An energy efficient flexible delay tolerant network with adaptive secured framework (ASF-DTN)

Delay tolerant networks are widely used in mobile communications because of network withstanding capability of delay. However, when the connectivity of nodes increases, data loss may occur while transmission. Due to the malicious behavior of nodes the data may get permanently lost, which is known as black hole attacks and there is a chance to partial data loss because of the lower energy level of a node, which is known as a gray hole attack. In existing work, the data capacity of a node is not limited, so most of the highly energy efficient node contains a huge amount of data, when compared to other nodes which may lead to random attack. To overcome this, we propose a subjective capability model (SCM) for each and every node to limit the capacity of each node. ASF-DTN prevents collision attack and injection attack by implementing Kalman filtering, which can statistically analyze the behavior of nodes while each and every transmission. Here, we propose an effective node optimization scheme using genetic algorithm with a fitness function to find out energy efficient nodes among the optimal path for effective communication and its performance.