Intensity Based Event Detection in Sensor Based IoT

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-03 DOI:10.1109/TNSE.2025.3556057

Anubhav Shivhare;Adarsh Prasad Behera;Manish Kumar

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引用次数: 0

Abstract

Finding an optimum trade-off between event detection and network lifetime is a major problem in the sensor-based Internet of Things framework. Further, reliable, effective, and accurate event detection is a perennial research problem explored in the domain of Sensor Based Internet of Things (SBIoT). Major research problems focusing on event detection depend upon models like Boolean and probabilistic sensing models. However, event detection is practically dependent upon the intensity and persistence of the event. The traditional non-intensity-based event sensing models fix a predefined sensing radius. Any occurrence outside the sensing radius is not considered an event, independent of its severity. The present work argues that the intensity and persistence of the event are also relevant parameters for event detection. This paper proposes two novel event intensity and persistence-based models for detecting different types of events and improving upon the quality of detection. The proposed ’Improved' model proves to be more efficient than the proposed ’Conventional' model. Further, the simulation results indicate the proposed algorithm's efficiency and effectiveness, and compare it with Non-intensity based models. Additionally, the results are compared in terms of detection accuracy, node activation, and network lifetime to show the efficiency and trade-offs of the proposed scheme.

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传感器物联网中基于强度的事件检测

在基于传感器的物联网框架中，在事件检测和网络生命周期之间寻找最佳平衡点是一个主要问题。此外，可靠、有效、准确的事件检测是基于传感器的物联网（sbot）领域长期探索的研究问题。关注事件检测的主要研究问题依赖于布尔模型和概率感知模型。然而，事件检测实际上依赖于事件的强度和持久性。传统的非基于强度的事件感知模型固定了预定义的感知半径。任何发生在感应半径之外的事件都不被视为事件，与其严重程度无关。本研究认为，事件的强度和持续时间也是事件检测的相关参数。本文提出了两种新的基于事件强度和持久性的事件检测模型，用以检测不同类型的事件，提高检测质量。所提出的“改进”模型证明比所提出的“传统”模型更有效。仿真结果验证了该算法的有效性，并与非强度模型进行了比较。此外，还比较了检测精度、节点激活和网络生命周期方面的结果，以显示所提出方案的效率和权衡。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.