Efficient goods movement is a vital aspect of logistics and urban planning, impacting the flow of goods and the quality of life for residents. To aid in this, we present an interactive map-based system for visualizing and analyzing goods’ movements using GPS traces. The system takes raw GPS signal data and road network data as input, then performs preprocessing and spatial analysis on the data using Flask framework and Python scripts. The system offers a user-friendly interface to explore the patterns of goods movement dynamically. It displays the temporal and spatial movement trips in the city, providing an intuitive way to analyze and optimize goods movement. Our system’s ability to explore and visualize goods movement patterns makes it an essential addition to the existing literature on urban transportation analysis and a valuable tool for logistics companies and urban planners.
{"title":"An Interactive Map-based System for Visually Exploring Goods Movement based on GPS Traces","authors":"Reza Safarzadeh, Yunli Wang, Sun Sun, Xin Wang","doi":"10.1145/3609956.3609975","DOIUrl":"https://doi.org/10.1145/3609956.3609975","url":null,"abstract":"Efficient goods movement is a vital aspect of logistics and urban planning, impacting the flow of goods and the quality of life for residents. To aid in this, we present an interactive map-based system for visualizing and analyzing goods’ movements using GPS traces. The system takes raw GPS signal data and road network data as input, then performs preprocessing and spatial analysis on the data using Flask framework and Python scripts. The system offers a user-friendly interface to explore the patterns of goods movement dynamically. It displays the temporal and spatial movement trips in the city, providing an intuitive way to analyze and optimize goods movement. Our system’s ability to explore and visualize goods movement patterns makes it an essential addition to the existing literature on urban transportation analysis and a valuable tool for logistics companies and urban planners.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129327032","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}
Accurate estimation of shipping CO2 emissions is important for developing regulations to combat the greenhouse effect. Many shipping CO2 emissions models have been proposed in the past decades. However, most of them are only validated for a few specific ships, and there is a lack of data-driven validation and comparison of these models on a large scale. To fill this gap, this study proposes a general evaluation framework to quantitatively validate and compare different emission models. This framework is based on data integration of three types of data sources: ship technical details, AIS trajectory, and weather. Along with emission models, these data are fed into three carefully-designed modules that perform analysis at both grid and trajectory level as well as use annually aggregated fuel consumption ground truth. Extensive experiments are conducted on one-month data from 1,571 ships passing Danish waters to demonstrate the utility of the framework and insights into the accuracy of five popular CO2 emission models are presented.
{"title":"Evaluation of Vessel CO2 Emissions Methods using AIS Trajectories","authors":"Song Wu, K. Torp, M. Sakr, E. Zimányi","doi":"10.1145/3609956.3609960","DOIUrl":"https://doi.org/10.1145/3609956.3609960","url":null,"abstract":"Accurate estimation of shipping CO2 emissions is important for developing regulations to combat the greenhouse effect. Many shipping CO2 emissions models have been proposed in the past decades. However, most of them are only validated for a few specific ships, and there is a lack of data-driven validation and comparison of these models on a large scale. To fill this gap, this study proposes a general evaluation framework to quantitatively validate and compare different emission models. This framework is based on data integration of three types of data sources: ship technical details, AIS trajectory, and weather. Along with emission models, these data are fed into three carefully-designed modules that perform analysis at both grid and trajectory level as well as use annually aggregated fuel consumption ground truth. Extensive experiments are conducted on one-month data from 1,571 ships passing Danish waters to demonstrate the utility of the framework and insights into the accuracy of five popular CO2 emission models are presented.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127950427","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}
Vasilis Ethan Sarris, Panos K. Chrysanthis, Constantinos Costa
The exposure to viral airborne diseases is higher in crowded and congested spaces, the COVID-19 pandemic has revealed the need of pedestrian recommendation systems that can recommend less congested paths which minimize exposure to infectious crowd diseases in general. In this paper, we introduce ASTRO-C, an extension of previous work ASTRO, which optimizes for minimum congestion. To our knowledge, ASTRO-C is the only solution to this problem of constraint-satisfying, indoor-outdoor, congestion-based path finding. Our experimental evaluation using randomly generated Indoor-Outdoor graphs with varying constraints matching various real-world scenarios, show that ASTRO-C is able to recommend paths with, on average a 0.62X reduction in average congestion, while on average, total travel time increases by 1.06X and never exceeds 1.10X compared to ASTRO.
