INFOCOM 2017 论文列表

Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

Energy harvesting is emerging as a promising alternative source to power the next generation of wireless networks. This paper introduces a new energy harvesting strategy that uses a dedicated energy source to optimally replenish energy for radio frequency (RF) based wireless communication systems. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that allows an energy harvesting device to effectively obtain energy from a dedicated energy source. While minimizing the system energy consumption, DTER takes into account the practical constraints on both the energy source and the energy harvesting device. Additionally, the overhead issue and the charge characteristics of an energy storage component are examined to make the proposed strategy practical. To solve the nonlinear optimization problem in DTER, we convert the design of optimal energy requesting problem into a classic shortest path problem and thus enable us to find a global optimal solution through dynamic programming algorithms. Theoretical analysis and simulation study verify that DTER outperforms two other schemes in the literature.

Recent diversification of mobile computing devices allows a mobile user to own multiple types of devices for different application scenarios, but also results in various restrictions on the performance and usability of these devices. A viable solution to such restriction is to incorporate and interconnect mobile devices towards a personal mobile cloud where these devices can complement each other via cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. In this paper, we propose a novel design of resource sharing framework to address these challenges and generically interconnect heterogeneous mobile devices. Our basic idea is to mask the hardware and software heterogeneity in mobile systems by exploiting the existing mobile OS services as the interface of resource sharing, and further develop the resource sharing framework as a middleware in the mobile OS. We have implemented our design over various mobile platforms with diverse characteristics and resource limits, and demonstrated that our design can efficiently support generic resource sharing among heterogeneous mobile devices without incurring significant system overhead or requiring individual system modification.

Network Function Virtualization (NFV) is a promising technique to greatly improve the effectiveness and flexibility of network services through a process named Service Function Chain (SFC) mapping, with which different network services are deployed over virtualized and shared platforms in data centers. However, such an evolution towards software-defined network functions introduces new challenges to network services which require high availability. One effective way of protecting the network services is to use sufficient redundancy. By doing so, however, the efficiency of physical resources may be greatly decreased. To address such an issue, this paper defines an optimal availability-aware SFC mapping problem and presents a novel online algorithm that can minimize the physical resources consumption while guaranteeing the required high availability within a polynomial time. Simulation results show that our proposed algorithm can significantly improve SFC mapping request acceptance ratio and reduce resource consumption.

Lightweight control planes are techniques that create a control plane in WiFi networks without any additional spectrum requirements. Flash signals are an example of such control signals that exploit the link margin that typically exists in WiFi communication. In this paper we consider the problem of allowing transmitters and receivers of a transmission to exploit such control channels while the communication is ongoing. We present a mechanism called switch that facilitates switches in communication modes (Tx to Rx and vice-versa). We then use switch as the core building block to solve problems in WiFi networks such as starvation due to hidden terminals, early collision termination, and frequency backoffs. We rely on WARP radios to experimentally verify that switch is indeed possible, and use ns-3 simulations to study the impact of using switch to solve the aforementioned WiFi problems.

Cloud radio access network (C-RAN) is deemed as a promising architecture to meet the exponentially increasing traffic demand in mobile networks, where baseband processing is separated from remote radio heads (RRHs) and performed in a centralized baseband unit (BBU) pool. However, the densely deployed RRHs, as well as the passive optical network which provides high capacity backhauls between the RRHs and the BBU pool, consume a large amount of energy. In this paper, we propose efficient RRH switching schemes to achieve a tradeoff between the system energy saving and the load balance among the RRHs in the C-RAN. We first develop an approximation algorithm to address the intractable user association problem for a given set of RRHs, based on which we introduce efficient local search algorithms to perform RRH selection procedure, which can reduce the load fairness index of the C-RAN by controlling the active/inactive state of each RRH. We also discuss the handover signalling overhead issue and introduce an adaptive trigger mechanism to avoid switching on/off too many RRHs simultaneously so as to keep the signalling overhead of the C-RAN below an acceptable level. Numerical results demonstrate that the proposed RRH switching schemes can improve the system performance of the C-RAN significantly. Moreover, our proposal sheds light on how to design effective and efficient handover schemes for next generation mobile networks.

