Task complexity is widely regarded as a major barrier to cooperation in mobile crowdsensing (MCS), often leading to trust collapse and market failure. However, this view overlooks the constructive role of task complexity in shaping cooperative evolution. In this article, we propose a three-party evolutionary game framework involving workers, platforms, and task requesters, in which task complexity is explicitly modeled as an endogenous driver of trust dynamics and strategic interactions. We derive a set of anti-collapse conditions under which the marginal benefits of cooperative behavior overcompensate for the marginal costs induced by task complexity. Task complexity thereby propels an evolutionary phase transition from a low-trust trap to a stable cooperative equilibrium by reshaping the payoff structure of cooperative strategies. We further characterize the critical complexity thresholds that govern this phase transition through theoretical stability analysis. Extensive numerical simulations validate the theoretical predictions and demonstrate the robustness and effectiveness of the proposed mechanism in sustaining cooperative behavior across a wide range of task complexities.

The growing popular awareness of personal privacy raises the following quandary: what is the new paradigm for collecting and protecting the data produced by ever-increasing sensor devices. Most previous studies on co-design of data aggregation and privacy preservation assume that a trusted fusion center adheres to privacy regimes. Very recent work has taken steps towards relaxing the assumption by allowing data contributors to locally perturb their own data. Although these solutions withhold some data content to mitigate privacy risks, they have been shown to offer insufficient protection against disclosure attacks. Aiming at providing a more rigorous data safeguard for the Internet of Things (IoTs), this paper initiates the study of privacy-preserving data aggregation. We propose a novel paradigm (calledRASE), which can be generalized into a 3-step sequential procedure–noise addition, followed by random permutation, and then parameter estimation. Specially, we design a differentially private randomizer, which carefully guides data contributors to obfuscate the truth. Then, a shuffler is employed to receive the noisy data from all data contributors. After that, it breaks the correct linkage between senders and receivers by applying a random permutation. The estimation phase involves using inaccurate data to calculate an approximate aggregate value. Extensive simulations are provided to explore the privacy-utility landscape of ourRASE.

Abstract While encrypted traffic protects user privacy and data security, it is also frequently exploited by malicious actors for illegal activities, e.g., phishing and malware distribution. Therefore, accurately and efficiently identifying user behavior behind encrypted traffic is crucial to maintaining network security. However, existing encrypted traffic classification methods rely on flow-level feature extraction, which is ineffective for short traffic flow. Additionally, these methods only analyze the graph interaction structure between local clients and remote servers, failing to effectively utilize the byte-level information in packets. This results in limited adaptability and low accuracy. To address these limitations, in this paper, we propose an encrypted traffic classification method, named Global-Statistical features and Packet-Bytes, for feature extraction and fusion. This method effectively utilizes byte-level information and constructs a graph structure based on sliding windows and Jaccard similarity between packet bytes. In particular, we design a triple embedding layer to embed traffic flow features and packet byte features. Feature fusion is achieved through an encoder-decoder module and a cross-gated feature fusion mechanism. Experiments on public datasets show that our method outperforms several state-of-the-art methods in fine-grained encrypted traffic classification tasks.

Selfishness detection offers an effective way to mitigate the routing performance degradation caused by selfish behaviors in Opportunistic Networks but leads to extra network traffic and computational burden. Most existing efforts focus on designing the selfishness detection scheme by exploiting the behavioral records of nodes. In this paper, we investigate the scheduling strategy of selfishness detection during the message lifespan with the game theory. Specifically, the Long-term Selfishness Detection Game (LSDG) is proposed based on the differential game and the payoff in the integral form. LSDG formulates the selfishness detection and the node’s selfishness with the Ordinary Differential Equations (ODEs). Then, we prove the existence of the Nash equilibrium in LSDG and deduce the necessary conditions of the equilibrium strategy based on Pontryagin’s maximum principle. The recursion-based algorithm is designed in this paper to compute the numerical solution of the equilibrium strategy via Euler’s method. Both the soundness of our modeling approach and solution properties are verified by extensive experiments. The simulations also show that the obtained solution can achieve the Nash equilibrium, where neither the source node nor relay nodes can benefit more by solely changing their own strategies.

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.

