Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00110
Yu Tian, Jiankun Wang, Z. Zhao
Wi-Fi signals vary over time due to multipath fading and dynamic indoor environment. Hence in the long-run deployment of Wi-Fi fingerprinting localization, to retain high accuracy the fingerprint database has to be updated regularly, which is usually labor-intensive and time-consuming. In this paper, we propose a novel unsupervised domain adaptation model TransLoc for Wi-Fi fingerprint update, to keep high accuracy yet at a low cost. TransLoc consists of a feature extractor, a generator, a discriminator, and a location predictor. The feature extractor learns domain-invariant features by cooperating with other components. To further guarantee localization accuracy, the location predictor is designed as a semi-supervised regressor with three parallel sub-modules. We carry out extensive experiments in two typical real-world indoor environments with a total area of over 8,200 $m^{2}$ across three months. Experimental results show that with only an initial fingerprint database and current unlabeled fingerprints, TransLoc maintains high localization accuracy at a low cost in the long run.
{"title":"Wi-Fi Fingerprint Update for Indoor Localization via Domain Adaptation","authors":"Yu Tian, Jiankun Wang, Z. Zhao","doi":"10.1109/ICPADS53394.2021.00110","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00110","url":null,"abstract":"Wi-Fi signals vary over time due to multipath fading and dynamic indoor environment. Hence in the long-run deployment of Wi-Fi fingerprinting localization, to retain high accuracy the fingerprint database has to be updated regularly, which is usually labor-intensive and time-consuming. In this paper, we propose a novel unsupervised domain adaptation model TransLoc for Wi-Fi fingerprint update, to keep high accuracy yet at a low cost. TransLoc consists of a feature extractor, a generator, a discriminator, and a location predictor. The feature extractor learns domain-invariant features by cooperating with other components. To further guarantee localization accuracy, the location predictor is designed as a semi-supervised regressor with three parallel sub-modules. We carry out extensive experiments in two typical real-world indoor environments with a total area of over 8,200 $m^{2}$ across three months. Experimental results show that with only an initial fingerprint database and current unlabeled fingerprints, TransLoc maintains high localization accuracy at a low cost in the long run.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126008309","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00034
Zhao Ming, Xiuhua Li, Chuan Sun, Qilin Fan, Xiaofei Wang, Victor C. M. Leung
With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.
{"title":"Dependency-Aware Hybrid Task Offloading in Mobile Edge Computing Networks","authors":"Zhao Ming, Xiuhua Li, Chuan Sun, Qilin Fan, Xiaofei Wang, Victor C. M. Leung","doi":"10.1109/ICPADS53394.2021.00034","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00034","url":null,"abstract":"With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124809436","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}
Pub Date : 2021-12-01DOI: 10.48550/arXiv.2203.03327
Shaolin Yu, Jihong Zhu, Jiali Yang
For tolerating Byzantine faults of both the terminal and communication components in self-stabilizing clock synchronization, the two-dimensional self-stabilizing Byzantine-fault-tolerant clock synchronization problem is investigated and solved. By utilizing the time-triggered (TT) stage provided in the underlying networks as TT communication windows, the approximate agreement, hopping procedure, and randomized grandmasters are integrated into the overall solution. It is shown that with partitioning the communication components into 3 arbitrarily connected subnetworks, efficient synchronization can be achieved with one such subnetwork and less than 1/3 terminal components being Byzantine. Meanwhile, the desired stabilization can be reached for the specific networks in one or several seconds with high probabilities. This helps in developing various distributed hard-real-time systems with stringent time, resources, and safety requirements.
