Abstract Mobile crowdsensing (MCS) leverages the multi-sensory capabilities of mobile devices to collect diverse data efficiently. However, in MCS, inefficient task assignment strategies seriously affect the overall effectiveness, while efficient task assignment frequently requires the collection of sensitive information about users and tasks. In order to effectively trade-off task assignment efficiency and bilateral privacy security, we propose a privacy-preserving task assignment framework based on location fingerprinting. In this investigation, we propose a location fingerprinting-based for privacy preservation mechanism (LFPM) based on Monte Carlo stochastic algorithm to bidirectionally protect the location privacy of workers and tasks. Meanwhile, to overcome the challenge of utilizing location information while protecting privacy, a two-stage task allocation algorithm (TSTA) is proposed. This mechanism facilitates precise task assignment through segmental location fingerprinting, aiming to minimize the total cost of completing all tasks. We theoretically analyze its lightweight design and privacy features. Comparative experiments on real datasets show that this strategy achieves significant improvements in communication efficiency, computational performance, and task assignment accuracy compared to other methods.

Mobile crowdsensing (MCS), a human-in-the-loop IoT sensing paradigm, relies on active user participation. However, in untrusted environments, incentive design faces a fundamental trilemma among strict local differential privacy (LDP), individual rationality (IR), and system efficiency under limited budgets. LDP perturbation can obscure cost–quality correlations and undermine quality-aware selection, and it may even push selected users into negative utility. Meanwhile, most existing mechanisms either overlook privacy concerns or depend on heavyweight cryptographic frameworks, which are often impractical for resource-constrained mobile devices. To address these challenges, we propose IR-LDP, a lightweight LDP auction mechanism that explicitly manages the economic risk introduced by LDP noise under fixed budgets while guaranteeing deterministic IR. Specifically, IR-LDP introduces two design notions for LDP auctions: a public safety threshold that ensures ex-post IR for selected low-cost users, and a publicly bounded loss guarantee that caps each winner’s worst-case utility loss using only public system parameters. To realize these guarantees, IR-LDP employs a public projection strategy that projects noisy bids onto the safety threshold to bound downside utility risk, together with a nonlinear quality-aware ranking rule that prioritizes high-ROI users and mitigates the “lemon market” effect induced by LDP noise. We prove that IR-LDP satisfies ϵ-LDP for reported bids, maintains strict budget feasibility, and runs inO(NlogN) time. Extensive simulations across diverse budget regimes and user populations further demonstrate that IR-LDP outperforms representative baselines in social welfare, sensing quality, and budget efficiency, while providing ex-post IR for users with costs below the public threshold and a publicly known bound on utility loss for all participants.

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.

While ridesharing provides substantial convenience, it also raises several security concerns, with location privacy being a primary issue. A common state-of-the-art solution is to add random noise to user locations to preserve privacy. However, this approach often degrades matching efficiency due to reduced location accuracy. In this paper, we study the real-time matching problem between ridesharing requests and drivers, aiming to maintain high matching efficiency despite obfuscated locations. We model the order dispatching process as an online bipartite matching problem, where drivers are offline and requests arrive sequentially following a known distribution. We construct benchmark linear programs (LPs) and propose an LP-based online matching algorithm with provable performance guarantees. To address privacy concerns, we further develop a privacy-aware LP-based method that mitigates the impact of Laplace noise. Experiments on real-world datasets demonstrate the effectiveness of our algorithms and support our theoretical findings.

With the continuous advancement of transportation systems, leveraging Vehicular Crowdsensing (VCS) for data collection and analysis within digital cities has become a promising paradigm. Most existing research focuses on improving task completion rates using location information but often overlooks the impact of drivers’ rational decision-making processes. To address this issue, we propose novel task scheduling and incentive mechanisms grounded in two distinct rational decision-making models. Specifically, drivers are categorized as Individual Rationality and Bounded Rationality based on their sensitivity to utility and cost. For drivers with Individual Rationality, who only accept tasks that ensure non-negative utility, we formulate the maximum weighted subset coverage problem (MWSC-Problem). On the other hand, for drivers exhibiting Bounded Rationality, who accept tasks with a certain probability influenced by their sensing utility and cost, we introduce the maximum probability task coverage problem (MPTC-Problem). The driver recruitment problem in both scenarios is proven to be NP-hard. For each case, we design customized scheduling and incentive algorithms to optimize both the platform’s task completion rate and cost efficiency. Meanwhile, the performance bounds and computational complexity of the proposed algorithms are theoretically analyzed. By extensive simulations on a real-world taxi dataset, the effectiveness of our strategies is validated.