{"title":"Recommending the Least Congested Indoor-Outdoor Paths without Ignoring Time","authors":"Vasilis Ethan Sarris, Panos K. Chrysanthis, Constantinos Costa","doi":"10.1145/3609956.3609969","DOIUrl":"https://doi.org/10.1145/3609956.3609969","url":null,"abstract":"The exposure to viral airborne diseases is higher in crowded and congested spaces, the COVID-19 pandemic has revealed the need of pedestrian recommendation systems that can recommend less congested paths which minimize exposure to infectious crowd diseases in general. In this paper, we introduce ASTRO-C, an extension of previous work ASTRO, which optimizes for minimum congestion. To our knowledge, ASTRO-C is the only solution to this problem of constraint-satisfying, indoor-outdoor, congestion-based path finding. Our experimental evaluation using randomly generated Indoor-Outdoor graphs with varying constraints matching various real-world scenarios, show that ASTRO-C is able to recommend paths with, on average a 0.62X reduction in average congestion, while on average, total travel time increases by 1.06X and never exceeds 1.10X compared to ASTRO.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134500989","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}
Traffic spatial-temporal prediction is essential for intelligent transportation systems. However, the current approach relies heavily on expert knowledge and time-consuming manual modeling. Neural architecture search can build models adaptively, but it is rarely used for traffic spatial-temporal prediction, nor is it designed specifically for traffic spatial-temporal feature. In response to the above problems, we propose neural architecture search spatial-temporal prediction (NASST), which is a method to automatically generate a traffic spatial-temporal prediction network by performing a differentiable neural network architecture search in an optimized search space. First, we adopt a differentiable neural architecture search method to continuously relax the discrete traffic spatial-temporal prediction model architecture search, and adopt a fusion strategy of comprehensive concatenate and addition (CA) to achieve efficient neural architecture search. Second, we optimize the search space and introduce a series of classic traffic spatial-temporal feature extraction modules, which are more in line with the architectural requirements of traffic spatial-temporal prediction network. Finally, our model is validated on two public traffic datasets and achieves the best predictions. Compared with traditional manual modeling methods, our method can realize the automatic search of high-precision predictive model architectures, which improves the modeling efficiency.
{"title":"Traffic Spatial-Temporal Prediction Based on Neural Architecture Search","authors":"Dongran Zhang, Gang Luo, Jun Li","doi":"10.1145/3609956.3609962","DOIUrl":"https://doi.org/10.1145/3609956.3609962","url":null,"abstract":"Traffic spatial-temporal prediction is essential for intelligent transportation systems. However, the current approach relies heavily on expert knowledge and time-consuming manual modeling. Neural architecture search can build models adaptively, but it is rarely used for traffic spatial-temporal prediction, nor is it designed specifically for traffic spatial-temporal feature. In response to the above problems, we propose neural architecture search spatial-temporal prediction (NASST), which is a method to automatically generate a traffic spatial-temporal prediction network by performing a differentiable neural network architecture search in an optimized search space. First, we adopt a differentiable neural architecture search method to continuously relax the discrete traffic spatial-temporal prediction model architecture search, and adopt a fusion strategy of comprehensive concatenate and addition (CA) to achieve efficient neural architecture search. Second, we optimize the search space and introduce a series of classic traffic spatial-temporal feature extraction modules, which are more in line with the architectural requirements of traffic spatial-temporal prediction network. Finally, our model is validated on two public traffic datasets and achieves the best predictions. Compared with traditional manual modeling methods, our method can realize the automatic search of high-precision predictive model architectures, which improves the modeling efficiency.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"859 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746504","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 Automatic Identification System (AIS) provides global vessel positioning data used in a variety of maritime applications. However, AIS suffers from transmission signal gaps, which causes vessels to disappear from AIS records for prolonged periods and poses a major challenge for the use of AIS data. In this paper, we propose a novel Data-driven AIS Trajectory INterpolation method (DAISTIN) to address AIS signal gaps. DAISTIN first makes use of massive raw AIS data to delicately construct a graph that well represents vessel movements. Next, given a gap between two locations A and B in an AIS trajectory, DAISTIN searches the graph for the shortest path from A to B and uses the path to interpolate the vessel’s whereabouts in between. To cope with large amounts of AIS data, we design a geometric sampling method for DAISTIN to select representative AIS data points for the graph construction. Finally, we design a postprocessing step for DAISTIN to fine-tune the quality of interpolated results. We conduct extensive experiments to compare DAISTIN with selected existing methods. The results verify the superiority of DAISTIN in terms of multiple performance metrics.
