Network intrusion detection is a crucial line of defense for protecting network security. Despite the significant advancements made by deep learning in this field, existing methods are primarily based on closed-set classification and are ineffective in detecting unknown attacks. To address this research gap, we propose an open-set recognition-based network intrusion detection method. We first provide a network traffic classification model based on open-set recognition, OpenPN , to classify known classes of network traffic and recognize unknown network traffic. Then we introduce a novel attack detection algorithm involving expert intervention, which reduces manual costs through expert verification and utilizes a density-based k-reciprocal nearest neighbor clustering algorithm for optimization. Finally, we perform continuous learning for the classes that have been verified as novel attacks. Extensive experiments conducted on three public datasets demonstrate that the proposed method outperforms existing methods in both closed-set classification and open-set recognition. In addition, the impact of each critical parameter on the performance of the relevant algorithms is comprehensively analyzed.

The widespread integration of the Internet of Things with sensors like depth-of-field cameras, LiDAR scanners, and eye-tracking infrared sensors, in head-mounted devices, has ushered in a new era of immersive digital experiences. Full-scene volumetric video (VV), a key innovation in this integration, provides a deeply immersive experience by capturing the richness and detail of the 3D world. However, its massive data volume presents significant streaming challenges. While 3D tile-based viewport approaches have been proposed, they struggle to full-scene VV given the small video buffer limitation, high tile segmentation overhead, and lack of full-scene consideration. In this work, inspired by the advancements of implicit neural radiance field (NeRF), we present \({\mathsf{V}^{2}\mathsf{NeRF}}\) , a novel full-scene VV streaming system featured by layered representation. It harmonizes the NeRF with explicit point clouds to represent the static background and dynamic foreground, thereby avoiding large data transfers and achieving photorealistic content representation. To tackle the issues of intensive computation requirements and multiscale adaptation scheduling within \({\mathsf{V}^{2}\mathsf{NeRF}}\) system, we propose a lightweight non-visible background removal method and a two-stage decoupled architecture. In addition, an efficient buffer-aware simulated annealing algorithm is developed, alongside the utilization of a perceptually learned metric, to enhance user experience. We further discuss the concerns about practical development and deployment. Extensive prototype evaluations demonstrate \({\mathsf{V}^{2}\mathsf{NeRF}}\) ’s superior streaming and viewing performance on a wide variety of networks, viewing motions, and scenes. For instance, compared to state-of-the-art approaches, it achieves a 24% increment in perceptual quality, an 83% reduction in rebuffering time, and a 54% enhancement in user experience on average.

Emerging low-latency video (LLV) services, such as cloud gaming, video conferencing, and virtual reality, demand ultra-low latency for smooth interaction. However, existing methods often overlook the misalignment between frame-level perception and packet-level scheduling in ubiquitous Wi-Fi networks, causing high tail latency and degraded user experience. To this end, we propose Flick, a frame-perceptive packet scheduling framework at Wi-Fi access points (APs). It leverages the periodic per-frame transmission behavior and the LLV traffic distribution characteristics to infer the end-to-end frame latency at APs. Flick consists of three components: a Frame Boundary Identifier that detects video frame boundaries using only packet size, an End-to-End Frame Latency Estimator that estimates the end-to-end latency without sender or receiver timestamps, and a Fast-Send, Slow-Recovery Scheduler that dynamically adjusts scheduling priority based on inferred latency. We implement Flick on a commercial Wi-Fi AP. Testbed results show that Flick reduces P99 latency and stall rate by 57% and 81%, respectively, while preserving 95% throughput and maintaining high fairness.

Networked 360$^\circ$video has become increasingly popular. Despite the immersive experience for users, its sheer data volume, even with the latest H.266 coding and viewport adaptation, remains a significant challenge to today's networks. Recent studies have shown that integrating deep learning into video coding can significantly enhance compression efficiency, providing new opportunities for high-quality video streaming. In this work, we conduct a comprehensive analysis of the potential and issues in applying neural codecs to 360$^\circ$video streaming. We accordingly present$\mathsf {NETA}$, a synergistic streaming scheme that merges neural compression with traditional coding techniques, seamlessly implemented within an edge intelligence framework. To address the non-trivial challenges in the short viewport prediction window and time-varying viewing directions, we propose implicit-explicit buffer-based prefetching grounded in content visual saliency and bitrate adaptation with smart model switching around viewports. A novel Lyapunov-guided deep reinforcement learning algorithm is developed to maximize user experience and ensure long-term system stability. We further discuss the concerns towards practical development and deployment and have built a working prototype that verifies$\mathsf {NETA}$’s excellent performance. For instance, it achieves a 27% increment in viewing quality, a 90% reduction in rebuffering time, and a 64% decrease in quality variation on average, compared to state-of-the-art approaches.

Immersive full-scene volumetric video (VV) showcases the richness and detail of the 3D world, yet poses significant streaming challenges given its massive data volume. Existing 3D tile-based viewport approaches struggle to effectively adapt to full-scene VV owing to their small video buffer limitation, high tile segmentation overhead, and lack of full-scene consideration.