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.

Nowadays, the use of Domain Generation Algorithm (DGA) in botnets has made the detection of DGA domain names very important. Compared with blacklist and character-based detection, traffic analysis has the advantages of small datasets and vocabulary. However, the current research of traffic analysis mainly use a single type of traffic, and few features in the traffic has been considered. In this paper, a DGA domain name detection method based on spatiotemporal features of DNS and NetFlow traffic is proposed. Based on the difference between the traffic performance of DGA and benign domain name after the attack behavior, the method selects 8 effective features in time and space from DNS and NetFlow traffic, including the number of user IP visits, the suddenness of user access, and the standard deviation mean ratio of the occurrence of each resolved IP address. Using these spatiotemporal features, we can detect DGA domain names from the network. During the detection process, the features extracted from the training set were put into the C4.5 supervised machine learning classification model for training, and the DGA domain name detection system with classification ability was obtained. Experimental results in China Telecom network show that the model can detect DGA domain names with a high accuracy rate of 99.14% in the actual network, including anti-detection DGA domain names. This indicates that the model has achieved long-term effectiveness and stability, and copes well with adversarial attacks.

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.

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.

Abstract The high efficiency of mobile crowdsensing (MCS) relies heavily on motivating users to participate in sensing tasks. Designing an auction-based incentive mechanism is a widely adopted approach. However, platforms operating in the unrestricted Internet environment are inevitably vulnerable to various types of malicious behaviors. While most existing studies focus solely on countering a single type of malicious behaviors, their approaches often lead to a decline in task acceptance rates, ultimately impacting the system’s utility. To address this challenge, we propose an incentive mechanism to resist multiple malicious user behaviors in a reverse auction, including monopoly and malicious competition. The PT-IM model is first introduced to identify and exclude monopolistic users through the calculation of tolerance price and user capability. Additionally, a novel task area division method is implemented within PT-IM to improve the task acceptance rate. Building on this foundation, we develop the enhanced model EPT-IM to further mitigate malicious competition among users through primary selection and secondary selection conditions. We conduct both theoretical and experimental analysis to evaluate EPT-IM. The results demonstrate that the proposed mechanism effectively resists malicious behaviors of users and surpasses other incentive mechanisms in terms of overall performance.

Zero-power communication, enabled by energy harvesting, backscattering, and low-power computing, is capable of fulfilling the requirements of emerging Internet of Things (IoT) communication scenarios that demand low cost, compact size, and minimal power consumption. Thus, it holds great potential as a transformative technology for the future of IoT. Trusted access and secure transmission remain essential in zero-power communication scenarios. Nevertheless, conventional complex security mechanisms become impractical due to limited power consumption and resources. This work presents a lightweight security protocol for authentication. Initially, a sliding window algorithm, utilizing the Hamming distance, is designed to generate the message digest. This algorithm leverages the remaining electric quantity of the transmitter as a secret parameter for authentication. Subsequently, a key distribution function based on the hash chain is employed to ensure the security of the session key. The protocol’s security attributes regarding transmitted data and its ability to withstand common attacks are demonstrated through formal security analysis and the utilization of the ProVerif analysis tool. Extensive simulations validate the efficacy of the proposed security algorithms, which are well suited for lightweight IoT devices with severely constrained resources and outperform benchmark algorithms.

