INFOCOM 2018 Papers

We propose and analyze a new shadowing field model meant to capture spatial correlations. The interference field associated with this new model is compared to that of the widely used independent shadowing model. Independent shadowing over links is adopted because of the resulting closed forms for performance metrics, and in spite of the well-known fact that the shadowing fields of networks are spatially correlated. The main purpose of this paper is to challenge this independent shadowing approximation. For this, we analyze the interference measured at the origin in networks where 1) nodes which are in the same cell of some random shadowing tessellation share the same shadow, or 2) nodes which share a common mother point in some cluster process share the same shadow. By leveraging stochastic comparison techniques, we give the order relation of the three main user performance metrics, namely coverage probability, Shannon throughput and local delay, under both the correlated and the independent shadowing assumptions. We show that the evaluation of the considered metrics under the independent approximation is systematically pessimistic compared to the correlated shadowing model. The improvement in each metric when adopting the correlated shadow model is quantified and shown to be quite significant.

Search over encrypted data (SE) enables a client to delegate his search task to a third-party server that hosts a collection of encrypted documents while still guaranteeing some measure of query privacy. Software-based solutions using diverse cryptographic primitives have been extensively explored, leading to a rich set of secure search indexes and algorithm designs. However, each scheme can only implement a small subset of information retrieval (IR) functions and often with considerable search information leaked. Recently, the hardware-based secure execution has emerged as an effective mechanism to securely execute programs in an untrusted software environment. In this paper, we exploit the hardware-based execution environment (TEE) and explore a software and hardware combined approach to address the challenging secure search problem. For functionality, our design can support the same spectrum of plaintext IR functions. For security, we present oblivious keyword search techniques to mitigate the index search trace leakage. We build a prototype of the system using Intel SGX. We demonstrate that the proposed system provides broad support of a variety of search functions and achieves computation efficiency comparable to plaintext data search with elevated security protection.

For many applications that use RFID technology, it is important to count the number of RFID tags accurately. However, the wireless channel between the RFID tags and readers can introduce communication errors, and the error rate may vary significantly over time. No existing protocol can perform RFID counting robustly (i.e., maintaining the estimation quality) over time-varying channels. In this paper, we design RRC, a Robust RFID Counting protocol that offers provable guarantees on estimation quality over time-varying channels. Specifically, regardless of how the communication errors occur, the final output generated by RRC is always a standard (e, δ) estimate of the correct count n. Furthermore, the expected amount of time needed by RRC is O(Y + 1/2 + (log log n)2) for a constant 6, where Y is the number of communication errors encountered by RRC. This makes the efficiency of RRC asymptotically near-optimal.

Enabling functionality in modern network is achieved through the use of middleboxes. Middleboxes suffer from temporal unavailability due to various reasons, such as hardware faults. We design a backup scheme that takes advantage of Network Function Virtualization (NFV), an emerging paradigm of implementing network functions in software, deployed on commodity servers. We utilize the agility of software-based systems, and the gap between the resource utilization of active and standby components, in order to design an optimal limited-resource backup scheme. We focus on the case where a small number of middleboxes fail simultaneously, and study the backup resources required for guaranteeing full recovery from any set of failures, of up to some limited size. Via a novel graph-based presentation, we develop a provably optimal construction of such backup schemes. Since full recovery is guaranteed, our construction does not rely on failure statistics, which are typically hard to obtain. Simulation results show that our proposed approach is applicable even for the case of larger numbers of failures.

Carrier frequency offset (CFO) arises from the intrinsic mismatch between the operating frequencies of the transmitter and the receiver, as well as their relative speeds (i.e., Doppler effect). Despite advances in CFO estimation techniques, estimation errors are still present. Residual CFO creates time-varying phase error. Modern wireless systems, including WLANs, 5G cellular systems, and satellite communications, use high-order modulation schemes, which are characterized by dense constellation maps. Accounting for the phase error is critical for the demodulation performance of such schemes. In this paper, we analyze the post-estimation probability distribution of residual CFO and use it to develop a CFO-aware demodulation approach for a set of modulation schemes (e.g., QAM and APSK). For a given distribution of the residual CFO, symbols with larger amplitudes are less densely distributed on the constellation map. We explore one important application of our adaptive demodulation approach in the context of PHY-layer security, and more specifically modulation obfuscation (MO) mechanisms. In such mechanisms, the transmitter attempts to hide the modulation order of a frame's payload from eavesdroppers, which could otherwise exploit such information to breach user privacy or launch selective attacks. We go further and complement our CFO-aware demodulation scheme by optimizing the design of a low-complexity MO technique with respect to phase errors. Our results show that when combined, our CFO-aware demodulation and optimized MO techniques achieve up to 5 dB gain over conventional demodulation schemes that are not obfuscated and are oblivious to residual CFO.

Public Bike Systems (PBSs) offer convenient and green travel service and become popular around the world. In many cities, the local governments build thousands of fixed stations for PBS to alleviate the city traffic jam and solve the last-mile problem. However, the increasing use of PBSs leads to new congestion problems in the form that users have, such as no bike to rent or no dock to return the bike. Further, users wish to receive assistance on deciding how to select bike trips with minimal time cost while taking congestion into account. Meanwhile, crowdsourcing attracted increasing attention in recent years. This paper applies it to help users share information and select bike trips before the bikes or docks are occupied. An interesting and important problem is how to help users select bike trips so that the time consumed on the trips can be minimized. We model the problem as a Bike Trip Selection (BTS) game which is shown to be equivalent to the symmetric network congestion game. This equivalence allows us to design a BTS algorithm by which the users can find at least one Nash Equilibria (NE) distributively. Furthermore, this paper evaluates the algorithm based on real datasets collected from the PBS of Hangzhou City in China. We also design a BTS system including an Android APP and a server to conduct the experiment for the distributed BTS algorithm in practice.

We use a crowdsourcing approach for RF spectrum patrolling, where heterogeneous, low-cost spectrum sensors are deployed widely and are tasked with detecting unauthorized transmissions in a collaborative fashion while consuming only a limited amount of resources. We pose this as a collaborative signal detection problem where the individual sensor's detection performance may vary widely based on their respective hardware or software configurations, but are hard to model using traditional approaches. Still an optimal subset of sensors and their configurations must be chosen to maximize the overall detection performance subject to given resource (cost) limitations. We present the challenges of this problem in crowdsourced settings and present a set of methods to address them. The proposed methods use data-driven approaches to model individual sensors and develops mechanisms for sensor selection and fusion while accounting for their correlated nature. We present performance results using examples of commodity-based spectrum sensors and show significant improvements relative to baseline approaches.