This paper presents a secure ranging system for proximity-based authentication using orthogonal frequency division multiplexing (OFDM) and Physically Unclonable Function (PUF). It specifically targets applications such as keyless entry systems that require accurate and tamper-resistant distance measurement. The proposed system utilizes PUF-based noiselike signatures embedded in the OFDM modulation process to mitigate distance manipulation attacks from adversaries. We implemented the design on a USRP X310 platform, leveraging both software and hardware-based processing to achieve real-time performance. Experimental evaluations conducted in wireless channels demonstrate that our PUF-based secure ranging system achieves decimeter-level accuracy and successfully resists distance reduction attacks. This approach offers high security and precision, making it a promising solution for secure and robust proximity-based authentication.

Extant studies predominantly address catastrophic forgetting within a simplified continual learning paradigm, typically confined to a singular data domain. Conversely, real-world applications frequently encompass multiple, evolving data domains, wherein models often struggle to retain many critical past information, thereby leading to performance degradation. This paper addresses this complex scenario by introducing a novel dynamic expansion approach called Learning Expandable and Adaptable Representations (LEAR). This framework orchestrates a collaborative backbone structure, comprising global and local backbones, designed to capture both general and task-specific representations. Leveraging this collaborative backbone, the proposed framework dynamically create a lightweight expert to delineate decision boundaries for each novel task, thereby facilitating the prediction process. To enhance new task learning, we introduce a novel Mutual Information-Based Prediction Alignment approach, which incrementally optimizes the global backbone via a mutual information metric, ensuring consistency in the prediction patterns of historical experts throughout the optimization phase. To mitigate network forgetting, we propose a Kullback–Leibler (KL) Divergence-Based Feature Alignment approach, which employs a probabilistic distance measure to prevent significant shifts in critical local representations. Furthermore, we introduce a novel Hilbert-Schmidt Independence Criterion (HSIC)-Based Collaborative Optimization approach, which encourages the local and global backbones to capture distinct semantic information in a collaborative manner, thereby mitigating information redundancy and enhancing model performance. Moreover, to accelerate new task learning, we propose a novel Expert Selection Mechanism that automatically identifies the most relevant expert based on data characteristics. This selected expert is then utilized to initialize a new expert, thereby fostering positive knowledge transfer. This approach also enables expert selection during the testing phase without requring any task information. Empirical results demonstrate that the proposed framework achieves state-of-the-art performance.

Misaligned incentives in secure software development have long been a challenge in security economics. Product liability, a powerful legal framework in other industries, has been largely ineffective for software products until recent times. However, the rapid regulatory responses to recent global cyber attacks by both the US and EU, together with the (relative) success of the General Data Protection Regulation in defining both duty and standard of care for software vendors, may enable regulators to use liability to re-align incentives for the benefit of the digital society. The United States National Cybersecurity Strategy suggests shifting responsibility for cyber incidents back to software vendors and proposes the concept of the liability waiver: if a software company voluntarily undergoes and passes an IT security audit, its future product liability is (fully or partially) waived. This article examines this audit-liability framework from both vendor and auditor perspectives. For vendors, we model the decision process as a sequential problem: a vendor must pass an audit to release a product and can attempt the audit multiple times. We show that the optimal strategy for an opt-in vendor is to never quit and to exert cumulative investments in either a “one-and-done” or “incremental” manner. For auditors, we explore how to design audits that encourage voluntary participation while maximizing vendor effort. We further investigate dynamic audit designs that can amplify vendors’ cumulative investments in security. Our findings provide insights into how liability waivers and audit strategies can re-align incentives, fostering a more secure digital ecosystem.

We consider a federated learning (FL) system consisting of multiple clients and a server, where the clients aim to collaboratively learn a common decision model from their distributed data. Unlike the conventional FL framework that assumes the client's data is static, we consider scenarios where the clients' data distributions may be reshaped by the deployed decision model. In this work, we leverage the idea of distribution shift mappings in performative prediction to formalize this model-dependent data distribution shift and propose a performative FL framework. We first introduce necessary and sufficient conditions for the existence of a unique performative stable solution and characterize its distance to the performative optimal solution. Then we propose the performative FedAvg algorithm and show that it converges to the performative stable solution at a rate of O(1/T) under both full and partial participation schemes. In particular, we use novel proof techniques and show how the clients' heterogeneity influences the convergence. Numerical results validate our analysis and provide valuable insights into real-world applications.

Dynamically planning in complex systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing context-dependent decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes make it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we formulate the sequential decision-making process as a conditional trajectory generation process. Based on the formulation, we introduce PlayBest (PLAYer BEhavior SynThesis), a method to improve player decision-making. We extend the diffusion probabilistic model to learn challenging environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained with corresponding rewards. To accomplish reward-guided trajectory generation, we condition the diffusion model on the value function via classifier-guided sampling. We validate the effectiveness of PlayBest through simulation studies, contrasting the generated trajectories with those employed by professional basketball teams. Our results reveal that the model excels at generating reasonable basketball trajectories that produce efficient plays. Moreover, the synthesized play strategies exhibit an alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.

Federated learning (FL) is a distributed learning framework that by design allows local edge devices to keep their training data. However, privacy leakage occurs through model updates and is a privacy protection concern that needs to be addressed. Over-the-air FL (OTA-FL) is a variant of FL designed for wireless edge networks by utilizing the inherent superposition property of the wireless medium. The wireless physical layer (PHY), in addition to providing resource and communication-efficient collaborative training via OTA-FL, can also be leveraged to enhance privacy for FL. This paper presents the PHY design to ensure differentially private (DP) OTA-FL. Specifically, by leveraging the Gaussian noise naturally present in the wireless channel, and deploying a dedicated artificial noise generator (cooperative jammer) when needed, a fully decentralized, dynamic power control strategy is proposed. This design relies on a resource-efficient FL framework with first-order approximation applied at every even iteration, thereby reducing the amount of information needed from clients. This approach can eliminate the need for artificial noise injection at the client side, typically required to achieve DP; the cooperative jammer is used for higher privacy requirement without transmission efficiency loss. The privacy analysis is provided via the Moments Accountant method, providing a tight privacy assessment. The convergence analysis is provided for non-convex learning objectives. Experiments conducted on real-world non-i.i.d. data demonstrate that our scheme outperforms the state-of-the-art method under the same DP requirement and illustrate the effectiveness of cooperative jammer in the case of stringent privacy requirements.

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates.

In this paper, we present and evaluate an automated pipeline for the large-scale analysis of corporate privacy policies. Organizations usually develop their privacy policies in isolation to best balance their business needs, user rights, as well as regulatory requirements. A wide-ranging and structured analysis of corporate privacy policies is essential to facilitate a deeper understanding of how organizations have balanced competing requirements. Our approach consists of a web crawler that can navigate to and scrape content from web pages that contain privacy policies, and a set of AI chatbot task prompts to process and extract structured/labeled annotations from the raw data. The analysis includes the types of collected user data, the purposes for which data is collected and processed, data retention and protection practices, and user rights and choices. Our validation shows that our annotations are highly accurate and consistent. We use this architecture to gather data on the privacy policies of companies in the Russell 3000 index, resulting in hundreds of thousands of annotations across all categories. Analysis of the resulting data allows us to obtain unique insights into the state of the privacy policy ecosystem as a whole.

In this paper we propose the use of large language models (LLMs) for characterizing, clustering, and fingerprinting raw text obtained from network measurements. To this end, We first train a transformer-based masked language model, namely RoBERTa, on a dataset containing hundreds of millions of banners obtained from Internet-wide scans. We further fine-tune this model using a contrastive loss function (driven by domain knowledge) to produce temporally stable numerical representations (embeddings) that can be used out-of-the-box for downstream learning tasks. Our embeddings are robust, resilient to small random changes in the content of a banner, and maintain proximity between embeddings of similar hardware/software products. We further cluster HTTP banners using a density-based approach (HDBSCAN), and examine the obtained clusters to generate text-based fingerprints for the purpose of labeling raw scan data. We compare our fingerprints to Recog, an existing database of manually curated fingerprints, and show that we can identify new IoT devices and server products that were not previously captured by Recog. Our proposed methodology poses an important direction for future research by utilizing state-of-the-art language models to automatically analyze, interpret, and label the large amounts of data generated by Internet scans.

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness as an online reinforcement learning problem for a policy affecting human populations. This formulation accommodates dynamical control objectives, such as achieving equitable population states, that cannot be incorporated into static formulations of fairness. We demonstrate that algorithmic solutions to the proposed fairness problem can adapt to unknown dynamics and, by sacrificing short-term incentives, drive the policy-population system towards more desirable equilibria. For the proposed setting, we develop an algorithm that adapts recent work in online learning and prove that this algorithm achieves simultaneous probabilistic bounds on cumulative loss and cumulative violations of fairness. In the classification setting subject to group fairness, we compare our proposed algorithm to several baselines, including the repeated retraining of myopic or distributionally robust classifiers, and to a deep reinforcement learning algorithm that lacks fairness guarantees. Our experiments model human populations according to evolutionary game theory and integrate real-world datasets.

Many data analytics applications rely on temporal data, generated (and possibly acquired) sequentially for online analysis. How to release this type of data in a privacy-preserving manner is of great interest and more challenging than releasing one-time, static data. Because of the (potentially strong) temporal correlation within the data sequence, the overall privacy loss can accumulate significantly over time; an attacker with statistical knowledge of the correlation can be particularly hard to defend against. An idea that has been explored in the literature to mitigate this problem is to factor this correlation into the perturbation/noise mechanism. Existing work, however, either focuses on the offline setting (where perturbation is designed and introduced after the entire sequence has become available), or requires a priori information on the correlation in generating perturbation. In this study we propose an approach where the correlation is learned as the sequence is generated, and is used for estimating future data in the sequence. This estimate then drives the generation of the noisy released data. This method allows us to design better perturbation and is suitable for real-time operations. Using the notion of differential privacy, we show this approach achieves high accuracy with lower privacy loss compared to existing methods.

Professional basketball provides an intriguing example of a dynamic spatio-temporal game that incorporates both hidden strategy policies and situational decision making. During a game, the coaches and players are assumed to follow a general game plan, but players are also forced to make spur-of-the-moment decisions based on immediate conditions on the court. However, because it is challenging to process heterogeneous signals on the court and the space of potential actions and outcomes is massive, it is hard for players to find an optimal strategy on the fly given a short amount of time to observe conditions and take action. In this work, we present ReLiable (ReinforcemEnt Learning In bAsketBaLl gamEs). Specifically, we investigate the possibility of using reinforcement learning (RL) to guide player decisions. We train an offline deep Q-network (DQN) on historical National Basketball Association (NBA) game data from 2015-2016. The data include play-by-play and player movement sensor data. We apply our trained agent to games that it has not seen. Our method is able to propose potentially smarter tactical strategies, compared with replay gameplay data, producing expected final game scores comparable to elite NBA teams. Our approach can be useful for learning strategy policies from other game-like domains characterized by competing groups and sequential spatio-temporal event data.