Recent studies have suggested that the stability of peer-to-peer networks may rely on persistent peers, who dwell on the network after they obtain the entire file. In the absence of such peers, one piece becomes extremely rare in the network, which leads to instability. Technological developments, however, are poised to reduce the incidence of persistent peers, giving rise to a need for a protocol that guarantees stability with nonpersistent peers. We propose a novel peer-to-peer protocol, the group suppression protocol, to ensure the stability of peer-to-peer networks under the scenario that all the peers adopt non-persistent behavior. Using a suitable Lyapunov potential function, the group suppression protocol is proven to be stable when the file is broken into two pieces, and detailed experiments demonstrate the stability of the protocol for arbitrary number of pieces. Straightforward incorporation of the group suppression protocol into BitTorrent while retaining most of BitTorrent's core mechanisms is also presented. Subsequent simulations show that under certain assumptions, BitTorrent with the official protocol cannot escape from the missing piece syndrome, but BitTorrent with group suppression does.

Video streaming applications based on Peer-to-Peer (P2P) systems are popular for their scalability, which is hard to achieve with traditional client-server approaches. In particular, layered video streaming has been much-studied due to its ability to differentiate users' streaming qualities in heterogeneous user environments. Previous work, however, has shown that user misbehavior (e.g., free-riding and protocol deviation) poses a serious threat to P2P systems that are not equipped with proper incentive mechanisms. We propose a method to disincentivize such misbehavior. Our SVC-TChain is a layered P2P video streaming method based on scalable video coding (SVC), which uses the recently proposed T-Chain incentive mechanism to discourage free-riding. After introducing T-Chain, we present the first analytical framework to study SVC piece selection with multiple video layers, using it to efficiently choose SVC-TChain's optimal piece selection parameters and thus discourage deviations from the piece selection policy. Extensive experimental results show that SVC-TChain outperforms layered extensions of BiTos and Give-to-Get, two popular P2P video streaming approaches, both in the absence of user misbehavior and when some users misbehave.

In this paper we study the following problem: given a set of m sensors that collectively cover a set of n target points with heterogeneous coverage requirements (target j needs to be covered every fjslots), how to schedule the sensor duty cycles such that all coverage requirements are satisfied and the maximum number of sensors turned on at any time slot is minimized. The problem models varied real-world applications in which sensing tasks exhibit high discrepancy in coverage requirements - critical locations often need to be covered much more frequently. We provide multiple algorithms with best approximation ratio of O (log n + log m) for the maximum number of sensors to turn on, and bi-criteria algorithm with (α, β)-approximation factors with high probability, where the number of sensors turned on is an α = O(δ(log (n) + log(m))/β)-approximation of the optimal (satisfying all requirements) and the coverage requirement is a β-approximation; δ is the approximation ratio achievable in an appropriate instance of set multi-cover. When the sensor coverage exhibits extra geometric properties, the approximation ratios can be further improved. We also evaluated our algorithms via simulations and experiments on a camera testbed. The performance improvement (energy saving) is substantial compared to turning on all sensors all the time, or a random scheduling baseline.

The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling a parameter V in the algorithm, the backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is [O(1/V), O(V)] and is attained by a backpressure algorithm based on a drift-pluspenalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, the existing backpressure algorithms necessarily yield an arbitrarily large queue length. However, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.

