Duc Van Le, Joy Qiping Yang, Siyuan Zhou, Daren Ho, Rui Tan
Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers interest of applying Artificial Intelligence of Things (AIoT) systems for industrial applications. The in situ inference and decision made based on the sensor data allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks. Such a scheme avoids the wireless bandwidth bottleneck and unreliability issues, as well as the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer lessons for the relevant research and industry communities. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of HP Inc.’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the work, which could be useful to the developments of other industrial AIoT systems for quality control in manufacturing.
{"title":"Design, Deployment, and Evaluation of an Industrial AIoT System for Quality Control at HP Factories","authors":"Duc Van Le, Joy Qiping Yang, Siyuan Zhou, Daren Ho, Rui Tan","doi":"10.1145/3618300","DOIUrl":"https://doi.org/10.1145/3618300","url":null,"abstract":"Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers interest of applying Artificial Intelligence of Things (AIoT) systems for industrial applications. The in situ inference and decision made based on the sensor data allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks. Such a scheme avoids the wireless bandwidth bottleneck and unreliability issues, as well as the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer lessons for the relevant research and industry communities. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of HP Inc.’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the work, which could be useful to the developments of other industrial AIoT systems for quality control in manufacturing.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49548288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hang Zhou, Xiaoyan Wang, M. Umehira, Biao Han, Hao Zhou
The integration of small cell architecture and edge intelligence is expected to make high-grade mobile connectivity accessible and thus provide smart and efficient services for various aspects of urban life. It is well known that small cell architecture will cause high inter-cell interference since the adjacent cells share the same frequency band. One of the most promising techniques to mitigate inter-cell interference is beamforming, however, how to coordinate the beamformers in a multicell dynamic network to reach a global optimum is an extremely challenging problem. In this paper, we consider analog beamforming with low-resolution phase shifters, and propose a distributed learning and multicell coordination based energy efficient beamforming approach for multiple-input and single-output (MISO) small cell system. The goal is to maximize the energy efficiency (EE) of the whole system by jointly optimizing the beamformer and transmit power. We perform extensive simulations in both static and dynamic scenarios, and validate the performance of the proposed approach by comparing with baseline and existing schemes. The simulation results demonstrate that the proposed approach outperforms the baseline and existing schemes with an significant improvement in terms of EE for both static and dynamic network settings.
{"title":"Energy Efficient Beamforming for Small Cell Systems: A distributed Learning and Multicell Coordination Approach","authors":"Hang Zhou, Xiaoyan Wang, M. Umehira, Biao Han, Hao Zhou","doi":"10.1145/3617997","DOIUrl":"https://doi.org/10.1145/3617997","url":null,"abstract":"The integration of small cell architecture and edge intelligence is expected to make high-grade mobile connectivity accessible and thus provide smart and efficient services for various aspects of urban life. It is well known that small cell architecture will cause high inter-cell interference since the adjacent cells share the same frequency band. One of the most promising techniques to mitigate inter-cell interference is beamforming, however, how to coordinate the beamformers in a multicell dynamic network to reach a global optimum is an extremely challenging problem. In this paper, we consider analog beamforming with low-resolution phase shifters, and propose a distributed learning and multicell coordination based energy efficient beamforming approach for multiple-input and single-output (MISO) small cell system. The goal is to maximize the energy efficiency (EE) of the whole system by jointly optimizing the beamformer and transmit power. We perform extensive simulations in both static and dynamic scenarios, and validate the performance of the proposed approach by comparing with baseline and existing schemes. The simulation results demonstrate that the proposed approach outperforms the baseline and existing schemes with an significant improvement in terms of EE for both static and dynamic network settings.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44835995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this paper, we propose iToLoc, a deep learning based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training based semi-supervised learning framework. Extensive experiments across 7 months with 8 different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92m and > 95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains > 90%, which significantly outperforms previous works.
{"title":"Train Once, Locate Anytime for Anyone: Adversarial Learning based Wireless Localization","authors":"Danyang Li, Jingao Xu, Zheng Yang, Chengpei Tang","doi":"10.1145/3614095","DOIUrl":"https://doi.org/10.1145/3614095","url":null,"abstract":"Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this paper, we propose iToLoc, a deep learning based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training based semi-supervised learning framework. Extensive experiments across 7 months with 8 different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92m and > 95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains > 90%, which significantly outperforms previous works.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135033628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chaofan Ma, W. Liang, M. Zheng, Xiaofang Xia, Lin Chen
Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications which require high reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this paper. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by VGG. In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.