{"title":"DAISTIN: A Data-Driven AIS Trajectory Interpolation Method","authors":"Búgvi Benjamin Magnussen, Nikolaj Bläser, Huan Lu","doi":"10.1145/3609956.3609961","DOIUrl":"https://doi.org/10.1145/3609956.3609961","url":null,"abstract":"The Automatic Identification System (AIS) provides global vessel positioning data used in a variety of maritime applications. However, AIS suffers from transmission signal gaps, which causes vessels to disappear from AIS records for prolonged periods and poses a major challenge for the use of AIS data. In this paper, we propose a novel Data-driven AIS Trajectory INterpolation method (DAISTIN) to address AIS signal gaps. DAISTIN first makes use of massive raw AIS data to delicately construct a graph that well represents vessel movements. Next, given a gap between two locations A and B in an AIS trajectory, DAISTIN searches the graph for the shortest path from A to B and uses the path to interpolate the vessel’s whereabouts in between. To cope with large amounts of AIS data, we design a geometric sampling method for DAISTIN to select representative AIS data points for the graph construction. Finally, we design a postprocessing step for DAISTIN to fine-tune the quality of interpolated results. We conduct extensive experiments to compare DAISTIN with selected existing methods. The results verify the superiority of DAISTIN in terms of multiple performance metrics.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994750","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}
Meghdad Mirabi, Leila Fathi, Anton Dignös, J. Gamper, Carsten Binnig
This paper presents the extended temporal aligner as a temporal primitive, and proposes a set of reduction rules that employ this primitive to convert a temporal join operator to its non-temporal equivalent. The rules cover all types of temporal joins, including inner join, outer joins, and anti-join. Preliminary experimental results demonstrate that the integration of the extended temporal aligner and the reduction rules can efficiently process temporal join queries.
{"title":"A New Primitive for Processing Temporal Joins","authors":"Meghdad Mirabi, Leila Fathi, Anton Dignös, J. Gamper, Carsten Binnig","doi":"10.1145/3609956.3609968","DOIUrl":"https://doi.org/10.1145/3609956.3609968","url":null,"abstract":"This paper presents the extended temporal aligner as a temporal primitive, and proposes a set of reduction rules that employ this primitive to convert a temporal join operator to its non-temporal equivalent. The rules cover all types of temporal joins, including inner join, outer joins, and anti-join. Preliminary experimental results demonstrate that the integration of the extended temporal aligner and the reduction rules can efficiently process temporal join queries.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129897051","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}
Nelson Tavares de Sousa, Yannick Wölker, M. Renz, A. Biastoch
A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.
{"title":"VoCC: Vortex Correlation Clustering Based on Masked Hough Transformation in Spatial Databases","authors":"Nelson Tavares de Sousa, Yannick Wölker, M. Renz, A. Biastoch","doi":"10.1145/3609956.3609971","DOIUrl":"https://doi.org/10.1145/3609956.3609971","url":null,"abstract":"A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128113646","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}
Spatial regionalization is the process of combining a collection of spatial polygons into contiguous regions that satisfy user-defined criteria and objectives. Numerous techniques for spatial regionalization have been proposed in the literature, which employ varying methods for region growing, seeding, optimization and enforce different user-defined constraints and objectives. This paper introduces a scalable unified system for addressing seeding spatial regionalization queries efficiently. The proposed system provides a usable and scalable framework that employs a wide-range of existing spatial regionalization techniques and allows users to submit novel combinations of queries that have not been previously explored. This represents a significant step forward in the field of spatial regionalization as it provides a robust platform for addressing different regionalization queries. The system is mainly composed of three components: query parser, query planner, and query executor. Preliminary evaluations of the system demonstrate its efficacy in efficiently addressing various regionalization queries.