Traditionally, the mission of intercepting malicious traffic between the Internet and the internal network of entities like organizations and corporations, is largely fulfilled by techniques such as deep packet inspection (DPI). However, steganography, the methodology of hiding secret data in seemingly benign public mediums (e.g., images), has been leveraged by advanced persistent threat (APT) groups in recent years, and is almost impossible to be detected and intercepted by traditional techniques, posing a pervasive and realistic threat to cybersecurity. Additionally, internal networks’ vulnerability to steganography is further exacerbated by the connectivity and large attack surface of the Internet of Things (IoT), whose adoption and deployment are quickly expanding. To protect computer systems against malicious communications that apply steganographic methods potentially unknown to cybersecurity stakeholders, we propose StegEraser, an approach to removing the secret information embedded in public mediums by adversaries, that is fundamentally distinct from existing research which is primarily designed for known steganographic methods. Implemented for images, StegEraser injects an excessively huge amount of random binary data with a novel steganographic method into the images, by utilizing the information-merging capabilities of invertible neural networks (INNs), in order to “overload” adversaries’ steganographic hiding capacity of images transmitted through the firewall performing DPI. In the meantime, StegEraser preserves the perceptual quality of the images. In other words, StegEraser “defeats unknown steganography with steganography”. Extensive evaluation verifies that StegEraser significantly outperforms state-of-the-art (SOTA) methods in terms of removing secret information embedded with both traditional and neural network-based steganographic methods, while visually maintaining the image quality.

Ultra-High-Definition (UHD) videos have been getting increasing attention. However, existing video streaming solutions fail to deliver them due to the extremely high bandwidth requirement. The emerging cloud native 5G networks have opened up the possibility of enhancing UHD video quality by leveraging in-network video streaming. Unfortunately, the restricted storage and bandwidth of in-network servers could become the main bottleneck. To this end, we present${\sf EMS}$, a novel UHD video streaming framework, by integratingErasure-coded storage withMulti-sourceStreaming. We respectively introduce a deadline-aware and a latency-sensitive metric to indicate the service quality of video servers and advocate a federated learning paradigm for the adaptive service quality update, including a reinforcement learning based multi-server selection (i.e., user local training) and a global service quality aggregation. To facilitate user local training without sacrificing streaming Quality-of-Experience (QoE), we cast the multi-server selection associated with the restriction on the average number of selected servers per video chunk into two kinds of Multi-Armed Bandit (MAB) models in terms of the proposed service quality metrics. We design lightweight Upper Confidence Bound (UCB) based algorithms with a theoretical performance guarantee. We implement a prototype of${\sf EMS}$, and extensive experiments confirm the superiority of the proposed algorithms.

In this paper, we advocate${\sf NetDPI}$, a novel and efficient Deep Packet Inspection (DPI) solution built-in 5G Data Plane for multi-access edge computing, leveraging the unique forwarding while computing capability of emerging programmable switches. As the cornerstone, we propose${\sf FIVE}$, the firstFiltering-plus-Verification algorithm tailored to programmable switches to achieve efficient multiple pattern matching (i.e., the core of DPI). Briefly, the filtering phase introduces a multi-window parallel shift-or algorithm to rapidly screen out all the “suspicious” packet payloads. Meanwhile, the verification phase innovates a level-based state encoding scheme for the Aho–Corasick (AC) algorithm, which substantially increases the number of supported patterns and consequently figures out more “guilty” payloads. We implement the prototype of${\sf NetDPI}$in both software and hardware programmable switches (i.e., BMv2 and Barefoot Tofino2) and make them publicly available. Extensive evaluations indicate that${\sf NetDPI}$provides orders of magnitude improvement in throughput compared to the typical cloud-delivered DPI solutions, and besides${\sf FIVE}$greatly reduces the memory consumption compared to the alternative in-network exact match algorithms under a variety of system settings including different DPI pattern sets and malware-packet percentages.

With the popularization of consortium blockchain, how to supervise numerous business chains has become a problem. Recently, some researchers have proposed the concept of ‘governing chain by chain’, which means that multiple organizations deploy a consortium blockchain to supervise many business chains in the industry. However, as the number of business chains increases, a performance bottleneck occurs on the supervision chain. Sharding is a promising direction to improve scalability, but existing studies focus on public chains. If transplanted to the supervision chain, these solutions will bring two challenges. Firstly, data from multiple business chains will cause storage conflicts and unbalanced sharding. Secondly, sharding makes it difficult for inter-shard transaction validation. Aiming at the challenges, we propose $\mathcal{S}$-chain, a shard-based supervision chain scheme. The contribution of our work lies in three points. Firstly, we design a unified label-based data sharding method that can evenly map data from different business chains to all shards without conflict. Secondly, we propose a mechanism to validate general smart-contract-based inter-shard transactions. At first, we design a smart contract splitting algorithm that splits one inter-shard smart contract into multiple sub-contracts and depicts their dependencies via a Directed Acyclic Graph(DAG). Then, we design an inter-shard transaction validation method by analyzing the dependencies of all sub-contracts. Finally, we implement a prototype system of $\mathcal{S}$-chain and test its performance. The results show that our $\mathcal{S}$-chain outperforms the scheme without sharding on throughput and storage overhead.

