Transitive reasoning: A high-performance computing model for significant pattern discovery in cognitive IoT sensor network

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Ad Hoc Networks Pub Date : 2024-10-31 DOI:10.1016/j.adhoc.2024.103700
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Abstract

Current research on the Internet of Things (IoT) has given rise to a new field of study called cognitive IoT (CIoT), which aims to incorporate cognition into the designs of IoT systems. Consequently, the CIoT inherits specific attributes and challenges from IoT. The CIoT applications generate vast, diverse, constantly changing, and time-dependent data due to the billions of devices involved. The efficient operation of these CIoT systems requires the extraction of valuable insights from vast data sources in a computationally efficient manner. Therefore, this study proposes transitive reasoning to glean significant concepts and patterns from a 21.25-year environmental dataset. To reduce the effects of missing entries, the proposed methodology includes a grouping of data using probabilistic clustering and applying total variance regularization in the alternate direction method of multipliers (ADMM) to regularize the sensory data. As a result, noisy entries will be less conspicuous. Afterward, it calculates the transitional plausibility value for each cluster using the transited value and then turns it into binary data to create concept lattices. In addition, each concept that is formed is assigned a weight, and the concept with the largest transitive strength value is chosen, followed by calculating the mean value. Therefore, this pattern is seen as significant. Experimental results on 21.25-year environmental data show an accuracy of over 99.5%, outperforming competing methods, as shown by cross-validation using multiple metrics.
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传递推理:认知物联网传感器网络中发现重要模式的高性能计算模型
当前对物联网(IoT)的研究催生了一个名为认知物联网(CIoT)的新研究领域,其目的是将认知纳入物联网系统的设计中。因此,CIoT 继承了物联网的特定属性和挑战。由于涉及数十亿台设备,CIoT 应用会产生大量、多样、不断变化且与时间相关的数据。这些 CIoT 系统的高效运行需要以计算高效的方式从庞大的数据源中提取有价值的见解。因此,本研究提出了从 21.25 年的环境数据集中提取重要概念和模式的直观推理方法。为了减少缺失条目的影响,所提出的方法包括使用概率聚类对数据进行分组,并在交替乘数方向法(ADMM)中应用总方差正则化对感官数据进行正则化。因此,噪声条目将不那么明显。之后,它利用过渡值计算每个聚类的过渡可信度值,然后将其转化为二进制数据,创建概念网格。此外,形成的每个概念都会被赋予一个权重,并选择过渡强度值最大的概念,然后计算平均值。因此,这种模式被认为是重要的。对 21.25 年环境数据的实验结果表明,该方法的准确率超过 99.5%,优于其他竞争方法。
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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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