With the concern of privacy, a user usually encrypts the images before they were uploaded to the cloud service providers. Classification over encrypted images is essential for the service providers to collect coarse-grained statistical information about the images, therefore offering better services without sacrificing users' privacy. In this paper, we propose CryptoEyes to address the challenges of privacy-preserving classification over encrypted images. We present a two-stream convolutional network architecture for classification over encrypted images to capture the contour of encrypted images, therefore significantly boosting the classification accuracy. By sharing a secret sequence between the service provider and the image owner, CryptoEyes allows the service provider to obtain category information of encrypted images while preventing the unauthorized users from learning it. We implemented and evaluated CryptoEyes on popular datasets and the experimental results demonstrate the superiority of CryptoEyes over existing state of the arts in terms of classification accuracy over encrypted images and better privacy preservation performance.

Currently, an unprecedentedly vast amount of videos are hosted on the Internet and shared by users across the world. Within these videos, a considerable portion is duplicate or near-duplicate. Consequently, building an effective yet efficient content-based redundancy detection system is of importance, as this research would be beneficial to a variety of applications. Despite the progress in this field, designing a practical detection system for web videos continues to be difficult, because of the contradictions between the accuracy and speed requirements. In this paper, we propose a novel near-duplicate video detection system, CompoundEyes, whose design philosophy deviates from the conventional feature-centered paradigm. Instead, the focus of our system has been shifted from the design of an advanced feature representation to the design of system architecture. This design methodology not only ensures a decent detection accuracy by the collaboration of the classifiers but also substantially accelerates the detection speed due to the low dimensionality of the feature representations and the exploitation of the parallelism among the components. Experiments have been conducted to demonstrate that the CompoundEyes is both accurate and fast.

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.

Designing an efficient defense framework is challenging with respect to a network's complexity, widespread sophisticated attacks, attackers' ability, and the diversity of security appliances. The Intrusion Response System (IRS) is intended to respond automatically to incidents by attuning the attack damage and countermeasure costs. The existing approaches inherit some limitations, such as using static countermeasure effectiveness, static countermeasure deployment cost, or neglecting the countermeasures' negative impact on service quality (QoS). These limitations may lead the IRS to select inappropriate countermeasures and deployment locations, which in turn may reduce network performance and disconnect legitimate users. In this paper, we propose a dynamic defense framework that selects an optimal countermeasure against different attack damage costs. To measure the attack damage cost, we propose a novel defense-centric model based on a service dependency graph. To select the optimal countermeasure dynamically, we formulate the problem at hand using a multi-objective optimization concept that maximizes the security benefit, minimizes the negative impact on users and services, and minimizes the security deployment cost with respect to the attack damage cost.

A lot of researches exploit the flexibility of Software-Defined Networking (SDN) to conduct traffic engineering in order to improve network performance and enhance robustness to failures. As the upgrade of a traditional network to a full SDN deployment is an incremental process, the coexistence of SDN switches and legacy switches forms a hybrid SDN. Due to the different forwarding characteristics of these switches, it is essential to coordinate the forwarding of SDN control and distributed routing to avoid inconsistency and achieve high network utilization. In this paper, we note that the effectiveness of traffic engineering in hybrid SDN strongly depends on both the structures of forwarding graphs and traffic distribution, while existing approaches mainly focus on the latter. We first define the consistent forwarding graph, and then construct forwarding graphs with potential high throughput for effective traffic engineering, while maintaining forwarding consistency. The evaluation results show that the proposed forwarding graph construction approach improves network throughput and achieves better load balancing compared with existing simple forwarding path constructing approaches, especially with more fraction of SDN deployment.

The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.

Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user's context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.

With SDN control programs from multi-domains configuring the network, it is inevitable that control programs make conflicting control decisions, which probably lead to misconfiguration or performance degradation. Existing control coordination approaches either compose control programs to derive consistent solutions jointly or examine the generated rules of each control program to ensure they are consistent. However, the former is usually of great complexity and hard to be conducted automatically, and the latter probably results in suboptimal solutions due to the independent execution of control programs. Moreover, these approaches all fail to consider the control utility of control programs. In this paper, we propose Redactor to optimize the consistency and utility of network control in an automatic and dynamic manner. To make network control consistent, we implement SDN control programs with declarative language Prolog, and compose control programs automatically to execute together to make consistent decisions. When conflicts occur, we use a heuristic approach to compromise a subset of control programs to maximize the control utility. We compare Redactor with the static priority mechanism and Athens [1], and the results show that Redactor always satisfies more control objectives to achieve better control consistency and utility.

At the present time, billions of videos are hosted and shared in the cloud of which a sizable portion consists of near-duplicate video copies. An efficient and accurate content-based online near-duplicate video detection method is a fundamental research goal; as it would benefit applications such as duplication-aware storage, pirate video detection, polluted video tag detection, searching result diversification. Despite the recent progress made in near-duplicate video detection, it remains challenging to develop a practical detection system for large-scale applications that has good efficiency and accuracy performance. In this paper, we shift the focus from feature representation design to system design, and develop a novel system, called CompoundEyes, accordingly. The improvement in accuracy is achieved via well-organized classifiers instead of advanced feature design. Meanwhile, by applying simple features with reduced dimensionality and exploiting the parallelism of the detection architecture, we accelerate the detection speed. Through extensive experiments we demonstrate that the proposed detection system is accurate and fast. It takes approximately 1.45 seconds to process a video clip from a large video dataset, CC_WEB_VIDEO, with a 89% detection accuracy.