Covert communication technology serves as a crucial tool for safeguarding not only the content of communication but also the identities of the parties involved. In this regard, blockchain emerges as a promising solution due to its decentralized nature, flood propagation of data, and inherent anonymity features. This makes blockchain an ideal candidate for covert communication channels, effectively addressing the weaknesses associated with traditional covert communication methods susceptible to detection, tracing, and interruption. However, the current efforts encounter obstacles like limited practicality, constrained channel capacity, and insufficient concealment capabilities, impeding their broad adoption in real-world scenarios. To address these issues, we propose DarkTrans, a blockchain-based covert communication scheme consisting of an address binary tree and a novel embedding mechanism. The address binary tree as a dynamic label method enables rapid recognition of specific transactions by the recipient, rendering detection by third parties challenging. The embedding mechanism encodes secret messages into transaction values for transmission to augment channel capacity, which can be practically realized within an Ethereum private blockchain. Our experiments with three aspects demonstrate that, compared with the existing scheme, DarkTrans achieves a low embedding time and a high channel capacity. Additionally, Kolmogorov-Smirnov test and sample entropy analysis are conducted to validate the robust concealment of this scheme.

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings.

Due to the limited capabilities of user devices, such as smart phones, and the Internet of Things (IoT), edge intelligence is being recognized as a promising paradigm to enable effective analysis of the data generated by these devices with complex artificial intelligence (AI) models, and it often entails either fully or partially offloading the computation of neural networks from user devices to edge computing servers. To protect users’ data privacy in the process, most existing researches assume that the private (sensitive) attributes of user data are known in advance when designing privacy-protection measures. This assumption is restrictive in real life, and thus limits the application of these methods. Inspired by the research in image steganography and cyber deception, in this paper, we propose StegEdge, a conceptually novel approach to this challenge. StegEdge takes as input the user-generated image and a randomly selected “cover” image that does not pose any privacy concern (e.g., downloaded from the Internet), and extracts the features such that the utility tasks can still be conducted by the edge computing servers, while potential adversaries seeking to reconstruct/recover the original user data or analyze sensitive attributes from the extracted features sent from users to the server, will largely acquire information of the cover image. Thus, users’ data privacy is protected via a form of deception. Empirical results conducted on the CelebA and ImageNet datasets show that, at the same level of accuracy for utility tasks, StegEdge reduces the adversaries’ accuracy of predicting sensitive attributes by up to 38% compared with other methods, while also defending against adversaries seeking to reconstruct user data from the extracted features.

360° videos generally require a large amount of bandwidth between video servers and users, which puts much burden on the current CDN-based single-source video streaming solutions. The emerging cloud native 5G networks can bridge the distance between video servers and users by leveraging in-network single-source video streaming to enhance 360° video quality. Unfortunately, the restricted bandwidth of in-network servers becomes the main bottleneck. Although tile-based video streaming is promising to reduce video transmission size while keeping user QoE, it highly depends on the accuracy of user FoV prediction, which existing prediction methods cannot guarantee. Recently, some researchers advocate the idea of “super FoV” (i.e., an extended range of predicted FoV) to cope with the inaccurate FoV prediction, which however could lower the effect of tile-based video streaming. Alternatively, we present Muster, a multi-source streaming for tile-based 360° videos within cloud native 5G networks. We detail the system components, provide a comprehensive model, formulate joint server selection and tile requesting problems, and correspondingly propose efficient online algorithms with a performance guarantee. Small-scale testbed and large-scale simulation based evaluation confirm the superiority of the proposed algorithms.

360° video streaming requires ultra-high bandwidth to provide an excellent immersive experience. Traditional viewport-aware streaming methods are theoretically effective but unreliable in practice due to the adverse effects of time-varying available bandwidth on the small playback buffer. To this end, we ponder the complementarity between the large buffer-based approach and the viewport-aware strategy for 360°video streaming. In this work, we present Sophon, a buffer-based and neural-enhanced streaming framework, which exploits the double buffer design, super-resolution technique, and viewport-aware strategy to improve user experience. Furthermore, we propose two well-suited ideas: visual saliency-aware prefetch and super-resolution model selection scheme to address the challenges of insufficient computing resources and dynamic user preferences. Correspondingly, we respectively introduce the prefetch and model selection metric, and develop a lightweight buffer occupancy-based prefetch algorithm and a deep reinforcement learning method to trade off bandwidth consumption, computing resource utilization, and content quality enhancement. We implement a prototype of Sophon and extensive evaluations corroborate its superior performance over state-of-the-art works.

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we proposeCocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework.

360° video services require extremely high bitrate and frame rate videos for a good immersive experience. Traditional solutions for adaptive bitrate streaming are still limited by currently insufficient and fluctuating bandwidth. Besides, viewpoint-aware or tile-based solutions would lead more rebuffering due to the short viewpoint prediction window. In this paper, we present EC-360, a novel video streaming framework to speed up 360° video streaming. Specifically, it creatively integrates tilebased online erasure coding into multi-source content delivery to mitigate “cask effect” or impact of straggler node, which oftentimes leads to failure to improve delivery speed in multisource streaming. We formulate the critical tile-based request scheduling problem in EC-360 based on the Combinatorial Multiarmed Bandit (CMAB) model and develop a low complexity online algorithm. We further theoretically verify the efficiency of the CMAB-based algorithm by deriving its regret upper bound. Extensive experiments based on prototype implementation and real network traces corroborate the efficiency, flexibility, and lightweight of proposed solution. EC-360 achieves superior performance improvement compared to state-of-the-art works in various network scenes and system settings.

