INFOCOM 2017 论文列表

Despite the increased capabilities of mobile devices, resource-demanded mobile applications still transcend what can be accomplished on a single device. As such, mobile device cloud (MDC), an environment that enables computation-intensive tasks to be performed among a set of nearby mobile devices, offers a promising architecture to support real-time mobile applications. To stimulate mobile devices to execute tasks for others, it is essential to design an incentive mechanism that appropriately charges the owners of the tasks, acted as the buyers, and rewards the mobile devices, acted as the sellers. In this paper, we propose two truthful auction mechanisms for two different task models, heterogeneous and homogeneous task models, which assume the different and the same resource requirements of the tasks, respectively. Specifically, for heterogeneous task model, we propose an efficient heuristic winning bids determination algorithm to allocate the tasks, and decide the payment of each seller for its winning bids. For homogeneous task model, we design an optimal winning bid determination algorithm, and propose a Vickrey-Clarke-Groves (VCG) based auction mechanism to determine the payment of each bid. Both theoretical analysis and simulations show that the proposed auction mechanisms achieve several desirable properties such as individual rationality, truthfulness and computational efficiency.

Massive trust assessment (MTA) in an Online Social Network (OSN), i.e., computing the trustworthiness of all users in the network, is crucial in various OSN-related applications. Existing solutions are either too slow or inaccurate in addressing the MTA problem. We propose the OpinionWalk algorithm that accurately and efficiently conducts MTA in an OSN. OpinionWalk models trust by the Dirichlet distribution and uses a matrix to represent the direct trust relations among users. From the perspective of a user, other users' trustworthiness are stored in a column vector that is iteratively updated when the algorithm “walks” through the network, in a breadth-first search manner. We identify the overlapping subproblems property in MTA and prove OpinionWalk is a more efficient solution. The accuracy and execution time of OpinionWalk are evaluated and compared to benchmark algorithms including EigenTrust, TrustRank, MoleTrust, TidalTrust and AssessTrust, using two real-world datasets (Advogato and Pretty Good Privacy). Experimental results indicate that OpinionWalk is an efficient and accurate solution to MTA, compared to previous algorithms.

Network verification has been recently proposed to detect network misconfigurations. Existing work focuses on the reachability. This paper proposes a framework that verifies the Service Level Agreement (SLA) compliance of the network using static verification. This work proposes a quantitative model and a set of algorithms for verifying performance properties of a network with switches and middleboxes, i.e., service chains. We develop SLA-Verifier and evaluate its efficiency using simulation on real-world data and testbed experiments. To improve the SLA violation detection accuracy, our system uses verification results to optimize online monitoring.

Monitoring the traffic volumes of elephant flows, including the total byte count per flow, is a fundamental capability for online network measurements. We present an asymptotically optimal algorithm for solving this problem in terms of both space and time complexity. This improves on previous approaches, which can only count the number of packets in constant time. We evaluate our work on real packet traces, demonstrating an up to X2.5 speedup compared to the best alternative.

In this paper, we study the tag size profiling (TSP) problem in RFID systems with multiple readers, which is to estimate the number of tags in every subregion in the system covered by different set of readers. The TSF problem is vitally important to reader scheduling and many other related operations in large scale multi-reader RFID systems. To our knowledge, however, it is not well solved in previous researches. We propose a novel approach to the TSF problem. The key idea is to treat the size of subregions as variables and construct a linear system in these variables to solve them. We theoretically prove that for any multi-reader RFID system, a linear system that can be used to uniquely solve the variables corresponding to the subregion sizes can always be constructed. We then propose a time-efficient algorithm that uses two heuristics to quickly find enough linearly independent equations to construct the linear system. Extensive simulation results show that the proposed approach achieves very high accuracy. When the estimation results of individual readers contain 5% errors, our approach achieves median estimation error of smaller than 0.02 and 90-percentile estimation error of smaller than 0.04 in large systems containing more than one hundred readers.

Software Defined Networking (SDN) can benefit a Network Function Virtualization solution by chaining a set of network functions (NF) to create a network service. Currently, control on NFs is isolated from the SDN, which creates routing inflexibility, flow imbalance and choke points in the network as the controller remains oblivious to the number, capacity and placement of NFs. Moreover, a NF may modify packets in the middle, which makes flow identification at a SDN switch challenging. In this paper, we postulate native NFs within the SDN data plane, where the same logical controller controls both network services and routing. This is enabled by extending SDN to support stateful flow handling based on higher layers in the packet beyond layers 2-4. As a result, NF instances can be chained on demand, directly on the data plane. We present an implementation of this architecture based on Open vSwitch, and show that it enables popular NFs effectively using detailed evaluation and comparison with other alternative solutions.

