The Device-as-a-service (DaaS) Internet of Things (IoT) business model enables distributed IoT devices to sell collected data to other devices, paving the way for machine-to-machine (M2M) economy applications. Cryptocurrencies are widely used by various IoT devices to undertake the main settlement and payment task in the M2M economy. However, the cryptocurrency market, which lacks effective supervision, has fluctuated wildly in the past few years. These fluctuations are breeding grounds for arbitrage in IoT data trading. Therefore, a practical cryptocurrency market supervision framework is very imperative in the process of IoT data trading to ensure that the trading is completed safely and fairly. The difficulty stems from how to combine these unlabeled daily trading data with supervision strategies to punish abnormal users, who disrupt the data trading market in IoT. In this article, we propose a closed-loop hybrid supervision framework based on the unsupervised anomaly detection to solve this problem. The core is to design the multi-modal unsupervised anomaly detection methods on trading prices to identify malicious users. We then design a dedicated control strategy with three levels to defend against various abnormal behaviors, according to the detection results. Furthermore, to guarantee the reliability of this framework, we evaluate the detection rate, accuracy, precision, and time consumption of single-modal and multi-modal detection methods and the contrast algorithm Adaptive KDE [19]. Finally, an effective prototype framework for supervising is established. The extensive evaluations prove that our supervision framework greatly reduces IoT data trading risks and losses.
{"title":"A Closed-loop Hybrid Supervision Framework of Cryptocurrency Transactions for Data Trading in IoT","authors":"Liushun Zhao, Deke Guo, Junjie Xie, Lailong Luo, Yulong Shen","doi":"10.1145/3568171","DOIUrl":"https://doi.org/10.1145/3568171","url":null,"abstract":"The Device-as-a-service (DaaS) Internet of Things (IoT) business model enables distributed IoT devices to sell collected data to other devices, paving the way for machine-to-machine (M2M) economy applications. Cryptocurrencies are widely used by various IoT devices to undertake the main settlement and payment task in the M2M economy. However, the cryptocurrency market, which lacks effective supervision, has fluctuated wildly in the past few years. These fluctuations are breeding grounds for arbitrage in IoT data trading. Therefore, a practical cryptocurrency market supervision framework is very imperative in the process of IoT data trading to ensure that the trading is completed safely and fairly. The difficulty stems from how to combine these unlabeled daily trading data with supervision strategies to punish abnormal users, who disrupt the data trading market in IoT. In this article, we propose a closed-loop hybrid supervision framework based on the unsupervised anomaly detection to solve this problem. The core is to design the multi-modal unsupervised anomaly detection methods on trading prices to identify malicious users. We then design a dedicated control strategy with three levels to defend against various abnormal behaviors, according to the detection results. Furthermore, to guarantee the reliability of this framework, we evaluate the detection rate, accuracy, precision, and time consumption of single-modal and multi-modal detection methods and the contrast algorithm Adaptive KDE [19]. Finally, an effective prototype framework for supervising is established. The extensive evaluations prove that our supervision framework greatly reduces IoT data trading risks and losses.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90445391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mart Lubbers, P. Koopman, Adrian Ramsingh, Jeremy Singer, P. Trinder
Internet of Things (IoT) software is notoriously complex, conventionally comprising multiple tiers. Traditionally an IoT developer must use multiple programming languages and ensure that the components interoperate correctly. A novel alternative is to use a single tierless language with a compiler that generates the code for each component and ensures their correct interoperation. We report a systematic comparative evaluation of two tierless language technologies for IoT stacks: one for resource-rich sensor nodes (Clean with iTask) and one for resource-constrained sensor nodes (Clean with iTask and mTask). The evaluation is based on four implementations of a typical smart campus application: two tierless and two Python-based tiered. (1) We show that tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. Careful analysis attributes this code reduction to reduced interoperation (e.g., two embedded domain-specific languages and one paradigm versus seven languages and two paradigms), automatically generated distributed communication, and powerful IoT programming abstractions. (2) We show that tierless languages have the potential to significantly improve the reliability of IoT systems, describing how Clean iTask/mTask maintains type safety, provides higher-order failure management, and simplifies maintainability. (3) We report the first comparison of a tierless IoT codebase for resource-rich sensor nodes with one for resource-constrained sensor nodes. The comparison shows that they have similar code size (within 7%), and functional structure. (4) We present the first comparison of two tierless IoT languages, one for resource-rich sensor nodes and the other for resource-constrained sensor nodes.
