INFOCOM 2015 论文列表

Spectrum auctions are efficient mechanisms for licensed users to relinquish their under-utilized spectrum to secondary links for monetary remuneration. Truthfulness and social welfare maximization are two natural goals in such auctions, but cannot be achieved simultaneously with polynomial-time complexity by existing methods, even in a static network with fixed parameters. The challenge escalates in practical systems with QoS requirements and volatile traffic demands for secondary communication. Online, dynamic decisions are required for rate control, channel evaluation/bidding, and packet dropping at each secondary link, as well as for winner determination and pricing at the primary user. This work proposes an online spectrum auction framework with cross-layer decision making and randomized winner determination on the fly. The framework is truthful-in-expectation, and achieves close-to-offline-optimal time-averaged social welfare and individual utilities with polynomial time complexity. A new method is introduced for online channel evaluation in a stochastic setting. Simulation studies further verify the efficacy of the proposed auction in practical scenarios.

Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. Meanwhile, static configurations of networks and hosts simplify this adversarial reconnaissance. In this paper, we present a novel proactive-adaptive defense technique that turns end-hosts into untraceable moving targets, and establishes dynamics into static systems by monitoring the adversarial behavior and reconfiguring the addresses of network hosts adaptively. This adaptability is achieved by discovering hazardous network ranges and addresses and evacuating network hosts from them quickly. Our approach maximizes adaptability by (1) using fast and accurate hypothesis testing for characterization of adversarial behavior, and (2) achieving a very fast IP randomization (i.e., update) rate through separating randomization from end-hosts and managing it via network appliances. The architecture and protocols of our approach can be transparently deployed on legacy networks, as well as software-defined networks. Our extensive analysis and evaluation show that by adaptive distortion of adversarial reconnaissance, our approach slows down the attack and increases its detectability, thus significantly raising the bar against stealthy scanning, major classes of evasive scanning and worm propagation, as well as targeted (hacking) attacks.

Optimal CSMA, which is fully distributed wireless MAC theory, has provided a rule of dynamically adapting CSMA parameters according to some theoretically developed principles, and has reported to offer nice analytical guarantees on throughput and fairness. Despite a couple of research efforts that transfer Optimal CSMA to practical protocols, e.g., O-DCF, our evaluation results show that they are still far from being deployable in practice mainly due to bad performance with TCP. In this paper, we first investigate how Optimal CSMA based MAC conflicts with TCP and degrades end-to-end performance, if poorly transferred to practice. Then, we propose a new wireless MAC protocol, called A-DCF, that inherits the basic framework and rationale of Optimal CSMA and O-DCF, but are largely redesigned to make A-DCF work well with TCP. The key idea of A-DCF lies in smartly exploiting both queue length and delay which widens our design space for compatibility with TCP. Our extensive simulation and experimental results demonstrate that A-DCF outperforms the traditional 802.11 and O-DCF. Particularly, we report our implementation code of A-DCF as a device driver module. To our knowledge, it is the first driver-level implementation of an Optimal CSMA based MAC protocol, being of broad interest to the community.

Wireless link correlation can greatly affect the performance of wireless protocols such as flooding, and opportunistic routing. Researchers have proposed a variety of approaches to optimize existing protocols exploiting link correlation. Most existing works directly measure link correlation using packet-level transmissions and receptions. Measurement alone is insufficient because it lacks predictive power and scalability. In this paper, we present CorModel, a model for predicting link correlation in low-power wireless networks. Based on the underlying causes of link correlation, we explore four easily measurable parameters for our modeling. Besides PHY-layer parameters that previous studies have explored, we find that network-layer parameters can also have significant impact on link correlation. We validate our model and illustrate its usefulness by integrating it into existing protocols for more accurate correlation estimation. Experimental results show that our model can significantly increase the accuracy of wireless link estimation, resulting in better protocol performance.

As a part of the international standard IEC 61375, the multifunction vehicle bus (MVB) has been used in most of the modern train control systems. It is highly desirable to check the temporal properties of the data transmitted on the bus. However, we are not aware of any published work on this problem. We proposed VeRV, the first temporal and data-concerned verification framework for the vehicle bus systems. A domain-specific language, called VeSpec, is proposed to specify the packet formats and the desired properties. The language is expressive, modular and easy to use. Given a VeSpec script, the VeRV allows automatic generation of runtime analyzer. We have applied our technique to a real tube train system and succeeded in diagnosing a real failure in this system. The industry application illustrates the effectiveness and efficiency of our technique.

Major cellular operators are planning to upgrade to high-speed 4G networks, but due to budget constraints, they have to dynamically plan and deploy the 4G networks through multiple stages of time. By considering one-time deployment cost, daily operational cost and 3G network congestion, this paper studies how an operator financially manages the cash flow and plans the 4G deployment in a finite time horizon to maximize his final-stage profit. The operator provides both the traditional 3G service and the new 4G service, and we show that users will start to use the 4G service only when it reaches a sizable coverage. At each time stage, the operator first decides an additional 4G deployment size, by predicting users' responses in choosing between the 3G and 4G services. We formulate this problem as a dynamic programming problem, and propose an optimal threshold-based 4G deployment policy. We show that the operator will not deploy to a full 4G coverage in an area with low user density or high deployment/operational cost. Perhaps surprisingly, during the 4G deployment process, we show that the 4G subscriber number first increases and then decreases, as the 4G service helps mitigate 3G network congestion and increases its QoS.

