Pub Date : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043857
Zhen Qin, Yilei Wang, Yong Xia, Hongrong Cheng, Yingjie Zhou, Zhengguo Sheng, Victor C. M. Leung
Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).
{"title":"Demographic information prediction based on smartphone application usage","authors":"Zhen Qin, Yilei Wang, Yong Xia, Hongrong Cheng, Yingjie Zhou, Zhengguo Sheng, Victor C. M. Leung","doi":"10.1109/SMARTCOMP.2014.7043857","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043857","url":null,"abstract":"Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133884802","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043859
Kin-Man Lam, Xuejian He, K. Choi
Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict 5-year mortality of radical cystectomy. The clinico-pathological data from a urology unit of a district hospital in Hong Kong were used to train and test the model. The outcome of the surgery was computed by an artificial neural network based on the risk factors identified by a conventional statistical method. It was found that the best overall accuracy of the neural network model was 77.8% and the 5-year mortality predicted by the model was comparable to that achieved by conventional statistical methods. The results of this study reflect that artificial intelligence has great development potential in medicine.
{"title":"Using artificial neural network to predict mortality of radical cystectomy for bladder cancer","authors":"Kin-Man Lam, Xuejian He, K. Choi","doi":"10.1109/SMARTCOMP.2014.7043859","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043859","url":null,"abstract":"Surgical removal of bladder, i.e. radical cystectomy, is a standard treatment option for muscle invasive bladder cancer. Unfortunately, the treatment is associated with significant morbidities and mortalities. Many studies have been conducted to predict the morbidities and mortalities of radical cystectomy based on statistical analysis. In this paper, an artificial neural network is employed to predict 5-year mortality of radical cystectomy. The clinico-pathological data from a urology unit of a district hospital in Hong Kong were used to train and test the model. The outcome of the surgery was computed by an artificial neural network based on the risk factors identified by a conventional statistical method. It was found that the best overall accuracy of the neural network model was 77.8% and the 5-year mortality predicted by the model was comparable to that achieved by conventional statistical methods. The results of this study reflect that artificial intelligence has great development potential in medicine.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131834169","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043854
Chunmei Ma, Nianbo Liu, Xiaomin Wang, Hai-gang Gong, Xili Dai, Ming Liu
To facilitate the safe and comfortable driving, vehicular ad hoc networks (VANETs) will be flooded with plenty of multimedia files, such as images, music and video clips. However, due to the dynamic and transient contacts between moving vehicles, these multimedia files distribution over VANETs often involves transmission failure and terrible user experience. In this paper, we propose an efficient infrastructure-less multimedia publish/subscribe scheme for an urban area. In cities, there are lots of parked vehicles, presenting as parking clusters, owning the ability of calculation, storage and communication. Our scheme relies on these parking clusters to cache and distribute the multimedia files for moving users. For each subscription, the parking cluster distributes the file chunks to the subscriber during their contact time. For the remained content chunks, the parking cluster will distribute them to slave vehicles that have no downloading request. Then, the slave vehicles transfer the received file chunks to a parking cluster, where the subscriber can continue the unfinished downloading when it drives through. Theoretical results illustrate the effectiveness of our approach and extensive simulations results demonstrate that the proposed scheme achieves a higher downloading ratio with different file sizes, especially in sparse traffic and multiple subscribers conditions.
{"title":"Towards efficient multimedia publish/subscribe in urban VANETs","authors":"Chunmei Ma, Nianbo Liu, Xiaomin Wang, Hai-gang Gong, Xili Dai, Ming Liu","doi":"10.1109/SMARTCOMP.2014.7043854","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043854","url":null,"abstract":"To facilitate the safe and comfortable driving, vehicular ad hoc networks (VANETs) will be flooded with plenty of multimedia files, such as images, music and video clips. However, due to the dynamic and transient contacts between moving vehicles, these multimedia files distribution over VANETs often involves transmission failure and terrible user experience. In this paper, we propose an efficient infrastructure-less multimedia publish/subscribe scheme for an urban area. In cities, there are lots of parked vehicles, presenting as parking clusters, owning the ability of calculation, storage and communication. Our scheme relies on these parking clusters to cache and distribute the multimedia files for moving users. For each subscription, the parking cluster distributes the file chunks to the subscriber during their contact time. For the remained content chunks, the parking cluster will distribute them to slave vehicles that have no downloading request. Then, the slave vehicles transfer the received file chunks to a parking cluster, where the subscriber can continue the unfinished downloading when it drives through. Theoretical results illustrate the effectiveness of our approach and extensive simulations results demonstrate that the proposed scheme achieves a higher downloading ratio with different file sizes, especially in sparse traffic and multiple subscribers conditions.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294721","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043865
Xiu Li, Jin Yu, Zhuo Jia, Jingdong Song
Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation (BP) neural network, generalized regression neural network (GRNN) and support vector machine (SVM) respectively. The experimental results show that the improved BP algorithm and SVM work better than GRNN methods, and the models based on SVM present the best performance in terms of goodness-of-fit measures, but need to be further improved in the running time. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably, but it is not ideally suited for exact predictions. The use of higher frequency data may improve the accuracy of the predictions.