Recently, vehicular ad hoc networks (VANETs) have garnered significant attention in the industry, thanks to their distinctive characteristics of mobility and real-time capabilities. In order to protect the privacy of data sharing in VANETs, a privacy computing scheme based on homomorphic encryption and blockchain has been proposed. First, the Paillier algorithm of homomorphic encryption is used to encrypt the data, making it available but not invisible to ensure the security of the data. Second, the data structure of a directed acyclic graph (DAG) is applied to replace the chain structure so that it can be processed in parallel and reduce the delay time of transmission confirmation. Finally, a single point cluster is set up in the Bigchain database, and Docker virtual technology is used to simulate the high dynamic and high load environment of VANETs, so as to verify the computational efficiency and security of the proposed scheme. The experimental results show that the privacy computing scheme combining homomorphic encryption and DAG blockchain not only eliminates the need for repeated encryption and decryption in the communication process but also reduces the computational cost associated with private data. Compared with traditional chain blockchain, this scheme achieves a comprehensive delay reduction of 68.51% and shortens the average confirmation time by 85.73%, effectively meeting the high-performance requirements of VANETs.

Utilizing mobile crowdsensing (MCS) for data collection and analysis has become a prominent paradigm in the Internet of Things (IoTs). However, the existing research predominantly focuses on platform-user interactions, often neglecting the potential for user collaboration, which is crucial for improving data quality and task efficiency. In practical applications, mobile users tend to cooperate with familiar individuals based on their preferences in sensing tasks. To tackle this issue, we introduce a novel MCS model that integrates user cooperation, significantly enhancing the system's overall effectiveness. Specifically, users’ capabilities and costs are synthesized and managed through a cooperation degree matrix. Additionally, cooperation is updated based on historical behaviors and user preferences. To incentivize user participation, currencies are employed for recruitment. Within this framework, we investigate the maximum collaborative user selection (MCUS) problem, which is dedicated to the problem of maximizing the amount of recruitment under user cooperation. The MCUS problem is proved to be an NP-hard problem and thus intractable. To address this, we propose the minimum weighted cost replacement (MWCR) algorithm. Experimental results demonstrate that the MWCR algorithm exhibits low complexity and high efficiency across various scales, making it an excellent solution for collaborative crowd recruitment.

Opportunistic Networks (OppNets) can provide a low-cost and reliable way for the message forwarding in urban areas, especially in which traffic jams occur frequently. However, in terms of the OppNet based on the bicycle-sharing system (BSS), how to predict bicycle trips and improve routing performance still remains unsolved. Moreover, the exchange of auxiliary information among OppNet nodes (bike stations) will compromise the privacy of nodes/users. Thus we design the Two-Tier Probability Model (TTPM), including the InteR-day pattern and the IntrA-day pattern, to predict the trips accurately. Then the Discrete Optimization Differential Privacy (DODP) method is utilized to disturb the estimated InteR-day and IntrA-day probabilities, which will further protect the privacy of nodes and users. With TTPM and DODP, we propose an efficient privacy-preserving routing scheme for OppNet, which transforms the relay selection problem into the shortest path problem approximately. Extensive simulations show that the proposed routing scheme (TTPM) outperforms the benchmarks with the delivery ratio of more than 0.75 when the time-to-live is 5 days and the message generation rate is 6 pkts/hour. Compared with TTPM+Lap and TTPM+GRR, the proposed TTPM+DODP improves the delivery ratio by 30% and 3%, respectively.

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions.

Graph kernels are a significant class of tools for measuring the similarity of graph data, which is the basis of a wide range of graph learning methods. However, graph kernels often suffer from high computing overhead. With the shining of cloud computing, it is desirable to transfer the computing burden to the server with abundant computing resources to reduce the cost of local machines. Nonetheless, under the honest-but-curious cloud assumption, the server may peek at the data, raising privacy concerns. To eliminate the risk of data privacy leakage, we propose CloudRGK to securely perform Random walk Graph Kernel(RGK), one of the most well-known graph kernels, on the cloud. We first prove that the edge- and vertex-labeled graphs could be transformed into an equivalent matrix representation. Afterward, we prove that the cloud could perform the core operations in RGK on the encrypted graphs without feature information loss. Evaluations of the real-world graph data demonstrate that our strategy significantly reduces the overhead of the local party to perform RGK without performance degradation. Meanwhile, it introduces only a small amount of extra computation cost. To the best of our knowledge, it is the first work towards private graph kernel computation on the cloud.

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.

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.

