Gesture recognition has significant applications in areas such as assisted living, e-health, and human-device interactions. Moving beyond conventional computer vision techniques, recent studies have increasingly adopted ubiquitous methods like Wi-Fi and acoustic signals, which provide a cost-effective solution for device deployment. In this paper, we explore these two ubiquitous techniques to enhance gesture recognition, focusing on overcoming challenges associated with multi-modal fusion. To harmonize information from these inherently different signal types, we propose a tailored fusion strategy specifically designed for Wi-Fi and acoustic signals. Traditional multi-modal fusion methods often lack a theoretical framework due to insufficient analysis of the fundamental characteristics of different signals. To address this gap, we introduce the concept of the Hybrid Zone, a novel theoretical framework that models the interaction and fusion of acoustic and Wi-Fi sensing signals. The Hybrid Zone offers a unified perspective on the interaction between acoustic and Wi-Fi sensing areas and delivers insights into the granular synthesis of their velocity profiles. Our experimental results demonstrate strong performance, achieving a gesture recognition accuracy rate of 94.69%.

Gesture recognition has significant applications such as assisted living, e-health, and human-device interactions. Moving away from conventional computer vision techniques, recent studies are turning towards ubiquitous methods such as Wi-Fi and acoustic signals, which offer device deployment at minimal costs. In this paper, we explore these two ubiquitous techniques to enhance gesture recognition, addressing challenges related to multi-modal fusion. Due to the inherent differences in signal types, we employ a tailored method to harmonize information from these distinct signals. Traditional multi-modal fusion methods often lack theoretical models due to insufficient analysis of the foundational characteristics of different signals. In particular, we propose a concept, Hybrid Zone, which is a theoretical model illustrating the fusion of acoustic and Wi-Fi sensing. Hybrid zone offers a comprehensive perspective on the fusion of acoustic and Wi-Fi sensing areas. Moreover, it provides an intricate view of the synthesis of acoustic and Wi-Fi velocities at a granular level. Our experimental results have been promising, achieving a high accuracy rate of 93.75% in gesture recognition.

Spectrum prediction is a key enabler for the forthcoming coexistence paradigm where various Radio Access Technologies share overlapping radio spectrum, to substantially improve spectrum efficiency in 5G and beyond systems. Although this fundamental issue has received tremendous research attention, existing algorithms are designed for and validated against spectrum usage data in low time-frequency granularities, which causes inevitable errors when applied to spectrum prediction in realistic resolutions. Therefore, in this paper, we redesign three key components along the spectrum prediction pipeline to propose Effi-Ace, an efficient and accurate prediction for high-resolution spectrum tenancy. First, we obtain raw spectrum data in the same resolutions as scheduling, which reflects the actual dynamics of the subject to be predicted. We improve the Deep Q-Network (DQN) prediction algorithm with enhanced experience replay to reduce sample complexity, so that the proposed DQN is more efficient in terms of sample quantities. New prediction features are extracted from high-resolution measurement data to improve prediction accuracy. According to our detailed experiments, the proposed prediction algorithm substantially reduces sample complexity by 88. 9%, and the improvements in prediction accuracy are up to 14%, when compared with various state-of-the-art counterparts.

With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (${{\rm{E}}^{2}}\rm{L}$) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy.

It is time-consuming to collect a huge number of pixel-level annotations for accurately extracting buildings by deep neural networks. Supported by class activation map (CAM), weakly supervised semantic segmentation (WSSS) methods with image-level annotations serve as an efficient solution for building extraction. However, it is a great challenge to generate highquality CAM heatmaps for buildings from high-resolution remote sensing images. On one hand, image-level labels lack spatial information, resulting in partial integrity and hollow phenomenon for building extraction. On the other hand, complex backgrounds in remote sensing images can lead to inaccurate extraction of building boundaries. In this study, we propose a novel weakly supervised building extraction method called FlipCAM to deal with these challenges. The Flip module based on feature-level flipping augmentation is designed to improve the integrity of CAM heatmaps by fusing the original and flipped feature maps. In addition, by combining Flip module with slice and merge (SAM) module based on consistency architecture, FlipCAM is able to generate high-quality CAM heatmaps with both boundary fineness and internal integrity in an end-to-end manner, which also alleviates special difficulties for building extraction, including adhesions in dense buildings and confusions with background and shadows, providing reliable pixel-level pseudo masks for training segmentation network to extract buildings. Extensive experiments on three high-resolution datasets show that FlipCAM achieves excellent performance and outperforms other weakly supervised methods in terms of effectiveness and robustness capabilities. Our code is public at https://github.com/NJU-LHRS/FlipCAM-master.