{"title":"Harmful algal blooms prediction with machine learning models in Tolo Harbour","authors":"Xiu Li, Jin Yu, Zhuo Jia, Jingdong Song","doi":"10.1109/SMARTCOMP.2014.7043865","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043865","url":null,"abstract":"Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation (BP) neural network, generalized regression neural network (GRNN) and support vector machine (SVM) respectively. The experimental results show that the improved BP algorithm and SVM work better than GRNN methods, and the models based on SVM present the best performance in terms of goodness-of-fit measures, but need to be further improved in the running time. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably, but it is not ideally suited for exact predictions. The use of higher frequency data may improve the accuracy of the predictions.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115473821","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043860
C. Li, Zhiyong Feng, Chao Xu
A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.
{"title":"Physiological-based emotion recognition with IRS model","authors":"C. Li, Zhiyong Feng, Chao Xu","doi":"10.1109/SMARTCOMP.2014.7043860","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043860","url":null,"abstract":"A major challenge in physiology-based emotion recognition is to establish an effective emotion recognizer for multi-users in the user-independent scenario. The recognition result is not satisfied because it ignores the difference in individual response pattern, which can be attributed to IRS (Individual Response Specificity) and SRS(Stimuli Response Specificity) in psychophysiology. To improve the performance of emotion recognition, this paper proposes a Group-Based IRS model by adaptively matching a suitable recognizer in accordance with user's IRS level. Specifically, the users are put into distinct groups by using cluster analysis techniques, where users within the same group have similar IRS level than other groups. Then physiological data of users from each group is utilized to build the corresponding emotion recognizers. After categorizing a new user into one group according to his IRS level, the new user's emotion state is predicted by the corresponding emotion recognizer. To validate our model, the affective physiological data was collected from 11 subjects in four induced emotions(neutral, sadness, fear and pleasure), three-channel bio-sensors were used to measure users electrocardiogram (ECG), galvanic skin response (GSR) and photo plethysmography (PPG). The results show that the recognition precision in Group-based IRS model is higher than general model.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760847","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043837
Yuwu Lu, Lunke Fei, Yan Chen
In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses the hybrid fusion scheme to combine these three kinds of matching scores for classification of the test sample. The hybrid fusion scheme exploits low- and high-order components of three kinds of matching scores based on the sum and product rule. Scores from each color channel generated from TPTSR includes both little correlated and very correlated scores, to extract low- and high-order components of these scores will allow them to be well integrated and used for classification. For evaluating the proposed method, we not only make a comparison of our method with some global and local methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel PCA (KPCA), kernel LDA (KLDA), locality preserving projection (LPP) and TPTSR. We also make a comparison of our method with some recently proposed local feature based methods, such as color local Gabor wavelets (CLGW), color local binary pattern (CLBP) and tensor discriminant color space (TDCS).
{"title":"A hybrid fusion scheme for color face recognition","authors":"Yuwu Lu, Lunke Fei, Yan Chen","doi":"10.1109/SMARTCOMP.2014.7043837","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043837","url":null,"abstract":"In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses the hybrid fusion scheme to combine these three kinds of matching scores for classification of the test sample. The hybrid fusion scheme exploits low- and high-order components of three kinds of matching scores based on the sum and product rule. Scores from each color channel generated from TPTSR includes both little correlated and very correlated scores, to extract low- and high-order components of these scores will allow them to be well integrated and used for classification. For evaluating the proposed method, we not only make a comparison of our method with some global and local methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel PCA (KPCA), kernel LDA (KLDA), locality preserving projection (LPP) and TPTSR. We also make a comparison of our method with some recently proposed local feature based methods, such as color local Gabor wavelets (CLGW), color local binary pattern (CLBP) and tensor discriminant color space (TDCS).","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736591","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043853
Geoffrey Poon, Y. Yeung, Wai-Man Pang
Online shopping systems nowadays are constructed with the capability of displaying text and 2D images to the customers. Although it is enough for the purpose of exchanging product information between sellers and buyers, the plain and dull shopping experience is not really appreciated. With the recent advancement of 3D technologies in the web environment, we can enhance such an experience with 3D visualized products. However, the creation of 3D content is a major challenge in realizing a 3D enabled shopping system. The high cost of 3D scanning devices inhibits the popularity of scanned 3D objects on the web and related applications. Therefore, one major targets of our system is to provide a low cost 3D scanning solution suitable for naïve web users. We exploit the Kinect device, which is a low-cost and fast depth sensor with color, in the development of a 3D scanning system. With an integration of a marker-based tracking system to estimate the current view angle of Kinect sensor during depth acquisition, the obtained depth data can reconstruct the 3D model by a preliminary coordinate frame alignment and a registration of all point clouds. The whole process of capturing, storing, uploading, compressing and displaying 3D models of products on web application is done with minimal user involvements. The introduction of attractive and fashionable 3D product on the web will gain significant attention from customers and evolve habits and traditions in e-commerce, especially for C2C business, in which sellers can promote their goods for sale with scanned 3D models.