PowerShell is a powerful and versatile task automation tool. Unfortunately, it is also widely abused by cyber attackers. To bypass malware detection and hinder threat analysis, attackers often employ diverse techniques to obfuscate malicious PowerShell scripts. Existing deobfuscation tools suffer from the limitation of static analysis, which fails to simulate the real deobfuscation process accurately. Accurate, complete, and robust PowerShell script deobfuscation is still a challenging problem.

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.

Abstract Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models’ classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions.

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.

While ride-hailing brings great convenience to our daily life, it also poses a threat to the passengers' location privacy. Although many perturbation-based methods have been proposed to protect the passengers' location privacy, imprecise can still result in poor assignments of the ride-hailing platform. In addition, the platform often has to balance multiple conflicting objectives when dispatching drivers. Thus, trading these objectives in an appropriate way is critical to the long-term development of the platform. In this paper, we focus on the assignment strategy design in ride-hailing, aiming to promote the performance of the dispatching system while protecting the passengers' location privacy. We first model the online order dispatching as a bipartite matching problem, where drivers are assumed to be offline available and orders arrive sequentially following a known distribution. Then, we construct several linear programs (LPs) to obtain the obfuscation matrix (for privacy-preserving) and the guiding solutions of assignments (for online dispatching). Finally, a parameterized LP-based online dispatching algorithm is proposed to flexibly trade the two assignment objectives, i.e., dispatch efficiency and fairness. Experimental results on real-world datasets demonstrate the effectiveness of our algorithms.

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.

Internet of Things (IoT) has been ubiquitous in both industrial and living areas, but also known for its weak security. Being as the first defense line against various cyberattacks, authentication is even more critical to IoT applications. Moreover, there has been a growing demand for cross-domain collaboration, leading to an increasing need for cross-domain authentication. Recently, certificate-based authentication schemes have been extensively studied. However, many of these schemes are not efficient in computation, storage, and communication, which are highly required in IoT. In this paper, we propose a lightweight authentication scheme based on consortium blockchain and design a cryptocurrency-like digital token to build trust. Furthermore, trust lifecycle management is performed by manipulating the amount of tokens. The comprehensive analysis and evaluation demonstrate that the proposed scheme is resistant to various common attacks and more efficient than competitor schemes in terms of storage, communication, and authentication cost.

With the rapid development of various applications such as 5G, smart cities, and industrial Internet of Things(IIoT) in recent years, IoT terminals have put forward new requirements for data storage and computing capabilities. Based on the phase change random access memory(PCRAM) technology, a hybrid memory management scheme, scList, has been designed and implemented for the first time. The scList scheme is used to manage storage-computation integrated variables in the lightweight IoT terminal operating system. Meanwhile, the scheme also takes the power-off non-volatile characteristics of PCRAM into account. The testing results of the prototype implementations show that the scheme can effectively improve the performance in typical application scenarios, while the power consumption is reduced by over 20% compared to existing methods.

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.

In order to achieve the desired performance of crowdsensing, the incentive mechanism, which can stimulate the workers to serve the sensing tasks efficiently, is usually indispensable. Different from the existing research efforts of incentive mechanisms, we propose an incentive mechanism to facilitate the delegation of tasks among the workers in hierarchical crowdsensing. Considering the task converging at some skillful workers, which will degrade the system stability and unbalance the workload among the workers, we construct a Stable and Truthful Incentive Mechanism (STIM) to model and restrict the interactions between the requester and the workers. STIM mechanism comprises a queue control algorithm for the workers and an auction scheme with Multi-sEllers for the Divisible tAsks (MEDA), which exploits an optimal winning bids determination strategy and conducts a truthful payment algorithm. The soundness of the modeling and the accuracy of the analysis are verified through extensive simulations.

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of 1/4 from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.

