INFOCOM 2017 Papers

Molecular Communication (MC) is an enabling paradigm for the interconnection of future devices and networks in the biological environment, with applications ranging from bio-medicine to environmental monitoring and control. The engineering of biological circuits, which allows to manipulate the molecular information processing abilities of biological cells, is a candidate technology for the realization of MC-enabled devices. In this paper, inspired by recent studies favoring the efficiency of analog computation over digital in biological cells, an analog decoder design is proposed based on biological circuit components. In particular, this decoder computes the a-posteriori log-likelihood ratio of parity-check-encoded bits from a binary-modulated concentration of molecules. The proposed design implements the required L-value and the box-plus operations entirely in the biochemical domain by using activation and repression of gene expression, and reactions of molecular species. Each component of the circuit is designed and tuned in this paper by comparing the resulting functionality with that of the corresponding analytical expression. Despite evident differences with classical electronics, biochemical simulation data of the resulting biological circuit demonstrate very close performance in terms of Mean Squared Error (MSE) and Bit Error Rate (BER), and validate the proposed approach for the future realization of MC components.

Virtualized datacenter networks have been deployed in production platforms, e.g., Amazon VPC and VMware's NVP, to offer the flexibility of network management to enterprise-level clients. A common characteristic of these platforms is that they adopt software switches, such as Open vSwitch (OvS), instead of hardware switches to transfer data between VMs. Although group communication is common in enterprise applications, the unique characteristics of software switches have posed new challenges to the design of multicast protocols. How logical multicast can be optimally performed with software switches is still not well understood. In this paper, we observe that unlike hardware switches, the per-stream output rate in a software switch critically depends on the packet processing overhead of flow cloning. We study the optimal OvS multicast topology with or without the help of additional dedicated software switches called service nodes, and formulate the throughput maximization as a new class of degree-supervised combinatorial graph problems due to the presence of flow cloning costs. We propose a lineartime optimal solution that translates into simple forwarding rules installed at each software switch. Through emulation-based OvS profiling and extensive simulation results, we demonstrate that our proposed logical multicast solutions can significantly improve session throughput with the ability to handle load balancing and latency issues, as compared to the state-of-the-art in the literature.

Shared Risk Link Group (SRLG) is a failure the network is prepared for, which contains a set of links subject to a common risk of single failure. During planning a backbone network, the list of SRLGs must be defined very carefully, because leaving out one likely failure event will significantly degrade the observed reliability of the network. Regional failures are manifested at multiple locations of the network, which are physically close to each other. In this paper we show that operators should prepare a network for only a small number of possible regional failure events. In particular, we give a fast systematic approach to generate the list of SRLGs that cover every possible circular disk failure of a given radius r. We show that this list has O((n + x)σr) SRLGs, where n is the number of nodes in the network, x is the number of link crossings, and σris the maximal number of links that could be hit by a disk failure of radius r. Finally through extensive simulations we show that this list in practice has size of ≈ 1.2 n.

Emerging 5G networks will not only offer higher link rates, but also integrate a variety of Radio Access Technologies (RATs) in order to provide ultra-reliable broadband access to a wide range of applications with high throughput and low latency requirements. SDN-enabled dynamic path selection is of critical importance in exploiting the collective transmission resources in such heterogeneous multi-RAT environments and delivering excellent user performance. In the present paper we propose the `best-rate' path selection algorithm for multi-RAT networks with various types of traffic flows. The best-rate algorithm accounts for the radio conditions and performance requirements of individual flows as well as the load conditions at the various access points. We analytically establish that the rates received by the various flows under the best-rate path selection, in conjunction with local fair resource sharing at the individual access points, are close to globally Proportional Fair. Detailed simulation experiments demonstrate that the best-rate algorithm achieves significant gains in terms of user-perceived throughput performance over various baseline policies.

DNS caches are an extremely important tool, providing services for DNS as well as for a multitude of applications, systems and security mechanisms, such as anti-spam defences, routing security (e.g., RPKI), firewalls. Subverting the security of DNS is detrimental to the stability and security of the clients and services, and can facilitate attacks, circumventing even cryptographic mechanisms. We study the caching component of DNS resolution platforms in diverse networks in the Internet, and evaluate injection vulnerabilities allowing cache poisoning attacks. Our evaluation includes networks of leading Internet Service Providers and enterprises, and professionally managed open DNS resolvers. We test injection vulnerabilities against known payloads as well as a new class of indirect attacks that we define in this work. Our Internet evaluation indicates that more than 92% of the Internet's DNS resolution platforms are vulnerable to records injection and can be persistently poisoned.

