The Industrial Internet of Things (IIoT) enables smart manufacturing through advanced sensing, monitoring, and control capabilities. However, securing these resource-constrained IIoT devices poses critical challenges due to limited memory and energy resources, while the emergence of quantum computing threatens existing security mechanisms. This study addresses the urgent need for practical quantum-resistant security solutions for deployment in resource-limited industrial environments. It presents QLight-IIoT, a quantum-resistant authentication and key agreement (AKA) scheme established solely on hash-based hardness assumption, demonstrating that lightweight quantum-resistant authentication is achievable without relying on computationally intensive primitives like lattices or codes. The proposed scheme is deployed and tested over Raspberry Pi 4 to demonstrate its practicality in real-world IIoT environments. Experimental validation shows that the scheme ensures mutual authentication with session key establishment with an average key generation time of 0.3200 seconds. The security of the proposed QLight-IIoT scheme is systematically evaluated using the Real-or-Random (ROR) model, the Scyther tool, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) framework. Performance evaluation reveals significant efficiency improvements, achieving 9.29–77.36% reduction in communication cost and 4.41–95.37% reduction in Computational overhead relative to existing schemes. The scheme requires minimal storage of 632 bits per IIoT device, resulting in up to 64.84% reduction in total system storage cost. Comparative security analysis further validates the scheme’s security against various attack vectors. This work provides a practical pathway for industrial organizations to prepare IIoT infrastructure for quantum-resistant security challenges while maintaining compatibility with current resource-constrained hardware.

Denoising diffusion models have emerged as state-of-the-art in generative tasks across image, audio, and video domains, producing high-quality, diverse, and contextually relevant data. However, their broader adoption is limited by high computational costs and large memory footprints. Post-training quantization (PTQ) offers a promising approach to mitigate these challenges by reducing model complexity through low-bandwidth parameters. Yet, direct application of PTQ to diffusion models can degrade synthesis quality due to accumulated quantization noise across multiple denoising steps, particularly in conditional tasks like text-to-audio synthesis. This work introduces PTQ4ADM, a novel framework for quantizing audio diffusion models(ADMs). Our key contributions include (1) a coverage-driven prompt augmentation method and (2) an activation-aware calibration set generation algorithm for text-conditional ADMs. These techniques ensure comprehensive coverage of audio aspects and modalities while preserving synthesis fidelity. We validate our approach on TANGO, Make-An-Audio, and AudioLDM models for text-conditional audio generation. Extensive experiments demonstrate PTQ4ADM’s capability to reduce the model size by up to 70% while achieving synthesis quality metrics comparable to full-precision models(<5% increase in FD scores). We show that specific layers in the backbone network can be quantized to 4-bit weights and 8-bit activations without significant quality loss. This work paves the way for more efficient deployment of ADMs in resource-constrained environments.

A core data-centric learning challenge is the identification of training samples that are detrimental to model performance. Influence functions serve as a prominent tool for this task and offer a robust framework for assessing training data influence on model predictions. Despite their widespread use, their high computational cost associated with calculating the inverse of the Hessian matrix pose constraints, particularly when analyzing large-sized deep models. In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection. This transformation not only presents a straightforward and Hessian-free formulation but also provides insights into the role of the gradient in sample impact. Through systematic empirical evaluations, we first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets. We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models. We also extend its use to influential sample identification for fine-tuning Large Language Models.

Object identification is a pivotal enabling technique for smart home and manufacturing applications. Traditional methodologies for object identification predominantly rely on a singular sensor modality, which inherently limits their ability to furnish a detailed characterization of the target object. Addressing this deficiency, in this paper, we fill this gap by introducing CRFUSION, the first-of-its-kind system that integrates the object RGB image and the radio frequency (RF) signal reflected by the object for fine-grained object identification. CRFUSION leverages the complementary characteristics between visible light and radio frequency modalities to simultaneously determine the category and material of target objects. We design a multifaceted object feature from the RF signal, called the Energy Reflection Factor (ERF), which not only reveals the object texture but complements the image modality for identifying the object category. By integrating the characteristics of radar, we obtain radar feature maps based on the ERF of target objects. Additionally, we have developed a modality fusion network to comprehensively integrate the image and ERF features. We conducted a comprehensive evaluation of CRFUSION using a commercial mmWave radar development board and camera. The results show that CRFUSION achieves a classification accuracy of over 96%, demonstrating its robustness, and potential for application.

With the increasing popularity of virtual reality applications, there is an increasing demand for more interactive entertainment, learning, social interactions, and other activities on eyewear devices. Recognizing users’ emotion and providing reliable feedback can significantly improve the immersive experience for users. However, previous works in emotion recognition required modifications to existing eyewear devices and the integration of additional sensors, or relied on specialized sensors in expensive commercial-grade eyewear devices, making direct deployment on existing consumer-grade eyewear devices challenging. In this paper, we proposeAccEmo, the first system that analyzes the data from the built-in accelerometer sensor on eyewear devices to accurately recognize human emotion.AccEmofirst employs signal processing technologies to process raw accelerometer data, and then uses a binary classification network to determine whether the accelerometer data is influenced by emotional changes. Subsequently,AccEmoproposes a network architecture based on residual neural network and channel-wise attention mechanism as a universal feature extractor to extract complex features related to human emotions from the accelerometer data. Finally,AccEmouses personalized classifiers to achieve emotion recognition for different users. Extensive performance evaluation ofAccEmoacross diverse users demonstrates an exceptional average accuracy of 94.3%. Additionally, the robustness ofAccEmois validated through evaluations in various scenarios, yielding promising results.

Classification models are ubiquitously deployed in society and necessitate high utility, fairness, and robustness performance. Current research efforts mainly focus on improving model architectures and learning algorithms on fixed datasets to achieve this goal. In contrast, in this paper, we address an orthogonal yet crucial problem: given a fixed convex learning model (or a convex surrogate for a non-convex model) and a function of interest, we assess what data benefits the model by interpreting the feature space, and then aim to improve performance as measured by this function. To this end, we propose the use of influence estimation models for interpreting the classifier's performance from the perspective of the data feature space. Additionally, we propose data selection approaches based on influence that enhance model utility, fairness, and robustness. Through extensive experiments on synthetic and real-world datasets, we validate and demonstrate the effectiveness of our approaches not only for conventional classification scenarios, but also under more challenging scenarios such as distribution shifts, fairness poisoning attacks, utility evasion attacks, online learning, and active learning.

Semantic segmentation has emerged as a pivotal area of study in computer vision, offering profound implications for scene understanding and elevating human-machine interactions across various domains. While 2D semantic segmentation has witnessed significant strides in the form of lightweight, high-precision models, transitioning to 3D semantic segmentation poses distinct challenges. Our research focuses on achieving efficiency and lightweight design for 3D semantic segmentation models, similar to those achieved for 2D models. Such a design impacts applications of 3D semantic segmentation where memory and latency are of concern, such as autonomous driving, medical imaging, multimedia, and similar applications. This paper introduces a novel approach to 3D semantic segmentation, distinguished by incorporating a hybrid blend of 2D and 3D computer vision techniques, enabling a streamlined, efficient process. We conduct 2D semantic segmentation on RGB images linked to 3D point clouds and extend the results to 3D using an extrusion technique for specific class labels, reducing the point cloud subspace. We perform rigorous evaluations with the Deep-View Agg model on the complete point cloud as our baseline by measuring the Intersection over Union (IoU) accuracy, inference time latency, and memory consumption. This model is the current state-of-the-art 3D semantic segmentation model on the KITTI-360 dataset. We can achieve heightened accuracy outcomes, sur-passing the baseline for 6 out of the 15 classes while maintaining a marginal 1 % deviation below the baseline for the remaining class labels. Our segmentation approach demonstrates a 1.347x speedup and about a 43% reduced memory usage compared to the baseline.

Summary The growing sophistication of cyber attacks, vulnerabilities in high computing systems and increasing dependency on cryptography to protect our digital data, make it more important to keep secret keys safe and secure. A few major issues of secret keys, like incorrect use of keys, inappropriate storage of keys, inadequate protection of keys, insecure movement of keys, lack of audit logging, insider threats and nondestruction of keys can compromise the whole security system severely. In this work, we propose a field programmable gate array (FPGA)‐based trusted platform module (TPM) framework for operating system companies and OS users, utilizing blockchain to address NIST‐recommended secret key management issues. The security processor used in OS user machines is partitioned into three areas such that processor area, confidential area, and crypto area. The isolated secret key memory in confidential area, along with a private blockchain (BC) can log the life cycle of secret keys of TPM standard. We have also implemented a special custom bus interconnect, which receives custom crypto instructions from Processing Element (PE). During the execution of crypto instructions, the architecture ensures that secret keys are present in confidential area and crypto area but never in the processor area. The movements of secret keys between confidential area, and crypto area are recorded cryptographically after the proper authentication process controlled by the proposed hardware‐based private BC framework. To the best of our knowledge, this work is the first attempt to implement a blockchain‐based framework between OS company and OS users to address NIST recommended secret key management issues of TPM standard hardware environment. The additional cost of resource usage and timing complexity we spent to implement the proposed idea is nominal. The proposed architecture is implemented with Xilinx EDA tool using FPGA board.

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we proposeIota, a logic programming-based framework to perform system-level security analysis for IoT systems.Iotagenerates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In buildingIota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions.Iotaautomatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows thatIota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We applyIotato 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we presentmilliEar, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio.milliEarcombines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluatemilliEarusing off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show thatmilliEarcan accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

Many vehicular applications, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by vehicles. In the absence of a secure authentication scheme, invalid spatial-temporal messages may be sent out by malicious vehicles. Meanwhile, malicious applications may also collect a lot of personal information from spatial-temporal messages. Since inter-vehicle connections are often deployed in high-moving traffic, any authentication must be implemented in real-time. To meet all these properties, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) scheme for inter-vehicle connections. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is mostly designed on hash chains and an entropy-based commitment, and is able to secure periodic spatial-temporal messages. FastTrust also protects vehicles’ privacy by deploying a pseudonym-varying scheduling mechanism to satisfy the anonymity and unlinkability requirements. Finally, in order to efficiently isolate malicious vehicles, a lightweight certificate management scheme is proposed for the limited bandwidth of vehicular networks. We provide analytical evaluations to show that our FastTrust achieves the security and privacy properties. Extensive validations are done to show that FastTrust can authenticate dozens of times faster than the existing signature algorithms, and isolate malicious vehicles at a low cost in terms of communication and computational resources.

Introduction to the Special Section on Contact-free Smart Sensing in AloTArtificial Intelligence (AI) and the Internet of Things (IoT) are two powerful forces that have been reshaping our world in recent years.When they converge, they create a new field of AIoT that enables ubiquitous intelligence through the integration of smart algorithms and connected devices.One of the key enablers of AIoT is contact-free sensing, which leverages the availability of portable and highly integrated WiFi, radar, and sonar-style devices to monitor humans and environments without physical contact.This technology has transformed the traditional computer vision-based paradigms and opened up novel possibilities for data collection and analysis.However, contactfree sensing also poses new challenges and risks for AIoT applications.The dynamic and complex wireless environments require innovative solutions for efficient data processing and interpretation.The security and privacy issues of WiFi, radar, and sonar-enabled sensing devices also demand urgent attention, as they may expose sensitive information to malicious attacks.Therefore, it is imperative to explore the potential and pitfalls of contact-free sensing in AIoT and to develop effective strategies for ensuring the robustness and reliability of AIoT applications.This special issue is dedicated to highlighting the cutting-edge methods and latest research in the field of contact-free sensing, which leverages WiFi, radar, and sonar-style devices to monitor humans and environments without physical contact.The main focus of this issue is to explore the latest machine learning analytics to extract information from the sensory data and to investigate the potential risks and countermeasures to ensure the security and privacy of sensing devices.The call for papers attracted with 44 submissions and after a rigorous review, 18 papers have been accepted for this special issue.A brief summary of some papers in this special issue is presented in the following:In "Feasibility of Remote Blood Pressure Estimation via Narrow-band Multi-wavelength Pulse Transit Time, " the authors investigate the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner using a modified RGB camera.A narrowband triple band-pass filter makes it possible to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths.They design a color-channel model and a novel channel-separation method to further resolve the inter-channel influence and band overlap.The results showed a good absolute Pearson's correlation coefficient between both MW PTT and systolic BP as well as diastolic BP, pointing to the feasibility of the proposed novel remote MW BP estimation via PTT.In "LiteWiSys: A Lightweight System for WiFi-based Dual-task Action Perception, " Sheng et al. propose a lightweight system named LiteWiSys that can simultaneously detect and recognize WiFi-based human actions.This work addresses two major drawbacks of existing methods: heavy

Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user’s personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an empirical perspective. Unreliable explanations can bear strong consequences such as attackers leveraging explanations for manipulating and tempting users to purchase target items that the attackers would want to promote. In this paper, we study the vulnerability of existent feature-oriented explainable recommenders, particularly analyzing their performance under different levels of external noises added into model parameters. We conducted experiments by analyzing three important state-of-the-art (SOTA) explainable recommenders when trained on two widely used e-commerce based recommendation datasets of different scales. We observe that all the explainable models are vulnerable to increased noise levels. Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease. Our study presents an empirical verification on the topic of robust explanations in recommender systems which can be extended to different types of explainable recommenders in RS.

Millimeter-wave (mmWave) communication is poised to significantly enhance vehicle-to-vehicle (V2V) networks by facilitating real-time data transmission between vehicles at gigabits-per-second (Gbps) data rates. However, the high relative mobility between vehicles results in substantial beamforming overhead, negatively affecting V2V network throughput and latency. In this paper, we introduce a novel real-time radarassisted beamforming approach for V2V networks and assess its performance in four typical scenarios using commercial off-the-shelf (COTS) devices. In the transmitter static scenario, our proposed scheme surpasses the default 802.11ad protocol by up to 54% in throughput. In the highly dynamic scenario, our approach yields a 67% improvement in throughput and exhibits 90% lower latency than the default 802.11ad protocol. Furthermore, we investigate a non-line-of-sight (NLOS) scenario, demonstrating that our proposed scheme can achieve higher data throughput rates by opting for the most robust beam sector rather than frequently alternating between weak beam patterns. Finally, the preliminary result in the highway scenario shows that our protocol can improve the throughput by 66% more than the default protocol.

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.

This paper initiates the study for the basic primitive of distributed message dissemination in multi-hop wireless networks under a strong adversarial jamming model. Specifically, the message dissemination problem is to deliver a message initiating at a source node to the whole network. An efficient algorithm for message dissemination can be an important building block for solving a variety of high-level network tasks. We consider the hard non-spontaneous wakeup case, where a node only wakes up when it receives a message. Under the realistic SINR model and a strong adversarial jamming model that removes the budget constraint commonly adopted in previous work by the adversary, we present a distributed randomized algorithm that can accomplish message dissemination in$\mathscr{T}(O(D(\log n+\log R)))$time slots with a high probability performance guarantee, where$\mathscr{T}(U)$is the number of time slots in the interval from the beginning of the algorithm's execution that contains U unjammed time slots, n is the number of nodes in the network, D is the network diameter,$R$is the distance with respect to which the network is connected. Our algorithm is shown to be almost asymptotically optimal by lower bound$\Omega(D\log n)$for non-spontaneous message dissemination in networks without jamming.

Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data science, and seek to partition the dataset into clusters while generating a hierarchical relationship between the data samples. HAC algorithms are employed in many applications, such as biology, natural language processing, and recommender systems. Thus, it is imperative to ensure that these algorithms are fair– even if the dataset contains biases against certain protected groups, the cluster outputs generated should not discriminate against samples from any of these groups. However, recent work in clustering fairness has mostly focused on center-based clustering algorithms, such as k-median and k-means clustering. In this paper, we propose fair algorithms for performing HAC that enforce fairness constraints 1) irrespective of the distance linkage criteria used, 2) generalize to any natural measures of clustering fairness for HAC, 3) work for multiple protected groups, and 4) have competitive running times to vanilla HAC. Through extensive experiments on multiple real-world UCI datasets, we show that our proposed algorithm finds fairer clusterings compared to vanilla HAC as well as the only other state-of-the-art fair HAC approach.

Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science. With recent advancements in deep learning-- deep clustering models have emerged as the current state-of-the-art over traditional clustering approaches, especially for high-dimensional image datasets. While traditional clustering approaches have been analyzed from a robustness perspective, no prior work has investigated adversarial attacks and robustness for deep clustering models in a principled manner. To bridge this gap, we propose a blackbox attack using Generative Adversarial Networks (GANs) where the adversary does not know which deep clustering model is being used, but can query it for outputs. We analyze our attack against multiple state-of-the-art deep clustering models and real-world datasets, and find that it is highly successful. We then employ some natural unsupervised defense approaches, but find that these are unable to mitigate our attack. Finally, we attack Face++, a production-level face clustering API service, and find that we can significantly reduce its performance as well. Through this work, we thus aim to motivate the need for truly robust deep clustering models.

Stochastic zeroth-order optimization algorithms have been predominantly analyzed under the assumption that the objective function being optimized is time-invariant. Motivated by dynamic matrix sensing and completion problems, and online reinforcement learning problems, in this work, we propose and analyze stochastic zeroth-order optimization algorithms when the objective being optimized changes with time. Considering general nonconvex functions, we propose nonstationary versions of regret measures based on first-order and second-order optimal solutions, and provide the corresponding regret bounds. For the case of first-order optimal solution based regret measures, we provide regret bounds in both the low- and high-dimensional settings. For the case of second-order optimal solution based regret, we propose zeroth-order versions of the stochastic cubic-regularized Newton's method based on estimating the Hessian matrices in the bandit setting via second-order Gaussian Stein's identity. Our nonstationary regret bounds in terms of second-order optimal solutions have interesting consequences for avoiding saddle points in the nonstationary setting.

Having an efficient onboarding process is a pivotal step to utilize and provision the IoT devices for accessing the network infrastructure. However, the current process to onboard IoT devices is time-consuming and labor-intensive, which makes the process vulnerable to human errors and security risks. In order to have a streamlined onboarding process, we need a mechanism to reliably associate each digital identity with each physical device. We design an onboarding mechanism called MAIDE to fill this technical gap. MAIDE is an Augmented Reality (AR)-facilitated app that systematically selects multiple measurement locations, calculates measurement time for each location and guides the user through the measurement process. The app also uses an optimized voting-based algorithm to derive the device-to-ID mapping based on measurement data. This method does not require any modification to existing IoT devices or the infrastructure and can be applied to all major wireless protocols such as BLE, and WiFi. Our extensive experiments show that MAIDE achieves high device-to-ID mapping accuracy. For example, to distinguish two devices on a ceiling in a typical enterprise environment, MAIDE achieves ~95% accuracy by measuring 5 seconds of Received Signal Strength (RSS) data for each measurement location when the devices are 4 feet apart.

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, there is an increasing interest in using the received signal strength (RSS) to detect IBAs in wireless networks. However, current schemes tend to generate excessive false alarms in the mobile scenario. In this paper, we propose a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for the mobile wireless networks, which exploits the reciprocity of the wireless fading channel and RSS variations naturally incurred by mobility to improve the detection performance. Different from current schemes only detect IBAs, RCVIC scheme conducts a multi-stage detection processes. If the IBAs are detected, RCVIC scheme partitions the received frames into two classes. The frames in the same class should be sent from the same senders, which could benefit the further analysis, such as network forensics, attacker localizing and trajectory analysis, etc. The feasibility of RCVIC are numerically evaluated through theoretical analysis and simulations. It is further validated through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios.

COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User’s confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1][2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems.We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.

IoT systems are revolutionizing our life by providing ubiquitous computing, inter-connectivity, and automated control. However, the increasing system complexity poses huge challenges for security as IoT devices are distributed, highly heterogeneous, and can directly interact with the physical environment. In IoT systems, bugs in device firmware, defects in network protocols, and design flaws in automation rules can lead to system breach or failure. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the existing vulnerability analysis frameworks inapplicable. In this paper, we present ForeSee, a model checking-based framework to comprehensively evaluate IoT system security. It builds a multi-layer IoT hypothesis graph by simultaneously modeling all of the essential components in IoT systems, including the physical environment, devices, communication protocols, and applications. The model checker can then analyze the generated hypothesis graph to validate system security properties or generate attack paths if there are any violations. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. Our framework verifies hypothesis graphs with millions of nodes in less than 100 seconds. The illustrative case studies show that our framework can detect more potential threats than the existing approaches.

While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

Clustering algorithms are used in a large number of applications and play an important role in modern machine learning– yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised learning. In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for clustering models for linearly separable clusters. Our attack works by perturbing a single sample close to the decision boundary, which leads to the misclustering of multiple unperturbed samples, named spill-over adversarial samples. We theoretically show the existence of such adversarial samples for the K-Means clustering. Our attack is especially strong as (1) we ensure the perturbed sample is not an outlier, hence not detectable, and (2) the exact metric used for clustering is not known to the attacker. We theoretically justify that the attack can indeed be successful without the knowledge of the true metric. We conclude by providing empirical results on a number of datasets, and clustering algorithms. To the best of our knowledge, this is the first work that generates spill-over adversarial samples without the knowledge of the true metric ensuring that the perturbed sample is not an outlier, and theoretically proves the above.

In the past few years, the computer vision community has developed numerous novel technologies of 3D vision (e.g., 3D object detection and classification and 3D scene segmentation). In this work, we explore the opportunities brought by these innovations for enabling real-time 3D vision on mobile devices. Mobile 3D vision finds various use cases for emerging applications such as autonomous driving, drone navigation, and augmented reality (AR). The key differences between 3D vision and 2D vision mainly stem from the input data format (i.e., point clouds or 3D meshes vs. 2D images). Hence, the key challenge of 3D vision is that it is could be more computation intensive and memory hungry than 2D vision, due to the additional dimension of input data. For example, our preliminary measurement study of several state-of-the-art machine learning models for 3D vision shows that none of them can execute faster than one frame per second on smartphones. Motivated by these challenges, we present in this position paper a research agenda on offering systems support for real-time mobile 3D vision, focusing on improving its computation efficiency and memory utilization.

Internet of Things (IoT) has become the most promising technology for service automation, monitoring, and interconnection, etc. However, the security and privacy issues caused by IoT arouse concerns. Recent research focuses on addressing security issues by looking inside platform and apps. In this work, we creatively change the angle to consider security problems from a wireless context perspective. We propose a novel framework called IoTGaze, which can discover potential anomalies and vulnerabilities in the IoT system via wireless traffic analysis. By sniffing the encrypted wireless traffic, IoTGaze can automatically identify the sequential interaction of events between apps and devices. We discover the temporal event dependencies and generate the Wireless Context for the IoT system. Meanwhile, we extract the IoT Context, which reflects user's expectation, from IoT apps' descriptions and user interfaces. If the wireless context does not match the expected IoT context, IoTGaze reports an anomaly. Furthermore, IoTGaze can discover the vulnerabilities caused by the inter-app interaction via hidden channels, such as temperature and illuminance. We provide a proof-of-concept implementation and evaluation of our framework on the Samsung SmartThings platform. The evaluation shows that IoTGaze can effectively discover anomalies and vulnerabilities, thereby greatly enhancing the security of IoT systems.

Network communications, despite being encrypted, leak crucial information via side channels. WiFi networks are more prone to such side-channel attacks since any attacker within the network’s range can passively eavesdrop the channel. With the increasing number of smart home devices and sensors connecting to private WiFi networks, it is essential to understand the inadvertent information leakage through WiFi side-channels. Our work demonstrates how fine-granular information on the activities happening inside a house can be inferred by passively monitoring WiFi network traffic. In particular, we were able to correctly classify various user interactions with simple IoT devices such as smart bulbs or power sockets as well as advanced voice-based intelligent assistants.