Although machine learning (ML) algorithms are widely used to make decisions about individuals in various domains, concerns have arisen that (1) these algorithms are vulnerable to strategic manipulation and "gaming the algorithm"; and (2) ML decisions may exhibit bias against certain social groups. Existing works have largely examined these as two separate issues, e.g., by focusing on building ML algorithms robust to strategic manipulation, or on training a fair ML algorithm. In this study, we set out to understand the impact they each have on the other, and examine how to characterize fair policies in the presence of strategic behavior. The strategic interaction between a decision maker and individuals (as decision takers) is modeled as a two-stage (Stackelberg) game; when designing an algorithm, the former anticipates the latter may manipulate their features in order to receive more favorable decisions. We analytically characterize the equilibrium strategies of both, and examine how the algorithms and their resulting fairness properties are affected when the decision maker is strategic (anticipates manipulation), as well as the impact of fairness interventions on equilibrium strategies. In particular, we identify conditions under which anticipation of strategic behavior may mitigate/exacerbate unfairness, and conditions under which fairness interventions can serve as (dis)incentives for strategic manipulation.

We study the design of a class of incentive mechanisms that can effectively prevent cheating in a strategic classification and regression problem. A conventional strategic classification or regression problem is modeled as a Stackelberg game, or a principal-agent problem between the designer of a classifier (the principal) and individuals subject to the classifier's decisions (the agents), potentially from different demographic groups. The former benefits from the accuracy of its decisions, whereas the latter may have an incentive to game the algorithm into making favorable but erroneous decisions. While prior works tend to focus on how to design an algorithm to be more robust to such strategic maneuvering, this study focuses on an alternative, which is to design incentive mechanisms to shape the utilities of the agents and induce effort that genuinely improves their skills, which in turn benefits both parties in the Stackelberg game. Specifically, the principal and the mechanism provider (which could also be the principal itself) move together in the first stage, publishing and committing to a classifier and an incentive mechanism. The agents are (simultaneous) second movers and best respond to the published classifier and incentive mechanism. When an agent's strategic action merely changes its observable features, it hurts the performance of the algorithm. However, if the action leads to improvement in the agent's true label, it not only helps the agent achieve better decision outcomes, but also preserves the performance of the algorithm. We study how a subsidy mechanism can induce improvement actions, positively impact a number of social well-being metrics, such as the overall skill levels of the agents (efficiency) and positive or true positive rate differences between different demographic groups (fairness).

Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage this multi-scale structure, and derive sufficient conditions for convergence of these to a Nash equilibrium. Our numerical experiments demonstrate that the proposed approaches enable orders of magnitude improvements in scalability when computing Nash equilibria in such games. For example, we can solve previously intractable instances involving up to 1 million agents in under 15 minutes.

Recent studies show that convolutional neural networks (CNNs) are vulnerable under various settings, including adversarial attacks, common corruptions, and backdoor attacks. Motivated by the findings that human visual sys-tem pays more attention to global structure (e.g., shapes) for recognition while CNNs are biased towards local texture features in images, in this work we aim to analyze whether "edge features" could improve the recognition robustness in these scenarios, and if so, to what extent? To answer these questions and systematically evaluate the global structure features, we focus on shape features and pro-pose two edge-enabled pipelines EdgeNetRob and Edge-GANRob, forcing the CNNs to rely more on edge features. Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm. Then EdgeNetRob trains down-stream learning tasks directly on the extracted edge features, while EdgeGANRob reconstructs a new image by refilling the texture information with a trained generative adversarial network (GANs). To reduce the sensitivity of edge detection algorithms to perturbations, we additionally propose a robust edge detection approach Robust Canny based on vanilla Canny. Based on our evaluation, we find that EdgeNetRob can help boost model robustness under different attack scenarios at the cost of the clean model accuracy. EdgeGANRob, on the other hand, is able to improve the clean model accuracy compared to EdgeNetRob while preserving the robustness. This shows that given such edge features, how to leverage them matters for robustness, and it also depends on data properties. Our systematic studies on edge structure features under different settings will shed light on future robust feature exploration and optimization.

In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on camera-or LiDAR-based AD perception alone. However, production AD systems today predominantly adopt a Multi-Sensor Fusion (MSF) based design, which in principle can be more robust against these attacks under the assumption that not all fusion sources are (or can be) attacked at the same time. In this paper, we present the first study of security issues of MSF-based perception in AD systems. We directly challenge the basic MSF design assumption above by exploring the possibility of attacking all fusion sources simultaneously. This allows us for the first time to understand how much security guarantee MSF can fundamentally provide as a general defense strategy for AD perception.We formulate the attack as an optimization problem to generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. To systematically generate such a physical-world attack, we propose a novel attack pipeline that addresses two main design challenges: (1) non-differentiable target camera and LiDAR sensing systems, and (2) non-differentiable cell-level aggregated features popularly used in LiDAR-based AD perception. We evaluate our attack on MSF algorithms included in representative open-source industry-grade AD systems in real-world driving scenarios. Our results show that the attack achieves over 90% success rate across different object types and MSF algorithms. Our attack is also found stealthy, robust to victim positions, transferable across MSF algorithms, and physical-world realizable after being 3D-printed and captured by LiDAR and camera devices. To concretely assess the end-to-end safety impact, we further perform simulation evaluation and show that it can cause a 100% vehicle collision rate for an industry-grade AD system. We also evaluate and discuss defense strategies.

IoT-driven smart societies are modern service-networked ecosystems, whose proper functioning is hugely based on the success of supply chain relationships. Robust security is still a big challenge in such ecosystems, catalyzed primarily by naive cyber-security practices (e.g., setting default IoT device passwords) on behalf of the ecosystem managers, i.e., users and organizations. This has recently led to some catastrophic malware-driven DDoS and ransomware attacks (e.g., the Mirai and WannaCry attacks). Consequently, markets for commercial third-party cyber-risk management (CRM) services (e.g., cyber-insurance) are steadily but sluggishly gaining traction with the rapid increase of IoT deployment in society, and provides a channel for ecosystem managers to transfer residual cyber-risk post attack events. Current empirical studies have shown that such residual cyber-risks affecting smart societies are often heavy-tailed in nature and exhibit tail dependencies. This is both, a major concern for a profit-minded CRM firm that might normally need to cover multiple such dependent cyber-risks from different sectors (e.g., manufacturing and energy) in a service-networked ecosystem, and a good intuition behind the sluggish market growth of CRM products. In this article, we provide: 1) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies and 2) a real-data-driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails. To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate CRM.

As authors of our recently accepted article:Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes, published in the IEEE IoT Journal, we regret that we have found a few errors in the numerical evaluation setup of the works in[1]and[2]that we had borrowed for our accepted paper. In this correction statement, we describe the errors in detail, correct it, and present our revised results with a renewed experimental setup, hoping it to replace the existing incorrect numerical results in the accepted paper. We apologize for the inconvenience caused to the reader. We emphasize that the numerical evaluation section does not in any way hamper the theoretical contributions in this article, and was initially only meant to provide some empirical evidence for whether the theory proposed in this article generalizes to behavioral settings introduced in[2].

In-app advertising is a multi-billion dollar industry that is an essential part of the current digital ecosystem, and is amenable to sensitive consumer information often being sold downstream without the knowledge of consumers, and in many cases to their annoyance. While this practice, in cases, may result in long-term benefits for the consumers, it can result in serious information privacy (IP) breaches of very significant impact (e.g., breach of genetic data) in the short term. The question we raise through this article is: does the type of information being traded downstream play a role in the degree of IP risks generated? We investigate two general (one-many) information trading market structures between a single data aggregating seller (e.g., enterprise app) and multiple competing buyers (e.g., ad-networks, retailers), distinguished by mutually exclusive and privacy sanitized aggregated consumer data (information) types: (i) data entailing strategically complementary actions among buyers and (ii) data entailing strategically substituting actions among buyers. Our primary question of interest here is: trading which type of data might pose less information privacy risks for society? To this end, we show that at market equilibrium IP trading markets exhibiting strategic substitutes between buying firms pose lesser risks for IP in society, primarily because the `substitutes' setting, in contrast to the `complements' setting, economically incentivizes appropriate consumer data distortion by the seller in addition to restricting the proportion of buyers to which it sells. Moreover, we also show that irrespective of the data type traded by the seller, the likelihood of improved IP in society is higher if there is purposeful or free-riding based transfer/leakage of data between buying firms. This is because the seller finds itself economically incentivized to restrict the release of sanitized consumer data with respect to the span of its buyer space, as well as in improved data quality.

A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. Several works have shown this vulnerability via adversarial attacks, but how to improve the robustness of DRL under this setting has not been well studied. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, are ineffective for many RL tasks. We propose the state-adversarial Markov decision process (SA-MDP) to study the fundamental properties of this problem, and develop a theoretically principled policy regularization which can be applied to a large family of DRL algorithms, including deep deterministic policy gradient (DDPG), proximal policy optimization (PPO) and deep Q networks (DQN), for both discrete and continuous action control problems. We significantly improve the robustness of DDPG, PPO and DQN agents under a suite of strong white box adversarial attacks, including two new attacks of our own. Additionally, we find that a robust policy noticeably improves DRL performance in a number of environments.

Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamics of population qualification and algorithmic decisions under a partially observed Markov decision problem setting. By characterizing the equilibrium of such dynamics, we analyze the long-term impact of static fairness constraints on the equality and improvement of group well-being. Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions. We also consider possible interventions that can effectively improve group qualification or promote equality of group qualification. Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.

Information sharing among organizations has been gaining attention as a method for improving cybersecurity. However, the associated disclosure costs act as deterrents for firms' voluntary cooperation. In this work, we take a game-theoretic approach to understanding firms' incentives in these agreements. We propose the design of inter-temporal incentives (i.e. conditioning future cooperation on past interactions). Specifically, we show that incentives for full cooperation can be designed if firms share their private assessments of other firms' disclosure decisions through a common communication platform. We further show that similar incentives can be designed based on outcomes of a public rating/assessment system.

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-accuracy tradeoff. We propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. Moreover, R-ADMM can be further modified (MR-ADMM) such that each node independently determines its own penalty parameter over iterations. We obtain a sufficient condition for the convergence of both algorithms and provide the privacy analysis based on objective perturbation. It can be shown that the privacy-accuracy tradeoff can be improved significantly compared with conventional ADMM.

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.

Deep neural networks (DNNs) have been widely applied in various applications, including autonomous driving and surveillance systems. However, DNNs are found to be vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead a learner to make incorrect predictions. While several defense and detection approaches are proposed for static image classification, many security-critical tasks use videos as their input and require efficient processing. In this paper, we propose an efficient and effective method advIT to detect adversarial frames within videos against different types of attacks based on temporal consistency property of videos. In particular, we apply optical flow estimation to the target and previous frames to generate pseudo frames and evaluate the consistency of the learner output between these pseudo frames and target. High inconsistency indicates that the target frame is adversarial. We conduct extensive experiments on various learning tasks including video semantic segmentation, human pose estimation, object detection, and action recognition, and demonstrate that we can achieve above 95% adversarial frame detection rate. To consider adaptive attackers, we show that even if an adversary has access to the detector and performs a strong adaptive attack based on the state of the art expectation of transformation method, the detection rate stays almost the same. We also tested the transferability among different optical flow estimators and show that it is hard for attackers to attack one and transfer the perturbation to others. In addition, as efficiency is important in video analysis, we show that advIT can achieve real-time detection in about 0.03--0.4 seconds.

