Direct-sequence spread spectrum (DSSS) communication, as a key technology in code-division multiple access networks, demonstrates excellent performance in multiple-access capability and interference suppression. However, its effectiveness significantly degrades in mobile underwater acoustic (UWA) networks, where the platform mobility induces severe Doppler effect and exacerbates the instability of multipath propagation. These impairments are further exacerbated by the inherently low data rates of UWA DSSS systems, where the prolonged signal duration leads to time-varying Doppler effect and fast-fading multipath structure within a single frame, collectively degrading the UWA DSSS receiver performance. To address these limitations, this paper proposes an improved DSSS receiver to achieve time-varying UWA multipath tracking, which employs Kalman filtering for channel parameter estimation. A multipath management technique is implemented to determine the appearance and disappearance of paths, followed by a coherent combination of all valid paths. Comprehensive simulations demonstrate the superior performance of the proposed receiver under various time-varying channel scenarios. The effectiveness of the proposed receiver is further validated by experimental data collected from Danjiangkou Lake and Zhang River experiments.

People have diverse privacy requirements. This is best modeled using a personalized local differential privacy model where each user privatizes their data using a possibly different privacy parameter. While the model of personalized local differential privacy is a natural and important one, prior work has failed to give meaningful error bounds. In this paper, we study the foundational sum/mean estimation problem under this model. We present two novel protocols that achieve strong error guarantees. The first gives a guarantee based on the radius of the data, suiting inputs that are centered around zero. The second extends the guarantee to the diameter of the data, capturing the case when the points are situated arbitrarily. Experimental results on both synthetic and real data show that our protocols significantly outperform existing methods in terms of accuracy while providing a strong level of privacy.

Underwater wireless sensor network (UWSNs) have enormous potential for development in fields, such as ocean resource development, military security, and environmental monitoring. However, the traditional localization methods for underwater networks are hampered by the challenge of acquiring real-time sound speed profiles (SSPs), leading to substantial localization inaccuracies. The advancement of the Argo program in recent years has facilitated the global collection of millions of temperature and salinity profiles, making it possible to invert SSP using big data in conjunction with various methodologies. Nevertheless, the utilization of the Argo dataset confronts two issues: 1) the presence of outliers in historical data and 2) the potential for data to originate from multiple subspaces, both of which can adversely affect the fitting capability of the basis functions for the subspaces. To address these challenges, this article proposed a subspace shrinkage algorithm based on robust PCA via outlier pursuit (OP-RPCA) algorithm, enhancing the expressive capacity of the basis functions and thereby improving the estimation accuracy of the SSP and locations. Based on the proposed algorithm, we present a joint estimation method for the locations of underwater nodes and the SSP. This method integrates sound ray tracing and circular intersection techniques for the localization of underwater network nodes, employing an alternating minimization approach to solve for node locations and SSP alternately, thus estimating both the SSP and node locations simultaneously. Numerical experiments based on Argo data from the North Atlantic are conducted to validate our approach. The results indicate that compared to traditional methods, the root mean square error (RMSE) of the horizontal localization results obtained using our method was reduced by approximately 80%, with the maximum inversion error of the SSP kept within ±2 m/s.

Differential Privacy (DP) is a widely adopted standard for privacy-preserving data analysis, but it assumes a uniform privacy budget across all records, limiting its applicability when privacy requirements vary with data values. Per-record Differential Privacy (PrDP) addresses this by defining the privacy budget as a function of each record, offering better alignment with real-world needs. However, the dependency between the privacy budget and the data value introduces challenges in protecting the budget's privacy itself. Existing solutions either handle specific privacy functions or adopt relaxed PrDP definitions. A simple workaround is to use the global minimum of the privacy function, but this severely degrades utility, as the minimum is often set extremely low to account for rare records with high privacy needs. In this work, we propose a general and practical framework that enables any standard DP mechanism to support PrDP, with error depending only on the minimal privacy requirement among records actually present in the dataset. Since directly revealing this minimum may leak information, we introduce a core technique called privacy-specified domain partitioning , which ensures accurate estimation without compromising privacy. We also extend our framework to the local DP setting via a novel technique, privacy-specified query augmentation . Using our framework, we present the first PrDP solutions for fundamental tasks such as count, sum, and maximum estimation. Experimental results show that our mechanisms achieve high utility and significantly outperform existing Personalized DP (PDP) methods, which can be viewed as a special case of PrDP with relaxed privacy protection.