Three-Dimensional (3D) semantic segmentation is an essential building block for interactive Augmented Reality (AR). However, existing Deep Neural Network (DNN) models for segmenting 3D objects are not only computation-intensive but also memory heavy, hindering their deployment on resource-constrained mobile devices. We present the design, implementation and evaluation of Slimmer, a generic and model-independent framework for accelerating 3D semantic segmentation and facilitating its real-time applications on mobile devices. In contrast to the current practice that directly feeds a point cloud to DNN models, Slimmer is motivated by our observation that these models remain high accuracy even if we remove a fraction of points from the input, which can significantly reduce the inference time and memory usage of these models. Our design of Slimmer faces two key challenges. First, the simplification method of point clouds should be lightweight. Otherwise, the reduced inference time may be canceled out by the incurred overhead of input-data simplification. Second, Slimmer still needs to accurately segment the removed points from the input to create a complete segmentation of the original input, again, using a lightweight method. Our extensive performance evaluation demonstrates that, by addressing these two challenges, Slimmer can dramatically reduce the resource utilization of a representative DNN model for 3D semantic segmentation. For example, if we can tolerate 1% accuracy loss, the reduction could be ~20% for inference time and ~9% for memory usage. The reduction increases to around ~27% for inference time and ~15% for memory usage when we can tolerate 2% accuracy loss.

In this paper, we show that under over-parametrization several standard stochastic optimization algorithms escape saddle-points and converge to local-minimizers much faster. One of the fundamental aspects of over-parametrized models is that they are capable of interpolating the training data. We show that, under interpolation-like assumptions satisfied by the stochastic gradients in an over-parametrization setting, the first-order oracle complexity of Perturbed Stochastic Gradient Descent (PSGD) algorithm to reach an $\epsilon$-local-minimizer, matches the corresponding deterministic rate of $O(1/\epsilon^{2})$. We next analyze Stochastic Cubic-Regularized Newton (SCRN) algorithm under interpolation-like conditions, and show that the oracle complexity to reach an $\epsilon$-local-minimizer under interpolation-like conditions, is $O(1/\epsilon^{2.5})$. While this obtained complexity is better than the corresponding complexity of either PSGD, or SCRN without interpolation-like assumptions, it does not match the rate of $O(1/\epsilon^{1.5})$ corresponding to deterministic Cubic-Regularized Newton method. It seems further Hessian-based interpolation-like assumptions are necessary to bridge this gap. We also discuss the corresponding improved complexities in the zeroth-order settings.

Internet of Things (IoT) has been emerging as one of the most significant technologies that may change the world. The development and deployment of IoT systems significantly improve the efficiency of work, advance the development of productive force, and affect the human society's modes of production, working, and life deeply. The number of deployed IoT devices and applications has had explosive growth in the past years. While people enjoy the benefits brought by IoT, the addressing of its security and privacy issues does not keep pace with the development of IoT technologies. We find that encrypted wireless IoT traffic can still leak information about user privacy. We can infer what the user is doing at home by deploying a wireless sniffer outside the user's house. In this paper, we propose a method to eavesdrop different kinds of user privacy via analyzing the wireless context. First, we extract the packet sequence features to fingerprint and detect the IoT events. Then we analyze the detected IoT events and infer the user's activities and even the user's moods. Furthermore, by analyzing the wireless context, which is discovered by mining the IoT event dependencies, we can infer the user's living habits, routines, and even installed IoT applications. The leakage of such information not only exposes the user's privacy but also extends the attack surface that an attacker can utilize to control the smart home. We implement our eavesdropping approach and conduct extensive experiments on the SmartThings platform. The evaluation demonstrates the feasibility and effectiveness of our eavesdropping approach.

In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more security. The massive idle computing resources closing to the edge also enhance the deployment of edge services. Instead of using cloud services from some primary providers, edge computing provides people a great chance to join the market of computing resources actively. However, edge computing also has some critical impediments that we have to overcome.In this paper, we design an edge computing service platform that can receive and distribute the computing resources from the end-users in a decentralized way. Without the centralized trade control, we propose a novel Blockchain-enabled decentralized technique to establish the trade trust among users and implement it with using embedded immutable intermediary smart contract. Our system also considers and resolves a variety of security and privacy challenges when utilizing the Blockchain technique. We implement our system and conduct extensive experiments to show the feasibility and effectiveness of our proposed system.

As a massive number of the Internet of Things (IoT) devices are deployed, the security and privacy issues in IoT arouse more and more attention. The IoT attacks are causing tremendous loss to the IoT networks and even threatening human safety. Compared to traditional networks, IoT networks have unique characteristics, which make the attack detection more challenging. First, the heterogeneity of platforms, protocols, software, and hardware exposes various vulnerabilities. Second, in addition to the traditional high-rate attacks, the low-rate attacks are also extensively used by IoT attackers to obfuscate the legitimate and malicious traffic. These low-rate attacks are challenging to detect and can persist in the networks. Last, the attackers are evolving to be more intelligent and can dynamically change their attack strategies based on the environment feedback to avoid being detected, making it more challenging for the defender to discover a consistent pattern to identify the attack. In order to adapt to the new characteristics in IoT attacks, we propose a reinforcement learning-based attack detection model that can automatically learn and recognize the transformation of the attack pattern. Therefore, we can continuously detect IoT attacks with less human intervention. In this paper, we explore the crucial features of IoT traffics and utilize the entropy-based metrics to detect both the high-rate and low-rate IoT attacks. Afterward, we leverage the reinforcement learning technique to continuously adjust the attack detection threshold based on the detection feedback, which optimizes the detection and the false alarm rate. We conduct extensive experiments over a real IoT attack data set and demonstrate the effectiveness of our IoT attack detection framework.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses, etc. Such communication has a unique property that a user can visually identify a transmitter (i.e., LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement, etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 7.7 Kbps on Nexus 5, 3.7 Kbps on iPhone 5S, and 2.9 Kbps on Samsung Note8. It is also shown that lower CSK modulations (e.g., four and eight CSK) provide extremely low symbol error rates (-3), making them a desirable choice for reliable LED-to-camera communication.

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

Recently, millimeter-wave (mmWave) is augmenting popularity in wireless communications since it provides multi-Gbps throughput by exploiting the broad unlicensed 60 GHz spectrum. Highly directional adaptive beamforming antennas are inherently employed in 60GHz to counteract the severe propagation and penetration losses experienced in this spectrum. Compared to traditional omnidirectional antennas, beamforming introduces critical challenges in medium access control, especially in link establishment and maintenance. In particular, node mobility and object blockage become crucial, which necessitates frequent beam steering to re-establish links. The state-of-the-art protocols fail to jointly address both the mobility and blockage challenges. Their approaches trigger exhaustive beam search to pair antenna sectors on every link failure which produces an excessive delay that conceivably disrupts communication and lowers the quality-of-service(QoS). In this paper, we propose BeamSniff, a novel protocol that resolves the mobility and blockage issues jointly while sustaining a seamless communication. Upon link failure, it intelligently estimates the possible cause of link failure and instantly foresees plausible paths to recover the broken link, thereby eliminating the frequent exhaustive beam search which results in reducing the overhead for link maintenance. BeamSniff is assessed extensively in our custom-built 60GHz system platform with a ray-tracing based simulator under various indoor environments. The evaluation manifests the efficiency of the protocol in sustaining seamless communication along with multi-fold throughput gain compared to state-of-the-art protocols. We observe up to 14× throughput increase in typical scenarios involving motion and blockage.

This paper presents StrLight, the first practical VLC system that leverages widely-deployed string lights to transmit data. The data transmission is imperceptible to human eyes. Users can decode the data by mobile devices (e.g., smartphones) equipped with cameras. StrLight primarily differs from existing VLC systems in using string lights which are composed of a large number of small LEDs and thus the unique design to address practical issues including a special data modulation/encoding scheme, a data representation with unstructured/unknown topologies of LEDs in the string light, and a fault tolerance against broken and blocked LEDs. To the best of our knowledge, StrLight is the first practical VLC system of its kind. We build several prototypes of string light transmitters and test with different smartphone models and a customized mobile device as receivers. The experiment results show that StrLight provides an efficient and robust data broadcasting. A string light of 100 LEDs working in 450 Hz and a camera with a capture rate of 30 Hz and an image resolution of as low as 320 × 240 pixels, delivers data rate of ~1 kbps, without observable light flickers.

In this paper, we investigate whether IEEE 802.11ac WiFi can support VR applications. To this end we conduct a controlled study of WiFi performance in an indoor setting. Our measurements reveal that WiFi transmissions suffer from high latency and jitter, which makes WiFi systems inadequate for VR applications. For example, the round-trip delay can be as high as 228ms, and more than 24.2% packets experience jitter higher than 1ms. To locate the root cause of the high latency and jitter, we dissect the network stack layer by layer and find that the main culprit is the wireless channel transmission time. To reduce the channel transmission time, we propose using multiple network interfaces running on non-overlapping channels. By using only two interfaces, we (1) reduce the median round-trip delay by 28.6% and jitters of higher than 1ms by 11.5% compared to the best single interface in UDP transmissions, and (2) reduce the median round-trip delay by 38.9% in TCP transmissions. We believe that this paper sheds some light on whether we can make today's WiFi systems VR-ready by using multiple interfaces.

Recent observations have shown that most of the attacks are fruits of collaboration among attackers. In this work we have developed a coalition formation game to model the collusive behavior among attackers. The novelty of this work is that we are the first to investigate the coalition formation dynamics among attackers with different efficiency. Most of the related works have modeled the attacker as a single entity. We define a new parameter called friction to represent the unwillingness of an attacker to collude. We have shown that the proportion of attackers in the Maximum Average Payoff Coalition (MAPC) decreases with efficiency. We have also shown that as the friction increases, size and heterogeneity of MAPC decrease. We show, using text analysis on a hacker web forum chat data, that the hacker collaboration network shows a strong small-world characteristics. We identify the leaders in these coalitions. The cluster compositions of the hacker collaboration network agree with our model. We also develop method to estimate the friction parameters for the attackers to decide optimal coalition to join. As this model provides insight into coalition formation among attackers, e.g., leaders, composition, and homogeneity, this model will be helpful to develop better defender strategies.

Bluetooth low energy (BLE) based devices are already deployed in massive quantity as Internet-of-things (IoT) becomes prominent in the last two decades. In order to lower the energy consumption, BLE devices have to compromise with security and privacy problems. Existing research work shows that BLE devices can be easily spoofed and leveraged to gain access to a networking system. In this paper, we propose BF-IoT, the first IoT secure communication framework for BLE-based networks that guards against device spoofing via monitoring the work-life cycles of devices. We dig into the BLE protocol stack and extract the unique network-flow features from the link layer and ATT/GATT service layer so as to generate the fingerprints for device authentication. BF-IoT provides two-phase defense against malicious entities: continuously authenticating device identity before the connection setup and during session establishment. We build a customized system to validate the effectiveness of our mechanism. We extensively evaluate BF-IoT with a dozen of different off-the-shelf commodity IoT devices which shows that the devices can be accurately authenticated via only sniffing the transmission characteristics.

60 GHz millimeter-wave networks have emerged as a potential candidate for designing the next generation of multi-gigabit WLANs. Since the 60 GHz links suffer from frequent outages due to blockages caused by human mobility, deploying 60 GHz WLANs that can provide robust coverage in presence of blockages is a challenging problem. In this paper, we study blockage-aware coverage and deployment of 60 GHz WLANs. We first show that the reflection profile of an indoor environment can be sensed using a few measurements. A novel coverage metric (angular spread coverage) which captures the number of available paths and their spatial diversity is proposed. Additionally, it is shown that using relays can extend the coverage of the AP at a lower cost and provide added spatial diversity in the available paths. We propose a heuristic algorithm that determines the AP and relay locations while maximizing the angular spread coverage metric for the clients. Our testbed-based evaluation shows that for five different rooms, our proposed deployment can guarantee an average connectivity of 91.7%, 83.9%, and 74.1% of client locations in the presence of 1, 3 and 5 concurrent human blockages respectively, substantially increasing the robustness of 60 GHz links against blockages.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

This paper presents DopEnc, an acoustic-based encounter profiling system on commercial off-the-shelf smartphones. DopEnc automatically identifies the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones, and accelerometers integrated on mainstream smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9 percent false positive and 9.7 percent false negative in real usage.

Cybersecurity is increasingly threatened by advanced and persistent attacks. As these attacks are often designed to disable a system (or a critical resource, e.g., a user account) repeatedly, it is crucial for the defender to keep updating its security measures to strike a balance between the risk of being compromised and the cost of security updates. Moreover, these decisions often need to be made with limited and delayed feedback due to the stealthy nature of advanced attacks. In addition to targeted attacks, such an optimal timing policy under incomplete information has broad applications in cybersecurity. Examples include key rotation, password change, application of patches, and virtual machine refreshing. However, rigorous studies of optimal timing are rare. Further, existing solutions typically rely on a pre-defined attack model that is known to the defender, which is often not the case in practice. In this work, we make an initial effort towards achieving optimal timing of security updates in the face of unknown stealthy attacks. We consider a variant of the influential FlipIt game model with asymmetric feedback and unknown attack time distribution, which provides a general model to consecutive security updates.The defender's problem is then modeled as a time associative bandit problem with dependent arms. We derive upper confidence bound based learning policies that achieve low regret compared with optimal periodic defense strategies that can only be derived when attack time distributions are known.

Location data collection at a societal scale is increasingly becoming common - examples of this are call and data detail records in telecommunication companies, GPS samples collected by car companies, and GPS samples from mobile devices in mapping companies (e.g., Google, Microsoft). Such large scale mobility datasets have applications in urban planning, network planning, surveillance, and real-time traffic estimations. This paper addresses the problem of subsampling location traces while preserving the amount of information present in such datasets. We present a novel subsampling technique that is based on a hierarchical geographical encoding mechanism (geohash), that allows for efficient spatial cluster sampling. We analyze this subsampling technique through various information theoretic measures to quantify the total "amount" of information in a dataset from a location trace perspective and evaluate these metrics in the context of two large scale mobility datasets from telecommunication companies - one is that of call detail records and the second is that of data detail records. We show that subsampling data in both these cases by as much as 75% does not significantly reduce the total amount of information, i.e. the dataset can be used similar to the original version. This paves way for the creation of better space and CPU efficient models that can support various applications reliant on collective location traces.

Evolved-Multimedia Broadcast Multicast Services (eMBMS) is a set of features in LTE networks to deliver bandwidth-intensive multimedia content on a point-to-multipoint basis to subscribers. The notion of a Single Frequency Network (SFN) in eMBMS allows base stations to synchronize and transmit signals in a coordinated fashion across the same frequency-time radio resources using a common modulation rate. While SFN boosts the channel quality of users via transmit diversity gain, the use of a common rate across base stations results in reduced utilization for those that can individually support much higher data rates for their users, even without the notion of an SFN. Excluding such base stations from the SFN helps them utilize their resources better by not being constrained by the common rate, but creates additional inter-cell interference from their independent transmissions. Striking a balance between SFN cooperation and resource utilization is crucial for efficiently delivering broadcast content as well as other unicast flows. We design BoLTE, which carefully addresses this tradeoff and evaluate it using a prototype implementation over an SFN testbed, realized over a cloud-based radio access network system, as well as large-scale NS3 simulations. We show that BoLTE improves overall system throughput by around 40%.

Incentive-driven advanced attacks have become a major concern to cyber-security. Traditional defense techniques that adopt a passive and static approach by assuming a fixed attack type are insufficient in the face of highly adaptive and stealthy attacks. In particular, a passive defense approach often creates information asymmetry where the attacker knows more about the defender. To this end, moving target defense (MTD) has emerged as a promising way to reverse this information asymmetry. The main idea of MTD is to (continuously) change certain aspects of the system under control to increase the attacker's uncertainty, which in turn increases attack cost/complexity and reduces the chance of a successful exploit in a given amount of time. In this paper, we go one step beyond and show that MTD can be further improved when combined with information disclosure. In particular, we consider that the defender adopts a MTD strategy to protect a critical resource across a network of nodes, and propose a Bayesian Stackelberg game model with the defender as the leader and the attacker as the follower. After fully characterizing the defender's optimal migration strategies, we show that the defender can design a signaling scheme to exploit the uncertainty created by MTD to further affect the attacker's behavior for its own advantage. We obtain conditions under which signaling is useful, and show that strategic information disclosure can be a promising way to further reverse the information asymmetry and achieve more efficient active defense.

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 presents iType, a system that uses eye gaze for typing private information on commodity mobile platforms. The design combats three primary challenges: 1) relatively low accuracy of mobile gaze tracking; 2) difficulties in correcting input errors due to lacking the comparison with the true text-entry value; and 3) device motions and other noises that may interfere gaze tracking accuracy and thus the iType performance. We devise a set of effective techniques, including leveraging a collective behavior of the gaze tracking results, unique correlation of the typing error spatial distributions, and motion sensor hints from mobile devices, to address above challenges. A set of enhancement techniques are applied to further improve iType's robustness and reliability. We consolidate above designs and implement iType on iOS platform. Evaluations show that iType achieves high keystroke detection accuracy for the secure typing within a reasonable short latency.

Wireless cameras can be used to gather situation awareness information (e.g., humans in distress) in disaster recovery scenarios. However, blindly sending raw video streams from such cameras, to an operations center or controller can be prohibitive in terms of bandwidth. Further, these raw streams could contain either redundant or irrelevant information. Thus, we ask "how do we extract accurate situation awareness information from such camera nodes and send it in a timely manner, back to the operations center?" Towards this, we design ACTION, a framework that (a) detects objects of interest (e.g., humans) from the video streams, (b) combines these streams intelligently to eliminate redundancies and (c) transmits only parts of the feeds that are sufficient in achieving a desired detection accuracy to the controller. ACTION uses small amounts of metadata to determine if the objects from different camera feeds are the same. A resource-aware greedy algorithm is used to select a subset of video feeds that are associated with the same object, so as to provide a desired accuracy, for being sent to the operations center. Our evaluations show that ACTION helps reduce the network usage up to threefold, and yet achieves a high detection accuracy of ≈ 90%.

Major Wi-Fi Access Point (AP) vendors worldwide seek to provide gigabit wireless connectivity, by densely deploying MU-MIMO capable APs, which can support multiple, concurrent data streams to a group of clients, connected to them. However, MU-MIMO gains can only be achieved if an AP can identify groups of clients with homogenous configurations and orthogonal wireless channels, where concurrent transmissions will not cause inter-client interference. Hence, MU-MIMO performance is fundamentally depending on how the clients are assigned to APs. Our experiments with 802.11ac commodity testbeds show that state-of-the-art client assignment algorithms are MU-MIMO oblivious and limit the MU-MIMO grouping opportunities in realistic settings. In this paper, we design and implement MAPS, an MU-MIMO-Aware AP Selection algorithm that is 802.11-compliant and can boost network's MU-MIMO throughput gains. We verified MAPS' gains over legacy designs via extensive experiments with 802.11ac commodity testbeds.

User's personal portable devices such as smartphone, tablet and laptop require continuous authentication of the user to prevent against illegitimate access to the device and personal data. Current authentication techniques require users to enter password or scan fingerprint, making frequent access to the devices inconvenient. In this work, we propose to exploit user's on-body wearable devices to detect their proximity from her portable devices, and use the proximity for continuous authentication of the portable devices. We present PCASA which utilizes acoustic communication for secure proximity estimation with sub-meter level accuracy. PCASA uses Differential Pulse Position Modulation scheme that modulates data through varying the silence period between acoustic pulses to ensure energy efficiency even when authentication operation is being performed once every second. It yields an secure and accurate distance estimation even when user is mobile by utilizing Doppler effect for mobility speed estimation. We evaluate PCASA using smartphone and smartwatches, and show that it supports up to 34 hours of continuous authentication with a fully charged battery.

Estimation of building occupancy has emerged as an important research problem with applications ranging from building energy efficiency, control and automation, safety, communication network resource allocation, etc. In this research work, we propose the estimation of occupancy using non-intrusive information that is already available from existing sensing modes, namely, number of WiFi devices, electrical energy demand and water consumption rate. Using data collected from 76 buildings in a university campus, we study the feasibility of multi-modal fusion between the three data sources for estimating fine-grained occupancy. In order to make the estimation model scalable, we propose three different clustering schemes to identify similarity in building characteristics and training per-cluster occupancy estimation models. The presented multi-modal fusion estimation framework achieves a mean absolute percentage error of 13.22% and we find that leveraging all three modalities provide an improvement of 48% in accuracy as compared to WiFi-only occupancy estimation. Our evaluation also shows that clustering buildings greatly increases the scalability of the proposed approach through significant reduction in training overhead, while providing an accuracy comparable to exhaustive, per-building estimation models.

Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.

Presents the farewell address from the editor of this publication.

Today, many smartphone users are unaware of what applications (apps) they should stop using to prevent their battery from running out quickly. The problem is identifying such apps is hard due to the fact that there exist hundreds of thousands of apps and their impact on the battery is not well understood. We show via extensive measurement studies that the impact of an app on battery consumption depends on both environmental (wireless) factors and usage patterns. Based on this, we argue that there exists a critical need for a tool that allows a user to: 1) identify apps that are energy hungry and 2) understand why an app is consuming energy, on her phone. Toward addressing this need, we present TIDE, a tool to detect high energy apps on any particular smartphone. TIDE's key characteristic is that it accounts for usage-centric information while identifying energy hungry apps from among a multitude of apps that run simultaneously on a user's phone. Our evaluation of TIDE on a test bed of Android-based smartphones, using week-long smartphone usage traces from 17 real users, shows that TIDE correctly identifies over 94% of energy-hungry apps and has a false positive rate of <; 6%.

Continuous monitoring of human’s breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This article presents mmVital, a system that uses 60GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human’s body and the Received Signal Strength (RSS) of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital also provides sleep monitoring with sleeping posture identification and detection of central apnea and hypopnea events. It relies on a novel human finding procedure where a human can be located within a room by reflection loss-based object/human classification. We evaluate mmVital using a 60GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 breaths per minute (Bpm; breathing rate) and 2.15 beats per minute (bpm; heart rate). Also, it can locate the human subject with 98.4% accuracy within 100ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind a wall.

The increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users’ quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords, or similar apps being made available several times and across diverse categories. Market operators maintain antispam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% and 95% and recall varying between 38% and 98%. We further show that a limited number of features, in the range of 10--30, generated from app metadata is sufficient to achieve a satisfactory level of performance. On a set of 180,627 apps that were present at the app market during our crawl, our classifier predicts 2.7% of the apps as potential spam. Finally, we perform additional manual verification and show that human reviewers agree with 82% of our classifier predictions.

Synchronizing videos over file-hosting services on personal cloud such as Dropbox, Box or Onedrive leads to wastage in bandwidth and storage, which can be critical, while using mobile devices. Users can alternatively download the video on-the-go, but that leads to high latency, depending on network bandwidth and video file size. In contrast, adaptive video streaming allows near-real-time viewing by streaming the best possible quality in a given network condition. This feature is achieved by keeping multiple versions of video in cloud, leading to additional costs in cloud storage. Moreover, current solutions can only support a small set of bitrates, leading to abrupt switches in video resolution especially when the network condition is unstable, as often experienced by mobile users. This paper introduces Vsync, a framework for cloud based video synchronization for mobile devices. A video content is streamed using a cloud-based real-time transcoding and transmission framework to provide smooth video quality. Built over prediction models for video transcoding sessions and a QoE based adaptive video streaming protocol, Vsync is able to obtain the improvements of 37 ~ 80% than other compared schemes. The dataset and evaluation was done on a pool of 220K video clips.

