A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems

The tradeoff between the computational complexity and system performance in multipleinput multiple-output (MIMO) wireless communication systems is critical to practical applications. In this dissertation, we investigate efficient low-complexity data detection schemes from conventional small-scale to recent large-scale MIMO systems, with the targeted applications in terrestrial wireless communication systems, vehicular networks, and underwater acoustic communication systems. In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA) systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition (SQRD) based turbo equalization scheme under imperfect channel estimation. We demonstrate the performance enhancement of the proposed scheme over the conventional minimum mean-square error (MMSE) based turbo equalization scheme in terms of system bit error rate (BER) and convergence performance. Furthermore, by jointly considering channel estimation error and the a priori information from the channel decoder, we develop low-complexity turbo equalization schemes conditioned on channel estimate for MIMO systems. Our proposed methods generalize the expressions used for MMSE and MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special cases. In addition, we extend the SQRD-based soft interference cancelation scheme to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show an improved system BER performance of the proposed turbo detection scheme over the conventional MMSE-based detection scheme. In the large-scale MIMO scenario, we focus on low-complexity detection schemes because computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive MIMO detection schemes. We demonstrate the applicability of the stair matrix through the study of the convergence conditions. We then investigate the system performance and demonstrate that the convergence rate and the system BER are much improved over the diagonal matrix based approach with the same system configuration. We further investigate low-complexity and fast processing detection schemes for massive MIMO systems where a block diagonal matrix is utilized in the development. Using a parallel processing structure, the processing time can be much reduced. We investigate the convergence performance through both the probability that the convergence conditions are satisfied and the convergence rate, and evaluate the system performance in terms of computational complexity, system BER, and the overall processing time. Using our proposed approach, we extend the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic (UWA) communications. By utilizing a recently proposed computational efficient statistic UWA channel model, we show that the proposed scheme can effectively approach the system performance of the original Gauss-Seidel method, but with much reduced processing delay

Mobile node-aided localization and tracking in terrestrial and underwater networks

In large-scale wireless sensor networks (WSNs), the position information of individual sensors is very important for many applications. Generally, there are a small number of position-aware nodes, referred to as the anchors. Every other node can estimate its distances to the surrounding anchors, and then employ trilateration or triangulation for self-localization. Such a system is easy to implement, and thus popular for both terrestrial and underwater applications, but it suffers from some major drawbacks. First, the density of the anchors is generally very low due to economical considerations, leading to poor localization accuracy. Secondly, the energy and bandwidth consumptions of such systems are quite significant. Last but not the least, the scalability of a network based on fixed anchors is not good. Therefore, whenever the network expands, more anchors should be deployed to guarantee the required performance. Apart from these general challenges, both terrestrial and underwater networks have their own specific ones. For example, realtime channel parameters are generally required for localization in terrestrial WSNs. For underwater networks, the clock skew between the target sensor and the anchors must be considered. That is to say, time synchronization should be performed together with localization, which makes the problem complicated. An alternative approach is to employ mobile anchors to replace the fixed ones. For terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very good choices, while autonomous underwater vehicles (AUVs) can be used for underwater applications. Mobile anchors can move along a predefined trajectory and broadcast beacon signals. By listening to the messages, the other nodes in the network can localize themselves passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much improved with the increased number of virtual anchors, which can be boosted at negligible cost; thirdly, the coverage can be easily extended, which makes the solution and the network highly scalable. Motivated by this idea, this thesis investigates the mobile node-aided localization and tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system design, modeling, and performance analyses will be presented for various applications, including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based underwater localization and time synchronization; (3) the Doppler-based underwater localization; (4) the underwater target detection and tracking based on the convolutional neural network and the fractional Fourier transform. In these applications, different challenges will present, and we will see how these challenges can be addressed by replacing the fixed anchors with mobile ones. Detailed mathematical models will be presented, and extensive simulation and experimental results will be provided to verify the theoretical results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless communications. A pilot decontamination method will be presented for the massive multiple-input-multiple-output communications, and the data-aided channel tracking will be discussed for millimeter wave communications. We will see that the localization problem is highly coupled with the channel estimation in wireless communications