The Internet of Things (IoT) encompasses a diverse array of physical entities, including sensors, electronics, networks, devices, and software, all interconnected to facilitate data exchange. IoT technology, while still emerging, promises transformative applications across domestic and commercial domains. However, the nascent nature of IoT presents numerous challenges that necessitate innovative solutions. Among these challenges, the paramount task is the development of robust methods for identifying anomalies within sensor data, a pervasive issue in IoT environments. This literature review provides a thorough examination of the extensive body of research dedicated to anomaly detection algorithms tailored for both univariate and multivariate time-series data, a common data format encountered within the realm of the Internet of Things.