{"title":"A Voronoi Diagram and Q-Learning based Relay Node Placement Method Subject to Radio Irregularity","authors":"Chaofan Ma, W. Liang, M. Zheng, Xiaofang Xia, Lin Chen","doi":"10.1145/3617124","DOIUrl":"https://doi.org/10.1145/3617124","url":null,"abstract":"Industrial Wireless Sensor Networks (IWSNs) have been widely used in industrial applications which require high reliable and real-time wireless transmission. A lot of works have been done to optimize the Relay Node Placement (RNP), which determines the underlying topology of IWSNs and hence impacts the network performance. However, existing RNP algorithms use a fixed communication radius to compute the deployment result at once offline, while ignoring that the radio environment may vary drastically across different locations, also known as radio irregularity. To address this limitation, we propose a Voronoi diagram and Q-learning based RNP (VQRNP) method in this paper. Instead of using a fixed communication radius, VQRNP employs the Q-learning algorithm to dynamically update the radio environment of measured areas, uses a Voronoi diagram based method to estimate the radio environment of unmeasured areas, and proposes a coverage extension location selection algorithm to place RNs so as to extend the coverage of the deployed network based on the results estimated by VGG. In this way, the VQRPN method can adapt itself well to the variation of radio environment and largely speed up deployment process. Extensive simulations verify that VQRNP significantly outperforms existing RNP algorithms in terms of reliability.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43101671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudio Savaglio, Vincenzo Barbuto, Faraz Malik Awan, R. Minerva, N. Crespi, G. Fortino
Although Digital Twins (DTs) became very popular in industry, nowadays they represent a pre-requisite of many systems across different domains, by taking advantage of the disrupting digital technologies such as Artificial Intelligence (AI), Edge Computing and Internet of Things (IoT). In this paper we present our “opportunistic” interpretation, which advances the traditional DT concept and provides a valid support for enabling next-generation solutions in dynamic, distributed and large scale scenarios as smart cities. Indeed, by collecting simple data from the environment and by opportunistically elaborating them through AI techniques directly at the network edge (also referred to as Edge Intelligence), a digital version of a physical object can be built from the bottom up as well as dynamically manipulated and operated in a data-driven manner, thus enabling prompt responses to external stimuli and effective command actuation. To demonstrate the viability of our Opportunistic Digital Twin (ODT) a real use case focused on a traffic prediction task has been incrementally developed and presented, showing improved inference performance and reduced network latency, bandwidth and power consumption.
{"title":"Opportunistic Digital Twin: an Edge Intelligence enabler for Smart City","authors":"Claudio Savaglio, Vincenzo Barbuto, Faraz Malik Awan, R. Minerva, N. Crespi, G. Fortino","doi":"10.1145/3616014","DOIUrl":"https://doi.org/10.1145/3616014","url":null,"abstract":"Although Digital Twins (DTs) became very popular in industry, nowadays they represent a pre-requisite of many systems across different domains, by taking advantage of the disrupting digital technologies such as Artificial Intelligence (AI), Edge Computing and Internet of Things (IoT). In this paper we present our “opportunistic” interpretation, which advances the traditional DT concept and provides a valid support for enabling next-generation solutions in dynamic, distributed and large scale scenarios as smart cities. Indeed, by collecting simple data from the environment and by opportunistically elaborating them through AI techniques directly at the network edge (also referred to as Edge Intelligence), a digital version of a physical object can be built from the bottom up as well as dynamically manipulated and operated in a data-driven manner, thus enabling prompt responses to external stimuli and effective command actuation. To demonstrate the viability of our Opportunistic Digital Twin (ODT) a real use case focused on a traffic prediction task has been incrementally developed and presented, showing improved inference performance and reduced network latency, bandwidth and power consumption.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44528778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently using deep learning to achieve WiFi-based human activity recognition (HAR) has drawn significant attention. While capable to achieve accurate identification in a single domain (i.e., training and testing in the same consistent WiFi environment), it would become extremely tough when WiFi environments change significantly. As such, domain adversarial neural networks based approaches have been proposed to handle such diversities across domains, yet often found to share the same limitation in practice: the imbalance between high-capacity of feature extractors and data insufficiency of source domains. This paper proposes i-Sample, an intermediate sample generation-based framework, striving to tackle this issue for WiFi-based HAR. i-Sample is mainly designed as two-stage training, where four data augmentation operations are proposed to train a coarse domain-invariant feature extractor in the first stage. In the second stage, we leverage the gradients of classification error to generate intermediate samples to refine the classifiers together with original samples, making i-Sample also capable to be integrated into most domain adversarial adaptation methods without neural network modification. We have implemented a prototype system to evaluate i-Sample, which shows that i-Sample can effectively augment the performance of nowadays mainstream domain adversarial adaptation models for WiFi-based HAR, especially when source domain data is insufficient.
