The integration of Internet of Things (IoT) networks and anomaly detection systems can enhance and improve spaces such as smart offices. By including anomaly detection, the reliability can increase and systems can become more useful. In this paper, we present an unsupervised transformer-based model capable of detecting anomalies in multivariate IoT sensor data, optimized for edge deployment. Through the use of real-world data provided by Sony's smart office system called Nimway, the model applies a reconstruction-based approach with multi-head attention to capture temporal and contextual dependencies. The model has been evaluated on test dataset, full dataset, and a synthetic anomaly dataset. The proposed transformer detected 65 anomalous data points distributed in 7 anomaly groups on test dataset, and 169 anomalies in 23 anomaly groups in the full dataset. By incorporating a dynamic quantization, model size has been reduced by 75% (to 1.14MB), which makes it more suitable for edge deployment and enables faster inference in realtime smart office environments. The quantized model achieved the test loss of 0.0495 (Mean Absolute Error). Moreover, models with and without quantization achieved the same performance by correctly flagging 10 out of 15 anomalies, indicating that the size reduction did not affect the model's accuracy. Preliminary results of attention-based feature importance visualization offer early insights into the features that explain the anomalies. Although the work is still in progress, this study addresses important aspects of IoT anomaly detection such as resource constraint, unsupervised learning and contextual anomalies.