Machine Learning (ML) models trained on data from multiple demographic groups can inherit representation disparity (Hashimoto et al., 2018) that may exist in the data: the model may be less favorable to groups contributing less to the training process; this in turn can degrade population retention in these groups over time, and exacerbate representation disparity in the long run. In this study, we seek to understand the interplay between ML decisions and the underlying group representation, how they evolve in a sequential framework, and how the use of fairness criteria plays a role in this process. We show that the representation disparity can easily worsen over time under a natural user dynamics (arrival and departure) model when decisions are made based on a commonly used objective and fairness criteria, resulting in some groups diminishing entirely from the sample pool in the long run. It highlights the fact that fairness criteria have to be defined while taking into consideration the impact of decisions on user dynamics. Toward this end, we explain how a proper fairness criterion can be selected based on a general user dynamics model.

The energy efficiency and delivery robustness are two critical issues for low duty-cycled wireless sensor networks. The asynchronous receiver-initiated duty-cycling media access control (MAC) protocols have shown their effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this article, we present CD-MAC, an energy-efficient and robust contention-detectable mechanism for addressing the collision-catching problem in receiver-initiated MACs. By exploring the temporal diversity of the acknowledgments, a receiver recognizes the potential senders and subsequently polls individual senders one by one. On that basis, CD-MAC can successfully avoid packet collision even though multiple senders have data packets to transmit to the same receiver. We implement CD-MAC in TinyOS and evaluate its performance on an indoor testbed with single-hop and multi-hop network scenarios. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion. During this iterative process the leakage of data privacy arises. A differentially private ADMM was proposed in prior work (Zhang & Zhu, 2017) where only the privacy loss of a single node during one iteration was bounded, a method that makes it difficult to balance the tradeoff between the utility attained through distributed computation and privacy guarantees when considering the total privacy loss of all nodes over the entire iterative process. We propose a perturbation method for ADMM where the perturbed term is correlated with the penalty parameters; this is shown to improve the utility and privacy simultaneously. The method is based on a modified ADMM where each node independently determines its own penalty parameter in every iteration and decouples it from the dual updating step size. The condition for convergence of the modified ADMM and the lower bound on the convergence rate are also derived.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

Cyber insurance is a viable method for cyber risk transfer. However, it has been shown that depending on the features of the underlying environment, it may or may not improve the state of network security. In this paper, we consider a single profit-maximizing insurer (principal) with voluntarily participating insureds/clients (agents). We are particularly interested in two distinct features of cybersecurity and their impact on the contract design problem. The first is the interdependent nature of cybersecurity, whereby one entity's state of security depends not only on its own investment and effort, but also the efforts of others' in the same eco-system (i.e., externalities). The second is the fact that recent advances in Internet measurement combined with machine learning techniques now allow us to perform accurate quantitative assessments of security posture at a firm level. This can be used as a tool to perform an initial security audit, or pre-screening, of a prospective client to better enable premium discrimination and the design of customized policies. We show that security interdependency leads to a “profit opportunity” for the insurer, created by the inefficient effort levels exerted by interdependent agents who do not account for the risk externalities when insurance is not available; this is in addition to risk transfer that an insurer typically profits from. Security pre-screening then allows the insurer to take advantage of this additional profit opportunity by designing the appropriate contracts which incentivize agents to increase their effort levels, allowing the insurer to “sell commitment” to interdependent agents, in addition to insuring their risks. We identify conditions under which this type of contract leads to not only increased profit for the principal, but also an improved state of network security.

We analyze an online learning problem that arises in crowdsourcing systems for users facing crowdsourced data: a user at each discrete time step t can choose K out of a total of N options (bandits), and receives randomly generated rewards dependent on user-specific and option-specific statistics unknown to the user. Each user aims to maximize her expected total rewards over a certain time horizon through a sequence of exploration and exploitation steps. Different from the typical regret/bandit learning setting, in this case a user may also exploit crowdsourced information to augment her learning process, i.e., other users' choices or rewards from using these options. We consider two scenarios, one in which only their choices are shared, and the other in which users share full information including their choices and subsequent rewards. In both cases we derive bounds on the weak regret, the difference between the user's expected total reward and the reward from a user-specific best single-action policy; and show how they improve over their individual-learning counterpart. We also evaluate the performance of our algorithms using simulated data as well as the real-world movie ratings dataset MovieLens.

We consider a crowd-sourcing problem where in the process of labeling massive data sets, multiple labelers with unknown annotation quality must be selected to perform the labeling task for each incoming data sample or task, with the results aggregated using for example simple or weighted majority voting rule. In this paper, we approach this labeler selection problem in an online learning framework, whereby the quality of the labeling outcome by a specific set of labelers is estimated so that the learning algorithm over time learns to use the most effective combinations of labelers. This type of online learning in some sense falls under the family of multi-armed bandit (MAB) problems, but with a distinct feature not commonly seen: since the data is unlabeled to begin with and the labelers' quality is unknown, their labeling outcome (or reward in the MAB context) cannot be readily verified; it can only be estimated against the crowd and be known probabilistically. We design an efficient online algorithm LS_OL using a simple majority voting rule that can differentiate high and low quality labelers over time, and is shown to have a regret (with respect to always using the optimal set of labelers) of O(log2T ) uniformly in time under mild assumptions on the collective quality of the crowd, thus regret free in the average sense. We discuss further performance improvement by using a more sophisticated majority voting rule, and show how to detect and filter out “bad” (dishonest, malicious or very incompetent) labelers to further enhance the quality of crowd-sourcing. Extension to the case when a labeler's quality is task-type dependent is also discussed using techniques from the literature on continuous arms. We establish a lower bound on the order of O(log T D2(T )), where D2(T) is an arbitrary function such that D2(T ) > O(1). We further provide a matching upper bound through a minor modification of the algorithm we proposed and studied earlier on. We present numerical results using both simulation and set of images labeled by amazon mechanic turks.

We consider an online learning problem (classification or prediction) involving disparate sources of sequentially arriving data, whereby a user over time learns the best set of data sources to use in constructing the classifier by exploiting their similarity. We first show that, when (1) the similarity information among data sources is known, and (2) data from different sources can be acquired without cost, then a judicious selection of data from different sources can effectively enlarge the training sample size compared to using a single data source, thereby improving the rate and performance of learning; this is achieved by bounding the classification error of the resulting classifier. We then relax assumption (1) and characterize the loss in learning performance when the similarity information must also be acquired through repeated sampling. We further relax both (1) and (2) and present a cost-efficient algorithm that identifies a best crowd from a potentially large set of data sources in terms of both classifier performance and data acquisition cost. This problem has various applications, including online prediction systems with time series data of various forms, such as financial markets, advertisement and network measurement.

In a system of interdependent users, the security of an entity is affected not only by that user's effort towards securing her system, but also by the security decisions of other users. The provision of security in such environment is modeled as a public good provision problem, and is referred to as a security game. In this paper, we propose the notion of exit equilibrium to study users' voluntary participation in mechanisms for provision of non-excludable public goods. We show the fundamental result that, due to the non-excludable nature of security, there exists no reliable mechanism which can incentivize the socially optimal investment profile, while ensuring voluntary participation and maintaining a weakly balanced budget, for all instances of security games. To better understand the features of the games that lead to this result, we consider the class of weighted effort games, and apply the two well-known Pivotal (VCG) and Externality mechanisms. Through analysis and simulation, we identify the effects of several features of the problem environment, including diversity in user types, multiplicity of exit equilibria, and users' self-dependence levels, on the performance of these mechanisms.

The threshold model is widely used to study the propagation of opinions and technologies in social networks. In this model individuals adopt the new behavior based on how many neighbors have already chosen it. We study cascades under the threshold model on sparse random graphs with community structure to see whether the existence of communities affects the number of individuals who finally adopt the new behavior. Specifically, we consider the permanent adoption model where nodes that have adopted the new behavior cannot change their state. When seeding a small number of agents with the new behavior, the community structure has little effect on the final proportion of people that adopt it, i.e., the contagion threshold is the same as if there were just one community. On the other hand, seeding a fraction of population with the new behavior has a significant impact on the cascade with the optimal seeding strategy depending on how strongly the communities are connected. In particular, when the communities are strongly connected, seeding in one community outperforms the symmetric seeding strategy that seeds equally in all communities.

In a network of interdependent users, the expenditure in security measures by an entity affects not only herself, but also other users interacting with her. As a result, users' efforts toward security can be viewed as a public good; the optimal provision of which in a system of self-interested entities requires the design of appropriate incentives through an external mechanism. In this paper, we propose the notion of exit equilibrium to study users' voluntary participation in such incentive mechanisms. We show a fundamental result that, due to the non-excludable nature of security, there exists no reliable mechanism, which can incentivize socially optimal investments, while ensuring voluntary participation and maintaining a weakly balanced budget, for all instances of security games. To further illustrate this result, we analyze the performance of two well-known incentive mechanisms, namely the Pivotal (VCG) and Externality mechanisms, in security games. We illustrate how, given a mechanism, stable coalitions of participating users may emerge, leading to an improved, yet sub-optimal security status. We further extend the impossibility result to risk-averse users, and discuss its implications on the viability of using cyber-insurance contracts to improve the state of cyber security.

We consider a distributed multiuser system where individual entities possess observations or perceptions of one another, while the truth is only known to themselves and they might have an interest in withholding or distorting the truth. We ask the question whether it is possible for the system as a whole to arrive at the correct perceptions or assessment of all users, referred to as their reputation, by encouraging or incentivizing the users to participate in a collective effort without violating private information and self-interest. In this paper, we investigate this problem using a mechanism design theoretic approach. We introduce a number of utility models representing users' strategic behavior, each consisting of one or both of a truth element and an image element, reflecting the user's desire to obtain an accurate view of others and an inflated image of itself. For each model, we either design a mechanism that achieves the optimal performance (solution to the corresponding centralized problem), or present individually rational suboptimal solutions. In the latter case, we demonstrate that even when the centralized solution is not achievable, by using a simple punish-reward mechanism, not only does a user have the incentive to participate and provide information, but also that this information can improve the system performance.

Existing work on pursuit-evasion problems typically either assumes stationary or heuristic behavior of one side and examines countermeasures of the other, or assumes both sides to be strategic which leads to a game theoretical framework. Results from the former often lack robustness against changes in the adversarial behavior, while those from the second category, typically as equilibrium solution concepts, may be difficult to justify: either due to the implied knowledge of other players' actions/beliefs and knowledge of their knowledge, or due to a lack of efficient dynamics to achieve such equilibria. In this paper, we take a different approach by assuming an intelligent pursuer/evader that is adaptive to the information available to it and is capable of learning over time with performance guarantee. Within this context we investigate two cases. In the first case we assume either the evader or the pursuer is aware of the type of learning algorithm used by the opponent, while in the second case neither side has such information and thus must try to learn. We show that the optimal policies in the first case have a greedy nature. This result is then used to assess the performance of the learning algorithms that both sides employ in the second case, which is shown to be mutually optimal and there is no loss for either side compared to the case when it knows perfectly the adaptive pattern used by the adversary and responses optimally. We further extend our model to study the application of jamming defense.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we propose Hitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with five nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Sniffer channel assignment (SCA) is a fundamental building block for wireless data capture, which is essential for traffic monitoring and network forensics. Most of the existing SCA approaches for cognitive radio networks (CRNs) adopt optimization-based methods and rely on the prior knowledge of the secondary user (SU) activities. To relax this constraint, learning-based methods have been recently developed; however, there is still insufficient theoretical understanding within the learning framework for SCA. In this paper, we aim to maximize the total amount of the captured SU traffic, and we formulate the SCA problem as a nonstochastic/adversarial multiarmed bandit problem. Moreover, the inherent error in wireless capturing, i.e., imperfect monitoring, is considered in our model. We propose two online learning algorithms for the SCA scenarios with and without channel switching costs, respectively, and their regret performances are proved uniformly sublinear in time and polynomial in the number of channels. The numerical evaluation shows, in addition to their robust regret performances, the proposed algorithms greatly outperform the existing SCA approaches in the amount of effectively captured SU traffic.

