INFOCOM 2017 论文列表

A low-end embedded platform for Internet of Things (IoT) often suffers from a critical trade-off dilemma between security enhancement and computation overhead. We propose PUFSec, a new device fingerprint-based security architecture for IoT devices. By leveraging intrinsic hardware characteristics, we aim to design a computationally lightweight security software system architecture so that complex cryptography computation can dramatically be prohibited. We exploit the innovative idea of Public Physical Unclonable Functions (PPUFs) that fundamentally protects attackers from recovering the secret key from public gate delay information. We implement its hardware logic in a real-world FPGA board. On top of the PPUF fingerprint hardware, we present an adaptive security control mechanism consisting of adaptive key generation and key exchange protocol, which adjusts security strength depending on system load dynamics. We demonstrate that our PPUF FPGA implementation embeds distinctive variability enough to distinguish between two different PPUFs with high fidelity. We validate our PUFSec architecture by implementing necessary algorithms and protocols in a real-world IoT platform, and performing empirical evaluation in terms of computation and memory usages, proving its practical feasibility.

We consider a parallel-server system with K homogeneous servers where incoming tasks, arriving at rate λ, are dispatched by n dispatchers. Servers are FCFS queues and dispatchers implement a size-based policy such that the servers are equally loaded. We compare the performance of a system with n> 1 dispatchers and of a system with a single dispatcher. Every dispatcher handles a fraction 1/n of the incoming traffic and balances the load to K/n servers. We show that the performance of a system with n dispatchers, K servers and arrival rate λ coincides with that of a system with one dispatcher, K/n servers and arrival rate λ/n. Therefore, the performance comparison can be interpreted as the economies of scale in a system with one dispatcher when we scale up the number of servers and the arrival rate proportionately. We consider two continuous service time distributions: uniform and Bounded Pareto that have increasing and decreasing failure rates, respectively; and a discrete distribution with two values, which is the distribution that maximizes the variance for a given mean. We show that the performance degradation is small for uniformly distributed job sizes, but that for Bounded Pareto and two points distributions it can be unbounded.

The 5.9 GHz band is being actively explored for possible spectrum sharing opportunities between Dedicated Short Range Communications (DSRC) and IEEE 802.11ac networks in order to address the increasing demand for bandwidth-intensive Wi-Fi applications. In this paper, we study the implications of this spectrum sharing to the performance of Wi-Fi systems. Through experiments performed on our testbed, we first investigate band sharing options available for Wi-Fi devices. Using experimental results, we show the need for using conservative Wi-Fi transmission parameters to enable harmonious coexistence between DSRC and Wi-Fi. Moreover, we show that under the current 802.11ac standard, certain channelization options, particularly the high bandwidth ones, cannot be used by Wi-Fi devices without causing interference to the DSRC nodes. Under these constraints, we propose a Real-time Channelization Algorithm (RCA) for Wi-Fi Access Points (APs) operating in the shared spectrum. Evaluation of the proposed algorithm using a prototype implementation on commodity hardware as well as via simulations show that informed channelization decisions can significantly increase Wi-Fi throughput compared to static channelization schemes.

Two related online problems: knapsack and truthful bipartite matching are considered. For these two problems, the common theme is how to `match' an arriving left vertex in an online fashion with any of the available right vertices, if at all, so as to maximize the sum of the value of the matched edges, subject to satisfying a sum-weight constraint on the matched left vertices. Assuming that the left vertices arrive in an uniformly random order (secretary model), two almost similar algorithms are proposed for the two problems, that are 2e competitive and 24 competitive, respectively. The proposed online bipartite matching algorithm is also shown to be truthful: there is no incentive for any left vertex to misreport its bid/weight. Direct applications of these problems include job allocation with load balancing, generalized adwords, crowdsourcing auctions, and matching wireless users to cooperative relays in device-to-device communication enabled cellular network.

In this research, we conduct a comprehensive measurement study on the predictability of human mobility with respect to demographic differences. We leverage an extensive WLAN dataset collected on a large university campus. Specifically, our dataset includes over 41 million WLAN entries gathered from over 5,000 students (with demographic information) during a four-month period in 2015. We observed surprising patterns on large increases of long-term mobility entropy by age, and the impact of academic majors on students long-term mobility entropy. The distribution of long-term entropy follows a bimodal distribution, which is different from previous studies. We also find that the predictability of students' short-term (daily or weekly) mobility varies on different days of the week and with student gender. Because of the large campus size, our results can mimic people's mobility patterns in metropolitan areas. We also anticipate that our results will provide insight that guides academic administrators' decisions regarding facilities planning, emergency management, etc. on campus.

