Mobile Ad Hoc Networks (MANETs) are autonomous wireless networks that do not rely on fixed infrastructure for communication among the mobile nodes. The mobile nodes communicate with each other with no centralized control, and routes are not established in advance. This makes MANETs vulnerable to routing attacks such as Black Hole and Sybil attacks, which divert or drop packets and thereby degrade network performance. This paper presents a novel hybrid approach combining Federated Learning (FL) and Graph Attention Network (GAT), which is termed as FLAT. GAT dynamically assigns importance to neighbouring nodes, allowing the model to capture the mobility, link instability, and heterogeneous node behaviour inherent in MANETs. FL, in turn, enables routing and attack patterns to be learned in a decentralized manner without sharing raw data. The approach was evaluated in NS-3.36 on networks ranging from 10 to 150 nodes, under Black Hole and Sybil attacks with 20% of the nodes acting maliciously, and was compared against AODV, SAODV, AOMDV, and the optimization-based Dolphin Cat Optimizer, using PDR, PLR, throughput, and end-to-end delay as evaluation metrics. It is found that the PDR of FLAT is approximately 15.1% higher than AODV and 8.2% higher than the Dolphin Cat Optimizer, while its PLR is reduced by about 77% relative to AODV, 75% relative to SAODV, and 50% relative to the Dolphin Cat Optimizer. With respect to throughput performance, FLAT shows a performance level of about 4.4, 2.8, 2.3, and 2.1 times higher than AODV, SAODV, AOMDV, and Dolphin Cat Optimizer respectively. The above findings clearly show that FLAT outperforms current techniques with regard to all measured parameters.

Wireless Sensor Networks (WSNs) are one of the most researched areas worldwide as the wide-scale networks possess low cost, are small in size, consume low power, and can be deployed in various environments. Among various applications of WSNs, target tracking is a highly demanding and broadly investigated application of wireless sensor networks. The parameter of accurate tracking is restricted because of the limited resources present in the wireless sensor networks, noise of the network, environmental factors, and faulty sensor nodes. Our work aims to enhance the accuracy of the tracking process as well as energy utilization by combing the mechanism of clustering with the prediction. Here, we present a hybrid energy-regulated constant gain Kalman filter-based target detection and tracking method, which is an algorithm to make the best use of energy and enhance precision in tracking. Our proposed algorithm is compared with the existing approaches where it is observed that the proposed technique possesses efficient energy utilization by decreasing the transference of unimportant data within the sensor network, achieving accurate results.