众所周知，物联网(IoT)软件非常复杂，通常由多层组成。传统上，物联网开发人员必须使用多种编程语言，并确保组件正确互操作。一种新颖的替代方案是使用单一的无层语言和编译器，该编译器为每个组件生成代码并确保它们的正确互操作。我们报告了物联网堆栈的两种无层语言技术的系统比较评估:一种用于资源丰富的传感器节点(Clean with iTask)，另一种用于资源受限的传感器节点(Clean with iTask和mTask)。评估基于典型智能校园应用的四种实现:两种分层和两种基于python的分层。(1)我们表明，分层语言有可能显著减少物联网系统的开发工作量，比分层实现所需的代码少70%。仔细分析将这种代码减少归因于减少的互操作性(例如，两种嵌入式领域特定语言和一种范式，而不是七种语言和两种范式)，自动生成的分布式通信以及强大的物联网编程抽象。(2)我们表明，分层语言具有显著提高物联网系统可靠性的潜力，描述了Clean iTask/mTask如何维护类型安全，提供高阶故障管理，并简化可维护性。(3)我们报告了针对资源丰富的传感器节点的无层物联网代码库与针对资源受限的传感器节点的代码库的首次比较。对比表明，它们的代码大小(在7%以内)和功能结构相似。(4)我们首次比较了两种无层次物联网语言，一种用于资源丰富的传感器节点，另一种用于资源受限的传感器节点。
{"title":"Could Tierless Languages Reduce IoT Development Grief?","authors":"Mart Lubbers, P. Koopman, Adrian Ramsingh, Jeremy Singer, P. Trinder","doi":"10.1145/3572901","DOIUrl":"https://doi.org/10.1145/3572901","url":null,"abstract":"Internet of Things (IoT) software is notoriously complex, conventionally comprising multiple tiers. Traditionally an IoT developer must use multiple programming languages and ensure that the components interoperate correctly. A novel alternative is to use a single tierless language with a compiler that generates the code for each component and ensures their correct interoperation. We report a systematic comparative evaluation of two tierless language technologies for IoT stacks: one for resource-rich sensor nodes (Clean with iTask) and one for resource-constrained sensor nodes (Clean with iTask and mTask). The evaluation is based on four implementations of a typical smart campus application: two tierless and two Python-based tiered. (1) We show that tierless languages have the potential to significantly reduce the development effort for IoT systems, requiring 70% less code than the tiered implementations. Careful analysis attributes this code reduction to reduced interoperation (e.g., two embedded domain-specific languages and one paradigm versus seven languages and two paradigms), automatically generated distributed communication, and powerful IoT programming abstractions. (2) We show that tierless languages have the potential to significantly improve the reliability of IoT systems, describing how Clean iTask/mTask maintains type safety, provides higher-order failure management, and simplifies maintainability. (3) We report the first comparison of a tierless IoT codebase for resource-rich sensor nodes with one for resource-constrained sensor nodes. The comparison shows that they have similar code size (within 7%), and functional structure. (4) We present the first comparison of two tierless IoT languages, one for resource-rich sensor nodes and the other for resource-constrained sensor nodes.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78402050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wouter Moedt, R. Bernsteiner, M. Hall, Ann L. Fruhling
Worldwide spending on Internet of Things (IoT) applications is forecasted to surpass $1 trillion by 2022. To stay competitive in this growing technological industry segment, lowering costs while increasing productivity and shortening time-to-market will become increasingly important. Adopting Agile Software Development practices for IoT projects may provide this competitive advantage, as it enables organizations to respond to change, while being dynamic and innovative. Applying a mixed-methods approach, agile IoT practitioners around the world and from diverse industries were surveyed and interviewed. Our study recommends that Agile Software Development team makeup, practices, and methods should be tailored to the specific industry, culture, people, and IT application of an organization. People play an important role in the success of agile projects; therefore, our research focuses on identifying the critical attributes of agile teams to maximize success. Our study identified the five critical agile practices: Collective Code Ownership, Continuous Integration, Single Team, Dedicated Customer, and Sprint Planning and found that both technical and soft skills are essential for successful IoT development.