LTE-Advanced (LTE-A) macro-cell deployments are being enhanced with small cells, i.e., low-power base stations, to increase the network coverage and capacity. However, simultaneous co-channel transmissions from macro and small cells cause increased inter-cell interference and under-utilize the spectrum resources at the small cells. The following LTE-A design techniques are used to improve system performance in such deployments: (i) Carrier Aggregation (CA) to increase capacity by using additional carrier bandwidth; (ii) enhanced Inter-Cell Interference Coordination (elCIC), that includes (a) Cell Selection Biasing (CSB) to increase small cell spectrum utilization via cell range expansion; and (b) blanking data transmission on the macro cells for a certain duration of time to increase cell-edge user throughput Our objective is to maximize the CSB of the small cell, subject to user QoS constraints and blanking support from the macro cell. Towards this end, we develop an analytical model that captures the inter-dependency between elCIC techniques. We observe that, not accounting for the complex inter-dependencies between these techniques leads to a degraded network performance. We propose a framework that jointly optimizes elCIC and the assignment of multiple component carriers in an LTE-A deployment for increasing spectrum utilization at the small cells with appropriate blanking support from the macro cells. Our simulation results show that our approach increases the small cell spectrum utilization and aggregate cell-edge throughput by as much as 200%.

There has been a growing interest in equipping the objects and environment surrounding the user with sensing capabilities. Smart indoor spaces such as smart homes and offices can implement the sensing and processing functionality, relieving users from the need of wearing or carrying smart devices. Enabling such smart spaces requires device-free effortless sensing of user's identity and activities. Device-free sensing using WiFi has shown great potential in such scenarios, however, fundamental questions such as person identification have remained unsolved. In this paper, we present WiWho, a framework that can identify a person from a small group of people in a device-free manner using WiFi. We show that Channel State Information (CSI) used in recent WiFi can identify a person's steps and walking gait. The walking gait being distinguishing characteristics for different people, WiWho uses CSI-based gait for person identification. We demonstrate how step and walk analysis can be used to identify a person's walking gait from CSI, and how this information can be used to identify a person. WiWho does not require a person to carry any device and is effortless since it only requires the person to walk for a few steps (e.g. entering a home or an office). We evaluate WiWho using experiments at multiple locations with a total of 20 volunteers, and show that it can identify a person with average accuracy of 92% to 80% from a group of 2 to 6 people. We also show that in most cases walking as few as 2-3 meters is sufficient to recognize a person's gait and identify the person. We discuss the potential and challenges of WiFi- based person identification with respect to smart space applications.

We propose human mobility tracking and activity monitoring using 60 GHz millimeter wave (mmWave). We discuss the benefits of using mmWave signals for the purpose over existing 2.4/5 GHz based techniques. We also identify related challenges of determining human's initial location and tracking, and demonstrate the feasibility of activity monitoring using an example of walking activity.

This paper presents DopEnc, an acoustic-based encounter profiling system on smartphones. DopEnc can automatically identify the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones and accelerometers integrated on commercial-off-the-shelf smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9% false positive and 9.7% false negative in real usage.

Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of- concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

Context-aware services nowadays offer incentive to user-reported context information , which inevitably solicits malicious users to cheat by submitting fabricated context claims. Conventional countermeasures based on Trusted Computing Base typically focus on particular context of interest, while disregarding the availability of various types of context information and the intrinsic correlation among them. In this work we propose a context claim verification scheme that interrogates correlated contexts of multiple dimensions to corroborate or contradict the reported context. Specifically, it first learns and models the context correlation with a Bayesian Multinet. Given a claim consisting of reported context and witnessing evidence, the scheme performs Bayesian inference with the evidence to verify the reported context. The verification process is light-weight, and can be applied to arbitrary types of context with a single model learnt. Evaluations on Reality Mining dataset and synthetic dataset validates choice of Multinet for data modeling, and demonstrate the feasibility of our scheme in context verification.

In vehicular networks, broadcast communications are critically important, as many safety-related applications rely on single-hop beacon messages broadcast to neighbor vehicles. However, it becomes a challenging problem to design a broadcast authentication scheme for secure vehicle-to-vehicle communications. Especially when a large number of beacons arrive in a short time, vehicles are vulnerable to computation-based Denial of Service (DoS) attacks that excessive signature verification exhausts their computational resources. In this paper, we propose an efficient broadcast authentication scheme called Prediction-Based Authentication (PBA) to not only defend against computation-based DoS attacks, but also resist packet losses caused by high mobility of vehicles. In contrast to most existing authentication schemes, our PBA is an efficient and lightweight scheme since it is primarily built on symmetric cryptography. To further reduce the verification delay for some emergency applications, PBA is designed to exploit the sender vehicle’s ability to predict future beacons in advance. In addition, to prevent memory-based DoS attacks, PBA only stores shortened re-keyed Message Authentication Codes (MACs) of signatures without decreasing security. We analyze the security of our scheme and simulate PBA under varying vehicular network scenarios. The results demonstrate that PBA fast verifies almost 99 percent messages with low storage cost not only in high-density traffic environments but also in lossy wireless environments.

With the prevalence of mobile computing, lots of wireless devices need to establish secure communication on the fly without pre-shared secrets. Device pairing is critical for bootstrapping secure communication between two previously unassociated devices over the wireless channel. Using auxiliary out-of-band channels involving visual, acoustic, tactile, or vibrational sensors has been proposed as a feasible option to facilitate device pairing. However, these methods usually require users to perform additional tasks, such as copying, comparing, and shaking. It is preferable to have a natural and intuitive pairing method with minimal user tasks. In this paper, we introduce a new method, called MagPairing, for pairing smartphones in close proximity by exploiting correlated magnetometer readings. In MagPairing, users only need to naturally tap the smartphones together for a few seconds without performing any additional operations in authentication and key establishment. Our method exploits the fact that smartphones are equipped with tiny magnets. Highly correlated magnetic field patterns are produced when two smartphones are close to each other. We design MagPairing protocol and implement it on Android smartphones. We conduct extensive simulations and real-world experiments to evaluate MagPairing. Experiments verify that the captured sensor data on which MagPairing is based has high entropy and sufficient length, and is nondisclosure to attackers more than few centimeters away. Usability tests on various kinds of smartphones by totally untrained users show that the whole pairing process needs only 4.5 s on average with more than 90% success rate.

The number of wireless-enabled devices owned by a user has had huge growth over the past few years. Over one third of adults in the United States currently own three wireless devices: a smartphone, laptop, and tablet. This article provides a study of the network usage behavior of today’s multidevice users. Using data collected from a large university campus, we provide a detailed multidevice user (MDU) measurement study of more than 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affects the network usage behavior of users. Specifically, we characterize the usage pattern of the different device types in terms of total and intermittent usage, how the usage of different devices overlap over time, and uncarried device usage statistics. We also study user preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about MDUs. We see how the use of tablets and laptops are interchangeable and how the overall multidevice usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices, which results in inefficient utilization of available IP address space. All findings about multidevice usage patterns have the potential to be utilized by different entities, such as app developers, network providers, security researchers, and analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two devices among a smartphone, tablet, and laptop.

Many smartphone applications, e.g., file backup, are intrinsically delay-tolerant so that data processing and transfer can be delayed to reduce smartphone battery usage. In the literature, these energy-delay tradeoff issues have been addressed independently in the forms of Dynamic Voltage and Frequency Scaling (DVFS) problems and network selection problems when smartphones have multiple wireless interfaces. In this paper, we jointly optimize the CPU speed and network speed to determine how much more energy can be saved through the joint optimization when applications can tolerate delays. We propose a dynamic speed scaling scheme called SpeedControl that jointly adjusts the processing and networking speeds using four controls: application scheduling, CPU speed control, wireless interface selection, and transmit power control. Through invoking the “Lyapunov drift-plus-penalty” technique, the scheme is demonstrated to be near optimal because it substantially reduces energy consumption for a given delay constraint. This paper is the first to reveal the energy-delay tradeoff relationship from a holistic perspective for smartphones with multiple wireless interfaces, DVFS, and multitasking capabilities. The trace-driven simulations based on real measurements of CPU power, network power, WiFi/3G throughput, and CPU workload demonstrate that SpeedControl can reduce battery usage by more than 42% through trading a 10 minutes delay when compared with the same delay in existing schemes; moreover, this energy conservation level increases as the WiFi coverage extends.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high ( > 0.9) collusion detection accuracy.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (≈ 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (< 10--3), making them a desirable choice for reliable LED-to-camera communication.

Energy Expenditure (EE) Estimation is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EE estimation using small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of individuals wearing mobile sensors. We use Convolution Neural Networks (CNNs) to automatically detect important features from data collected from triaxial accelerometer and heart rate sensors. Using CNNs, we find a significant improvement in EE estimation compared to other state-of-the-art models. We compare our results against state-of-the-art Activity-Specific Linear Regression as well as Artificial Neural Networks (ANN) based models. Using a universal CNN model, we obtain an overall low Root Mean Square Error (RMSE) of 1.12 which is 30% and 35% lower than existing models. The results were calibrated against a COSMED K4b2 indirect calorimeter readings.

Building security systems are commonly deployed to detect intrusion and burglary in home and business structures. Such systems can accurately detect door open/close events, but their high-cost of installation and maintenance makes them unsuitable for certain building monitoring applications, such as times of high/low entrance traffic, estimating building occupancy, etc. In this paper, we show that barometer sensors found in latest smartphones can directly detect the building door open/close events anywhere inside an insulated building. The sudden pressure change observed by barometers is sufficient to detect events even in presence of user mobility (e.g. climbing stairs). We study various characteristics of the pressure variation due to door events, and demonstrate that door open/close events can be recognized with an accuracy range of 99.34% -- 99.81% based on the data collected from 3 different buildings. Such a low-cost ubiquitous solution of door event detection enables many monitoring applications without any infrastructure integration, and it can also work as an augmentation to the existing expensive security systems.

Mobile advertisements have become the dominant source of revenue for mobile application developers, advertisers and brokers. Using novel sensing techniques and the advanced sensors of mobile devices, it has become feasible to determine a user's fine-grained context such as her location, activity, and interests. This information can be used by the advertisement (ad) brokers to provide more relevant ads to the user based on her context. However, this has led to serious privacy risks, since a user can be tracked by the broker or an adversary based on her context. In this paper, we present AnonAd, an ad delivery scheme that allows users to protect their privacy when receiving micro-targeted ads from the broker. AnonAd utilizes the encryption of the user's context based on a split-secret scheme that guarantees that the broker can decrypt the context only when there exists k other users in the same context. This way, a user's privacy is protected with k-anonymity during the context report. We show that the split-secret scheme integrates seamlessly with existing homomorphic encryption-based schemes that can provide differential privacy for ad click reports. We implement AnonAd on Android smartphones and evaluate it with real users as well as simulated users that follow real mobility traces. Our results show that AnonAd achieves a balance between user's privacy and relevancy of advertisements without the requirement of any additional proxy servers.

Today, many smartphone users are unaware of what applications (apps) they should stop using to prevent their battery from running out quickly. The problem is identifying such apps is hard due to the fact that there exist hundreds of thousands of apps and their impact on the battery is not well understood. We show via extensive measurement studies that the impact of an app on battery consumption depends on both environmental (wireless) factors and usage patterns. Based on this, we argue that there exists a critical need for a tool that allows a user to (a) identify apps that are energy hungry, and (b) understand why an app is consuming energy, on her phone. Towards addressing this need, we present TIDE, a tool to detect high energy apps on any particular smartphone. TIDE's key characteristic is that it accounts for usage-centric information while identifying energy hungry apps from among a multitude of apps that run simultaneously on a user's phone. Our evaluation of TIDE on a testbed of Android-based smartphones, using weeklong smartphone usage traces from 17 real users, shows that TIDE correctly identifies over 94% of energy-hungry apps and has a false positive rate of <; 6%.

The quality of mobile videos is usually quantified through the Quality of Experience (QoE), which is usually based on network QoS measurements, user engagement, or post-view subjective scores. Such quantifications are not adequate for real-time evaluation. They cannot provide on-line feedback for improvement of visual acuity, which represents the actual viewing experience of the end user. We present a visual acuity framework which makes fast online computations in a mobile device and provide an accurate estimate of mobile video QoE. We identify and study the three main causes that impact visual acuity in mobile videos: spatial distortions, types of buffering and resolution changes. Each of them can be accurately modeled using our framework. We use machine learning techniques to build a prediction model for visual acuity, which depicts more than 78% accuracy. We present an experimental implementation on iPhone 4 and 5s to show that the proposed visual acuity framework is feasible to deploy in mobile devices. Using a data corpus of over 2852 mobile video clips for the experiments, we validate the proposed framework.

The landscape of cyber security has been reformed dramatically by the recently emerging Advanced Persistent Threat (APT). It is uniquely featured by the stealthy, continuous, sophisticated and well-funded attack process for long-term malicious gain, which render the current defense mechanisms inapplicable. A novel design of defense strategy, continuously combating APT in a long time-span with imperfect/incomplete information on attacker's actions, is urgently needed. The challenge is even more escalated when APT is coupled with the insider threat (a major threat in cyber-security), where insiders could trade valuable information to APT attacker for monetary gains. The interplay among the defender, APT attacker and insiders should be judiciously studied to shed insights on a more secure defense system. In this paper, we consider the joint threats from APT attacker and the insiders, and characterize the fore-mentioned interplay as a two-layer game model, i.e., a defense/attack game between defender and APT attacker and an information-trading game among insiders. Through rigorous analysis, we identify the best response strategies for each player and prove the existence of Nash Equilibrium for both games. Extensive numerical study further verifies our analytic results and examines the impact of different system configurations on the achievable security level.

LTE network service reliability is highly dependent on the wireless coverage that is provided by cell towers (eNB). Therefore, the network operator's response to outage scenarios needs to be fast and efficient, in order to minimize any degradation in the Quality of Service (QoS). In this paper, we propose an outage mitigation framework for LTE-Advanced (LTE-A) wireless networks. Our framework exploits the inherent design features of LTE-A; it performs a dual optimization of the transmission power and beamforming weight parameters at each neighbor cell sector of the outage eNBs, while taking into account both the channel characteristics and residual eNB resources, after serving its current traffic load. Assuming statistical Channel State Information about the users at the eNBs, we show that this problem is theoretically NP-hard; thus we relax it as a convex optimization problem and solve for the optimal points using an iterative algorithm. Contrary to previously-proposed power control studies, our framework is specifically designed to alleviate the effects of sudden LTE-A eNB outages, where a large number of mobile users need to be efficiently offloaded to nearby towers. We present the detailed analytical design of our framework, and we assess its efficacy via extensive NS-3 simulations on an LTE-A topology. Our simulations demonstrate that our framework provides adequate coverage and QoS across all examined outage scenarios.

Smartphones are closely coupled with their users and smartphone sensors can perceive users' private information. The existing studies in this area focus on user activity recognition and short-term context detection. In this paper, we show that smartphone sensors are able to profile users' long-term privacy and more sensitive information. We present the techniques of discovering users' spending level by merely using smartphone sensors. We do not access users' contacts, calendar, or call log, so that the profiling is performed in a non-intrusive manner. This paper is an alert towards the public that the privacy leakage could be far worse than imagination by just carrying smartphones.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

The potential to provide multi-gbps throughput has made 60 GHz communication an attractive choice for next-generation WLANs. Due to highly directional nature of the communication, a 60 GHz link faces frequent outages in the presence of mobility. In this work, we present a sensor-assisted multi-level codebook-based beam width adaptation and beam switching to address the mobility challenges in 60 GHz WLANs. First, we show that by combining antenna element selection with codebook design, it is possible to generate a multilevel codebook that can cover different beam forming directions with many possible beam widths and directive gain. Second, we propose that accelerometer and magnetometer sensors which are commonly available on mobile devices can be used to better account for mobility, and perform near-real time beam width adaptation and beam switching. We evaluate the sensor-assisted multi-level codebook-based beam forming with trace-driven simulations using real mobility traces. Numeric evaluation shows that such beam forming can maintain the connectivity over 84% of the time even in presence of high device mobility.

Voice control has emerged as a popular method for interacting with smart-devices such as smartphones, smartwatches etc. Popular voice control applications like Siri and Google Now are already used by a large number of smartphone and tablet users. A major challenge in designing a voice control application is that it requires continuous monitoring of user?s voice input through the microphone. Such applications utilize hotwords such as "Okay Google" or "Hi Galaxy" allowing them to distinguish user?s voice command and her other conversations. A voice control application has to continuously listen for hotwords which significantly increases the energy consumption of the smart-devices.

No abstract available.

Mobile device users use applications that require text input. Today there are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each of these input modalities have different energy demands, and as a result, their use will have a significant impact on the battery life of the mobile device. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three text input modalities. We show that the length of interaction determines the most energy efficient modality. If the interactions is short, on average less than 30 characters, using the device SK is the most energy efficient. For longer interactions, the use of a STT applications is more energy efficient. Swype is more energy efficient than STT for very short interactions, less than 5 characters on average, but is never as efficient as SK. This is primarily due to STT enabling the users to complete tasks more quickly than when using SK or Swype. We also show that these results are independent of “user style”, the experience of using different input modalities and device characteristics. Finally we show that STT energy efficiency is dependent on application logic of whether speech samples are for a given period of time before transmitting to a server for analysis as opposed to streaming the speech to a sever for analysis. Based on these observations we recommend that the users should use SK for short interactions of less than 30 characters, and STT for longer interactions. In addition, they should use STT applications which uses storing and transmit logic, if they are willing to trade off battery life to QoE. Finally we proposed the development of an adaptive storing and analyze STT to improve the energy efficiency of it.

There has been a huge growth in the number of wireless-enabled devices possessed by a user. Over two third of adults in United States currently own three devices - laptop, smartphone and tablet. In this paper, we provide a first look at the network usage behavior of today's multi-device users. Using the data collected from a large university campus, we provide a detailed measurement-based characterization study of over 30,000 users. Our objective is to understand how existence of multiple wireless devices affect the network usage behavior of users. Specifically, we study the usage pattern of devices, how the usage of difference devices overlap in time, user's preferences of accessing sensitive content and device-specific factors that govern their choice of WiFi encryption type. The study reveals numerous interesting findings such as how current DHCP configurations are oblivious to multiple devices which results in inefficient utilization of available IP address space.

Wearable smart devices such as smart-glasses, smart-watches and life-logging devices are becoming increasingly popular, and majority of them are being equipped with first-person cameras. Such first-person cameras on smart-glasses or lifeloggers capture photos/videos from user's point of view, allowing them to record and share user's everyday events. However, these wearable devices with first-person cameras raise serious privacy concerns because they can also capture extremely private moments and sensitive information of the user. Currently, such devices lack the intelligence to understand user's preferences about certain scenarios being sensitive/private. To address this problem, we present PriFir, a scheme that enables Privacy-preserving First-person cameras. PriFir is based on the idea that low-power sensors (e.g. accelerometer, light sensor, etc.) embedded in smartphones and smart-watches can be leveraged to identify sensitive scenarios. Learning from user's preferences, PriFir employs a cascade of classifiers that tags a scenario to be sensitive simply based on the characteristics of the low-power sensor data. We evaluate PriFir using real sensor traces spanning over multiple days and show that it performs highly accurate classification at a low energy cost.

The IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks established itself as a leading forum for the presentation and exchange of ideas and results among researchers and practitioners in the field of wireless, mobile, and multimedia systems.

Increased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam apps, which reduce the users' quality of experience and increase the workload of app market operators. Apps can be "spammy" in multiple ways including not having a specific functionality, unrelated app description or unrelated keywords and publishing similar apps several times and across diverse categories. Market operators maintain anti-spam policies and apps are removed through continuous human intervention. Through a systematic crawl of a popular app market and by identifying a set of removed apps, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. Finally, we build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy over 95% with precision varying between 85%-95% and recall varying between 38%-98%. By applying the classifier on a set of apps present at the app market during our crawl, we estimate that at least 2.7% of them are spam apps.

Video source identification is very important in validating video evidence, tracking down video piracy crimes, and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method that is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed. We conduct extensive real-world experiments to validate our method. The results show that the source identification accuracy of the proposed scheme largely outperforms the existing methods in the presence of video blocking and blurring. Moreover, our method is able to identify the video source in a near-real-time fashion, which can be used to detect the wireless camera spoofing attack.

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It is understood from past decade of research that a wireless multi-hop network can achieve maximum network throughput only when its nodes operate at a minimum common transmission power level that ensures network connectivity (availability). This point of optimality where maximum availability and throughput is guaranteed in an interference-optimal network has been the basis of numerous design problems in wireless networks. In this paper, we claim that when performability (availability weighted performance) is considered as opposed to average case throughput performance, there does not exist a transmission power (or node density) that can maximize both availability and performability. Since the current mesh networks are expected to deliver carrier-grade services to its users, the availability-performability tradeoff presented in this paper holds a special importance. While availability metric is a necessary one for any networking system intended to provide continuous service, past research has shown a strong correlation between performability and quality of user experience in case of wireless networks. The contributions of the paper are as follows: (1) We first define availability and performability in the context of wireless mesh networks, and then develop efficient algorithms on the basis of intelligent state sampling that can calculate both the quantities with reasonable accuracy. (2) We apply the evaluation methods to two existing mesh networks (GoogleWiFi and PoncaCityMesh) to demonstrate that their current design can not guarantee a reasonable level of availability or performability. (3) Using hundreds of hours of simulations, we analyze the impact of two basic deployment factors (node density and transmission power) on availability and performability. We outline numerous novel results that emerge due to joint availability-performability analysis including the observation about availability-performability tradeoff.

Wireless communication systems are highly prone to channel errors. With video being a major player in Internet traffic and undergoing exponential growth in wireless domain, we argue for the need of a Video-aware MAC (VMAC) to significantly improve the throughput and delay performance of real-time video streaming service. VMAC makes two changes to optimize wireless LAN for video traffic: (a) It incorporates a Perceptual-Error-Tolerance (PET) to the MAC frames by reducing MAC retransmissions while minimizing any impact on perceptual video quality; and (b) It uses a group NACK-based Adaptive Window (NAW) of MAC frames to improve both throughput and delay performance in varying channel conditions. Through simulations and experiments, we observe 56--89% improvement in throughput and 34--48% improvement in delay performance over legacy DCF and 802.11e schemes. VMAC also shows 15--78% improvement over legacy schemes with multiple clients.

In recent times we have witnessed the emergence of large online markets with two-sided preferences that are responsible for businesses worth billions of dollars. Recommendation systems are critical components of such markets. It is to be noted that the matching in such a market depends on the preferences of both sides, consequently, the construction of a recommendation system for such a market calls for consideration of preferences of both sides. The online dating market, and the online freelancer market are examples of markets with two-sided preferences. Recommendation systems for such markets are fundamentally different from typical rating based product recommendations. We pose this problem as a bipartite ranking problem. There has been extensive research on bipartite ranking algorithms. Typically, generalized linear regression models are popular methods of constructing such ranking on account of their ability to be learned easily from big data, and their computational simplicity on engineering platforms. However, we show that for markets with two sided preferences, one can improve the AUC (Area Under the receiver operator Curve) score by considering separate models for preferences of both the sides and constructing a two layer architecture for ranking. We call this a two-level model algorithm. For both synthetic and real data we show that the two-level model algorithm has a better AUC performance than the direct application of a generalized linear model such as L1logistic regression or an ensemble method such as random forest algorithm. We provide a theoretical justification of AUC optimality of two-level model and pose a theoretical problem for a more general result.