In this paper, we address the fundamental problem of improving the performance of many-to-one and many-to-many communications. Our approach is Time Division Multiple Access (TDMA) based but addresses the limitations of existing TDMA implementations in a novel way. In a nutshell, we combine packets from many senders into a single large packet transmission by exploiting capture effect achieved through fine grained power control at the level of segments within a single packet. We applied our technique to the design of a one-hop, many-to-one communication protocol, SyncMerge, and a multi-hop, many-to-many communication protocol, ByteCast. Our evaluation shows that SyncMerge is able to achieve 2 to 15 times improvement over traditional many-to-one communication schemes. In addition, ByteCast is able to disseminate 1 byte of data from every node to all other nodes in about 600 ms with 99.5% reliability on a 90 node testbed. Compared to the state-of-the-art protocols such as LWB and Chaos, ByteCast is able to reduce the radio-on time by up to 90% while achieving similar reliability.

Face authentication emerges as a powerful method for preventing unauthorized access to mobile devices. It is, however, vulnerable to photo-based forgery attacks (PFA) and videobased forgery attacks (VFA), in which the adversary exploits a photo or video containing the user's frontal face. Effective defenses against PFA and VFA often rely on liveness detection, which seeks to find a live indicator that the submitted face photo or video of the legitimate user is indeed captured in real time. In this paper, we propose FaceHeart, a novel and practical face authentication system for mobile devices. FaceHeart simultaneously takes a face video with the front camera and a fingertip video with the rear camera on COTS mobile devices. It then achieves liveness detection by comparing the two photoplethysmograms independently extracted from the face and fingertip videos, which should be highly consistent if the two videos are for the same live person and taken at the same time. As photoplethysmograms are closely tied to human cardiac activity and almost impossible to forge or control, FaceHeart is strongly resilient to PFA and VFA. Extensive user experiments on Samsung Galaxy S5 have confirmed the high efficacy and efficiency of FaceHeart.

Traditional DDoS anti-spoofing scrubbers require dedicated middleboxes thus adding CAPEX, latency and complexity in the network. This paper starts by showing that the current SDN match-and-action model is rich enough to implement a collection of anti-spoofing methods. Secondly we develop and utilize advance methods for dynamic resource sharing to distribute the required mitigation resources over a network of switches. None of the earlier attempts to implement anti-spoofing in SDN actually directly exploited the match and action power of the switch data plane. They required additional functionalities on top of the match-and-action model, and are not implementable on an SDN switch as is. Our method builds on the premise that an SDN data path is a very fast and efficient engine to perform low level primitive operations at wire speed. The solution requires a number of flow-table rules and switch-controller messages proportional to the legitimate traffic. To scale when protecting multiple large servers the flow tables of multiple switches are harnessed in a distributed and dynamic network based solution. We have fully implemented all our methods in either Open-Flow1.5 in Open-vSwitch and in P4. The system mitigates spoofed attacks on either the SDN infrastructure itself or on downstream servers.

Mobile vehicles, equipped with low-cost sensors, can provide unprecedented opportunities for urban monitoring with a wide coverage. The quality of collected data is very important for many applications. The low-cost mobile sensors can, however, suffer from limited accuracy, high instability, and sensor drift. Therefore, they need to be frequently calibrated to preserve a good data quality. Frequent sensor calibration can be achieved by deploying static high-precision sensors (called reference sensors) and exploiting meeting points (i.e., rendezvous) between the reference sensors and mobile sensors. A calibrated mobile sensor can also be used to calibrate an uncalibrated mobile sensor when they meet, referred to as multihop calibration. In this paper, we introduce a novel concept, k-hop calibratability, in the context of multihop calibration: a sensor is k-hop calibratable if the calibration path is no larger than k. We consider the problem of how to deploy the reference sensors to ensure that all sensors in the network are k-hop calibratable. To address this problem, we formally define the rendezvous connection graph to precisely describe the relationship between mobile sensors and (virtual) reference sensors. Based on this definition, we formulate the reference sensor deployment problem as a set cover problem. We also extend our problem to consider practical deployment requirements. We propose efficient algorithms and conduct an extensive evaluation in the context of mobile air quality monitoring. We present a detailed prototype implementation of the mobile sensors and reference sensors. Evaluations using real-world datasets show the effectiveness of the algorithms.