{"title":"Enhancing IoT Project Success through Agile Best Practices","authors":"Wouter Moedt, R. Bernsteiner, M. Hall, Ann L. Fruhling","doi":"10.1145/3568170","DOIUrl":"https://doi.org/10.1145/3568170","url":null,"abstract":"Worldwide spending on Internet of Things (IoT) applications is forecasted to surpass $1 trillion by 2022. To stay competitive in this growing technological industry segment, lowering costs while increasing productivity and shortening time-to-market will become increasingly important. Adopting Agile Software Development practices for IoT projects may provide this competitive advantage, as it enables organizations to respond to change, while being dynamic and innovative. Applying a mixed-methods approach, agile IoT practitioners around the world and from diverse industries were surveyed and interviewed. Our study recommends that Agile Software Development team makeup, practices, and methods should be tailored to the specific industry, culture, people, and IT application of an organization. People play an important role in the success of agile projects; therefore, our research focuses on identifying the critical attributes of agile teams to maximize success. Our study identified the five critical agile practices: Collective Code Ownership, Continuous Integration, Single Team, Dedicated Customer, and Sprint Planning and found that both technical and soft skills are essential for successful IoT development.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87172173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Absar-Ul-Haque Ahmar, Emekcan Aras, T. D. Nguyen, Sam Michiels, W. Joosen, D. Hughes
Low-power wide-area networks enable large-scale deployments of low-power wireless devices. LoRaWAN is a long-range wireless technology that has emerged as a low-power and low data rate solution to support Internet of Things applications. Although LoRaWAN provides a low-power and cost-efficient networking solution, recent literature shows that it performs poorly in terms of reliability and security in dense deployments due to the uncoordinated (ALOHA-based) nature of the MAC (medium access control) protocol. Furthermore, LoRaWAN is not robust against selective jamming attacks. This article proposes CRAM: a time-synchronized cryptographic frequency hopping MAC protocol designed for the LoRa physical layer. CRAM reduces the contention by fairly exploiting the available frequency space and maximizes the entropy of the channel hopping algorithm. We develop a large physical testbed and a simulator to thoroughly evaluate the proposed protocol. Our evaluations show that CRAM significantly improves reliability and scalability and increases channel utilization while making selective jamming difficult to perform compared to the standard LoRaWAN protocol.
{"title":"Design of a Robust MAC Protocol for LoRa","authors":"Absar-Ul-Haque Ahmar, Emekcan Aras, T. D. Nguyen, Sam Michiels, W. Joosen, D. Hughes","doi":"10.1145/3557048","DOIUrl":"https://doi.org/10.1145/3557048","url":null,"abstract":"Low-power wide-area networks enable large-scale deployments of low-power wireless devices. LoRaWAN is a long-range wireless technology that has emerged as a low-power and low data rate solution to support Internet of Things applications. Although LoRaWAN provides a low-power and cost-efficient networking solution, recent literature shows that it performs poorly in terms of reliability and security in dense deployments due to the uncoordinated (ALOHA-based) nature of the MAC (medium access control) protocol. Furthermore, LoRaWAN is not robust against selective jamming attacks. This article proposes CRAM: a time-synchronized cryptographic frequency hopping MAC protocol designed for the LoRa physical layer. CRAM reduces the contention by fairly exploiting the available frequency space and maximizes the entropy of the channel hopping algorithm. We develop a large physical testbed and a simulator to thoroughly evaluate the proposed protocol. Our evaluations show that CRAM significantly improves reliability and scalability and increases channel utilization while making selective jamming difficult to perform compared to the standard LoRaWAN protocol.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78418872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Video surveillance systems are increasingly becoming common in many private and public campuses, city buildings, and facilities. They provide many useful smart campus/city monitoring and management services based on data captured from video sensors. However, the video surveillance services may also breach personally identifiable information, especially human face images being monitored; therefore, it may potentially violate the privacy of human subjects involved. To address this privacy issue, we introduced a large-scale distributed video surveillance service model, called Smart-city Video Surveillance (SCVS). SCVS is a video surveillance data collection and processing platform to identify important events, monitor, protect, and make decisions for smart campus/city applications. In this article, the specific research focus is on how to identify and anonymize human faces in a distributed edge cloud computing infrastructure. To preserve the privacy of data during video anonymization, SCVS utilizes a two-step approach: (i) parameter server-based distributed machine learning solution, which ensures that edge nodes can exchange parameters for machine learning-based training. Since the dataset is not located on a centralized location, the data privacy and ownership are protected and preserved. (ii) To improve the machine learning model’s accuracy, we presented an asynchronous training approach to protect data and model privacy for both data owners and data users, respectively. SCVS adopts an in-memory encryption approach, where edge computing nodes collect and process data in the memory of edge nodes in encrypted form. This approach can effectively prevent honest but curious attacks. The performance evaluation shows the presented privacy protection platform is efficient and effective compared to traditional centralized computing models as presented in Section 5.