Existing measurements and modeling of radio spectrum usage have shown that exclusive access leads to low efficiency. Thus, the next generation of wireless networks is adopting new paradigms of spectrum sharing and coexistence among heterogeneous networks. However, two significant limitations in current spectrum tenancy models hinder the development of essential functions in nonexclusive spectrum access. First, these models rely on data with much coarser resolutions than those required for wireless scheduling, rendering them ineffective for spectrum prediction or characterizing spectrum access behavior in a wireless coexistence setting. Second, due to a lack of detailed data, current models cannot describe the access dynamics of individual users, leading to unjustified adoption of simplistic traffic models, such as the on/off model and the M/G/1 queue, in spectrum access algorithm research. To address these limitations, we propose the Frame-Level spectrum Model (FLuMe), a data-driven model that characterizes individual spectrum usage based on high-resolution data. This lightweight model tracks the spectrum tenancy movements of individual users using four variables. The proposed model is applied to high-resolution LTE spectrum tenancy data, from which model parameters are extracted. Comprehensive validations demonstrate the goodness-of-fit of the model and its applicability to spectrum prediction.

The rapid growth of IoT devices and applications has resulted in the generation and transmission of large volumes of private data, which encompass various dimensions and are observable by multiple entities. To address the need for balancing confidentiality and data utility, the Quantitative Information Flow (QIF) approach has been developed to quantify information leakage in computer systems. However, it assumes a single adversary seeking to infer a single piece of secret information. This paper examines leakage measures in the Multiple Attributes and Adversaries (MAA) scenario where attributes are under coordinated attacks from multiple adversaries through individual side channels. The paper reveals that the current model, when applied individually to each attribute or adversary, only considers scenarios where all attributes are leaked to all adversaries. However, any leakage of an attribute to any adversary should be deemed a breach of private data. To tackle this, the paper introduces the MAA leakage model and validates its compliance with a leakage measure and operational interpretation of the MAA threat model. Additionally, it presents a closed-form expression for the upper bound of MAA leakage within a given set of side channels, demonstrating that the upper bound is achieved when all attributes conform to a uniform distribution.

The applications of spectrum prediction span over a wide range of crucial fields in wireless networks, such as spectrum efficiency improvement, service quality enhancement, and network management. Despite such broad ranges of fundamental applications of spectrum prediction, existing methods are based on coarse measurement of power spectral density values. Few predictions target the actual binary tenancy of whether the spectrum slices are occupied or left unused, but their data resolution and prediction accuracy are far from satisfactory. To improve the accuracy of spectrum prediction, we propose the framework of Total Occupancy guided Prediction (TOP). It is a general prediction scheme that is flexible to incorporate an arbitrary algorithm into its framework with enhanced accuracy. Through characterizing the prediction of binary spectrum tenancy as data transmissions over the Binary Symmetric Channel (BSC), we analytically and numerically show that the two key assumptions justifying the superior performance of TOP are valid. To evaluate the accuracy of the TOP framework on spectrum tenancy from real world measurement, we set up a Software Defined Radio (SDR) testbed to measure LTE spectrum tenancy by decoding the Downlink Control Information (DCI) to gain high resolution usage at the same granularity with LTE scheduling. Armed with the high resolution data, we adapt the Multi-Layer Perceptron (MLP) algorithm into the TOP framework to validate its performance. The thorough experiments reveal that the TOP framework significantly improves MLP accuracy from 0.84 to 0.91, outperforming many state-of-the-art prediction schemes.