The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery based on user's location. Since sharing precise location remains a major privacy concern among the users, many location-based services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100 gigabytes of total data. Our results show that PACL (built using decision tree) can predict user's contextual location with the accuracy of around 87%.

Energy-delay tradeoffs in smartphone applications have been studied independently in dynamic voltage and frequency scaling (DVFS) problem and network interface selection problem. We optimize the two problems jointly to quantify how much energy can be saved further and propose a scheme called SpeedControl which jointly manages application scheduling, CPU speed control and wireless interface selection. The scheme is shown to be near-optimal in that it tends to minimize energy consumption for given delay constraints. This paper is the first to reveal energy-delay tradeoffs in a holistic view considering multiple wireless interfaces, DVFS and multitasking in smartphone. We perform real measurements on WiFi/3G coverage and throughput, power consumption of CPU and WiFi/3G interfaces, and CPU workloads. Trace-driven simulations based on the measurements demonstrate that SpeedControl can save over 30% of battery by trading 10 min delay as compared to existing schemes when WiFi temporal coverage is 65%, moreover, the saving tendency increases as WiFi coverage increases.

With the proliferation of sensor-embedded mobile computing devices, mobile sensing is becoming a popular paradigm to collect information from participating mobile users. Unlike the well-calibrated and well-tested sensor networks, mobile sensing relies on participants with unknown reliability. Data collected from mobile users may be untrustworthy. There are various solutions proposed in the literature for assessing the trustworthiness of the sensing data that describe an individual event or observation. In addition to single-event based trust models, we propose the concept of Provenance Logic, to reason about the logical relations between multiple events by jointly recognizing and linking events from successive sensing observations. We propose an approach that combines logical reasoning and statistical learning techniques. To the best of our knowledge, our work is the first attempt for trust evaluation based on the logical relation among multiple events in the mobile sensing context. We motivate and illustrate our approach with a use case of traffic monitoring mobile sensing. Performance validation has shown that improved trust assessment can be achieved efficiently and effectively on top of single-event based analysis.

Indoor navigation is typically achieved by an operational localization system among a range of location-based services it provides. However, the construction of a localization map, which is prerequisite for binding sensed observation in sensing space to individual locations in geographic space, remains a challenging task to date. In this work, we propose an indoor navigation system that alleviates the need for constructing a localization map and instead provides navigation in signal space. The main idea behind our approach is to construct a sensing map consisting of signal observations (WiFi Clusters) and connecting dead-reckoning segments obtained through mobile sensing capabilities (traces of accelerometer and digital compass reading). To this end, we design a prototype to demonstrate effective construction of such sensing map with energy-efficient sensors and crowd-sourcing, and its ability to support accurate navigation.

Facial recognition is a popular biometric authentica-tion technique, but it is rarely used in practice for de-vice unlock or website / app login in smartphones, alt-hough most of them are equipped with a front-facing camera. Security issues (e.g. 2D media attack and vir-tual camera attack) and ease of use are two important factors that impede the prevalence of facial authentica-tion in mobile devices. In this paper, we propose a new sensor-assisted facial authentication method to over-come these limitations. Our system uses motion and light sensors to defend against 2D media attacks and virtual camera attacks without the penalty of authenti-cation speed. We conduct experiments to validate our method. Results show 95-97% detection rate and 2-3% false alarm rate over 450 trials in real-settings, indicat-ing high security obtained by the scheme ten times faster than existing 3D facial authentications (3 sec-onds compared to 30 seconds).

Crowd-Cache is a novel crowd-sourced content caching system which provides cheap and convenient content access for mobile users. Our system exploits both transient colocation of devices and the spatial temporal correlation of content popularity, where users in a particular location and at specific times would be likely interested in similar content. We demonstrate the feasibility of Crowd-Cache system through a prototype implementation on Android smartphones.

This paper is first of its kind in presenting a detailed characterization of IEEE 802.11ac using real experiments. 802.11ac is the latest WLAN standard that is rapidly being adapted due to its potential to deliver very high throughput. The throughput increase in 802.11ac can be attributed to three factors — larger channel width (80/160 MHz), support for denser modulation (256 QAM) and increased number of spatial streams for MIMO. We provide an experiment evaluation of these factors and their impact using a 18-nodes 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice. Since utilization of larger channel width is one of the most significant changes in 802.11ac, we focus our study on understanding its impact on energy efficiency and interference. Using experiments, we show that utilizing larger channel width is in general less energy efficient due to its higher power consumption in idle listening mode. Increasing the number of MIMO spatial streams is comparatively more energy efficient for achieving the same percentage increase in throughput. We also show that 802.11ac link witnesses severe unfairness issues when it coexists with legacy 802.11. We provide a detailed analysis to show how medium access in heterogeneous channel width environment leads to the unfairness issues. We believe that these and many other findings presented in this work will help in understanding and resolving various performance issues of next generation WLANs.

Mobile applications used in smartphones are increasingly using Open APIs, and the trend is likely to continue in the foreseeable future. However, the performance of the Open APIs integrated in smartphone apps (Mobile Open APIs) remains hidden from app developers and app users because of the lack of a method to isolate the Open API calls from the whole app execution process. In this paper, we present the very first effort on characterizing Mobile Open APIs and analyzing their performance in terms of four metrics: response latency, network traffic, energy consumption, and CPU usage. We first develop APIExtractor (APIX), a software tool to extract the Mobile Open API calls as fine-grained as in the function level from Android app files (.apk). Then the popularity of the Open API functions were ranked by running APIX on 200 top popular apps downloaded from the Android app store. We then perform in-depth case studies on the the top 17 most popular Mobile Open APIs, by wrapping each of them in a specifically designed app (called APISymphone) and test the apps on both Wi-Fi and cellular network. Furthermore, we conduct a global scale measurements of the Mobile Open APIs by using Amazon Elastic Computing service. Our comprehensive measurement-based results provides very intriguing as well as interesting insights to the performance characteristics of Mobile APIs.

Time-critical Location Based Service (LBS) applications in mobile ad hoc networks require fast localization. The conventional localization techniques are, unfortunately, unsuitable for such applications, for they neglect the time needed for localization. As a result, time-critical information may become obsolete, and the mobile users such as vehicles may have moved to new locations before the localization procedure is completed. To address this issue, we formulate a notion of On-Demand Fast Localization (ODFL) and devise a framework to implement this concept over existing routing protocols in MANETs. We present analytical and simulation results to demonstrate that ODFL can significantly reduce the time solely needed for localization before starting time-critical applications. Moreover, we show that ODFL can also improve location privacy and reduce energy consumptions.

Video telephony is becoming popular over smart-phones and tablets. Unlike the Desktop era, smartphone users are often `mobile' and this impacts how the video is processed and transmitted over the network. The significant increase in the motion content in such videos change the composition of video frames. Coupled with wireless packet losses, it often leads to poor quality of video received by the end user. In this work, we propose RVD, a framework for Reliable Video Delivery in mobile telephony by accounting for video object motion comprising foreground end-user motion and background scene changes in the network transmission of video. Multilayer perceptron (MLP) based non-linear regression model is used to analyze the impact of redundancy on received video quality under network variations and different degrees of video motion. RVD achieves 17-25% bandwidth savings for a target video quality, and 50-56% quality improvement over video-oblivious approaches.

Digital camera identification can be accomplished based on sensor pattern noise, which is unique to a device, and serves as a distinct identification fingerprint. Camera identification and authentication have formed the basis of image/video forensics in legal proceedings. Unfortunately, real-time video source identification is a computationally heavy task, and does not scale well to conventional software implementations on typical embedded devices. In this paper, we propose a hardware architecture for source identification in networked cameras. The underlying algorithms, an orthogonal forward and inverse discrete wavelet transform and minimum mean square error-based estimation, have been optimized for 2-D frame sequences in terms of area and throughput performance. We exploit parallelism, pipelining, and hardware reuse techniques to minimize hardware resource utilization and increase the achievable throughput of the design. A prototype implementation on a Xilinx Virtex-6 FPGA device was optimized with a resulting throughput of 167 MB/s, processing 30 640 × 480 video frames in 0.17 s.

A FTER the initial learning phase, I am catching up on my role as the editor-in-chief of IEEE Transactions on Mobile Com- puting (TMC).The paper submission rate is very healthy and we are trying our best to expedite the review process while maintaining the high quality of reviews.Our Editorial Board continues to evolve, and I would like to take this opportunity to introduce the new members, whose biographies appear below.These new editors fill gaps left by Associate Editors whose terms expired, and will strengthen the Editorial Board's expertise in emerging areas.It is my pleasure to introduce Sajal Das, Pan Hui, Biplab Sikdar, and Yanchao Zhang.I would like to thank them for taking time from their busy schedules to volunteer and commit for this important service to the community.TMC continues to be a very desirable publication venue for mobile and wireless computing and networking, and this would not be possible without the support of our readers and authors, the members of the Editorial Board, the Steering Committee, and the Computer Society staff.I look forward to hearing feedback from our readership and receiving suggestions for ways to improve the journal in the coming years.

Mobile sensing is becoming a popular paradigm to collect information from and outsource tasks to mobile users. These applications deal with lot of personal information, e.g., identity and location. Therefore, we need to pay a deeper attention to privacy and anonymity. However, the knowledge of the data source is desired to evaluate the trustworthiness of the sensing data. Anonymity and trust become two conflicting objectives in mobile sensing. In this paper, we proposeARTSense, a framework to solve the problem of “trust without identity” in mobile sensing. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. In contrast to other recent solutions, our scheme does not require a trusted third party and both positive and negative reputation updates can be enforced. In the trust assessment, we consider contextual factors that dynamically affects the trustworthiness of the sensing data as well as the mutual support and conflict among data from difference sources. Security analysis shows that ARTSense achieves our desired anonymity and security goals. Our prototype implementation on Android demonstrates that ARTSense incurs minimal computation overhead on mobile devices, and simulation results justify that ARTSense captures the trust of information and reputation of participants accurately.

In a multi-tenant data center environment, the current paradigm for route control customization involves a labor-intensive ticketing process where tenants submit route control requests to the landlord. This results in tight coupling between tenants and the landlord, extensive human resource deployment, and long ticket resolution time. We propose Routing-as-a-Service (RaaS), a framework for tenant-directed route control in data centers. We show that RaaS-based implementation provides a route control platform where multiple tenants can perform route control independently with little administrative involvement, and the landlord can set the overall network policies. RaaS-based solutions can run on commercial off-the-shelf (COTS) hardware and leverage existing technologies, so it can be implemented in existing networks without major infrastructural overhaul. We present the design of RaaS, introduce its components, and evaluate a prototype based on RaaS.

Network forensics is an emerging interdiscipline used to track down cyber crimes and detect network anomalies for a multitude of applications. Efficient capture of data is the basis of network forensics. Compared to traditional networks, data capture faces significant challenges in cognitive radio networks. In traditional wireless networks, usually one monitor is assigned to one channel for traffic capture. This approach will incur very high cost in cognitive radio networks because it typically has a large number of channels. Furthermore, due to the uncertainty of the primary user's behavior, cognitive radio devices change their operating channels dynamically, which makes data capturing more difficult. In this paper, we propose a systematic method to capture data in cognitive radio networks with a small number of monitors. We utilize incremental support vector regression to predict packet arrival time and intelligently switch monitors between channels. We also propose a protocol that schedules multiple monitors to perform channel scanning and packet capturing in an efficient manner. Monitors are reused in the time domain, and geographic coverage is taken into account. The real-world experiments and simulations show that our method is able to achieve the packet capture rate above 70% using a small number of monitors, which outperforms the random scheme by 200%-300%.

The Ethernet switch is a primary building block for today's enterprise networks and data centers. As network technologies converge upon a single Ethernet fabric, there is ongoing pressure to improve the performance and efficiency of the switch while maintaining flexibility and a rich set of packet processing features. The OpenFlow architecture aims to provide flexibility and programmable packet processing to meet these converging needs. Of the many ways to create an OpenFlow switch, a popular choice is to make heavy use of ternary content addressable memories (TCAMs). Unfortunately, TCAMs can consume a considerable amount of power and, when used to match flows in an OpenFlow switch, put a bound on switch latency. In this paper, we propose enhancing an OpenFlow Ethernet switch with per-port packet prediction circuitry in order to simultaneously reduce latency and power consumption without sacrificing rich policy-based forwarding enabled by the OpenFlow architecture. Packet prediction exploits the temporal locality in network communications to predict the flow classification of incoming packets. When predictions are correct, latency can be reduced, and significant power savings can be achieved from bypassing the full lookup process. Simulation studies using actual network traces indicate that correct prediction rates of 97% are achievable using only a small amount of prediction circuitry per port. These studies also show that prediction circuitry can help reduce the power consumed by a lookup process that includes a TCAM by 92% and simultaneously reduce the latency of a cut-through switch by 66%.

Femtocells offer many advantages in wireless networks such as improved cell capacity and coverage in indoor areas. As these femtocells can be deployed in an ad-hoc manner by different consumers in the same frequency band, the femtocells can interfere with each other. To fully realize the potential of the femtocells, it is necessary to allocate resources to them in such a way that interference is mitigated. We propose a distributed resource allocation algorithm for femtocell networks that is modelled after link-state routing protocols. Resource Allocation using Link State Propagation (RALP) consists of a graph formation stage, where individual femtocells build a view of the network, and an allocation stage, where every femtocell executes an algorithm to assign OFDMA resources to all the femtocells in the network. Our evaluation shows that RALP performs better than existing femtocell resource allocation algorithms with respect to spatial reuse and satisfaction rate of required throughput.

We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 ≤ MSE ≤ 0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.

Many practical problems in mobile social networks such as routing, community detection, and social behavior analysis, rely on accurate user contact detection. The frequently used method for detecting user contact is through Bluetooth on smartphones. However, Bluetooth scans consume lots of power. Although increasing the scan duty cycle can reduce the power consumption, it also reduces the accuracy of contact detection. In this paper, we address this problem based on the observation that user contact changes (i.e., starts and ends of user contacts) are mainly caused by user movement. Since most smartphones have accelerometers, we can use them to detect user movement with much less energy and then start Bluetooth scans to detect user contacts. By conducting experiments on smartphones, we discover three relationships between user movement and user contact changes. According to these relationships, we propose a Mobility-Assisted User Contact detection algorithm (MAUC), which triggers Bluetooth scans only when user movements have a high possibility to cause contact changes. Moreover, we propose energy-aware MAUC (E-MAUC) to further reduce energy consumption during Bluetooth discovery, while keeping the same detection accuracy as MAUC. Via trace driven simulations, we show that MAUC can reduce the number of Bluetooth scans by half while maintaining similar contact detection rates compared to existing algorithms, and E-MAUC can further reduce the energy consumption by 45% compared to MAUC.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. In contrast to most existing location proof systems which rely on infrastructure like wireless APs, STAMP is based on co-located mobile devices mutually generating location proofs for each other. This makes STAMP desirable for a wider range of applications. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. We also examine different collusion scenarios and propose a light-weight entropy-based trust evaluation approach to detect fake proofs resulting from collusion attacks. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high (> 0.9) collusion detection accuracy.

Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.

RSS (Received Signal Strength) has been widely utilized in wireless applications. It is, however, susceptible to environmental unknowns from both temporal and spatial domains. As a result, the fluctuation of RSS may degrade performance of RSS based applications. In this work, we propose a novel RSS processing method at the receiver for three antenna based systems. The output of our approach is `RSS-Ratio', which eliminates the environmental unknowns and thus is a more stable variable compared to RSS itself. To validate the efficacy of the proposed method, we conduct a series of experiments in a range of wireless scenarios, including indoor laptop based measurement, indoor software defined radio - WARP based measurement, and outdoor wireless measurement. In addition, we also give an analysis to the relationship between the location of transmitter and the value of RSS-Ratio, and examine the accuracy of the estimated RSS-Ratio value via both simulations and experiments. All the experimental, analytical, and simulated results demonstrate that RSS-Ratio will be a better replacement for RSS to improve the performance of RSS based applications.

Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms.

With the proliferation of sensor-embedded mobile computing devices, participatory sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in participatory sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately.

Received signal strength based fingerprinting approaches have been widely exploited for localization. The received signal strength (RSS) plays a very crucial role in determining the nature and characteristics of location fingerprints stored in a radio-map. The received signal strength is a function of distance between the transmitter and receiving device, which varies due to various in-path interferences. A detailed analysis of factors affecting the received signal for indoor localization is presented in this paper. The paper discusses the effect of factors such as spatial, temporal, environmental, hardware and human presence on the received signal strength through extensive measurements in a typical IEEE 802.11b/g/n network. It also presents the statistical analysis of the measured data that defines the reliability of RSS-based location fingerprints for indoor localization.

LTE is the next generation of all-IP mobile communication system designed and developed by 3GPP. It offers unprecedented data transmission speed and low latency to support a variety of applications and services. However, compared to wireline networks, efficient QoS provisioning for diversified applications in wireless access networks such as LTE is challenging due to unreliable and resource-constrained radio interface. In this paper, we investigate an important problem of downlink resource allocation in recently enhanced LTE-Advanced systems where a newly added feature carrier aggregation provides more flexibility in radio resource management in addition to the existing resource block level packet scheduling. The resource allocation problem can be formulated as a complex combinatorial problem with multiple constraints and is solved every time slot. We decompose this highly complex optimization problem and construct a two-tier resource allocation framework which incorporates dynamic component carrier assignment and backlog based scheduling schemes with intelligent link adaptation. An efficient algorithm is developed to dynamically allocate component carriers to users to achieve load balancing. We also present novel backlog based scheduling policies and weighted-CQI based link adaptation scheme to obtain significantly better throughput and delay fairness. Performance of the proposed schemes is evaluated against the static round-robin component carrier assignment, the well-known proportional fairness scheduling rule and the existing link adaptation scheme. Extensive simulation results demonstrate that our schemes offer both better throughput and delay performance as well as user fairness.

In an orthogonal frequency division multiplexing (OFDM) communication system, data bits carried by each subcarrier are not delivered at an equal error probability due to the effect of multipath fading. The effect can be exploited to provide unequal error protections (UEP) to wireless data by carefully mapping bits into subcarriers. Previous works have shown that this frequency-aware approach can improve the throughput of wireless data delivery significantly over conventional frequency-oblivious approaches. We are inspired to explore the frequency-aware approach to improve the quality of wireless streaming, where video frames are naturally not of equal importance. In this work, we present FAVICS, a Frequency-Aware Video Communication System. In particular, we propose three techniques in FAVICS to harvest the frequency-diversity gain. First, FAVICS employs a searching algorithm to identify and provide reliable subcarrier information from a receiver to the transmitter. It effectively reduces the channel feedback overhead and decreases the network latency. Second, FAVICS uses a series of special bit manipulations at the MAC layer to counter the effects that alter the bits-to-subcarrier mapping at the PHY layer. In this way, FAVICS does not require any modifications to wireless PHY and can benefit existing wireless systems immediately. Third, FAVICS adopts a greedy algorithm to jointly deal with channel dynamics and frequency diversity, and thus can further improve the system performance. We prototype an end-to-end system on a software defined radio (SDR) platform that can stream video real-time over wireless medium. Our extensive experiments across a range of wireless scenarios demonstrate that FAVICS can improve the PSNR of video streaming by 5~10 dB.

Many popular mobile applications require the continuous monitoring and sharing of a mobile user's location. However, exploiting a user's location leads to disclosing sensitive information about the users daily activity. Several location privacy-preserving schemes have been proposed, but it remains challenging for a user to achieve visibility of the associated threats as well as to control the impact of those threats. This paper presents an adaptive location privacy-preserving system (ALPS) that allows for a user to control the level of privacy disclosure with different quality of location-based service (LBS). We have identified key attack models on location tracking using powerful map-matching algorithms, and then defined a scheme that allows a user to control the privacy of tracking information. We have implemented ALPS on Android OS and evaluated the implementation extensively via trace-based simulation, showing the effectiveness of user-controllable privacy preservation.

While conventional cognitive radio (CR) system is striving at providing best possible protections for the usage of primary users (PU), little attention has been given to ensure the quality of service (QoS) of applications of secondary users (SU). When loading real-time applications over such a CR system, we have found that existing spectrum sensing schemes create a major hurdle for real-time traffic delivery of SU. For example, energy detection based sensing, a widely used technique, requires possibly more than 100 ms to detect a PU with weak signals. The delay is intolerable for real-time applications with stringent QoS requirements, such as voice over internet protocol (VoIP) or live video chat. This delay, along with other delays caused by backup channel searching, channel switching, and possible buffer overflow due to the insertion of sensing periods, makes supporting real-time applications over CR system very difficult if not impossible. In this paper, we present the design and implementation of a sensing-based CR system - RECOG, which is able to support realtime communications among SUs. We first redesign the conventional sensing scheme. Without increasing the complexity or trading off the detection performance, we break down a long sensing period into a series of shorter blocks, turning a disruptive long delay into negligible short delays. To enhance the sensing capability as well as better protect the QoS of SU traffic, we also incorporate an on-demand sensing scheme based on MAC layer information. In addition, to ensure a fast and reliable switching when PU returns, we integrate an efficient backup channel scanning and searching component in our system. Finally, to overcome a potential buffer overflow, we propose a CR-aware QoS manager. Our extensive experimental evaluations validate that RECOG can not only support realtime traffic among SUs with high quality, but also improve protections for PUs.

Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). Although mobility is an inherent property of wireless networks, there has been no prior work studying the performance of collaborative spectrum sensing under attacks in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at bad locations with severe path-loss. In this paper, we propose to use two trust parameters, location reliability and malicious intention (LRMI), to improve both malicious user detection and primary user detection in mobile CRNs under attack. Location reliability reflects path-loss characteristics of the wireless channel and malicious intention captures the true intention of secondary users, respectively. We propose a primary user detection method based on location reliability (LR) and a malicious user detection method based on LR and Dempster-Shafer (D-S) theory. Simulations show that mobility helps train location reliability and detect malicious users based on our methods. Our proposed detection mechanisms based on LRMI significantly outperforms existing solutions. In comparison to the existing solutions, we show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.

In cognitive radio networks, an attacker transmits signals mimicking the characteristics of primary signals, in order to prevent secondary users from transmitting. Such an attack is called primary user emulation (PUE) attack. TV towers and wireless microphones are two main types of primary users in white space. Existing work on PUE attack detection only focused on the first category. For the latter category, primary users are mobile and their transmission power is low. These properties introduce great challenges on PUE detection and existing methods are not applicable. In this paper, we propose a novel method to detect the emulation attack of wireless microphones. We exploit the relationship between RF signals and acoustic information to verify the existence of wireless microphones. The effectiveness of our approach is validated through real-world implementation. Extensive experiments show that our method achieves both false positive rate and false negative rate lower than 0.1 even in a noisy environment.

Generating a shared key between two parties from the wireless channel is an increasingly interesting topic. The process of obtaining information from the wireless channel is called channel probing. Previous key generation schemes probe the channel at a preset and constant rate without any consideration of channel variation or probing efficiency. To satisfy the usersâ requirements for key generation rate (KGR) and to use the wireless channel efficiently, we propose an adaptive channel probing scheme based on the proportional-integral-derivative controller, which is used to tune the probing rate. Moreover, we use the Lempel-Ziv complexity to estimate the entropy rate of channel statistics (received signal strength), which is considered as an indicator of probing efficiency. The experimental results show that the controller can dynamically tune the probing rate and, meanwhile, to achieve a user desired KGR. It stabilizes the KGR at the desired value with error below 1 bit/s. Besides, channel probing process is efficient under different user velocities, motion types, and sites.