{"title":"SCVS: On AI and Edge Clouds Enabled Privacy-preserved Smart-city Video Surveillance Services","authors":"Sowmya Myneni, Garima Agrawal, Yuli Deng, Ankur Chowdhary, N. Vadnere, Dijiang Huang","doi":"10.1145/3542953","DOIUrl":"https://doi.org/10.1145/3542953","url":null,"abstract":"Video surveillance systems are increasingly becoming common in many private and public campuses, city buildings, and facilities. They provide many useful smart campus/city monitoring and management services based on data captured from video sensors. However, the video surveillance services may also breach personally identifiable information, especially human face images being monitored; therefore, it may potentially violate the privacy of human subjects involved. To address this privacy issue, we introduced a large-scale distributed video surveillance service model, called Smart-city Video Surveillance (SCVS). SCVS is a video surveillance data collection and processing platform to identify important events, monitor, protect, and make decisions for smart campus/city applications. In this article, the specific research focus is on how to identify and anonymize human faces in a distributed edge cloud computing infrastructure. To preserve the privacy of data during video anonymization, SCVS utilizes a two-step approach: (i) parameter server-based distributed machine learning solution, which ensures that edge nodes can exchange parameters for machine learning-based training. Since the dataset is not located on a centralized location, the data privacy and ownership are protected and preserved. (ii) To improve the machine learning model’s accuracy, we presented an asynchronous training approach to protect data and model privacy for both data owners and data users, respectively. SCVS adopts an in-memory encryption approach, where edge computing nodes collect and process data in the memory of edge nodes in encrypted form. This approach can effectively prevent honest but curious attacks. The performance evaluation shows the presented privacy protection platform is efficient and effective compared to traditional centralized computing models as presented in Section 5.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80197136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soteris Constantinou, Andreas Konstantinidis, Panos K. Chrysanthis, D. Zeinalipour-Yazti
The advancement of renewable energy infrastructure in smart buildings (e.g., photovoltaic) has highlighted the importance of energy self-consumption by energy-demanding IoT-enabled devices (e.g., heating/cooling, electromobility, and appliances), which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines but more importantly is good for the environment as energy from fossil sources with a high CO2 footprint is minimized. On the other hand, user comfort levels expressed in the form of Rule Automation Workflows (RAW), are usually not aligned with renewable production patterns. In this work, we propose an innovative framework, coined IoT Meta-Control Firewall (IMCF+), which aims to bridge this gap and balance the trade-off between comfort, energy consumption, and CO2 emissions. The IMCF+ framework incorporates an innovative Green Planner (GP) algorithm, which is an AI-inspired algorithm that schedules energy consumption with a variety of amortization strategies. We have implemented IMCF+ and GP as part of a complete IoT ecosystem in openHAB and our extensive evaluation shows that we achieve a CO2 reduction of 45–59% to satisfy the comfort of a variety of user groups with only a moderate ≈ 3% in reducing their comfort levels.
{"title":"Green Planning of IoT Home Automation Workflows in Smart Buildings","authors":"Soteris Constantinou, Andreas Konstantinidis, Panos K. Chrysanthis, D. Zeinalipour-Yazti","doi":"10.1145/3549549","DOIUrl":"https://doi.org/10.1145/3549549","url":null,"abstract":"The advancement of renewable energy infrastructure in smart buildings (e.g., photovoltaic) has highlighted the importance of energy self-consumption by energy-demanding IoT-enabled devices (e.g., heating/cooling, electromobility, and appliances), which refers to the process of intelligently consuming energy at the time it is available. This stabilizes the energy grid, minimizes energy dissipation on power lines but more importantly is good for the environment as energy from fossil sources with a high CO2 footprint is minimized. On the other hand, user comfort levels expressed in the form of Rule Automation Workflows (RAW), are usually not aligned with renewable production patterns. In this work, we propose an innovative framework, coined IoT Meta-Control Firewall (IMCF+), which aims to bridge this gap and balance the trade-off between comfort, energy consumption, and CO2 emissions. The IMCF+ framework incorporates an innovative Green Planner (GP) algorithm, which is an AI-inspired algorithm that schedules energy consumption with a variety of amortization strategies. We have implemented IMCF+ and GP as part of a complete IoT ecosystem in openHAB and our extensive evaluation shows that we achieve a CO2 reduction of 45–59% to satisfy the comfort of a variety of user groups with only a moderate ≈ 3% in reducing their comfort levels.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87158970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arun C. S. Kumar, Zhijie Wang, Abhishek Srivastava
Internet of Things’ (IoT) deployments are increasingly dependent upon learning algorithms to analyse collected data, draw conclusions, and take decisions. The norm is to deploy such learning algorithms on the cloud and have IoT nodes interact with the cloud. While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.
