Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used to solve it. Grey Wolf Optimization (GWO) is one of the most popular algorithms for solving such problems. However, standard GWO has several limitations, such as premature convergence, susceptibility to local minima, and unsuitability for dynamic environments due to its lack of adaptive learning. We propose a Q-learning-based GWO algorithm to address these issues in this study. QGWO introduces four key features: a Q-learning-based adaptive convergence factor, a segmented and parameterized position update strategy, a long-jump mechanism for population diversity preservation, and the replacement of non-dominant wolves for improved exploration. In addition, the Bayesian optimization algorithm is used to set parameters in QGWO for better performance. To evaluate the quality and robustness of QGWO, extensive numerical and simulation experiments were conducted on IEEE CEC 2022 benchmark functions, comparing it with standard GWO and some of its recent variants. In path planning simulation, QGWO lowers the path cost by 27.4%, improves the convergence speed by 19.06%, and reduces the area under the curve (AUC) by 23.8% over standard GWO, achieving optimal trajectory. Results show that QGWO is an efficient, reliable algorithm for UAV path planning in dynamic environments.

For the last decade or so wireless and mobile communication and its application has been enjoying tremendous attention because of their unique features. More recently, an increasing emphasis has been on the potential of infrastructure less wireless mobile networks that are easy, fast and in-expensive to set up, with the view that such technologies will enable numerous new applications in a wide range of areas. However, security in ILWN is still its primary stage as very little attention has been devoted so far to this topic by the research community. We identify the initial exchange of authentication and key credentials, referred to as pre-authentication, as well as authentication and key exchange as primary security goals. In particular, the problem of authentication has been widely neglected in existing security solutions, even though it is a necessary pre-requisite for other security goals. The Elliptic curve crypto system (ECC) provides more security in less key length which makes is energy efficient. Through a thorough study and implementation we show the scheme provide better security to infrastructure less wireless networks.