Chang et al. proposed the load-balanced switch in their seminal work [1], which has received wide attention due to its inherent scalability properties in both size and speed. These scalability properties continue to be of significant interest due to the relentless exponential growth in Internet traffic. The main drawback of the load-balanced switch is that packets can depart out-of-order from the switch, which can significantly degrade network performance by negatively interacting with TCP congestion control. Hence, a large body of subsequent work has proposed a variety of modifications for ensuring packet ordering, but all the proposed approaches tend to increase packet delay significantly in comparison to the basic load-balanced switch. In this paper, we show that the amount of packet reordering that can occur with the load-balanced switch is actually quite limited, which means that packet reordering can simply be rectified by employing reordering buffers at the switch outputs. In particular, we formally bound the worst-case amount of time that a packet has to wait in these output reordering buffers before it is guaranteed to be ready for in-order departure with high probability, and we prove that this bound is linear with respect to the switch size. This linear bound is significant because previous approaches can add quadratic or cubic delays to the load-balanced switch. In addition, we use a hash-grouping method that further reduces resequencing delays significantly. Although simple and intuitive, our experimental results show that our output packet reordering approach substantially outperforms existing load-balanced switch architectures.

Crowdsourcing applications are vulnerable to malicious behaviors, posing serious threats to their adoption and large deployment. Based on the notion that the requestor (i.e., the crowdsourcer) can block malicious behaviors via leveraging the market power through task allocation and pricing, we propose two novel frameworks based on the mechanism design game theory (i.e., the reverse game theory). To the best of our knowledge, we are the first to exploit the market power and to apply the mechanism design game theory in thwarting malicious behaviors in crowdsourcing. The first proposed framework is built on a requestor-dominant mechanism design game (Rd-MDG), where the game rule is determined solely by the requestor. The second proposed framework is based on the worker-assisted mechanism design game (WaMDG), where the worker (i.e., the contributor) can assist the requestor to determine the game rules by offering advices. These two frameworks have the following salient features: i) neither of them requires the workers to reveal their private information; ii) the game rules of each framework are designed to be able to force the workers to calculate their best strategies based on their actual private information; iii) our theoretical analysis shows that equilibriums exist for both frameworks; and iv) our extensive simulation results demonstrate that these two frameworks can thwart malicious behaviors by driving the workers with a higher attack intent into obtaining lower utilities.

This paper explores the limits of acoustic ranging on smart phone in the scenario of device-free hand tracking. Tracking the hand is challenging since it requires continuously locating the moving hand in the air with fine resolution. Existing work on hand tracking relies on special hardware or requires users hold the mobile device. This paper presents EchoTrack, which continuously locates the hand by leveraging mobile audio hardware advances without special infrastructure supported. EchoTrack measures the distance from the hand to the speaker array embedded in smart phone via the chirp's Time of Flight (TOF). The speaker array and hand yield a unique triangle. The hand can be located with this triangular geometry. The trajectory accuracy can be improved with the method of Doppler shift compensation and trajectory correction (i.e., roughness penalty smoothing method). We implement a prototype on smart phone and the evaluation shows that EchoTrack can achieve tracking accuracy within about three centimeters of 76% and two centimeters of 48%.

In datacenter networks, flows can have different performance objectives. We use a tenant-objective division to denote all flows of a tenant that share the same objective. Bandwidth allocation in datacenters should support not only performance isolation among divisions but also objective-oriented scheduling among flows within the same division. This paper studies the Multi-Tenant Multi-Objective (MT-MO) bandwidth allocation problem. To our best knowledge, no existing practical work support performance isolation and objective scheduling simultaneously. We propose Stacked Congestion Control (SCC), a distributed host-based bandwidth allocation design, where an underlay congestion control (UCC) layer handles contention among divisions, and a private congestion control (PCC) layer for each division optimizes its performance objective. Via the tenant-objective tunnel abstraction, SCC achieves weighted bandwidth sharing for each division in a distributed and transparent way. By adding a rate-limiting send queue in the ingress of each tunnel, mechanisms between performance isolation and objective scheduling are completely decoupled. We evaluate SCC both on a small-scale testbed and with large-scale NS-2 simulations. Compared to the direct coexistence cases, SCC reduces latency by up to 40% for Latency-Sensitive flows, deadline miss ratio by up to 3.2× for Deadline-Sensitive flows, and average flow-completion-time by up to 53% for Completion-Sensitive flows.

