INFOCOM 2019 Papers

Online misinformation has been considered as one of the top global risks as it may cause serious consequences such as economic damages and public panic. The misinformation prevention problem aims at generating a positive cascade with appropriate seed nodes in order to compete against the misinformation. In this paper, we study the misinformation prevention problem under the prominent independent cascade model. Due to the #P-hardness in computing influence, the core problem is to design effective sampling methods to estimate the function value. The main contribution of this paper is a novel sampling method. Different from the classic reverse sampling technique which treats all nodes equally and samples the node uniformly, the proposed method proceeds with a hybrid sampling process which is able to attach high weights to the users who are prone to be affected by the misinformation. Consequently, the new sampling method is more powerful in generating effective samples used for computing seed nodes for the positive cascade. Based on the new hybrid sample technique, we design an algorithm offering a (1-1/e-€)-approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.

Conventionally, mobile network operators charge users for data plan subscriptions. To create new revenue streams, some operators now also incentivize users to watch ads with data rewards and collect payments from advertisers. In this work, we study two such rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' existing data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among a capacity-constrained operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We provide some counter-intuitive insights for the design of data rewards. For example, the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function. When each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and nonsubscribers) if and only if it has a small network capacity.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to support rechargeable sensors to enrich a series of energy-consuming applications. Unfortunately, two major design restrictions hinder the applicability of rechargeable sensor networks. First, most of the sensor placement schemes are focusing on the sensing tasks instead of the charging utility, which leaves a considerably high performance gap towards the optimal result. Second, the charging scheduling is non-flexible, where full or nothing charging policy suffers from the relatively low charging coverage as well as efficiency. In this paper, we focus on how to efficiently improve the charging utility when introducing charging oriented sensor placement and flexible scheduling policy. To this end, we jointly consider optimizing node positions and charging allocations. In particular, we formulate a general convex optimization problem under a general routing constraint, which generates great difficulty. We utilize area partition and charging discretization methods to reformulate a submodular function maximization problem. Thus a constant approximation algorithm is delivered to construct a near optimal charging tour. To this end, we analyze the performance loss from the discretization to guarantee that the output of the proposed algorithm has more than $(1 -\varepsilon)/4 (1 - 1 /e)$ of the optimal solution, where $\varepsilon$ is an arbitrarily small positive parameter $(0 \leq \varepsilon \leq 1)$. Both simulations and field experiments are conducted to evaluate the performance of our proposed algorithm.

Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learning this function from the examples of marked point patterns in order to predict the marks of new point patterns. To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect to some local point distance representation. In a more advanced approach, we use a global data representation via the scattering moments of random measures, which build informative and stable to deformations data representation, already proven useful in image analysis and related application domains. In this case, the regression of the scattering moments of the marked point patterns with respect to the non-marked ones is combined with the numerical solution of the inverse problem, where the marks are recovered from the estimated scattering moments. Considering some simple, generic marks, often appearing in the modeling of wireless networks, such as the shot-noise values, nearest neighbour distance, and some characteristics of the Voronoi cells, we show that the scattering moments can capture similar geometry information as the baseline approach, and can reach even better performance, especially for non-local marking functions. Our results motivate further development of statistical learning tools for stochastic geometry and analysis of wireless networks, in particular to predict cell loads in cellular networks from the locations of base stations and traffic demand.

Cross-technology coexistence is crucial to avoid collisions of wireless transmissions and improve the efficiency of spectrum utilization in today's large-scale wireless network systems, especially the Internet of Things. However, existing approaches to cross-technology coexistence incur additional transmission delay and signal processing overhead, which are unaffordable by extremely weak wireless devices such as embedded sensors and computational RFIDs. These schemes hence fail when being applied to emerging application scenarios, such as smart cities and connected healthcare where weak devices play important roles. In this paper, we design and implement EmBee, a new wireless PHY technique that enables cross-technology coexistence at zero cost or performance loss to these extremely weak wireless devices. The basic idea of EmBee is to exploit the diversity of different wireless technologies' spectrum utilization, so as to adaptively reserve occupied spectrum from the strong devices for weak wireless devices' concurrent data transmissions. We have implemented EmBee over custom wireless hardware and evaluated EmBee under different wireless scenarios. Experiment results show that EmBee can effectively support ZigBee transmissions over a fully occupied WiFi channel without causing any extra delay, while only resulting in 10% WiFi throughput loss.