The Domain Name System (DNS) provides a critical service for the Internet -- mapping of user-friendly domain names to their respective IP addresses. Yet, there is no standard set of metrics quantifying the Quality of Domain Name Service (QoDNS), let alone a thorough evaluation of it. This article attempts to fill this gap from the perspective of a DNS proxy/cache, which is the bridge between clients and authoritative servers. We present an analytical model of DNS proxy operations that offers insights into the design trade-offs of DNS infrastructure and the selection of critical DNS parameters. Due to the critical role DNS proxies play in QoDNS, they are the focus of attacks including cache poisoning attack. We extend the analytical model to study DNS cache poisoning attacks and their impact on QoDNS metrics. This analytical study prompts us to present Domain Name Cross-Referencing (DoX), a peer-to-peer systems for DNS proxies to cooperatively defend cache poisoning attacks. Based on QoDNS, we compare DoX with the cryptography-based DNS Security Extension (DNSSEC) to understand their relative merits.

As the next generation of all-IP mobile communication system, LTE offers unprecedented data transmission speed and low latency for a variety of applications and services. However efficient QoS provisioning for wireless networks is challenging due to unreliable and resource-constrained radio interface. In this paper, we investigate the important downlink scheduling problem in LTE networks with a focus on video streaming applications. Unlike the conventional scheduling rules which exploit the network layer metrics, our scheme is directly targeted on optimizing the application-layer video quality within the required end-to-end delay bound. The video quality optimized scheduling is formulated as a complex combinatorial optimization problem with an exponentially-growing search space. To solve this complex optimization problem, we exploit the GA (genetic algorithm) based metaheuristic approach. The intrinsic strength of population based solution of GA offers a superior advantage for this type of scheduling problems. Performance of the proposed crosslayer design and the GA solution is evaluated against the well-known M-LWDF scheduling rule and a trajectory method. The simulation results demonstrate the effectiveness of the GA based quality-optimized approach. It can enhance the video quality significantly and satisfy the delay bound.

Secure socket layer (SSL) is the most popular protocol used in the Internet for facilitating secure communications. In this retrospective, we summarize our original paper which analyzed the performance and architectural impact of SSL on the servers and provided insights into the functioning and acceleration of SSL. In addition, we describe advancements in the area that have occurred on this topic and also discuss future research opportunities.

Secure socket layer (SSL) is the most popular protocol used in the Internet for facilitating secure communications. In this paper, we analyze the performance and architectural impact of SSL on the servers in terms of various parameters such as throughput, utilization, cache sizes, cache miss ratios, number of processors, control dependencies, file access sizes, bus transactions, network load, etc. The major conclusions from this study are as follows: The use of SSL increases computational cost of the transactions by a factor of 5-7. SSL transactions do not benefit much from a larger L2 cache, but a larger LI cache would be helpful. A complex logic for handling control dependencies is not useful for SSL transaction as the frequency of branches is very low. Because SSL workload is highly CPU bound, it may be possible to enhance SSL performance by using a number of other architectural features as well.

Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). It is inaccurate if spectrum sensing nodes are malicious. Although mobility is an inherent property of wireless networks, there has been no prior work studying the detection of malicious users for collaborative spectrum sensing in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at locations with severe pathloss. In this paper, we propose to use two trust parameters, Location Reliability and Malicious Intention (LRMI), to improve malicious and primary user detection in mobile CRNs under attacks. Location Reliability reflects pathloss characteristics of the wireless channel and Malicious Intention captures the true intention of secondary users, respectively. Simulations of our proposed detection mechanisms, LRMI, show that mobility helps train location reliability and detect malicious users. We show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.

Adaptive modulation and coding is an important characteristic of OFDMA based wireless networks. A group of subcarriers and symbols (which we refer to as “allocation unit”) can be assigned to an user equipment (UE) based on its channel conditions. The “allocation units” can have different bandwidths for different UEs depending on the current channel conditions of the UEs. The UEs typically have a minimum throughput requirement. In this article our goal is to design a scheduler that maximizes the number of scheduled UEs while meeting their minimum throughput requirements. First we define an analytical model for scheduling and then propose three algorithms for the optimization problem. We show that, given the sets of “allocation units”, maximizing the number of UEs is equivalent to finding the maximum independent set of a bounded degree graph. We also define the set allocation problem that minimizes the number of intersecting sets subject to certain constraints.

Evolved Multimedia Broadcast Multicast Services (eMBMS) in LTE standards provides Raptor code as Forward Error Correction (FEC) scheme in application layer. Hybrid automatic repeat request (HARQ) is also used to increase reliability at MAC layer for packet recovery. The two mechanisms, with no interactions between them, may either lead to more redundancy in download link (DL) network resource or meaningless drops of recovery data at application layer. In this paper, we first analyze tradeoff between two recovery mechanisms and then present a probabilistic model to find optimal Raptor encoding rate and number of HARQ retransmissions for a given network condition. This can achieve a saving of upto 13-15% in DL network resources compared to existing schemes while ensuring reliable file delivery. It was also found to reduce the transmission delay (by minimizing the number of re-transmissions). The model was evaluated using LTE-A simulation framework.

In contrast to conventional static fixed-width channel allocation, on-demand dynamic variable-width channel allocation has shown that it can effectively improve the fairness, throughput, and spectrum efficiency of wireless networks. Air-time utilization (the percentage of time spent on transmissions) is often used to characterize the spectrum demand of networks. The higher the airtime utilization of a network is, the more spectrum the network should be allocated to. Normally, if all wireless devices in a network utilize spectrum effectively, the airtime utilization can faithfully reflect the spectrum usage. In practice, however, spectrum can be ineffectively used due to the misconfiguration of wireless devices, such as inappropriate bit rate configuration, conservative transmit power setting, or mismatch between channel-width and bit rate. The misconfiguration not only degrades the performance of its local network, but also causes the inflation of local network's airtime utilization and thus results in an unfair spectrum allocation. To address the problem, we present Pinokio, a system that monitors spectrum usage at access points, detects spectrum misuse and improves spectrum efficiency. Our extensive evaluations suggest that Pinokio can accurately detect spectrum misuse, and limit the inflation of airtime utilization from more than 730% to less than 20%.

Video quality assessment in mobile devices, for instances smart phones and tablets, raises unique challenges such as unavailability of original videos, the limited computation power of mobile devices and inherent characteristics of wireless networks (packet loss and delay). In this paper, we present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. Despite its simplicity, it shows a high correlation coefficient of 0.875 to optical flow which captures all motion information in a video. We use the TVM values to derive a reduced-reference temporal quality assessment metric, Temporal Variation Index (TVI), which quantifies the quality degradation incurred in network transmission. Subjective assessments demonstrate that TVI is a very good predictor of users' Quality of Experience (QoE). Its prediction shows a 92.5% of correlation to subjective Mean Opinion Score (MOS) ratings. Through video streaming experiments, we show that TVI can also estimate the network conditions such as packet loss and delay. It depicts an accuracy of almost 95% in extensive tests on 183 video traces.

Cognitive radio is considered as a promising technology that enables dynamic spectrum access and improves spectrum utilization. To bootstrap the communication, rendezvous process is crucial for cognitive radio users to establish communication links among each other. Blind rendezvous is a representative technology for rendezvous purpose without relying on a common control channel. Existing works mainly focus on channel hopping (CH) sequence design to speed up or guarantee users meeting on the same channel, while largely ignored the MAC overhead and PHY layer characteristics. This paper proposes new blind rendezvous protocols that take into account the handshaking overhead and power leakage at adjacent channels. Our basic idea is that a cognitive radio user can infer the transmission at adjacent channels by exploiting adjacent channel power leakage, then it can launch a local channel search to find the other user even when they are not on the same channel initially, thus speeding up the rendezvous process. We analyze the time to rendezvous (TTR) of our protocols with two-user and multi-user settings, and identify the conditions under which our protocols outperform the existing ones. We have conducted extensive simulations to evaluate our protocols. Both analytical and simulation results show that our protocols can significantly decrease the time to rendezvous (TTR) by by 53.5% over the packets decoding based rendezvous.

Transmit power estimation is widely used in network monitoring, power-aware design of MANETs, primary user detection in cognitive radio networks and many other areas. Traditional methods for transmit power estimation are trilateration-based, which require an underlying infrastructure with at least three monitors. In this paper, we propose a novel transmit power estimation method which utilizes the nuance of the received signal strength at different frequencies. Our method only needs one monitor, thus has less hardware requirement and is much easier to carry around. We use a support vector machine to facilitate the estimation, and conduct real-world experiments to validate our method. The experimental results demonstrate that our method is able to achieve the accuracy as high as 90%, which in practice outperforms the trilateration method using multiple monitors.

Crowd-sourced Wi-Fi-based localization systems utilize user input for RF scene analysis and map construction. Such systems reduce the deployment cost and privacy concerns that expert-based site survey systems can create. However, the main bottleneck of such crowd-sourcing localization systems is a bootstrapping stage, where lack of contributions by users results in no accuracy guarantee and frequent unnecessary prompting for users' input, even for explored areas. In this paper, we propose a crowd-sourcing localization system that uses both Wi-Fi scene analysis and Bluetooth beacons to address the insufficient contribution challenge. After prompting for user input, the mobile device not only submits Wi-Fi fingerprint to a map server, but also enables Bluetooth beacons to disseminate/share its location and fingerprint information to quickly populate the signal map. Then, subsequent user devices entering the area can discover the Bluetooth beacons and are able to instantly obtain room-level location information without causing unnecessary prompting to users. We implement our proposed system in the Linux OS and evaluate the prototype extensively through both experiments and simulation. Our evaluation results show that using Bluetooth beacons help to improve signal map growth, while maintaining reasonable localization accuracy.

It is my pleasure to welcome you to the Thirteenth IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). It is indeed my honor to serve as the General chair of this symposium, which is being organized at San Francisco, California, USA during June 25–28, 2012.

LTE-Advanced (LTE-A) systems support wider transmission bandwidths and hence, higher data rates for bulk traffic, as a result of Carrier Aggregation (CA). However, existing literature lacks efforts on channel-aware CA, especially in the uplink. The cell-edge users particularly suffer from exhaustion of resources, higher fading losses, lower SINR values (hence, requiring a higher power consumption) due to lossy channels that their traffic requirements are least-satisfied by channel-blind CA. This paper addresses the above concern by proposing an edge-prioritized channel- and traffic-aware uplink CA comprising Component Carrier (CC) assignment and resource scheduling. The LTE-A UEs are spatially-grouped and the under-represented edge UE groups, having the least assignable resources (good CCs), are prioritized for CA. This results in assigning the best channels to the edge groups. The frequency resources are scheduled to the groups based on inter-group and intra-group Proportional Fair Packet Scheduling (PFPS) in the time and frequency domains respectively, to resolve resource contention. The proposed approach outperforms the existing channel-blind Round-Robin and channel-aware Opportunistic CA, in terms of overall uplink throughput, by 33% in CC assignment and 21% in PFPS, in addition to significant throughput improvements for the edge UEs.

We implement a new software-based multihop TDMA MAC protocol (Soft-TDMAC) with microsecond synchronization using a novel system interface for development of 802.11 overlay TDMA MAC protocols (SySI-MAC). SySI-MAC provides a kernel independent message-based interface for scheduling transmissions and sending and receiving 802.11 packets. The key feature of SySI-MAC is that it provides near deterministic timers and transmission times, which allows for implementation of highly synchronized TDMA MAC protocols. Building on SySI-MAC's predictable transmission times, we implement Soft-TDMAC, a software-based 802.11 overlay multihop TDMA MAC protocol. Soft-TDMAC has a synchronization mechanism, which synchronizes all pairs of network clocks to within microseconds of each other. Building on pairwise synchronization, Soft-TDMAC achieves tight network-wide synchronization. With network-wide synchronization independent of data transmissions, Soft-TDMAC can schedule arbitrary TDMA transmission patterns. For example, Soft-TDMAC allows schedules that decrease end-to-end delay and take end-to-end rate demands into account. We summarize hundreds of hours of testing Soft-TDMAC on a multihop testbed, showing the synchronization capabilities of the protocol and the benefits of flexible scheduling.

Network measurement is a discipline that provides the techniques to collect data that are fundamental to many branches of computer science. While many capturing tools and comparisons have made available in the literature and elsewhere, the impact of these packet capturing tools on existing processes have not been thoroughly studied. While not a concern for collection methods in which dedicated servers are used, many usage scenarios of packet capturing now requires the packet capturing tool to run concurrently with operational processes. In this paper we perform experimental evaluations of the performance impact that packet capturing process have on webbased services; in particular, we observe the impact on web servers. We find that packet capturing processes indeed impact the performance of web servers, but on a multi- core system the impact varies depending on whether the packet capturing and web hosting processes are co-located or not. In addition, the architecture and behavior of the web server and process scheduling is coupled with the behavior of the packet capturing process, which in turn also affect the web server's performance.

Long Term Evolution (LTE) is emerging as a major candidate for 4G cellular networks to satisfy the increasing demands for mobile broadband services, particularly multimedia delivery. MIMO (Multiple Input Multiple Output) technology combined with OFDMA and more efficient modulation/coding schemes (MCS) are key physical layer technologies in LTE networks. However, in order to fully utilize the benefits of the advances in physical layer technologies MIMO configuration and MCS need to be dynamically adjusted to derive the promised gains of 4G at the application level. This paper provides a performance evaluation of video traffic with variations in the physical layer transmission parameters to suit the varying channel conditions. A quantitative analysis is provided using the perceived video quality (evaluated using no-reference blocking and blurring metrics) along with transmission delay, as video quality measures. Experiments are performed to measure performance with changes in modulation as well as code rates in poor and good channel conditions. We discuss how an adaptive scheme can optimize the performance over a varying channel.

Trust plays an important role in protecting the security of mobile ad hoc networks. The node mobility brings challenge to trust propagation, where traditional graph-based propagation methods are difficult to apply. In this paper, we propose a trust establishment and propagation scheme by exploiting natural mobility of mobile ad hoc network nodes. We estimate the moving state of neighboring nodes, and select the group of nodes with higher movement diversity as the next hop. In this way, we expedite trust propagation with the help of mobility instead of treating it as a hurdle. Two classes of mobility models are considered: random direction model and cluster-based model. Extensive experiments are conducted on trust propagation performance using our scheme. Results show that the detection rate of highly untrustworthy node is improved by about 400% in random direction mobility model and about 300% in cluster-based mobility model compared to static case. Further, we observe that trust convergence time and communication overhead varies dramatically in different mobility models. Based on these observations, we discuss the usage of different mobility models and metrics in different trust applications in mobile ad hoc networks.

The Domain Name System (DNS) is a foundational component of today's Internet for mapping Internet names to addresses. With the DNS Security Extensions (DNSSEC) DNS responses can be cryptographically verified to prevent malicious tampering. The protocol complexity and administrative overhead associated with DNSSEC can significantly impact the potential for name resolution failure. We present metrics for assessing the quality of a DNSSEC deployment, based on its potential for resolution failure in the presence of DNSSEC misconfiguration. We introduce a metric to analyze the administrative complexity of a DNS configuration, which contributes to its failure potential. We then discuss a technique which uses soft anchoring to increase robustness in spite of misconfigurations. We analyze a representative set of production signed DNS zones and determine that 28% of the validation failures we encountered would be mitigated by the soft anchoring technique we propose.

Traditionally, the performance of wireless mesh networks (WMNs) is measured by long-term averaged metrics, such as long-term averaged packet delivery ratio or throughput. However, due to the dynamic nature of the wireless networks, long-term averaged metrics cannot reflect the short-term behaviors of the network. In the meanwhile, the users may require a sustained performance for a certain period of time in many realtime applications. Prediction of network performance of WMNs at the level of individual flows in a small time granularity becomes very important, but is missing in the literature. In this paper, we propose a measurement-based model to predict the short-term performance of both goodput and packet loss for individual flows in WMNs. This model captures the complex dependencies among the different queues in the system, traffic demand, and wireless interference in the network. We developed two tools Rater and CalMedium to calculate the probabilities of packet loss along all the layers in the protocol stack at each node. Real-world experiments on an indoor mesh network testbed demonstrate that our prediction method can achieve accurate performance prediction under both single-flow and multiple-flow scenarios. We also discuss two application examples of utilizing our prediction model: finding the bottleneck rate of a flow and admission control.

Network forensics is widely used in tracking down criminals and detecting network anomalies, and data capture is the basis of network forensics. Compared to traditional networks, data capture faces significant challenges in cognitive radio networks. In traditional wireless networks, one monitor is usually assigned to one channel to capture traffic, which incurs very high cost in a cognitive radio network because the latter typically has a large number of channels. Furthermore, due to the uncertainty of the primary user's activity, cognitive radio devices change their operating channels randomly, which makes data capturing more difficult. In this paper, we propose a systematic method to capture data in cognitive radio networks with a small number of monitors. We utilize incremental support vector regression to predict packet arrival time and intelligently switch monitors between channels. In addition, a protocol is proposed to schedule multiple monitors to perform channel scan and packet capturing in an efficient manner. The real-world experiments and simulations show that our method is able to achieve the packet capture rate above 70% using a small number of monitors, which outperforms the random scheme by 200%-300%.

Software bugs in distributed systems are notoriously hard to find due to the large number of components involved and the non-determinism introduced by race conditions between messages. This paper introduces Pop Mine, a tool for diagnosing corner-case bugs by finding the minimal causal directed acyclic graph (DAG) of events, spanning multiple processes, which captures a bug-triggering condition. Being based on causal order, a global notion of time is not required in uncovering bug-triggering distributed event patterns. Bug triggering event DAGs can be identified by comparing execution graphs from successful runs to those where bug manifestations were observed, and exposing the minimal discriminative event DAGs that may be responsible for the problem. This is a significant extension to prior debugging tools, in that prior work considered much simpler bug-triggering conditions such as single events, event sets, or ordered chains of events. To the authors' knowledge, this is the first paper that considers bug-triggering conditions in the form of distributed event graphs. To prove the effectiveness of our approach, we applied our tool to VCP, Chord and GreenGPS and diagnosed bugs. We also present performance analysis results to demonstrate the scalability of our approach.

In a multi-tenant data center environment, the current paradigm for route control customization involves a labor-intensive ticketing process, in which tenants submit route control requests to the landlord. This results in a tight coupling between tenants and landlord, extensive human resource deployment, and long ticket resolution time. We propose Routing-as-a-Service (RaaS), a framework for tenant-directed route control in data centers. We show that RaaS-based implementation provides a route control platform for multiple tenants to perform route control independently with little administrative involvement, and for the landlord to set the overall network policies. RaaS-based solutions can run on commercial off-the-shelf (COTS) hardware and leverage existing technologies, so it can be implemented in existing networks without major infrastructural overhaul. We present the design of RaaS, introduce its components, and evaluate a prototype based on RaaS.

In cognitive radio networks, an adversary transmits signals whose characteristics emulate those of primary users, in order to prevent secondary users from transmitting. Such an attack is called primary user emulation (PUE) attack. There are two main types of primary users in white space: TV towers and wireless microphones. Existing work on PUE attack detection focused on the first category. However, for the latter category, primary users are mobile and their transmission power is low. These unique properties of wireless microphones introduce great challenges and existing methods are not applicable. In this paper, we propose a novel method to detect the PUE attack of mobile primary users. We exploit the correlations between RF signals and acoustic information to verify the existence of wireless microphones. The effectiveness of our approach is validated through extensive real-world experiments. It shows that our method achieves both false positive rate and false negative rate lower than 0.1.

Mobile data usage over cellular networks has been dramatically increasing over the past years. Wi-Fi based wireless networks offer a high-bandwidth alternative for offloading such data traffic. However, intermittent connectivity, and battery power drain in mobile devices, inhibits always-on connectivity even in areas with good Wi-Fi coverage. This paper presents WiFisense, a system that employs user mobility information retrieved from low-power sensors (e.g., accelerometer) in smartphones, and further includes adaptive Wi-Fi sensing algorithms, to conserve battery power while improving Wi-Fi usage. We implement the proposed system in Android-based smartphones and evaluate the implementation in both indoor and outdoor Wi-Fi networks. Our evaluation results show that WiFisense saves energy consumption for scans by up to 79% and achieves considerable increase in Wi-Fi usage for various scenarios.

Generating a shared key between two parties from the wireless channel is of increasing interest. The procedure for obtaining information from wireless channel is called channel probing. Previous works used a constant channel probing rate to generate a key, but they neither consider the tradeoff between the bit generation rate (BGR) and channel resource consumption, nor adjust the probing rate according to different scenarios. In order to satisfy users' requirement for BGR and to use the wireless channel efficiently, we first build a mathematical model of channel probing and derive the relationship between BGR and probing rate. Second, we introduce an adaptive channel probing system based on Lempel-Ziv complexity (LZ76) and Proportional-Integral-Derivative (PID) controller. Our scheme uses LZ76 to estimate the entropy rate of the channel statistics, e.g. the Received Signal Strength (RSS), and uses the PID controller to control the channel probing rate. Our experiments show that this system is able to dynamically adjust its probing rate to achieve a desired BGR under different moving speeds, different mobile types, and different sites. Our results also show that the standard deviation of the LZ76 calculator is less than 0.15 bits/s. The PID controller is able to stabilize the bit generation rate at a desired value with mean error of less than 0.9 bits/s.

Identity-based attacks (IBAs) are one of the most serious threats to wireless networks. Recently, received signal strength (RSS) based detection mechanisms were proposed to detect IBAs in static networks. Although mobility is an inherent property of wireless networks, limited work has addressed IBA detection in mobile scenarios. In this paper, we propose a novel RSS based technique, Reciprocal Channel Variation-based Identification (RCVI), to detect IBAs in mobile wireless networks. RCVI takes advantage of the location decorrelation, randomness, and reciprocity of the wireless fading channel to decide if all packets come from a single sender or more. If the packets are only coming from the genuine sender, the RSS variations reported by the sender should be correlated with the receiver's observations. Otherwise, the correlation should be degraded, then an attack can be flagged. We evaluate RCVI through theoretical analysis, and validate it through experiments using off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios. We show that RCVI can detect IBAs with a high probability even when the attacker is half a meter away from the genuine user.

Arithmetic Coding (AC) is widely used for the entropy coding of text and multimedia data. It involves recursive partitioning of the range [0,1) in accordance with the relative probabilities of occurrence of the input symbols. In this paper, we present a data (image or video) encryption scheme based on arithmetic coding, which we refer to as Chaotic Arithmetic Coding (CAC). In CAC, a large number of chaotic maps can be used to perform coding, each achieving Shannon optimal compression performance. The exact choice of map is governed by a key. CAC has the effect of scrambling the intervals without making any changes to the width of interval in which the codeword must lie, thereby allowing encryption without sacrificing any coding efficiency. We next describe Binary CAC (BCAC) with some simple Security Enhancement (SE) modes which can alleviate the security of scheme against known cryptanalysis against AC-based encryption techniques. These modes, namely Plaintext Modulation (PM), Pair-Wise Independent Keys (PWIK), and Key and cipher text Mixing (MIX) modes have insignificant computational overhead, while BCAC decoder has lower hardware requirements than BAC coder itself, making BCAC with SE as excellent choice for deployment in secure embedded multimedia systems. A bit sensitivity analysis for key and plaintext is presented along with experimental tests for compression performance.