In this paper we present a methodology to uncover hidden cliques/communities among a set of nodes when observations of their relationships or connectivities are noisy. Existing literature in community detection typically starts with the assumption that the statistical properties of community structure is known a priori, as well as the number of communities, so the task at hand is solely to partition the set into the given number of groups. In practice neither assumption is necessarily true. Motivated by this, we set out to determine a detectability condition (from spectral analysis) prior to performing the partitioning task, and further illustrate how to combine this detectability condition with clustering algorithms to arrive at desirable partitions without a priori information on the clique structure. We validate our results via simulation and make comparison with existing heuristics to demonstrate its advantages.

In this paper an optimal relay selection algorithm with non-negligible probing time is proposed and analyzed for cooperative wireless networks. Relay selection has been introduced to solve the degraded bandwidth efficiency problem in cooperative communication. Yet complete information of relay channels often remain unavailable for complex networks which renders the optimal selection strategies impossible for transmission source without probing the relay channels. Particularly when the number of relay candidate is large, even though probing all relay channels guarantees the finding of the best relays at any time instant, the degradation of bandwidth efficiency due to non-negligible probing times, which was often neglected in past literature, is also significant. In this work, a stopping rule based relay selection strategy is determined for the source node to decide when to stop the probing process and choose one of the probed relays to cooperate with under wireless channels' stochastic uncertainties. This relay selection strategy is further shown to have a simple threshold structure. At the meantime, full diversity order and high bandwidth efficiency can be achieved simultaneously. Both analytical and simulation results are provided to verify the claims.

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across 6 months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB. Moreover, AcMu provides 2x improvement on localization accuracy by maintaining an up-to-date radio map.

WiFi technology has fostered numerous mobile computing applications, such as adaptive communication, finegrained localization, gesture recognition, etc., which often achieve better performance or rely on the availability of Line-Of-Sight (LOS) signal propagation. Thus the awareness of LOS and Non-Line-Of-Sight (NLOS) plays as a key enabler for them. Realtime LOS identification on commodity WiFi devices, however, is challenging due to limited bandwidth of WiFi and resulting coarse multipath resolution. In this work, we explore and exploit the phase feature of PHY layer information, harnessing both space diversity with antenna elements and frequency diversity with OFDM subcarriers. On this basis, we propose PhaseU, a real-time LOS identification scheme that works in both static and mobile scenarios on commodity WiFi infrastructure. Experimental results in various indoor scenarios demonstrate that PhaseU consistently outperforms previous approaches, achieving overall LOS and NLOS detection rates of 94.35% and 94.19% in static cases and both higher than 80% in mobile contexts. Furthermore, PhaseU achieves real-time capability with millisecond-level delay for a connected AP and 1-second delay for unconnected APs, which is far beyond existing approaches.

Making judicious channel access and transmission scheduling decisions is essential for improving performance (delay, throughput, etc.) as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in the past decade, and the resulting dynamic and opportunistic channel access schemes can bring potentially significant improvement over traditional schemes. However, a common and severe limitation of these dynamic schemes is that they almost always require some form of a priori knowledge of the channel statistics. A natural remedy is a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy (i.e., to stay in the best channel), with performance measured by weak regret, rather than learning a good dynamic channel access policy. There is thus a clear disconnect between what an optimal channel access policy can achieve with known channel statistics that actively exploits temporal, spatial and spectral diversity, and what a typical existing learning algorithm aims for, which is the static use of a single channel devoid of diversity gain. In this paper we bridge this gap by designing learning algorithms that track known optimal or sub-optimal dynamic channel access and transmission scheduling policies, thereby yielding performance measured by a form of strong regret, the accumulated difference between the reward returned by an optimal solution when a priori information is available and that by our online algorithm. We do so in the context of two specific algorithms that appeared in [1] and [2], respectively, the former for a multiuser single-channel setting and the latter for a single-user multichannel setting. In both cases we show that our algorithms achieve sub-linear regret uniform in time and outperforms the standard weak-regret learning algorithms.

The energy efficiency and delivery robustness are two critical issues for low duty cycled wireless sensor networks. The asynchronous receiver-initiated duty cycling media access control (MAC) protocols have shown the effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this paper, we present Contention Detectable MAC (CD-MAC), an energy efficient and robust duty-cycled MAC for general wireless sensor network applications. By exploring the temporal diversity of the acknowledgements, a receiver recognizes the potential senders and subsequently polls individual senders one by one. We further design efficient algorithm to avoid the possible acknowledgement collision. We implement CD-MAC in TinyOS and evaluate the performance on an indoor testbed with single-hop and multi-hop networks. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

We consider a crowd-sourcing problem where in the process of labeling massive datasets, multiple labelers with unknown annotation quality must be selected to perform the labeling task for each incoming data sample or task, with the results aggregated using for example simple or weighted majority voting rule. In this paper we approach this labeler selection problem in an online learning framework, whereby the quality of the labeling outcome by a specific set of labelers is estimated so that the learning algorithm over time learns to use the most effective combinations of labelers. This type of online learning in some sense falls under the family of multi-armed bandit (MAB) problems, but with a distinct feature not commonly seen: since the data is unlabeled to begin with and the labelers' quality is unknown, their labeling outcome (or reward in the MAB context) cannot be directly verified; it can only be estimated against the crowd and known probabilistically. We design an efficient online algorithm LS_OL using a simple majority voting rule that can differentiate high- and low-quality labelers over time, and is shown to have a regret (w.r.t. always using the optimal set of labelers) of O(log2 T) uniformly in time under mild assumptions on the collective quality of the crowd, thus regret free in the average sense. We discuss performance improvement by using a more sophisticated majority voting rule, and show how to detect and filter out "bad" (dishonest, malicious or very incompetent) labelers to further enhance the quality of crowd-sourcing. Extension to the case when a labeler's quality is task-type dependent is also discussed using techniques from the literature on continuous arms. We present numerical results using both simulation and a real dataset on a set of images labeled by Amazon Mechanic Turks (AMT).

In this paper we study opportunistic spectrum access (OSA) policies in a multiuser multi-channel random access cognitive radio network, where users perform channel probing and switching in order to obtain better channel condition or higher instantaneous transmission quality. Prior studies in this area include those on channel probing and switching policies for a single user to exploit spectral diversity, and those on probing and access policies for multiple users over a single channel to exploit temporal and multiuser diversity. By contrast, in this study we consider the collective switching of multiple users over multiple channels. This inevitably necessitates explicit modeling of the effect of collision. Furthermore, we consider finite arrivals, whereby users are not assumed to always have data to send and the demand for channel follows a certain arrival process. Under such a scenario, the users' ability to opportunistically exploit temporal diversity (the temporal variation in channel quality over a single channel) and spectral diversity (quality variation across multiple channels at a given time) is greatly affected by the level of congestion in the system. We investigate the associated decision process in this case, and show that the optimal policy is given by a nested stopping rule which may be viewed as a type of generalization to results found in existing literature in this area. We analytically and numerically evaluate the extent to which congestion affects potential gains from opportunistic dynamic channel switching.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for better understanding of the characteristics of current spectrum utilization as well as better tools for analysis. A number of measurement studies have been conducted recently, revealing previously unknown features. On the other hand, analytical studies largely continues to rely on standard models like the two-state Markov (Gilbert-Elliot) model. In this paper, we present an alternative, stochastic differential equation (SDE) based spectrum utilization model that captures dynamic changes in channel conditions induced by primary users' activities. The SDE model is in closed form, can generate spectrum dynamics as a temporal process, and is shown to provides very good fit for real spectrum measurement data. We show how synthetic spectrum data can be generated in a straightforward manner using this model to enable realistic simulation studies. Moreover, we show that the SDE model can be viewed as a more general modeling framework (continuous in time and continuous in value) than commonly used discrete Markovian models: it is defined by only a few parameters but can be used to obtain the transition matrix of any N-state Markov model. This is verified by comparing the two-state GE model generated by the SDE model and that trained directly from the data. We show that the GE model is a good fit for the (quantized) data, thereby a fine choice when binary descriptions of the channel condition is sufficient. However, when highly resolution (in channel condition) is needed, the SDE model is much more accurate than an N-state model, and is much easier to train and store.

In this paper we study the problem of how to detect and extract a particular type of propagation structure that arises in phishing activities. One of the most interesting phenomena induced by phishing is fast-flux, whereby a single malicious domain is mapped to a constantly changing IP address in order to evade capture and shut-down. This leads to malicious activities observed to be propagating through different networks, even though they originate from the same phishing campaign. To be able to detect and extract such a propagation is of significant importance as it can help us understand and analyze phishing activities. To achieve this goal, we propose a multi-layered propagation model, where layers correspond to different delay stages in the propagation and each is given by an adjacency matrix called the propagation matrix which models pairwise propagation relationships. A regression problem is then formulated to estimate this set of matrices so that the model prediction best fits the data; a Gibbs sampling based randomized algorithm is developed to efficiently find solutions with guaranteed performance. We evaluate our method using both simulation and Internet measurement data.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we proposeHitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with 5 nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., Side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Existing work on pursuit-evasion problems typically either assumes stationary or heuristic behavior of one side and examines countermeasures of the other, or assumes both sides to be strategic which leads to a game theoretical framework. Results from the former may lack robustness against changes in the adversarial behavior, while those from the latter are often difficult to justify due to the implied full information (either as realizations or as distributions) and rationality, both of which may be limited in practice. In this paper, we take a different approach by assuming an intelligent pursuer/evader that is adaptive to the information available to it and is capable of learning over time with performance guarantee. Within this context we investigate two cases. In the first case we assume either the evader or the pursuer is aware of the type of learning algorithm used by the opponent, while in the second case neither side has such information and thus must try to learn. We show that the optimal policies in the first case have a greedy nature, hiding/seeking in the location that the opponent is the least/most likely to appear. This result is then used to assess the performance of the learning algorithms that both sides employ in the second case, which is shown to be mutually optimal and there is no loss for either side compared to the case when it completely knows the adaptive pattern used by the adversary and responses optimally.

In this paper we study a transmission power-tune/control problem in the context of 802.11 Wireless Local Area Networks (WLANs) with multiple (and possibly densely deployed) access points (APs). Previous studies on power control tend to focus on one aspect of the control, either its effect on transmission capacity (PHY layer) assuming simultaneous transmissions, or its effect on contention order (MAC layer) by maximizing spatial reuse. We observe that power control has a dual effect: it affects both spatial reuse and capacity of active transmission; moreover, maximizing the two separately is not always aligned in maximizing system throughput and can even point in opposite directions. In this paper we introduce an optimization formulation that takes into account this dual effect, by measuring the impact of transmit power on system performance from both PHY and MAC layers. We show that such an optimization problem is intractable and develop an analytical framework to construct simple yet efficient solutions. Through numerical results, we observe clear benefits of this dual-effect model compared to solutions by trying to maximize spatial reuse and transmission capacity separately. This problem does not invoke cross-layer design, as the only degree of freedom in design resides with transmission power. It however highlights the complexity in tuning certain design parameters, as the change may manifest itself differently at different layers which may be at odds.

