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":"28 1","pages":"1 - 26"},"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":"21 1","pages":"1 - 30"},"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":"39 1","pages":"1 - 21"},"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":"80 1","pages":"1 - 30"},"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":"22 1","pages":"1 - 30"},"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":"31 1","pages":"1 - 39"},"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}
Miraqa Safi, S. Dadkhah, Farzaneh Shoeleh, Hassan Mahdikhani, Heather Molyneaux, A. Ghorbani
The proliferation of heterogeneous Internet of things (IoT) devices connected to the Internet produces several operational and security challenges, such as monitoring, detecting, and recognizing millions of interconnected IoT devices. Network and system administrators must correctly identify which devices are functional, need security updates, or are vulnerable to specific attacks. IoT profiling is an emerging technique to identify and validate the connected devices’ specific behaviour and isolate the suspected and vulnerable devices within the network for further monitoring. This article provides a comprehensive review of various IoT device profiling methods and provides a clear taxonomy for IoT profiling techniques based on different security perspectives. We first investigate several current IoT device profiling techniques and their applications. Next, we analyzed various IoT device vulnerabilities, outlined multiple features, and provided detailed information to implement profiling algorithms’ risk assessment/mitigation stage. By reviewing approaches for profiling IoT devices, we identify various state-of-the-art methods that organizations of different domains can implement to satisfy profiling needs. Furthermore, this article also discusses several machine learning and deep learning algorithms utilized for IoT device profiling. Finally, we discuss challenges and future research possibilities in this domain.
{"title":"A Survey on IoT Profiling, Fingerprinting, and Identification","authors":"Miraqa Safi, S. Dadkhah, Farzaneh Shoeleh, Hassan Mahdikhani, Heather Molyneaux, A. Ghorbani","doi":"10.1145/3539736","DOIUrl":"https://doi.org/10.1145/3539736","url":null,"abstract":"The proliferation of heterogeneous Internet of things (IoT) devices connected to the Internet produces several operational and security challenges, such as monitoring, detecting, and recognizing millions of interconnected IoT devices. Network and system administrators must correctly identify which devices are functional, need security updates, or are vulnerable to specific attacks. IoT profiling is an emerging technique to identify and validate the connected devices’ specific behaviour and isolate the suspected and vulnerable devices within the network for further monitoring. This article provides a comprehensive review of various IoT device profiling methods and provides a clear taxonomy for IoT profiling techniques based on different security perspectives. We first investigate several current IoT device profiling techniques and their applications. Next, we analyzed various IoT device vulnerabilities, outlined multiple features, and provided detailed information to implement profiling algorithms’ risk assessment/mitigation stage. By reviewing approaches for profiling IoT devices, we identify various state-of-the-art methods that organizations of different domains can implement to satisfy profiling needs. Furthermore, this article also discusses several machine learning and deep learning algorithms utilized for IoT device profiling. Finally, we discuss challenges and future research possibilities in this domain.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"46 1","pages":"1 - 39"},"PeriodicalIF":2.7,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90803755","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}
The IPv6 over IEEE 802.15.4e TSCH mode (6TiSCH) network is intended to provide reliable and delay bounded communication in multi-hop and scalable Industrial Internet of Things (IIoT). The IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) link layer protocol allows the nodes to change their physical channel after each transmission to eliminate interference and multi-path fading on the channels. However, due to this feature, new nodes (aka pledges) take more time to join the 6TiSCH network, resulting in significant energy consumption and inefficient data transmission, which makes the communication unreliable. Therefore, the formation of 6TiSCH network has gained immense interest among the researchers. To date, numerous solutions have been offered by various researchers in order to speed up the formation of 6TiSCH networks. This article briefly discusses about the 6TiSCH network and its formation process, followed by a detailed survey on the works that considered 6TiSCH network formation. We also perform theoretical analysis and real testbed experiments for a better understanding of the existing works related to 6TiSCH network formation. This article is concluded after summarizing the research challenges in 6TiSCH network formation and providing a few open issues in this domain of work.