{"title":"Efficient Two-Dimensional Self-Stabilizing Byzantine Clock Synchronization in WALDEN","authors":"Shaolin Yu, Jihong Zhu, Jiali Yang","doi":"10.48550/arXiv.2203.03327","DOIUrl":"https://doi.org/10.48550/arXiv.2203.03327","url":null,"abstract":"For tolerating Byzantine faults of both the terminal and communication components in self-stabilizing clock synchronization, the two-dimensional self-stabilizing Byzantine-fault-tolerant clock synchronization problem is investigated and solved. By utilizing the time-triggered (TT) stage provided in the underlying networks as TT communication windows, the approximate agreement, hopping procedure, and randomized grandmasters are integrated into the overall solution. It is shown that with partitioning the communication components into 3 arbitrarily connected subnetworks, efficient synchronization can be achieved with one such subnetwork and less than 1/3 terminal components being Byzantine. Meanwhile, the desired stabilization can be reached for the specific networks in one or several seconds with high probabilities. This helps in developing various distributed hard-real-time systems with stringent time, resources, and safety requirements.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139768","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00046
Simeng Wang, Xing Chen, Fei Tong, Yujian Zhang
Vehicle Ad-hoc Network (VANET) faces a large number of potential threats due to its openness and complexity. Identity authentication is the basis for resisting various attacks. Common approaches usually employ public key infrastructure, identity-based signature and cryptography-based algorithms, which either bring high computation and storage costs, have certificate issuance, revocation, and management problems, or exist centralization problems. In this paper, we propose an identity authentication scheme for VANET based on consortium blockchain, in which vehicle or Road-Side Unit (RSU) authenticity is verified by on-chain transactions instead of certificates in other blockchain schemes. To the end, a new data structure based on unspent transaction output is introduced to initiate a set of online operations. Furthermore, we put forward an RSU-aided scheme, in which only one additional step, namely fillToken operation, is required to reduce the communication delay of authentication after a vehicle first joins an RSU group through a series of online operations. We implement the proposed scheme in the Hyperledger Fabric platform and conduct security and performance analysis, which shows the effectiveness of our scheme.
{"title":"RSU-Aided Authentication for VANET Based on Consortium Blockchain","authors":"Simeng Wang, Xing Chen, Fei Tong, Yujian Zhang","doi":"10.1109/ICPADS53394.2021.00046","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00046","url":null,"abstract":"Vehicle Ad-hoc Network (VANET) faces a large number of potential threats due to its openness and complexity. Identity authentication is the basis for resisting various attacks. Common approaches usually employ public key infrastructure, identity-based signature and cryptography-based algorithms, which either bring high computation and storage costs, have certificate issuance, revocation, and management problems, or exist centralization problems. In this paper, we propose an identity authentication scheme for VANET based on consortium blockchain, in which vehicle or Road-Side Unit (RSU) authenticity is verified by on-chain transactions instead of certificates in other blockchain schemes. To the end, a new data structure based on unspent transaction output is introduced to initiate a set of online operations. Furthermore, we put forward an RSU-aided scheme, in which only one additional step, namely fillToken operation, is required to reduce the communication delay of authentication after a vehicle first joins an RSU group through a series of online operations. We implement the proposed scheme in the Hyperledger Fabric platform and conduct security and performance analysis, which shows the effectiveness of our scheme.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184252","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00068
Hongsheng Hao, Liang Wang, Zenggang Xia, Zhiwen Yu, Jianhua Gu, Ning Fu
In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.
{"title":"Traffic Congestion Prediction: A Spatial-Temporal Context Embedding and Metric Learning Approach","authors":"Hongsheng Hao, Liang Wang, Zenggang Xia, Zhiwen Yu, Jianhua Gu, Ning Fu","doi":"10.1109/ICPADS53394.2021.00068","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00068","url":null,"abstract":"In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428882","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00065
Ziyi Zhou, Baoshen Guo, Cao Zhang
Postoperative pain cause discomfort to the patient, and even postoperative complications in severe cases, which suggests there is a severe need for predicting the postoperative pain. A number of studies have investigated the correlation between different physiological parameters and nociception, and developed indicators for evaluating the degree of intraoperative nociception. However, these technologies require additional monitoring equipment, which increases the difficulty of deployment and popularization of postoperative pain prediction. In this paper, We propose DoseGuide, a graph-based dynamic time-aware prediction system based on the patient data collected from existing standard infrastructure. DoseGuide takes as input the static physical data and the dynamic intraoperative data of the patient, and output the prediction of postoperative pain level for the certain patient, in which the two types of features are fused via a hybrid feature encoder. Additionally, a graph attention mechanism is introduced to utilize the similarity relationships between patients, which promoted the accuracy of prediction further. We evaluate the system with the medical records of 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The Experimental results show that our model achieves 78% accuracy for postoperative pain, and has the best comprehensive performance in comparison with baselines.