With diversified demands for location-based services (LBS), smartphone-based indoor pedestrian positioning becomes a research hotspot in the academic and industrial society. Due to the complexity of the indoor environments and the insufficient accuracy of smartphone inertial sensors, it is still challenging to get an indoor pedestrian positioning solution with stable positioning accuracy and good environmental adaptability. Aiming at this problem, a novel iBeacon deployment scheme for indoor pedestrian is proposed in this paper. Firstly, we introduce an abstract method for complex and diverse plane structures of the indoor environments. Secondly, a mapping function between positioning accuracy and pedestrian walking distance is deduced by error analysis of pedestrian dead-reckoning (PDR) method. Finally, this paper proposes a generation algorithm of iBeacon deployment scheme which not only satisfies the positioning accuracy requirement of LBSs but also greatly reduces the number of deployed iBeacons. We have carried out experimental analysis on three different plane structures of real indoor environments. And it turns out that the proposed iBeacon deployment scheme can help PDR-based indoor pedestrian positioning solutions to achieve breakthrough in indoor environment adaptability.

The payment channel networks (PCNs), as the main method of blockchain off-chain expansion, have received extensive attention in recent years. Using the PCNs, two unconnected parties in a transaction can forward payments through existing payment channels of other nodes, which dramatically reduces interactions with the blockchain. But in large-scale dynamic PCNs, the routing mechanism is a challenge. Existing PCNs routing algorithms have some limitations. The landmark routing destroys the decentralization of blockchain. The static routing requires nodes having a global view and ultra-high computing power, which can not be applied to light nodes. We propose VEIN, a dynamic multi-path source routing algorithm, which is suitable for not only full nodes but also light nodes. We present an ingenious routing protocol, a modified max-flow algorithm to find edge-disjoint paths, and a path selection algorithm to deal with the NP-hard multi-path selection problem. Extensive experiments show that VEIN increases the transaction success ratio by 34% with the state-of-art algorithm, and realizes multiple orders of magnitude reduction in storage. In addition, we implement a prototype of VEIN on the Ethereum testnet to verify its feasibility.

The incoming 5G cellular network is stepping into a densification era, where various kinds of base stations are densely deployed to provide fruitful mobile services such as video streaming. In order to improve the performance of these mobile services, the way to optimally allocate cellular resources for the network-wide users is a crucial problem. In this paper, we consider the mobile Ultra-High-Definition (UHD) video streaming service, envision a 5G ultra dense network (UDN) consisting of a series of Video Base Stations (VBSs) dedicated to video streaming services for multiple users, and mainly explore the impact of cellular resource allocation on video streaming. We incorporate two important video streaming states into cellular resource allocation and formulate a streaming-aware and fairness-aware cellular resource allocation problem. To deal with the formulated problem, we provide a novel graph transformation and design an optimal matching algorithm which can be solved in polynomial time. Extensive trace-driven simulations validate the superior performance of the proposed algorithm under different user mobility patterns and network scales.

Ultra-High-Definition (UHD) videos have absorbed great attention in recent years. However, as they are of significant size, streaming them require an extremely high bandwidth to achieve a good quality of experience, which poses a great challenge on the current cellular networks. Realizing the great potentials of coordinated multi-point joint transmission (JT-CoMP) in 5G Ultra Dense Network, we propose a novel Tuner framework for the UHD video streaming service. In this framework, we strive to design an efficient algorithm for VBS sleeping and VBS grouping to maximize the data rates of overall video users while reducing the overhead of cellular networks. To this end, we provide a comprehensive framework model and formulate a single-timescale VBS sleeping & grouping problem. We design a novel master-slave based algorithm to solve the formulated mixed-integer programming problem optimally with low complexity. In addition, we extend it to facilitate the more practical setting, i.e., two-timescale VBS sleeping & grouping. Extensive simulations validate the superior performance of our framework in various system settings.

360° or panoramic video applications have seen booming development and absorbed great attention in recent years. However, as they are of significant size and usually watched from a close distance, they require an extremely higher bandwidth and frame rate for a good immersible experience, which poses a great challenge on mobile networks. Realizing the great potentials of tile-based transcoding, viewport adaptive streaming, and edge caching, we propose Allies, a tile-based joint transcoding, delivery, and caching framework for 360° video services in edge cloud networks. Meanwhile, an innovative idea about 360° video caching way is proposed and applied to improve the cache utilization of edge clouds. In this framework, we formulate the joint optimization problem as an integer nonlinear program and propose a greedy suboptimal algorithm with polynomial running time to minimize video service costs. Finally, extensive simulations with real user's head movement traces and corresponding 360° video datasets corroborate the efficiency, flexibility, and lightweight of our proposed algorithm; for instance, it achieves over 25% performance improvement compared to state-of-the-art works in various system settings.