The rapid growth of sensor-embedded smartphones has led to a new data sensing and collecting paradigm, known as crowdsensing. Many auction-based incentive mechanisms have been proposed to stimulate smartphone users to participate in crowdsensing. However, none of them have taken into consideration the Sybil attack where a user illegitimately pretends multiple identities to gain benefits. This attack may undermine existing inventive mechanisms. To deter the Sybil attack, we design Sybil-proof auction-based incentive mechanisms for crowdsensing in this paper. We investigate both the single-minded and multi-minded cases and propose SPIM-S and SPIM-M, respectively. SPIM-S achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SPIM-M achieves individual rationality, truthfulness, and Sybil-proofness. We evaluate the performance and validate the desired properties of SPIM-S and SPIM-M through extensive simulations.

Millimeter-wave communication is one of the enabling technologies to meet high data-rate requirements of 5G wireless systems. Millimeter-wave systems due large available bandwidth enable gigabit-per-second data rates for line-of-sight (LOS) transmissions in short distances. However, for non-line-of-sight (NLOS) transmissions, millimeter-wave systems suffers performance degradation because the received signal strengths at user equipments (UEs) are not satisfactory. In this paper, the NLOS problem in millimeter-wave systems is treated from SoftAir (a wireless software-defined networking architecture) perspective. In particular, a so-called dynamic base station (BS) formation is introduced, which adaptively coordinates BSs and their multiple antennas to always satisfy UEs' quality-of-service (QoS) requirements in NLOS cases. First, the architecture for software-defined millimeter-wave system is introduced, where remote radio heads (RRHs) coordination is explained and millimeter-wave channel model between RRHs and UEs is analyzed. A ubiquitous millimeter-wave coverage problem is formulated, which jointly optimizes RRH-UE associations and beamforming weights of RRHs to maximize the UE sum-rate while guaranteeing QoS and system-level constraints. After proving the np-hardness of the coverage optimization problem with non-convex constraints, an iterative algorithm is developed for dynamic BS formation that achieves ubiquitous coverage with high data rates in LOS and NLOS cases. Through successive convex approximations, the proposed dynamic BS formation algorithm transforms the original mixed-integer nonlinear programming into a mixed-integer second-order cone programming, which is efficiently solved by convex tools. Simulations validate the efficacy of our solution that completely solves NLOS problem by facilitating ubiquitous coverage in 5G millimeter-wave systems.

Mobile applications (apps) often transmit sensitive data through network with various intentions. Some transmissions are needed to fulfill the app's functionalities. However, transmissions with malicious receivers may lead to privacy leakage and tend to behave stealthily to evade detection. The problem is twofold: how does one unveil sensitive transmissions in mobile apps, and given a sensitive transmission, how does one determine if it is legitimate? In this paper, we propose LeakSemantic, a framework that can automatically locate abnormal sensitive network transmissions from mobile apps. LeakSemantic consists of a hybrid program analysis component and a machine learning component. Our program analysis component combines static analysis and dynamic analysis to precisely identify sensitive transmissions. Compared to existing taint analysis approaches, LeakSemantic achieves better accuracy with fewer false positives and is able to collect runtime data such as network traffic for each transmission. Based on features derived from the runtime data, machine learning classifiers are built to further differentiate between the legal and illegal disclosures. Experiments show that LeakSemantic achieves 91% accuracy on 2279 sensitive connections from 1404 apps.

This paper concerns the smart exploitation of multimodal communication capabilities of underwater nodes to enable reliable and swift underwater networking. To contrast adverse and highly varying channel conditions we define a smart framework enabling nodes to acquire knowledge on the quality of the communication to neighboring nodes over time. Following a model-based reinforcement learning approach, our framework allows senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. We name the resulting forwarding method MARLIN, for MultimodAl Reinforcement Learning-based RoutINg. Applications can choose whether to seek reliable routes to the destination, or whether faster packet delivery is more desirable. We evaluate the performance of MARLIN in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN is compared to state-of-the-art forwarding protocols, including a channel-aware solution, a machine learning-based solution and to a flooding protocol extended to use multiple modems. Our results show that a smartly learned selection of relay and modem is key to obtain a packet delivery ratio that is twice as much that of other protocols, while maintaining low latencies and energy consumption.

