network are defined and modeled formally. In the following, the Controllability problem is applied to the temporal SIoT network (CSIoT) to identify the Minimum Driver nodes Set (MDS). Then proposed CSIoT is compared with the state-of-the-art methods for performance analysis. In the obtained results, the heterogeneity (different types, brands, and models) has been investigated. Also, 69.80 % of the SIoT sub-graphs nodes have been identified as critical driver nodes in 152 different sets. The proposed controllability deals with network control in a distributed manner.
{"title":"A controllability method on the social Internet of Things (SIoT) network","authors":"Zahra Aghaee , Afsaneh Fatemi , Peyman Arebi","doi":"10.1016/j.pmcj.2024.101992","DOIUrl":"10.1016/j.pmcj.2024.101992","url":null,"abstract":"<div><div>In recent years, one type of complex network called the Social Internet of Things (SIoT) has attracted the attention of researchers. Controllability is one of the important problems in complex networks and it has essential applications in social, biological, and technical networks. Applying this problem can also play an important role in the control of social smart cities, but it has not yet been defined as a specific problem on SIoT, and no solution has been provided for it. This paper addresses the controllability problem of the temporal SIoT network. In this regard, first, a definition for the temporal SIoT network is provided. Then, the unique relationships of this network are defined and modeled formally. In the following, the Controllability problem is applied to the temporal SIoT network (CSIoT) to identify the Minimum Driver nodes Set (MDS). Then proposed CSIoT is compared with the state-of-the-art methods for performance analysis. In the obtained results, the heterogeneity (different types, brands, and models) has been investigated. Also, 69.80 % of the SIoT sub-graphs nodes have been identified as critical driver nodes in 152 different sets. The proposed controllability deals with network control in a distributed manner.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101992"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359679","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 : 2024-12-01Epub Date: 2024-09-19DOI: 10.1016/j.pmcj.2024.101990
Tao Feng, Jie Wang, Lu Zheng
Although the Internet of Things (IoT) brings efficiency and convenience to various aspects of people’s lives, security and privacy concerns persist as significant challenges. Certificateless Signatures eliminate digital certificate management and key escrow issues and can be well embedded in resource-constrained IoT devices for secure access control. Recently, Ma et al. designed an efficient and pair-free certificateless signature (CLS) scheme for IoT deployment. Unfortunately, We demonstrate that the scheme proposed by Ma et al. is susceptible to signature forgery attacks by Type-II adversaries. That is, a malicious-and-passive key generation center (KGC) can forge a legitimate signature for any message by modifying the system parameters without the user’s secret value. Therefore, their identity authentication scheme designed based on vehicular ad-hoc networks also cannot guarantee the claimed security. To address the security vulnerabilities, we designed a blockchain-enhanced and anonymous CLS scheme and proved its security under the Elliptic curve discrete logarithm (ECDL) hardness assumption. Compared to similar schemes, our enhanced scheme offers notable advantages in computational efficiency and communication overhead, as well as stronger security. In addition, a mutual authentication scheme that satisfies the cross-domain scenario is proposed to facilitate efficient mutual authentication and negotiated session key generation between smart devices and edge servers in different edge networks. Performance evaluation shows that our protocol achieves an effective trade-off between security and compute performance, with better applicability in IoT scenarios.
尽管物联网（IoT）为人们生活的各个方面带来了效率和便利，但安全和隐私问题仍然是重大挑战。无证书签名消除了数字证书管理和密钥托管问题，可以很好地嵌入到资源有限的物联网设备中，实现安全访问控制。最近，Ma 等人为物联网部署设计了一种高效、无配对的无证书签名（CLS）方案。不幸的是，我们证明了 Ma 等人提出的方案容易受到第二类对手的签名伪造攻击。也就是说，恶意和被动的密钥生成中心（KGC）可以通过修改系统参数，在没有用户秘密值的情况下伪造任何信息的合法签名。因此，他们基于车载 ad-hoc 网络设计的身份验证方案也无法保证所宣称的安全性。针对这些安全漏洞，我们设计了一种区块链增强匿名 CLS 方案，并在椭圆曲线离散对数（ECDL）硬度假设下证明了其安全性。与类似方案相比，我们的增强方案在计算效率和通信开销方面具有显著优势，而且安全性更强。此外，我们还提出了一种满足跨域场景的相互验证方案，以促进不同边缘网络中智能设备与边缘服务器之间的高效相互验证和协商会话密钥生成。性能评估表明，我们的协议在安全性和计算性能之间实现了有效权衡，在物联网场景中具有更好的适用性。
{"title":"Blockchain-enhanced efficient and anonymous certificateless signature scheme and its application","authors":"Tao Feng, Jie Wang, Lu Zheng","doi":"10.1016/j.pmcj.2024.101990","DOIUrl":"10.1016/j.pmcj.2024.101990","url":null,"abstract":"<div><div>Although the Internet of Things (IoT) brings efficiency and convenience to various aspects of people’s lives, security and privacy concerns persist as significant challenges. Certificateless Signatures eliminate digital certificate management and key escrow issues and can be well embedded in resource-constrained IoT devices for secure access control. Recently, Ma et al. designed an efficient and pair-free certificateless signature (CLS) scheme for IoT deployment. Unfortunately, We demonstrate that the scheme proposed by Ma et al. is susceptible to signature forgery attacks by Type-II adversaries. That is, a malicious-and-passive key generation center (KGC) can forge a legitimate signature for any message by modifying the system parameters without the user’s secret value. Therefore, their identity authentication scheme designed based on vehicular ad-hoc networks also cannot guarantee the claimed security. To address the security vulnerabilities, we designed a blockchain-enhanced and anonymous CLS scheme and proved its security under the Elliptic curve discrete logarithm (ECDL) hardness assumption. Compared to similar schemes, our enhanced scheme offers notable advantages in computational efficiency and communication overhead, as well as stronger security. In addition, a mutual authentication scheme that satisfies the cross-domain scenario is proposed to facilitate efficient mutual authentication and negotiated session key generation between smart devices and edge servers in different edge networks. Performance evaluation shows that our protocol achieves an effective trade-off between security and compute performance, with better applicability in IoT scenarios.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101990"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318677","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 : 2024-12-01Epub Date: 2024-09-20DOI: 10.1016/j.pmcj.2024.101979