Duty-cycling techniques have been widely adopted to save energy for energy-constrained wireless sensor networks, while they also cause the sleep latency issue, especially in a multihop linear sensor network (LSN). So the duty-cycling and pipelined-forwarding (DCPF) techniques have been proposed to alleviate this issue. However, most of existing DCPF protocols have no effective scheme to handle the contention and interference among those proximately-located nodes which maintain the same sleep-wakeup schedule. As a result, the network performance degrades with low energy efficiency and high packet delivery latency, particularly when experiencing a heavy traffic load. To this end, this paper proposes an enhanced pipelined data collection (EPDC) MAC protocol for LSN. In EPDC, three algorithms are proposed to guarantee that those nodes located within the interference range of each other have mutually staggered sleep-wakeup schedules, so that the contention and interference among them can be eliminated. The extensive OP-NET simulations show that EPDC significantly outperforms an existing DCPF protocol in terms of packet delivery ratio, network throughput, packet delivery latency, and energy efficiency.

The authentication protocol is commonly served as the first defense line against various attacks in vehicular ad hoc networks (VANETs). Conventional schemes usually employ public key infrastructure or cryptography-based algorithms, which suffer from high computational and storage cost. In this paper, we propose a privacy-preserving authentication scheme for VANETs based on consortium blockchain. The authenticity of a vehicle or a road-side unit is represented by its transaction capability on blockchain instead of a certificate or a cryptographic key. In support of that, we design a novel data structure based on the unspent transaction output (UTXO) combined with a set of online operations, including issue, transfer, query and revocation. Thus, the authentication between two entities is accomplished by on-chain verification and corresponding communications. We conduct a set of security and privacy analysis as well as implementing a prototype on the Hyperledger Fabric platform, to evaluate the effectiveness and the efficiency of the proposed scheme.

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.

Sampling traffic traces to collect data from Internet nodes offers a new approach to reduce the traffic amount of sampling and the memory depletion. However, the loss of accuracy in traffic detection may be large due to the impertinent sampling probability. Many elephant flows detection methods suffer the high time consumption and the poor detection results from the empirical sampling. In this paper, the self-adaptive sampling method for elephant flow detection is investigated based on the heavy-tailed distribution of Internet. Specifically, the SaPS (Self-adaptive Probabilistic Sampling) algorithm is proposed to capture the characteristics of heavy-tailed flows through periodically calculating the kurtosis of tailedness for the flows. We employ this algorithm to provide simplified but representative samples to four well- known detection algorithms. The results of extensive simulations show that our sampling algorithm achieves the high performance in terms of time and memory consumption while maintaining a high accuracy through dynamically adjusting sampling probability for elephant flows detection.

The campus Wi-Fi access trace is usually recorded when the users log in the campus Wi-Fi and access Internet. The network traffic, which records the users' network access information after log in successfully, e.g., source/destination IP, URL address, packet size, access time, is utilized to perform users profiling to figure out the campus users. In this paper, we utilize the network access trace and the network traffic in SEU university to profile the campus users. Here the Wi-Fi access records from wireless APs can be regarded as the mobility behaviors of users in the campus. The network traffic, which will be classified into several categories first, is quantified with the temporal dimension. With these two network datasets, we propose a Conditioned Reclassifying Algorithm based on BPNN, CRAB algorithm, to distinguish the faculty members from the students. Then the graduates and the undergraduates are identified through the binary classifying approaches. The disciplines of graduates are predicted with multi-classification approaches. Finally, the performances of the user identification prediction, i.e., Faculty/Student, Graduate/Undergraduate, and the discipline prediction of graduates are evaluated in terms of accuracy, precision and recall. Experimental results validate the effectiveness of our profiling method.

3D Wireless Sensor Networks (WSNs) have attracted considerable attentions due to their geometrical topology characteristics and various application scenarios. Because of the unbalanced energy consumption among the nodes and the obstacles in the sensing space, the holes of the network topology have been one of the main obstacles in the application of the 3D WSNs. The data collection, especially for the data converged at the nodes nearby the holes, depends on the data relaying by the nodes on the boundary of the hole space, which may result in the exhaustive energy consumption of the involved nodes, quickly draining their limited energy and enlarge the hole space further. In this paper, we adopt a 3D clustering approach and propose a Precision-Adjustable Shape Retrieval (PASR) scheme for hole detection in 3D WSNs. In order to provide a fast and efficient hole detection for the network topology, a hierarchical clustering approach is designed by using the adjustable cluster size to conduct data collection for hole detection. Through extensive simulations, our scheme is demonstrated to be able to serve accurate shape retrieval and achieve good balance between the detection precision and the energy consumption.

