WiFi fingerprint based indoor localization has become a key research direction in the field of indoor localization due to its high positioning accuracy and low equipment deployment cost. Increasing the number of reference point collected offline can improve the positioning accuracy, however it yields excessive cost of offline collection. Fingerprint augment is an effective solution to reduce the cost while ensuring the positioning accuracy. In this paper, we are pioneering to propose a fingerprint augment framework based on super-resolution (FASR), which achieves the fusion of fingerprint augment and super-resolution based on mutual conversion between fingerprint data and fingerprint image. The processing framework of FASR is formulated and the implementation of three modules in FASR are given, including Fingerprint-To-Image Conversion module, Super-Resolution module and Image-To-Fingerprint Conversion module. Simulated and real data experiments reveal the feasibility and effectiveness of the FASR. In addition, we explore the impact of two key engineering parameters on the performance of the FASR method. Our work demonstrates the new application of super-resolution in image processing field in wireless indoor localization topics.

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on centralized topology poses challenges when applying FL in a sensors network, including imbalanced communication congestion and possible single point of failure, especially on the PS. To alleviate these problems, we devise a Dynamic Average Consensus-based Federated Learning (DACFL) for implementing FL in a decentralized sensors network. Different from existing studies that replace the model aggregation roughly with neighbors’ average, we first transform the FL model aggregation, which is the most intractable in a decentralized topology, into the dynamic average consensus problem by treating a local training procedure as a discrete-time series.We then employ the first-order dynamic average consensus (FODAC) to estimate the average model, which not only solves the model aggregation for DACFL but also ensures model consistency as much as possible. To improve the performance with non-i.i.d data, each user also takes the neighbors’ average model as its next-round initialization, which prevents the possible local over-fitting. Besides, we also provide a basic theoretical analysis of DACFL on the premise of i.i.d data. The result validates the feasibility of DACFL in both time-invariant and time-varying topologies and declares that DACFL outperforms existing studies, including CDSGD and D-PSGD, in most cases. Take the result on Fashion-MNIST as a numerical example, with i.i.d data, our DACFL achieves 19∼34% and 3∼10% increases in average accuracy; with non-i.i.d data, our DACFL achieves 30∼50% and 0∼10% increases in average accuracy, compared to CDSGD and D-PSGD.

Federated learning(FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less uplink transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.

Federated learning (FL) enables multiple devices to collaboratively accomplish a machine learning task by iteratively exchanging their model updates instead of raw data with a parameter server (PS), thus protecting users' data privacy. Nevertheless, the communication efficiency and the nonindependent and identically distributed (non-IID) data are remaining intractable challenges in FL. This article proposes a hybrid FL framework called FedSeq, based on user clustering and sequential in-cluster training, to improve the communication efficiency and the test accuracy, especially on non-IID data. The FedSeq first divides users into multiple clusters and selects a cluster head (CH) on behalf on each cluster to upload the model updates to the PS, thus drastically reducing the uplink communication overhead. Within each cluster, a sequential training method is designed, which enables the CHs' models witness more categories of training data and go through more meta-updates per training epoch, thus promoting the test accuracy. Moreover, we also provide the convergence analysis of FedSeq on IID data with Random user clustering, with specific experiments validating our theoretical analysis from a simulation perspective. In the experiments, we compare our FedSeq with other baselines, including FedSGD, FedAVG, FedProx, FedCluster, and HierFAVG, of which the results demonstrate that FedSeq outperforms other FL paradigms in terms of model accuracy and training efficiency. We also test FedSeq with Random and Low-Energy Adaptive Clustering Hierarchy clustering strategies to demonstrate the robustness of FedSeq to different clustering strategies. The FedSeq offers a potential solution to cope with non-IID data and reduce uplink communication overhead in FL.

WiFi fingerprinting-based indoor localization system is extensively researched with the advent of the high-density wireless networks deployment, but is limited by heavy site survey in the offline phase, for which fingerprint augment is an effective solution. In this paper, we innovatively propose a fingerprint augment method based on super-resolution (FASR) and formulate the processing framework. In order to perform super-resolution on sparse fingerprint database, the conversions between WiFi fingerprint data and fingerprint images are proposed. EDSR, a method based on deep learning in super-resolution, is adopted in FASR to obtain high-resolution fingerprint images, which are then reconstructed to augmented fingerprint database. Experiments on simulated and real scenarios verified the feasibility of FASR. Our work demonstrates a new application of machine learning in wireless communication.

In the process of military informatization, there is data isolation, lack of associated organizations, and the problem that data is difficult to use effectively. To solve the above problems, a military knowledge graph construction technology based on open source and multiple data is proposed. Firstly, the military application advantages of knowledge graph are analyzed. Then, based on the internet multi-source data, the military knowledge graph is constructed, and the key process of constructing the military knowledge graph is described in detail. Finally, the knowledge graph construction process is completed. The key technologies in the paper are briefly introduced to provide reference for the application of knowledge graph technology in the military field.