With the popular applications of SDN in load balancing and failure recovery, the controller schedules affected flows to redundant paths to avoid network congestions and failures by updating flow tables in data plane. However, inconsistent flow table updating may lead to transient incorrect network behaviors or undesired performance degradation. Therefore, the consistency imposes dependencies among updates, so that the order of updates must be carefully considered to keep the consistency. To update flow tables consistently and efficiently, in this paper, we propose an update ordering approach — Cupid. To avoid high overhead in update ordering, we divide the global dependencies among updates into local restrictions by: 1) partitioning a new routing path into several independent segments, 2) identifying critical nodes controlling traffic shifting between the old path and new path, and 3) constructing a dependency graph among critical nodes for potential congested links. We then design a heuristic algorithm to resolve the dependency graph. To save the flow table space, a switch keeps only one flow entry with multiple ports for a flow during updating. Our simulation shows that Cupid schedules updates at least 2 times faster and has less throughput losses than the state-of-the-art approaches in both fat-tree and mesh networks.

Software-defined networking enables applications act as blackboxes independently to control the network flexibly. However, these independent applications may generate conflicting control decisions. To reconcile applications automatically and dynamically, we implement control applications with Prolog, which enables applications to execute jointly to make consistent control decisions. When conflicts occur, we design a compromise algorithm by sacrificing a subset of applications to maximize the desired control objectives.

Recently, map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to the lack of a direct fully access to PoI databases, it is infeasible to exhaustively search and collect all PoIs within a large area using public APIs, which usually impose a limit on the maximum query rate. In this paper, we propose sampling methods to accurately estimate PoI statistics such as sum and average aggregates from as few queries as possible. Experimental results based on real datasets show that our methods are efficient, and require six times less queries than state-of-the-art methods to achieve the same accuracy.

Radio Frequency Identification (RFID) technology plays an important role in supply chain logistics and inventory control. In these applications, a series of scanning operations at different locations are often needed to cover the entire inventory (tags). In such continuous scanning scenario, adjacent scans inevitably read overlapping tags multiple times. Most existing methods suffer from low scanning efficiency when the overlap is small, since they do not distinguish the size of overlap which is an important factor of scanning performance. In this paper, we analytically unveil the fundamental relationship between the performance of continuous scanning and the size of overlap, deriving a critical threshold for the selection of scanning strategy. Further, we design an accurate estimator to approximate the overlap. Combining the estimate and a compact data structure, an adaptive scanning scheme is introduced to achieve low communication time. Through detailed analysis and extensive simulations, we demonstrate that the proposed scheme significantly outperforms previous approach in total scanning time.

The papers in this special section focus on cyber-physical systems and applications for their use. A cyber-physical system (CPS) integrates a vast variety of static and mobile resources, including sensor and actuator networks, swarms of robots, remote-controlled vehicles, critical infrastructures, control and decision software, static data and just-in-time information from sensors, knowledge, data analytics and fusion software, event driven supply chains, and humans, and offers great potential for achieving tasks that are far beyond the capabilities of existing systems [1]. Individual users, organizations, and various communities can transform the vast space of cyberphysical entities into capabilities that no single entity can achieve alone. However, these capabilities do not come easily. Intelligence is needed for just-in-time composition of resources into services. Associated challenges include how to manage the vast number and diverse varieties of static and mobile physical entities, how to describe the capabilities of the cyber-physical entities, how to decompose high level goals into low-level control commands for the individual entities, how to achieve intelligent coordination and manage information flow among the entities.

The extreme volume and staggeringly increasing rate inevitably produce unprecedented pressure on any large scale video sharing and hosting systems. Among the efforts to mitigate this pressure, content-based video similarity search is becoming more and more important with the exponential growth of the data size. Though various approaches have been proposed to address this problem, they are mainly focusing on the retrieval accuracy thus bringing video features with high complexity. Due to the complexity of the feature, these systems are based on the assumption that features representing videos have been obtained offline and stored in the database statically. However, the on-call efforts to move the feature extraction and similarity search from offline to online have been ignored in previous work. In this paper, we propose Marlin, a streaming data processing pipeline that efficiently extracts video features and retrieves video similarity information in a large scale video data system. We design a streaming feature extractor to handle the videos streaming into the system and establish the fined-grained resource allocation with a resource-aware data abstraction layer over streaming data to allocate computing resources among the videos with various resource demands. Besides that, we are pipelining the feature extraction and similarity search process with a distributed feature index, which supports real-time query and incremental index update. The experimental and the extensive real-world workload driven simulation results show that the proposed stream processing architecture achieves 25X speedup against the sequential feature extraction algorithm and 23X speedup against the sequential similarity search with a subsecond similarity query latency for a single request.