Comparing the recognition of human gestures using cameras, radar, or LiDAR, a WiFi-based gesture recognition system has distinct advantages, such as being low-cost, being device-free, and having much less privacy leakage. Recently, there have been advancements in WiFi-based gesture recognition, but most of the research has primarily focused on single-person gesture recognition. However, in real-world scenarios like e-learning, it is common for multiple individuals to engage in different actions simultaneously. To this end, this paper focuses on multi-person gesture recognition, which presents two major challenges, that is, the recognition accuracy due to the WiFi signal interference, and the processing time for real-time applications. Multi-person gesture recognition is more challenging than single-person scenario due to the interference caused by the superposition of WiFi signals induced by multiple moving individuals. In this paper, we define a concept of super-gesture and propose a WiFi-based Super-Gesture recognition (WiSG) method. Through the decomposition of the super-gesture's DFS spectrogram by Multi-Motion Trajectory algorithm, we extract modified signals of each person. Moreover, a novel feature called Field Motion Velocity is proposed by fully exploiting the advantages of our multiple transmitter-receiver WiFi sensing system. The proposed feature is not significantly affected by domains such as position, orientation, and other factors irrelevant to gestures. As a result, our approach can effectively recognize gestures across different domains. Evaluation results show that the cross-domain recognition accuracy of our WiSG can achieve up to 89% in multi-person scenario. Moreover, our approach can reduce processing time by 20 times against Widar3.0, which satisfies the requirements of most real-time applications.

Task offloading, which refers to processing (computation-intensive) data at facilitating servers, is an exemplary service that greatly benefits from the fog computing paradigm, which brings computation resources to the edge network for reduced application latency. However, the resource-consuming nature of task execution, as well as the sheer scale of IoT systems, raises an open and challenging question: whether fog is a remedy or a resource drain, considering frequent and massive offloading operations? This question is nontrivial, because participants of offloading processes, i.e., fog nodes, may have diversified technical specifications, while task generators, i.e., task nodes, may employ a variety of criteria to select offloading targets, resulting in an unmanageable space for performance evaluation. To overcome these challenges of heterogeneity, we propose a gravity model that characterizes offloading criteria with various gravity functions, in which individual/system resource consumption can be examined by the device/network effort metrics, respectively. Simulation results show that the proposed gravity model can flexibly describe different offloading schemes in terms of application and node-level behavior. We find that the expected lifetime and device effort of individual tasks decrease as$O({}{1}/{N})$over the network size$N$, while the network effort decreases much slower, even remain$O(1)$when load balancing measures are employed, indicating a possible resource drain in the edge network.

Mobile networks are embracing Dynamic Spectrum Access (DSA) to unleash data capacities of spectrum holes caused by tidal traffic. Being the largest mobile system, LTE has been standardized to operate in the DSA mode where the knowledge on the spectrum tenancy of LTE systems is required. Although there exists rich literature on spectrum sensing, measurement and modeling, they cannot satisfy the needs of accurately acquiring the spectrum tenancy of LTE systems. This is because most traditional measurements only provide inaccurate tenancy in coarse granularities, and therefore models built upon them are defective. To enable the precise discovery of spectrum assignments of an LTE cell from an outsider perspective, we build U-CIMAN toUnCover spectrum occupancy and userInformation inMobileAccessNetworks. The LTE protocol fields parsed by U-CIMAN not only accurately reveal the spectrum occupancy at the same granularity with LTE scheduling, but also provide important details associated with spectrum usage, i.e., rough user locations and traffic types. Besides insightful observations based on measurements enabled by U-CIMAN, we propose to characterize LTE spectrum occupancy using Vector Autoregression that captures the statistical distributions of spectrum tenancy intervals in multiple channels and the correlations among them.