Encrypted communications, implemented for the confidential information exchange, facilitate the preservation of individual privacy. Unfortunately, some criminals abuse encrypted communications to conduct illegal activities, leading to the proliferation of the Darknet. To curb malicious darknet activities, the accurate and effective classification of darknet traffic is imperative. Considerable endeavors have been devoted to identifying the darknet traffic. However, the classification of darknet applications has not yielded a satisfactory result. This deficiency arises from the limitations of current approaches, e.g., some traditional methods rely on hand-crafted features that consume labor, and other neural network-based methods disregard the graph structure of the traffic. To tackle these challenges, we propose the Darknet Traffic Graph (DTG), a graph structure that captures the interactions between local clients and remote servers in darknet traffic. Furthermore, based on DTG, we combine the GNN model and attention mechanism to create the Darknet Graph Neural Networks, i.e., DGNN, a powerful model that sufficiently exploits the benign and darknet traffic features. As a result, on the CIC-Darknet2020 dataset, the accuracy of DGNN in traffic classification and application classification is 98.52% and 99.06%, respectively, which outperforms other classifiers.

Aiming at providing a more rigorous data safeguard for the Internet of Things (IoTs), this paper initiates the study of privacy-preserving data aggregation. We propose a "randomize-then-shuffle" paradigm, which can be generalized into a two-step procedure, that is, a noise addition step plus a random permutation step. More specially, we design an efficient randomizer, which carefully guides the Data Contributors (DCs) to choose the privacy level and obfuscates the truth to ensure local differential privacy. Then, a shuffler is employed to receive the noisy data from all DCs. After that, it breaks the correct linkage between the senders and the receivers by applying a random permutation. Extensive simulations are provided to explore the privacy-utility landscape of our proposed paradigm.

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%.

Nowadays, the flourish of the internet of things incurs a great demand for progressive technologies to prolong the lifetime of Wireless Sensor Networks. Exploiting a fleet of Mobile Chargers (MCs) to replenish the energy-critical sensor nodes provides a new dimension to maintain long-term network operations, but may suffer from high charging delay due to MC’s limited mobility. Most existing studies focus on the reduction of server-oriented delay, i.e., the overall time taken by MCs (servers) to carry out sensor charging and travel inside the sensing field. However, these solutions may not be robust enough as some energy-critical sensor nodes will run out of the stored energy before the charger’s arrival. In this article, we address this challenge by reducing the client-oriented delay—referred to as the wait-for delay —which is defined as the “arrival times” at the to-be-charged sensor nodes (clients). To this end, we first formulate a novel wait-for charging delay minimization problem under the multi-node energy charging scheme. We then prove the NP-hardness of the proposed problem. Inspired by empirical observations, we devise an efficient approximation algorithm with a provable approximation ratio for the problem. We have evaluated the proposed algorithm using real-life system settings. The experimental results suggest that the proposed algorithm certainly performs better than the existing benchmarks; it could reduce the wait-for delay by up to 87.4 percent.

Selfish node detection offers an effective means to mitigate the routing performance degradation caused by selfish behaviors in opportunistic Networks (OppNets), but leads to the extra network overload and computation cost. Most existing effort in the literature focuses on exploring the detection methods based on the traffic analysis or the cooperation among nodes. In this paper, we investigate the state transition of nodes in the message dissemination without detection. Specifically, the Ordinary Differential Equation (ODE) is constructed to approximatively model the periodic detection with complete detection requirements. Then we obtain the optimal solution of the selfish node detection by the Pontryagin’s maximum principle, and mathematically deduce the right detection time during the message lifetime. The model soundness is verified statistically and the analysis accuracy is evaluated via extensive simulations. The experiments also show that our solution can achieve the tradeoff between the reward and the detection cost.

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.

With the rapid growth in multimedia information, problems on how to discover the individual interests of users and recommend them with the proper goods have become increasingly difficult. Traditional recommendation algorithms simply utilize user rating logs for recommendations, but ignore lots of useful information which can be expressed as a Heterogeneous Information Network. In this paper, we propose a similarity measure, PW- PathSim, to calculate the relevance between two entities of the semi-symmetric weighted meta paths. Then a similarity regularization based recommendation algorithm is proposed to integrate the similarity of users and items with matrix factorization for recommendations. Furthermore, we compare the PWMFP algorithm with several benchmarks including FunkSVD, HeteFM and DSR. Experimental results with Douban dataset show that it outperforms other HIN-based algorithms in terms of recommendation accuracy.