Crowdsensing applications require individuals toshare local and personal sensing data with others to produce valuableknowledge and services. Meanwhile, it has raised concernsespecially for location privacy. Users may wish to prevent privacyleak and publish as many non-sensitive contexts as possible.Simply suppressing sensitive contexts is vulnerable to the adversariesexploiting spatio-temporal correlations in users' behavior.In this work, we present PLP, a crowdsensing scheme whichpreserves privacy while maximizes the amount of data collectionby filtering a user's context stream. PLP leverages a conditionalrandom field to model the spatio-temporal correlations amongthe contexts, and proposes a speed-up algorithm to learn theweaknesses in the correlations. Even if the adversaries are strongenough to know the filtering system and the weaknesses, PLPcan still provably preserves privacy, with little computationalcost for online operations. PLP is evaluated and validated overtwo real-world smartphone context traces of 34 users. Theexperimental results show that PLP efficiently protects privacywithout sacrificing much utility.

Rapid disaster relief is important to save human lives and reduce property loss. With the wide use of smartphones and their ubiquitous easy access to the Internet, sharing and uploading images to the cloud via smartphones offer a nontrivial opportunity to provide information of disaster zones. However, due to limited available bandwidth and energy, smartphone-based crowdsourcing fails to support the real-time data analytics. The key to efficiently and timely share and analyze the images is to determine the value/worth of the images based on their significance and redundancy, and only upload those valuable and unique images. In this paper, we propose a near-realtime and cost-efficient scheme, called SmartEye, in the cloud-assisted disaster environment. The idea behind SmartEye is to implement QoS-aware in-network deduplication over DiffServ in the software-defined networks (SDN). Due to the ease of use, simplicity and scalability, DiffServ supports the in-network deduplication to meet the needs of differentiated QoS. SmartEye aggregates the flows with similar features via a semantic hashing, and provides communication services for the aggregated, not a single, flow. To achieve these goals, we leverage two main optimization schemes, including semantic hashing and space-efficient filters. Efficient image sharing is helpful to disaster detection and scene recognition. To demonstrate the feasibility of SmartEye, we conduct two real-world case studies in which the loss in Typhoon Haiyan (2013) and Hurricane Sandy (2012) can be identified in a timely fashion by analyzing massive data consisting of more than 22 million images using our SmartEye system. Extensive experimental results illustrate that SmartEye is efficient and effective to achieve real-time analytics in disasters.

In traditional networks, it is difficult to manage the distributed detection and prevention nodes of IDS and IPS due to the laborious manual deployment and independent configuration. Software defined networking (SDN) provides a flexible approach to control the underlying network infrastructures efficiently. However, the OpenFlow flow table is too simple to provide complex functions with the match-action style processing. To support more functionalities, in this paper, we propose a middlebox management architecture with SDN - OpenMiddlebox, by extending OpenFlow to support middleboxes with ClickOS virtual machines (VM), so that programmable middleboxes could be deployed and managed in switches with fast booted ClickOS VMs flexibly. We then design automatic deployment and update schemes of network intrusion detection and prevention middleboxes with the centralized controller. The evaluation results show that OpenMiddlebox could manage the distributed middleboxes efficiently and is scalable to large networks, and the centralized control also improves the network intrusion detection and prevention accuracy.

Participatory sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of participatory sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth the important issues of false data detection and correction in participatory sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose DECO, a general framework to detect false values for participatory sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, DECO is able to accurately detect the false data and estimate both false and missing values for data correction. We validate our design through an experimental case study.

The increasing virtualization in data centers brings growing inter-node communication by running various applications on virtual machines located physically separated. Meanwhile, the virtual machines on a physical server also compete for limited Ethernet interface I/O resources. Load balancing with multipath and multihoming is usually the key to address the bandwidth and Ethernet I/O bottlenecks. Even though various variants of equal cost multipath (ECMP) schemes have been widely applied for load balancing, the equal and fairness assumption of ECMP results in imbalance and underutilization in asymmetric networks without considering network topology and traffic situation. The multihoming solutions usually require protocol modification and peer's support. In this paper, we propose a utilization & topology-aware multipath routing and multihoming scheduling with Software Defined Networking (SDN) in data centers to address these bottlenecks. The utilization & topology-aware multipath routing takes global network situation to avoid congestions and balances utilization of multiple paths. At a multi-homed server end, the multihoming scheduler balances the traffic among multiple Ethernet interfaces and ensures QoS guarantees when accessing the network without changing network stack. We compared our approach with traditional single path and equal cost multipath schemes, and the results showed that the utilization & topology-aware multipath routing and multihoming scheduling achieved much higher network utilization and better load balancing, especially for asymmetric networks.

End-to-end delay measurement has been an essential element in the deployment of real-time services in networked systems. Traditional methods of delay measurement based on time domain analysis, however, are not efficient as the network scale and the complexity increase. We propose a novel theoretical framework to analyze the end-to-end delay distributions of networked systems from the frequency domain. We use a signal flow graph to model the delay distribution of a networked system and prove that the end-to-end delay distribution is indeed the inverse Laplace transform of the transfer function of the signal flow graph. Two efficient methods, Cramer’s rule-based method and the Mason gain rule-based method, are adopted to obtain the transfer function. By analyzing the time responses of the transfer function, we obtain the end-to-end delay distribution. Based on our framework, we propose an efficient method using the dominant poles of the transfer function to work out the bottleneck links of the network. Moreover, we use the framework to study the network protocol performance. Theoretical analysis and extensive evaluations show the effectiveness of the proposed approach.

