Survey of location-centric target tracking with mobile elements in wireless sensor networks

介绍目标跟踪的过程以及移动跟踪的特点;通过区分目标定位为主的方法和目标探测为主的方法,介绍定位为主的移动式目标跟踪方法(称为目标的移动式定位跟踪; )的研究现状;分析和比较不同方法的特点和应用领域,发现现有方法虽然可以提高跟踪质量、降低网络整体能耗,但是还存在一些问题。基于此,总结目标的移动; 式定位跟踪方法在方法类型、网络结构和节点模型等方面可能存在的研究热点,指出其研究和发展趋势。The basic process of target tracking and the properties of tracking; solutions with mobile elements were introduced. By distinguishing; location-centric methods and detection-centric methods, the current; research status of the location-centric target tracking methods were; reviewed. The properties and application fields of different solutions; were analyzed and compared. Although the existing solutions can; significantly improve tracking quality and reduce energy consumption of; the whole network, there are also some problems. Based on these; discoveries, some possible research hotspots of mobile solutions were; summarized in many aspects, such as method types, network architecture,; node model, and so on, indicating the future direction of research and; development.国家自然科学基金资助项目; 国家科技支撑计划项

Towards Usable Parental Control for Voice Assistants

Voice Personal Assistants (VPA) have become a common household appliance. As one of the leading platforms for VPA technology, Amazon created Alexa and designed Amazon Kids for children to safely enjoy the rich functionalities of VPA and for parents to monitor their kids' activities through the Parent Dashboard. Although this ecosystem is in place, the usage of Parent Dashboard is not yet popularized among parents. In this paper, we conduct a parent survey to find out what they like and dislike about the current parental control features. We find that parents need more visuals about their children's activity, easier access to security features for their children, and a better user interface. Based on the insights from our survey, we present a new design for the Parent Dashboard considering the parents' expectations

IoTBeholder: A Privacy Snooping Attack on User Habitual Behaviors from Smart Home Wi-Fi Traffic

With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and user activity identification. IoTBeholder can be easily run and replicated on commercial off-the-shelf (COTS) devices such as mobile phones or personal computers, enabling attackers to infer user's habitual behaviors from smart home Wi-Fi traffic alone. We set up a testbed with 23 IoT devices for evaluation in the real world. The result shows that IoTBeholder has good device classification and device activity identification performance. In addition, IoTBeholder can infer the users' habitual behaviors and automation rules with high accuracy and interpretability. It can even accurately predict the users' future actions, highlighting a significant threat to user privacy that IoT vendors and users should highly concern

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

IoT comprises sensors and other small devices interconnected locally and via the Internet. Typical IoT devices collect data from the environment through sensors, analyze it and act back on the physical world through actuators. We can find them integrated into home appliances, Healthcare, Control systems, and wearables. This chapter presents a variety of applications where IoT devices are used for anomaly detection and correction. We review recent advancements in Machine/Deep Learning Models and Techniques for Anomaly Detection in IoT networks. We describe significant in-depth applications in various domains, Anomaly Detection for IoT Time-Series Data, Cybersecurity, Healthcare, Smart city, and more. The number of connected devices is increasing daily; by 2025, there will be approximately 85 billion IoT devices, spreading everywhere in Manufacturing (40%), Medical (30%), Retail, and Security (20%). This significant shift toward the Internet of Things (IoT) has created opportunities for future IoT applications. The chapter examines the security issues of IoT standards, protocols, and practical operations and identifies the hazards associated with the existing IoT model. It analyzes new security protocols and solutions to moderate these challenges. This chapter’s outcome can benefit the research community by encapsulating the Information related to IoT and proposing innovative solutions

BlockNet Report: Exploring the Blockchain Skills Concept and Best Practice Use Cases

In order to explore the practical potential and needs of interdisciplinary knowledge and competence requirements of Blockchain technology, the project activity "Development of Interdisciplinary Blockchain Skills Concept" starts with the literature review identifying the state of the art of Blockchain in Supply Chain Management and Logistics, Business and Finance, as well as Computer Science and IT-Security. The project activity further explores the academic and industry landscape of existing initiatives in education which offer Blockchain courses. Moreover, job descriptions and adverts are analyzed in order to specify today's competence requirements from enterprises. To discuss and define the future required competence, expert workshops are organized to validate the findings by academic experts. Based on the research outcome and validation, an interdisciplinary approach for Blockchain competence is developed. A second part focuses on the development of the Blockchain Best Practices activity while conducting qualitative empirical research based on case studies with industry representatives. Therefore, company interviews, based on the theoretical basis of Output 1, explore existing Blockchain use cases in different sectors. Due to the interdisciplinary importance of Blockchain technology, these skills will be defined by different perspectives of Blockchain from across multiple mentioned disciplines. The use cases and companies for the interviews will be selected based on various sampling criteria to gain results valid for a broad scale. The analysis of the various use cases will be conducted and defined in a standardized format to identify the key drivers and competence requirements for Blockchain technology applications and their adoption. On the one hand, this approach ensures comparability, on the other hand, it facilitates the development of a structured and systematic framework.Comment: arXiv admin note: text overlap with arXiv:2102.0322