Selecting high performance routes in wireless networks requires the exchange of link quality information among nodes. Adversaries can manipulate this functionality by advertising fake qualities for links; by doing so, they can attract routes and subsequently launch pernicious attacks. Our measurements suggest that malicious route attraction can fatally impact throughput. We design a framework that is effective against both independent and colluding attackers. In the latter case, we consider both local and remote colluders. With local collusion, malicious nodes exchange and advertise fake routing information to increase the probability of being selected as relays. Remote collusion refers to nodes residing in distant parts of the network that (i) create sybil identities in a local neighborhood and / or (ii) utilize link quality reports to advertise fake links. Our framework combines packet signing and frequency hopping to accurately detect the adversaries. We implement the framework on our testbed and conduct experiments to assess its efficacy. We observe that our framework provides significant throughput benefits by detecting attackers with 90% accuracy.

The quality of information is crucial for decision making in many dynamic information sharing environments such as sensor and tactical networks. Information trustworthiness is an essential parameter in assessing the information quality. In this paper, we present a trust model to evaluate the trustworthiness of information as well as information publishing entities based on information provenance. In our trust model, decision makers can give an evaluation on the information they receive and further adjust the evaluation result to a more accurate value by considering two factors: information similarity and path difference. We introduce Collusion Attacks that may bias the computation and present a mechanism to detect and reduce the effect of Collusion Attacks. Based on the final adjusted information trust, feedback is given to the information publishing nodes to adaptively update their trust scores. Therefore, our collusion-resistant scheme can dynamically assess the trustworthiness of information as well as participating entities in a network and thus effectively enhance the network security. Detailed analysis of the proposed approach is presented along with simulation results.

The dynamic and shared nature of wireless medium imposes an adverse barrier to supporting QoS for video streaming applications in wireless networks. In this paper, we investigate a case study of video streaming in a wireless mesh network to obtain important observations on the factors which impact video quality in multihop wireless mesh networks. Based on our analysis of the case study, we propose the solutions for enhancing video streaming experience in wireless mesh networks from a cross-layer perspective which leverage the information across network (routing) layer and link (MAC) layer. The MAC layer retransmission count is exploited to guide the interface queue management for video packets. In addition, this retransmission count is also used as a metric to construct a retransmission-aware QoS routing scheme for video streams. In our approach, the upper layers are aware of the dynamic network status via retransmission count, so timely QoS decision can be made to enhance video quality effectively. Simulation results demonstrate the proposed solutions improve video streaming quality significantly compared with the existing schemes.

Recent works have shown that the white-space spectrum opened to cognitive radio devices is far less than what the lobbyists claimed. With fast growing number of secondary users, carefully scheduling the spectrum allocation in cognitive radio networks operating on white space becomes vital. However, the frequent ON/OFF activity of primary users (PU) and the mobility of the cognitive users make the problem of spectrum scheduling extremely hard. By modeling the PUs activity in an opportunistic manner, this paper studies how to schedule the spectrum assignment for mobile cognitive radio devices. With the mobility information, we formally define the related problem as the Maximum Throughput Channel Scheduling problem (MTCS) which seeks a channel assignment schedule for each cognitive radio device such that the maximum expected throughput can be achieved. We present a general scheduling framework for solving the MTCS. Based on the proposed framework, we then present two polynomial time optimal algorithms to solve the MTCS in the homogeneous and the heterogeneous traffic load cases, respectively. Our algorithms are evaluated by simulations using the mobility trace obtained from a real world public transportation system. On average, the proposed algorithms outperform a greedy algorithm by 21.6%.

Ethernet is the indisputable de facto technology for local area networks due to its simplicity, low cost, and wide-scale adoption. In recent times, Ethernet has entered new networking areas, such as Metro Area Network (MAN) and Industrial Area Network (IAN), where specialized protocols dominate the market. In addition to the well known advantages, Ethernet acts as the common platform to integrate multiple protocols. However, Ethernet falls short of the stringent resilience requirements mandated by applications in MEN and IAN, despite progress made by the community on additional standardization. We describe a new approach for swift failure detection and recovery in Ethernet ring topologies called Rapid Ring Recovery (RRR). RRR is based on the novel usage of multiple virtual rings. Our implementation augmenting an off-the-shelf Ethernet switch shows that RRR reconverges after a fault in 294 microseconds while sustaining the loss of only eight large frames at 95 percent traffic load.

We study the integrated WiFi/WiMAX networks, where users are equipped with dual-radio interfaces that can connect to either a WiFi or a WiMAX network. Previous research on integrated heterogeneous networks (e.g., WiFi/cellular) usually considers one network as the main and the other as the auxiliary. The performance of the integrated network is compared with the "main” network. The gain is apparently due to the additional resources from the auxiliary network. In this study, we are interested in integration gain that comes from the better utilization of the resource rather than the increase of the resource. The heterogeneity of the two networks is the fundamental reason for the integration gain. To quantify it, we design a generic framework that supports different performance objectives. We focus on the max-min throughput fairness in this work and also briefly cover the proportional fairness metric. We first prove that it is NP-hard to achieve integral max-min throughput fairness, then propose a heuristic algorithm, which provides two-approximation to the optimal fractional solution. Simulation results demonstrate significant integration gain from three sources, namely, spatial multiplexing, multinetwork diversity, and multiuser diversity. For the proportional fairness metric, we derive the formulation and propose a heuristic algorithm, which shows satisfactory performance when compared with the optimal solution.

Traffic measurements provide critical input for a wide range of network management applications, including traffic engineering, accounting, and security analysis. Existing measurement tools collect traffic statistics based on some predetermined, inflexible concept of “flows.” They do not have sufficient built-in intelligence to understand the application requirements or adapt to the traffic conditions. Consequently, they have limited scalability with respect to the number of flows and the heterogeneity of monitoring applications. We present ProgME, a Programmable MEasurement architecture based on a novel concept of flowset-an arbitrary set of flows defined according to application requirements and/or traffic conditions. Through a simple flowset composition language, ProgME can incorporate application requirements, adapt itself to circumvent the scalability challenges posed by the large number of flows, and achieve a better application-perceived accuracy. The modular design of ProgME enables it to exploit the surging popularity of multicore processors to cope with 7-Gb/s line rate. ProgME can analyze and adapt to traffic statistics in real time. Using sequential hypothesis test, ProgME can achieve fast and scalable heavy hitter identification.

Probability density of the received power is well analyzed for wireless networks with static nodes. However, most of the present days networks are mobile and not much exploration has been done on statistical analysis of the received power for mobile networks in particular, for the network with random moving patterns. In this paper, we derive probability density of the received power for mobile networks with random mobility models. We consider the power received at an access point from a particular mobile node. Two mobility models are considered: Random Direction (RD) model and Random way-point (RWP) model. Wireless channel is assumed to have a small scale fading of Rayleigh distribution and path loss exponent of 4. 3D, 2D and 1D deployment of nodes are considered. Our findings show that the probability density of the received power for RD mobility models for all the three deployment topologies are weighted confluent hypergeometric functions. In case of RWP mobility models, the received power probability density for all the three deployment topologies are linear combinations of confluent hypergeometric functions. The analytical results are validated through NS2 simulations and a reasonably good match is found between analytical and simulation results.

Several underwater network characteristics, including long propagation delays and a bandwidth dependent on distance, provide unique challenges to protocol designers. In this paper we present STUMP-WR, a distributed routing and channel scheduling protocol, designed for heavily loaded underwater networks. STUMP-WR selects and schedules links using a distributed algorithm to overlap communications by leveraging the long propagation delays. Through simulation we show that STUMP-WR outperforms previously proposed channel access protocols for underwater networks by achieving higher throughput and lower energy consumption per bit delivered at nearly all traffic rates. We also determine the effect of using CDMA on protocol operation.

In this paper, we present a trust model to evaluate the trustworthiness of information as well as the information publishing nodes based on the information provenance. We consider two factors in evaluating the provenance-based information trust: Path Similarity and Information Similarity. In multihop networks, information can flow through multiple hops from multiple paths. We model the similarity between different paths which deliver information about the same event and the similarity between two information items about the same event which are delivered through different paths. Both path and information similarity factors are considered in determining the trust of the information. This information trust is indeed used as a feedback factor to adaptively adjust trust of the nodes in the network. Detailed analysis of the proposed approach is presented along with simulation results for validation.

Channel surfing is an effective method to prevent jamming attacks in wireless communications. In traditional channel surfing schemes, two parties have to negotiate beforehand, in order to agree on the channel switching sequence. However, the negotiation process itself is vulnerable to jamming attacks. In this paper, we propose a novel channel surfing method without relying on such negotiation. Taking advantage of the reciprocity of the wireless fading channel, our method switches channels according to the random channel states observed by the two parties during their communication. Therefore, it does not introduce any extra communication overhead and can achieve strong security. To evaluate our method, we carry out extensive experiments using off-the-shelf 802.11 devices in a real indoor environment. Experimental results validate the efficiency and security of our method.

Detection and diagnosis of failures in wireless networks is of crucial importance. It is also a very challenging task, given the myriad of problems that plague present day wireless networks. A host of issues such as software bugs, hardware failures, and environmental factors, can cause performance degradations in wireless networks. As part of this study, we propose a new approach for diagnosing performance degradations in wireless networks, based on the concept of ``fault signatures''. Our goal is to construct signatures for known faults in wireless networks and utilize these to identify particular faults. Via preliminary experiments, we show how these signatures can be generated and how they can help us in diagnosing network faults and distinguishing them from legitimate network events. Unlike most previous approaches, our scheme allows us to identify the root cause of the fault by capturing the state of the network parameters during the occurrence of the fault.

Peak Signal-to-Noise Ratio (PSNR) is the simplest and the most widely used video quality evaluation methodology. However, traditional PSNR calculations do not take the packet loss into account. This shortcoming, which is amplified in wireless networks, contributes to the inaccuracy in evaluating video streaming quality in wireless communications. Such inaccuracy in PSNR calculations adversely affects the development of video communications in wireless networks. This paper proposes a novel video quality evaluation methodology. As it not only considers the PSNR of a video, but also with modifications to handle the packet loss issue, we name this evaluation method MPSNR. MPSNR rectifies the inaccuracies in traditional PSNR computation, and helps us to approximate subjective video quality, Mean Opinion Score (MOS), more accurately. Using PSNR values calculated from MPSNR and simple network measurements, we apply linear regression techniques to derive two specific objective video quality metrics, PSNR-based Objective MOS (POMOS) and Rates-based Objective MOS (ROMOS). Through extensive experiments and human subjective tests, we show that the two metrics demonstrate high correlation with MOS. POMOS takes the averaged PSNR value of a video calculated from MPSNR as the only input. Despite its simplicity, it has a Pearson correlation of 0.8664 with the MOS. By adding a few other simple network measurements, such as the proportion of distorted frames in a video, ROMOS achieves an even higher Pearson correlation (0.9350) with the MOS. Compared with the PSNR metric from the traditional PSNR calculations, our metrics evaluate video streaming quality in wireless networks with a much higher accuracy while retaining the simplicity of PSNR calculation.

The domain name system (DNS) is critical to Internet functionality. The availability of a domain name refers to its ability to be resolved correctly. We develop a model for server dependencies that is used as a basis for measuring availability. We introduce the minimum number of servers queried (MSQ) and redundancy as availability metrics and show how common DNS misconfigurations impact the availability of domain names. We apply the availability model to domain names from production DNS and observe that 6.7% of names exhibit sub-optimal MSQ, and 14% experience false redundancy. The MSQ and redundancy values can be optimized by proper maintenance of delegation records for zones.

Multi-radio nodes in wireless mesh networks introduce extra complexity in utilizing channel resources. Depending on the configuration of the radios, bad mappings between radio to wireless frequencies may result in sub-optimal network topologies. Static channel assignments in wireless mesh networks have been studied in theory and through simulation but very little work has been done through experiments. This paper focuses on evaluating static channel assignments on a live wireless mesh network. We chose three popular types of static channel assignment algorithms for implementation and comparison purposes. The three types are breadth-first search, priority-based selection and integer linear programming. We find that there is no single channel assignment algorithm that does well overall. BFS algorithm can create the shortest paths to the gateway and also generate balanced channel usage topologies. The PBS algorithm can use all the best links in the network but have poor performance from each radio to the gateway. Overall, we find the channel assignments given by the algorithms to be suboptimal when applied to a live mesh network because temporal variations in the link quality metrics are not taken into account. Looking at the interflow and intraflow performance of these channel assignment algorithms in a live mesh network, we can conclude that routing protocols must be modified to take advantage of the underlying channel assignment algorithms.

Generating a secret key between two parties by extracting the shared randomness in the wireless fading channel is an emerging area of research. Previous works focus mainly on single-antenna systems. Multiple-antenna devices have the potential to provide more randomness for key generation than single-antenna ones. However, the performance of key generation using multiple-antenna devices in a real environment remains unknown. Different from the previous theoretical work on multiple-antenna key generation, we propose and implement a shared secret key generation protocol, Multiple-Antenna KEy generator (MAKE) using off-the-shelf 802.11n multiple-antenna devices. We also conduct extensive experiments and analysis in real indoor and outdoor mobile environments. Using the shared randomness extracted from measured Received Signal Strength Indicator (RSSI) to generate keys, our experimental results show that using laptops with three antennas, MAKE can increase the bit generation rate by more than four times over single-antenna systems. Our experiments validate the effectiveness of using multi-level quantization when there is enough mutual information in the channel. Our results also show the trade-off between bit generation rate and bit agreement ratio when using multi-level quantization. We further find that even if an eavesdropper has multiple antennas, she cannot gain much more information about the legitimate channel.

Wireless simulators provide full control to researchers in investigating traffic flow behavior, but do not always reflect real-world scenarios. Although previous work pointed out such shortages are due to the limitation of radio propagation models in the simulators, it is still unclear how these imperfect models affect network behavior and to what degree. In this paper, we quantify network behavioral differences between simulations and real-world testbed experiments. We compare and analyze the experimental results from indoor and outdoor experiments with the results from NS-2 and Qualnet simulations. We find that in the PHY layer, the distribution of received signal strength in experiments is usually different from simulation due to the antenna diversity. However, path loss, which is regarded as a dominating factor in simulator channel modeling, can be configured to match the real-world behavior. For the MAC layer, increasing traffic load on a flow may cause significant performance degradation in experiments, but it is not the case in simulations. Interference is inadequately captured in simulations and cannot show the flow level unfairness phenomenon. In the network layer, a few dominant routes exist in testbed experiments while routes in simulations are less stable. These findings give the wireless research community an improved overview about the differences between simulations and testbed experiments. They will help researchers to choose between simulations and experiments according to their particular research requirements.

Growing popularity of high-speed wireless broadband access for real-time applications makes it increasingly relevant to study the admission control and scheduling of flows in a service differentiated manner. Next-generation wireless broadband networks employ orthogonal frequency division multiple access (OFDMA) technology that enables multiple users to communicate at the same time using a time-frequency grid. In this paper, we provide a mathematical model for prioritized admission control and scheduling in OFDMA-based multihop wireless networks. The problem is formulated as an integer linear program (ILP) that does joint admission control and scheduling of flows while satisfying its rate and latency requirements. We propose different heuristic algorithms for scheduling priority-based flows in centralized multihop OFDMA networks. We define the “Flow Admittance” (FA) metric and compare the different scheduling schemes based on this metric. Simulation results show that the Start from Frame Beginning (SFB) heuristic performs well in most of the scenarios. We also propose a combination approach that merges multiple heuristics. The FA values obtained from the combination approach are close to the ILP while incurring computation time orders of lower magnitude.

The domain name system (DNS) is integral to today's Internet. Name resolution for a domain is often dependent on servers well outside the control of the domain's owner. In this paper we propose a formal model for analyzing the name dependencies inherent in DNS, based on protocol specification and actual implementations. We derive metrics to quantify the extent to which domain names affect other domain names. It is found that under certain conditions, the name resolution for over one-half of the queries exhibits influence of domains not expressly configured by administrators. This result serves to quantify the degree of vulnerability of DNS due to dependencies that administrators are unaware of. The model presented in the paper also shows that the set of domains whose resolution affects a given domain name is much smaller than previously thought. The model also shows that with caching of NS target addresses, the number of influential domains expands greatly, thereby making the DNS infrastructure more vulnerable.

We design and implement Soft-TDMAC, a software Time Division Multiple Access (TDMA) based MAC protocol, running over commodity 802.11 hardware. Soft-TDMAC has a synchronization mechanism, which synchronizes all pairs of network clocks to within microseconds of each other. Building on pairwise synchronization, Soft-TDMAC achieves network wide synchronization. With, out-of-band, network wide synchronization Soft-TDMAC can schedule arbitrary TDMA transmission patterns. We summarize hundreds of hours of testing Soft-TDMAC on a multi-hop testbed. Our experimental results show that Soft-TDMAC synchronizes multi-hop networks to within a few microsecond sized TDMA slots. Soft-TDMAC can schedule transmissions to take end-to-end demands into account and in a way that decreases end-to-end delay. With no collisions, under good channel conditions, TCP achieves almost the full wireless channel bandwidth.

Measurement of available bandwidth in a network has always been a topic of great interest. This knowledge can be applied to a wide variety of applications and can be instrumental in providing quality of service to end users. Several probe-based tools have been proposed to measure available bandwidth in wired networks. However, the performance of these tools in the realm of wireless networks has not been evaluated extensively. In recent years, there has also been some work on estimating bandwidth in wireless networks via passively monitoring the channel and determining the 'busy' and 'idle' periods. However, such techniques have primarily been evaluated via simulations only. In this work, we perform an extensive experimental comparison study of both passive and active bandwidth estimation tools for 802.11-based wireless mesh networks. We investigate the impact of interference, packet loss, and 802.11 rate-adaptation, on the performance of these tools. Our results indicate that for wireless networks, a passive technique provides much greater accuracy than the probe-based tools.

In this paper, we propose the Staggered TDMA Underwater MAC Protocol (STUMP), a scheduled, collision free TDMA-based MAC protocol that leverages node position diversity and the low propagation speed of the underwater channel. STUMP uses propagation delay information to overlap node communication and increase channel utilization. Our work yields several important conclusions. First, leveraging node position diversity through scheduling yields large improvements in channel utilization. Second, STUMP does not require tight node synchronization to achieve high channel utilization, allowing nodes to use simple or more energy efficient synchronization protocols. Finally, we briefly present and evaluate algorithms that derive STUMP schedules.

Despite the increasing number of wireless mesh network deployments and research, there is a lack of understanding on how robust these networks are in practice. In this paper, we perform a systematic experimental study to investigate the impacts of different factors on the unreliability of a mesh network and the sources causing such unreliability. We use packet loss rate as a metric for defining reliability. The factors that we studied include traffic load, number of hops and flows, transmission rates, maximum retransmission limits and the RTS/CTS mechanism. Our results are based on measurements performed on our real- world mesh network testbed. In addition to performing the experiments on multiple hops, we include the results of one hop experiments for comparison. In identifying the sources of packet loss, we developed a tool, FlowPaC, to collect flow-based statistics at different points in the system to understand the effects of the MAC layer parameters and the traffic attributes. We also explore the potential remedies for the system configuration and thereby improve the reliability of wireless mesh networks.

In this paper we propose an adaptive scheduling algorithm for IEEE 802.16j based wireless broadband networks. Computation of an optimal schedule for prioritized traffic in OFDMA based IEEE 802.16 wireless network is an NP-Hard problem. Hence, we propose a scheduling heuristic for an OFDMA based WiMAX relay network. The ORS (OFDMA Relay Scheduler) heuristic computes the zone boundaries (relay and access) in an uplink scheduling frame based on the number of RSs and MSs, the bandwidth demands and the link conditions. The ORS heuristic determines a schedule which assigns subchannels and timeslots to prioritized traffic based on the demand for various nodes while implementing frequency selectivity. The ORS adapts zone boundaries and the schedule to link and demand conditions at every scheduling period. We perform extensive simulations to demonstrate the effectiveness of adaptive zone scheduling and changes in rate conditions for various topologies.

The amount of intelligent packet processing in an Ethernet switch continues to grow, in order to support of embedded applications such as network security, load balancing and quality of service assurance. This increased packet processing is contributing to greater per-packet latency through the switch.

Interference among concurrent transmissions complicates QoS provisioning for multimedia applications in wireless mesh networks. In this paper we propose MARIA (mesh admission control and QoS routing with interference awareness), a scheme towards enhancing QoS support for multimedia in wireless mesh networks. We characterize interference in wireless networks using a conflict graph based model. Nodes exchange their flow information periodically and compute their available residual bandwidth based on the local maximal clique constraints. Admission decision is made based on the residual bandwidth at each node. We implement an on-demand routing scheme that explicitly incorporates the interference model in the route discovery process. It directs routing message propagations and avoids "hot-spots" with severe interference. Simulation results demonstrate that by taking interference into account MARIA outperforms the conventional approach. It finds routes with less interference and enhances the performance significantly. We use video as an example application and MARIA improves the quality of delivered videos, with up to 7.3 dB average PSNR gain.

802.11-based multi-hop wireless mesh networks have become increasingly prevalent over the last few years. Recently, a lot of focus has been on deploying monitoring frameworks for enterprise and municipal multi-hop wireless networks. A lot of work has also been done on developing measurement-based schemes for resource management and fault management in these networks. The above goals require an efficient monitoring infrastructure to be deployed in the wireless network, which can provide the maximum amount of information regarding the network status, while utilizing the least possible amount of network resources. However, network monitoring introduces overheads, which can impact network performance, from the perspective of the end user. The impact of monitoring overheads on data traffic has been overlooked in most of the previous works. It remains unclear, as to how parameters such as number of monitoring agents, frequency of reporting monitoring data, and others, impact the performance of a wireless network. In this work, we first evaluate the impact of monitoring overheads on data traffic, and show that even small amounts of overheads can cause large degradation in network performance. We then explore several different techniques for reducing monitoring overheads, while maintaining the objective (resource management, fault management and others) that needs to be achieved. Via extensive simulations, we investigate whether a monitoring framework, which is constrained in terms of number of monitors or periodicity of monitoring, can achieve similar performance as a monitoring framework spanning the entire network. We show that different techniques lend themselves to different application scenarios, and evaluate the trade-offs involved in terms of monitoring overheads and quality of monitoring data.

Wi-Fi-based mesh networks have been considered as a viable option to provide wireless coverage for a vast area, such as community-wide or city-wide. However, interference due to multihop transmissions and potential isolated (disconnected) nodes are the major obstacles to achieve high performance. In this paper, we propose a heterogeneous wireless network architecture, consisting of Wi-Fi and WiMAX, to overcome these limitations. We first construct an optimization problem to analyze the benefits of heterogeneous networks. Then, we design a practical protocol to efficiently combine the resources of Wi-Fi and WiMAX networks. The evaluations show that our new scheme greatly improves the system performance in terms of throughput and fairness.

Supporting multimedia services in wireless mesh networks is receiving more attention from the research community. While wired networks have mature infrastructure and protocols providing QoS for multimedia, supporting multimedia in multihop wireless mesh networks faces greater technical challenges. The unreliable nature and shared media of multihop communications make the deployment of multimedia applications in wireless mesh networks a difficult task. To identify and understand the issues and problems of providing multimedia in multihop wireless mesh networks, we take video streaming as an example, setting up a real testbed to conduct extensive experiments in various scenarios and analyze its performance. In contrast to simulation or network-layer statistics based studies, our investigation is directly focused on video quality in multihop scenarios. The results better represent real networks and reveal interesting aspects of video performance in multihop wireless mesh networks, which we believe is helpful in designing efficient QoS solutions for multimedia services in the wireless mesh networks.

