Towards Secure and Reliable IoT: A Review on Anomaly Detection and Predictive Analytics in Sensor Networks
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P116Keywords:
Internet Of Things (IoT), Anomaly Detection, Predictive Analytics, Sensor Networks, Cybersecurity, Machine LearningAbstract
As IoT sensor networks spread into new domains like smart cities, healthcare, industry, transportation infrastructure, and environmental monitoring, predictive analytics and anomaly detection have taken centre stage in improving the security, reliability, and continuity of these networks. The raises in data volumes, distributed designs, non-homogenous sensing instruments, and real time decision needs augment the hazards of sensor faults, cyberattacks, spoofing, data drift, noisy of interfering data, and miscalibration. Smart analytical processes are thus important in detecting abnormal patterns and predicting failures and trusted sensing environments. In this review, the traditional statistical methods, ML methods, and the latest DL and hybrid models are examined in terms of accuracy, scalability, computing footprint, and the ability to be deployed at an edge, fog, and cloud layer. The latest developments such as federated learning, adaptive contextual models, transfer learning, edge intelligence, and automated thresholding are discussed as opportunities to decrease false alarms and increase detection accuracy. This paper also identifies the issues associated with the lack of data sets, the problem of inconsistency of benchmarking, streaming data processing, and measurement scales. The general synthesis shows that combining anomaly detection and predictive analytics can be used to build proactive maintenance, risk mitigation and resilient IoT performance in more dynamic and interconnected environments.
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