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14th CCF Conference on Computer Supported Cooperative Work and Social Computing

作者： Cai, Saihua Sun, Ruizhi Mu, Huiyu Shi, Xiaochen Yuan, Gang China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Sci Res Base Integrated Technol Precis Agr Anim H Beijing 100083 Peoples R China

ISBN: (纸本)9789811513770;9789811513763

In recent years, the scale of data stream is becoming much larger in real life. However, the anomaly data often exists in the collected data stream, while the existence of anomaly is a main reason for the decrease of the accuracy of data-based operations. The anomaly data have two main characteristics, that is, appear rarely and deviate much from most data elements, thus, the anomaly detection methods should accurately detect the anomaly data by considering these two attributes. Because the data stream is continuously generated and constantly flowing, thus, the previous static anomaly detection methods are not suitable for processing data streams. In addition, the large amount of data stream makes the time consumption and memory occupation of rare itemset mining phase very high. To effectively solve these problems, this paper first proposes an efficient MRI-Mine method for mining minimum rare itemsets, and then proposes an accurately anomaly detection method called MRI-AD based on anomaly index to identify the implicit anomaly data. The experiments indicate the proposed MRI-Mine method can mine the minimum rare itemsets in less time consumption and memory occupation, and the detection accuracy of MRIAD method is also competitive.

关键词： Anomaly detection Minimum rare itemsets Anomaly index sensor data stream data mining

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Mining Associated Patterns from Wireless sensor Networks

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IEEE TRANSACTIONS ON COMPUTERS 2015年第7期64卷 1998-2011页

作者： Rashid, Md. Mamunur Gondal, Iqbal Kamruzzaman, Joarder Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Federat Univ Mt Helen Vic Australia Monash Univ Clayton Vic 3800 Australia

Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

关键词： Wireless sensor networks data mining knowledge discovery behavioral patterns sensor data stream

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Online conflict resolution strategies for human activity recognition in smart homes

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Journal of Control and Decision 2023年第3期10卷 402-416页

作者： Amina Jarraya Amel Bouzeghoub Amel Borgi Facultédes Sciences de Tunis Universitéde Tunis El ManarTunisTunisia Samovar Télécom SudParisInstitut Polytechnique de ParisPalaiseauFrance Facultédes Sciences de Tunis Institut Supérieur d’InformatiqueUniversitéde Tunis El ManarTunisTunisia

DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current ***, they learn from their collaborations to improve their own performance in activity *** resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other *** this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .

关键词： Human activity recognition distributed reasoning learning agents smart homes online learning conflict resolution strategies sensor data stream

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Transformer based Early Classification for Real-time Human Activity Recognition in Smart Homes 23

Transformer based Early Classification for Real-time Human A...

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38th Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Lee, Tae-Hoon Kim, Hyunju Lee, Dongman Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781450395175

Human activity recognition (HAR) plays a key role in intelligent systems. Ambient sensors are utilized to avoid privacy concerns and to collect data streams in a less intrusive manner in smart homes. In scenarios requiring immediate intervention, the systems must perform HAR in real-time. Since it is difficult to segment the exact transition point in real-time, data unrelated to the target activity can appear at the beginning of the time series, which we call unrefined data. It leads us to a new challenge that the HAR model recognizes a user's activity as early as possible with unrefined data. To explore the impact of unrefined data on real-time HAR, we design an experimental system that consists of a Transformer-based filtering network and an LSTM-based early classifier. We evaluate the experimental system with 3 public datasets collected on testbeds with ambient sensors installed. Our results reveal that unrefined data degrade HAR performance in terms of accuracy and earliness, and the use of the filtering network that filters out unrefined data improves recognition performance.

关键词： Human activity recognition Early classification Transformer Smart homes sensor data stream

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Design and Implementation of a System for Environmental Monitoring sensor Network

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9th Asia-Pacific Web Conference/8th International Conference on Web-Age Information Management (APWeb/WAIM 2007)

作者： Lee, Yang Koo Jung, Young Jin Ryu, Keun Ho Chungbuk Natl Univ Database Bioinformat Lab Cheongju South Korea

ISBN: (纸本)9783540729082

In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss Of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system.

关键词： Wireless sensor Network sensor data stream Spatiotemporal data stream Continuous Query Historical Query

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A Deep Anomaly Detection System for IoT-Based Smart Buildings

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sensorS 2023年第23期23卷 9331-9331页

作者： Cicero, Simona Guarascio, Massimo Guerrieri, Antonio Mungari, Simone Natl Res Council Italy Inst High Performance Comp & Networking ICAR CNR Via P Bucci 8-9C I-87036 Amantea CS Italy Univ Calabria Via P Bucci I-87036 Arcavacata Di Rende CS Italy