In order to alleviate the traffic congestion caused by the bike-sharing system, the bicycles should be parked in designated parking sites, particularly around the hot scenic spots. A proper route, which guides the cyclers to select a vacant place among the sites to park the bike, is required. In this paper, the Expected Travel Distance (ETD) and the Probability of Successful Parking (PSP) are formulated to evaluate the routes, which will guide the users to travel all the parking sites. We exploit the Poisson process to model the increment of the bicycle number in the parking site and construct the travel tree for the route plan problem. To provide a proper route, we propose the GOR algorithm and the F-M method based on the travel tree. Through extensive simulations, our algorithms are compared with TSP in terms of ETD, PSP and the execution time.

Exploiting community structure for opportunistic forwarding decisions in mobile social networks offers a promising paradigm to improve the transmission performance and reduce the extra network overhead. Actually, people will have closer relationships and more opportunities to contact with each other if they are in the same community. In this paper, the activeness of nodes and the probability of reaching the destination are investigated based on the node contacts in the trace. Then the Contacts-Aware Opportunistic Forwarding (CAOF) scheme, which includes inter-community and intra-community phase, is proposed. In the inter-community phase, the node with higher global activeness and source-to-destination probability is selected to serve as the relay. Besides, in the intra-community phase, the forwarding decisions are determined by the local metrics. Furthermore, we compare the proposed CAOF scheme with several benchmark forwarding algorithms, including BUBBLE Rap, SPRINT, Epidemic and JDER. The validity of the modeling and the soundness of the analysis are verified through extensive experiments with real traces, which illustrates that it outperforms other routing strategies in heavy traffic scenarios.

The collection of sensory data is crucial for cyber‐physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on‐demand MDC; that is, MAs collect data based on the real‐time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve‐based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.

Worker selection for location-based crowdsensing can be described as the strategy of choosing the proper cooperative participants to complete the allocated tasks in specified regions. Due to the mobility pattern of vehicles and regular road networks, the Vehicular Ad-hoc Networks (VANETs) are expected to provide many opportunities for task execution in opportunistic crowdsensing, enabling some emerging applications. To fulfill tasks with the least execution time under the spatial-temporal restrictions, we propose a Location-Aware Worker Selection scheme (LAWS) for mobile opportunistic crowdsensing in urban areas. Different from the traditional worker selection schemes assigning a task to one designated worker, LAWS exploits the vehicles contacts provided by taxicabs and buses and makes full advantage of prior knowledge of vehicles to promote the performance of task execution. Real-world vehicle traces are introduced to construct the extensive simulations. The simulation results show that our scheme outperforms the well-known algorithms, e.g., Epidemic, Prophet, in terms of the task execution success ratio, the execution time and the network load.

Exploiting camera sensors to conduct intruder detection has attracted a lot of research attention. Different from the traditional sensor with omni-directional sensing model, a camera sensor usually has a specified direction with a fixed sensing angle of sensing area. The sensing quality of coverage is critical to the application of coverage scheme. In this paper, we first investigate the complete coverage issue with two alternative layouts of sensing area by 2 camera sensors. Considering the weighted image quality and the importance of sensing area, we propose a quality-enhancing coverage scheme for camera sensor networks, QCC, to improve the coverage performance. We then mathematically present a geometrical probability-based analysis to theoretically evaluate the performance of intruder detection approach. Furthermore, the extension of QCC scheme, QCC-D, is proposed to cover the sensing areas with differentiated importance. Through extensive simulations, our scheme is demonstrated to outperform the best known coverage algorithms, in terms of both overlap area ratio and total weighted quality.