In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behavior-based features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.

Counting the number of RFID tags is a fundamental issue and has a wide range of applications in RFID systems. Most existing protocols, however, only apply to the scenario where a single reader counts the number of tags covered by its radio, or at most the union of tags covered by multiple readers. They are unable to achieve more complex counting objectives, i.e., counting the number of tags in a composite set expression such as (S_1 big cup S_2) - (S_3 big cap S_4). This type of counting has realistic significance since it provides more diversity than existing counting scenario, and can be applied in various applications. In this paper, we formally introduce the RFID composite counting problem, which aims at counting the tags in arbitrary set expression. We obtain strong lower bounds on the communication cost of composite counting. We then propose a generic Composite Counting Framework (CCF) that provides estimates for any set expression with desired accuracy. The communication cost of CCF is proved to be within a small factor from the optimal. We build a prototype system for CCF using USRP software defined radio and Intel WISP computational tags. Also, extensive simulations are conducted to evaluate the performance of CCF. The experimental results show that CCF is generic, accurate and time-efficient.

Recently map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to the lack of a direct fully access to PoI databases, it is infeasible to exhaustively search and collect all PoIs within a large area using public APIs, which usually impose a limit on the maximum query rate. In this paper, we propose an effective and efficient method to sample PoIs on maps, and give unbiased estimators to calculate PoI statistics such as sum and average aggregates. Experimental results based on real datasets show that our method is efficient, and requires six times less queries than state-of-the-art methods to achieve the same accuracy.

Smartphones nowadays are installed with diverse applications, each of which consumes energy and bandwidth. As more and more applications are crowded into a smart- phone, they cause serious problems with regard to battery life and bandwidth utilization. Existing proposals to tackle such challenges usually resort to two ways: avoiding energy- consuming network activities or improving communication efficiency in terms of power consumption. Those approaches either affect the smartphone users' experience, or offer little benefit in prolonging the battery life. Motivated by insightful understanding of users' habit, we in this paper propose a novel approach to orchestrate network activities of smartphone applications, based on user's habit. We implement our approach on smartphones as a middleware service called NetMaster. The performance evaluation with real traces shows that NetMaster reduces energy consumption of network activities by 77.8% in average and increases network bandwidth utilization by over 200%. The user experience is surprisingly well preserved. The chance of undesired interrupt during normal usage is less than 1%.

Radio Frequency Identification (RFID) technology plays an important role in supply chain logistics and inventory control. In these applications, a series of scanning operations at different locations are often needed to cover the entire inventory (tags). In such continuous scanning scenario, adjacent scans inevitably read overlapping tags multiple times. Most existing methods suffer from low scanning efficiency when the overlap is small, since they do not distinguish the size of overlap which is an important factor of scanning performance. In this paper, we analytically unveil the fundamental relationship between the performance of continuous scanning and the size of overlap, deriving a critical threshold for the selection of scanning strategy. Further, we design an accurate estimator to approximate the overlap. Combining the estimate and a compact data structure, an adaptive scanning scheme is introduced to achieve low communication time. Through detailed analysis and extensive simulations, we demonstrate that the proposed scheme significantly outperforms previous approach in total scanning time.

With the rapid growth of data, query performance is an important concern in cloud storage applications. To reduce the query response time, cuckoo hashing via d hash functions has been adopted to achieve O(1) query efficiency. However, in practice the cuckoo hashing consumes a large amount of system resources, since an item insertion may suffer from frequent “kick-out” operations or even endless loops. In order to address this problem, we propose an efficient loop-oblivious scheme, called Necklace, in the cloud. The idea behind Necklace is to minimize data migration among servers and alleviate the collisions in the insertion operation. We identify the shortest path via indexing the auxiliary table, without retrieving the actual storage contents or carrying out the tentative “kick-out” operations. We have implemented Necklace in a real cloud system and examined the performance by using a real-world trace. Extensive experimental results demonstrate the efficiency and efficacy of Necklace.

FlexRay is a new industry standard for next-generation communication in automotives. Though there are a few recent researches on performance analysis of FlexRay, two important aspects of the FlexRay design have been overlooked. The first is a holistic integrated scheduling scheme that can handle both static and dynamic segments in a FlexRay network. The second is cost-effective and scalable performance. To address these aspects, we propose a novel holistic scheduling scheme, called HOSA, which can provide scalable performance by using flexible and ease-of-use dual channel communication in FlexRay. HOSA is built upon a novel slot pilfering technique to schedule and optimize the available slots in both static and dynamic segments. Moreover, to achieve efficient implementation, we propose approximate computation, which can efficiently support cost-effective and holistic scheduling by judiciously obtaining the tradeoff between computation complexity and available pilfered slots. HOSA hence offers the salient feature of improving bandwidth utilization. Moreover, to deliver high performance and bridge the gap between real-time communications in networks and storage management in end devices, we implement a deduplication-aware scheme in a real SSD prototype that can reduce space overhead and extend SSD lifespan (by significantly reducing duplicate write operations). The deduplication scheme meets the needs of message updates in FlexRay. Extensive experiments based on synthetic test cases and real-world case studies demonstrate the efficiency and efficacy of HOSA.