{"title":"DoseGuide: A Graph-based Dynamic Time-aware Prediction System for Postoperative Pain","authors":"Ziyi Zhou, Baoshen Guo, Cao Zhang","doi":"10.1109/ICPADS53394.2021.00065","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00065","url":null,"abstract":"Postoperative pain cause discomfort to the patient, and even postoperative complications in severe cases, which suggests there is a severe need for predicting the postoperative pain. A number of studies have investigated the correlation between different physiological parameters and nociception, and developed indicators for evaluating the degree of intraoperative nociception. However, these technologies require additional monitoring equipment, which increases the difficulty of deployment and popularization of postoperative pain prediction. In this paper, We propose DoseGuide, a graph-based dynamic time-aware prediction system based on the patient data collected from existing standard infrastructure. DoseGuide takes as input the static physical data and the dynamic intraoperative data of the patient, and output the prediction of postoperative pain level for the certain patient, in which the two types of features are fused via a hybrid feature encoder. Additionally, a graph attention mechanism is introduced to utilize the similarity relationships between patients, which promoted the accuracy of prediction further. We evaluate the system with the medical records of 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The Experimental results show that our model achieves 78% accuracy for postoperative pain, and has the best comprehensive performance in comparison with baselines.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132773597","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}
Pub Date : 2021-12-01DOI: 10.1109/icpads53394.2021.00125
{"title":"Organizing Committee ICPADS 2021","authors":"","doi":"10.1109/icpads53394.2021.00125","DOIUrl":"https://doi.org/10.1109/icpads53394.2021.00125","url":null,"abstract":"","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000363","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00070
Liang Dong, Xinjun Cai, Zheng Yang
Mobile multiplayer augmented reality(AR) emerges in various applications including games, education, training, etc. Edge computing technique enables real-time environmental perception ability(e.g. object detection and segmentation) of devices by offloading complex computation to the nearby edge server. However, with more players involved and offloading video streams, bandwidth competition intensifies and lengthens the transmission latency, which severely impairs the accuracy of environmental perception in multiplayer AR applications. We realize staggering offloading period of each device can reduce transmission latency and maintain real-time environmental perception when more players involved. We propose Bonus containing two key techniques: collaborative offloading scheduler to eliminate bandwidth competition among multiple devices, which improve the performance of the overall system to achieve “Pareto Efficiency”; time-aware video reshaper based on mainstream 802.11 protocol to enable Bonus compatible with most wireless scenarios. we evaluate Bonus and four SOTA solutions across 24 videos on different environmental perception tasks. Results demonstrate that Bonus achieves 119.5% accuracy and 1.9x player capacity compared to the closest baseline under wireless environment.
{"title":"Gain Without Pain: Enabling Real-time Environmental Perception on 2x Mobile Devices in Multiplayer Augmented Reality","authors":"Liang Dong, Xinjun Cai, Zheng Yang","doi":"10.1109/ICPADS53394.2021.00070","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00070","url":null,"abstract":"Mobile multiplayer augmented reality(AR) emerges in various applications including games, education, training, etc. Edge computing technique enables real-time environmental perception ability(e.g. object detection and segmentation) of devices by offloading complex computation to the nearby edge server. However, with more players involved and offloading video streams, bandwidth competition intensifies and lengthens the transmission latency, which severely impairs the accuracy of environmental perception in multiplayer AR applications. We realize staggering offloading period of each device can reduce transmission latency and maintain real-time environmental perception when more players involved. We propose Bonus containing two key techniques: collaborative offloading scheduler to eliminate bandwidth competition among multiple devices, which improve the performance of the overall system to achieve “Pareto Efficiency”; time-aware video reshaper based on mainstream 802.11 protocol to enable Bonus compatible with most wireless scenarios. we evaluate Bonus and four SOTA solutions across 24 videos on different environmental perception tasks. Results demonstrate that Bonus achieves 119.5% accuracy and 1.9x player capacity compared to the closest baseline under wireless environment.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132043469","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00079
Zhaoxing Yang, Guiyun Fan, Haiming Jin
Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.