Wearables are small and have limited user interfaces, so they often wirelessly interface with a personal smartphone/computer to relay information from the wearable for display or other interactions. In this paper, we envision a new method, LightTouch, by which a wearable can establish a secure connection to an ambient display, such as a television or a computer monitor, while ensuring the user's intention to connect to the display. LightTouch uses standard RF methods (like Bluetooth) for communicating the data to display, securely bootstrapped via the visible-light communication (the brightness channel) from the display to the low-cost, low-power, ambient light sensor of a wearable. A screen `touch' gesture is adopted by users to ensure that the modulation of screen brightness can be securely captured by the ambient light sensor with minimized noise. Wireless coordination with the processor driving the display establishes a shared secret based on the brightness channel information. We further propose novel onscreen localization and correlation algorithms to improve security and reliability. Through experiments and a preliminary user study we demonstrate that LightTouch is compatible with current display and wearable designs, is easy to use (about 6 seconds to connect), is reliable (up to 98% success connection ratio), and is secure against attacks.

The ability to detect which wearables and smartphones are on the same body has the potential to support a wealth of applications, including user authentication, automatic data synchronization, and personalized profile loading. This paper brings this feature to commercial off-the-shelf (COTS) wearables and smartphones, by creating a virtual “on-body detection sensor” based on devices' inherent wireless capabilities. We investigate using the peculiar propagation characteristics of creeping waves to discern on-body wearables. To this end, we decompose signals into multiple independent components to exploit the variation features of creeping waves. We implement our system on COTS wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can identify on-body devices at 92.3% average true positive rate and 5% average false positive rate.

Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.

It is crucial for cellular data network operators to understand the service quality perceived by its customers. The state-of-art systems deployed in cellular networks mostly report service quality aggregated on cell site level, which is typically an aggregation of tens or hundreds of customers depending on the locations of the cell sites. In this paper, we propose to enhance the measurement of customer-perceived service quality by leveraging M2M devices as sensors in the field, which provide an unprecedented opportunity for cellular network operators to measure what end-users experience with better accuracy and coverage. Our approach is to identify a set of M2M devices which are stationary and communicate continuously over the cellular network over an indefinite period of time. We use these M2M devices to estimate the customer-perceived service quality during cell site outages. We implement our methodology as a system called M2MScan and evaluate M2MScan with both synthetic outages and real outages from a large-scale operational cellular network. To the best of our knowledge, this is the first work that employs M2M devices to measure the service quality perceived by customers in operational cellular networks at a large scale.

Identifying an object's spatial direction (or orientation) plays a fundamental role in a variety of applications, such as automatic assembly, indoor navigation, and robot driving. In this paper, we design a fine-grained direction finding system called Tag-Compass that attaches a single tag to an object (whose size may be small) and identifies the tagged object's orientation by determining the spatial direction of the tag. We exploit the polarization properties of the RF waves used in the communications between an RFID reader and the tag on the object. Polarization mismatch between the tag and the reader's antenna affects the received signal strength at the reader. From the measured signal strength values, we are able to deduce the tag's direction through a series of transformations and deviation minimization. We propose a system design for Tag-Compass and implement a prototype. We evaluate the performance of TagCompass through extensive experiments using the prototype. The experimental results show that Tag-Compass provides accurate direction estimates with a median error of just 2.5° when the tag's position is known and a median error of 3.8° when the tag's position is unknown.

Current wireless underground (UG) communication techniques are limited by their achievable distance. In this paper, a novel framework for underground beamforming using adaptive antenna arrays is presented to extend communication distances for practical applications. Based on the analysis of propagation in wireless underground channel, a theoretical model is developed which uses soil moisture information to improve wireless underground communications performance. Array element in soil is analyzed empirically and impacts of soil type and soil moisture on return loss (RL) and resonant frequency are investigated. Accordingly, beam patterns are analyzed to communicate with underground and above ground devices. Depending on the incident angle, refraction from soil-air interface has adverse effects in the UG communications. It is shown that beam steering improves UG communications by providing a high-gain lateral wave. To this end, the angle, which enhances lateral wave, is shown to be a function of dielectric properties of the soil, soil moisture, and soil texture. Evaluations show that this critical angle varies from 0° to 16° and decreases with soil moisture. Accordingly, a soil moisture adaptive beamforming (SMABF) algorithm is developed for planar array structures and evaluated with different optimization approaches to improve UG communication performance.

The completion times of jobs in a computing cluster may be influenced by a variety of factors including job size and machine processing variability. In this paper, we explore online resource allocation policies which combine size-dependent scheduling with redundant execution and opportunistic checkpointing to minimize the overall job flowtime. We introduce a simplified model for the job service capacity of a computing cluster while leveraging redundant execution/checkpointing. In this setting, we propose two resource allocation algorithms, SRPT+R and LAPS+R(β) subject to checkpointing overhead not exceeding the number of jobs which are processed. We provide new theoretical performance bounds for these algorithms: SRPT+R is shown to be O(1/∊) competitive under (1 + ∊)-speed resource augmentation, while LAPS+R(β) is shown to be O(1/β∊) competitive under (2+ 2β + 2∊)-speed resource augmentation.