Side-channel information leaks have been reported in various online applications, especially, in wireless local area networks (WLANs) due to the shared-medium nature of wireless links and the ease of eavesdropping. Even when Wi-Fi traffic is encrypted, its characteristics are identifiable, which can be used to infer sensitive user activities and data. Existing countermeasures do not offer effective and efficient protection: packet padding and traffic morphing often bring in substantial communication overheads; attempts to anonymize user identifiers are vulnerable to the analysis based upon traffic statistics. In this paper, we present a new technique, called traffic demultiplexing, which offers effective protection against Wi-Fi traffic analysis without incurring noticeable overhead and performance degradation. Our approach utilizes Media Access Control (MAC) layer virtualization and packet scheduling over multiple virtual MAC interfaces to shape the traffic on each virtual MAC interface, so as to hide the original traffic characteristics. Traffic demultiplexing operates at the MAC layer and is transparent to users and other protocol stacks. We implemented our technique over Multiband Atheros Driver for Wi-Fi (MadWifi) and evaluated it in real WLAN environments. Our experimental study demonstrates that traffic demultiplexing is effective and efficient in defending against traffic analysis attacks and easy to deploy.

ABSTRACT Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end‐to‐end delay analysis in wireless networks that employs inter‐session network coding. Prior work on performance analysis in wireless network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the delay of each flow in the network. The theoretical basis of our approach is network calculus. In order to use network calculus to analyze the performance of traffic flows in the network, we have to address three specific problems: identifying traffic flows, characterizing broadcast links, and measuring coding opportunities. We propose solutions for these problems and discuss the practical issues when applying the approach in practice. We make three main contributions. First, we obtain theoretical formulations for computing the queueing delay bounds of traffic flows in wireless networks with network coding. Second, with the formulations, we figure out the factors that affect the queueing delay of a flow and find that first‐in first‐out scheduling cannot fully exploit the benefit of network coding. Third, in order to exploit our findings, we introduce a new scheduling scheme that can improve the performance of current practical wireless network coding. Copyright © 2012 John Wiley & Sons, Ltd.

Internet based service providers, such as Amazon, Google, Yahoo etc, build their data centers (IDC) across multiple regions to provide reliable and low latency of services to clients. Ever-increasing service demand, complexity of services and growing client population cause enormous power consumptions by these IDCs incurring a major part of their running costs. Modern electric power grid provides a feasible way to dynamically and efficiently manage the electricity cost of distributed IDCs based on the Locational Marginal Pricing (LMP) policy. While recent works exploit LMP by electricity-price based geographic load distribution, the dynamic workload and high volatility of electricity prices induce highly volatile power demand and critical power peak problem. The benefit of cost minimization via geographic load distribution is counterbalanced with the high cost incurred by violating the peak power. In this paper, we study the dynamic control of electricity cost to provide low volatility in power demand and shaving of power peaks. To this end, a Model Predictive Control (MPC) electricity cost minimization problem is formulated based on a time-continuous differential model. The proposed solution minimizes electricity costs, provides low variation in power demand by penalizing the change in workload and alleviates the power peaks by tracking the available power budget. By providing extensive simulation results based on real-life electricity price traces we show the effectiveness of our approach.

FlexRay is a new industry standard for next-generation communication in automotives. Though there are a few recent researches on performance analysis of FlexRay, two important aspects of the FlexRay design have been overlooked. The first is a holistic integrated scheduling scheme that can handle both static and dynamic segments in a FlexRay network. The second is cost-effective and scalable fault-tolerance. In order to address these aspects, we propose a novel holistic scheduling scheme, called HOSA, which can provide scalable fault tolerance by using flexible and ease-of-use dual channel communication in FlexRay. HOSA is built upon a novel slot pilfering technique to schedule and optimize the available slots in both static and dynamic segments. Moreover, in order to achieve efficient implementation, we propose approximate computation, which can efficiently support cost-effective and holistic scheduling by judiciously obtaining the tradeoff between computation complexity and available pilfered slots. HOSA hence offers two salient features, i.e., providing fault-tolerance and improving bandwidth utilization. Extensive experiments based on synthetic test cases and real-world case studies demonstrate the efficiency and efficacy of HOSA.

The emerging participatory sensing applications rely on individuals' inputs which may be highly correlated with individuals' sensitive information or personal data. Hence, privacy protection is crucial to encourage individual participation for participatory sensing applications to generate trustworthy and high quality data. A widely used technique for privacy preservation is data perturbation, which adds noises to original data at the client side to protect individuals' privacy and allows the information server to reconstruct the statistics of the original data. In this paper, we find a serious vulnerability of existing data perturbation algorithms that an adversary may exploit to restore other users' private information (e.g., mean, variance and the distribution of original data) from the perturbed data, because all the participants share the same noise distribution. To overcome such vulnerability, we propose a privacy enhanced state-dependent perturbation (PESP), which assigns different noise distributions to individuals and the noises vary according to the state of real data. PESP is not only able to reconstruct community statistics, but also provides better privacy protection for individuals even when the adversaries acquire the perturbed data. We evaluate PESP through two participatory sensing applications: one analyzes the speed variations of vehicles on the road and the other computes the weight statistics for a particular diet. The results demonstrate the efficiency of the PESP in privacy preservation and reconstruction of the community statistics.

Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. We present a privacy-preserving participatory sensing scheme for multidimensional data which uses negative surveys. Multidimensional data, such as vectors of attributes that include location and environment fields, are challenging for privacy protection and are common in participatory sensing applications. When reporting data in a negative survey, an individual participant randomly selects a value from the set complement of the sensed data value, once for each dimension, and returns the negative values to a central collection server. Using algorithms described in this paper, the server can reconstruct the probability density functions of the original distributions of sensed values, without knowing the participants' actual data. Our algorithms avoid computationally expensive encryption and key management schemes, conserving energy. We study trade-offs between accuracy and privacy, and their relationships to the number of dimensions, categories, and participants. We introduce dimensional adjustment, a method that reduces the magnification of error associated with earlier work. Two simulation scenarios illustrate how the approach can protect the privacy of a participant's multidimensional data while allowing useful aggregate information to be collected.

Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this paper, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this paper, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.

Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end-to-end delay analysis in wireless networks that employ inter-session coding. Prior work on performance analysis in wireless network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the end-to-end delay performance of each flow in the network. The theoretical basis of our approach is network calculus. In order to apply network calculus to the analysis of wireless network coding, we address three specific problems: identifying traffic flows, characterizing broadcast links, and measuring coding opportunities. We make three main contributions. First, we obtain theoretical formulations for computing the delay bounds of bursty flows in wireless networks employing network coding. Second, based on the formulations, we figure out the factors that affect the end-to-end delays, and find an interesting phenomenon that, as traffic grows, the overall delay can potentially decrease. Third, in order to exploit the benefit of our findings, we introduce a new scheduling scheme that can improve the performance of current practical wireless network coding.

Traffic analysis has been exploited by attackers to threaten user privacy in wireless networks. As an example, a user's on line activities may be exposed to strangers, even if the traffic is encrypted. However, the existing defense mechanisms against traffic analysis, such as packet padding and traffic morphing, are inefficient because they add noise traffic to blur the traffic features, therefore introducing significant overhead. In this paper, we propose the traffic reshaping technique to thwart traffic analysis. It creates multiple virtual media access control (MAC) interfaces over a single wireless card, dynamically schedules packets over these interfaces, thereby reshaping the packet features on each virtual interface. Hence, features of the original traffic are obscured and unavailable for the adversary to infer users' on line activities. Unlike the existing solutions, traffic reshaping enhances privacy protection without incurring overhead in items of adding noise traffic. We evaluate the performance of traffic reshaping through trace-based experiments. The results show that traffic reshaping is effective and efficient in defending against the traffic analysis attacks.

When wireless sensor networks accumulate sensitive or confidential data, privacy becomes an important concern. Sensors are often resource-limited and power-constrained, and data aggregation is commonly used to address these issues. However, providing privacy without disrupting in-network data aggregation is challenging. Although privacy-preserving data aggregation for additive and multiplicative aggregation functions has been studied, nonlinear aggregation functions such as maximum and minimum have not been well addressed. We present KIPDA, a privacy-preserving aggregation method, which we specialize for maximum and minimum aggregation functions. KIPDA obfuscates sensitive measurements by hiding them among a set of camouflage values, enabling k-indistinguishability for data aggregation. In principle, KIPDA can be used to hide a wide range of aggregation functions, although this paper considers only maximum and minimum. Because the sensitive data are not encrypted, it is easily and efficiently aggregated with minimal in-network processing delay. We quantify the efficiency of KIPDA in terms of power consumption and time delay, studying tradeoffs between the protocol's effectiveness and its resilience against collusion.

Traffic analysis may threaten user privacy, even if the traffic is encrypted. In this paper, we use IEEE 802.11 wireless local area networks (WLANs) as an example to show that inferring users' online activities accurately by traffic analysis without the administrator's privilege is possible during very short periods (e.g., a few seconds). The online activities we investigated include web browsing, chatting, online gaming, downloading, uploading and video watching, etc. We implement a hierarchical classification system based on machine learning algorithms to discover what a user is doing on his/her computer. Furthermore, we conduct experiments in different network environments (e.g., at home, on university campus, and in public areas) with different application scenarios to evaluate the performance of the classification system. Results show that our system can distinguish different online applications on the accuracy of about 80% in 5 seconds and over 90% accuracy if the eavesdropping lasts for 1 minute.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme--- Cluster-based Private Data Aggregation (CPDA)---leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme--- Slice-Mix-AggRegaTe (SMART)---builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

Communication reliability is one of the most important concerns and fundamental issues in network systems, such as cyber-physical systems, where network components, sensors, actuators, controllers are interconnected with each other. These systems are prevalent in many safety-critical areas, including aerospace, automotive, civil infrastructure, energy, healthcare, manufacturing, and transportation, etc. In such systems, a single link failure, or communication delay could lead to catastrophic consequences. Hence, there is an urgent demand on efficient methodologies to model and analyze the delay distribution of control messages or feedback signals, especially when networks grow more complex and more heterogenous. In this paper, a calculus based on frequency domain analysis is developed to address this goal, so we can model and analyze the reliability of communication in large-scale compositional networked systems. Several network structures (e.g. serial, parallel, circular and backup) are defined as building blocks to model a wide variety of connections in networked systems. The advantages of the proposed theoretical framework over the traditional time domain approaches include the capability to capture higher order moments of system characteristics, calability to analyze the reliability of complex systems, efficiency in calculation and practicability in simulation.