{"title":"A Scalable Unified System for Seeding Regionalization Queries","authors":"Hussah Alrashid, A. Magdy","doi":"10.1145/3609956.3609980","DOIUrl":"https://doi.org/10.1145/3609956.3609980","url":null,"abstract":"Spatial regionalization is the process of combining a collection of spatial polygons into contiguous regions that satisfy user-defined criteria and objectives. Numerous techniques for spatial regionalization have been proposed in the literature, which employ varying methods for region growing, seeding, optimization and enforce different user-defined constraints and objectives. This paper introduces a scalable unified system for addressing seeding spatial regionalization queries efficiently. The proposed system provides a usable and scalable framework that employs a wide-range of existing spatial regionalization techniques and allows users to submit novel combinations of queries that have not been previously explored. This represents a significant step forward in the field of spatial regionalization as it provides a robust platform for addressing different regionalization queries. The system is mainly composed of three components: query parser, query planner, and query executor. Preliminary evaluations of the system demonstrate its efficacy in efficiently addressing various regionalization queries.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122586741","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}
Li Ruan, Heng Guo, Yunzhi Xue, Tao Ruan, Yuetiansi Ji, Limin Xiao
The workloads of autonomous driving traffic accident cloud data centers exhibit high variance and uncertainty. Accurate modeling and prediction of the variance and uncertainty of cloud workloads are crucial for the realization of reliable resource management in cloud data centers. Existing solutions are point prediction methods that can not capture the variance and uncertainty of the cloud workloads. In this paper, we propose a workload probabilistic prediction method with deep learning to model and predict the variance and uncertainty of cloud workload. Our method is a hybrid deep learning model which combines exponential smoothing, bidirectional long short-term memory (BLSTM) and quantile regression. First, a cloud workload pre-processing method based on exponential smoothing is proposed to smooth the high variance feature of cloud workloads. Then, a BLSTM based cloud workload algorithm is introduced. Finally, a differentiable quantile loss function is introduced into the prediction model to generate predictions of multiple quantiles. The experimental results on the Google cluster trace show that our method outperforms other four baseline models.
{"title":"Towards Workload Trend Time Series Probabilistic Prediction via Probabilistic Deep Learning","authors":"Li Ruan, Heng Guo, Yunzhi Xue, Tao Ruan, Yuetiansi Ji, Limin Xiao","doi":"10.1145/3609956.3609979","DOIUrl":"https://doi.org/10.1145/3609956.3609979","url":null,"abstract":"The workloads of autonomous driving traffic accident cloud data centers exhibit high variance and uncertainty. Accurate modeling and prediction of the variance and uncertainty of cloud workloads are crucial for the realization of reliable resource management in cloud data centers. Existing solutions are point prediction methods that can not capture the variance and uncertainty of the cloud workloads. In this paper, we propose a workload probabilistic prediction method with deep learning to model and predict the variance and uncertainty of cloud workload. Our method is a hybrid deep learning model which combines exponential smoothing, bidirectional long short-term memory (BLSTM) and quantile regression. First, a cloud workload pre-processing method based on exponential smoothing is proposed to smooth the high variance feature of cloud workloads. Then, a BLSTM based cloud workload algorithm is introduced. Finally, a differentiable quantile loss function is introduced into the prediction model to generate predictions of multiple quantiles. The experimental results on the Google cluster trace show that our method outperforms other four baseline models.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116111509","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}
Ning Li, Winston K.G. Seah, Zhengyu Hou, Bing Jia, Baoqi Huang, Wuyungerile Li
Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a "backup cluster head" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.
{"title":"An Energy Aware Adaptive Clustering Protocol for Energy Harvesting Wireless Sensor Networks","authors":"Ning Li, Winston K.G. Seah, Zhengyu Hou, Bing Jia, Baoqi Huang, Wuyungerile Li","doi":"10.1145/3609956.3609958","DOIUrl":"https://doi.org/10.1145/3609956.3609958","url":null,"abstract":"Wireless sensor network (WSN) has many applications, such as, military scenarios, habitat monitoring and home security. In recent years, with the advancement of energy harvesting (EH) technology, nodes can obtain available energy from the surrounding environment for their own use, thus extending their lifetimes. Under these conditions, research aimed at improving the WSN lifecycle has further shifted towards improving the performance of the network, albeit subject to unique energy harvesting constraints. This paper proposes an energy prediction algorithm for the devices and an Energy and Density Adaptive Clustering (EDAC) protocol to improve network throughput and transmission ratio for EH-powered WSNs. Based on the EH characteristics, we first employed Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (Bi-LSTM) algorithm for energy prediction, then we divide the energy of the sensor nodes into three levels: low, medium, and high energy levels. At high energy levels, nodes can be selected as cluster head nodes, while at low energy levels, nodes must sleep and charge. EDAC first uses the K-Means clustering algorithm to dynamically cluster the surviving nodes in each round and sets a threshold to partition the clustering density. On this basis, a new adaptive cluster head election formula is proposed for cluster head election based on the energy levels of nodes, the predicted energy of the next stage, and the density of clusters. In the stable communication stage of the network, we introduce a \"backup cluster head\" to temporarily forward the remaining data packets within the cluster when the current cluster head expires. Our simulation results show that our algorithm significantly improves throughput and data transfer rate compared to the traditional and improved clustering protocols.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325212","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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