Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

Abstract Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum-rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations on the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information

Optimum range of angle tracking radars: a theoretical computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other

Guest editorial: Special issue on enabling massive iot with 6g: Applications, architectures, challenges, and research directions

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Guest Editorial: Special issue on green internet of things: Challenges and future opportunities - Part II

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Guest Editorial Special Issue on Green Internet of Things: Challenges and Future Opportunities - Part i

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Secrecy Outage Probability of Relay Selection Based Cooperative NOMA for IoT Networks

As an important partner of fifth generation (5G) communication, the internet of things (IoT) is widely used in many fields with its characteristics of massive terminals, intelligent processing, and remote control. In this paper, we analyze security performance for the cooperative non-orthogonal multiple access (NOMA) networks for IoT, where the multi-relay Wyner model with direct link between the base station and the eavesdropper is considered. In particular, secrecy outage probability (SOP) for two kinds of relay selection (RS) schemes (i.e., single-phase RS (SRS) and two-phase RS (TRS)) is developed in the form of closed solution. As a benchmark for comparison, the SOP for random RS (RRS) is also obtained. To gain more meaningful insights, approximate derivations of SOP under the high signal-to-noise ratio (SNR) region are provided. Results of statistical simulation confirm the theoretical analysis and testify that: i) Compared with RRS scheme, SRS and TRS may improve secure performance because of obtaining smaller SOPs; ii) There exists secrecy performance floor for the SOP in strong SNR regime, which is dominated by NOMA protocol; iii) The security performance can be enhanced by augmenting the quantity of relays for SRS and TRS strategies. The purpose of this work is to provide theoretical basis for the analysis and design of anti-eavesdropping for NOMA systems in IoT

A Parallel Military Dog based Algorithm for Clustering Big data in Cognitive Industrial Internet of Things

With the advancement of wireless communication, internet of things, and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big data based problems, since it does not require labeled datasets. Recently, meta-heuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big data sets produced from IoT devices, these algorithm fail to respond within desired time due to high computation cost. This paper presents a new meta-heuristic based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big data sets. The optimization efficacy the proposed method is validated against 17 benchmark functions and the results are compared with 5 other recent algorithms namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Further, a parallel version of the proposed method is introduced using MapReduce (MR-MDBO) for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on 2 benchmark UCI datasets and 3 real IoT based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the 6 other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times