We revisit the jamming defense problem in a multi-channel wireless system, using a general formulation of online learning against an adversary via repeated game-playing. We provide the explicit form of the worst-case optimal channel-hopping strategy of a legitimate user in a multi-stage interaction with a resource-replenishing jamming attacker. Interestingly, we show that the worst imaginary enemy can be given as an adversary who behaves in an i.i.d. manner in this multi-stage interaction, and the optimal strategy of the user is determined by the induced random walk of the adversarial behavior. In addition to the jamming defense, our framework is also applicable to other competitive game problems with finite action spaces.

This paper considers a widely studied stochastic control problem arising from opportunistic spectrum access in a multichannel system, with the goal of providing a unifying analytical framework whereby a number of prior results may be viewed as special cases. Specifically, we consider a single wireless transceiver/user with access to N channels, each modeled as an independent identically distributed discrete-time two-state Markov chain. In each time step, the user is allowed to sense k ≤ N channels, and subsequently use up to m ≤ k channels out of those sensed to be available. Channel sensing is assumed to be perfect, and for each channel used in each time step the user gets a unit reward. The user's objective is to maximize its total discounted or average reward over a finite or infinite horizon. This problem has previously been studied in various special cases including k = 1 and m = k ≤ N, often cast as a restless bandit problem, with optimality results derived for a myopic policy that seeks to maximize the immediate one-step reward when the two-state Markov chain model is positively correlated. In this paper, we study the general problem with 1 m ≤ k ≤ N, and derive sufficient conditions under which the myopic policy is optimal for the finite and infinite horizon reward criteria, respectively. It is shown that these results reduce to those derived in prior studies under the corresponding special cases, and thus may be viewed as a set of unifying optimality conditions. Numerical examples are also presented to highlight how and why an optimal policy may deviate from the otherwise-optimal myopic sensing given additional exploration opportunities, i.e., when m ≤ k.

In this paper, we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access, and cannot provide deterministic service guarantees to a buyer. At the same time, buyers are of different types, characterized by different communication needs, tolerance for the channel uncertainty, and so on, all of which are a buyer's private information. The license holder must then try to design different contracts catered to different types of buyers in order to maximize its profit. We address this problem by adopting as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. We fully characterize the optimal solution in the cases where there is a single buyer type, and when multiple types of buyers share the same known channel condition as a result of the primary user activity. In the most general case, we construct an algorithm that generates a set of contracts in a computationally efficient manner and show that this set is optimal when the buyer types satisfy a monotonicity condition.

We consider the problem of monitoring soil moisture evolution using a wireless network of in-situ underground sensors. To reduce cost and prolong lifetime, it is highly desirable to rely on fewer measurements and estimate with higher accuracy the original signal (the temporal evolution of soil moisture). In this article, we explore the use of results from the theory of sparse sampling, including Compressive Sensing (CS) and Matrix Completion (MC), in this application context. We first consider the problem of reconstructing the soil moisture process at a single location using CS. Our physical constraint leads to very sparse measurement matrices, which makes finding a suitable representation basis very challenging: it needs to make the underlying signal sufficiently sparse while at the same time being sufficiently incoherent with the measurement matrix, two common preconditions for CS techniques to work well. We construct a representation basis by exploiting unique features of soil moisture evolution and show that this basis attains a very good tradeoff between its ability to sparsify the signal and its incoherence with measurement matrices that are consistent with our physical constraints. We next consider the problem of jointly reconstructing soil moisture processes at multiple locations, assuming sparse measurements can be taken at each location. We show that the spatial soil moisture process enjoys a low-rank property, a priority for MC. Accordingly, we introduce a spatiotemporal measurement matrix and apply the MC framework to reconstruct the soil moisture field. Extensive numerical evaluation is performed on both real, high-resolution soil moisture data and simulated data and through comparison with a closed-loop scheduling approach. Our results demonstrate that, for a single location, a uniform measurement scheduling followed by CS recovery results in a very nice tradeoff between estimation accuracy, sampling rate, flexibility, and feasibility in implementation. When multiple locations are available, our results show that joint reconstruction using MC in general produces better estimation accuracy than using a single location alone, but it requires the use of independent and random measurement schedules across locations. We also show that these sparse sampling techniques can be augmented so as to be robust against sporadic data outliers/corruption caused by, for example, intermittent sensor faults.

This poster presents a distributed model-based fault detection algorithm which is based on local pair-wise verification. We first show that there exists a linear relationship between the outputs of any pair of sensors. Therefore, a network can be partitioned into sensor pairs, and the relationship between a pair of sensors can be modeled by a linear model. In addition to detecting general faults happened within a sensor pair, we developed an algorithm for identifying non-linearity type of fault without the use of reference sensors. Preliminary performance analysis shows that this scalable algorithm achieves high diagnosis accuracy. Communication power is also greatly reduced by the distributed nature of the algorithm.

This paper introduces a novel use of concepts from combinatorial group testing and Kalman filtering in detecting faulty sensors in a network when faults are relatively rare. By assigning sensors to specific groups and performing Kalman filter-based fault detection over these groups, we can obtain a small binary detection outcome, which can be decoded to reveal the fault state of all sensors in the network. Compared to existing methods, our algorithm achieves similar or better detection accuracy with fewer tests and thus lower computational complexity. We perform extensive numerical analysis using a set of real vibration data collected from the New Carquinez Bridge in California using an 18-sensor network mounted on the bridge.

In this paper we study opportunistic spectrum access (OSA) policies in a multiuser multichannel random access setting, where users perform channel probing and switching in order to obtain better channel condition or higher instantaneous transmission quality. However, unlikely many prior works in this area, including channel probing and switching policies for a single user to exploit spectral diversity, and probing and access policies for multiple users over a single channel to exploit temporal and multiuser diversity, in this study we consider the collective switching of multiple users over multiple channels. In addition, we consider finite arrivals, i.e., users are not assumed to always have data to send and demand for channel follow a certain arrival process. Under such a scenario, the users' ability to opportunistically exploit temporal diversity (the temporal variation in channel quality over a single channel) and spectral diversity (quality variation across multiple channels at a give time) is greatly affected by the level of congestion in the system. We investigate the optimal decision process in this case, and evaluate the extent to which congestion affects potential gains from opportunistic dynamic channel switching.

In this paper we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access, and cannot provide deterministic service guarantees to a buyer. At the same time, buyers are of different types, characterized by different communication needs, tolerance for the channel uncertainty, and so on, all of which a buyer's private information. The license holder must then try to design different contracts catered to different types of buyers in order to maximize its profit. We address this problem by adopting as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. We fully characterize the optimal solution in the cases where there is a single buyer type, and when multiple types of buyers share the same, known channel condition as a result of the primary user activity. In the most general case we construct an algorithm that generates a set of contracts in a computationally efficient manner, and show that this set is optimal when the buyer types satisfy a monotonicity condition.

We consider the optimal transmission power control of a single wireless node with stochastic energy harvesting and an infinite/saturated queue with the objective of maximizing a certain reward function, e.g., the total data rate. We develop simple control policies that achieve near optimal performance in the finite-horizon case with finite energy storage. The same policies are shown to be asymptotically optimal in the infinite horizon case for sufficiently large energy storage. Such policies are typically difficult to directly obtain using a Markov Decision Process (MDP) formulation or through a dynamic programming framework due to the computational complexity. We relate our results to those obtained in the unsaturated regime, and highlight a type of threshold-based policies that is universally optimal.

We evaluate the performance of an opportunistic multi-channel medium access control protocol and compare it to that of the corresponding single-channel MAC (S-MAC) and a non-opportunistic multi-channel MAC (M-MAC). We do this in three different settings: (1) an ideal scenario where no control channel is used and no sensing delay is incurred, (2) a more realistic scheme where users compete for access on a control channel using random access, and (3) a scheme similar to (2) but with a time-division multiplexing (TDM) based access scheme on the control channel. Our analysis and numerical results show that in terms of delay performance, the random access and competition on the control channel, which typically occupy a fraction of the total bandwidth, almost always wipe out the channel diversity gain, a main motivation behind an opportunistic multi-channel MAC. On the other hand opportunistic access increases bandwidth utilization which reduces the system's total busy time. As a result it helps reduce power consumption in general. When TDM is employed on the control channel, the data sub-channel sensing delay becomes the main bottleneck to attaining better performance. In this case the performance of opportunistic multi-channel MAC gets closer to that of the single-channel MAC when the channel sensing overhead is substantially reduced.

We observe that in a multichannel wireless system, an opportunistic channel/spectrum access scheme that solely focuses on channel quality sensing measured by received SNR may induce users to use channels that, while providing better signals, are more congested. Ultimately the notion of channel quality should include both the signal quality and the level of congestion, and a good multichannel access scheme should take both into account in deciding which channel to use and when. Motivated by this, we focus on the congestion aspect and examine what type of dynamic channel switching schemes may result in the best system throughput performance. Specifically, we derive the stability region of a multiuser multichannel WLAN system and determine the throughput optimal channel switching scheme within a certain class of schemes. We also empirically examine the impact of considering congestion in addition to signal quality in making channel selection decisions.

In this paper we analyze the delay performance of an opportunistic multi-channel medium access control scheme and compare it to that of the corresponding single channel MAC scheme. In the opportunistic multi-channel MAC scheme, we assume that the pair of sender/receiver is able to evaluate the channel quality after a certain amount of channel sensing delay and to choose the best one for data communication. We consider three settings: (1) an ideal scenario where no control channel is needed and no sensing delay is incurred, (2) a more realistic scheme where users compete for access on a control channel using random access, and (3) a scheme similar to (2) but with a Time Division Multiplex (TDM) based access scheme on the control channel. Our analysis show that in terms of delay performance, the random access overhead on the control channel almost always wipe out the channel diversity gain, which is the main motivation behind an opportunistic multi-channel MAC. Using a TDM based access scheme on the control channel can help remove this bottleneck, but only when channel sensing can be done sufficiently fast.

In this paper we develop an adaptive learning algorithm which is approximately optimal for an opportunistic spectrum access (OSA) problem with polynomial complexity. In this OSA problem each channel is modeled as a two state discrete time Markov chain with a bad state which yields no reward and a good state which yields reward. This is known as the Gilbert-Elliot channel model and represents variations in the channel condition due to fading, primary user activity, etc. There is a user who can transmit on one channel at a time, and whose goal is to maximize its throughput. Without knowing the transition probabilities and only observing the state of the channel currently selected, the user faces a partially observed Markov decision problem (POMDP) with unknown transition structure. In general, learning the optimal policy in this setting is intractable. We propose a computationally efficient learning algorithm which is approximately optimal for the infinite horizon average reward criterion.

This poster presents a distributed reference-free fault detection algorithm which is based on local pair-wise verification. We show there exist a linear relationship between the output of any pair of sensors if the system excitations can be aggregated as a single system input. Using this relationship, faulty sensors suffering from sparse spike errors can be identified with high accuracy by our algorithm. An appealing feature of our method is that existence of reference sensors and knowledge of system input are not required. preliminary performance analysis shows that the algorithm is scalable, robust and able to detect most of the faults exist in the sensors. Communication power is also greatly reduced by the distributed nature of the algorithm.