In the standard model of differential privacy (DP), every user's privacy is treated equally, which is captured by a single privacy parameter \varepsilon. However, in many real-world situations, users may have diverse privacy concerns and requirements, some conservative while others liberal. This is formalized by the model of personalized differential privacy (PDP), where each user may have a different privacy parameter \varepsilon. However, existing techniques for PDP cannot provide good utility for many fundamental problems such as basic counting and sum estimation. In this paper, we present the personalized truncation mechanism for these problems under PDP. We first show that, theoretically, it is never worse than previous mechanisms (up to polylogarithmic factors) on any instance, while can be much better in certain cases. Then we use extensive experiments on both real and synthetic data to demonstrate its empirical advantages. Our mechanism also works for user-level DP, thus supporting a large class of SJA queries over relational databases under foreign-key constraints.

Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of SQLs while maintaining DP has become the holy grail in privacy-preserving query release. In this demonstration, we present DOP-SQL, a DP SQL system that can answer a broad class of queries consisting of the selection, projection, aggregation, join, and group by operators. DOP-SQL has integrated a suite of down-neighborhood optimal DP mechanisms, thus achieving state-of-the-art utility. The current implementation of DOP-SQL is based on PostgreSQL, but its extensible feature allows it to be used in conjunction with any standard SQL engine.

Answering relational queries under differential privacy has attracted a lot of attention in recent years due to growing concerns on personal privacy, and instance-optimal mechanisms have been developed for a single query. However, most real-world data analytical tasks require multiple queries to be answered under a total privacy budget. The standard solution to extend the single-query mechanism to multiple queries is via privacy composition. However, we observe that this may yield an error bound that could be a d0.5-factor worse from the optimal, where d is the number of queries. In this paper, we present a different, more holistic approach that closes this gap. In addition to theoretical optimality, our new mechanism also significantly outperforms privacy composition in practice, especially on more skewed data and large d.

Whenever randomness is involved in query processing, confidence intervals are commonly returned to the user to indicate the statistical significance of the query answer. However, this problem has not been explicitly addressed under differential privacy, which must use randomness by definition. For some classical mechanisms whose noise distribution does not depend on the input, such as the Laplace and the Gaussian mechanism, deriving confidence intervals is easy. But the problem becomes nontrivial for queries whose global sensitivity is large or unbounded, for which these classical mechanisms cannot be applied. There are three main techniques in the literature for dealing with such queries: the exponential mechanism, the sparse vector technique, and the smooth sensitivity. In this paper, for each of the three techniques we design mechanisms to produce confidence intervals that are (1) differentially private; (2) correct, i.e., the interval contains the true query answer with the specified confidence level; and (3) have a utility guarantee matching that of the original mechanism, up to constant factors. Then we show how to apply our techniques to a variety of problems ranging from simple statistics (e.g., mean, median, maximum) to graph pattern counting and conjunctive queries.

One of the challenges of Internet of Underwater Things (IoUT) is the design of nodes for collecting information from underwater, with features of low cost, long term, long range, voyage routing, and real-time communications. Wave glider, has shown great potential acting as IoUT nodes through its persistent, long-range traveling, and flexibility underwater. In this article, we propose an architecture of IoUT, involving multiple wave gliders as nodes for acoustical observation. We present the target localization method via acoustical observation of nodes with multiple wave gliders deployed underwater, by which precision of bearing estimation of each node is required to achieve high precision of localization. With the data collected, we apply a compensation method of bearing estimate when the hydrophone array is rotating during the observation. The feasibility of acoustical observation of wave gliders has been validated through both simulation and sea trial data, which is of great potential to be nodes for constructing IoUT.

Due to the time‐varyingproperty of the underwater acoustic (UWA) channel, the significant Doppler spread will severely degrade the performance of direct‐sequence spread‐spectrum (DSSS) communications. The relative velocity variation between the transmitter and the receiver will cause both the phase rotation and the magnitude loss of correlation peak, during the long transmission of the DSSS packet. To solve this problem, the authors propose a novel transceiver design for the UWA DSSS communications. At the transmitter, the triple differential phase shift keying (D PSK) modulation is adopted to overcome the phase rotation, whereas the phase noise will be amplified resulting in the signal‐to‐noise ratio (SNR) loss. At the receiver, the improved bit‐interleaved coded modulation with iterative decoding algorithm for D PSK is used to recover the SNR loss, in which the D PSK demodulator is treated as the convolutional decoder, and the linear prediction is adopted to track the channel variation. Furthermore, an adaptive selection of local reference signal is also applied to recover the correlation loss. Theoretical simulation shows that the proposed transceiver can effectively mitigate the performance loss caused by the motion acceleration, and the performance gain is significant over the conventional.