{"title":"A Novel Approach for Classification in Resource-Constrained Environments","authors":"Arun C. S. Kumar, Zhijie Wang, Abhishek Srivastava","doi":"10.1145/3549552","DOIUrl":"https://doi.org/10.1145/3549552","url":null,"abstract":"Internet of Things’ (IoT) deployments are increasingly dependent upon learning algorithms to analyse collected data, draw conclusions, and take decisions. The norm is to deploy such learning algorithms on the cloud and have IoT nodes interact with the cloud. While this is effective, it is rather wasteful in terms of energy expended and temporal latency. In this article, the endeavour is to develop a technique that facilitates classification, an important learning algorithm, within the extremely resource constrained environments of IoT nodes. The approach comprises selecting a small number of representative data points, called prototypes, from a large dataset and deploying these prototypes over IoT nodes. The prototypes are selected in a manner that they appropriately represent the complete dataset and are able to correctly classify new, incoming data. The novelty lies in the manner of prototype selection for a cluster that not only considers the location of datapoints of its own cluster but also that of datapoints in neighboring clusters. The efficacy of the approach is validated using standard datasets and compared with state-of-the-art classification techniques used in constrained environments. A real world deployment of the technique is done over an Arduino Uno-based IoT node and shown to be effective.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90958949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. C. Kirchhof, A. Kleiss, Bernhard Rumpe, David Schmalzing, Philipp Schneider, A. Wortmann
Today’s Internet of Things (IoT) applications are mostly developed as a bundle of hardware and associated software. Future cross-manufacturer app stores for IoT applications will require that the strong coupling of hardware and software is loosened. In the resulting IoT applications, a quintessential challenge is the effective and efficient deployment of IoT software components across variable networks of heterogeneous devices. Current research focuses on computing whether deployment requirements fit the intended target devices instead of assisting users in successfully deploying IoT applications by suggesting deployment requirement relaxations or hardware alternatives. This can make successfully deploying large-scale IoT applications a costly trial-and-error endeavor. To mitigate this, we have devised a method for providing such deployment suggestions based on search and backtracking. This can make deploying IoT applications more effective and more efficient, which, ultimately, eases reducing the complexity of deploying the software surrounding us.
{"title":"Model-driven Self-adaptive Deployment of Internet of Things Applications with Automated Modification Proposals","authors":"J. C. Kirchhof, A. Kleiss, Bernhard Rumpe, David Schmalzing, Philipp Schneider, A. Wortmann","doi":"10.1145/3549553","DOIUrl":"https://doi.org/10.1145/3549553","url":null,"abstract":"Today’s Internet of Things (IoT) applications are mostly developed as a bundle of hardware and associated software. Future cross-manufacturer app stores for IoT applications will require that the strong coupling of hardware and software is loosened. In the resulting IoT applications, a quintessential challenge is the effective and efficient deployment of IoT software components across variable networks of heterogeneous devices. Current research focuses on computing whether deployment requirements fit the intended target devices instead of assisting users in successfully deploying IoT applications by suggesting deployment requirement relaxations or hardware alternatives. This can make successfully deploying large-scale IoT applications a costly trial-and-error endeavor. To mitigate this, we have devised a method for providing such deployment suggestions based on search and backtracking. This can make deploying IoT applications more effective and more efficient, which, ultimately, eases reducing the complexity of deploying the software surrounding us.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87146822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from limited sensing coverage. On the other hand, data can also be gathered dynamically through crowdsensing contributed by voluntary users but suffer from its unreliability and the lack of incentives for users’ contributions. In this article, we explore an integrated paradigm called “hybrid sensing” that harnesses both IoT-sensing and crowdsensing in a complementary manner. In hybrid sensing, users are incentivized to provide sensing data not covered by IoT sensors and provide crowdsourced feedback to assist in calibrating IoT-sensing. Their contributions will be rewarded with credits that can be redeemed to retrieve synthesized information from the hybrid system. In this article, we develop a hybrid sensing system that supports explicit user privacy—IoT sensors are obscured physically to prevent capturing private user data, and users interact with a crowdsensing server via a privacy-preserving protocol to preserve their anonymity. A key application of our system is smart parking, by which users can inquire and find the available parking spaces in outdoor parking lots. We implemented our hybrid sensing system for smart parking and conducted extensive empirical evaluations. Finally, our hybrid sensing system can be potentially applied to other information-driven services in smart cities.
