As the construction of the new power system progresses, it has become increasingly evident that a change in the production model driven by digital technology is an inevitable choice to promote the high-quality development of the power grid. The interconnection and interoperability of multiple IoT sensing devices for digital converter stations are achieved through the utilisation of broadband and narrowband communication technologies, meeting diverse needs such as real-time monitoring and data collection. However, with the increase in the number of sensing devices and the adoption of integrated broadband and narrowband networks, the issue of co-channel interference (CCI) due to spectrum scarcity has become increasingly prominent.To enhance the utilisation efficiency of spectrum resources and communication reliability, and to establish a novel digital power system based on Symbiotic Internet of Things (Symbiotic IoT), this paper investigates anti-interference optimisation methods leveraging Non-Orthogonal Multiple Access (NOMA) technology. For broadband and narrowband wireless networks applied to the IoT sensing devices in digital converter stations, the access point (AP) selection and power allocation strategies of the sensing devices are modeled as an optimisation problem. An AP-UE association algorithm is proposed to achieve optimal matching between sensing devices and access points. On this basis, to address the issue of CCI, a power allocation algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to enable efficient power allocation. This further enhances the anti-interference capability of the Symbiotic IoT network within the converter station, ensuring reliable communication among Symbiotic IoT devices to achieve efficient collaboration and intelligent interaction.

In recent years, the extraction of extensive spatiotemporal datasets from various sources has surged. One notable example is the cloud-to-ground (CG) lightning dataset in atmospheric area, where each entry records the flash location, including latitude and longitude, and the time the flash occurred.The flashes can be clustered into cells with a set of rules applied to the time-ordered sequence of flash locations, which is called as a thunderstorm cell. Analysis of the cell structure plays an important part in a description of a multicell thunderstorm system in atmospheric area. Clustering algorithms such as K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) cannot be directly applied to these datasets due to their inability to handle spatial and temporal dimensions simultaneously. Our study aims to investigate a clustering method capable of handling large-scale spatiotemporal data in a reliable and efficient manner. The proposed method, called Spatiotemporal-DBSCAN, is applied to both idealized clusters and real datasets, such as the cloud-to-ground (CG) lightning dataset. Experiements demonstrates that the spatiotemporal-DBSCAN performs effectively on spatiotemporal datasets.

To promote development of Mobile Crowdsensing Systems (MCSs), numerous auction schemes have been proposed to motivate mobile users' participation. But, task diversity of MCSs has not been fully explored by most existing works. To further exploit task diversity and improve performance of MCSs, in this paper, we investigate the joint problem of sensing task assignment and schedule with considering multi-dimensional task diversity, including partial fulfillment, bilaterally-multi-schedule, attribute diversity, and price diversity. First, task owner-centric auction model is formulated and two distributed auction schemes (CPAS and TPAS) are proposed such that each task owner can locally process auction procedure. Then, mobile user-centric auction model is established and two distributed auction schemes (VPAS and DPAS) are developed to facilitate local auction implementation. These four auction schemes differ in their approaches to determine winners and compute payments. We further rigorously prove that all the four auction schemes (CPAS, TPAS, VPAS, and DPAS) are computationally-efficient, individually-rational, and incentive-compatible and that both CPAS and TPAS are budget-feasible. Finally, we comprehensively evaluate the effectiveness of CPAS, TPAS, VPAS, and DPAS via comparing with the state-of-the-art in real-data experiments.

We present a curricular model for introducing data analytics concepts into an introductory computer science course for non-majors. This is accomplished through the design and implementation of hands-on laboratories projects using the Python programming language and associated tools. While introducing students to an important research area, we believe the use of these projects improves students' learning experiences, enabling them to apply and relate fundamental computational thinking concepts of algorithmic reasoning, data representation, and computational efficiency to data analytics problems. We present the curricular modules, as well as preliminary experiences using them.

Motivating the mobile users to participate in sensing services for efficient data generation and collection is one of the most critical issues in Mobile Crowdsensing Systems (MCSs). Auction based mechanisms are seen to be promising and effective solutions to incentivize mobile users. However, price is not the unique factor dominating participants' contribution in MCSs. Participant's preference for different sensing tasks is also a pivotal factor which should be considered in the auction mechanisms as assigning the least favorite tasks discourages them to participate in future sensing tasks. Unfortunately, participant's preference has been overlooked by most existing works, which motivates us to fill this gap in this paper. We first propose a new concept "mutual preference degree" to capture participant's preference and then design a preference-based auction mechanism (PreAM) to simultaneously guarantee individual rationality, budget feasibility, preference truthfulness, and price truthfulness. Finally, both the theoretical analysis and simulation results demonstrate the effectiveness of PreAM.

With the emergence of Mobile Crowdsensing Systems (MCSs), many auction schemes have been proposed to incentivize mobile users to participate in sensing activities. However, in most of the existing work, the heterogeneity of MCSs has not been fully exploited. To tackle this issue, in this paper, we study the joint problem of sensing task assignment and scheduling while considering partial fulfillment, attribute diversity, and price diversity. We first elaborately model the problem as a reverse auction and design a distributed auction framework. Then, based on this framework, we propose two distributed auction schemes, cost-preferred auction scheme (CPAS) and time schedule-preferred auction scheme (TPAS), which differ on the methods of task scheduling, winner determination, and payment computation. We further rigorously prove that both CPAS and TPAS can achieve computational-efficiency, individual-rationality, budget-balance, and truthfulness. Finally, the simulation results validate the effectiveness of both CPAS and TPAS in terms of sensing task's allocation efficiency, mobile user's working time utilization and utility, and truthfulness.

Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries’ arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via Jenson-Shannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose ϵ -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the ϵ -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.