Delay and rate-based alternatives to TCP congestion-control have been around for nearly three decades and have seen a recent surge in interest. However, such designs have faced significant resistance in being deployed on a wide-scale across the Internet - this has been mostly due to serious concerns about noise in delay measurements, pacing inter-packet gaps, and/or required changes to the standard TCP stack/headers. With the advent of high-speed networking, some of these concerns become even more significant. In this paper, we consider Rapid, a recent proposal for ultra-high speed congestion control, which perhaps stretches each of these challenges to the greatest extent. Rapid adopts a framework of continuous fine-scale bandwidth probing, which requires a potentially different and finely-controlled gap for every packet, high-precision timestamping of received packets, and reliance on fine-scale changes in inter-packet gaps. While simulation-based evaluations of Rapid show that it has outstanding performance gains along several important dimensions, these will not translate to the real-world unless the above challenges are addressed. We design a Linux implementation of Rapid after carefully considering each of these challenges. Our evaluations on a 10Gbps testbed confirm that the implementation can indeed achieve the claimed performance gains, and that it would not have been possible unless each of the above challenges was addressed.

In the non-time-slotted cognitive radio networks (CRNs), the synchronization between PUs and secondary users (SUs) cannot be guaranteed, resulting in two challenging problems: the reactivation-failure of PUs and the frequently unexpected hand-offs among SUs. The reactivation-failure of PUs is the incident that the SUs cannot detect the PUs' reactivation when the SUs are occupying the channels to transmit their data in non-time-slotted CRNs. The frequently unexpected handoffs among SUs are the events that the SU cannot distinguish between the PUs' reactivation and the other SUs' contention, thus causing many unexpected hand-offs among SUs, which severely degrade the achieved throughput of SUs in non-time-slotted CRNs. Employing the energy-detection based wireless full-duplex spectrum sensing schemes, the PUs' reactivation-failure problem can be efficiently solved, thus guaranteeing the required throughput of PUs. However, the key of CRNs is not only the throughput-guarantees for PUs, but also the throughput-boosts for SUs. To optimize the throughput of SUs in multichannel non-time-slotted CRNs, in this paper we develop the pilot-based full-duplex spectrum sensing (PF-SS) scheme and the pilot-based medium access control (P-MAC) protocol to not only guarantee the required throughput of PUs, but also significantly increase the throughput of SUs in multichannel non-time-slotted CRNs. Using the PF-SS scheme, the SUs can identify whether the PUs' signal or the SUs' signal, thus significantly reducing the frequently unexpected hand-offs among SUs. Then, based on the PF-SS scheme, the P-MAC protocol can significantly increase the throughput of SUs. We conduct extensive numerical analyses to show that our developed PF-SS scheme and P-MAC protocol can significantly increase the throughput of SUs while guaranteeing the required throughput for PUs in multichannel non-time-slotted CRNs.

Given the ever-expanding scale of WiFi deployments in metropolitan areas, we have reached the point where accurate GPS-free outdoor localization becomes possible by relying solely on the WiFi infrastructure. Nevertheless, the existing industrial practices do not seem to have the right implementation to achieve an adequate accuracy, while the academic researches that are mostly attracted by indoor localization have largely neglected this outdoor aspect. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a solution that offers meter-level accuracy, by holistically treating the large number of WiFi hotspot labels gather by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, we accommodate all the labeled and unlabeled data for a given area using a semi-supervised manifold learning technique, and the output concerning the unlabeled part will become the estimated locations for both users and WiFi hotspots. We conduct extensive experiments with WOLoc in several outdoor areas, and the results have strongly indicated the efficacy of our solution.

We study the problem of personalized Quality of Service (QoS) estimation for web services. State-of-the-art methods use matrix factorization or collaborative prediction to estimate web service response times and throughput for each user based on partial measurements collected from past invocations. We point out that in reality, both the response times and through-put of web services follow highly skewed distributions. In this case, the conditional mean QoS estimates generated by traditional matrix completion approaches can be heavily biased toward a few outliers, leading to poor web service recommendation performance. In this paper, we propose the Quantile Matrix Factorization (QMF) technique for web service recommendation by introducing quantile regression into the matrix factorization framework. We propose a novel and efficient algorithm based on Iterative Reweighted Least Squares (IRLS) to solve the QMF problem involving a non-smooth objective function. We further extend the proposed QMF approach to take into account user and service side attributes. Extensive evaluation based on a large-scale QoS dataset has shown that our schemes significantly outperform various state-of-the-art web service QoS estimation schemes in terms of personalized recommendation performance.