Cloudlets, which refer to the edge computing services deployed at the proximity of end devices, are key providers of connectivity, storage, and computation resources to many applications. While access to cloudlets is pervasive in typical settings, it can be difficult in challenging, even hostile environments, such as military or post-disaster scenarios, featuring multi-hop communication and energy-constrained end devices. In these cases, cloudlets may have become the only equipment powerful enough to execute life-critical applications, such as battle-field situation awareness, tactic cooperation, and search-and-rescue missions. Quality of these services is greatly influenced by the minimum time that a packet can be delivered, i.e., the cloudlet access delay (CAD), whose characteristics remain unknown. To address the open question of fast and efficient cloudlet access, we establish a packet mobility model that allows CAD and energy consumption to be analyzed as a function of the initial device-cloudlet distance. We find that the expected CAD scales either linearly or quadratically under distinct types of packet mobility, and the successful access rate (SAR) can be bounded by functions of the delay constraint. Based on these findings, we develop a packet shedding algorithm that saves 24% transmission power, and reduces the average CAD by 2%, while maintaining a similar SAR in simulated cloudlet access environments.

Spectrum activity surveillance (SAS) is essential to dynamic spectrum access (DSA)-enabled systems with a two-fold impact: it is a primitive mechanism to collect usage data for spectrum efficiency improvement; it is also a prime widget to collect misuse forensics of unauthorized or malicious users. While realizing SAS for DSA-enabled systems appears to be intuitive and trivial, it is, however, a challenging yet open problem. On one hand, a large-scale SAS function is costly to implement in practice; on the other hand, it is not clear how to characterize the efficacy and performance of monitor deployment strategies. To address such challenges, we introduce a three-factor space, composed ofspectrum,time, andgeographic region, over which the SAS problem is formulated by a two-step solution: 3D-tessellation for sweep (monitoring)coverageand graph walk for detectingspectrum culprits, that is, devices responsible for unauthorized spectrum occupancy. In particular, our system model transforms SAS from a globally collective activity to localized actions, and strategy objectives from qualitative attributes to quantitative measures. With this model, we design low-cost deterministic strategies for dedicated monitors, which outperform strategies found by genetic algorithms, and performance-guaranteed random strategies for crowd-source monitors, which can detect adversarial spectrum culprits in bounded time.

Dynamic Spectrum Access (DSA) is proposed to improve spectrum efficiency by enabling opportunistic access of underutilized spectrum resources. The key to successful DSA operations is the correct understanding of spectrum hole distributions. Though huge amounts of studies have been conducted on spectrum tenancy due to the significance of spectrum hole distributions, there are still two overlooked aspects. One is the measurement resolution, and the other is the spectrum distribution in the spectral perspective. Since the spectrum hole analysis relies on the measurement data, we decode the LTE downlink control information to obtain the spectrum tenancy at the same time-frequency granularity with LTE scheduling. We analyze the spectrum hole distributions in fine resolutions along both the temporal and the spectral dimensions, and investigate the performance of two widely used spectrum tenancy models, the Markov and the on/off models, in terms of their capabilities on capturing the distributions of spectrum holes. Our observations include but are not limited to the following. The spectrum holes follow the power law distributions when examined in the LTE scheduling unit from both the time and the frequency perspectives. Both Markov and on/off models should be fitted to the spectrum tenancy along the frequency perspective to achieve their best performance.

Effective clustering is vital to mitigate routing scalability and reliability issues in heterogeneous vehicular networks. In this paper, we propose an adaptive clustering scheme to maximize the cluster stability in vehicular networks. The scheme uses the predicted driving behavior of vehicles over a time horizon to maximize the clusters' lifetime. To this end, we first define the stability degree of vehicles by exploiting the unique aspects of vehicular environments. We then formulate the clustering problem as an optimization problem, which is used within a rolling horizon framework in the cluster formation process. Our scheme is based on a heterogeneous vehicular network architecture, which allows the coexistence of dedicated short-range communication and cellular network for vehicular communications. The simulation results demonstrate that our scheme significantly outperforms alternative clustering algorithms in terms of the overall clusters' lifetime under different traffic conditions. Our scheme can also be utilized to provide a well-grounded comprehension of the optimally of the existing and future distributed clustering algorithms.

