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.

By locating computational and storage resources at the edge of the network, the emerging paradigm of Mobile Edge Computing (MEC) yields a significant enhancement in user Quality of Experience (QoE). However, the limited resources at edge nodes, coupled with the dynamism of user requests, present a considerable challenge to decision-making in service placement and computational resource assignment. This paper investigates the resource management problem within an edge-cloud cooperative network. Aiming to minimize long-term network latency, the original problem is first modeled as a Markov Decision Process (MDP) featuring a hybrid discrete-continuous action space. To address dynamically arriving tasks and varying network conditions, we develop a Dynamic Service Placement and Computation Resource Allocation (DSPCRA) scheme based on deep reinforcement learning (DRL). DSPCRA integrates a deep deterministic policy gradient (DDPG) with a parameterized action mechanism for online decision-making. Numerous simulations confirm that the proposed scheme exhibits good convergence properties and achieves lower latency performance compared to the benchmark algorithms.

With the rapid growth of smart devices, mobile crowdsensing is becoming one of the most important and attractive paradigms to acquire information from physical environments. Low-quality data, a notorious but widely found issue, degrades the availability and preciseness of sensing services, especially for these complex sensing task scenarios. However, few existing incentive mechanisms frequently ignore the issue of data quality. In this paper, we define user reputation and user task preferences in a new perspective, while predicting the number of users likely to upload high-quality data by combining Poisson distribution. Then, the maximum expectation algorithm is employed to evaluate the parameter values of the Poisson distribution. Subsequently, a two-way auction mechanism is proposed, which encourages users to participate in the sensing task and improves the match between tasks and users. We adopt the number of high-quality data that the user may upload as a factor in the user’s offer to maximize the quality of data received by the platform. The analysis based on the model lays a theoretical foundation on the incentive process of mobile crowdsensing considering data quality. The evaluation results show that our mechanism outperforms other existing techniques, in terms of robustness and efficiency.

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.

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT.

Exploiting mobile crowdsensing to conduct data collection and analysis brings unprecedented opportunities to promote the development of the Internet of Things(IoT). However, malicious platforms may provide untrusted data or illegally leak users’ information, which leads users in crowdsensing networks to be reluctant to participate in sensing activities. Besides, users are unwilling to report malicious platforms without sufficient incentives. To tackle the problem, a new incentive mechanism is proposed by modeling users’ preferences in this paper. Specifically, two scenarios are considered to detect malicious platforms when users join sensing activities according to the system grasps user’s information, i.e., complete information scenario and partial information scenario. Different incentive algorithms are designed for each scenario to optimize the systems incentive cost. In the complete information scenario, we minimize the total incentive cost by ranking users’ preferences. In the partial information scenario, uniform Distribution and Laplace Distribution are employed to model the distribution of users’ preferences to find the optimal cost. Specifically, we incorporate the concept of non-convexity into design the incentive mechanism, when user preferences obey the Laplace Distribution. By conducting an in-depth exploration the properties of Laplace Distribution, we can transform it into a convex problem to solve it efficiently. The analysis based on these mechanisms lays a theoretical foundation on the detection of malicious platforms. Furthermore, the soundness of modeling and the accuracy of analysis are verified through extensive simulation, which also guides the design of more sophisticated incentive schemes for the detection of malicious platforms.

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%.

Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.