Yi Ke, Quan Wan, Fangting Xie, Zhen Liang, Ziyu Wu, Xiaohui Cai
In-bed pose estimation holds significant potential in various domains, including healthcare, sleep studies, and smart homes. Pressure-sensitive bed sheets have emerged as a promising solution for addressing this task considering the advantages of convenience, comfort, and privacy protection. However, existing studies primarily rely on ideal datasets that do not consider the presence of common daily objects such as pillows and quilts referred to as interference, which can significantly impact the pressure distribution. As a result, there is still a gap between the models trained with ideal data and the real-life application. Besides the end-to-end training approach, one potential solution is to recognize the interference and fuse the interference information to the model during training. In this study, we created a well-annotated dataset, consisting of eight in-bed scenes and four common types of interference: pillows, quilts, a laptop, and a package. To facilitate the analysis, the pixels in the pressure image were categorized into five classes based on the relative position between the interference and the human. We then evaluated the performance of five neural network models for pixel-level interference recognition. The best-performing model achieved an accuracy of 80.0% in recognizing the five categories. Subsequently, we validated the utility of interference recognition in improving pose estimation accuracy. The ideal model initially shows an average joint position error of up to 30.59 cm and a Percentage of Correct Keypoints (PCK) of 0.332 on data from scenes with interferences. After retraining on data including interference, the error is reduced to 13.54 cm and the PCK increases to 0.747. By integrating interference recognition information, either by excluding the parts of the interference or using the recognition results as input, the error can be further minimized to 12.44 cm and the PCK can be maximized up to 0.777. Our findings represent an initial step towards the practical deployment of pressure-sensitive bed sheets in everyday life.
{"title":"Pressure distribution based 2D in-bed keypoint prediction under interfered scenes","authors":"Yi Ke, Quan Wan, Fangting Xie, Zhen Liang, Ziyu Wu, Xiaohui Cai","doi":"10.1016/j.pmcj.2024.101979","DOIUrl":"10.1016/j.pmcj.2024.101979","url":null,"abstract":"<div><div>In-bed pose estimation holds significant potential in various domains, including healthcare, sleep studies, and smart homes. Pressure-sensitive bed sheets have emerged as a promising solution for addressing this task considering the advantages of convenience, comfort, and privacy protection. However, existing studies primarily rely on ideal datasets that do not consider the presence of common daily objects such as pillows and quilts referred to as interference, which can significantly impact the pressure distribution. As a result, there is still a gap between the models trained with ideal data and the real-life application. Besides the end-to-end training approach, one potential solution is to recognize the interference and fuse the interference information to the model during training. In this study, we created a well-annotated dataset, consisting of eight in-bed scenes and four common types of interference: pillows, quilts, a laptop, and a package. To facilitate the analysis, the pixels in the pressure image were categorized into five classes based on the relative position between the interference and the human. We then evaluated the performance of five neural network models for pixel-level interference recognition. The best-performing model achieved an accuracy of 80.0% in recognizing the five categories. Subsequently, we validated the utility of interference recognition in improving pose estimation accuracy. The ideal model initially shows an average joint position error of up to 30.59 cm and a Percentage of Correct Keypoints (PCK) of 0.332 on data from scenes with interferences. After retraining on data including interference, the error is reduced to 13.54 cm and the PCK increases to 0.747. By integrating interference recognition information, either by excluding the parts of the interference or using the recognition results as input, the error can be further minimized to 12.44 cm and the PCK can be maximized up to 0.777. Our findings represent an initial step towards the practical deployment of pressure-sensitive bed sheets in everyday life.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101979"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314386","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 : 2024-12-01Epub Date: 2024-09-23DOI: 10.1016/j.pmcj.2024.101991
JiaYi Feng, Lang Li, LiuYan Yan, ChuTian Deng
The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation. Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources. To address these challenges, a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front–back symmetry. This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN). Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP). This optimization leads to a 47.02% reduction in area compared to the of Midori, using UMC technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency. In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 J/bit, representing 25.21% reduction compared to Midori. Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption.
{"title":"INLEC: An involutive and low energy lightweight block cipher for internet of things","authors":"JiaYi Feng, Lang Li, LiuYan Yan, ChuTian Deng","doi":"10.1016/j.pmcj.2024.101991","DOIUrl":"10.1016/j.pmcj.2024.101991","url":null,"abstract":"<div><div>The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation. Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources. To address these challenges, a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front–back symmetry. This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN). Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP). This optimization leads to a 47.02% reduction in area compared to the <span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> of Midori, using UMC <span><math><mrow><mn>0</mn><mo>.</mo><mn>18</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency. In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 <span><math><mi>μ</mi></math></span>J/bit, representing 25.21% reduction compared to Midori. Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"105 ","pages":"Article 101991"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318676","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}
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