The widespread integration of Internet of Things (IoT) technology in the military domain has brought significant attention to the security concerns surrounding the Internet of Battlefield Things (IoBT). Given the limited communication resources within IoBT, there is a growing focus on detecting network security without interrupting normal network operations. Topology serves as a crucial foundation for the detection of network security in IoBT, facilitating the discovery of abnormal devices and the detection of unauthorized access. Security detection based on topology can effectively enhance the information security and operational levels of IoBT. This paper utilizes matching analysis of time series for information exchanged between neighboring nodes and implements IoBT topology inference based on flow rate estimation, and a threshold parameter adaptive adjustment strategy is innovatively proposed to improve the accuracy of topology inference. The non-cooperative inference method proposed in this paper enables network topology inference without network access and information parsing, exhibiting strong generality and independence from the discovery of acknowledgment frames during information exchange processes. The simulation results demonstrate the feasibility and superiority of this method.

The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based greedy learning (MGL) algorithm is proposed, which can ensure convergence to Nash equilibrium. Compared with existing representative algorithms, our proposed algorithm performs the best in terms of average coverage, best value, and standard deviation within within a suitable time. In addition, increasing memory length helps to generate a better Nash equilibrium.

The widespread integration of Internet of Things (IoT) technology in the military domain has brought significant attention to the security concerns surrounding the Internet of Battlefield Things(IoBT). Given the limited communication resources within IoBT, there's a growing focus on conducting network security detection without disrupting normal network operations. Topology serves as a crucial foundation for conducting network security detection in IoBT, facilitating the discovery of abnormal devices and unauthorized access detection. Security detection based on topology can effectively enhance the information security and operational levels of IoBT. This paper utilizes matching analysis of time series for information exchanged between neighboring nodes and implements IoBT topology inference based on flow rate estimation, and a threshold parameter adaptive adjustment strategy is innovatively proposed to improve the accuracy of topology inference. The non-cooperative inference method proposed in this paper enables network topology inference without network access and information parsing, exhibiting strong generality and independence from the discovery of acknowledgment frames during information exchange processes. Simulation results demonstrate the feasibility and superiority of this method.