{"title":"Constrained Multi-Agent Reinforcement Learning for Managing Electric Self-Driving Taxis","authors":"Zhaoxing Yang, Guiyun Fan, Haiming Jin","doi":"10.1109/ICPADS53394.2021.00079","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00079","url":null,"abstract":"Electric self-driving taxis (es-taxis) draw great attention nowadays and hold the promise for future transportation due to their convenient and environment-friendly nature. However efficiently managing large-scale es-taxis remains an open problem. In this paper, we focus on scheduling es-taxis under charging budget constraint. Specifically, we design safe-controller to guarantee the satisfaction of budget constraint, and propose HAT framework to enlarge the sight for decision-making on deactivating es-taxis. As for the non-stationary induced by HAT, we analyze and limit its influence with theoretical guarantees. The overall framework Safe-HAT achieves superior performance in real-world data against other strong baselines.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133439163","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}
Pub Date : 2021-12-01DOI: 10.1109/ICPADS53394.2021.00120
Nan Jing, Bowen Zhang, Guannan Liu, LiuJie Yang, Lin Wang
In recent years, wireless sensor networks have been used in a wide range of indoor localization-based applications. Although promising, the existing works are dependent on a large number of anchor nodes to achieve localizations, which brings the issues of increasing of the cost and additional maintenance. Inspired by the cross-technology communication, an emerging technique that enables direct communication among heterogeneous wireless devices, we propose an anchor-free distributed method, which leverages the installed Wi-Fi APs to calculate the distance instead of traditional anchor nodes. More specifically, for the asymmetric coverage of Wi-Fi and ZigBee nodes, we first design a progressive method, where the first unknown node estimates its location based on two Wi-Fi APs and a sink node, then once achieving its position, it acts as the alternative sink node of the next hop. This process is repeated until the new members can obtain their positions. Second, as a low-power technology, ZigBee signal may be submerged in strong signals such as Wi-Fi. To overcome this problem, a prime number is deployed to be the Wi-Fi broadcasting period based on the numerical analysis theory. Among lots of prime numbers, we have the opportunity to select an appropriate one with the relatively small packet collisions. Last, numerical simulations and experiments are performed to evaluate the proposal. The evaluation results show that the proposal can achieve decimeter level accuracy without deploying any anchor node. Moreover, the proposal demonstrates the anti-interference ability in the crowded open spectrum environment.
{"title":"Anchor-Free Self-Positioning in Wireless Sensor Networks via Cross-Technology Communication","authors":"Nan Jing, Bowen Zhang, Guannan Liu, LiuJie Yang, Lin Wang","doi":"10.1109/ICPADS53394.2021.00120","DOIUrl":"https://doi.org/10.1109/ICPADS53394.2021.00120","url":null,"abstract":"In recent years, wireless sensor networks have been used in a wide range of indoor localization-based applications. Although promising, the existing works are dependent on a large number of anchor nodes to achieve localizations, which brings the issues of increasing of the cost and additional maintenance. Inspired by the cross-technology communication, an emerging technique that enables direct communication among heterogeneous wireless devices, we propose an anchor-free distributed method, which leverages the installed Wi-Fi APs to calculate the distance instead of traditional anchor nodes. More specifically, for the asymmetric coverage of Wi-Fi and ZigBee nodes, we first design a progressive method, where the first unknown node estimates its location based on two Wi-Fi APs and a sink node, then once achieving its position, it acts as the alternative sink node of the next hop. This process is repeated until the new members can obtain their positions. Second, as a low-power technology, ZigBee signal may be submerged in strong signals such as Wi-Fi. To overcome this problem, a prime number is deployed to be the Wi-Fi broadcasting period based on the numerical analysis theory. Among lots of prime numbers, we have the opportunity to select an appropriate one with the relatively small packet collisions. Last, numerical simulations and experiments are performed to evaluate the proposal. The evaluation results show that the proposal can achieve decimeter level accuracy without deploying any anchor node. Moreover, the proposal demonstrates the anti-interference ability in the crowded open spectrum environment.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114753851","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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