Owing to the rapid growth in high-quality video streaming over the Internet, preserving high-level robustness against data loss and low latency, while maintaining higher data transmission rates, is becoming an increasingly important issue for high-quality real-time delay-sensitive streaming. In this paper, we propose a low latency, low loss streaming mechanism, L4C2, specialized for high-quality delay-sensitive streaming. With L4C2, nodes in the network estimate the acceptable delay and packet loss probability in their uplinks, aiming at retrieving lost data packets from in-network cache and/or coded data packets using in-network coding within an acceptable delay, by extending the Content-Centric Networking (CCN) approach. Further, L4C2 naturally provides multiple paths and multicast technologies to efficiently utilize network resources while sharing network resources fairly with competing data flows by adjusting the video quality when necessary. We validate through comprehensive simulations that L4C2 achieves a high success probability of data transmission considering the acceptable one-way delay, and higher QoE while suppressing the interest and redundant data traffic than the proposed multipath congestion control mechanism in CCN.

In recent years, both the RFID and computer vision technologies have been widely employed in indoor scenarios aimed at different goals while faced with respective limitations. For example, the RFID-based EAS system is useful in quickly identifying tagged objects but the accompanying false alarm problem is troublesome and hard to tackle with except that the accurate trajectory of the target tag can be easily acquired. On the other side, the CV system performs fairly well in tracking multiple moving objects precisely while finding it difficult to screen out the specific target among them. To overcome the above limitations, we present TagVision, a hybrid RFID and computer vision system for fine-grained localization and tracking of tagged objects. A fusion algorithm is proposed to organically combine the position information given by the CV subsystem, and phase data output by the RFID subsystem. In addition, we employ the probabilistic model to eliminate the measurement error caused by thermal noise and device diversity. We have implemented TagVision with COTS camera and RFID devices and evaluated it extensively in our lab environment. Experimental results show that TagVision can achieve 98% blob matching accuracy and 10.33mm location tracking precision.

Recent real-world attacks against Certification Authorities (CAs) and fraudulently issued certificates arouse the public to rethink the security of public key infrastructure for web-based connections. To distribute the trust of CAs, notaries, as an independent party, are introduced to record certificates, and a client can request an audit proof of certificates from notaries directly. However, there are two challenges. On one hand, existing works consider the security of notaries insufficiently. Due to lack of systematic mutual verification, notaries might bring safety bottlenecks. On the other hand, the service of these works is not sustainable, when any party leaks its private key or fails. In this paper, we propose a Tripartite Public Key Infrastructure (TriPKI), using Certificates Authorities, Integrity Log Servers, and Domain Name Servers, to provide a basis for establishing secure SSL/TLS connections. Specifically, we apply checks-and balances among those three parties in the structure to make them verify mutually, which avoids any single party compromise. Furthermore, we design a collaborative certificate management scheme to provide sustainable services. The security analysis and experiment results demonstrate that our scheme is suitable for practical usage with moderate overhead.

Incentive-driven advanced attacks have become a major concern to cyber-security. Traditional defense techniques that adopt a passive and static approach by assuming a fixed attack type are insufficient in the face of highly adaptive and stealthy attacks. In particular, a passive defense approach often creates information asymmetry where the attacker knows more about the defender. To this end, moving target defense (MTD) has emerged as a promising way to reverse this information asymmetry. The main idea of MTD is to (continuously) change certain aspects of the system under control to increase the attacker's uncertainty, which in turn increases attack cost/complexity and reduces the chance of a successful exploit in a given amount of time. In this paper, we go one step beyond and show that MTD can be further improved when combined with information disclosure. In particular, we consider that the defender adopts a MTD strategy to protect a critical resource across a network of nodes, and propose a Bayesian Stackelberg game model with the defender as the leader and the attacker as the follower. After fully characterizing the defender's optimal migration strategies, we show that the defender can design a signaling scheme to exploit the uncertainty created by MTD to further affect the attacker's behavior for its own advantage. We obtain conditions under which signaling is useful, and show that strategic information disclosure can be a promising way to further reverse the information asymmetry and achieve more efficient active defense.

Shelf scanning is one of the most important processes for inventory management in a library. It helps the librarians and library users discover the miss-shelved books and pinpoint where they are, improving the quality of service. By traditional means, however, manually checking each bookshelf suffers from extremely intensive labor and long scanning delay. Although some existing RFID-enabled approaches have been proposed, they suffer from either high-cost infrastructure or complicated system deployment, forming a great barrier to commercial adoption. In light of this, we in this paper propose a smart system called RF-Scanner that can perform the shelf scanning automatically by combining the robot technology and the RFID technology. The former is used for replacing the librarians and liberating them from intensively manual labor. The later is installed on the robot and moves with the robot to scan the on-the-shelf books. We formulate two important issues concerned by librarians, the book localization and the lying-down book detection, and give the sophisticated solutions to them. Besides, we implement RF-Scanner and put it into practical use in our school library. Long-term experiments and studies show that RF-Scanner provides fine-grained book localization with a mean error of just 1.3 cm and accurate detection accuracy of lying-down books with a mean error of 6%.