We investigate the problem of optimal request routing and content caching in a heterogeneous network supporting in-network content caching with the goal of minimizing average content access delay. Here, content can either be accessed directly from a back-end server (where content resides permanently) or be obtained from one of multiple in-network caches. To access a piece of content, a user must decide whether to route its request to a cache or to the back-end server. Additionally, caches must decide which content to cache. We investigate the problem complexity of two problem formulations, where the direct path to the back-end server is modeled as i) a congestion-sensitive or ii) a congestion-insensitive path, reflecting whether or not the delay of the uncached path to the back-end server depends on the user request load, respectively. We show that the problem is NP-complete in both cases. We prove that under the congestion-insensitive model the problem can be solved optimally in polynomial time if each piece of content is requested by only one user, or when there are at most two caches in the network. We also identify a structural property of the user-cache graph that potentially makes the problem NP-complete. For the congestion-sensitive model, we prove that the problem remains NP-complete even if there is only one cache in the network and each content is requested by only one user. We show that approximate solutions can be found for both models within a (1 - 1/e) factor of the optimal solution, and demonstrate a greedy algorithm that is found to be within 1% of optimal for small problem sizes. Through trace-driven simulations we evaluate the performance of our greedy algorithms, which show up to a 50% reduction in average delay over solutions based on LRU content caching.

Given a universe U of n elements and a collection of subsets S of U, the maximum disjoint set cover problem (DSCP) is to partition S into as many set covers as possible, where a set cover is defined as a collection of subsets whose union is U. We consider the online DSCP, in which the subsets arrive one by one (possibly in an order chosen by an adversary), and must be irrevocably assigned to some partition on arrival with the objective of minimizing the competitive ratio. The competitive ratio of an online DSCP algorithm A is defined as the maximum ratio of the number of disjoint set covers obtained by the optimal offline algorithm to the number of disjoint set covers obtained by A across all inputs. We propose an online algorithm for solving the DSCP with competitive ratio ln n. We then show a lower bound of Ω(√ln n) on the competitive ratio for any online DSCP algorithm. The online disjoint set cover problem has wide ranging applications in practice, including the online crowd-sourcing problem, the online coverage lifetime maximization problem in WSNs, and in online resource allocation problems.

We study the convergence properties of distributed network selection in HetNets with priority-based service. Clients in such networks have different priority weights (e.g., QoS requirements, scheduling policies, etc.) for different access networks and act selfishly to maximize their own throughput. We formulate the problem as a non-cooperative game, and study its convergence for two models: (i) A purely client-centric model where each client uses its own preference to select a network, and (ii) a hybrid client-network model that uses a combination of client and network preferences to arrive at pairings. Our results reveal that: (a) Pure client-centric network selection with generic weights can result in infinite oscillations for any improvement path (i.e., shows strongly cyclic behavior). However, we show that under several classes of practical priority weights (e.g., weights that achieve different notions of fairness) or under additional client-side policies, convergence can be guaranteed; (b) We study convergence time under client-centric model and provide tight polynomial and linear bounds; (c) We show that applying a minimal amount of network control in the hybrid model, guarantees convergence for clients with generic weights. We also introduce a controllable knob that network controller can employ to balance between convergence time and its network-wide objective with predictable tradeoff.

In this paper SDN-WISE, a software defined networking (SDN) solution for wireless sensor networks, is introduced. Differently from the existing SDN solutions for wireless sensor networks, SDN-WISE is stateful and pursues two objectives: (i) to reduce the amount of information exchanged between sensor nodes and the SDN network controller, and (ii) to make sensor nodes programmable as finite state machines so enabling them to run operations that cannot be supported by stateless solutions. A detailed description of SDN-WISE is provided in this paper. SDN-WISE offers APIs that allow software developers to implement the SDN Controller using the programming language they prefer. This represents a major advantage of SDN-WISE as compared to existing solutions because it increases flexibility and simplicity in network programming. A prototype of SDN-WISE has been implemented and is described in this paper. Such implementation contains the modules that allow a real SDN Controller to manage an OMNeT++ simulated network. Finally, the paper illustrates the results obtained through an experimental testbed which has been developed to evaluate the performance of SDN-WISE in several operating conditions.

Trajectory data, i.e., human mobility traces, is extremely valuable for a wide range of mobile applications. However, publishing raw trajectories without special sanitization poses serious threats to individual privacy. Recently, researchers begin to leverage differential privacy to solve this challenge. Nevertheless, existing mechanisms make an implicit assumption that the trajectories contain a lot of identical prefixes or n-grams, which is not true in many applications. This paper aims to remove this assumption and propose a differentially private publishing mechanism for more general time-series trajectories. One natural solution is to generalize the trajectories, i.e., merge the locations at the same time. However, trivial merging schemes may breach differential privacy. We, thus, propose the first differentially-private generalization algorithm for trajectories, which leverage a carefully-designed exponential mechanism to probabilistically merge nodes based on trajectory distances. Afterwards, we propose another efficient algorithm to release trajectories after generalization in a differential private manner. Our experiments with real-life trajectory data show that the proposed mechanism maintains high data utility and is scalable to large trajectory datasets.