With the proliferation of Dynamic Spectrum Access (DSA), Internet of Things (IoT), and Mobile Edge Computing (MEC) technologies, various methods have been proposed to deduce key network and user information in cellular systems, such as available cell bandwidths, as well as user locations and mobility. Not only is such information dominated by cellular networks of vital significance on other systems co-located spectrum-wise and/or geographically, but applications within cellular systems can also benefit remarkably from inferring such information, as exemplified by the endeavours made by video streaming to predict cell bandwidth. Hence, we are motivated to develop a new tool to uncover as much information used to be closed to outsiders or user devices as possible with off-the-shelf products. Given the wide-spread deployment of LTE and its continuous evolution to 5G, we design and implement U-CIMAN, a client-side system to accurately UnCover as much Information in Mobile Access Networks as allowed by LTE encryption. Among the many potential applications of U-CIMAN, we highlight one use case of accurately measuring the spectrum tenancy of a commercial LTE cell. Besides measuring spectrum tenancy in unit of resource blocks, U-CIMAN discovers user mobility and traffic types associated with spectrum usage through decoded control messages and user data bytes. We conduct 4-month detailed accurate spectrum measurement on a commercial LTE cell, and the observations include the predictive power of Modulation and Coding Scheme on spectrum tenancy, and channel off-time bounded under 10 seconds, to name a few.

Emerging mobile applications enable people to connect with one another more easily than ever, which causes networked systems, e.g., online social networks (OSN) and Internet-of-Things (IoT), to grow rapidly in size, and become more complex in structure. In these systems, different, even conflicting information, e.g., rumor v.s. truth, and malware v.s. security patches, can compete with each other during their propagation over individual connections. For such information pairs, in which a desired information kills its undesired counterpart on contact, an interesting yet challenging question is when and how fast the undesired information dies out. To answer this question, we propose a Susceptible-Infectious-Cured (SIC) propagation model, which captures short-term competitions between the two pieces of information, and define extinction time and half-life time, as two pivots in time, to quantify the dying speed of the undesired information. Our analysis revealed the impact of network topology and initial conditions on the lifetime of the undesired information. In particular, we find that, the Cheeger constant that measures the edge expansion property of a network steers the scaling law of the lifetime with respect to the network size, and the vertex eccentricities that are easier to compute provide accurate estimation of the lifetime. Our analysis also sheds light on where to inject the desired information, such that its undesired counterpart can be eliminated faster.

Recent years have witnessed the rapid growth of mobile users, which further accelerates the deployment of mobile communication networks, especially LTE networks, due to its high data rate, as well as comprehensive functionality. Compared to its predecessors, LTE networks have incorporated a number of security measures specified by 3GPP, including amalgamation of temporary identities, mutual authentication, and enhanced signaling procedures, which are meant to protect the system and individual subscribers against various forms of attacks. However, as we show in this paper, flaws in real-world implementation render commercial LTE systems vulnerable to several attacks, including identity spoofing and denial-of-service (DoS), which have severe impacts on subscriber's data integrity, QoS, and even privacy. Specifically, we identify the vulnerabilities by carefully analyzing LTE specifications, list possible attacks targeting these vulnerabilities, and successfully implement two attacks on a commercial LTE network with a USRP-based testbed. Our work reveals severe security risks in real-world LTE systems, which call for immediate enhancement from both standardization organizations and cellular service providers.

The mainstay of current spectrum access grants exclusive rights to proprietary occupants who exhibit tidal traffic patterns, leading to low usage of valuable spectrum resources. To remedy this situation, Dynamic Spectrum Access (DSA) is proposed to allow Secondary Users (SUs) to opportunistically exploit idle spectrum slices left by Primary Users (PUs). The key to the success of DSA lies in SUs' knowledge on radio activities of PUs. To enhance the understanding of PU spectrum tenancy patterns, various mathematical models have been proposed to describe spectrum occupancy dynamics. However, there are still two overlooked aspects in existing studies on spectrum tenancy modeling, i.e., time-varying spectrum tenancy patterns and multiple channels within the same Radio Access Technology (RAT). To address the two issues, we apply a change detection algorithm to discover time points where spectrum tenancy patterns vary, and propose to characterize spectrum usage in a multi-channel RAT by the Vector Autoregressive (VAR) model. Through analyzing LTE spectrum tenancy data with the algorithm and the model, we validate that the segment size discovered by the online change detection method coincides with the one obtained by brute force, and VAR outperforms the widely adopted on/off model.