Millions of people nowadays share their television (TV) experience with other people through social media like Twitter or Facebook with mobile devices. It is generally believed that TV has been repurposed for social networks, called as social television. A key functionality of social television is that it allows the viewers to interchange their comments through mobile devices, thus creating the impression of watching TV like alongside a group of friends. To do so, mobile devices have to be aware of the current media context (identifier and progress) of what the TV is playing, known as synchronizing context from TVs to mobile devices. Unfortunately, most legacy systems are not able to track the playing progresses or need to upgrade TV devices. To address the issue, we design a purely software-based solution, called as TagScreen, which inserts a series of hidden sound markers into the audio of the content. TagScreen supports longrange or multipath-resistant synchronization at second-level, being independent of device diversity over severely frequency-selective acoustic channels. We implement TagScreen by using COTS TVs and mobile devices. The system has been extensively tested on 150 movies and 150 TV series across five different environments. Results show that TagScreen has a mean recognition accuracy of 98% up to 35m, and a mean tracking accuracy of 97%.

Live virtual machine (VM) migration is commonly used for enabling dynamic resource or fault management, or for load balancing in datacenters or cloud platforms. A service hosted by a VM may also be migrated to prevent its visibility to an external adversary who may seek to disrupt its operation by launching a DDoS attack against it. We first show that current systems cannot adequately hide a VM migration from an external adversary. The key reason for this is that a migration typically manifests a traffic pattern with distinguishable statistical properties. We introduce two new attacks that can allow an adversary to effectively track a migration in progress, by leveraging observations of these properties. As our primary contribution, we design and implement a stealth migration framework that causes migration traffic to be indistinguishable from regular Internet traffic, with a negligible latency overhead of approximately 0.37 seconds, on average.

We consider a cognitive radio network, where primary users have priority over the spectrum resources, and secondary users can exploit the unused resources through channel sensing. Due to sensing inaccuracy, the secondary traffic may obstruct the primary traffic. A penalty for collision has been used to protect the primary traffic, which is often designed to provide a fixed per-collision compensation or to restrict the collision rate at an acceptable level. In this work, we develop a framework that can protect the primary traffic taking into account the Quality of Service of the primary traffic. In particular, we pay attention to the delay performance, which is determined not only by the collision rate but also by the amount of traffic in both networks. We design a novel model with coupled queues, and successfully incorporate dynamic interactions between the two systems through the standard optimization problem. We also consider the practical requirement of no direct sharing of the system information between the two networks, and develop a close-to-optimal solution of per-collision price and channel sensing under mild assumptions. We evaluate its performance through simulations.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a three-dimensional tensor as compared to interpolation methods based on two-dimensional matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose Reshape-Align scheme to form the regular tensor with data from dynamic measurements, and introduce user-domain and temporal-domain factor matrices which takes full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CP decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of two metrics: error ratio and mean absolute error (MAE).

Modern communication and computation systems consist of large networks of unreliable nodes. Yet, it is well known that such systems can provide aggregate reliability via information redundancy, duplicating paths, or replicating computations. While redundancy may increase the load on a system, it can also lead to major performance improvements through the judicious management of additional system resources. Two important examples of this abstract paradigm are content access from multiple caches in content delivery networks and master/slave computations on compute clusters. Many recent articles in the area have proposed bounds on the latency performance of redundant systems, characterizing the latency-redundancy tradeoff under specific load profiles. Following a similar line of research, this article introduces new analytical bounds and approximation techniques for the latency-redundancy tradeoff for a range of system loads and two popular redundancy schemes. The proposed framework allows for approximating the equilibrium latency distribution, from which various metrics can be derived including mean, variance, and the tail decay of stationary distributions.