DeepSense—Deep neural network framework to improve the network lifetime of IoT‐MANETs

Summary With the advancement of Internet of Things (IoT), the devices are allowed to interact with other networks like mobile ad hoc network (MANET). The MANET‐IoT systems often undergo energy balancing problem between the sensor nodes, whereas the MANETs operate on mobile sensor nodes. Hence, proper utilization of battery power is required to maintain the network connectivity during a multi‐hop transmission. In this paper, we propose a DeepSense IoT‐MANET framework that effectively routes the packets from the IoT nodes via mobile sensor nodes in MANETs. The routings between the MANETs are organized by DeepSense interconnected with deep neural network (DNN) learning methods. The performance of the DeepSense DNN method is evaluated against various network metrics to evaluate the efficacy of the model. This paper designs and analyzes the DeepSense deep neural network model in terms of routing over an integrated IoT‐MANETs architecture. The system design involves three different planes that include control, data, and routing plane to manage the process of routing w.r.t high data rate of input IoT devices.