Location estimation has become essential for mobile and pervasive computing applications of indoor wireless networks. Trilateration is a basic building block of the range based localization techniques, which requires the set of at least three distances from the known locations, and these distances may be estimated by Received Signal Strength (RSS) using the radio propagation models. The distance estimation of a mobile device from known access points is erroneous due to the imperfection of the radio propagation models for the complex indoor environment. Since distance estimation is erroneous, so it would be beneficial to use less number of distances for location estimation. In this paper, we have proposed a Heron-Bilateration location estimation method which requires only two distances to locate the mobile device. As we are using less number of erroneous distances, therefore, our proposed method not only improves the accuracy but also reduces the computation complexity. The experimental results show that the proposed approach outperforms in comparison of conventional trilateration method.

Event detection on real-time traffic data is crucial for transportation systems, enhancing safety in autonomous systems and the resilience of urban mobility infrastructure. However, the complex, heterogeneous traffic stream data and spatial-temporal patterns pose a significant challenge to existing methods, which often fail to capture higher-order interactions among nodes and edges and semantic context. This paper proposes a generative, self-attention model layered with two stacked content-based layers based on a framework that incrementally handles the process of traffic stream networks. Experimental analysis on benchmark event -detection dataset demonstrates the effectiveness of the proposed methodology, attaining peak performance values of 0.80 in Normalized mutual information(NMI), 0.73 IN Adjusted Mutual Information (AMI), and 0.72 in Adjusted Rand Index(ARI) across the incremental block of heterogeneous data, the proposed method consistently outperforms state-of-the-art baselines.