We consider the problem of monitoring soil moisture evolution using a wireless network of in-situ underground sensors. To reduce cost and prolong lifetime, it is highly desirable to rely on fewer measurements and estimate with higher accuracy the original signal (soil moisture temporal evolution). In this paper we explore results from the compressive sensing (CS) literature and examine their applicability to this problem. Our main challenge lies in the selection of two matrices, the measurement matrix and a representation basis. The physical constraints of our problem make it highly non-trivial to select these matrices, so that the latter can sufficient sparsify the underlying signal while at the same time be sufficiently incoherent with the former, two common pre-conditions for CS techniques to work well. We construct a representation basis by exploiting unique features of soil moisture evolution. We show that this basis attains very good tradeoff between its ability to sparsify the signal and its incoherence with measurement matrices that are consistent with our physical constraints. Extensive numerical evaluation is performed on both real, high-resolution soil moisture data and simulated data, and through comparison with a closed-loop scheduling approach. Our results demonstrate that our approach is extremely effective in reconstructing the soil moisture process with high accuracy and low sampling rate.

Combinatorial network optimization algorithms that compute optimal structures taking into account edge weights form the foundation for many network protocols. Examples include shortest path routing, minimal spanning tree computation, maximum weighted matching on bipartite graphs, etc. We present CLRMR, the first online learning algorithm that efficiently solves the stochastic version of these problems where the underlying edge weights vary as independent Markov chains with unknown dynamics. The performance of an online learning algorithm is characterized in terms of regret, defined as the cumulative difference in rewards between a suitably-defined genie, and that obtained by the given algorithm. We prove that, compared to a genie that knows the Markov transition matrices and uses the single-best structure at all times, CLRMR yields regret that is polynomial in the number of edges and nearly-logarithmic in time.

In this paper, we study the online learning problem involving rested and restless bandits, in both a centralized and a decentralized setting. In a centralized setting, the system consists of a single player/user and a set of$K$finite-state discrete-time Markov chains (arms) with unknown state spaces (rewards) and statistics. The objective of the player is to decide in each step which$M$of the$K$arms to play over a sequence of trials so as to maximize its long-term reward. In a decentralized setting, multiple uncoordinated players each makes its own decision on which arm to play in a step, and if two or more players select the same arm simultaneously, a collision results and none of the players selecting that arm gets a reward. The objective of each player again is to maximize its long-term reward. We first show that logarithmic regret algorithms exist both for the centralized rested and restless bandit problems. For the decentralized setting, we propose an algorithm with logarithmic regret with respect to the optimal centralized arm allocation. Numerical results and extensive discussion are also provided to highlight insights obtained from this study.

Estimating link capacity in a wireless network is a complex task because the available capacity at a link is a function of not only the current arrival rate at that link, but also of the arrival rate at links which interfere with that link as well as of the nature of interference between these links. Models which accurately characterize this dependence are either too computationally complex to be useful or lack accuracy. Further, they have a high implementation overhead and make restrictive assumptions, which makes them inapplicable to real networks. In this paper, we propose CapEst, a general, simple yet accurate, measurement-based approach to estimating link capacity in a wireless network. To be computationally light, CapEst allows inaccuracy in estimation; however, using measurements, it can correct this inaccuracy in an iterative fashion and converge to the correct estimate. Our evaluation shows that CapEst always converged to within 5 percent of the correct value in less than 18 iterations. CapEst is model-independent; hence, it is applicable to any MAC/PHY layer and works with autorate adaptation. Moreover, it has a low implementation overhead, can be used with any application which requires an estimate of residual capacity on a wireless link and can be implemented completely at the network layer without any support from the underlying chipset.

The knowledge of channel statistics can be very helpful in making sound opportunistic spectrum access decisions. It is therefore desirable to be able to efficiently and accurately estimate channel statistics. In this paper, we study the problem of optimally placing sensing/sampling times over a time window so as to get the best estimate of the parameters of an on-off renewal channel. We are particularly interested in a sparse sensing regime with a small number of samples relative to the time window size. Using Fisher information as a measure, we analytically derive the best and worst sensing sequences under a sparsity condition. We also present a way to derive the best/worst sequences without this condition using a dynamic programming approach. In both cases the worst turns out to be the uniform sensing sequence, where sensing times are evenly spaced within the window. Interestingly the best sequence is also uniform but with a much smaller sensing interval that requires a priori knowledge of the channel parameters. With these results we argue that without a priori knowledge, a robust sensing strategy should be a randomized strategy. We then compare different random schemes using a family of distributions generated by the circular \beta ensemble, and propose an adaptive sensing scheme to effectively track time-varying channel parameters. We further discuss the applicability of compressive sensing in the context of this problem.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literatures calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper, we present a detailed spectrum measurement study, with data collected in the 20 MHz to 3 GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the statistics of the collected data, including channel vacancy statistics, channel utilization within each individual wireless service, and the spectral and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2D frequent pattern mining algorithm that can predict channel availability based on past observations with considerable accuracy.

This paper studies the problem of distributed computation over a network of wireless sensors. While this problem applies to many emerging applications, to keep our discussion concrete, we will focus on sensor networks used for structural health monitoring. Within this context, the heaviest computation is to determine the singular value decomposition (SVD) to extract mode shapes (eigenvectors) of a structure. Compared to collecting raw vibration data and performing SVD at a central location, computing SVD within the network can result in significantly lower energy consumption and delay. Using recent results on decomposing SVD, a well-known centralized operation, we seek to determine a near-optimal communication structure that enables the distribution of this computation and the reassembly of the final results, with the objective of minimizing energy consumption subject to a computational delay constraint. We show that this reduces to a generalized clustering problem and establish that it is NP-hard. By relaxing the delay constraint, we derive a lower bound. We then propose an integer linear program (ILP) to solve the constrained problem exactly as well as an approximate algorithm with a proven approximation ratio. We further present a distributed version of the approximate algorithm. We present both simulation and experimentation results to demonstrate the effectiveness of these algorithms .

In this paper, we introduce and analyze the properties of a class of games, the atomic congestion games on graphs (ACGGs), which is a generalization of the classical congestion games. In particular, an ACGG captures the spatial information that is often ignored in a classical congestion game. This is useful in many networking problems, e.g., wireless networks where interference among the users heavily depends on the spatial information. In an ACGG, a player's payoff for using a resource is a function of the number of players who interact with it and use the same resource. Such spatial information can be captured by a graph. We study fundamental properties of the ACGGs: under what conditions these games possess a pure strategy Nash equilibrium (PNE), or the finite improvement property (FIP), which is sufficient for the existence of a PNE. We show that a PNE may not exist in general, but that it does exist in many important special cases including tree, loop, or regular bipartite networks. The FIP holds for important special cases including systems with two resources or identical payoff functions for each resource. Finally, we present two wireless network applications of ACGGs: power control and channel contention under IEEE 802.11.

We study the problem of optimal sensor placement in the context of soil moisture sensing. We show that the soil moisture data possesses some unique features that can be used together with the commonly used Gaussian assumption to construct more scalable, robust, and better performing placement algorithms. Specifically, there exists a coarse-grained monotonic ordering of locations in their soil moisture level over time, both in terms of its first and second moments, a feature much more stable than the soil moisture process itself at these locations. This motivates a clustered sensor placement scheme, where locations are classified into clusters based on the ordering of the mean, with the number of sensors placed in each cluster determined by the ordering of the variances. We show that under idealized conditions the greedy mutual information maximization algorithm applied globally is equivalent to that applied cluster by cluster, but the latter has the advantage of being more scalable. Extensive numerical experiments are performed on a set of three-dimensional soil moisture data generated by a state-of-the-art soil moisture simulator. Our results show that our clustering approach outperforms applying the same algorithms globally, and is very robust to lack of training and errors in training data.

In this paper, we consider a cognitive radio network where multiple heterogenous secondary users (SUs) compete for transmissions on idle primary channels. We model this as a singleton congestion game, where the probability for an SU to successfully access a channel decreases with the number of SUs selecting the same channel. In particular, we consider player-specific payoffs that depend not only on the shares of the channel but also on different preference constants. Such system can be modeled as a congestion game, and we study the price of anarchy (PoA) for four families of such a game: identical, player-specific symmetric, resource-specific symmetric, and asymmetric games. We characterize the worst-case PoA in terms of the number of SUs and channels, and illustrate the network scenarios under which the worse case performance is reached. We further illustrate the PoA results with two Medium Access Control (MAC) schemes: uniform MAC and slotted Aloha. For both cases, we observe that the average performance of the game equilibrium is better than the worst-case PoA. Our study sheds light on how to design stable systems with smaller efficiency loss of the equilibrium.

We consider a combinatorial generalization of the classical multi-armed bandit problem that is defined as follows. There is a given bipartite graph of M users and N≥M resources. For each user-resource pair (i,j), there is an associated state that evolves as an aperiodic irreducible finite-state Markov chain with unknown parameters, with transitions occurring each time the particular user i is allocated resource j. The user i receives a reward that depends on the corresponding state each time it is allocated the resource j. The system objective is to learn the best matching of users to resources so that the long-term sum of the rewards received by all users is maximized. This corresponds to minimizing regret, defined here as the gap between the expected total reward that can be obtained by the best-possible static matching and the expected total reward that can be achieved by a given algorithm. We present a polynomial-storage and polynomial-complexity-per-step matching-learning algorithm for this problem. We show that this algorithm can achieve a regret that is uniformly arbitrarily close to logarithmic in time and polynomial in the number of users and resources. This formulation is broadly applicable to scheduling and switching problems in communication networks including cognitive radio networks and significantly extends prior results in the area.

We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activities) is modeled as an arbitrary finite-state Markov chain. At each instance of time, a (secondary) user probes a channel and collects a certain reward as a function of the state of the channel (e.g., good channel condition results in higher data rate for the user). Each channel has potentially different state space and statistics, both unknown to the user, who tries to learn which one is the best as it goes and maximizes its usage of the best channel. The objective is to construct a good online learning algorithm so as to minimize the difference between the user's performance in total rewards and that of using the best channel (on average) had it known which one is the best from a priori knowledge of the channel statistics (also known as the regret). This is a classic exploration and exploitation problem and results abound when the reward processes are assumed to be iid. Compared to prior work, the biggest difference is that in our case the reward process is assumed to be Markovian, of which iid is a special case. In addition, the reward processes are restless in that the channel conditions will continue to evolve independent of the user's actions. This leads to a restless bandit problem, for which there exists little result on either algorithms or performance bounds in this learning context to the best of our knowledge. In this paper we introduce an algorithm that utilizes regenerative cycles of a Markov chain and computes a samplemean based index policy, and show that under mild conditions on the state transition probabilities of the Markov chains this algorithm achieves logarithmic regret uniformly over time, and that this regret bound is also optimal. We numerically examine the performance of this algorithm along with a few other learning algorithms in the case of an OSA problem with Gilbert-Elliot channel models, and discuss how this algorithm may be further improved (in terms of its constant) and how this result may lead to similar bounds for other algorithms.