Frequency‐hopped M‐ary frequency‐shift‐keyed (FH‐MFSK) has the ability to handle with long multipath, large Doppler spreads, high ambient noise and rapid phase fluctuation, and is proved to be a robust modulation scheme in mobile shallow underwater acoustic channels. Aiming at this topic, a convolutionally encoded FH‐MFSK modulation scheme with a novel iterative multi‐channel reception is proposed. To obtain the novel reception suitable for non‐coherent M‐ary frequency‐shift‐keyed, the ideas of iterative multi‐channel demodulation and decoding are combined and extended. A modified multi‐channel soft‐input–soft‐output demodulator based on maximum‐a‐posterior criterion is derived, and substantial gains of several decibels in power efficiency are achieved. Extra gain can be obtained by utilising time diversity with long multipath delay. Simulation shows that a non‐coherent receiver equipped with five arrays obtains around 5.5 dB gain over the single non‐iterative receiver. A shallow water field testing at Songhua Lake with 300 m distance confirms that the proposed receiver verifies its robustness and usability.

Based on dichotomous coordinate descent (DCD) iterations and with the use of the variable forgetting factor (VFF), a widely linear (WL) l1‐norm recursive least squares (RLS) adaptive filtering algorithm is proposed for sparse underwater acoustic channelequalization. In the proposed l1‐norm WL‐RLS algorithm with VFF, the WL model is employed to exploit the second order statistics of the non‐circular signals and the VFF is employed to improve the tracking ability of the RLS algorithms. DCD iterations are incorporated in the proposed l1‐norm WL‐RLS‐DCD algorithm with VFF to reduce the computing complexity. Moreover, the proposed algorithms are employed by the direct adaptive decision feedback equalizer (DA‐DFE). Numerical results indicate that compared with the conventional RLS, l1‐norm RLS, WL‐RLS with VFF, l1‐norm WL‐RLS‐DCD algorithms, the proposed algorithms achieve a better performance in terms of the convergence rate, mean square errorand symbol error ratein the DA‐DFE receiver. Experimental results also show that the proposed algorithms can promote the DA‐DFE receiver to obtain a better performance in the sparse time‐varying underwater acoustic communication system. Even though the transmitted signals are circular quadrature phase‐shift keying (QPSK) through the underwater acoustic channel, the proposed adaptive RLS algorithms can still obtain a better performance.

In this study, the authors propose an l 0 ‐norm penalised shrinkage linear least mean squares ( l 0 ‐SH‐LMS) algorithm and an l 0 ‐norm penalised shrinkage widely linear least mean squares ( l 0 ‐SH‐WL‐LMS) algorithm for sparse system identification. The proposed algorithms exploit the priori and the posteriori errors to calculate the varying step‐size, thus they can adapt to the time‐varying channel. Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l 0 ‐SH‐WL‐LMS algorithm also makes full use of the non‐circular properties of the signals of interest to improve the tracking capability and estimation performance. Quantitative analysis of the convergence behaviour for the l 0 ‐SH‐WL‐LMS algorithm verifies the capabilities of the proposed algorithms. Simulation results show that compared with the existing least mean squares‐type algorithms, the proposed algorithms perform better in the sparse channels with a faster convergence rate and a lower steady‐state error. When channel changes suddenly, a filter with the proposed algorithms can adapt to the variation of the channel quickly.

This paper studies the impact of distance measurement error on node localization in large scale Underwater Wireless Sensor Networks (UWSNs) where only a small percent of nodes are anchor nodes with accurate location information and a large percent of ordinary nodes are to be localized. The recursive localization algorithm is re-evaluated under realistic assumptions that low-cost underwater sensor node may experience large system errors and random errors of distance measurement. Our results show that the errors indeed cause severe performance degradation in localization coverage and normalized localization errors. The sources of distance measurement errors are also identified and their remedies are suggested.

In this study, the authors propose a novel decision feedback equaliser (DFE)‐based receiver, which combines channel shortening methods and dichotomous coordinate descent (DCD) recursive least squares (RLS) adaptive algorithm with variable forgetting factor (VFF). The proposed receiver can obtain its performance gain via two aspects: (i) reducing the computing complexity by channel shortening method, such as passive time reversal (pTR) filter or minimised mean‐square error (MMSE) filter and (ii) improving the performance of the receiver, such as a lower bit error rate and mean‐square error. Underwater acoustic (UWA) channel has the features of long multi‐path spread and time‐varying property. When the multi‐path spread is very long, pTR or MMSE filter is used as a channel shortening pre‐processing method to reduce the computing complexity in the DFE receiver. When the channel is time varying, VFF is incorporated into DCD‐RLS adaptive algorithm to improve the tracking capability of the DFE receiver. The proposed receiver is appealing for practical implementations. Numerical examples and lake experimental results show that, the proposed DFE receiver can achieve a better performance in the UWA communication system.