In WiFi-based content distribution community infrastructure (CDCI), file servers are deployed in diverse locations around cities, caching popular files interesting to a community. They serve file download requests from mobile users in proximity via WiFi. In this paper, we study the optimal caching strategy for file servers in CDCI so that the mean file retrieval probability within deadline is maximized, subject to storage capacity constraints of each file server. The optimal caching strategy depends on many factors, such as users' mobility patterns, access point topology, file popularity, etc. We have formalized this content management problem as a mixed integer optimization problem. Because of NPhardness and a large variable space of this optimization problem, we propose a heuristic algorithm MobaSsign to allocate file blocks. Extensive experiments show that our heuristic caching strategy considering mobility patterns improves the file retrieval probability within deadline.

Broadcast communication prevails for data dissemination and resource discovery. In mission-critical applications, extensive information sharing and coordination endow broadcast with new features: a large number of active broadcast sources, probabilistic broadcast reception and high receiving rate. We identify this type of broadcast traffic as ASP Bcast traffic. Many efforts have been made to authenticate broadcast source and prevent content modification in a light-weighted way using one-way hash chain (TESLA). However, they do not scale to a large number of senders. In addition, authentication delay increases under packet losses and probabilistic broadcast. The longer authentication is postponed, the longer packets are buffered, which poses a memory-based denial-of-service (DoS) threat. In this paper, we will present an efficient authentication protocol for ASP Bcast traffic, called ChainFarm. We propose an algorithm for parameter configuration to satisfy both memory and delay requirements with maximal resilience against compromise. Simulation results agree with our analysis and show distinct performance improvement.

Mission-critical networks show great potential in emergency response and/or recovery, health care, critical infrastructure monitoring, etc. Such mission-critical applications demand that security service be ldquoanywhere,rdquo ldquoanytime,rdquo and ldquoanyhow.rdquo However, it is challenging to design a key management scheme in current mission-critical networks to fulfill the required attributes of secure communications, such as data integrity, authentication, confidentiality, nonrepudiation, and service availability. In this paper, we present a self-contained public key-management scheme, a scalable method of cryptographic key management (SMOCK), which achieves almost zero communication overhead for authentication, and offers high service availability. In our scheme, a small number of cryptographic keys are stored offline at individual nodes before they are deployed in the network. To provide good scalability in terms of the number of nodes and storage space, we utilize a combinatorial design of public-private key pairs, which means nodes combine more than one key pair to encrypt and decrypt messages. We also show that SMOCK provides controllable resilience when malicious nodes compromise a limited number of nodes before key revocation and renewal.

Fault tolerance is an important aspect in real-time computing. In real-time control systems, tasks could be faulty due to various reasons. Faulty tasks may compromise the performance and safety of the whole system and even cause disastrous consequences. In this paper, we describe On-demand real-time guard (ORTEGA), a new software fault tolerance architecture for real-time control systems. ORTEGA has high fault coverage and reliability. Compared with existing real-time fault tolerance architectures, such as Simplex, ORTEGA allows more efficient resource utilizations and enhances flexibility. These advantages are achieved through the on-demand detection and recovery of faulty tasks. ORTEGA is applicable to most industrial control applications where both efficient resource usage and high fault coverage are desired.

The proliferation of multimedia applications over mobile, resource-constrained wireless networks has raised the need for techniques that adapt these applications both to clients' Quality of Service (QoS) requirements and to network resource constraints. This article investigates the upper-layer adaptation mechanisms to achieve end-to-end delay control for multimedia applications. The proposed adaptation approach spans application layer, middleware layer and network layer. In application layer, the requirement adaptor dynamically changes the requirement levels according to end-to-end delay measurement and acceptable QoS requirements for the end-users. In middleware layer, the priority adaptor is used to dynamically adjust the service classes for applications using feedback control theory. In network layer, the service differentiation scheduler assigns different network resources (e.g., bandwidth) to different service classes. With the coordination of these three layers, our approach can adaptively assign resources to multimedia applications. To evaluate the impact of our adaptation scheme, we built a real IEEE 802.11 ad hoc network testbed. The test-bed experiments show that the proposed upper-layer adaptation for end-to-end delay control successfully adjusts multimedia applications to meet delay requirements in many scenarios.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