Internet of Things (IoT) systems produce great amount of data, but usually have insufficient resources to process them in the edge. Several time-critical IoT scenarios have emerged and created a challenge of supporting low latency applications. At the same time cloud computing became a success in delivering computing as a service at affordable price with great scalability and high reliability. We propose an intelligent resource allocation system that optimally selects the important IoT data streams to transfer to the cloud for processing. The optimization runs on utility functions computed by predictor algorithms that forecast future events with some probabilistic confidence based on a dynamically recalculated data model. We investigate ways of reducing specifically the upload bandwidth of IoT video streams and propose techniques to compute the corresponding utility functions. We built a prototype for a smart squash court and simulated multiple courts to measure the efficiency of dynamic allocation of network and cloud resources for event detection during squash games. By continuously adapting to the observed system state and maximizing the expected quality of detection within the resource constraints our system can save up to 70% of the resources compared to the naive solution.

Secret key establishment is a fundamental requirement for private communication between two wireless entities. An intriguing solution is to extract secret keys from the inherent randomness shared between them. Although several works have been done to extract secret keys from different kinds of mediums (e.g., RSSI, CSI, CIR), the efficiency and security problems are not fully solved. In this paper, we consider the problem of secret key establishment for wireless devices, and propose MAGIK, a secure and efficient scheme based on dynamic geoMAGnetic field in Indoor environment for Key establishment. We carefully study the feasibility of utilizing indoor geomagnetic field for key extraction through extensive measurements. Our results demonstrate that geomagnetic field has several dynamic properties, including space-varying, time-varying, sensitive to measurement device, and correlative between two observed points in proximity. We also optimize the key extraction process and present two rotation-angle-based quantification methods, which can achieve faster key generation rates and lower bit mismatching ratios. Besides, we build a prototype on commodity mobile devices, and evaluate its performance by conducting real-word experiments in indoor scenarios. The experiment results confirm that our system is efficient, in terms of key extraction rate, and robust in secret key establishment without requiring additional overhead on mobile devices.

Consider a set of point-to-point communication requests in a multi-channel multihop wireless network, each of which is associated with a traffic demand of at most one unit of transmission time, and a weight representing the utility if its demand is fully met. A subset of requests is said to be schedulable without spectral splitting if they can be scheduled within one unit of time subject to the constraint each request is assigned with a unique channel throughout its transmission. This paper develops efficient and provably good approximation algorithms for finding a maximum-weighted subset of communication requests schedulable without spectral splitting.

In Named Data Networking (NDN), data synchronization plays an important role similar to transport protocols in IP. Many distributed applications, including pub-sub applications such as news and weather services, require a synchronization protocol where each consumer can subscribe to a different subset of a producer's data streams. However, existing Sync protocols support only full-data synchronization, which is a special case of this problem. We propose PSync to efficiently address different types of data synchronization. Names are used in PSync messages to carry producers' latest namespace information and each consumer's subscription information, which allows producers to maintain a single state for all consumers and enables consumers to synchronize with any producer that replicates the same data. We have implemented PSync in the NDN codebase and used it to develop a prototype pub-sub module for building management. Our experimental results show that PSync scales well as the number of consumers, subscriptions, and data streams increases and it outperforms the state-of-the-art Sync protocol in supporting full-data synchronization.

Multiview videos offer unprecedented experience by allowing users to explore scenes from different angles and perspectives. Thus, such videos have been gaining substantial interest from major content providers such as Google and Facebook. Adaptive streaming of multiview videos is, however, challenging because of the Internet dynamics and the diversity of user interests and network conditions. To address this challenge, we propose a novel rate adaptation algorithm for multiview videos (called MASH). Streaming multiview videos is more user centric than single-view videos, because it heavily depends on how users interact with the different views. To efficiently support this interactivity, MASH constructs probabilistic view switching models that capture the switching behavior of the user in the current session, as well as the aggregate switching behavior across all previous sessions of the same video. MASH then utilizes these models to dynamically assign relative importance to different views. Furthermore, MASH uses a new buffer-based approach to request video segments of various views at different qualities, such that the quality of the streamed videos is maximized while the network bandwidth is not wasted. We have implemented a multiview video player and integrated MASH in it. We compare MASH versus the state-of-the-art algorithm used by YouTube for streaming multiview videos. Our experimental results show that MASH can produce much higher and smoother quality than the algorithm used by YouTube, while it is more efficient in using the network bandwidth. In addition, we conduct large-scale experiments with up to 100 concurrent multiview streaming sessions, and we show that MASH maintains fairness across competing sessions, and it does not overload the streaming server.

