Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this paper presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.
{"title":"TONARI: Reactive Detection of Close Physical Contact using Unlicensed LPWAN Signals","authors":"Chenglong Shao, Osamu Muta","doi":"10.1145/3648572","DOIUrl":"https://doi.org/10.1145/3648572","url":null,"abstract":"Recognizing if two objects are in close physical contact (CPC) is the basis of various Internet-of-Things services such as vehicle proximity alert and radiation exposure reduction. This is achieved traditionally through tailor-made proximity sensors that proactively transmit wireless signals and analyze the reflection from an object. Despite its feasibility, the past few years have witnessed the prosperity of reactive CPC detection techniques that do not need spontaneous signal transmission and merely exploit received wireless signals from a target. Unlike existing approaches entailing additional effort of multiple antennas, dedicated signal emitters, human intervention, or a back-end server, this paper presents TONARI, an effortless CPC detection framework that performs in a reactive manner. TONARI is developed for the first time with LoRa, the representative of unlicensed low-power wide area network (LPWAN) technologies, as the wireless signal for CPC detection. At the heart of TONARI lies a novel feature arbitrator that decides whether two devices are in CPC or not by distinguishing different types of LoRa chirp-based additive sample magnitude sequences. Software-defined radio-based experiments are conducted to show that the achievable CPC detection accuracy via TONARI can reach 100% in most practical cases.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835839","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 Internet of Things (IoT) is a dynamic network of devices and infrastructure supporting instances composed to platforms being based on cloud/fog and blockchain technologies. Its intervention in more and more sensitive areas requires IoT entities (devices and platform instances) to communicate with each other via secure channels generally established by using cryptographical methods. This needs an authentic key exchange which in turn requires an authentication process. Moreover, it has to be ensured that client entities can access only authorized services provided by authorized server entities. Additionally, requirements specifically introduced by IoT complicate realizing these security goals even more. This paper introduces a novel approach providing authentication, authorization, access control, and key exchange in instance-to-instance, device-to-instance, and device-to-device communications to handle cloud/fog-based and blockchain-based platforms. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identity-based schemes while meeting the IoT security requirements. Thus, it does not require any public data pre-distribution or secret pre-sharing between communicating entities, and no entity has to hold any device-specific or instance-specific data to be used for authentication or authorization. While supporting the autonomous character of IoT, our approach is independent of application and platform types without requiring additional components or procedures. Moreover, it is resistant to active man in the middle attacks and does not include costly cryptographic operations. This paper also demonstrates the high performance of our approach with regard to multiple affecting factors.
{"title":"Authentication, Authorization, Access Control, and Key Exchange in Internet of Things","authors":"I. Simsek","doi":"10.1145/3643867","DOIUrl":"https://doi.org/10.1145/3643867","url":null,"abstract":"The Internet of Things (IoT) is a dynamic network of devices and infrastructure supporting instances composed to platforms being based on cloud/fog and blockchain technologies. Its intervention in more and more sensitive areas requires IoT entities (devices and platform instances) to communicate with each other via secure channels generally established by using cryptographical methods. This needs an authentic key exchange which in turn requires an authentication process. Moreover, it has to be ensured that client entities can access only authorized services provided by authorized server entities. Additionally, requirements specifically introduced by IoT complicate realizing these security goals even more. This paper introduces a novel approach providing authentication, authorization, access control, and key exchange in instance-to-instance, device-to-instance, and device-to-device communications to handle cloud/fog-based and blockchain-based platforms. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identity-based schemes while meeting the IoT security requirements. Thus, it does not require any public data pre-distribution or secret pre-sharing between communicating entities, and no entity has to hold any device-specific or instance-specific data to be used for authentication or authorization. While supporting the autonomous character of IoT, our approach is independent of application and platform types without requiring additional components or procedures. Moreover, it is resistant to active man in the middle attacks and does not include costly cryptographic operations. This paper also demonstrates the high performance of our approach with regard to multiple affecting factors.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808508","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 Internet of Things (IoT) is a dynamic network of devices and infrastructure supporting instances composed to platforms being based on cloud/fog and blockchain technologies. Its intervention in more and more sensitive areas requires IoT entities (devices and platform instances) to communicate with each other via secure channels generally established by using cryptographical methods. This needs an authentic key exchange which in turn requires an authentication process. Moreover, it has to be ensured that client entities can access only authorized services provided by authorized server entities. Additionally, requirements specifically introduced by IoT complicate realizing these security goals even more. This paper introduces a novel approach providing authentication, authorization, access control, and key exchange in instance-to-instance, device-to-instance, and device-to-device communications to handle cloud/fog-based and blockchain-based platforms. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identity-based schemes while meeting the IoT security requirements. Thus, it does not require any public data pre-distribution or secret pre-sharing between communicating entities, and no entity has to hold any device-specific or instance-specific data to be used for authentication or authorization. While supporting the autonomous character of IoT, our approach is independent of application and platform types without requiring additional components or procedures. Moreover, it is resistant to active man in the middle attacks and does not include costly cryptographic operations. This paper also demonstrates the high performance of our approach with regard to multiple affecting factors.
