Task-Driven Cooperative Internet of Robotic Things Crowdsourcing: From the Perspective of Hierarchical Game Theoretic

Smart and connected communities (SCC) is a new area of the Internet of Things with the potential to improve people's lives. The goal of developing SCC for a community is to encourage people to focus on the present, consider the future, and reflect on the past. In SCC, nodes (vehicles) can use their mobility to collect data from interconnected devices and send it to a variety of delay‐tolerant applications. Vehicular delay tolerant networks are an ideal fit for such services. However, the problem is that the devices and nodes (vehicles), having limited resources, may become unwilling to cooperate due to their selfish behavior. This article presents a reliable community card system (RCCS) with two main parts: a reliable community and community‐based card tracking system (CBCTS). The reliable community contains all trustworthy vehicles with a certain level of community reputation determined by their honesty level. In CBCTS, every vehicle needs a community card to participate in the network and the neighboring vehicles act as monitoring vehicle. The evaluation results show that the proposed RCCS is quite effective, with detection time and packet delivery. Similarly, it causes minimal overhead and energy consumption compared to all other existing techniques.

Section: Related Workmentioning

confidence: 99%

Reliable community card system for detecting and isolating selfish vehicles in smart and connected communities‐based VDTN's

Rehman,

Haq,

Badshah

et al. 2024

“…In addition, current sensitive data identification methods do not take into account the application scenarios of data-efficient transmission technology. [1][2][3][4][5][6] This paper proposes a sensitive data identification method based on batch data and streaming data for the problem of identifying sensitive data in multiple categories and scenarios within Beike Inc. To mitigate the problems posed by the size and complexity of the data, this method further divides data into incremental data, stock data, structured data, semi-structured and unstructured data, then uses different sensitive data identification government schemes respectively. In addition, the method constructs a classification & annotation platform and a dataset management platform to provide a reference for the definition and classification of sensitive data, therefore further supporting the training and evaluation of machine learning models for sensitive data identification.…”

Section: Introductionmentioning

confidence: 99%

Sensitive data identification for multi‐category and multi‐scenario data

Cui,

Huang,

Bai

et al. 2024

Sensitive data identification is the prerequisite for protecting critical user and business data. Traditional methods usually only target a certain type of application scenario or a certain type of data, thus making it difficult to meet the needs of enterprise‐level data protection. This paper proposes an introduction to the end‐to‐end sensitive data identification system of Beike Inc. The system consists of the data identification & annotation platform, dataset management platform, and sensitive data identification model, which propose different governance methods for batch data and streaming data respectively. Specifically, we propose a sliding window‐based identification method for long text to improve the identification of streaming data. Evaluation results show that this method can improve the effect of identifying long text sensitive data without losing the ability on short text, for the open source test dataset, the value can be up to 94.15, so it is applicable in diverse scenarios.

“…The rapid increase in the number of mobile terminals and Internet-of-Things (IoT) devices in network deployments [1][2][3][4][5][6] introduces security challenges due to the broadcast nature of the wireless transmissions. 7 This is because the mobile terminals are vulnerable to cyberattacks and overhearing process.…”

Section: Introductionmentioning

confidence: 99%

Achievable secrecy rate analysis of OFDM‐based multi‐user untrusted relay networks with imperfect CSI

Özduran,

Nomikos,

Ansari

2024

This study investigates the impact of imperfect channel state information (CSI), caused by channel estimation errors and feedback delays, on the physical‐layer security performance of multi‐user relay networks. More specifically, a multi‐user untrusted half/full‐duplex (HD/FD) uni/bi‐directional wireless amplify‐and‐forward (AF) relay network is studied, adopting orthogonal frequency division multiplexing (OFDM). In order to minimize the information leakage, it is considered that a limited number of friendly jammers affect the untrusted relay. In this context, the achievable secrecy rate of the relay network is theoretically analyzed and optimization problems are formulated and solved for the untrusted relay's location and power allocation. Monte Carlo simulations are conducted to validate the theoretical findings, revealing that channel estimation errors cause coding gain losses for low signal‐to noise ratio (SNR) while their impact becomes negligible for high SNR. Also, a large number of user‐pairs achieves higher secrecy rate while FD relaying further improves the secrecy performance. Finally, bi‐directional relaying provides higher achievable secrecy rate over uni‐directional relaying.