Summary In Vehicular Ad hoc Networks (VANETs), because of the selfishness of the vehicles, the resource allocation in VANET has become one of the primary tasks. Exploiting the Road Side Units (RSUs), which constructs the Cloud Computing environment, provides more data access opportunities and stable communication time for the vehicles. We investigated the cloud resource allocation for data access with noncooperative game based on a Gauss–Seidel iteration method. We further proposed a repeated game scheme, which can approximately achieve the near Pareto‐optimal flow allocation among the vehicles. Considering the vehicles' irrational behavior, a punishment strategy was designed to prevent the vehicles from behavior deviation. The analysis based on these models lays a theoretical method foundation on cloud resource allocation process. The validity of the modeling and the accuracy of the analysis were verified through the extensive simulations, which also guide the future design of more sophisticated cloud resource allocation schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Exploiting Radio Signal Strength Indicator (RSSI) estimation method to conduct indoor localization in wireless sensor networks offers a new approach to serve object tracking and improve the tracking precision. When the object is closer the line-of-sight link between the transmitter and the receiver, it is more likely to arouse the attenuation or amplification of the RSSI signal. In this paper, the exponential model is used to analyze the fluctuation of the measured RSSI with the nodes, TT - CC2430, in the indoor environment. The indoor object tracking scenario is investigated through a Markov analytical model. The analysis based on Markov probability estimation lays a theoretical foundation on the object tracking process. Furthermore, PITM scheme is presented for object tracking based on the analytical results. The soundness of the analytical approach and the tracking accuracy of the PITM scheme are verified through extensive simulation, which also guides the design of more sophisticated indoor object tracking schemes.

Mobile Elements (MEs) are widely employed for data collection in Wireless Sensor Networks (WSNs) to balance the energy consumption and prolong the network lifetime. However, the limited travel speed of the MEs causes a large data collection latency, which in turn degrades the performance of the data collection task and weakens the applicability of the data collection schemes. Considering the deterministic concentric circle-like trajectories-covered sensing field, we propose a Low-latency Data Sweeping scheme, LDS, which utilizes the communication opportunities among MEs to shorten the data collection latency. The data are swept by the MEs, which traverse along the trajectories, and are relayed to the adjacent MEs towards the sink node. Besides, the transmission range of sensor nodes is limited to the minimum distance to the nearest trajectory for energy-conservation purposes. The performance of the proposed scheme is first analyzed with probabilistic methods. Extensive simulations further show that our scheme outperforms the well-known heuristic algorithms in terms of the data collection latency, energy dissipation, and network lifetime.

Exploiting social characteristics to make opportunistic forwarding decisions in mobile social networks offers a new approach to improve the forwarding performance and reduce the additional network workload. Actually, people in social networks will have more opportunities to contact with each other if they have more similar characteristics. In this paper, the data forwarding strategy is investigated through the weighted Characteristic-based Opportunistic Forwarding (COF) scheme, in which the weight for each characteristic is computed by exploring the contact frequency of the nodes. In our approach, the message is forwarded to the node which has more common characteristics with the destination. Furthermore, we compare the proposed COF scheme with several benchmark forwarding algorithms, including PeopleRank, Spray&Wait, Epidemic and Wait destination. The validity of the modeling and the accuracy of the analysis are verified through the extensive simulations with real traces, which also guide the design of more sophisticated data forwarding schemes.

In mobile opportunistic networks, the ad hoc nodes with high mobility are expected to have many opportunities of node contacts and serving message forwarding. Here we considering two typical mobility models, e.g., the RWP(Random WayPoint) model and Manhattan model, where the probabilistic characteristics of the node contact provided by the mobile nodes are explored. Furthermore we propose a Location-Aware Opportunistic Forwarding scheme, which exploits the location information of the messages and the node mobility for message forwarding. The message is forwarded to its destination regions or neighbor regions to improve the delivery ratio and reduce the network overhead. We construct a simulation environment, integrated with the RWP mobility trace and the city taxis' traces. Through extensive simulations, our scheme is demonstrated to outperform the best known opportunistic forwarding algorithms in terms of both delivery ratio/latency and transmission overhead.