360° UHD videos have absorbed great attention in recent years. However, as they are of significant size and usually watched from a close range, they require extremely high bandwidth for a good immersive experience, which poses a great challenge on the current single-source adaptive streaming strategies. Realizing the great potentials of tile-based video streaming and pervasive edge services, we advocate a tile-based multi-source adaptive streaming strategy for 360° UHD videos over edge networks. In order to reap its benefits, we consider a comprehensive model which captures the key components of tile-based multi-source adaptive streaming. Then we formulate a joint bitrate selection and request scheduling problem, aiming at maximizing the system utility (i.e., user QoE minus service overhead) while satisfying the service integrity and latency constraints. To solve the formulated non-linear integer programming problem efficiently, we decouple the control variables and resort to matroid theory to design an optimal master-slave algorithm. In addition, we improve our proposed algorithm with a deep learning-based bitrate selection algorithm, which can achieve a rationalization result in a short running time. Extensive datadriven simulations validate the superior performance of our proposed algorithm.

Deep Neural Networks (DNNs) have been extensively applied in a variety of tasks, including image classification, object detection, etc. However, DNNs are computationally expensive, making on-device inference impractical due to limited hardware capabilities and high energy consumption. This paper first incorporates downside risk into characteristics of per-request processing latency for DNN serving systems. Then, considering applications' diverse preferences of latency and accuracy, we introduce a scheme for assigning applications to different DNN models in an edge site, in order to maximize QoS of all applications while reducing the risk of having large processing latency and to meet requirements of minimum accuracy at the same time. Empirical results show that our approach improves system performance and takes an acceptable amount of time for computation.

In this paper, we consider MEC in NGFI-based C-RAN, a novel and practical MEC framework to facilitate the emerging mobile applications such as AR/VR and video surveillance. However, it is challenging to implement it in a cost-efficient manner (i.e., optimized operational expenditures and service performance), due to the coupled resource provision, service deployment and workload distribution. To solve this joint resource scheduling problem, we resort to rounding and decomposition to devise Matryoshka, a novel approximation algorithm with polynomial running time. Extensive data-driven simulations corroborate that Matryoshka achieves superior performance (e.g., 43% and 52% performance gain compared with two state-of-the-art works, CSPP and Octopus) and scales well to support a variety of system settings.

In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20%-68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.

As the cornerstone of future network research, network traffic classification plays an important role on network management, cyberspace security and quality of service. Recently, many researches have used Machine Learning technologies for traffic classification. Most of them only focus on classifying the samples into predefined classes but ignoring the "strangers". In this paper, we use stranger to represent the traffic not belonging to any predefined application, and propose a novel scheme named FlowCop to achieve stranger detection in network traffic classification. By constructing multiple one-class classifiers, FlowCop can divide testing traffic into N classes and a stranger class. Since samples of stranger class are not required during the training stage, FlowCop works in an inexperienced way to detect strangers, just like the cops searching the crowd for strangers. Besides, for accurate classification and low overhead, a feature subspace algorithm is proposed to select outstanding features for each one-class classifier. To verify the effectiveness of FlowCop, we make contrast experiments on two real-world datasets. The results show that FlowCop can not only identify the predefined traffic flows but also detect the strangers. It outperforms four state-of-the-art approaches on both precision and recall.

In this paper, we propose MIU, a novel mobile edge computing framework for IoT applications in ultra dense networks, via the control of the macro base station. We present a comprehensive framework model, and formulate a joint task offloading, user association and small base station sleeping problem, aiming at minimizing the energy consumptions of network-wide IoT devices and total SBSs while respecting a series of practical constraints. We design an efficient algorithm by invoking dual-decomposition and subgradient method to solve the formulated mixed-integer quadratic programming problem. Extensive simulation results show that our proposed algorithm achieves better performance in energy consumption than several benchmark schemes.

In this paper, we advocate edge caching in cloud radio access networks (C-RAN) to facilitate the ever-increasing mobile multimedia services. In our framework, central offices will cooperatively allocate cloud resources to cache popular contents and satisfy user requests for those contents, so as to minimize the system costs in terms of storage, VM reconfiguration, content access latency, and content migration. However, this joint resource allocation, content placement and request routing, is nontrivial, since it needs to be continuously adjusted to accommodate system dynamics, such as user movement and content slashdot effect, while taking into account the time-correlated adjustment costs for VM reconfiguration and content migration. To this end, we build a comprehensive model to capture the key components of edge caching in C-RAN and formulate a joint optimization problem, aiming at minimizing the system costs over time and meanwhile satisfying the time-varying user requests and respecting various practical constraints (e.g., storage and bandwidth). Then, we propose a novel online approximation algorithm by resorting to the regularization, rounding, and decomposition technique, which can be proved to have a parameterized competitive ratio with a polynomial running time. Extensive trace-driven simulations corroborate the efficiency, flexibility, and lightweight of our proposed online algorithm; for instance, it achieves an empirical competitive ratio around 2 - 4 and gains over 30% improvement compared with many state-of-the-art algorithms in various system settings.

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.

