The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such as convolution neural network (CNN) and recurrent neural network (RNN), which mainly accept formats of images, time series, and text data in the application layer. Therefore, it is of considerable interest to bridge the gap between signal waveforms to DL amenable data formats. In this paper, we develop a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waveforms while being represented in an image data format. In this paper, we explore several potential application scenarios and present effective CSI-based solutions to address the signal recognition challenges. Our investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.

The ever-growing demand for electrical energy of sensing devices in the Internet of Things (IoT) has led to generating large amounts of electricity consumption data. Electricity service providers often use wireless sensor networks to collect sensing devices' electricity consumption data for statistical analysis, so as to provide sensing devices with improved electrical services. As an important data mining technique, while data clustering excels in dealing with such massive data, it imposes the risk of privacy disclosure in the process of data clustering. In an effort of solving this problem, Blum et al. proposed a differential privacy k-means algorithm, effectively preventing privacy disclosure. However, the availability of data clustering results is reduced due to the data distortion in Blum's algorithm. In this paper, we propose a privacy and availability data clustering (PADC) scheme based on k -means algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point. Moreover, PADC attempts to reduce the outlier effect through detecting outliers during the clustering process. Security analysis indicates that our scheme satisfies the goal of differential privacy and prevents privacy information disclosure. Meanwhile, performance evaluation shows that our scheme, at the same privacy level, improves the availability of clustering results compared to the existing differential privacy k-means algorithms, suggesting that our proposed PADC scheme outperforms others for intelligent electrical service in IoT.

The mobile crowdsensing technology with a large number of IoT devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers mobile crowdsensing to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, we propose an AI-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS. Specifically, based on the random forest classifier and the k-anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analysis and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.

The designs of existing incentive mechanisms in mobile crowdsensing are primarily platform-centered or user-centered, while overlooking the multidimensional consideration of sensing task requirements. To tackle the above issues, we propose a sensing task-oriented user selection incentive mechanism (TRIM) based on edge computing, in an effort toward a task-centered design framework in mobile crowdsensing. Considering the constraint of the task budget, the edge node performs a secondary user selection based on the ratio of the similarity between the sensing task and the sensing user and the expected reward of the sensing user. By adopting a secure multi-party sorting protocol, enhanced by fuzzy closeness and the fuzzy comprehensive evaluation method, the target user set is determined aiming at maximizing the similarity of the task and the users preferences, while minimizing the payment of the edge node, and ensuring the fairness of the sensing user being selected. Simulation results show that TRIM achieves feasible and efficient user selection while ensuring the privacy and security of sensing users. Among the dynamic changes of task requirements, TRIM excels with user selections reaching nearly 90% on the data quality level compliance rate and 70% on the task budget consumption ratio, superior to existing incentive mechanism.

A small, affordable computer, the size of a debit card called Raspberry Pi, that uses a regular mouse and keyboard and connects to either a desktop screen or television. It makes it possible for people of all generations to learn about computer devices and various programming languages like Python and Scratch. It has all the characteristics of a desktop computer, including the potential to browse the Internet, view high-definition video, work with spreadsheets, write documents, and play games. In addition, the Raspberry Pi was involved with several digital maker projects, such as song players and climate forecasting. It is therefore effective in communicating with people around the world. In this article, detailed features and information of the Raspberry Pi have been discussed very clearly as well as each version along with its alternative boards. The hardware components utilized, as well as the software needed to program it, will be discussed in this article (Raspberry Pi board). This article will offer a broad overview of the Raspberry Pi and is helpful for individuals who want to learn and do projects using the Raspberry Pi by giving them a proper guide and details regarding the Pi boards. It serves as the comprehensive guide for the basic ideas and uses of the Raspberry Pi boards.