{"title":"Authentication, Authorization, Access Control, and Key Exchange in Internet of Things","authors":"I. Simsek","doi":"10.1145/3643867","DOIUrl":"https://doi.org/10.1145/3643867","url":null,"abstract":"The Internet of Things (IoT) is a dynamic network of devices and infrastructure supporting instances composed to platforms being based on cloud/fog and blockchain technologies. Its intervention in more and more sensitive areas requires IoT entities (devices and platform instances) to communicate with each other via secure channels generally established by using cryptographical methods. This needs an authentic key exchange which in turn requires an authentication process. Moreover, it has to be ensured that client entities can access only authorized services provided by authorized server entities. Additionally, requirements specifically introduced by IoT complicate realizing these security goals even more. This paper introduces a novel approach providing authentication, authorization, access control, and key exchange in instance-to-instance, device-to-instance, and device-to-device communications to handle cloud/fog-based and blockchain-based platforms. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identity-based schemes while meeting the IoT security requirements. Thus, it does not require any public data pre-distribution or secret pre-sharing between communicating entities, and no entity has to hold any device-specific or instance-specific data to be used for authentication or authorization. While supporting the autonomous character of IoT, our approach is independent of application and platform types without requiring additional components or procedures. Moreover, it is resistant to active man in the middle attacks and does not include costly cryptographic operations. This paper also demonstrates the high performance of our approach with regard to multiple affecting factors.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868332","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}
In this work, we propose MiSleep , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems, MiSleep is not privacy-invasive and does not require users to wear anything on their body. MiSleep leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging, and specularity in signals. MiSleep builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction, MiSleep designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate MiSleep with real data collected from Commercial-Off-The-Shelf mmWave devices for 8 volunteers of diverse ages, genders, and heights performing different sleep postures. We observe that MiSleep identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.
{"title":"MiSleep: Human Sleep Posture Identification from Deep Learning Augmented Millimeter-Wave Wireless Systems","authors":"Aakriti Adhikari, Sanjib Sur","doi":"10.1145/3643866","DOIUrl":"https://doi.org/10.1145/3643866","url":null,"abstract":"\u0000 In this work, we propose\u0000 MiSleep\u0000 , a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems,\u0000 MiSleep\u0000 is not privacy-invasive and does not require users to wear anything on their body.\u0000 MiSleep\u0000 leverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging, and specularity in signals.\u0000 MiSleep\u0000 builds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction,\u0000 MiSleep\u0000 designs a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluate\u0000 MiSleep\u0000 with real data collected from Commercial-Off-The-Shelf mmWave devices for 8 volunteers of diverse ages, genders, and heights performing different sleep postures. We observe that\u0000 MiSleep\u0000 identifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.\u0000","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683834","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}
Huadong Ma, Yuan He, Mo Li, Neal Patwari, Stephan Sigg
ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.
{"title":"Introduction to the Special Issue on Wireless Sensing for IoT","authors":"Huadong Ma, Yuan He, Mo Li, Neal Patwari, Stephan Sigg","doi":"10.1145/3633078","DOIUrl":"https://doi.org/10.1145/3633078","url":null,"abstract":"ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197586","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}
{"title":"Special Issue on Wireless Sensing for IoT: A Word from the Editor-in-Chief","authors":"G. Picco","doi":"10.1145/3633752","DOIUrl":"https://doi.org/10.1145/3633752","url":null,"abstract":"","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139203625","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}
Due to the limited resources of Internet of Things (IoT) devices, Symmetric Key Cryptography (SKC) is typically favored over resource-intensive Public Key Cryptography (PKC) to secure communication between IoT devices. To utilize SKC, devices need to execute a key exchange protocol to establish a session key before initiating communication. However, existing SKC-based key exchange protocols assume communication devices have a pre-shared secret or there are trusted intermediaries between them; neither is always realistic in IoT. We introduce a new SKC-based key exchange protocol for IoT devices. While also intermediary-based, our protocol fundamentally departs from existing intermediary-based solutions in that intermediaries between two key exchange devices may be malicious, and moreover, our protocol can detect cheating behaviors and identify malicious intermediaries. We prove our protocol is secure under the universally composable model, and show it can detect malicious intermediaries with probability 1.0. We implemented and evaluated our protocol on different IoT devices. We show our protocol has significant improvements in computation time and energy cost. Compared to the PKC-based protocols ECDH, DH, and RSA, our protocol is 2.3 to 1591 times faster on one of the two key exchange devices and 0.7 to 4.67 times faster on the other.