Millimeter wave (mmW) communications have recently attracted considerable attention as a key element of next-generation (5G) wireless systems. Despite significant efforts in this domain, establishing and maintaining directional mmW links in a dynamic environment are still quite challenging, largely due to the search-time overhead of beam scanning, and the vulnerability of directional links to beam misalignment, blockage, and outages. In this paper, we propose SmartLink, a protocol that exploits the multi-cluster scattering phenomenon at mmW frequencies to establish a multi-directional link between a base station and a user. By exploiting multiple clusters, SmartLink enables fast initial access and link maintenance, along with sustained throughput. A search algorithm called multi-lobe beam search (MLBS) is used to discover multiple channel clusters by probing several directions simultaneously using carefully designed multilobe beam patterns. MLBS reduces the search time from linear to logarithmic with respect to the number of directions. We provide detailed analysis of the false alarm and misdetection probabilities for the designed beam patterns. Following cluster discovery, SmartLink divides antennas into sub-arrays to generate the optimal multi-lobe pattern with respect to cluster powers and blockage probabilities. Finally, extensive trace-driven simulations at 29 GHz frequency using phased-array antennas verify the efficiency of SmartLink.

Data is becoming an important kind of commercial good, and many online data marketplaces are set up to facilitate the trading of data. However, most existing data market models and the corresponding pricing mechanisms are simple, and fail to capture the unique economic properties of data. In this paper, we first characterize the distinctive features of IoT data as a commodity, and then present a new IoT data market model from an information design perspective. We further propose a family of data pricing mechanisms for revenue maximization under different market settings. Our MSimple mechanism extracts full surplus from the market for the model with one type of buyer. When multiple types of buyers coexist, our MGeneral mechanism optimally solves the problem of revenue maximization by formulating it as a polynomial size convex program. For a more practical setting where buyers have bounded rationality, we design MPractical mechanism with a tight logarithmic approximation ratio. We evaluate our pricing mechanisms on a real-world ambient sound dataset. Evaluation results show our pricing mechanisms achieve good performance and approach the revenue upper bound.

Estimating frequencies of certain items among a population is a basic step in data analytics, which enables more advanced data analytics (e.g., heavy hitter identification, frequent pattern mining), client software optimization, and detecting unwanted or malicious hijacking of user settings in browsers. Frequency estimation and heavy hitter identification with local differential privacy (LDP) protect user privacy as well as the data collector. Existing LDP algorithms cannot leverage 1) prior knowledge about the noise in the estimated item frequencies and 2) prior knowledge about the true item frequencies. As a result, they achieve suboptimal performance in practice. In this work, we aim to design LDP algorithms that can leverage such prior knowledge. Specifically, we design Calibrate to incorporate the prior knowledge via statistical inference. Calibrate can be appended to an existing LDP algorithm to reduce its estimation errors. We model the prior knowledge about the noise and the true item frequencies as two probability distributions, respectively. Given the two probability distributions and an estimated frequency of an item produced by an existing LDP algorithm, our Calibrate computes the conditional probability distribution of the item's frequency and uses the mean of the conditional probability distribution as the calibrated frequency for the item. It is challenging to estimate the two probability distributions due to data sparsity. We address the challenge via integrating techniques from statistics and machine learning. Our empirical results on two real-world datasets show that Calibrate significantly outperforms state-of-the-art LDP algorithms for frequency estimation and heavy hitter identification.

The emergence of programmable switches has sparked a significant amount of work on new techniques to perform more powerful measurement tasks, for instance, to obtain fine-grained traffic and performance statistics. Previous work has focused on the efficiency of these measurements alone and has neglected flexibility, resulting in solutions that are hard to reuse or repurpose and that often overlap in functionality or goals. In this paper, we propose the use of a set of reusable primitive building blocks that can be composed to express measurement tasks in a concise and simple way. We describe the rationale for the design of our primitives, that we have named MAFIA (Measurements As FIrst-class Artifacts), and using several examples we illustrate how they can be combined to realize a comprehensive range of network measurement tasks. Writing MAFIA code does not require expert knowledge of low-level switch architecture details. Using a prototype implementation of MAFIA, we demonstrate the applicability of our approach and show that the use of our primitives results in compiled code that is comparable in size and resource usage with manually written specialized P4 code, and can be run in current hardware.

