ACM Trans. Sens. Networks最新文献

{"title":"GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi","authors":"Yi Zhang, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, Zheng Yang","doi":"10.1145/3466638","DOIUrl":"https://doi.org/10.1145/3466638","url":null,"abstract":"\u0000 Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present\u0000 GaitSense\u0000 , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds,\u0000 GaitSense\u0000 first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis,\u0000 GaitSense\u0000 reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques.\u0000 GaitSense\u0000 also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.\u0000","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324660","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":"Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands","authors":"Junyang Shi, Di Mu, M. Sha","doi":"10.1145/3470452","DOIUrl":"https://doi.org/10.1145/3470452","url":null,"abstract":"Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295703","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":"AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling","authors":"F. Yang, A. Thangarajan, G. Ramachandran, W. Joosen, D. Hughes","doi":"10.1145/3467894","DOIUrl":"https://doi.org/10.1145/3467894","url":null,"abstract":"\u0000 Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no\u0000 a priori\u0000 modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees\u0000 Sustainability\u0000 by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves\u0000 Efficiency\u0000 by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.\u0000","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124082016","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":"S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices","authors":"A. Giaretta, N. Dragoni, F. Massacci","doi":"10.1145/3480462","DOIUrl":"https://doi.org/10.1145/3480462","url":null,"abstract":"The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132850740","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":"Queec: QoE-aware Edge Computing for IoT Devices under Dynamic Workloads","authors":"Borui Li, Wei Dong, Gaoyang Guan, Jiadong Zhang, Tao Gu, Jiajun Bu, Yi Gao","doi":"10.1145/3442363","DOIUrl":"https://doi.org/10.1145/3442363","url":null,"abstract":"Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883209","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":"LeaD: Learn to Decode Vibration-based Communication for Intelligent Internet of Things","authors":"Guangrong Zhao, Bowen Du, Yiran Shen, Zhenyu Lao, L. Cui, Hongkai Wen","doi":"10.1145/3440250","DOIUrl":"https://doi.org/10.1145/3440250","url":null,"abstract":"\u0000 In this article, we propose,\u0000 LeaD\u0000 , a new vibration-based communication protocol to\u0000 Lea\u0000 rn the unique patterns of vibration to\u0000 D\u0000 ecode the short messages transmitted to smart IoT devices. Unlike the existing vibration-based communication protocols that decode the short messages symbol-wise, either in binary or multi-ary, the message recipient in\u0000 LeaD\u0000 receives vibration signals corresponding to bits-groups. Each group consists of multiple symbols sent in a burst and the receiver decodes the group of symbols as a whole via machine learning-based approach. The fundamental behind\u0000 LeaD\u0000 is different combinations of symbols (1 s or 0 s) in a group will produce unique and reproducible patterns of vibration. Therefore, decoding in vibration-based communication can be modeled as a pattern classification problem.\u0000 \u0000 \u0000 We design and implement a number of different machine learning models as the core engine of the decoding algorithm of\u0000 LeaD\u0000 to learn and recognize the vibration patterns. Through the intensive evaluations on large amount of datasets collected, the Convolutional Neural Network (CNN)-based model achieves the highest accuracy of decoding (i.e., lowest error rate), which is up to 97% at relatively high bits rate of 40 bits/s. While its competing vibration-based communication protocols can only achieve transmission rate of 10 bits/s and 20 bits/s with similar decoding accuracy. Furthermore, we evaluate its performance under different challenging practical settings and the results show that\u0000 LeaD\u0000 with CNN engine is robust to poses, distances (within valid range), and types of devices, therefore, a CNN model can be generally trained beforehand and widely applicable for different IoT devices under different circumstances. Finally, we implement\u0000 LeaD\u0000 on both off-the-shelf smartphone and smart watch to measure the detailed resources consumption on smart devices. The computation time and energy consumption of its different components show that\u0000 LeaD\u0000 is lightweight and can run\u0000 in situ\u0000 on low-cost smart IoT devices, e.g., smartwatches, without accumulated delay and introduces only marginal system overhead.\u0000","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123708041","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":"Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network","authors":"Beilun Wang, Jiaqi Zhang, Yan Zhang, Meng Wang, Sen Wang","doi":"10.1145/3432312","DOIUrl":"https://doi.org/10.1145/3432312","url":null,"abstract":"Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different edge devices in the IoT network. Because data from different devices are heterogeneous and the variable dependencies can be characterized into a graphical model, we can focus on the problem that jointly estimating multiple, high-dimensional, and sparse Gaussian Graphical Models for many related tasks (edge devices). This is an important goal in many fields. Many IoT networks have collected massive multi-task data and require the analysis of heterogeneous data in many scenarios. Past works on the joint estimation are non-distributed and involve computationally expensive and complex non-smooth optimizations. To address these problems, we propose a novel approach: Multi-FST. Multi-FST can be efficiently implemented on a cloud-server-based IoT network. The cloud server has a low computational load and IoT devices use asynchronous communication with the server, leading to efficiency. Multi-FST shows significant improvement, over baselines, when tested on various datasets.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126994105","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":"Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control","authors":"D. V. Le, Rongrong Wang, Yingbo Liu, Rui Tan, Y. Wong, Yonggang Wen","doi":"10.1145/3439332","DOIUrl":"https://doi.org/10.1145/3439332","url":null,"abstract":"Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However, due to the complex psychrometric dynamics, operating the air free-cooled DC in the tropics generally requires adaptive control of supply air condition to maintain the computing performance and reliability of the servers. This article studies the problem of controlling the supply air temperature and RH in a free-cooled tropical DC below certain thresholds. To achieve the goal, we formulate the control problem as Markov decision processes and apply deep reinforcement learning (DRL) to learn the control policy that minimizes the cooling energy while satisfying the requirements on the supply air temperature and RH. We also develop a constrained DRL solution for performance improvements. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons among the unconstrained and constrained DRL approaches as well as two other baseline approaches show the superior performance of our proposed solutions.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132537826","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":"SDCN: Sensory Data-Centric Networking for Building the Sensing Layer of IoT","authors":"L. Liu, Bo Chen","doi":"10.1145/3402452","DOIUrl":"https://doi.org/10.1145/3402452","url":null,"abstract":"ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 1550-4859/2020/11-ART6 $15.00 https://doi.org/10.1145/3402452 ACM Transactions on Sensor Networks, Vol. 17, No. 1, Article 6. Publication date: November 2020.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124295901","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":"MORE: Multi-node Mobile Charging Scheduling for Deadline Constraints","authors":"Panlong Yang, Tao Wu, Haipeng Dai, Xunpeng Rao, Xiaoyu Wang, P. Wan, Xin He","doi":"10.1145/3410454","DOIUrl":"https://doi.org/10.1145/3410454","url":null,"abstract":"PANLONG YANG, University of Science and Technology of China TAO WU, National University of Defense Technology HAIPENG DAI, State Key Laboratory for Novel Software Technology, Nanjing University XUNPENG RAO, Communication Engineering, Army Engineering University of PLA XIAOYU WANG, State Key Laboratory for Novel Software Technology, Nanjing University PENG-JUN WAN, Department of Computer Science, Illinois Institute of Technology XIN HE, University of Science and Technology of China","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582605","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}