Recently, adopting mobile crowdsensing to collect data, analyze information, and share knowledge has gained increasing attention from the research community. However, in practice, strategic selfishness and privacy breaches lead to user reluctance to participate in sensing tasks. To tackle the problem, we combine a two-way auction model with differential privacy to incentivize user participation in sensing activities while ensuring the protection of their sensitive information. In order to improve the match between users and tasks, we fully consider users' historical behavior and task attributes during the auction process, and reward users for completing tasks based on their performance. Furthermore, Laplace noise is added to users' sensitive information based on differential privacy to prevent privacy leakage. Through extensive simulations with real-world system settings, we verify that the proposed algorithms outperform other algorithms. Furthermore, we validate the soundness of modeling and the accuracy of analysis, which also guides the design of a more sophisticated incentive mechanism.

Caching content at the edge network has emerged as a critical technique to alleviate backhaul congestion, minimize service latency, and improve user Quality of Experience (QoE). Driven by operational profitability, the edge service provider (ESP) necessitates access to user preference data to optimize its caching policies. However, disseminating such sensitive information raises significant privacy concerns. To address this challenge, we propose a privacy-preserving cooperative edge caching framework that jointly enhances caching efficiency and safeguards user preference privacy. Specifically, we design a privacy-oriented popularity estimation protocol, PSRSA, which guarantees the preservation of user privacy while delivering precise content popularity estimations. The PSRSA protocol integrates a randomize-then-shuffle mechanism to obfuscate user data locally prior to aggregation and accommodates heterogeneous privacy preservation requirements across users by dynamically adjusting differential privacy (DP) budgets. Subsequently, we formulate a Stackelberg Game-based Edge Caching (SGEC) algorithm that jointly optimizes the utility functions of both the content provider (CP) and the ESP, thereby ensuring efficient resource allocation at edge servers. Comprehensive evaluations conducted on real-world datasets demonstrate the superiority of the integrated PSRSA+SGEC framework, exhibiting a 35.22% increase in caching utility and a 3.4% improvement in cache hit ratio, while simultaneously achieving a 20.1% reduction in privacy leakage risks, compared to state-of-the-art baselines.

Edge network devices are typically resource-constrained, but intrusion detection requires real-time capabilities. Currently deep learning detection models for raw traffic data require significant computational resources, which does not meet our requirements. In addition, purely manual feature extraction may lead to lower accuracy, even though it can make the algorithm more lightweight. Taking these considerations into account, this paper proposes a machine learning network—LLAMNet, which offers lower latency and memory requirements. The proposed model takes a more comprehensive approach to capturing the deep structure of network traffic. It leverages attention mechanisms to effectively uncover the temporal characteristics among the packets that form the network flow. This enables a more thorough exploration of the sequential features within the data. To minimize latency and memory overhead, sparse self-attention mechanisms and self-attention distillation techniques are utilized in our approach. Additionally, in order to better suit the intrusion detection task, we have implemented enhancements that enable the lightweight network architecture to achieve accurate detection rates. Furthermore, experiments were conducted on publicly available datasets, including a series of ablation experiments to assess the effectiveness of our improvements.

Recently, adopting mobile-edge computing (MEC) to accommodate the compute-intensive and delay-sensitive tasks from mobile devices has gained increasing attention from the research community. In contrast to a cloud-centric scheme, deploying servers at the network edge offers the advantage of delivering faster and more efficient services. However, pioneering works primarily focus on a homogeneous server deployment strategy, which distributes the same quantity of servers among a specific number of selected locations. In this work, we aim to lay the theoretical foundation for budget-constrained profits maximization (BCPM) problem, which is a coupled problem of server deployment and task scheduling. Subsequently, a two-step optimization method is proposed. Through seeking the maximum matches in the constructed bipartite graph, a task scheduling algorithm is first designed to maximize the profits under the server deployment. Then, two approximation algorithms with provable approximation ratios are exploited to perform nearly optimal deployment of servers in a homogeneous and heterogeneous manner, respectively. Extensive simulations with real-world data set and system settings are conducted. The results show that the proposed algorithms can achieve at least a 10.54% increase in total profits and the average processing delay of tasks can be shortened by about 17%.

Blackhole detection in the opportunistic networks offers an effective means to mitigate the routing performance degradation but faces many challenges from corrupted nodes due to their collusion behaviors. Most existing effort in the literature focuses on the blackhole feature extraction from the message exchange. However, the decay effect of features and the forged features from the corrupted node, which acts as the rational node in performing message exchange, degrade the performance of the detection. In this article, we investigate the evidence construction, i.e., the direct and indirect evidence with the statistical parameters in message exchange. Specifically, we construct behavior classifiers to distinguish the blackhole behaviors from rational ones and design the collusion filtering strategy to improve the detection accuracy by separating corrupted nodes from rational ones, laying a behavior identification foundation. The contact evidence-driven blackhole detection (CEBD) based on machine learning is proposed to improve the routing performance. The soundness of the proposed scheme is verified statistically and the detection accuracy is evaluated based on random waypoint model (RWP) trace and Shanghai taxi trace. Extensive simulations show that our scheme outperforms the benchmarks, including SDBG, Li, and MDS, in terms of the delivery ratio in various scenarios.