We observe that in a multi-channel system, an opportunistic channel access scheme that solely focuses on channel quality sensing measured by received SNR may induce users to use channels that, while providing better signals, are more congested. Ultimately the notion of channel quality should include both the signal quality and the level of congestion, and a good multi-channel access scheme should take both into account in deciding which channel to use and when. Motivated by this, we focus on the congestion aspect and examine what type of dynamic channel switching schemes may result in the best system throughput performance. Specifically we derive the stability region of a multi-user multi-channel WLAN system and determine the throughput optimal channel switching scheme within a certain class of schemes.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for better understanding of the characteristics of current spectrum utilization as well as better tools for analysis. A number of measurement studies have been conducted recently, revealing previously unknown features. On the other hand, analytical studies largely continues to rely on standard models like the two-state Markov (Gilbert-Elliot) model. In this paper we present an alternative, stochastic differential equation (SDE) based spectrum utilization model that captures dynamic changes in channel conditions induced by primary users' activities. It is in closed form and verified using real spectrum measurement data. We also present a straightforward procedure to generate synthetic spectrum data that can be used for realistic simulation. We then compare this model (in its time-discretized and value-quantized form) with the standard Gilbert-Elliot model, and in particularly examine their performance when the same channel access policy is applied. Finally we introduce a 3-state Markov model that captures certain key features of the SDE model.

This paper considers a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time and from user to user. Each user has a buffer to store received packets before they are drained. At each time step, the source determines how much power to use for transmission to each user. The source's objective is to dynamically allocate power in a manner that minimizes total power consumption and packet holding costs, while satisfying strict buffer underflow constraints and a joint power constraint in each slot. The primary application motivating this problem is wireless media streaming. For this application, the buffer underflow constraints prevent the user buffers from emptying, so as to maintain playout quality. In the case of a single user, a state-dependent modified base-stock policy is shown to be optimal with linear power-rate curves, and a state-dependent finite generalized base-stock policy is shown to be optimal with piecewise-linear convex power-rate curves. When certain technical conditions are satisfied, efficient methods to compute the critical numbers that complete the characterizations of the optimal control laws in each of these cases are presented. The structure of the optimal policy for the case of two users is then analyzed.

This special issue highlights the state-of-the-art enabling technologies which are critical to sensor networking and explores today's application areas as well as expected future developments.

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.

In this paper, we model and analyze the interactions between secondary users in a spectrum overlay cognitive system as a cognitive MAC game. In this game, each secondary user can sense (and transmit) one of several channels, the availability of each channel is determined by the activity of the corresponding primary user. We show that this game belongs to the class of congestion game and thus there exists at least one Nash Equilibrium. We focus on analyzing the worst case efficiency loss (i.e., price of anarchy) at any Nash Equilibrium of such a game. Closed-form expressions of price of anarchy are derived for both symmetric and asymmetric games, with arbitrary channel and user heterogeneity. Several insights are also derived in terms of how to design better cognitive radio systems with less severe efficiency loss.

Dynamic spectrum access has been a subject of extensive research activity in recent years. The increasing volume of literature calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper we present a detailed spectrum measurement study, with data collected in the 20MHz to 3GHz spectrum band and at four locations concurrently in South China. We examine the first and second order statistics of the collected data, including channel occupancy/vacancy statistics, channel utilization within each individual wireless service, and the temporal, spectral, and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2-dimensional frequent pattern mining algorithm that can accurately predict channel availability based on past observations.

Spectrum is a critical yet scarce resource and it has been shown that dynamic spectrum access can significantly improve spectrum utilization. To achieve this, it is important to incentivize the primary license holders to open up their under-utilized spectrum for sharing. In this paper we present a secondary spectrum market where a primary license holder can sell access to its unused or under-used spectrum resources in the form of certain fine-grained spectrum-space-time unit. Secondary wireless service providers can purchase such contracts to deploy new service, enhance their existing service, or deploy ad hoc service to meet flash crowds demand. Within the context of this market, we investigate how to use auction mechanisms to allocate and price spectrum resources so that the primary license holder's revenue is maximized. We begin by classifying a number of alternative auction formats in terms of spectrum demand. We then study a specific auction format where secondary wireless service providers have demands for fixed locations (cells). We propose an optimal auction based on the concept of virtual valuation. Assuming the knowledge of valuation distributions, the optimal auction uses the Vickrey-Clarke-Groves (VCG) mechanism to maximize the expected revenue while enforcing truthfulness. To reduce the computational complexity, we further design a truthful suboptimal auction with polynomial time complexity. It uses a monotone allocation and critical value payment to enforce truthfulness. Simulation results show that this suboptimal auction can generate stable expected revenue.

This paper considers opportunistic communication over multiple channels where the state (ldquogoodrdquo or ldquobadrdquo) of each channel evolves as independent and identically distributed (i.i.d.) Markov processes. A user, with limited channel sensing capability, chooses one channel to sense and decides whether to use the channel (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a ldquogoodrdquo channel. The objective is to design a channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a partially observed Markov decision process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. This paper shows that a myopic policy that maximizes the immediate one-step reward is optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to two or three, while presenting a counterexample for the case of four channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and antijamming.

In this paper we study an optimal server allocation problem, where a single server is shared among multiple queues based on the queue backlog information. Due to the physical nature of the system this information is delayed, in that when the allocation decision is made, the server only has the backlog information from an earlier time. Queues have different arrival processes as well as different buffering/holding costs. The objective is to minimize the expected total discounted holding cost over a finite or infinite horizon. We introduce an index policy where the index of a queue is a function of the state of the queue. Our primary interest is to characterize conditions under which this index policy is optimal. We present a fairly general method bounding the reward of serving one queue instead of another. Using this result, sufficient conditions on the optimality of the index policy can be derived for a variety of arrival processes and packet holding costs. These conditions are in general in the form of sufficient separation among indices, and they characterize the part of the state space where the index policy is optimal. We provide examples and derive the indices and illustrate the region where the index policy is optimal.

We consider opportunistic communications over multiple channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user, with limited sensing and access capability, chooses one channel to sense and subsequently access (based on the sensed channel state) in each time slot. A reward is obtained when the user senses and accesses a "good" channel. The objective is to design the optimal channel selection policy that maximizes the expected reward accrued over time. This problem can be generally formulated as a Partially Observable Markov Decision Process (POMDP) or a restless multi-armed bandit process, to which optimal solutions are often intractable. We show in this paper that the myopic policy, with a simple and robust structure, achieves optimality under certain conditions. This result finds applications in opportunistic communications in fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and anti-jamming.

We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. One potential application is the Soil Moisture Active/Passive (SMAP) mission. The science measurements considered are the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in-situ sensors. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic data assimilation system subject to energy and accuracy constraints. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware are presented in this paper. We also present results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture.

We consider opportunistic spectrum access (OSA) strategies for a transmitter in a multichannel wireless system, where a channel may or may not be available and the transmitter must sense/probe the channel to find out before transmission. Applications for this work include joint probing and transmission for a secondary user in a cognitive radio network. Limited by resources, e.g., energy and time, the transmitter must decide on a subset of a potentially very large number of channels to probe and can only use for transmission those that have been found to be available. In contrast to previous works, we do not assume the user has a priori knowledge regarding the statistics of channel states. The main goal of this work is to design strategies that decide, based only on knowledge of the channel bandwidths/data rates, which channels to probe. We derive optimal strategies that maximize the total expected bandwidth/data rate in the worst-case, via a performance measure in the form of a competitive regret (ratio) between the average performance of a strategy and a genie (or omniscient observer). We examine the performance of these optimal strategies under a wide range of system parameters and practical channel models via numerical studies.

We consider opportunistic spectrum access (OSA) strategies for a transmitter in a multichannel wireless system, where a channel may or may not be available and the transmitter must sense/probe the channel to find out before transmission. Applications for this work include joint probing and transmission for a secondary user in a cognitive radio network. Limited by resources, e.g., energy and time, the transmitter must decide on a subset of a potentially very large number of channels to probe and can only use for transmission those that have been found to be available. In contrast to previous works, we do not assume the user has a priori knowledge regarding the statistics of channel states. The main goal of this work is to design robust strategies that decide, based only on knowledge of the channel bandwidths/data rates, which channels to probe. We derive optimal strategies that maximize the total expected bandwidth/data rate in the worst-case, via a performance measure in the form of a competitive regret (ratio) between the average performance of a strategy and a genie (or omniscient observer). This formulation can also be viewed as a two-player zero-sum game between the user and an adversary which chooses the channel state that minimizes the useriquests gain. We show that our results correspond to a Nash equilibrium (in the form of a mixed strategy) in this game. We examine the performance of the optimal strategies under a wide range of system parameters and practical channel models via numerical studies.

In this paper, we consider a constrained sequential resource allocation problem where an individual needs to accomplish a task by repeatedly guessing/investing a sufficient level of effort/input. If the investment falls short of a minimum required level that is unknown to the individual, she fails; with each unsuccessful attempt, the individual then increases the input and tries again until she succeeds. The objective is to complete the task with as little resources/cost as possible subject to a delay constraint. The optimal strategy lies in the proper balance between 1) selecting a level (far) below the minimum required and therefore having to try again, thus wasting resources, and 2) selecting a level (far) above the minimum required, and therefore, overshooting and wasting resources. A number of motivating applications arising from communication networks are provided. Assuming that the individual has no knowledge on the distribution of the minimum effort required to complete the task, we adopt a worst-case cost measure and a worst-case delay measure to formulate the above constrained optimization problem. We derive a class of optimal strategies, shown to be randomized, and obtain their performance as a function of the constraint.

In this study we consider optimal opportunistic spectrum access (OSA) policies for a transmitter in a multichannel wireless system, where a channel can be in one of multiple states. Each channel state is associated with either a probability of transmission success or a transmission rate. In such systems, the transmitter typically has partial information concerning the channel states, but can deduce more by probing individual channels, e.g. by sending control packets in the channels, at the expense of certain resources, e.g., energy and time. The main goal of this work is to derive optimal strategies for determining which channels to probe (in what sequence) and which channel to use for transmission. We consider two problems within this context,allthe constant data time (CDT) and the constant access time (CAT) problems. For both problems, we derive key structural properties of the corresponding optimal strategy. In particular, we show that it has a threshold structure and can be described by an index policy. We further show that the optimal CDT strategy can only take on one of three structural forms. Using these results we present a two-step lookahead CDT (CAT) strategy. This strategy is shown to be optimal for a number of cases of practical interest. We examine its performance under a class of practical channel models via numerical studies.

In this paper, we propose a simple yet effective method of identifying traffic conditions on surface streets given location traces collected from on-road vehicles—this requires only GPS location data, plus infrequent low-bandwidth cellular updates. Unlike other systems, which simply display vehicle speeds on the road, our system characterizes unique traffic patterns on each road segment and identifies unusual traffic states on a segment-by-segment basis. We developed and evaluated the system by applying it to two sets of location traces. Evaluation results show that higher than 90 % accuracy in characterization can be achieved after ten or more traversals are collected on a given road segment. We also show that traffic patterns on a road are very consistent over time, provided that the underlying road conditions do not change. This allows us to use a longer history in identifying traffic conditions with higher accuracy.

Abstract — In this paper we consider the problem of query and search in a network, e.g., searching for a specific node or a piece of data. We limit our attention to the class of TTL (time-to-live) based controlled flooding search strategies where query/search packets are broadcast and relayed in the network until a preset TTL value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. Every search attempt also incurs a cost (in terms of packet transmissions and receptions) and a delay (time till timeout or till the target is found). The primary goal is to derive search strategies (i.e., sequences of TTL values) that minimize a worst-case cost measure subject to a worst-case delay constraint. We present a constrained optimization framework and derive a class of optimal strategies, shown to be randomized strategies, and obtain their performance as a function of the delay constraint. We also use these results to discuss the trade-off between search cost and delay within the context of flooding search. Index Terms — data query and search, TTL, controlled flood-ing search, wireless sensor and ad hoc networks, constrained optimization, randomized strategy, competitive analysis I.