Significant progress has been made to achieve video streaming over wireless ad hoc networks. However, there is not much work on providing security. Is existing security solution good enough for securing video streaming over ad hoc networks? In this paper, we discover a cross-layer dropping attack against video streaming. We first identify a general IP layer dropping attack and then reveal its destructive impact by leveraging the application layer information (e.g., video streaming). Through simulations, we quantify the impact of this attack as a function of several performance parameters such as delivery ratio, hop number and the number of attackers. The surprising result with this attack is that with a 94% delivery ratio, the receiver still cannot watch the video! We also propose several possible solutions to address the dropping attacks. Due to the unique characteristics of this attack, as long as malicious nodes exist, the network will suffer from this dropping attack.

The economics and familiarity of Ethernet technology is motivating the vision of wide-scale adoption of Metro Ethernet Networks (MEN). Despite the progress made by the community on additional Ethernet standardization and commercialization of the first generation of MEN, the fundamental technology does not meet the expectations that carriers have traditionally held in terms of network resiliency and load management. These two important features of MEN have been addressed in this paper. We propose a new concept of Cross-Over Spanning Trees (COST) that increases the resiliency of the MEN while provisioning the support for load balancing. As a result, the capacity in terms of network throughput is greatly enhanced while almost avoiding any re-convergence time in the case of failures. The gain ranges from 1.69% to 7.3% of the total traffic in the face of failure; while load balancing increases an additional 12.76% to 37% of the total throughput.

The border gateway protocol (BGP) is known to take a long time to converge to a steady state following the failure of BGP routers or inter-router links. This has resulted in extensive analysis of BGP convergence delay and a number of schemes have been proposed to reduce this delay. But the convergence delay is a network centric metric and the end-user relevant effects of a failure are better characterized by the packet losses. In this paper we study BGP convergence from the packet delivery perspective and show that a reduction in the convergence delay does not necessarily translate into an improvement in packet delivery. Our measurements provide insights into which BGP modifications are likely to decrease packet loss, and how any shortcomings can be rectified. We also modify a couple of existing techniques, and show how to reduce the packet losses.

We explore the use of partially overlapped channels in wireless mesh networks that consist of multiple 802.11-based access points. We propose novel channel allocation and link scheduling algorithms in the MAC layer to enhance network performance. Due to different traffic characteristics in multi-hop WMNs compared to those in one-hop 802.11 networks, we perform our optimization based on end-to-end flow requirement, instead of the sum of link capacity. In addition, we discuss other factors affecting the performance of POC, including topology, node density, and distribution.

The domain name system (DNS) provides a critical service for the Internet -mapping of user-friendly domain names to their respective IP addresses. Yet, there is no standard set of metrics quantifying the quality of domain name service or QoDNS, let alone a thorough evaluation of it. This paper attempts to fill this gap from the perspective of a DNS proxy/cache, which is the bridge between clients and authoritative servers. We present an analytical model of DNS proxy operations that offers insights into the design tradeoffs of DNS infrastructure and the selection of critical DNS parameters. After validating our model against simulation results, we extend it to study the impact of DNS cache poisoning attacks and evaluate various DNS proposals with respect to the QoDNS metrics. In particular, we compare the performance of two newly proposed DNS security solutions: one based on cryptography and one using collaborative overlays.

The most common technology in local area networks is the Ethernet protocol. The continuing evolution of Ethernet has propelled it into the scope of metropolitan area networks. Even though Ethernet is fast and simple, the spanning tree in Ethernet is inefficient in terms of network utilization and load balancing. In this work, we compare the performance of spanning tree and link state algorithms in the context of layer 2 switching. In addition, we introduce a hybrid scheme that is customized for metro Ethernet networks. The results show that the hybrid scheme increases utilization and reduces the congestion ratio and delay. The performance gained as compared to RSTP, link state, and MSTP are 20.9%, 9.4%, and 11.4%, respectively. In addition, the hybrid scheme is more scalable than using pure link state.

Multi-hop WiMAX networks based on IEEE 802.16 has the potential of easily providing high-speed wireless broadband access to areas with little or no existing wired infrastructure. WiMAX technology can be used as "last mile" broadband connections to deliver streaming audio or video to clients. Thus, quality of service (QoS) is very important for WiMAX networks. Providing QoS in multi-hop WiMAX networks such as WiMAX mesh or mobile multi-hop relay networks is challenging as multiple links can interfere with each other if they are scheduled at the same time. We propose efficient heuristic algorithms for scheduling flows in a centrally scheduled multi-hop WiMAX network. The proposed algorithms guarantee bandwidth and delay constraints of flows and allow multiple non-interfering links to be scheduled at the same time. We also define a "schedule efficiency" metric for comparing different flow scheduling algorithms. The simulation results show that the "schedule flow subchannel" algorithm leads to the best schedule efficiency.

The increased deployment of wireless mesh networks (WMNs) should be complemented by a robust resource management scheme that can provide performance guarantees to mission-critical applications. Several admission control schemes have been presented for wireless LANs and wireless ad-hoc networks. However, wireless mesh networks, with static wireless back-bone and multi-hop communication, pose new design challenges. Evaluation of existing admission control schemes has been done primarily via simulations, which often do not have accurate models for capturing interference between adjacent wireless links and nodes. In this paper, we develop light-weight monitoring modules to measure current network/traffic conditions and estimate end-to-end path delay, which is then incorporated in our admission control decision. We utilize a novel layer-2 packet forwarding mechanism, based on the wireless distribution system (WDS) for WMNs. We evaluate our scheme via experiments conducted on a test-bed consisting of IEEE 802.11a-based nodes that form a wireless mesh. Results show that our proposed scheme can provide performance assurance without incurring too much control overhead.

Capacity limitation is one of the fundamental issues in wireless mesh networks. This paper addresses capacity improvement issues in multi-radio multi-channel wireless mesh networks. Our objective is to find a dynamic channel assignment and link schedule that maximizes the network capacity for ftp-type applications and video-type applications, respectively. Specifically, we minimize the number of time slots needed to schedule all the flows for ftp-type applications and maximize the minimal link satisfaction ratio for video-type applications. The problems are formulated as linear programming and we provide two heuristics to solve these problems. One heuristic uses a set covering strategy and the other uses a link-weight- adjusting strategy. We perform a trade-off analysis between network performance and hardware cost based on the number of radios and channels in different topologies. This work provides valuable insights for wireless mesh network designers during network planning and deployment.

Traffic measurements provide critical input for a wide range of network management applications, including traffic engineering, accounting, and security analysis. Existing measurement tools collect traffic statistics based on some pre-determined, inflexible concept of "flows". They do not have sufficient built-in intelligence to understand the application requirements or adapt to the traffic conditions. Consequently, they have limited scalability with respect to the number of flows and the heterogeneity of monitoring applications.

We study the deployment of data back-haul nodes for wireless networks with energy constraints. We address the following problem: given the required lifetime of a sensor network, the energy constraint of back-haul nodes, and the area to be covered, what is the minimum number of nodes needed to construct such a back-haul network and what is the corresponding deployment scheme? Finding an efficient deployment scheme involves location management, routing, and power management. We focus on linear networks and formulate a deployment optimization problem. We then propose and analyze a greedy deployment scheme that achieves close to optimal performance. We reveal the closed-form relationship among different design parameters, namely, the number of sensor nodes, the desired lifetime, and the coverage distance. We also study the effect of miscellaneous power consumptions and non-uniform data density, and consider extensions to planar networks

Abstract In this paper, we present a location aided knowledge extraction routing (LAKER) protocol for mobile ad hoc networks (MANETs). The novelty of LAKER is that it learns from past actions to guide future behaviors. In particular, LAKER cangradually discovercurrent topological characteristics of the network, such as population density distribution, residual battery map, and traffic load status. This knowledge can be organized in the form of a set ofguiding routes, each of which consists of a chain of guiding positions between a pair of source and destination locations. The guiding route information is learned by individual nodes during route discovery phase, and it can be used to guide future route discovery processes in a more efficient manner. LAKER is especially suitable for mobility models where nodes are not uniformly distributed. LAKER can exploit topological characteristics in these models and limit the search space in route discovery processes in a more refined granularity than location aided routing (LAR) protocol. Simulation results show that LAKER outperforms LAR and DSR in term of routing overhead, saving up to 30–45% broadcast routing messages compared to LAR approach. Copyright © 2006 John Wiley & Sons, Ltd.

In many applications of sensor networks, security is a very important issue. To be resistant against the various attacks, nodes in a sensor network can establish pairwise secret keys[5], [6], [10], authenticate all communications with cryptographic functions[8], and also apply secure information aggregation schemes[13] or hop-by-hop filtering methods[14], [16]. However, these security measures can take considerable overhead in terms of storage, communication and computation, which are scarce resources in sensor nodes. Previously proposed security measures can only resist against a limited number of compromised nodes, which we define as the resistance level. In this paper, we propose a separate solution to any security measure. This technique either significantly reduces the overhead, or increases the resistance level without increasing overhead. The solution is based on a new "Mixed Multi-Channel" (MMC) architecture. In this design, each node can only use one fixed channel. The whole network is thus divided into multiple "planes" by the different planes. Exploiting the characteristics of multi-channel communication, a series of methods are proposed, such as MMC-1, MMC-k and MMC-r. We then present designs to integrate the methods with current security measures, and analyze their resistance level and energy conservation.

Border gateway protocol (BGP) is the standard routing protocol used in the Internet for routing packets between the autonomous systems (ASes). It is known that BGP can take hundreds of seconds to converge after isolated failures. We have also observed that the convergence delay can be even greater for large-scale failures. In this study, we first investigate some of the factors affecting the convergence delay and their relative impacts. We observe that the minimum route advertisement interval (MRAI) and the processing overhead at the routers during the re-convergence have a significant effect on the BGP recovery time. We propose a couple of new schemes to reduce processing overload at BGP routers during large failures, which in turn leads to decreased convergence delays. We show that these schemes combined with the tuning of the MRAI value decrease the BGP convergence delay significantly, and can thus limit the impact of large scale failures in the Internet

As Internet computing gains speed, complexity, and becomes ubiquitous, the need for precise and accurate time synchronization increases. In this paper, we present a characterization of a clock synchronization network managed by Network Time Protocol (NTP), composed by thousands of nodes, including hundreds of Stratum 1 servers, based on data collected recently by a robot. NTP is the most common protocol for time synchronization in the Internet. Many aspects that define the quality of timekeeping are analyzed, as well as topological characteristics of the network. The results are compared to previous characterizations of the NTP network, showing the evolution of clock synchronization in the last fifteen years.

The ubiquitous Ethernet technology has propelled itself into a wide-scale adoption for Metro Ethernet Networks (MEN). Despite recent advancements in Ethernet and commercialization of the first generation of MEN, the fundamental technology does not meet the expectations that carriers have traditionally held in terms of network resiliency and load management. This paper addresses these two issues. We propose a new concept of overlay network in the Ethernet layer, called Etherlay, that increases the resiliency of the MEN while provisioning the support for load balancing. As a result, the capacity in terms of network throughput is greatly enhanced while almost avoiding performance hits for any re-convergence in case of failures. Compared to the standard protocols, Etherlay's total throughput gain ranges from 5.93% to 20.7% in the face of failure; while load balancing capability increases an additional 16% to 60% of the total throughput.

Border gateway protocol (BGP) is the standard routing protocol between various autonomous systems (AS) in the Internet. In the event of a failure, BGP may repeatedly withdraw some routes and advertise new ones until a stable state is reached. It is known that the corresponding recovery time could stretch into hundreds of seconds or more for isolated Internet outages and lead to high packet drop rates. In this paper we characterize BGP recovery time under large-scale failure scenarios, perhaps those caused by disastrous natural or man-made events. We show that the recovery time depends on a variety of topological parameters and can be substantial for massive failures. The study provides guidelines on reducing the impact of BGP convergence delay on the Internet.

The mapping service provided by the Domain Name System (DNS) is fundamental not only to the health of the Internet but also to the protection and integrity of the data. Recently, the DNS infrastructure has suffered several malicious attacks including DNS cache poisoning, which causes the DNS to return false name-to-IP mappings and can be used as a foothold for more insidious attacks. This paper proposes DoX, a peer-to-peer based scheme, to detect and correct inaccurate DNS records caused by cache poisoning attacks. DoX also helps DNS servers to improve cache consistency by detecting and removing obsolete records. DoX does not require modifications to the current infrastructure and can be deployed quickly. It does not use cryptographic techniques and thus does not suffer from the key management and processing overhead issues of those techniques.

The border gateway protocol (BGP) has been known to suffer from large convergence delays after failures. We have also found that the impact of a failure rises sharply with the size of the failure. In this paper we present and evaluate a speculative route invalidation scheme aimed at reducing the convergence delays for large-scale failures. Our scheme collects statistics from BGP updates received at a router and identifies Autonomous Systems (ASes) that are likely to be "unstable". Routes that contain these ASes are marked as invalid and not propagated further. This cuts down the number of invalid routes during the convergence process and results in a significant improvement in the convergence delay.

Wireless Mesh Networks are being deployed everywhere as an alternative to broadband connections. Their ease of setup and large coverage are attractive attributes. However, there are very few performance studies of mesh networks especially in the multiple channel arena. Our objective is to study the performance and characterize the 802.11b wireless mesh backbone as a linear topology with respect to multiple channel usage. We look at the relative performances of single and multiple channels, as well as the number of hops utilized. We introduce a number of communication flows into the network to study the interactions. Finally, we look at the physical placement of the antennas on an access point to determine its impact on performance. Several design decisions for the configuration of wireless mesh network deployments have been inferred from our experimental testbed.

In this paper we present an adaptive per hop differentiation (APHD) scheme towards achieving end-to-end delay assurance in multihop wireless networks. Our APHD scheme extends the capability of IEEE 802.11e EDCA technique into multihop environments by taking end-to-end delay requirement into consideration. At an intermediate node, based on data packet's end-to-end requirement, its accumulative delay so far, and the current node's channel status, APHD smartly adjusts a data packet's priority level in order to satisfy its end-to-end delay requirement. Simulation results show that APHD scheme can provide excellent end-to-end delay assurance while achieving much higher network utilization, compared to a pure EDCA scheme

Security concerns are becoming increasingly critical in networked systems. Firewalls provide important defense for network security. However, misconfigurations in firewalls are very common and significantly weaken the desired security. This paper introduces FIREMAN, a static analysis toolkit for firewall modeling and analysis. By treating firewall configurations as specialized programs, FIREMAN applies static analysis techniques to check misconfigurations, such as policy violations, inconsistencies, and inefficiencies, in individual firewalls as well as among distributed firewalls. FIREMAN performs symbolic model checking of the firewall configurations for all possible IP packets and along all possible data paths. It is both sound and complete because of the finite state nature of firewall configurations. FIREMAN is implemented by modeling firewall rules using binary decision diagrams (BDDs), which have been used successfully in hardware verification and model checking. We have experimented with FIREMAN and used it to uncover several real misconfigurations in enterprise networks, some of which have been subsequently confirmed and corrected by the administrators of these networks.

In a CSMA/CA based multihop wireless network, excessive interference at a receiver or a potential forwarding node causes severe blocking and reduction in throughput. The unbalanced interference forces the node to consume more time receiving packets rather than sending them, resulting in dropped packets due to buffer overflow. We discuss a novel flow control framework at the MAC layer for regulating the transmission and improving the overall throughput of multihop wireless networks based on CSMA/CA protocol. The framework prevents congestion, reduces packet loss and is attractive because per-flow information at each node is kept to a minimum. The techniques used to improve throughput include a hop-by-hop, hybrid rate and window based flow control scheme that paces the transmission of frames such that competition between frames originating from the same flow is reduced. Simulations show more than five fold improvement in throughput and energy cost over the existing scheme without flow control in some cases

Multicasting in Diffserv networks is a challenging problem due to the architectural conflicts between them, namely, stateful vs. stateless core. In this paper, we assume an edge-based multicast (EBM) model wherein the multicast tree is constructed such that the branching occurs only at the edge routers. We propose an algorithm to solve the problem of dynamic member join/leave in heterogeneous QoS multicasting under EBM model. We formally state the problem and propose an algorithm for it, which is optimal when the constraint on the member join/leave requires that there can be no service disruption for on-tree nodes. We then evaluate the performance of our algorithm with respect to a static multicast tree construction heuristic and a source-based shortest path algorithm using "tree QoS cost" as a primary metric. Our studies show that the proposed algorithm achieves good performance in terms of tree QoS cost, time taken for member join/leave, and number of service disruptions with acceptable storage overhead.

Surveillance has been a typical application of wireless sensor networks. To conduct surveillance of a given area in real life, one can use stationary watch towers, or can also use patrolling sentinels. Comparing them to solutions in sensor network surveillance, all current coverage based methods fall into the first category. In this paper, we propose and study patrol-based surveillance operations in sensor networks. Two patrol models are presented: the coverage-oriented patrol and the on-demand patrol. They achieve one of the following goals, respectively, i) to achieve surveillance of the entire field with low power drain but still bounded delay of detection; ii) to use an on-demand manner to achieve user initiated surveillance only to interested places. We propose the "SENSTROL" protocol to fulfill the patrol setup procedure for both models. With the implementation in the GloMoSim simulator, it is shown that patrol on arbitrary path can be set up in a network where each node follows a 98%-time-sleep-2%-time-wake power schedule.

Many potential applications of mobile ad hoc networks (MANETs) involve group communications among the nodes. Multicasting is an useful operation that facilitates group communications. Efficient and scalable multicast routing in MANETs is a difficult issue. In addition to the conventional multicast routing algorithms, recent protocols have adopted the following new approaches: overlays, backbone-based, and stateless. In this paper, we study these approaches from the protocol state management point of view, and compare their scalability behaviors. To enhance performance and enable scalability, we have proposed a framework for hierarchical multicasting in MANET environments. Two classes of hierarchical multicasting approaches, termed as domain-based and overlay-based, are proposed. We have considered a variety of approaches that are suitable for different mobility patterns and multicast group sizes. Results obtained through simulations demonstrate enhanced performance and scalability of the proposed techniques

A moderate amount of recent work has been dedicated to using overlay network to support value-added network service, such as overlay multicast, OverQoS, etc. Overlay service network is a generic service framework which is designed to provide a variety of services to overlay service customers. To design an overlay service network, the first step is to choose an overlay topology connecting all the overlay service nodes. For example, RON [Anderson, DG et al., 2001] has used full mesh topology connecting all the nodes. In this paper, we did a study on the impact of topology on the overlay routing service. We found that the overlay topology has significant impact on the overlay routing in terms of routing performance and routing overhead. For example, the full mesh topology does not always give us the best performance. Moreover, the physical topology information can benefit us a lot to construct an efficient overlay topology. In addition, some alternative topologies can provide us with better performance when considering both the routing performance and overhead.

Sensor networks usually have limited energy and transmission capacity. It is beneficial to reduce the data volume for dissemination in a sensor network that monitors continuous physical processes in order to reduce energy consumption. Data compression schemes in use should be able to adapt to limited bandwidth while preserving high data quality. We propose a wavelet-based, error aware compression algorithm that is targeted to achieving these goals. It is called RACE (rate adaptive compression with error bound). It can adjust its maximum normalized error to current network capacity. Additionally, errors due to multiple passes of compression during multi-hop relaying are additive and thus can be estimated easily upon data reconstruction. Moreover, during data dissemination, error ranges can be narrowed through an opportunistic patching process when excess bit rate is available. Consequently, the performance is less subject to the volatility of physical processes. The algorithm has been evaluated in various aspects and demonstrated to be effective in rate adaptivity, error range narrowing, and preservation of statistical interpretation.

Target tracking is an important application of wireless sensor networks. In this application, the sensor nodes collectively monitor and track the movement of an event or target object. The network operations have two states: the surveillance state during the absence of any event of interest, and the tracking state which is in response to any moving targets. Thus, the power saving operations, which is of critical importance for extending network lifetime, should be operative in two different modes as well. In this paper, we study the power saving operations in both states of network operations. During surveillance state, a set of novel metrics for quality of surveillance is proposed specifically for detecting moving objects. In the tracking state, we propose a collaborative messaging scheme that wakes up and shuts down the sensor nodes with spatial and temporal preciseness. This study, which is a combination of theoretical analysis and simulated evaluations, quantifies the trade-off between power conservation and quality of surveillance while presenting guidelines for efficient deployment of sensor nodes for target tracking application.

Recently, many overlay applications have emerged in the Internet. Currently, each of these applications requires their proprietary functionality support. A general unified framework may be a desirable alternative to application-specific overlays. We introduce the concept of overlay brokers (OBs). We assume that each autonomous system in the Internet has one or more OBs. These OBs cooperate with each other to form an overlay service network (OSN) and provide overlay service support for overlay applications, such as resource allocation and negotiation, overlay routing, topology discovery, and other functionalities. The scope of our effort is the support of quality-of-service (QoS) in overlay networks. Our primary focus is on the design of QoS-aware routing protocols for overlay networks (QRONs). The goal of QRON is to find a QoS-satisfied overlay path, while trying to balance the overlay traffic among the OBs and the overlay links in the OSN. A subset of OBs, connected by the overlay paths, can form an application specific overlay network for an overlay application. The proposed QRON algorithm adopts a hierarchical methodology that enhances its scalability. We analyze two different types of path selection algorithms. We have simulated the protocols based on the transit-stub topologies produced by GT-ITM. Simulation results show that the proposed algorithms perform well in providing a QoS-aware overlay routing service.

To avoid the problem of wireless broadcast storm, the random rebroadcast delay (RRD) approach was introduced in the process of flooding-based route discovery in DSR and AODV protocols. We identify the "next-hop racing" phenomena due to the RRD approach and propose a positional attribute based next-hop determination approach (PANDA) to address this problem. Based on positional attributes such as the relative distance, estimated link lifetime, transmission power consumption, an intermediate node will identify itself as good or bad candidate for the next-hop node and use different rebroadcast delay accordingly. Through simulations we evaluate the performance of PANDA using path optimality, end-to-end delay, and transmission power consumption. Simulation results show that PANDA can: (a) improve path optimality, and end-to-end delay, (b) help find data paths with only 15%/spl sim/40% energy consumption compared to the RRD approach.

Though the merits of IP-based multicast are undeniable, the deployment of IP multicast has met many difficulties. In the past several years, lots of research works have been done on overlay multicast. In this paper, we propose a new overlay multicast protocol: HostCast. Besides constructing a data delivery tree, HostCast uses a simple and efficient approach to form an overlay mesh for control and maintenance. The mesh can effectively facilitate the overlay multicasting. HostCast improves the reliability of overlay multicast tree and decreases the convergence time as demonstrated by the results obtained via simulation.

On demand routing protocols provide scalable and cost-effective solutions for packet routing in mobile wireless ad hoc networks. The paths generated by these protocols may deviate far from the optimal because of the lack of knowledge about the global topology and the mobility of nodes. Routing optimality affects network performance and energy consumption, especially when the load is high. In this paper, we define routing optimality using different metrics such as path length, energy consumption along the path, and energy aware load balancing among the nodes. We then propose a framework of Self-Healing and Optimizing Routing Techniques (SHORT) for mobile ad hoc networks. While using SHORT, all the neighboring nodes monitor the route and try to optimize it if and when a better local sub-path is available. Thus SHORT enhances performance in terms of bandwidth and latency without incurring any significant additional cost. In addition, SHORT can be also used to determine paths that result in low energy consumption or optimize the residual battery power. Thus, we have detailed two broad classes of SHORT algorithms: Path-Aware SHORT and Energy-Aware SHORT. Finally, we evaluate SHORT using the ns-2 simulator. The results demonstrate that the performance of existing routing schemes can be significantly improved using the proposed SHORT algorithms.