Recent years have witnessed the proliferation of mobile crowdsourcing that brings a new opportunity to leverage human intelligence and movement behaviors to wider application areas. In parallel with the development of online centralized platforms, we look into the realization of self-organized mobile crowdsourcing drawing on opportunistic networks, and propose the Crowd Foraging framework, in which a mobile task requester can proactively recruit a massive crowd of opportunistic encountered mobile workers in real time for quick and high-quality results. We present a comprehensive framework model that fully integrates human behavior factors for modeling task profile, worker arrival, and work ability, and then introduce a service quality concept to indicate the expected service gain that a requester can enjoy when she recruits an arrival worker by jointly considering the work ability of workers as well as timeliness and reward of tasks. Furthermore, we formulate a sequential worker recruitment problem as an online multiple stopping problem to maximize the expected sum of service quality, and accordingly derive an optimal worker recruitment policy through the dynamic programming principle, which exhibits a nice threshold-based structure. We provide data-driven case studies to validate the assumptions used in the policy design, and conduct extensive trace-driven numerical evaluations, which demonstrate that our policy can achieve superior performance (e.g., improve more than 30% performance over classic policies). Besides, our Android prototype shows that the Crowd Foraging framework is cost-efficient, such as requiring less than 7 s and 6 J in terms of time and energy consumption for the optimal threshold calculation in our policy in most cases.

In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.

The increasing market share of electric vehicles (EVs) makes charging facilities indispensable infrastructure for integrating EVs into the future intelligent transportation systems and smart grid. One promising facility called fast charging reservation(FCR) system was recently proposed. It allows people to reserve fast chargers ahead of time. In this system, fast chargers are the most scarce resource instead of electricity. Thus how to allocate these charging points requires careful designing. A good allocation policy should 1) ensure charging points to be allocated to EV users who really value them, and 2) prevent users' private information, e.g., identity, personal agenda, residing area and etc., from being inferred. A simple combination of classic multi-item auction and user identity anonymization cannot satisfy both criteria simultaneously. To find such an allocation, in this paper we investigate the design of privacy-preserving auctions in FCR systems. Traditional privacy-preserving strategies such as cryptography could incur high computation and communication overhead and hence jeopardize the efficiency of allocation. To this end, we propose Auc2Reserve, a differentially private randomized auction. Auc2Reserve applies an improved approximate sampler and the belief propagation (BP) technique to accelerate the resource allocation and pricing process. As a result, it is much more computationally efficient than generic exponential differentially private mechanisms and other theoretical approximate implementations. Through theoretical analysis, we show that Auc2Reserve is ?-incentive compatible, individual rational and ?-differentially private. And it provides a close-form approximation ratio in social welfare of FCR systems. In addition, we also demonstrate the efficacy of Auc2Reserve in terms of social welfare and privacy leakage via numerical simulation.

In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user's motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% ~ 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency.

In urban areas, traffic congestion has become a common phenomenon resulting in a great waste of time and fuel. Thus, the real-time detection for road condition becomes a significant challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for urban traffic condition only by volunteer probes. The travel time for detected road segment is used to evaluate the traffic condition. To reduce the influence of individuals, a T Window Algorithm (TWA) is designed for calculating the average travel time of probes. For performance optimization, three kinds of Adaptive T Window Algorithm (ATWA) are put forward to resize window according to the probe density. Besides, on analysis of travel time composition, a congestion line is built to identify the traffic status of road segment. Finally, all the approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic condition effectively.

SUMMARY In high‐speed network monitoring, the ever‐growing traffic calls for a high‐performance solution for the computation of frequent items. The increasing number of cores in the current commodity multi‐core processors opens up new opportunities in parallelization. In this paper, we present a novel precision integrated framework (PRIF) that exploits the great parallel capability of multi‐cores to speed up the famousfrequentalgorithm. PRIF equally distributes the input data stream into sub‐threads that use the optimized weightedfrequentalgorithm to track local frequent items. The items with frequency increments exceeding a pre‐defined threshold are sent to a merging thread which is able to return the global continuousε‐deficient frequent items. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic traces confirm the theoretical analyses and demonstrate the excellent performance as well as the effects of parameters and data skewness. The results show that PRIF is able to provide continuous frequent items and near‐linear speedup at the cost of greater memory use. Copyright © 2013 John Wiley & Sons, Ltd.

Mobility-assisted data collection in sensor networks creates a new dimension to reduce and balance the energy consumption for sensor nodes. However, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to schedule the movement of mobile elements throughout the field is of ultimate importance. In this paper, the on-demand scenario where data collection requests arrive at the mobile element progressively is investigated, and the data collection process is modelled as an$M/G/1/c$-$NJN$queuing system with an intuitive service discipline of nearest-job-next (NJN). Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. NJN is further extended by considering the possible requests combination (NJNC). The simulation results validate our models and offer more insights when compared with the first-come-first-serve (FCFS) discipline. In contrary to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have better performance than FCFS, in both the average and more importantly the worst cases, which offers the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes, as well as other similar scheduling scenarios.

Many mobile applications such as games and social applications are emerging for mobile devices. These powerful applications consume more and more running time and energy. So they are badly confined by mobile device with limited resource. Since cloud infrastructure has great potential to benefit task execution, this paper presents SmartVirtCloud (SmartVC). A system can offload methods in applications to achieve better performance in indoor environment. SmartVC decides at runtime whether and when the methods in application should be executed remotely. And two types of cloud service models, namely load-balancing and application-isolation, are constructed for concurrent requests. The empirical results show that, by using SmartVC, the CPU-intensive calculation application consumes two orders of magnitude less energy on average; the processing speed of latency-sensitive image translation application gets doubled; the performance of network-intensive picture download application is improved with the increase of picture amount. In addition, the proposed two cloud models support concurrent requests from smartphones very well.