Most existing studies for information dissemination in the online social network are based on variations of the classical epidemic model. In such a model, nodes recursively infect, or share information to, their neighboring nodes with a certain probability. The higher degree a node has, the more likely it gets infected by its neighbors. Although widely accepted, we found there are certain discrepancies between existing epidemic models and social interactions in reality. Firstly, the real-world social network is actually a dual-layered network, where a person shares information online to her online friends, and also offline to her real-life friends. More importantly, since a computer do not automatically share information, a computer exposed to information will not effectively receive it (i.e., getting infected and starting to infect others) unless its user receives it. Secondly, contrary to the epidemic model, the more friends a person has, the less likely she is going to effectively receive a certain piece of message (just imagine how easily a message can be flushed and ignored by a human user because of overwhelming newer information). In other words, in social networks, the infection rate of a node may not be positively correlated with its degree. Based on these observations, we develop the social-physical interdependent (SPI) model to capture and analyze the unique characters of social networks. Our study provides new observations, and sheds light on a new direction for the study of information dissemination in social networks.

Spectrum monitoring, run-time usage acquisition, and regulation enforcement, in general can be referred to as spectrum activity surveillance (SAS). It is essential to dynamic spectrum access with a two-fold impact: it is a primitive mechanism to continuously scan spectrum usage for system optimization purposes; it is also a prime widget to obtain spectrum footprints of legitimate users, and record misuse by unauthorized or malicious users. Seemingly trivial, large-scale SAS in wireless overlay networks is actually an open yet challenging problem. This is because on one hand, such a system is time and energy-sensitive and hence unlikely (or not necessary) to implement in practice, due to constraints of radio spectrum license and system deployment. On the other hand, it is not clear how to characterize the efficacy and performance of spectrum monitoring strategies in surveillance over a large geographical region, and detection of spectrum culprits, that is, unauthorized spectrum occupants. To address such a challenge, we consider SAS in a 3-dimensional space that is composed of spectrum, time, and geographical region, and then formulate monitoring strategies as graph walks by accounting for the locality of spectrum activities. In particular, our approach transforms the SAS problem from a globally collective activity to a set of localized, distributed actions, and strategy objectives from qualitative attributes to quantitative measures. We find that randomized strategies with m monitors can achieve a sweep-coverage over a space of n assignment points in Θ(n/m ln n) time, and detect an oblivious or adversarial spectrum culprit in Θ(n/m) time for SAS systems.

Internet of Things (IoT) is a networking paradigm that interconnects physical systems to the cyber world, to provide automation and intelligence via interdependent links between the two domains. Such interdependence renders IoT systems vulnerable to random failures, e.g., broken communication links or crashed cyber instances, because a single incident in one domain can develop into a cascade-of-failures across domains, which dissolves the network structure, and has devastating consequences. To answer how robust an IoT system is, this paper studies its resilience by examining the impact of edge- and jointly-induced cascades, that is, a sequence of failures caused by randomly broken physical links (and simultaneous failing cyber nodes). Resilience of an IoT system is quantified by two new metrics, the critical edge disconnecting probability φcr, i.e., the maximum intensity of random failures the system can withstand, and the cascade length τcf, i.e., the lifetime of a cascade. For IoT systems with Poisson degree distributions, we derive exact solutions for the critical disconnecting probability φcr, above which an edge-induced cascade will completely fragment the network. We also find that the critical condition φcrmarks a dichotomy of the expected cascade length E(τcf): for the super-critical (φ > φcr) scenario, we obtain E(τcf) ~ exp(1 - φ) through analysis, while for the subcritical scenario, we observe E(τcf) ~ exp(1/1 - φ) through simulations. With these results, the final outcome of a cascade can be anticipated upon the initial failures, while the reaction window of time-sensitive countermeasures can be obtained before a cascade fully unfolds.