{"title":"i-Sample: Augment Domain Adversarial Adaptation Models for WiFi-based HAR","authors":"Zhipeng Zhou, Feng Wang, Wei Gong","doi":"10.1145/3616494","DOIUrl":"https://doi.org/10.1145/3616494","url":null,"abstract":"Recently using deep learning to achieve WiFi-based human activity recognition (HAR) has drawn significant attention. While capable to achieve accurate identification in a single domain (i.e., training and testing in the same consistent WiFi environment), it would become extremely tough when WiFi environments change significantly. As such, domain adversarial neural networks based approaches have been proposed to handle such diversities across domains, yet often found to share the same limitation in practice: the imbalance between high-capacity of feature extractors and data insufficiency of source domains. This paper proposes i-Sample, an intermediate sample generation-based framework, striving to tackle this issue for WiFi-based HAR. i-Sample is mainly designed as two-stage training, where four data augmentation operations are proposed to train a coarse domain-invariant feature extractor in the first stage. In the second stage, we leverage the gradients of classification error to generate intermediate samples to refine the classifiers together with original samples, making i-Sample also capable to be integrated into most domain adversarial adaptation methods without neural network modification. We have implemented a prototype system to evaluate i-Sample, which shows that i-Sample can effectively augment the performance of nowadays mainstream domain adversarial adaptation models for WiFi-based HAR, especially when source domain data is insufficient.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46049186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Mobile Edge Computing, edge servers have limited storage and computing resources which can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc, a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc, an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least1.9 × of that of the baselines.
{"title":"DAG Scheduling in Mobile Edge Computing","authors":"Guopeng Li, Hailun Tan, Liuyan Liu, Hao Zhou, S. Jiang, Zhenhua Han, Xiangyang Li, Guoliang Chen","doi":"10.1145/3616374","DOIUrl":"https://doi.org/10.1145/3616374","url":null,"abstract":"In Mobile Edge Computing, edge servers have limited storage and computing resources which can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc, a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc, an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least1.9 × of that of the baselines.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46912083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zheng Wu, Kehua Guo, Liwei Wang, Min Hu, Sheng Ren
Face recognition is an essential technology in intelligent transportation and security within smart cities. Nevertheless, face images taken in nighttime urban environments often suffer from low brightness, small sizes, and low resolution, which pose significant challenges for accurate face feature recognition. To address this issue, we propose the Low-light Small-target Face Enhancement (LSFE) method, a collaborative learning-based image brightness enhancement approach specifically designed for small-target faces in low-light environments. LSFE employs a multilevel feature stratification module to acquire detailed face image features at different levels, revealing hidden facial image information within the dark. In addition, we design a network combining collaborative learning and self-attention mechanisms, which effectively captures long-distance pixel dependencies in low-brightness face images and enhances their brightness in a stepwise manner. The enhanced feature maps are then fused through a branch fusion module. Experimental results demonstrate that LSFE can more effectively enhance the luminance of small-target face images in low-light scenes while retaining more visual information, compared to other existing methods.
{"title":"A Collaborative Learning-based Urban Low-light Small-target Face Image Enhancement Method","authors":"Zheng Wu, Kehua Guo, Liwei Wang, Min Hu, Sheng Ren","doi":"10.1145/3616013","DOIUrl":"https://doi.org/10.1145/3616013","url":null,"abstract":"Face recognition is an essential technology in intelligent transportation and security within smart cities. Nevertheless, face images taken in nighttime urban environments often suffer from low brightness, small sizes, and low resolution, which pose significant challenges for accurate face feature recognition. To address this issue, we propose the Low-light Small-target Face Enhancement (LSFE) method, a collaborative learning-based image brightness enhancement approach specifically designed for small-target faces in low-light environments. LSFE employs a multilevel feature stratification module to acquire detailed face image features at different levels, revealing hidden facial image information within the dark. In addition, we design a network combining collaborative learning and self-attention mechanisms, which effectively captures long-distance pixel dependencies in low-brightness face images and enhances their brightness in a stepwise manner. The enhanced feature maps are then fused through a branch fusion module. Experimental results demonstrate that LSFE can more effectively enhance the luminance of small-target face images in low-light scenes while retaining more visual information, compared to other existing methods.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46296054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei You, Meixuan Ren, Yuzhuo Ma, Dié Wu, Jilin Yang, Xuxun Liu, Tang Liu
Benefitting from the maturation of Wireless Power Transfer (WPT) technology, Wireless Rechargeable Sensor Networks (WRSNs) have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this paper, we study a fundamental issue of Wireless chArger placement wIth obsTacles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1 − 1/e − ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.