{"title":"Resilient Intermediary‐Based Key Exchange Protocol for IoT","authors":"Zhangxiang Hu, Jun Li, Christopher Wilson","doi":"10.1145/3632408","DOIUrl":"https://doi.org/10.1145/3632408","url":null,"abstract":"Due to the limited resources of Internet of Things (IoT) devices, Symmetric Key Cryptography (SKC) is typically favored over resource-intensive Public Key Cryptography (PKC) to secure communication between IoT devices. To utilize SKC, devices need to execute a key exchange protocol to establish a session key before initiating communication. However, existing SKC-based key exchange protocols assume communication devices have a pre-shared secret or there are trusted intermediaries between them; neither is always realistic in IoT. We introduce a new SKC-based key exchange protocol for IoT devices. While also intermediary-based, our protocol fundamentally departs from existing intermediary-based solutions in that intermediaries between two key exchange devices may be malicious, and moreover, our protocol can detect cheating behaviors and identify malicious intermediaries. We prove our protocol is secure under the universally composable model, and show it can detect malicious intermediaries with probability 1.0. We implemented and evaluated our protocol on different IoT devices. We show our protocol has significant improvements in computation time and energy cost. Compared to the PKC-based protocols ECDH, DH, and RSA, our protocol is 2.3 to 1591 times faster on one of the two key exchange devices and 0.7 to 4.67 times faster on the other.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257855","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}
Devkishen Sisodia, Jun Li, Samuel Mergendahl, Hasan Cam
With the growth of the Internet of Things (IoT), the number of cyber attacks on the Internet is on the rise. However, the resource-constrained nature of IoT devices and their networks makes many classical security systems ineffective or inapplicable. We introduce TWINKLE, a two-mode, adaptive security framework that allows an IoT network to be in regular mode for most of the time, which incurs a low resource consumption rate, and to switch to vigilant mode only when suspicious behavior is detected, which potentially incurs a higher overhead. Compared to the early version of this work, this paper presents a more comprehensive design and architecture of TWINKLE, describes challenges and details in implementing TWINKLE, and reports evaluations of TWINKLE based on real-world IoT testbeds with more metrics. We show the efficacy of TWINKLE in two case studies where we examine two existing intrusion detection and prevention systems and transform both into new, improved systems using TWINKLE. Our evaluations show that TWINKLE is not only effective at securing resource-constrained IoT networks, but can also successfully detect and prevent attacks with a significantly lower overhead and detection latency than existing solutions.
{"title":"A Two-Mode, Adaptive Security Framework for Smart Home Security Applications","authors":"Devkishen Sisodia, Jun Li, Samuel Mergendahl, Hasan Cam","doi":"10.1145/3617504","DOIUrl":"https://doi.org/10.1145/3617504","url":null,"abstract":"With the growth of the Internet of Things (IoT), the number of cyber attacks on the Internet is on the rise. However, the resource-constrained nature of IoT devices and their networks makes many classical security systems ineffective or inapplicable. We introduce TWINKLE, a two-mode, adaptive security framework that allows an IoT network to be in regular mode for most of the time, which incurs a low resource consumption rate, and to switch to vigilant mode only when suspicious behavior is detected, which potentially incurs a higher overhead. Compared to the early version of this work, this paper presents a more comprehensive design and architecture of TWINKLE, describes challenges and details in implementing TWINKLE, and reports evaluations of TWINKLE based on real-world IoT testbeds with more metrics. We show the efficacy of TWINKLE in two case studies where we examine two existing intrusion detection and prevention systems and transform both into new, improved systems using TWINKLE. Our evaluations show that TWINKLE is not only effective at securing resource-constrained IoT networks, but can also successfully detect and prevent attacks with a significantly lower overhead and detection latency than existing solutions.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263350","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}
Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.
{"title":"Online learning for dynamic impending collision prediction using FMCW radar","authors":"Aarti Singh, Neal Patwari","doi":"10.1145/3616018","DOIUrl":"https://doi.org/10.1145/3616018","url":null,"abstract":"Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86369942","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}
Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this paper, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63 × with a communication success rate requirement of > (95% ) compared with state-of-the-art methods.
{"title":"CH-MAC: Achieving Low-latency Reliable Communication via Coding and Hopping in LPWAN","authors":"Junzhou Luo, Zhuqing Xu, Jingkai Lin, Ciyuan Chen, Runqun Xiong","doi":"10.1145/3617505","DOIUrl":"https://doi.org/10.1145/3617505","url":null,"abstract":"Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this paper, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63 × with a communication success rate requirement of > (95% ) compared with state-of-the-art methods.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84414636","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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