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Much of the previous work on SLP has focused on the development of various protocols to enhance the level of SLP imparted to the network, under various attacker models and other conditions. Other work has focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we adopt a different approach where we model the attacker movement as a time series and use information theoretic concepts to infer the properties of a routing protocol that imparts high levels of SLP. We propose the notion of a properly competing path that causes an attacker to “stall” when moving towards the source. This concept provides the basis for developing a perturbation model, similar to those in privacy-preserving data mining. We then show how to use properly competing paths to develop properties of an SLP-aware routing protocol. Further, we show how different SLP-aware routing protocols can be obtained through different instantiations of the framework. Those instantiations are obtained based on a notion of information loss achieved through the use of the perturbation model proposed.

Replicating redundant requests has been shown to be an effective mechanism to defend application performance from high capacity variability - the common pitfall in the cloud. While the prior art centers on single-tier systems, it still remains an open question how to design replication strategies for distributed multi-tier systems, where interference from neighboring workloads is entangled with complex tier interdependency. In this paper, we design a first of its kind PArtial REplication system, sPARE, that replicates and dispatches read-only workloads for multi-tier web applications, determining replication factors per tier. The two key components of sPARE are (i) the variability-aware replicator that coordinates the replication levels on all tiers via an iterative searching algorithm, and (ii) the replication-aware arbiter that uses a novel token-based arbitration algorithm (TAD) to dispatch requests in each tier. We evaluate sPARE on web serving and web searching applications, i.e., MediaWiki and Solr, deployed on our private cloud testbed. Our results based on various interference patterns and traffic loads show that sPARE is able to improve the tail latency of MediaWiki and Solr by a factor of almost 2.7x and 2.9x, respectively.

Social networks are the major routes for most individuals to exchange their opinions about new products, social trends and political issues via their interactions. It is often of significant importance to figure out who initially diffuses the information, i.e., finding a rumor source or a trend setter. It is known that such a task is highly challenging and the source detection probability cannot be beyond 31% for regular trees, if we just estimate the source from a given diffusion snapshot. In practice, finding the source often entails the process of querying that asks “Are you the rumor source?” or “Who tells you the rumor?” that would increase the chance of detecting the source. In this paper, we consider two kinds of querying: (a) simple batch querying and (b) interactive querying with direction under the assumption that queriees can be untruthful with some probability. We propose estimation algorithms for those queries, and quantify their detection performance and the amount of extra budget due to untruthfulness, analytically showing that querying significantly improves the detection performance. We perform extensive simulations to validate our theoretical findings over synthetic and real-world social network topologies.

Device-to-device (D2D) communications for Long Term Evolution (LTE) networks relies on a discovery process to enable User Equipment (UE) to determine which D2D applications and services are supported by neighboring UEs. This is especially important for groups of UEs that operate outside the coverage area of any base station. The amount of time required for discovery information to reach every UE in a group depends on the number of UEs in the group and the dimensions of the discovery resource pool associated with the Physical Sidelink Discovery Channel (PSDCH); an additional factor is the half-duplex property of current UEs. In this paper, we use a Markov chain to characterize the performance of Mode 2 direct discovery. The resulting analytical model gives the distribution of the time for a UE to discover all other UEs in its group. We validate the model using Monte Carlo and network simulations.

Software-Defined Networking (SDN) provides network administrators opportunities to control network devices more simply and easily than in traditional networking. However, heterogeneity in switch hardware, especially in forwarding pipeline architecture, renders the task of network application developers and network administrators tedious, by hampering portability across switch models. In this paper, we propose FlowConvertor, an algorithm capable of converting rules from any forwarding pipeline to any other different forwarding pipeline, as long as both pipelines offer compatible operations. More precisely, FlowConvertor is an online algorithm that operates on flow updates issued to the origin pipeline and computes the corresponding updates for the target pipeline in real time. Performance evaluation shows that the latency introduced by FlowConvertor on the path between the SDN controller and the target switch is of the order of 1ms in most cases, and is thus acceptable for practical deployment.