{"title":"Enabling 3D online shopping with affordable depth scanned models","authors":"Geoffrey Poon, Y. Yeung, Wai-Man Pang","doi":"10.1109/SMARTCOMP.2014.7043853","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043853","url":null,"abstract":"Online shopping systems nowadays are constructed with the capability of displaying text and 2D images to the customers. Although it is enough for the purpose of exchanging product information between sellers and buyers, the plain and dull shopping experience is not really appreciated. With the recent advancement of 3D technologies in the web environment, we can enhance such an experience with 3D visualized products. However, the creation of 3D content is a major challenge in realizing a 3D enabled shopping system. The high cost of 3D scanning devices inhibits the popularity of scanned 3D objects on the web and related applications. Therefore, one major targets of our system is to provide a low cost 3D scanning solution suitable for naïve web users. We exploit the Kinect device, which is a low-cost and fast depth sensor with color, in the development of a 3D scanning system. With an integration of a marker-based tracking system to estimate the current view angle of Kinect sensor during depth acquisition, the obtained depth data can reconstruct the 3D model by a preliminary coordinate frame alignment and a registration of all point clouds. The whole process of capturing, storing, uploading, compressing and displaying 3D models of products on web application is done with minimal user involvements. The introduction of attractive and fashionable 3D product on the web will gain significant attention from customers and evolve habits and traditions in e-commerce, especially for C2C business, in which sellers can promote their goods for sale with scanned 3D models.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122546326","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043833
G. Privitera, G. Ghidini, S. P. Emmons, David Levine, P. Bellavista, Jeffrey O. Smith
Commercial applications of wireless sensor networks, also known as machine-to-machine (M2M) communications, feature hundreds of thousands or even millions of devices. These M2M applications often rely on cellular networks like GSM that were not designed with such use cases in mind. Based on our first-hand experience at a large provider of M2M communications solutions, there is a need for soft real-time traffic analytics solutions to help engineers monitor and manage the millions of devices deployed in these M2M applications. We present a solution for soft real-time GPRS traffic analytics built on Apache Spark, a framework for distributed in-memory computing. The proposed solution captures GPRS traffic, processes it, and decorates it with details about the devices, networks, and M2M applications. It then computes a whole array of statistics that are presented in charts and maps on a live Web application dashboard, or may be fed to other systems for data mining. In a series of experiments, previously captured GPRS traffic from real-life commercial M2M applications is played back to the traffic analytics solution at different rates, and is processed on clusters of varying size. Results show that our solution handles GPRS traffic rates of 3,333 packets/sec, which are 2X the rates of an M2M application with close to one million devices, with a latency below one minute on a Spark cluster with four m1.large slave instances in Amazon EC2 at a cost of $7,665/year. These costs can be reduced to approx. $700/year by bidding on SPOT instances.