{"title":"A Practical Charger Placement Scheme for Wireless Rechargeable Sensor Networks with Obstacles","authors":"Wei You, Meixuan Ren, Yuzhuo Ma, Dié Wu, Jilin Yang, Xuxun Liu, Tang Liu","doi":"10.1145/3614431","DOIUrl":"https://doi.org/10.1145/3614431","url":null,"abstract":"Benefitting from the maturation of Wireless Power Transfer (WPT) technology, Wireless Rechargeable Sensor Networks (WRSNs) have become a promising solution for prolonging network lifetime. In practical charging scenarios, obstacles are ubiquitous. However, most prior arts have failed to consider the combined impacts of the material, size, and location of obstacles on the charging performance, making these schemes unsuitable for real applications. In this paper, we study a fundamental issue of Wireless chArger placement wIth obsTacles (WAIT), that is, how to place wireless chargers by comprehensively considering these parameters of obstacles, such that the overall charging utility is maximized. To tackle the WAIT problem, we first build a practical charging model with obstacles by introducing shadow fading, and conduct experiments to verify its correctness. Then, we design a piecewise constant function to approximate the nonlinear charging power. Afterwards, we develop a Dominating Coverage Set extraction algorithm to reduce the continuous solution space to a limited number. Finally, we prove the WAIT problem is a maximizing monotone submodular function problem, and propose a 1 − 1/e − ε approximation algorithm to address it. Extensive simulations and field experiments show that our scheme outperforms comparison algorithms by at least 20.6% in charging utility improvement.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42291742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuxun Liu, Xinyuan Zeng, Junyu Ren, Song Yin, Huan Zhou
Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this paper, we propose a novel connectivity restoration scheme to address this problem. This scheme comprises three connectivity mechanisms regarding relay segment selection in different regions. The first one is a data traffic decentralization mechanism, which establishes more transmission paths near the sink for reliability improvement and traffic load balancing. The second one is a segment shape selection mechanism, in which the segments with high-reliability preferably become the relay segments for greater network robustness. The third one is a traffic load transfer mechanism, in which data traffic is transferred from a high-load segment to a low-load segment for balancing energy depletion of the network. The distinctive characteristics of this work are twofold: different regions perform diverse connectivity restoration approaches according to the demand diversity of different regions, and traffic load can be balanced from upstream regions rather than only from downstream regions. Extensive simulation experiments validate the effectiveness and advantages of our proposed scheme in terms of connection cost, network robustness, load balance degree, and network longevity.
{"title":"Region-Different Network Reconfiguration in Disjoint Wireless Sensor Networks for Smart Agriculture Monitoring","authors":"Xuxun Liu, Xinyuan Zeng, Junyu Ren, Song Yin, Huan Zhou","doi":"10.1145/3614430","DOIUrl":"https://doi.org/10.1145/3614430","url":null,"abstract":"Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this paper, we propose a novel connectivity restoration scheme to address this problem. This scheme comprises three connectivity mechanisms regarding relay segment selection in different regions. The first one is a data traffic decentralization mechanism, which establishes more transmission paths near the sink for reliability improvement and traffic load balancing. The second one is a segment shape selection mechanism, in which the segments with high-reliability preferably become the relay segments for greater network robustness. The third one is a traffic load transfer mechanism, in which data traffic is transferred from a high-load segment to a low-load segment for balancing energy depletion of the network. The distinctive characteristics of this work are twofold: different regions perform diverse connectivity restoration approaches according to the demand diversity of different regions, and traffic load can be balanced from upstream regions rather than only from downstream regions. Extensive simulation experiments validate the effectiveness and advantages of our proposed scheme in terms of connection cost, network robustness, load balance degree, and network longevity.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47827700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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