Offloading computation to a mobile cloud is a promising approach for enabling the use of computationally intensive applications by mobile devices. In this paper we consider autonomous devices that maximize their own performance by choosing one of many wireless access points for computation offloading. We develop a game theoretic model of the problem, prove the existence of pure strategy Nash equilibria, and provide a polynomial time algorithm for computing an equilibrium. For the case when the cloud computing resources scale with the number of mobile devices we show that all improvement paths are finite. We provide a bound on the price of anarchy of the game, thus our algorithm serves as an approximation algorithm for the global computation offloading cost minimization problem. We use extensive simulations to provide insight into the performance and the convergence time of the algorithms in various scenarios. Our results show that the equilibrium cost may be close to optimal, and the convergence time is almost linear in the number of mobile devices.

The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations, identities) about individuals. This work studies ordinal data aggregation for distribution estimation meanwhile locally preserving individuals' data privacy (such as on their mobile devices). Under ε-geo-indistinguishable constraints, which capture intrinsic dissimilarity between ordinal categories in the framework of differential privacy, we provide an efficient and effective locally private mechanism: Subset Exponential Mechanism (SEM) for ordinal data distribution estimation. The mechanism randomly responds with a fixed-size subset of the categories with calibrated probability assignment. Specially for uniform ordinal data, we propose a circling technique to symmetrically randomizing categories and estimating frequencies of categories, hence the computational/space costs and estimation performance of SEM are further optimized. Besides contributing theoretical error bounds of SEM, we also evaluate the mechanism on extensive scenarios, the evaluation results show that SEM reduces distribution estimation error on average by exp(ϵ/2) factor over existing private mechanisms.

This paper proposes a disaster avoidance control method for use against heavy rainfall and discusses its effectiveness through actual weather data. The proposed control method uses geographical information data including weather data, hazard area data, and physical network data. By applying technologies related to meteorology, erosion control, and civil engineering to such data, the proposed method can evaluate the risk of a physical network being disconnected. On the basis of the evaluated risk, the proposed method reconfigures a logical network to reduce service disruption. The proposed method is applied to a cloud computing service network where, in addition to route changes, the relocation of virtual machines is possible, increasing its effectiveness. By using empirical data, we show that the proposed method reduces the probability of service disconnection to almost zero even for heavy rainfall causing landslides. Finally, an experimental system of the proposed method was implemented through software defined network technology and successfully controlled the experimental network.

Recent research on CTC (cross-technology communication) demonstrates the viability of direct coordination among heterogeneous devices (e.g., WiFi and ZigBee) with incompatible physical layers. Although encouraging, current solutions suffer from either severe inefficiency in channel utilization or low throughput using limited beacons. To address these limitations, this paper presents C-Morse, which leverages all traffic (such as through data packets, beacons and other control frames) to achieve a high cross-technology communication throughput. The key idea of C-Morse is to slightly perturb the transmission timing of existing WiFi packets to construct recognizable radio energy patterns without introducing noticeable delays to upper layers. At the receiver side, ZigBee captures such patterns by sensing the RSSI value, and then decodes the transmitted symbols. C-Morse also introduces a novel timing-based multiplexing technique to allow the coexistence of multiple C-Morse access points and reject other interference, showing a reliable symbol delivery ratio. As a result, C-Morse achieves a free side-channel, whose CTC throughput is as much as 9 χ of the present state of the art, while maintaining the through traffic within a negligible delay that goes unnoticed by applications and end-users.

Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in public transportation. Although the transaction messages of AFC systems are mostly transferred in plaintext, which is obviously insecure, system operators do not need to pay much attention to this issue, since the AFC network is well isolated from public network (e.g., the Internet). Nevertheless, in recent years, the advent of Near Field Communication (NFC)-equipped smartphones has bridged the gap between the AFC network and the Internet through Host-based Card Emulation (HCE). Motivated by this fact, we design and practice a novel paradigm of attack on modern distance-based pricing AFC systems, enabling users to pay much less than actually required. Our constructed attack has two important properties: 1) it is invisible to AFC system operators because the attack never causes any inconsistency in the backend database of the operators; and 2) it can be scalable to large number of users (e.g., 10,000) by maintaining a moderate-sized AFC card pool (e.g., containing 150 cards). Based upon this constructed attack, we developed an HCE app, named LessPay. Our real-world experiments on LessPay demonstrate not only the feasibility of our attack (with 97.6% success rate), but also its low-overhead in terms of bandwidth and computation.

Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the `share-constrained proportional allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource allocation model under strategic players.

Error estimation code estimates the bit error ratio of the received data bits with low overhead. It has many applications, especially in estimating the number of errors in a packet transmitted over a wireless link. In this paper, we propose a novel error estimation code, mEEC, that outperforms the existing code by more than 10%-20% depending on the packet sizes, at the same time being less biased. mEEC is mainly based on the idea of grouping multiple blocks of sampled data bits into a super-block, thus creating a multi-dimensional feature. It then compresses these features into a single number, called the color, as the coded bits. Through an intelligent coloring scheme, the blocks in a super-block share the cost of covering low probability events, which allows the decoder to recover the actual feature values from the color even in the presence of error. mEEC also adopts a lightweight redistribution step, which is guided by the solution of an optimization problem and further reduces the estimation errors and bias. We also show that mEEC can be implemented with reasonable storage sizes and low time complexity.