Al-Zabin Lial Raja, Al-Wesabi Ola A, Al Hajri Hamed, Abdullah Nibras, Khudayer Baidaa Hamza, Al Lawati Hala
Information Technology Department, Al-Zahra College for Women, Muscat 3365, Oman.
Faculty of Computer Science and Engineering, Hodeidah University, Hodeidah P.O. Box 3114, Yemen.
Sensors (Basel). 2023 Aug 3;23(15):6918. doi: 10.3390/s23156918.
Wireless sensor networks (WSNs) have been commonly utilized in event detection and environmental observation applications. The main aim of event detection is to define the presence or absence of an event. Various existing studies in the field of event detection depend on static or threshold values to reveal the occurrence of an event, which can result in imprecise sensor readings. Recently, many studies have utilized fuzzy logic to treat fluctuating sensor readings; as a result, they have decreased the number of false alarms created. However, there is some attention required when utilizing fuzzy logic. One aspect is that the efficiency and accuracy of the fuzzy membership function can be impacted by the utilization of heterogeneous sensors, which may increase the complexity of the fuzzy logic operation as the number of inputs rises. To address these issues, this paper proposes an approach named Probabilistic Collaborative Event Detection (PCED), which is a hybrid event detection technique that is based on a cluster WSN topology. The PCED approach utilizes a validated probabilistic technique for heterogeneous sensor nodes to transform sensing values into probability formulas and introduces a Cluster Head Decision Mechanism to make decisions based on the aggregated data from the sensors. The proposed approach employs fuzzy logic at the fusion center level to enhance the precision of event detection. The effectiveness of this method is thoroughly evaluated using MATLAB software, demonstrating an improvement in the probability of detection and a decrease in the probability of false alarms. PCED is compared to well-established event detection mechanisms such as the REFD mechanism. The results show that PCED reduces the occurrence of false alarms from 37 to 3 in certain scenarios, while improving detection accuracy by up to 19.4% over REDF and decreasing detection latency by up to 17.5%.
无线传感器网络（WSNs）已被广泛应用于事件检测和环境观测应用中。事件检测的主要目的是确定事件的存在与否。事件检测领域中现有的各种研究依赖于静态或阈值来揭示事件的发生，这可能导致传感器读数不准确。最近，许多研究利用模糊逻辑来处理波动的传感器读数；结果，它们减少了产生的误报数量。然而，在使用模糊逻辑时需要一些注意事项。一方面，模糊隶属函数的效率和准确性可能会受到异构传感器使用的影响，随着输入数量的增加，这可能会增加模糊逻辑操作的复杂性。为了解决这些问题，本文提出了一种名为概率协作事件检测（PCED）的方法，它是一种基于簇状WSN拓扑的混合事件检测技术。PCED方法利用经过验证的概率技术对异构传感器节点进行处理，将传感值转换为概率公式，并引入簇头决策机制，根据传感器的聚合数据进行决策。所提出的方法在融合中心级别采用模糊逻辑来提高事件检测的精度。使用MATLAB软件对该方法的有效性进行了全面评估，结果表明检测概率有所提高，误报概率有所降低。将PCED与成熟的事件检测机制（如REFD机制）进行了比较。结果表明，在某些场景下，PCED将误报的发生率从37次降低到3次，同时与REDF相比，检测准确率提高了19.4%，检测延迟降低了17.5%。
