Routing and security are the two main prerequisites for ensuring the correct operation of wireless networks. The importance of these cases doubles in wide networks such as IoT. This paper presents an algorithm to improve Secure Routing in IoT called SRAIOT. This algorithm uses a hierarchical structure to determine the connections between network components and data transfer routing. In SRAIOT, the network structure is managed hierarchically and through SDN. For this purpose, the IoT network is first divided into a set of subnets using the SDN solution, communication control and authentication are managed using the controller nodes of each subnet. The communication between two objects (located in different subnets) will be possible if their identity is confirmed through the controller nodes related to them. On the other hand, in order to identify the sources of attacks and network security threats, the controller nodes in each subnet monitor the network traffic pattern using an ensemble learning model and identify possible attacks in their subnet. The performance of SRAIOT was tested in the simulation, and the results were compared with previous methods. The results of these tests show that SRAIOT improves network performance regarding routing and detecting attacks.

Federated edge learning (FEEL) is an innovative approach that facilitates collaborative training among numerous distributed edge devices while eliminating the need to transfer sensitive information. However, the practical deployment of FEEL faces significant constraints, owing to the limited and asymmetric computational and communication resources of these devices, along with their energy availability. To this end, we propose a novel asymmetry-tolerant training approach for FEEL, enabled via simultaneous wireless information and power transfer (SWIPT). This framework leverages SWIPT to offer sustainable energy support for devices while enabling them to train models with varying intensities. Given a limited energy budget, we highlight the critical trade-off between heterogeneous local training intensities and the quality of wireless transmission, suggesting that the design of local training and wireless transmission should be closely integrated, rather than treated as separate entities. To elucidate this perspective, we rigorously derive a new explicit upper bound that captures the combined impact of local training accuracy and the mean square error of wireless aggregation on the convergence performance of FEEL. To maximize overall system performance, we formulate two key optimization problems: the first aims to maximize the energy harvesting capability among all devices, while the second addresses the joint learning–communication optimization under the optimal energy harvesting solution. Comprehensive experiments demonstrate that our proposed framework achieves significant performance improvements compared to existing baselines.