Overlay multicast protocol builds a virtual mesh spanning all member nodes of a multicast group. It employs standard unicast routing and forwarding to fulfill multicast functionality. The advantages of this approach are robustness and low overhead. However, efficiency is an issue since the generated multicast trees are normally not optimized in terms of total link cost and data delivery delay. In this paper, we propose an efficient overlay multicast protocol to tackle this problem in MANET environment. The virtual topology gradually adapts to the changes in underlying network topology in a fully distributed manner. A novel source-based Steiner tree algorithm is proposed for constructing the multicast tree. The multicast tree is progressively adjusted according to the latest local topology information. Simulations are conducted to evaluate the tree quality. The results show that our approach solves the efficiency problem effectively.

In this paper we present a location aided knowledge extraction routing (LAKER) protocol for MANETs, which utilizes a combination of caching strategy in dynamic source routing (DSR) and limited flooding area in location aided routing (LAR) protocol. The key novelty of LAKER is that it can gradually discover knowledge of topological characteristics such as population density distribution of the network. This knowledge can be organized in the form of a set of guiding/spl I.bar/routes, which includes a chain of important positions between a pair of source and destination locations. The guiding/spl I.bar/route information is learned during the route discovery phase, and it can be used to guide future route discovery process in a more efficient manner. LAKER is especially suitable for mobility models where nodes are not uniformly distributed. LAKER can exploit the topological characteristics in these models and limit the search space in route discovery process in a more refined granularity. Simulation results show that LAKER outperforms LAR and DSR in term of routing overhead, saving up to 30% broadcast routing message compared to the LAR approach.

Although many QoS-based multicast routing protocols have been proposed in recent years, most of them are based on per-flow resource reservation, which cannot be deployed within differentiated services (DiffServ) domains. In this paper, we propose a new QoS-aware multicast routing protocol called QMD, which is designed for DiffServ environments. QMD can provide scalable QoS-aware multicast services while greatly alleviating the core routers' multicast routing burden. In QMD, we separate the control and data forwarding functions. The edge routers are involved in multicast control plane functions: processing joining and leaving events, searching QoS-satisfied branch, and making resource reservation. On-tree routers (key nodes) maintain multicast routing states and forward multicast data traffic. The key nodes of one multicast group are a subset of all the on-tree routers, which uniquely identify a QoS-satisfied multicast tree connecting the group members. Though the other on-tree routers between any two key nodes do not keep any multicast routing states and QoS reservation information, the multicast traffic still can get guaranteed QoS when transmitted from one key node to another. In addition, QMD can provide higher QoS-satisfaction rate while incurring less message overhead compared to other protocols.

Advances in the areas of QoS and IP multicasting have necessitated the need of integration of these two important features of Internet. Differentiated services (DiffServ) has been proposed as a scalable solution for supporting QoS in the Internet. Coexistence of multicasting and DiffServ is promising since the DiffServ model can provide a scalable framework and may reduce the computational complexity to locate a QoS-satisfied multicast tree. We first identify the problems of provisioning multicasting in DiffServ domains. Next, we propose an efficient DiffServ-Aware Multicasting (DAM) scheme which has three novel features: weighted traffic conditioning (WTC), receiver-initiated marking (RIM) scheme, and Heterogeneous DSCP Headers encapsulation (HDE). The proposed technique solves many problems with the integration of DiffServ and multicasting while accommodating heterogeneous QoS requirements. The framework is scalable, flexible, and feasible. Performance evaluation through analyses and simulations demonstrate conformance of the QoS requirements and the potential benefits of DAM.

Many multicast applications, such as video-on-demand and tele-education have quality of service (QoS) requirements from the underlying network. Recently, many QoS-based multicast protocols have been proposed to meet these requirements. However, few of them can achieve high success ratios. We propose a new QoS-based multicast protocol, QoS-aware multicast protocol using bounded flooding (QMBF) technique. Every network node has local network cell topology information as well as QoS state information. The QMBF utilizes this information to increase the chance of finding the feasible branch. It is based on two methods to find a feasible branch - computing out partial feasible branches using local network cell information (collected from bounded flooding messages) and multiple path searching. The design of QMBF allows it to operate on top of any unicast routing protocol or cooperate with a QoS-based unicast routing protocol.

With the explosive use of the Internet, contemporary Web servers are susceptible to overloads and their services deteriorate drastically and often cause denial of services. In this paper, we propose two methods to prevent and control overloads in Web servers by utilizing the session-based relationship among HTTP requests. We first exploited the dependence among session-based requests by analyzing and predicting the reference patterns. Using the dependency relationships, we have derived traffic conformation functions that can be used for capacity planning and overload prevention in Web servers. Second, we have proposed a dynamic weighted fair sharing (DWFS) scheduling algorithm to control overloads in Web servers. DWFS is distinguished from other scheduling algorithms in the sense that it aims to avoid processing of requests that belong to sessions that are likely to be aborted in the near future. The experimental results demonstrate that DWFS can improve server responsiveness by as high as 50% while providing QoS support through service differentiation for a class of application environment.

The proportion of dynamic objects has been growing at a fast rate in the World Wide Web. In the e-commerce environment, these objects form the core of all web transactions. However, because of additional resource requirements and the changing nature of these objects, the performance of accessing dynamic Web contents has been observed to be poor in the current generation Web services. We propose a framework called WebGraph that helps in improving the response time for accessing dynamic objects. The WebGraph framework manages a graph for each of the Web pages. The nodes of the graph represent weblets, which are components of the Web pages that either stay static or change simultaneously. The edges of the graph define the inclusiveness of the weblets. Both the nodes and the edges have attributes that are used in managing the Web pages. Instead of recomputing and recreating the entire page, the node and edge attributes are used to update a subset of the weblets are then integrated to form the entire page. In addition to the performance benefits in terms of lower response time, the WebGraph framework facilitates Web caching, quality-of-service (QoS) support, load balancing, overload control, personalized services, and security for both dynamic as well as static Web pages. A detailed implementation methodology for the proposed framework is also described. We have implemented the WebGraph framework in an experimental setup and have measured the performance improvement in terms of server response time, throughput, and connection rate. The results demonstrate the feasibility and validates a subset of the advantages of the proposed framework.

The differentiated service approach has been proposed as a potential solution to provide quality of service (QoS) in the next generation Internet. The ultimate goal of end-to-end service differentiation can be achieved by complementing the network-level QoS with service differentiation at Internet servers. In this paper, we have presented a detailed study of the performance of service differentiating Web servers (SDIS). Various aspects, such as admission control, scheduling, and task assignment schemes for SDIS, have been evaluated through real workload traces. The impact of these aspects has been quantified in a simulation-based study. Under high system utilization, a service differentiating server provides significantly better services to high priority tasks compared to a traditional Internet server. A combination of selective early discard and priority-based task scheduling and assignment is required to provide efficient service differentiation at the servers. The results of these studies could be used as a foundation for further studies on service differentiating Internet servers.

The proportion of dynamic objects has been growing at a fast rate in the World Wide Web. However, because of additional resource requirements and the changing nature of these objects, the performance of accessing dynamic Web content has been observed to be poor in the current generation Web services. We propose a framework called WebGraph that helps in improving the response time for accessing dynamic objects. The WebGraph framework manages a graph for each of the Web pages. Both the nodes and the edges have attributes that are used in managing the Web pages. Instead of recomputing and recreating the entire page, the node and edge attributes are used to update a subset of the Weblets are then integrated to form the entire page. In addition to the performance benefits in terms of lower response time, the WebGraph framework facilitates Web caching, QoS support, load balancing, overload control, personalized services, and security for both dynamic as well as static Web pages.

The performance of servers for network-intensive workloads such as web services and online transaction processing applications depends on the effective utilization of the processor caches. A detailed analysis of the cache space utilization of web workloads shows us that several memory addresses are referenced only once during their lifetime in the cache. These references frequently reside in the cache for a long time contributing to the pollution of cache. The most commonly adopted least-recently-used (LRU) replacement scheme does not exploit this characteristic. In this paper, we propose an alternative block replacement policy called Single-Touch Aware Replacement (STAR) algorithm. This algorithm predicts blocks that will potentially be referenced only once and replaces them early enough to improve cache efficiency. The STAR scheme was implemented in a trace-driven cache simulator and the performance with several commercial workloads was analyzed. The use of the STAR algorithm results in up to 20% improvement in cache performance for web workloads (SPECweb96, SPECweb99) and up to 5% improvement in online transaction processing (TPC-C) workloads.

The diversity in web object types and their resource requirements contributes to the unpredictability of web service provisioning. In this paper, an efficient admission control algorithm, PACERS, is proposed to provide different levels of services based on the server workload characteristics. Service quality is ensured by periodical allocation of system resources based on the estimation of request rate and service requirements of prioritized tasks. Admission of lower priority tasks is restricted during high load periods to prevent denial-of-services to high priority tasks. A double-queue structure is implemented to reduce the effects of estimation inaccuracy and to utilize the spare capacity of the server, thus increasing the system throughput. Response delays of the high priority tasks are bounded by the length of the prediction period. Theoretical analysis and experimental study show that the PACERS algorithm provides desirable throughput and bounded response delay to the prioritized tasks, without any significant impact on the aggregate throughput of the system under various workload.

Secure socket layer (SSL) is the most popular protocol used in the Internet for facilitating secure communications. In this paper, we analyze the performance and architectural impact of SSL on the servers in terms of various parameters such as throughput, utilization, cache sizes, cache miss ratios, number of processors, control dependencies, file access sizes, bus transactions, network load, etc. The major conclusions from this study are as follows: The use of SSL increases computational cost of the transactions by a factor of 5-7. SSL transactions do not benefit much from a larger L2 cache, but a larger L1 cache would be helpful. A complex logic for handling control dependencies is not useful for SSL transaction as the frequency of branches is very low. Because SSL workload is highly CPU bound, it may be possible to enhance SSL performance by using a number of other architectural features as well.

The differentiated services (DiffServ) model, proposed to evolve the current best-effort Internet to a quality-of-service-aware Internet, provides packet level service differentiation on a per-hop basis. The end-to-end service differentiation may be provided by extending the per-hop behavior over multiple network domains through service level agreements between domains. The edge routers of each of the domains monitor the aggregate flow of the incoming packets and demote packets when the aggregate incoming traffic exceeds the negotiated interdomain service agreement. A demoted packet may encounter other edge routers on its path that have sufficient resources to route the packet with its original marking. In this paper, we propose a random early demotion and promotion (REDP) technique that works at the aggregate traffic level and allows (1) fair demotion of packets belonging to different flows, and (2) easy and fair detection and promotion of the demoted packets. Using early and random decisions on packets REDP ensures fairness in promotion and demotion. It uses a three color marking mechanism, reserving one color fur differentiating between a demoted packet and a packet with the original out-of-profile marking. We experiment with the proposed REDP scheme using the ns2 simulator for both TCP and UDP streams. The results demonstrate the fairness of REDP scheme in demoting and promoting packets. Furthermore, we show a variety of results that demonstrates that REDP provides better assured services compared to the previously proposed RIO scheme with or without the provision of promotion.

Differentiated service has been proposed as a potential solution for bandwidth allocation and is expected to be supported in the next-generation Internet. However, a service-differentiating Internet with best-effort servers may not meet the overall goals of the differentiated service. In this paper, approaches and performance issues on providing differentiated services from an Internet server are studied. Experimental study and analyses prove that under near-saturation of server utilization, differentiating service provides significantly better performance to high priority tasks compared to a traditional service mode. Quantitative performance estimation of different priority levels of tasks is presented. It is also observed that an enhanced shortest queue first task assignment scheme helps in decreasing the average response time of the server system.

We propose an alternative approach to branch resolution based on the earlier work on decoupled memory architectures. Branch decoupling is a technique for decoupling a single instruction stream program into two streams. One stream is solely dedicated to resolving branches as early as possible (both the branch condition and the branch target). The resolved branch targets are consumed by the other computing stream through a queue. We have proposed a compiler based static branch decoupling methodology earlier. In this paper, we propose a dynamic branch decoupled (DBD) architecture. Simulations show a speedup of 25.6% for SPEC95 integer benchmarks and 6.1% for SPEC95 FP benchmarks over a 2-level adaptive branch predictor. The average number of branch penalty cycles per instruction for DBD reduces to .0475 compared to .0835 for the 2-level branch predictor.

Multicast operation is an important operation in multicomputer communication systems and can be used to support several collective communication operations. A significant performance improvement can be achieved by supporting multicast operations at the hardware level. We propose an asynchronous tree-based multicasting (ATBM) technique for multistage interconnection networks (MINs). The deadlock issues in tree-based multicasting in MINs are analyzed first to examine the main causes of deadlocks. An ATBM framework is developed in which deadlocks are prevented by serializing the initiations of tree operations that have a potential to create deadlocks. These tree operations are identified through a grouping algorithm. The ATBM approach is not only simple to implement but also provides good communication performance using minimal overheads in terms of additional hardware requirements and synchronization delay. Using the ATBM framework, algorithms are developed for both unidirectional and bidirectional multistage interconnection networks. The performances of the proposed algorithms are evaluated through simulation experiments. The results indicate that the proposed hardware-based ATBM scheme reduces the communication latency when compared to the software multicasting approach proposed earlier.

Contemporary processor allocation schemes for multicomputers suffer from a fragmentation problem which causes underutilization of the processing nodes. The RSR and ANCA schemes, based on the concepts of size-reduction and non-contiguous allocation, show considerable performance improvement. However the penalties associated with these schemes limit their usage in environments with memory-bounded or communication-intensive jobs. We propose to use simple directives provided by the users to apply the appropriate allocation scheme and thus maximize the performance benefit. We present such a hybrid processor allocation scheme which combines the previously proposed conventional allocation, RSR and ANCA schemes. The hybrid allocation scheme is evaluated via extensive simulation. It outperforms the conventional allocation scheme and can be implemented easily while maximizing the resource utilizations.

We have proposed efficient stream scheduling algorithms for multimedia storage servers that are providers of variable bit rate media streams. We have developed three types of stream scheduling algorithms: In-Order Scheduling Algorithm (IOSA), Out-of-Order Scheduling Algorithm (OOSA), and Dynamic Merge Scheduling Algorithm (DMSA). In the IOSA scheme, media blocks must be transmitted according to their natural order. In the OOSA scheme, in-order transmission is not mandated and thus results in an out-of-order transmission. In the DMSA scheme, the requests that are possible to be merged care merged together at first. Then the OOSA scheduling scheme is used for the merged requests. The performance evaluations done through simulations show that the maximum bandwidth requirement, the fetch ahead distance, and the coefficient of variation for the bandwidth requirement are improved by using the IOSA, OOSA, and DMSA algorithms.

An analytical model for submesh reliability of mesh-connected systems is proposed in this paper. A mesh is considered operational as long as a functional submesh of the required size is available. We use the principle of inclusion and exclusion to find the exact probability of having a functional submesh within a partition of the mesh. The partitions are taken along either dimension of the mesh. The partitions along the rows are called row partitions (RPs) and along the columns are called column partitions (CPs). The reliability of a partition is then used to approximate the submesh reliability of the system and, thus, this model is called partitioned mesh (PM) model. Instead of using a computationally intensive recursive algorithm as done in the previous work, a closed form approximation of the submesh reliability is derived in this paper. The PM model is validated through simulation and compared with the earlier proposed approximation techniques. It is shown that the PM model provides better approximations for submesh reliability with constant computational complexity.

The performance of a multicomputer system depends on the processor management strategy. Processor management deals with processor allocation and job scheduling. Most of the processor allocation and job scheduling schemes proposed in the literature incur high implementation complexity and are therefore impractical to be integrated. In this paper, we propose an integrated processor management scheme that includes a bypass-queue scheduling policy and a fixed-orientation allocation algorithm. Both policies have very low complexities and are hence suitable to be integrated. Both policies improve the system performance considerably when applied in isolation. The integrated scheme provides even better performance.

In this peeper, we propose a tree-based multicasting algorithm for Multistage Interconnection Networks. We first analyze the necessary conditions for deadlocks in MINs. Based on these observations, an asynchronous tree-based multicasting algorithm is developed in which deadlocks are prevented by serializing the initiations of branching operations that have potential for creating deadlocks. The serialization is done using a technique based on grouping of the switching elements. The preliminary simulation results are encouraging as it lowers the latency by almost a factor of 4 when compared with the software multicasting approach proposed earlier.

In this paper, we analyze several issues involved in developing low latency adaptive wormhole routing schemes for two-dimensional meshes. It is observed that along with adaptivity, balanced distribution of traffic has a significant impact on the system performance. Motivated by this observation, we develop a new fully adaptive routing algorithm called positive-first-negative-first for two-dimensional meshes. The algorithm uses only two virtual channels per physical channel creating two virtual networks. The messages are routed positive-first in one virtual network and negative-first in the other. Because of this combination, the algorithm distributes the system load uniformly throughout the network and is also fully adaptive. It is shown that the proposed algorithm results in providing better performance in terms of the average network latency and throughput when compared with the previously proposed routing algorithms.

The fragmentation problem in multicomputer systems reduces the system utilization and prohibits the systems from performing at their full capacity. In this paper, we propose a generic job allocation method for multicomputer systems based on job size reduction. We reduce the subsystem size requirement adaptively according to the availability of processors. The fragmentation problem is greatly alleviated by this approach. To ensure that the benefit of reducing fragmentation is not outweighed by the penalty of executing jobs on less number of processors, we restrict the number of times the size of a job can be reduced; hence the name restricted size reduction (RSR). Extensive simulations are conducted to validate the RSR method for hypercubes and mesh-based systems with different allocation algorithms. It is observed in both mesh and hypercube that by using the RSR method a simple algorithm can provide better performance than the more sophisticated allocation algorithms. We have also compared RSR method with the limit allocation that is based on a similar idea. Our method outperforms the limit allocation and provides better fairness to different size jobs. The performance gain, fairness, and low complexity makes the RSR method highly attractive.

Task allocation using cubic partitioning of multistage interconnection networks (MINs) offers several advantages over random allocation of resources. The objective of this paper is to analyze MIN reliability considering the cubic allocation algorithm. A comprehensive analytical model is derived for predicting reliability of MIN-based systems where tasks are allocated using the buddy strategy. System reliability with the free list allocation policy is computed via simulation. It is shown that the system reliability is dependent on the allocation algorithm and the free list policy is superior to the buddy scheme in this respect. Two types of mapping algorithms known as conventional and bit reversal are used on a baseline MIN to show that the same allocation algorithm can result in different reliability and performance. A performance-related reliability measure is analyzed using probability of acceptance as the performance measure to demonstrate the trade-offs between performance and reliability.

We present a queueing model for performance analysis of finite-buffered multistage interconnection networks. The proposed model captures network behaviour in an asynchronous communication mode and is based on realistic assumptions. A uniform traffic model is developed first and then extended to capture nonuniform traffic in the presence of a hot-spot. Throughput and delay are computed using the proposed model and the results are validated via simulation. The analysis is extended to predict performance of MIN-based multiprocessors. The effects of buffer length, switch size, and the maximum allowable outstanding requests on the system performance are discussed. Various design decisions using this model are drawn with respect to delay, throughput, and system power.

In this paper, we present a new concept of region of adaptivity with respect to various routing algorithms in wormhole networks. Using this concept, we demonstrate that the previously proposed routing algorithms, though more adaptive, causes an uneven workload in the network which limits the performance improvement. A is observed that balanced distribution of traffic has greater impact on system performance than the adaptivity or efficiency of the algorithm. Based on these motivating factors, we have presented a new fully adaptive routing algorithm for 2-dimensional meshes using one extra virtual channel. The algorithm is more efficient in terms of the number of paths it offers between the source and the destination and also distributes the network load more evenly and symmetrically. The simulation results are presented and are compared with the results of previously proposed algorithms. It is shown that the proposed algorithm results in much better performance in terms of the average network latency and the throughput.>

Analytical techniques for reliability and availability prediction of mesh-connected systems are proposed. The models are based on the submesh requirements. First, a reliability model is proposed assuming that a submesh can be always recognized if it exits. Analysis of the linear consecutive n-out-of-N system is extended using an expanding row/column technique to evaluate the submesh reliability. An alternative approach called row folding is also discussed. Due to the high complexity involved in computing the exact reliability, both of these techniques use approximation to estimate lower bounds. Next, the submesh reliability is computed based on two different allocation policies, known as the two-dimensional buddy system (TDBS), and the frame sliding (FS). The model with the TDBS is further extended to estimate the reliability of multiple working submeshes, which is useful in a multiuser environment. Availability analysis for a submesh of the required size is conducted using a Markov chain (MC). State truncation is used to reduce the computation time, and the MC is solved using a software package called HARP. Validation of the analytical models is done through extensive simulation. Issues, such as reliability comparison based on allocation policies, and methods for improving system reliability are addressed using the analytical models.>

Concurrent access to a shared variable may cause network saturation in parallel computers. This problem, commonly termed as hot spot contention, can be alliviated by combining requests destined to the hot memory module. In this paper, we propose an analytical model to predict performance of combining multistage interconnection networks. The model considers realistic assumptions like finite length buffers in the switches, deterministic service time, finite degree of combining. Simulation results are used to validate the analytical model.

A queueing model for performance evaluation of cluster-based multiprocessors is proposed. Most system components are modeled as M/D/1/L queues to capture deterministic service time and finite buffer behavior. Various subsystems are analyzed independently and then integrated for the system level analysis. Average delay, throughput, and processor utilization are the performance parameters studied in this analysis. The analytical results are first validated via simulation. Next, several design alternatives are discussed using the model. These include the effect of buffer length and identification of bottleneck centers for various design configurations.>

In this paper, we present a queueing model for per formance analysis of finite-buffered multistage inter connection networks. The model captures network be havior in an asynchronous communication mode and is based on realistic assumptions. Throughput and de lay are computed using the proposed model and the results are validated via simulation. Various design decisions using this mode! are drawn with respect to delay, throughput, and system power.

Processor allocation and job scheduling are com plementary techniques to improve the performance of multiprocessors. It has been observed that all the hypercube allocation policies with the FCFS schedul ing show little performance difference. A greater im pact on the performance can be obtained by efficient job scheduling. This paper presents an effort in that direction by introducing a new scheduling algorithm called lazy scheduling for hypercubes. The motivation of this scheme is to eliminate the limitations of the FCFS scheduling. This is done by maintaining sep arate queues for different job sizes and delaying the allocation of a job if any other job(s) of the same di mension is(are) running in the system. Simulation studies show that the hypercube performance is dra matically enhanced by using the lazy scheme as com pared to the FCFS scheduling. Comparison with a re cently proposed scheme called scan indicates that the lazy scheme performs better than scan under a wide range of workloads.

System decomposition is a novel technique for modeling the dependability of complex systems without constructing a single-level Markov Chain (MC). This is demonstrated in this paper for the availability computation of a class of multiprocessors that uses 4*4 switching elements for the multistage interconnection network (MIN). The availability model is known as task-based availability, where a system is considered operational as long as the task requirements are satisfied. The authors develop two simple MC's for the processors and memories and solve them using a software package, called HARP. The probabilities of i processing elements (PE's) and j memory modules (MM's) working at any time t, denoted as Pi(t) and Pj(t), are obtained from their corresponding MC's. The effect of the MIN is captured in the model by finding the number of switches required for the connection of i PE's and j MM's. A third MC is then developed for the switches to find the probability that the MIN provides the required (i*j) connection. Multiplying this term with Pi(t) and Pj(t), the probability of an (i*j) working group is obtained. The methodology is generalized to model arbitrary as well as larger size systems. Transient and steady state availabilities are computed for a variety of MIN configurations and the results are validated through simulation.>