IPv6 over IEEE 802.15.4e TSCH模式(6TiSCH)网络旨在为多跳和可扩展的工业物联网(IIoT)提供可靠和延迟有限的通信。IEEE 802.15.4e时隙信道跳频(TSCH)链路层协议允许节点在每次传输后改变其物理信道，以消除信道上的干扰和多径衰落。然而，由于这一特性，新节点(即承诺节点)加入6TiSCH网络需要更多的时间，从而导致大量的能量消耗和低效的数据传输，使得通信不可靠。因此，6TiSCH网络的形成引起了研究者的极大兴趣。迄今为止，为了加快6TiSCH网络的形成，各种研究人员已经提出了许多解决方案。本文简要讨论了6TiSCH网络及其形成过程，然后对考虑6TiSCH网络形成的工作进行了详细的综述。为了更好地理解现有的与6TiSCH网络形成相关的工作，我们还进行了理论分析和实际试验台实验。本文总结了6TiSCH网络形成的研究挑战，并提出了该工作领域的一些有待解决的问题。
{"title":"6TiSCH – IPv6 Enabled Open Stack IoT Network Formation: A Review","authors":"Alakesh Kalita, M. Khatua","doi":"10.1145/3536166","DOIUrl":"https://doi.org/10.1145/3536166","url":null,"abstract":"The IPv6 over IEEE 802.15.4e TSCH mode (6TiSCH) network is intended to provide reliable and delay bounded communication in multi-hop and scalable Industrial Internet of Things (IIoT). The IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) link layer protocol allows the nodes to change their physical channel after each transmission to eliminate interference and multi-path fading on the channels. However, due to this feature, new nodes (aka pledges) take more time to join the 6TiSCH network, resulting in significant energy consumption and inefficient data transmission, which makes the communication unreliable. Therefore, the formation of 6TiSCH network has gained immense interest among the researchers. To date, numerous solutions have been offered by various researchers in order to speed up the formation of 6TiSCH networks. This article briefly discusses about the 6TiSCH network and its formation process, followed by a detailed survey on the works that considered 6TiSCH network formation. We also perform theoretical analysis and real testbed experiments for a better understanding of the existing works related to 6TiSCH network formation. This article is concluded after summarizing the research challenges in 6TiSCH network formation and providing a few open issues in this domain of work.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"12 1","pages":"1 - 36"},"PeriodicalIF":2.7,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83734341","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}
Michael Norris, Z. Berkay Celik, P. Venkatesh, Shulin Zhao, P. Mcdaniel, A. Sivasubramaniam, Gang Tan
IoT devices can be used to complete a wide array of physical tasks, but due to factors such as low computational resources and distributed physical deployment, they are susceptible to a wide array of faulty behaviors. Many devices deployed in homes, vehicles, industrial sites, and hospitals carry a great risk of damage to property, harm to a person, or breach of security if they behave faultily. We propose a general fault handling system named IoTRepair, which shows promising results for effectiveness with limited latency and power overhead in an IoT environment. IoTRepair dynamically organizes and customizes fault-handling techniques to address the unique problems associated with heterogeneous IoT deployments. We evaluate IoTRepair by creating a physical implementation mirroring a typical home environment to motivate the effectiveness of this system. Our evaluation showed that each of our fault-handling functions could be completed within 100 milliseconds after fault identification, which is a fraction of the time that state-of-the-art fault-identification methods take (measured in minutes). The power overhead is equally small, with the computation and device action consuming less than 30 milliwatts. This evaluation shows that IoTRepair not only can be deployed in a physical system, but offers significant benefits at a low overhead.
{"title":"IoTRepair: Flexible Fault Handling in Diverse IoT Deployments","authors":"Michael Norris, Z. Berkay Celik, P. Venkatesh, Shulin Zhao, P. Mcdaniel, A. Sivasubramaniam, Gang Tan","doi":"10.1145/3532194","DOIUrl":"https://doi.org/10.1145/3532194","url":null,"abstract":"IoT devices can be used to complete a wide array of physical tasks, but due to factors such as low computational resources and distributed physical deployment, they are susceptible to a wide array of faulty behaviors. Many devices deployed in homes, vehicles, industrial sites, and hospitals carry a great risk of damage to property, harm to a person, or breach of security if they behave faultily. We propose a general fault handling system named IoTRepair, which shows promising results for effectiveness with limited latency and power overhead in an IoT environment. IoTRepair dynamically organizes and customizes fault-handling techniques to address the unique problems associated with heterogeneous IoT deployments. We evaluate IoTRepair by creating a physical implementation mirroring a typical home environment to motivate the effectiveness of this system. Our evaluation showed that each of our fault-handling functions could be completed within 100 milliseconds after fault identification, which is a fraction of the time that state-of-the-art fault-identification methods take (measured in minutes). The power overhead is equally small, with the computation and device action consuming less than 30 milliwatts. This evaluation shows that IoTRepair not only can be deployed in a physical system, but offers significant benefits at a low overhead.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"6 1","pages":"1 - 33"},"PeriodicalIF":2.7,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86952542","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}
This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional classification algorithms is not possible. Thus, we claim that solutions for IoT scenarios should avoid using raw data directly, preferring their transformation to a new domain. In the ordinal patterns domain, it is possible to capture the temporal dynamics of raw data to distinguish them. However, to be applied to this challenging scenario, TSCLAS follows a strategy for selecting the best parameters for the ordinal patterns transformation based on maximizing the class separability of the time series dynamics. We show that our method is competitive compared to other classification algorithms from the literature. Furthermore, TSCLAS is scalable concerning the length of time series and robust to the presence of missing data gaps on them. By simulating missing data gaps as long as 50% of the data, our method could beat the accuracy of the compared classification algorithms. Besides, even when losing in accuracy, TSCLAS presents lower computation times for both training and testing phases.
{"title":"A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series Dynamics","authors":"J. B. Borges, Heitor S. Ramos, A. Loureiro","doi":"10.1145/3533049","DOIUrl":"https://doi.org/10.1145/3533049","url":null,"abstract":"This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional classification algorithms is not possible. Thus, we claim that solutions for IoT scenarios should avoid using raw data directly, preferring their transformation to a new domain. In the ordinal patterns domain, it is possible to capture the temporal dynamics of raw data to distinguish them. However, to be applied to this challenging scenario, TSCLAS follows a strategy for selecting the best parameters for the ordinal patterns transformation based on maximizing the class separability of the time series dynamics. We show that our method is competitive compared to other classification algorithms from the literature. Furthermore, TSCLAS is scalable concerning the length of time series and robust to the presence of missing data gaps on them. By simulating missing data gaps as long as 50% of the data, our method could beat the accuracy of the compared classification algorithms. Besides, even when losing in accuracy, TSCLAS presents lower computation times for both training and testing phases.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"19 1","pages":"1 - 30"},"PeriodicalIF":2.7,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84637334","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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