Localization is one of the critical issues on wireless sensor networks. Localization schemes are classified into range-based and range-free according to the method of whether to use range information. In this paper, we propose a novel range-based localization scheme which involves a movement strategy of mobile beacon, called mobile beacon-assisted localization (MBAL). Contrary to many research activities which have been carried out to design localization schemes using mobile beacons only based on random movement method, we consider totally a new scheme providing movement path selection with a low computational complexity. A new range check technique is also adopted into the MBAL as a useful solution to the position-ambiguity problem of bilateration in order to improve the performance of the proposed localization scheme. Simulation results verify that the MBAL impressively achieves energy efficiency because of its mobile beacon based approach using the proposed movement strategy and range check technique.

Directional antennas can divide the transmission range into several sectors. Thus, through switching off sectors in unnecessary directions in wireless networks, we can save bandwidth and energy consumption. In this paper, we will study a directional virtual backbone (VB) in the network where directional antennas are used. When constructing a VB, we will take routing and broadcasting into account since they are two common operations in wireless networks. Hence, we will study a VB with guaranteed routing costs, named α Minimum rOuting Cost Directional VB (α-MOC-DVB). Besides the properties of regular VBs, α-MOC-DVB also has a special constraint - for any pair of nodes, there exists at least one path all intermediate directions on which must belong to α-MOC-DVB and the number of intermediate directions on the path is smaller than α times that on the shortest path. We prove that construction of a minimum α-MOC-DVB is an NP-hard problem in a general directed graph. A heuristic algorithm is proposed and theoretical analysis is also discussed in the paper. Extensive simulations demonstrate that our α-MOC-DVB is much more efficient in the sense of VB size and routing costs compared to other VBs.

Deep learning has been widely applied to automatic modulation classification (AMC), and there have been many studies on data augmentation techniques using deep generative models to improve performance. However, existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. This letter presents UniQGAN, Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introduces multi-conditions embedding and multi-domains classification to leverage both conditions, i.e., modulation type and SNR. Experimental results show that UniQGAN effectively improves the AMC performance, while the training time is reduced.