As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless spectrum sharing between secondary users (SUs) and incumbent users. Despite its benefits, SAS's design requirements, as set by FCC, present privacy risks to SUs, merely because SUs are required to share sensitive operational information (e.g., location, identity, spectrum usage) with SAS to be able to learn about spectrum availability in their vicinity. In this paper, we propose TrustSAS, a trustworthy framework for SAS that synergizes state-of-the-art cryptographic techniques with blockchain technology in an innovative way to address these privacy issues while complying with FCC's regulatory design requirements. We analyze the security of our framework and evaluate its performance through analysis, simulation and experimentation. We show that TrustSAS can offer high security guarantees with reasonable overhead, making it an ideal solution for addressing SUs' privacy issues in an operational SAS environment.

Photo sharing on Online Social Networks (OSNs) has become one of the most popular social activities in our daily life. However, some associated friends or bystanders in the photos may not want to be viewed due to privacy concerns. In this paper, we propose the design, implementation and evaluation of HideMe, a framework to preserve the associated users’ privacy for online photo sharing. HideMe acts as a plugin to existing photo sharing OSNs, and it enables the following: a) extraction of factors when users upload their photos, b) associated friends in the uploaded photos are able to set their own privacy policies based on scenarios, instead of a photo-by-photo setting, c) any user in other friend’s uploaded photos could be hidden away from unwanted viewers based on one time policy generation. We also design a distance-based algorithm to identify and protect the privacy of bystanders. Moreover, HideMe not only protects users’ privacy but also reduces the system overhead by a carefully designed face matching algorithm. We have implemented a prototype of HideMe, and evaluation results have demonstrated its effectiveness and efficiency.

Thanks to the recent advances in energy-harvesting devices, nodes equipped with such devices are produced and enable Wireless Sensor Networks (WSNs) to be energy self-sustainable. Such networks are named as Battery-Free WSNs (BF-WSNs). Data aggregation is an essential operation in WSNs, and the Minimum Latency Aggregation Scheduling (MLAS) problem which seeks a collision-free aggregation scheduling with the minimum latency has been well studied in Battery-Powered WSNs (BP-WSN). In BP-WSNs, latency is mainly caused by the time overhead in collision-avoiding. However, the time-consumption for node recharging is the main cause of latency in BF-WSNs. Moreover, the collisions are time-independent while the recharge rate is time-varying. Therefore, the previous algorithms are not suitable for BF-WSNs. In addition, if aggregating data from all nodes, the latency would be determined by the node with the lowest recharge rate. Thus, we propose to aggregate a subset of nodes which can meet the given coverage quality requirement. Meanwhile, the aggregation tree and scheduling strategy should be adaptive to the current energy condition. We formulate this problem and propose a distributed algorithm which can select nodes adaptively according to their energy condition and schedule these nodes to achieve the minimum latency, simultaneously. To the best of our knowledge, it is the first distributed algorithm to solve the MLAS problem with coverage guarantee in BF-WSNs. The simulation results verify that our algorithm can reduce aggregation latency effectively, especially in bad energy condition.

Performing exact computations can require significant resources. Approximate computing allows to alleviate resource constraints, sacrificing the accuracy of results. In this work, we consider a generalization of the classical packet classification problem. Our major contribution is to introduce various representations for approximate packet classifiers with controlled accuracy and optimization techniques to reduce classifier sizes exploiting this new level of flexibility. We validate our theoretical results with a comprehensive evaluation study.

Increasing popularity of delay-sensitive and data-intensive online services such as video streaming has placed stronger requirements on the quality of content delivery. By negotiating premium peering agreement with an Internet service provider (ISP), a content provider (CP) is able to improve its service quality, which is beneficial for attracting end-users and increasing profits. Meanwhile, by deploying cache on its content delivery route with a proper caching mechanism, transit traffic is effectively reduced and bandwidth is saved, hence the CP can also achieve better service quality. In this paper, we study how a CP determines an optimal strategy that maximizes its utility, if both premium peering and caching are available from an ISP. We present the conditions that the CP's optimal strategy should comply with, and observe that the optimal quantity of purchasing one capacity is positively correlated to that of the other. We also find that the CP's optimal strategy indicates that the CP would purchase a moderate amount of capacity, or the maximum amount available in some case, to maximize its utility.