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across 6 months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB. Moreover, AcMu provides 2x improvement on localization accuracy by maintaining an up-to-date radio map.

As most of mobile apps rely on network connections for their operations, measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time as the metric. We use a multiple-sniffer testbed to overcome the challenge of obtaining a complete trace for acquiring the required timestamps. Our experiment results show that the RTTs measured by the apps are all inflated, ranging from a few milliseconds (ms) to tens of milliseconds. Moreover, the 95% confidence interval can be as high as 2.4ms. A finer-grained analysis reveals that the delay inflation can be introduced both in the Dalvik VM (DVM) and below the Linux kernel. The in-DVM overhead can be mitigated but the other cannot be. Finally, we propose and implement a native app which uses HTTP messages for network measurement, and the delay inflation can be kept under 5ms for almost all cases.

The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS. To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly (around 100%) while confirming that our system achieves incentive compatibility, individual rationality, and server profitability.

Cognitive Radio (CR) is an intelligent radio technology to boost spectrum utilization and is likely to be widely spread in the near future. However, its flexible software-oriented design may be exploited by an adversary to control CR devices to launch large scale attacks on a wide range of critical wireless infrastructures. To proactively mitigate the potentially serious threat, this paper presents MadeCR, a Correlation-based Malware detection system for CR. MadeCR exploits correlations among CR applications' component actions to detect malicious behaviors. In addition, a significant contribution of the paper is a general experimentation method referred to as mutation testing to comprehensively evaluate the effectiveness of the anomaly detection method against a large number of artificial malware cases. Evaluation shows that MadeCR detects malicious behaviors within 1.10s at an accuracy of 94.9%.

In this paper, we analyze a large dataset of passive wireless measurements and obtain insights about wireless performance. We monitor 167 homes continuously for 4 months from the vantage point of the gateway, which allows us to capture all the activity on the home wireless network. We report on the makeup of the home wireless network, traffic activity, and performance characteristics. We find that in most homes, a small number of devices account for most of the observed traffic volume and the bulk of this traffic activity occurs in the evenings. Studying link performance, we find that overall, the vast majority of transmissions are carried out at high data rates and the wireless networks have good coverage. We find a small number of episodes where performance is poor; a few homes have a disproportionate number of poor performance reports. Investigating further, we observe that most of these are not caused by poor coverage (pointing to network interference). Our results significantly add to the understanding of home wireless networks and will help ISPs to understand their subscriber networks.

Multi-touch mobile devices have penetrated into everyday life to support personal and business communications. Secure and usable authentication techniques are indispensable for preventing illegitimate access to mobile devices. This paper presents RhyAuth, a novel two-factor rhythm-based authentication scheme for multi-touch mobile devices. RhyAuth requires a user to perform a sequence of rhythmic taps/slides on a device screen to unlock the device. The user is authenticated and admitted only when the features extracted from her rhythmic taps/slides match those stored on the device. RhyAuth is a two-factor authentication scheme that depends on a user-chosen rhythm and also the behavioral metrics for inputting the rhythm. Through a 32-user experiment on Android devices, we show that RhyAuth is highly secure against various attacks and also very usable for both sighted and visually impaired people.

Most sampling techniques for online social networks (OSNs) are based on a particular sampling method on a single graph, which is referred to as a statistic. However, various realizing methods on different graphs could possibly be used in the same OSN, and they may lead to different sampling efficiencies, i.e., asymptotic variances. To utilize multiple statistics for accurate measurements, we formulate a mixture sampling problem, through which we construct a mixture unbiased estimator which minimizes the asymptotic variance. Given fixed sampling budgets for different statistics, we derive the optimal weights to combine the individual estimators; given a fixed total budget, we show that a greedy allocation towards the most efficient statistic is optimal. In practice, the sampling efficiencies of statistics can be quite different for various targets and are unknown before sampling. To solve this problem, we design a two-stage framework which adaptively spends a partial budget to test different statistics and allocates the remaining budget to the inferred best statistic. We show that our two-stage framework is a generalization of 1) randomly choosing a statistic and 2) evenly allocating the total budget among all available statistics, and our adaptive algorithm achieves higher efficiency than these benchmark strategies in theory and experiment.

Latency-sensitive applications (e.g., wireless gaming and TV remote play) are increasingly popular in home WiFi networks. Such millisecond-level latency requirements call for new fine-grained approaches at the link layer. In this paper, we show that current solutions work well for throughput but not for latency due to the long tail of the packet delay distribution. We thus propose LLRA, a new latency-aware rate adaptation scheme that reduces the tail latency for delay-sensitive applications. LLRA takes concerted design in rate control, frame aggregation scheduling and software/hardware retransmission dispatching. Our implementation and evaluation confirm the viability of LLRA in 802.11n home networks.