The introduction of mobile elements has created a new dimension to reduce and balance the energy consumption in wireless sensor networks. However, data collection latency may become higher due to the relatively slow travel speed of mobile elements. Thus, the scheduling of mobile elements, i.e., how they traverse through the sensing field and when they collect data from which sensor, is of ultimate importance and has attracted increasing attention from the research community. Formulated as the traveling salesman problem with neighborhoods (TSPN) and due to its NP-hardness, so far only approximation and heuristic algorithms have appeared in the literature, but the former only have theoretical value now due to their large approximation factors. In this paper, following a progressive optimization approach, we first propose a combine-skip-substitute (CSS) scheme, which is shown to be able to obtain solutions within a small range of the lower bound of the optimal solution. We then take the realistic multirate features of wireless communications into account, which have been ignored by most existing work, to further reduce the data collection latency with the multirate CSS (MR-CSS) scheme. Besides the correctness proof and performance analysis of the proposed schemes, we also show their efficiency and potentials for further extensions through extensive simulation.

Mobility-assisted data collection in wireless sensor networks brings in new opportunities to improve the energy efficiency of sensor nodes. However, it also introduces new challenges such as large data collection latency. The optimal usage of the limited mobility of mobile elements in the network is of great importance to reduce this latency, and a lot of research efforts have been devoted to it. In this paper, focusing on the scenario where a mobile sink is available to carry out the data collection, a simple and efficient Partition-based Nearest Job Next data collection scheme is proposed, which schedules the travel of the mobile sink based on a clustered structure of the network. Corresponding geometrical probability-based analysis is also presented to shed light on the performance of the scheme. The efficiency of the scheme, along with the accuracy of the analysis, is verified through extensive simulation.

The introduction of mobile elements in wireless sensor networks creates a new dimension to reduce and balance the energy consumption for resource-constrained sensor nodes; however, it also introduces extra latency in the data collection process due to the limited mobility of mobile elements. Therefore, how to arrange and schedule the movement of mobile elements throughout the sensing field is of ultimate importance. In this paper, the online scenario where data collection requests arrive progressively is investigated, and the data collection process is modeled as an M/G/1/c-NJN queuing system, where NJN stands for nearest-job-next, a simple and intuitive service discipline. Based on this model, the performance of data collection is evaluated through both theoretical analysis and extensive simulation. The NJN discipline is further extended by considering the possibility of requests combination (NJNC). The simulation results validate our analytical models and give more insights when comparing with the first-come-first-serve (FCFS) discipline. In contrast to the conventional wisdom of the starvation problem, we reveal that NJN and NJNC have a better performance than FCFS, in both the average and more importantly the worst cases, which gives the much needed assurance to adopt NJN and NJNC in the design of more sophisticated data collection schemes for mobile elements in wireless ad hoc sensor networks, as well as many other similar scheduling application scenarios.

By splitting up an underutilized physical host into several virtualized domains, Virtualization can make the optimal use of resources and reduce the energy consumption. By delving into the types of I/O applications, adjusting amount of resource, and combining different applications in one or more domains, several insights are observed in this paper: using a few exclusive physical CPU driver domain can improve the efficiency of forwarding, which makes over 17% performance improvement for I/O applications; in addition, the domain providing mixed services can achieve 70% performance gain, when compared with others that provide single service; last but not least, avoiding running too many network-provider domains simultaneously can relieve the inter-domain interference. These observations not only help to bring performance improvement to cloud consumers but also service stability to cloud providers.

Traffic congestion has become a global problem in urban areas, resulting in a great waste of time and fuel every year. Thus, the real-time detection for road congestion becomes a great challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for traffic congestion based on the communication between vehicles and road side unit (RSU). In our scheme, the length of waiting queue during red light acts as a measure index to evaluate the traffic status of intersections. In order to detect the queue length timely and accurately, a restricted greedy forwarding strategy is put forward to transmit the status information from tail vehicle by multihop broadcast. According to the driver-perceived performance, a congestion index is built to describe the traffic condition of detected intersections. Finally, all the proposed approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic congestion effectively.

Exploiting mobile elements to conduct data collection in wireless sensor networks offers a new approach to reducing and balancing the energy consumption of sensor nodes; however, the resultant data collection latency may be large due to the limited travel speed. Many research efforts have been made on reducing the data collection latency with the scenario where a single mobile element is available. A potential problem with this scenario is the scalability, and a straightforward solution is to employ multiple mobile elements to collect data collaboratively. In this paper, the network where multiple homogeneous mobile elements are available is modeled as an M/G/c queuing system, and insights on the data collection performance are obtained through theoretically analyzing the measures of the queue. In addition, a heuristic formula to determine the optimal number of mobile elements is proposed based on this model. The accuracy of our modeling and analysis, along with the performance evaluation of the proposed heuristic formula, is verified through extensive simulation.