{"title":"Soft real-time GPRS traffic analytics for commercial M2M communications using spark","authors":"G. Privitera, G. Ghidini, S. P. Emmons, David Levine, P. Bellavista, Jeffrey O. Smith","doi":"10.1109/SMARTCOMP.2014.7043833","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043833","url":null,"abstract":"Commercial applications of wireless sensor networks, also known as machine-to-machine (M2M) communications, feature hundreds of thousands or even millions of devices. These M2M applications often rely on cellular networks like GSM that were not designed with such use cases in mind. Based on our first-hand experience at a large provider of M2M communications solutions, there is a need for soft real-time traffic analytics solutions to help engineers monitor and manage the millions of devices deployed in these M2M applications. We present a solution for soft real-time GPRS traffic analytics built on Apache Spark, a framework for distributed in-memory computing. The proposed solution captures GPRS traffic, processes it, and decorates it with details about the devices, networks, and M2M applications. It then computes a whole array of statistics that are presented in charts and maps on a live Web application dashboard, or may be fed to other systems for data mining. In a series of experiments, previously captured GPRS traffic from real-life commercial M2M applications is played back to the traffic analytics solution at different rates, and is processed on clusters of varying size. Results show that our solution handles GPRS traffic rates of 3,333 packets/sec, which are 2X the rates of an M2M application with close to one million devices, with a latency below one minute on a Spark cluster with four m1.large slave instances in Amazon EC2 at a cost of $7,665/year. These costs can be reduced to approx. $700/year by bidding on SPOT instances.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124814587","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043868
Jinho Ahn
Mobile agent group concept is a versatile software paradigm capable of providing group task execution flexibility and collaborative adaptability in various application fields of distributed networked systems. However, as the size of these fields is rapidly increasing, improving the performance of the agent group communication in Internet-scale infrastructures should be reconsidered to be suitable for their scale. For this purpose, some appropriate inter-agent group communication protocols are required in distributed agent-based systems. Causal order delivery to a broadcast group is a very important issue for these systems. However, the existing message delivery protocols for mobile agent groups may not satisfy this important requirement. This paper presents a new causally ordered group communication protocol for mobile agent groups to consider weaker message ordering consistency than atomic broadcast semantics while ensuring reliability even with the assumption of unreliable networks for exchanging messages to agent groups and agent mobility. The simulation results show the proposed protocol performs better than the existing atomic broadcast protocol in terms of the total message delivery latency.
{"title":"Mobile agent group communication protocol ensuring causal order semantics","authors":"Jinho Ahn","doi":"10.1109/SMARTCOMP.2014.7043868","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043868","url":null,"abstract":"Mobile agent group concept is a versatile software paradigm capable of providing group task execution flexibility and collaborative adaptability in various application fields of distributed networked systems. However, as the size of these fields is rapidly increasing, improving the performance of the agent group communication in Internet-scale infrastructures should be reconsidered to be suitable for their scale. For this purpose, some appropriate inter-agent group communication protocols are required in distributed agent-based systems. Causal order delivery to a broadcast group is a very important issue for these systems. However, the existing message delivery protocols for mobile agent groups may not satisfy this important requirement. This paper presents a new causally ordered group communication protocol for mobile agent groups to consider weaker message ordering consistency than atomic broadcast semantics while ensuring reliability even with the assumption of unreliable networks for exchanging messages to agent groups and agent mobility. The simulation results show the proposed protocol performs better than the existing atomic broadcast protocol in terms of the total message delivery latency.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125291691","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 : 2014-11-01DOI: 10.1109/SMARTCOMP.2014.7043846
Simone Brienza, F. Bindi, G. Anastasi
The overall energy consumption due to ICT equipment has followed an increasing trend over the last years. A considerable fraction of the consumed energy is caused by user devices, such as Personal Computers (PCs) and displays. However, a large part of this energy is wasted due to an inefficient use. Users leave their PCs on for long periods even when unused, especially in workplaces. Hence, significant energy savings could be achieved just turning them off. However, it is not wise to rely on user collaboration, and, thus, automated tools are needed. In this paper, we present E-Net-Manager, a power management system for large environments, which turns unused PCs off and switches them on when the user is about to use them. To this end, E-Net-Manager leverages soft sensors, i.e., software/hardware tools already in use by the users, thus not introducing any additional cost. E-Net-Manager combines information provided by the users and data obtained from a number of these soft sensors. This way, it is possible to accurately determine the user presence/activity near her/his PC and, therefore, eliminate wastes also due to short periods of inactivity.
{"title":"E-Net-Manager: A power management system for networked PCs based on soft sensors","authors":"Simone Brienza, F. Bindi, G. Anastasi","doi":"10.1109/SMARTCOMP.2014.7043846","DOIUrl":"https://doi.org/10.1109/SMARTCOMP.2014.7043846","url":null,"abstract":"The overall energy consumption due to ICT equipment has followed an increasing trend over the last years. A considerable fraction of the consumed energy is caused by user devices, such as Personal Computers (PCs) and displays. However, a large part of this energy is wasted due to an inefficient use. Users leave their PCs on for long periods even when unused, especially in workplaces. Hence, significant energy savings could be achieved just turning them off. However, it is not wise to rely on user collaboration, and, thus, automated tools are needed. In this paper, we present E-Net-Manager, a power management system for large environments, which turns unused PCs off and switches them on when the user is about to use them. To this end, E-Net-Manager leverages soft sensors, i.e., software/hardware tools already in use by the users, thus not introducing any additional cost. E-Net-Manager combines information provided by the users and data obtained from a number of these soft sensors. This way, it is possible to accurately determine the user presence/activity near her/his PC and, therefore, eliminate wastes also due to short periods of inactivity.","PeriodicalId":169858,"journal":{"name":"2014 International Conference on Smart Computing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125079096","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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