This paper derives a lower bound of the form ngamma-1to the per-node throughput achievable by a wireless network when n source-destination pairs are randomly distributed throughout a disk of radius ngamma, 0alpha, alpha > 2

In this paper we present a trace-driven framework capable of building realistic mobility models for the simulation studies of mobile systems. With the goal of realism, this framework combines coarse-grained wireless traces, i.e., association data between WiFi users and access points, with an actual map of the space over which the traces were collected. Through a sequence of data processing steps, including filtering the data trace and converting the map to a graph representation, this framework generates a probabilistic mobility model that produces user movement patterns that are representative of real movement. This is done by adopting a set of heuristics that help us infer the paths users take between access points. We describe our experience applying this approach to a college campus, and study a number of properties of the trace data using our framework.

In this paper, we consider the problem of allocating bandwidth to two queues with arbitrary arrival processes, so as to minimize the total expected packet holding cost over a finite or infinite horizon. Bandwidth is in the form of time slots in a time-division multiple-access schedule. Allocation decisions are made based on one-step delayed queue backlog information. In addition, the allocation is done in batches, in that a queue can be assigned any number of slots not exceeding the total number in a batch. We show for a two queue system that if the holding cost as a function of the packet backlog in the system is nondecreasing, supermodular, and superconvex, then: 1) the value function at each slot will also satisfy these properties; 2) the optimal policy for assigning a single slot is of the threshold type; and 3) optimally allocating M slots at a time can be achieved by repeatedly using a policy that assigns each slot optimally given the previous allocations. Thus, the problem of finding the optimal allocation strategy for a batch of slots reduces to that of optimally allocating a single slot, which is conceptually much easier to obtain. These results are applied to the case of linear and equal holding costs, and we also present a special case where the above results extend to more than two queues.

Simulation has become an indispensable tool in the design and evaluation of mobile systems. By using mobility models that describe constituent movement, one can explore large systems, producing repeatable results for comparison between alternatives. In this paper, we show that a large class of mobility models - including all those in which nodal speed and distance or destination are chosen independently - have a transient period in which the average node speed decreases until converging to some long-term average. This speed decay provides an unsound basis for simulation studies that collect results averaged over time, complicating the experimental process. In this paper, we derive a general framework for describing this decay and apply it to a number of cases. Furthermore, this framework allows us to transform a given mobility model into a stationary one by initializing the simulation using the steady-state speed distribution and using the original speed distribution subsequently. This transformation completely eliminates the transient period and the decay in average node speed and, thus, provides sound models for the simulation of mobile systems.

This paper studies wireless sensor networks that operate in low duty cycles, measured by the percentage of time a sensor is on or active. The dynamic change in topology as a result of such duty-cycling has potentially disruptive effect on the performance of the network. We limit our attention to a class of surveillance and monitoring applications and random duty-cycling schemes, and analyze certain coverage property. Specifically, we consider coverage intensity defined as the probability distribution of durations within which a target or an event is uncovered/unmonitored. We derive this distribution using a semi-Markov model, constructed using the superposition of alternating renewal processes. We also present the asymptotic (as the number of sensors approaches infinity) distribution of the target uncovered duration when at least one sensor is required to cover the target, and provide an asymptotic lower bound when multiple sensors are required to cover the target. The analysis using the semi-Markov model serves as a tool with which we can find suitable random duty-cycling schemes satisfying a given performance requirement. Our numerical observations show that the stochastic variation of duty-cycling durations affects performance only when the number of sensors is small, whereas the stochastic mean of duty-cycling durations impacts performance in all cases studied. We also show that there is a close relationship between coverage intensity and the measure of path availability, defined as the probability distribution of durations within which a path (of a fixed number of nodes) remains available. Thus the results presented here are readily applicable to the study of path availability in a low duty-cycled sensor network

In this paper we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large wireless network. We consider the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. We derive search strategies that minimize the search cost in the worst-case, via a performance measure in the form of the competitive ratio between the average search cost of a strategy and that of an omniscient observer. This ratio is shown in prior work to be lower bounded by 4 among all deterministic search strategies. In this paper we show that by using randomized strategies this ratio is lower bounded by e. We derive an optimal strategy that achieves this lower bound, and discuss its performance under other performance criteria.

We study the optimality of an index policy for a bandwidth allocation problem, where a single server is allocated among N queues in a slotted system based on the queue backlog information. Due to the physical nature of the system this information is delayed, in that when the allocation decision is made, the server only has the backlog information from an earlier time. This results in imperfect and partial state observation. Queues have Bernoulli arrival processes with different probabilities of arrival, as well as different buffering/holding costs to differentiate heterogeneous classes of traffic/service. The objective is to minimize the expected total discounted holding cost over a finite or infinite horizon. We introduce an index policy with indices defined as functions of the state of a queue. We first show that when the state of the system is away from the boundary, i.e., no empty queues, the index policy is optimal. When there are empty queues, we show that under sufficient separation of the indices the index policy is still optimal. We show by example that if the separation does not hold, the index policy is not necessarily optimal. We then formulate the optimal bandwidth allocation as a restless bandit problem and show under what conditions the index policy calculated using Whittle's heuristics, which in general is only asymptotically optimal, is optimal for the finite case.

This paper investigates the problem of providing network coverage using wireless sensors that operate on low duty cycles (measured by the percentage time a sensor is on or active), i.e., each sensor alternates between active and sleep states to conserve energy with an average sleep period (much) longer than the active period. The dynamic change in topology as a result of such duty-cycling has potentially disruptive effect on the operation and performance of the network. This is compensated by adding redundancy in the sensor deployment. In this paper we examine the fundamental relationship between the reduction in sensor duty cycle and the required level of redundancy for a fixed performance measure, and explore the design of good sensor sleep schedules. In particular, we consider two types of mechanisms, the random sleep type where each sensor keeps an active-sleep schedule independent of another, and the coordinated sleep type where sensors coordinate with each other in reaching an active-sleep schedule. Both types are studied within the context of providing network coverage. We present specific scheduling algorithms within each type, and illustrate their coverage and duty cycle properties via both analysis and simulation. We show with either type of sleep schedule the benefit of added redundancy saturates at some point in that the reduction in duty cycles starts to diminish beyond a certain threshold in deployment redundancy. We also show that at the expense of extra control overhead, a coordinated sleep schedule is more robust and can achieve higher duty cycle reduction with the same amount of redundancy compared to a random sleep schedule.

In this paper we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large network. Applications of this problem include searching for a destination node in a mobile ad hoc network, querying for a piece of desired data in a wireless sensor network, and searching for a shared file in an unstructured peer-to-peer network. We limit our attention in this study to the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. The primary goal of this study is to derive search strategies (i.e., sequences of TTL values) that will minimize the cost of such searches associated with packet transmissions. The main results of this paper are as follows. When the probability distribution of the location of the object is known a priori, we present a dynamic programming formulation with which optimal search strategies can be derived that minimize the expected search cost. We also derive the necessary and sufficient conditions %on the location distribution for two very commonly used search strategies to be optimal. When the probability distribution of the location of the object is not known a priori and the object is to minimize the worst-case search cost, we show that the best strategies are randomized strategies, i.e., successive TTL values are chosen from certain probability distributions rather than deterministic values. We show that given any deterministic TTL sequence, there exists a randomized version that has a lower worst-case expected search cost. We also derive an asymptotically (as the network size increases) optimal strategy within a class of randomized strategies.

In this paper we consider a class of loss networks that have arbitrary topologies and routes of arbitrary length. Multiple traffic classes are present, each with different bandwidth requirement, and each routed according to a state-dependent routing scheme. In particular, we consider the least loaded routing method generalized to routes of arbitrary number of hops. The connection level performance metric of interest is the end-to-end blocking probability. We are interested in developing fast evaluation methods to provide reasonably accurate estimates of the blocking probability, especially under heavy traffic load. Our algorithms are based on the fixed-point method framework, also known as the reduced load approximation. In addition to what commonly examined by previous work, two more factors contribute to the complexity of the computation in the scenario under consideration in this paper. One is the state-dependent nature of the routing mechanism, the other is the possible overlapping between routes due to the general multihop topology of the network. We present two fast approximation algorithms to evaluate the blocking probability with state-dependent routing by simplifying the route overlapping computation. We discuss the computational complexity of our algorithms as well as sources of approximation error. We then compare the numerical results with that of simulation and show that our algorithms provide fairly accurate blocking probability estimates especially under heavy traffic load.

In this paper we present a Markov chain for TCP congestion avoidance phase. With this model we are able to analyze congestion window behavior as a discrete-time stochastic process and distinguish between window transient period and steady state. Using this result we are able to obtain more accurate estimate of TCP latency over lossy links compared to existing models. We then simplify the proposed model and show that the transient period evolves with an exponential rate. Our results are validated using NS2 simulation and show significant improvement in latency estimate for a wide range of file sizes.

This study examines the random waypoint model widely used in the simulation studies of mobile ad hoc networks. Our findings show that this model fails to provide a steady state in that the average nodal speed consistently decreases over time, and therefore should not be directly used for simulation. We show how unreliable results can be obtained by using this model. In particular, certain ad hoc routing metrics can drop by as much as 40% over the course of a 900-second simulation using the random waypoint model. We give both an intuitive and a formal explanation for this phenomenon. We also propose a simple fix of the problem and discuss a few alternatives. Our modified random waypoint model is able to reach a steady state and simulation results are presented.

Simulation has become an indispensable tool in the construction and evaluation of mobile systems. By using mobility models that describe constituent movement, one can explore large systems, producing repeatable results for comparison between alternatives. Unfortunately, the vast majority of mobility models---including all those in which nodal speed and distance or destination are chosen independently---suffer from decay; average speed decreases until converging to some long-term average. Such decay provides an unsound basis for simulation studies that collect results averaged over time, complicating the experimental process.This paper shows via analysis that such decay is inevitable in a wide variety of mobility models, including the most common in use today. We derive a general framework for describing this decay, and apply it to a number of practical cases. Furthermore, this framework allows us to transform any given mobility model into a stationary one: choose initial speeds from the steady-state distribution, and subsequent speeds from the original. This transformation provides sound models for simulation, eliminating variations in average nodal speed.

The paper examines the performance as well as energy consumption Issues of a wireless sensor network providing periodic data from a sensing field to a remote receiver. The sensors are assumed to be randomly deployed. We distinguish between two types of sensor organizations, one with a single layer of identical sensors (homogeneous) and one with an additional overlay of fewer but more powerful sensors (heterogeneous). We formulate the energy consumption and study their estimated lifetime based on a clustering mechanism with varying parameters related to the sensing field, e.g., size, and distance. We quantify the optimal number of clusters based on our model and show how to allocate energy between different layers.

We present a hierarchical loss network model for estimating the end-to end blocking probabilities of large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so far all work being done in this area is for flat networks with flat routing schemes. We aim at developing a more efficient approximation method for networks that have a natural hierarchy and/or when some form of hierarchical routing policy is used. We present two hierarchical models in detail for fixed hierarchical routing and dynamic hierarchical routing policies, respectively, via the notion of network abstraction, route segmentation, traffic segregation and aggregation. Computation is done separately within each cluster (local) and among clusters (global), and the fixed point is obtained by iteration between local and global computations. We present results from both numerical experiments and discrete event simulations.