Data collection with mobile elements in wireless sensor networks brings in new opportunities to reduce and balance the energy consumption of sensor nodes, however, it may result in high data collection latency. The optimal scheduling of mobile element's limited mobility is of great importance to reduce this latency, and a lot of research efforts have been made on it. Focusing on the on-demand data collection scenario, where data collection requests appear progressively, and based on the fact that several nearby requests can be combined and served from the same collection site, we propose a simplified combine-skip-substitute (CSS) scheme, which is shown to be able to reduce the data collection latency greatly. We also analyze the probability for the combination to happen, and how many requests can be combined, to gain more insights in its performance. The efficacy of the proposed scheme and the accuracy of the analytical results are verified through extensive simulation.

Nowadays, urban traffic congestion has become a global phenomenon, resulting in a lot of negative effects both on economy and ecology. Thus, the detection of road congestion in cities is a highly urgent problem to be solved by scientists throughout the world. One new promising approach is the utilization of vehicular ad-hoc networks (VANETs). In this paper, we focus on the data transmission in the detection applications and propose a novel broadcast method, restricted multi-hop broadcast (RMB), to improve the congestion detecting ability of road side unit (RSU). Furthermore, two strategies are presented as the optimization of RMB. In order to validate RMB, we make experiments in a realistic traffic scenario. Simulations show that RMB works efficiently and expands the detection range of RSU significantly.

With the advantage of high bandwidth and low cost, WLAN is widely regarded as an appealing technology for wireless access. However, WLAN only provides very limited coverage which is no more than hundreds of meters, making users experience intermittent connectivity when they move near Wi-Fi access points (AP). To fill in the gaps between APs, we present a new framework, which combines infrastructure composed of APs with the concept of DTN to improve network performance. In particular, when a mobile client has moved out of the reach of the infrastructure, our framework predicts the trajectory of this client and lets the APs in the proximity identify suitable relay nodes among the mobile clients that happen to visit those APs. If some passing by mobile clients have a chance to encounter the target client soon, the corresponding AP(s) will transfer the data destined to the target client to those mobile clients, in the hope that they will accomplish the delivery in a "store-carry-forward" fashion. In this way, the target client may be able to receive data even before it arrives at an AP. Our evaluation shows that our framework fairly improves the performance in terms of both delivery delay and delivery ratio, with controlled overhead.

Vehicle traffic congestion has become a serious urban phenomenon in recent years, resulting in a large number of negative effects both on economy and ecology. Thus, finding a way to detect traffic congestion is a highly urgent problem to be solved by scientists and politicians throughout the world. One new promising approach is the usage of vehicular ad-hoc network (VANET). In this paper, we study different models of detecting traffic congestion, and propose two broadcast methods, restricted 2-hop broadcast (R2HB) and probabilistic restricted 2-hop broadcast (PR2HB), used for data collection in the context of vehicle to road side unit (RSU) communication. Simulations show that R2HB and PR2HB work efficiently and expand the detection range of RSU significantly.

Exploring mobility to accomplish the data collection in wireless sensor networks (WSNs) has become the focus of recent studies, which can improve the energy efficiency of sensor nodes by shifting the data forwarding task from them to mobile elements (MEs). However, the data collection latency in this case can be much higher. We consider an on-demand data collection scenario in this paper, in which sensor nodes broadcast service requests when their buffer is about to be full. On receiving such requests, the ME moves toward the sensor nodes to collect data, and uploads the data to the sink when possible. An M/G/1 queue-based analytical model is presented, and analytical results on several important system performance metrics are derived. Furthermore, we propose an improved service scheme, which combines requests whenever they are in proximity. The work is evaluated through extensive simulations, which validate the accuracy of our model. The efficacy of the proposed service scheme to improve the system performance is also verified.

Integrating mobility into WSNs can significantly reduce the energy consumption of sensor nodes. However, this may lead to unacceptable data collection latency at the same time. In our previous work, we alleviated the problem under the assumption of a mobile base station (BS). In this paper, we discuss how the problem can be solved when the BS itself is not capable of moving, but it can instead employ some mobile elements (MEs). The data collection latency is mainly determined by the longest tour of the MEs in this case. Each ME should be assigned a similar workload to reduce the latency. Furthermore, the total length of the tours should be minimized to decrease the working cost of MEs. We propose three methods to solve the problem with these two-fold objectives. In the first two methods, we cluster the network according to some criteria, and then construct the data collection tour for each ME. We apply a heuristic operator based on the genetic algorithm in the third method, whose fitness function is defined according to the two-fold objectives. These methods are evaluated by comprehensive experiments. The results show that the genetic method can provide us more steady solutions in term of data collection latency. We also compare the mobile BS model and the multiple MEs model, whose results show that the latter can get us better solutions when the number of MEs gets larger.

In the anti-spam area, distributed spam processing technology that based on reputation mechanism is a currently main focus in research. The raw reputation plays an important role in computing the finial reputation of the target node. In this paper, we analyze the ratio-based raw reputation generation approach in detail, point out its problems, and propose an aggregation-based raw reputation generation approach. Furthermore, we discuss how the number of evaluations and the method using to choose these evaluations impact on the result of raw reputation. Theoretical analysis and simulation results demonstrate that aggregation-based approach is much more effective and accurate than ratio-based approach. As long as we choose the proper number of evaluations, the results can accurately reflect behavior of the target node.