In recent years, technological advancements in sensor, communication, and data storage technologies have led to the increasingly widespread use of smart devices in different types of buildings, such as residential homes, offices, and industrial installations. The main benefit of using these devices is the possibility of enhancing different crucial aspects of life within these buildings, including energy efficiency, safety, health, and occupant comfort. In particular, the fast progress in the field of the Internet of Things has yielded exponential growth in the number of connected smart devices and, consequently, increased the volume of data generated and exchanged. However, traditional Cloud-Computing platforms have exhibited limitations in their capacity to handle and process the continuous data exchange, leading to the rise of new computing paradigms, such as Edge Computing and Fog Computing. In this new complex scenario, advanced Artificial Intelligence and Machine Learning can play a key role in analyzing the generated data and predicting unexpected or anomalous events, allowing for quickly setting up effective responses against these unexpected events. To the best of our knowledge, current literature lacks Deep-Learning-based approaches specifically devised for guaranteeing safety in IoT-Based Smart Buildings. For this reason, we adopt an unsupervised neural architecture for detecting anomalies, such as faults, fires, theft attempts, and more, in such contexts. In more detail, in our proposal, data from a sensor network are processed by a Sparse U-Net neural model. The proposed approach is lightweight, making it suitable for deployment on the edge nodes of the network, and it does not require a pre-labeled training dataset. Experimental results conducted on a real-world case study demonstrate the effectiveness of the developed solution.

关键词： internet of things sensor data stream deep learning safety anomaly detection industry 4.0

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PCMCR: A Novel Conflict Resolution Strategy based on Possibility Theory for Human Activity Recognition

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Procedia Computer Science 2022年 207卷 926-935页

作者： Ilef Ben Slima Amina Jarraya Sourour Ammar Amel Borgi Digital Research Center of Sfax 3021 Sfax Tunisia SM@RTS: Laboratory of Signals systeMs aRtificial Intelligence and neTworkS Sfax Tunisia Université de Tunis El Manar Faculté des Sciences de Tunis LIPAH LR11ES14 2092 Tunis Tunisia Université de Tunis El Manar Institut Supérieur d'Informatique Faculté des Sciences de Tunis LIPAH LR11ES14 2092 Tunis Tunisia

DCR is a Distributed Collaborative Reasoning multi-agent model that aims to recognize human activities in smart homes from distributed, heterogeneous and dynamic sensor data. In this model, distributed agents with diverse classifiers, detect sensor stream data, make local predictions, communicate and collaborate to identify current activities. Conflict resolution strategies are applied to generate one final predicted activity when the local predicted activity of an agent is different from received predicted activities of other agents. In this paper, a possibilistic conflict resolution strategy, PCMCR , is proposed. Possibility theory is particularly efficient in combining multiple agents predictions that can be incomplete, uncertain, and conflicting. The PCMCR strategy deals with uncertainty factor which can be presented in the predictions of poor agents. It can take advantage of the complementary information given by each agent, even the weak ones. We experimentally test this strategy by performing an evaluation study on Aruba dataset. The obtained results indicate an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the existing conflict resolution method max-trust of DCR, to the centralized model and to an existing distributed system.

关键词： Activity Recognition Distributed reasoning Smart homes Possibility theory Uncertainty aggregation Conflict resolution strategies sensor data stream

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ZebraBAN: a heterogeneous high-performance energy efficient wireless body sensor network

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IET WIRELESS sensor SYSTEMS 2013年第4期3卷 247-254页

作者： Lu, Xiyu Chen, Xinlei Li, Yong Jin, Depeng Zeng, Lieguang Rashvand, Habib F. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Warwick Knowle Off Adv Commun Syst Solihull CV4 7AL W Midlands England

Recently, there are much research on wireless body sensor network (WBSN) as the growing demands of pervasive health and personal entertainment. Supporting high-speed data transmission becomes a challenge in development of WBSN in medical and entertainment applications. In this study, the authors present ZebraBAN, a high-speed low-power WBSN based on single-carrier ultra-wideband (SC-UWB) and IEEE 802.15.4. The heterogeneous network combines the strengths of SC-UWB and IEEE 802.15.4 technologies while offsetting their weakness. Like SC-UWB, the system achieves medium to high data rate and reduces radiation and power consumption at a low cost. Like IEEE 802.15.4, ZebraBAN achieves low data rate and low latency under low contention. In ZebraBAN, the authors introduce the idea of control plane and data plane for controlling multiple PHYs and MACs according to energy and data rate balance. Experimental results based on the prototype system are presented to evaluated the network performance. The result demonstrates that high reliability transmission for every sensor data stream within 4 m around the person, with a low-power consumption for fabricated application specific integrated circuits (ASIC) about 8.2 mW when it works on transmitting one channel video. The network has a peak capacity of 100 Mbps that could support multiple sensor nodes, including four channel video stream and multiple low-speed sensor datas.

关键词： entertainment performance evaluation telecommunication network reliability ultra wideband communication body sensor networks ZebraBAN heterogeneous high-performance energy efficient wireless body sensor network pervasive health personal entertainment high-speed data transmission multiple low-speed sensor datas four channel video stream multiple sensor nodes fabricated ASIC low-power consumption sensor data stream reliability transmission network performance MAC multiple PHY low latency radiation IEEE 802.15.4 technologies SC-UWB single-carrier ultra-wideband high-speed low-power WBSN entertainment applications medical applications

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