Nowadays, multi-radio multi-channel wireless mesh networks have been widely exploited to provide high-speed Internet access. By assigning different channels, the interference between wireless mesh network nodes can be greatly reduced, leading to a better network performance. However, the channel assignment attacks have imposed a serious security problem, where the diverse messages, that is, Channel Usage Message CUM, Channel Change Message CCM, Channel Switch Operation CSO and Security Alarm Message SAM, are often used to launch attacks by the malicious nodes in order to disturb the normal channel assignment and thus the overall network performance. In this paper, we propose a joint anti-attack scheme JAS against the channel assignment attacks. Our JAS is composed of four key components: CUM, CCM, CSO and SAM defense schemes to protect the channel assignment. The CUM defense scheme verifies the CUM messages and their sender. The CCM defense scheme employs three rounds of CCM verifications. Furthermore, three control messages are exploited by the CSO defense scheme. The SAM defense scheme offers two rounds of verification for the alarm messages. Moreover, we apply our JAS to a popular distributed channel assignment algorithm, that is, Hyacinth. Through extensive simulation, the performance of the proposed scheme is evaluated by comparing with SeCA and SmartC in terms of Goodput and additional overhead. Observed from the simulation results, our scheme is demonstrated to outperform the best-known secure channel assignment algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Nowadays, message propagation has been one of the major tasks of Vehicular Ad-Hoc Networks (VANETs). The main obstacle of message propagation, which relies on the data forwarding among vehicles, is the frequent change in network topology, as the intermittent link between the vehicles will degrade the performance of message propagation. Hence, RSUs are deployed to extend vehicle coverage and improve network performance in VANETs. However, non-optimal RSU deployment may result in greater power consumption and lower quality of network performance. In this paper, we study the vehicle mobility characteristics along the highway and propose a Cluster-based RSU Deployment (CRD) scheme with the Traffic-Aware Power Control (TAPC) method to maximize the network performance, as well as minimizing the energy consumption of RSUs. Moreover, we develop a data propagation algorithm, Data-Driven Message Propagation (DDMP), to improve the performance of message propagation in RSU-assisted VANETs. The performance of the proposed scheme is analytically evaluated by comparing performance in with and without our scheme scenarios. Through extensive simulations, with the aid of our scheme, the performance of message propagation is improved significantly, in terms of the propagation latency and the power consumption.

In wireless sensor networks, grid-based clustering and routing schemes have attracted considerable attention due to their simplicity and feasibility. In this paper, we adopt a hexagonal tessellation approach and propose a Fast Hexagonal Clustering (FHC) algorithm for efficient data collection. The traffic load is intentionally concentrated at a small portion of Cluster Heads (CHs) and Convergence Points (CPs). In order to provide a steady and efficient energy supply for the nodes under our clustering scheme, a mobile energy replenishment approach is designed by using the Mobile Elements (MEs) to conduct wireless energy replenishment for the CPs. We construct an optimal route of the MEs for a better energy replenishment efficiency. Through extensive simulations, our scheme is demonstrated to outperform the best known clustering algorithms, in terms of both energy dissipation of data collection and energy replenishment.

By leveraging node mobility and exploring a store-carry-and-forward paradigm, delay-tolerant networking enables and assists end-to-end message delivery in many scenarios, e.g., vehicular ad hoc networks and mobile social networks. Most existing work in the literature either focuses on the routing strategies for unicast, or history-based routing for multicast communications. In this paper, we discover the most important and independent social features from the Infocom 06 trace data, and propose a social profile-based multicast routing scheme. Our proposed scheme reduces the delivery cost greatly compared with flooding-based schemes and achieves a similar performance to the history-based schemes, without the cost of maintaining the contact history. The efficiency of the proposed scheme has been confirmed by trace-driven simulation, which also reflects the efficacy of exploring social features in delay-tolerant networks.

Using mobile elements (MEs) as mechanical carriers to collect data brings many opportunities to wireless sensor networks, such as improving the energy efficiency of sensor nodes and prolonging network lifetime. However, the limited travel speed of MEs leads to a higher data transfer latency, which in turn degrades the performance of the data collection task. The optimal use of the limited mobility of MEs is thus critical to the overall performance optimization. Considering the data-rate constraints of wireless communications, and by following a progressive optimization approach, we propose a sweeping tour optimization scheme with active skipping (SAS) in this paper. The performance of the proposed scheme is evaluated by investigating the tour length and the data collection latency. Through extensive simulations, our scheme is shown to outperform the best known heuristic algorithms in terms of the data collection latency.