Mobile ad hoc networks show great potential in emergency response and/or recovery. Such mission-critical applications demand security service be "anywhere", "anytime" and "anyhow". However, it is challenging to design a key management scheme in current wireless ad hoc networks to fulfill the required attributes of secure communications, such as data integrity, authentication, confidentiality, non-repudiation and service availability, when Sybil attacks are present. In this paper, we present a self-contained public key management scheme, called SMOCK, which is able to resist the Sybil attack, achieves zero communication overhead for authentication, and offers high service availability. In oar scheme, small number of cryptographic keys are stored off-line at individual nodes before they are deployed in the network. To provide good scalability in terms of number of nodes and storage space, we utilize a combinatorial design of public-private key pairs, which means nodes combine more than one key pair to encrypt and decrypt messages. We also show that SMOCK provides controllable resilience when malicious nodes break into a limited number of nodes before key revocation and renewal

Due to the shared medium nature of wireless networks, the uncertainties caused by collisions and interferences make the quality of service (QoS) issue harder than its wired counterpart. Many publications have been focused on network and MAC layer design to address the QoS issue in wireless networks. However, the middleware design has been overlooked. For QoS support, we need to map the QoS requirement of applications to performance metrics. Middleware is the place we do such mapping. In this paper, we use packet level priority to bridge the QoS requirements and performance. Through middleware priority adaptation, we aim to make the premium traffic meet the QoS requirement, we study the impact of middleware priority adaptation on QoS performance, including bandwidth and end- to-end delay, via experiments with multimedia flows over IEEE 802.11 environment. Our evaluation is based on experiments in both WLAN and ad hoc network environment. Our investigation shows that middleware adaptation is efficient in assisting to achieve QoS in many scenarios.

Wireless networks are accommodating more and more emerging multimedia traffic from entertainment to telephony. However, wireless network standards typically focus on MAC layer and physical layer signaling, leaving crucial end to end delay and security issues to application implementers. Delay and security provisioning issues in such an environment is very challenging. In this paper, we address an integrated solution to end-to-end delay and security, where the middleware adaptation provides tunable delay and security support according to network condition. The test-bed experiments show that the proposed solution successfully adjusts multimedia applications to meet both delay and security requirements in IEEE 802.11-based wireless environment

How to allocate computing and communication resources in a way that maximizes the effectiveness of control and signal processing, has been an important area of research. The characteristic of a multi-hop Real-Time Wireless Sensor Network raises new challenges. First, the constraints are more complicated and a new solution method is needed. Second, a distributed solution is needed to achieve scalability. This article presents solutions to both of the new challenges. The first solution to the optimal rate allocation is a centralized solution that can handle the more general form of constraints as compared with prior research. The second solution is a distributed version for large sensor networks using a pricing scheme. It is capable of incremental adjustment when utility functions change. This article also presents a new sensor device/network backbone architecture---Real-time Independent CHannels (RICH), which can easily realize multi-hop real-time wireless sensor networking.

We present a new feedback control scheme for resource allocation in wireless ad hoc environment. The congestion measure on a wireless link is symbolized as a virtual price, which depends on the recent history of wireless node's queue size. The aggregate price along a route is the feedback to control the transmission rate at the source. In this way, the transmission rate adjustment will dynamically achieve efficient resource allocation, and make the queue size on wireless links stabilize around desired equilibrium. Simulation results show that the proposed scheme can not only help allocate bandwidth fairly, but also achieve the goal of optimal sharing the channel capacity among multi-hop flows.

Deploying wireless LAN for industrial control (IC-WLAN) has many benefits, such as mobility, low deployment cost and ease of reconfiguration. However, the top concern is robustness of wireless communications. Wireless control loops must be maintained under persistent adverse channel conditions, such as noise, large-scale path loss and fading. Many electro-magnetic interference sources in industrial environments, e.g. electric motor and welding, make wireless communication more challenging. The conventional IEEE 802.11 WLANs, which are designed for providing high bandwidth instead of high robustness, are therefore inappropriate for IC-WLAN. On the other hand, if the low data rate feature of industrial control is fully exploited by the state-of-the-art direct sequence spread spectrum (DSSS) technology, much higher robustness can be achieved. We hereby propose using DSSS-CDMA to build IC-WLAN, and exploiting the low data rate feature of industrial control loops for enhanced robustness. We carried out fine-grained physical layer simulations and Monte Carlo comparisons. The results show that DSSS-CDMA IC-WLAN provides much higher robustness than IEEE 802.11 WLAN, so that reliable wireless industrial control loops are made feasible. The DSSS-CDMA IC-WLAN scheme also opens up a new problem space for interdisciplinary study, involving real-time scheduling and resource management, communication, networking and control. In this paper, we study the resource management problems on maximizing robustness and minimizing control utility loss. Analytical resource optimization solutions are given

How to allocate computing and communication resources in a way that maximizes the effectiveness of control and signal processing has been an important area of research. The characteristic of a multi-hop real-time wireless sensor network raises new challenges. First, the constraints are more complicated and a new solution method is needed. Second, we need a distributed solution to achieve scalability. This paper presents solutions to both of the new challenges. The first solution to the optimal frequency allocation is a centralized solution that can handle the more general form of constraints as compared with prior research. The second solution is a distributed version for large networks using a pricing scheme. It is capable of incremental adjustment when utility functions change.