{"title":"Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities","authors":"Hanwei Zhu, S. Chau, G. Guarddin, W. Liang","doi":"10.1145/3549550","DOIUrl":"https://doi.org/10.1145/3549550","url":null,"abstract":"Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from limited sensing coverage. On the other hand, data can also be gathered dynamically through crowdsensing contributed by voluntary users but suffer from its unreliability and the lack of incentives for users’ contributions. In this article, we explore an integrated paradigm called “hybrid sensing” that harnesses both IoT-sensing and crowdsensing in a complementary manner. In hybrid sensing, users are incentivized to provide sensing data not covered by IoT sensors and provide crowdsourced feedback to assist in calibrating IoT-sensing. Their contributions will be rewarded with credits that can be redeemed to retrieve synthesized information from the hybrid system. In this article, we develop a hybrid sensing system that supports explicit user privacy—IoT sensors are obscured physically to prevent capturing private user data, and users interact with a crowdsensing server via a privacy-preserving protocol to preserve their anonymity. A key application of our system is smart parking, by which users can inquire and find the available parking spaces in outdoor parking lots. We implemented our hybrid sensing system for smart parking and conducted extensive empirical evaluations. Finally, our hybrid sensing system can be potentially applied to other information-driven services in smart cities.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75823677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sikder, Leonardo Babun, Z. Berkay Celik, Hidayet Aksu, P. Mcdaniel, E. Kirda, A. Uluagac
Multiple users have access to multiple devices in a smart home system – typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique, trusted user that controls access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically-changing demands on multiple devices that cannot be handled by traditional access control techniques. Moreover, smart devices from different platforms/vendors can share the same home environment, making existing access control obsolete for smart home systems. To address these challenges, in this paper, we introduce Kratos+, a novel multi-user and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos+ has four main components: user interaction module, backend server, policy manager, and policy execution module. Users can easily specify their desired access control settings using the interaction module that are translated into access control policies in the back-end server. The policy manager analyzes these policies, initiates automated negotiation between users to resolve conflicting demands, and generates final policies to enforce in smart home systems. We implemented Kratos+ as a platform-independent solution and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (337 different policies including 231 demand conflicts and 69 restriction policies). These configurations also included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos+ is very effective in resolving conflicting access control demands with minimal overhead. We also performed an extensive user study with 72 smart home users to better understand the user’s needs before designing the system and a usability study to evaluate the efficacy of Kratos+ in a real-life smart home environment.
{"title":"Who’s Controlling My Device? Multi-User Multi-Device-Aware Access Control System for Shared Smart Home Environment","authors":"A. Sikder, Leonardo Babun, Z. Berkay Celik, Hidayet Aksu, P. Mcdaniel, E. Kirda, A. Uluagac","doi":"10.1145/3543513","DOIUrl":"https://doi.org/10.1145/3543513","url":null,"abstract":"Multiple users have access to multiple devices in a smart home system – typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique, trusted user that controls access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically-changing demands on multiple devices that cannot be handled by traditional access control techniques. Moreover, smart devices from different platforms/vendors can share the same home environment, making existing access control obsolete for smart home systems. To address these challenges, in this paper, we introduce Kratos+, a novel multi-user and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos+ has four main components: user interaction module, backend server, policy manager, and policy execution module. Users can easily specify their desired access control settings using the interaction module that are translated into access control policies in the back-end server. The policy manager analyzes these policies, initiates automated negotiation between users to resolve conflicting demands, and generates final policies to enforce in smart home systems. We implemented Kratos+ as a platform-independent solution and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (337 different policies including 231 demand conflicts and 69 restriction policies). These configurations also included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos+ is very effective in resolving conflicting access control demands with minimal overhead. We also performed an extensive user study with 72 smart home users to better understand the user’s needs before designing the system and a usability study to evaluate the efficacy of Kratos+ in a real-life smart home environment.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81447716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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