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks on computer clusters. With the increase of computational power, network communications have become one limiting factor on the system scalability. In this paper, we observe that many deep neural networks have a large number of layers with only a small amount of data to be communicated. Based on the fact that merging some short communication tasks into a single one may reduce the overall communication time, we formulate an optimization problem to minimize the training iteration time. We develop an optimal solution named merged-gradient wait-free backpropagation (MG-WFBP) and implement it in our open-source deep learning platform B-Caffe. Our experimental results on an 8-node GPU cluster with 10GbE interconnect and trace-based simulation results on a 64-node cluster both show that the MG-WFBP algorithm can achieve much better scaling efficiency than existing methods WFBP and SyncEASGD.

Modulation recognition plays a key role in emerging spectrum applications including spectrum enforcement, resource allocation, privacy and security. While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i)a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter's bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity.This paper bridges the gap between real-world spectrum sensing and the growing body of methods for modulation recognition designed under simplifying assumptions. We propose to use local features, besides global statistics, extracted from raw IQ data, which collectively enable a robust framework for modulation recognition that outperforms baselines from the state-of-the-art. Specifically, we exploit the discriminative power of local patterns from consecutive IQ samples extracted based on a Fisher Kernel framework that captures non-linearity in the underlying data. With these domain-informed features, we employ lightweight linear support vector machine classification for modulation detection. Our framework is robust to noise, partial transmitter scans and data biases without utilizing prior knowledge of the underlying transmitter technology. The recognition accuracy of our approach consistently outperforms baselines in both simulated and real-world traces. We demonstrate up to a 98% accuracy and a 30% improvement over several counterparts from the literature with partial scans in a USRP testbed.

With the increasing prevalence of mobile devices, face-to-face device-to-device (D2D) communication has been applied to a variety of daily scenarios such as mobile payment and short distance file transfer. In D2D communications, a critical security problem is verifying the legitimacy of devices when they share no secrets in advance. Previous research addressed the problem with device authentication and pairing schemes based on user intervention or exploiting physical properties of the radio or acoustic channels. However, a remaining challenge is to secure face-to-face D2D communication even in the middle of a crowd, within which an attacker may hide. In this paper, we present Nhuth, a nonlinearity-enhanced, location-sensitive authentication mechanism for such communication. Especially, we target at the secure authentication within a limited range such as 20 cm, which is the common case for face-to-face scenarios. Nhuth contains averification scheme based on the nonlinear distortion of speaker-microphone systems and a location-based-validation model. The verification scheme guarantees device authentication consistency by extracting acoustic nonlinearity patterns (ANP) while the validation model ensures device legitimacy by measuring the time difference of arrival (TDOA) at two microphones. We analyze the security of Nhuth theoretically and evaluate its performance experimentally. Results show that Nhuth can verify the device legitimacy in the presence of nearby attackers.

Wireless Power Transfer (WPT) has emerged into an inspiringly commercial and applicable era to charge devices. Existing studies mainly focus on general charging patterns and metrics while overlooking the collaborated task execution, which incurs charging inefficiency among nodes. In this paper we first advocate the collaborated tasks-driven mobile charging and scheduling to respect the energy requirement diversity. Specially, the mobile charging scheduling strategy is considered to maximize the overall task utility which concerns sensor selection and task cooperation. Unfortunately, solving this problem is non-trivial, because it involves solving two coupling NP-hard problems. In tackling with this difficulty, we construct a surrogate function with specific theoretical analysis of its submodularity and gap property. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint, where we propose $a (1-\ 1/e)/4$-approximation algorithm. Extensive simulations are conducted and the results show that our algorithm can achieve a near-optimal solution covering at least S4.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in office room and soccer field environment with 10 and 20 sensors are implemented respectively to validate our proposed algorithm.

With the rapid development of wireless communication technologies, the demand for higher data rate and more concurrent transmissions has been continually increasing. Due to the widespread deployment of various wireless technologies, interference has become a key roadblock to the improvement of network performance. Interference has long been known to be harmful, leading to development of numerous interference management (IM) mechanisms based on resource segmentation or signal processing to mitigate or suppress interference. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we propose Interference ReCycling (IRC). Under IRC, a recycling signal is generated with the interference a victim receiver (Rx) is subjected to, and then sent by the Rx's associated transmitter (Tx). Under the influence of the recycling signal, the desired data of the victim Tx-Rx pair can be recovered from the interference at the victim Rx. That is, by exploiting the interactions among multiple signals, i.e., recycling signal and interference, useful data information can be extracted from interference, or the unintended data carried in interference can be artificially converted to the desired one. Our theoretical analysis and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the spectral efficiency (SE) of the victim Rx compared to the other existing IM methods.