Network Multiple Input Multiple Output (MIMO) is able to increase spectrum efficiency and mitigate inter-cell interference. These benefits mainly result from one salient feature of network MIMO, the centralized control enabling coordinated scheduling. As Software Defined Networking (SDN) provides the innate centralized control, we propose a new cellular system architecture featuring SDN network MIMO. In this setting, the coordination of network MIMO downlink transmission is achieved in the SDN fashion. Two performance metrics, Cluster Average flow rule Storage Load (CASL) and Cluster Average flow table Miss Rate (CAMR) of Base Stations (BSs) are solved based on a Multivariate Markov Chain (MMC) model for flow rule dynamics of the BSs in a cluster. According to the analysis, we design an algorithm for the controller to assign flow rule timeout values to minimize the CASL for a given CAMR. Simulation results are presented to show the accuracy of the analysis, and the insight into the trade-off between the two metrics.

Internet of things (IoT) is expected to provide a fully informative and controllable environment that features networking, automation, and intelligence by interconnecting physical systems to cyber world. Such a correlation opens the interdependence between the two, upon which a single incident in one domain, e.g., a broken communication link, or an out-of-battery device, can cause a cascade-of-failures across physical and cyber domains. To understand the resilience of IoT systems against such detrimental cascades, this paper studies the aftermath of edge and jointly-induced cascades, that is, a sequence of failures induced by randomly broken physical links (and simultaneous failing cyber nodes) by answering how many nodes will survive the cascade with a newly defined node yield metric. Specifically, we construct a framework to establish self-consistent equations of node yield through an auxiliary graph, without requiring the exact network topology. Then two algorithms are proposed to numerically calculate node yield for interdependent networks with arbitrary degree distributions. For random graph with Poisson degree distributions, we prove the existence of a critical initial edge disconnecting probability φcr, under which an edge-induced cascade will result in dissolving the network topology, derive the closed form solution for φcr, and find that φcrincreases sub-linearly with the mean degree of the physical network.

The explosive number of IoT nodes and adoption of software-defined radio have enabled an efficient method of exploiting idle frequency spectrums called dynamic spectrum access (DSA). The foremost problem in DSA is for a pair of nodes to rendezvous and form a control channel prior to communication. Existing schemes require a channel hopping (CH) pattern with length O(N2), which is overly complex especially when the number of channels N is large. Moreover, the CH patterns are designed assuming DSA nodes have unlimited CH capability, which is hardly satisfied by nodes with long frequency switching time and limited sensing capacity. In this paper, we design a low-complexity rendezvous scheme that account for CH capability limits. The CH capability is captured using spectrum slice graphs that describe the possible channels for the next hop, given the currently-visited channel. By viewing the CH patterns as random walks over the spectrum graphs, we assign the walks with optimal transition probabilities that achieve the smallest rendezvous delay. The resulting symmetric random CH (S-RCH) scheme, which is suitable for IoT nodes without predetermined roles, achieves a lower rendezvous delay than existing Modular Modified Clock (MMC) scheme and offers more than 80 % successful rendezvous in mobile networks.

Determining the causality of abnormalities in a network is the prerequisite for developing countermeasures. In this paper, we focus on infection detection in heterogeneous networks. Given a snapshot of the network which demonstrates the condition of the nodes, the goal is to distinguish between random failures and epidemic scenarios. We model the network situation as a graph signal based on the nodes' status. Detection metrics motivated by graph signal processing are introduced for the infection detection problem in hand, and an effective algorithm is proposed to solve it. Simulation results indicate a dramatic improvement in terms of detection probability compared to the current state-of-the-art.

The ever-increasing traffic demand from social networking service (SNS) users and recent progress in device-to-device (D2D) technology have empowered a new D2D-based SNS paradigm, which enables multimedia content exchange via short-range wireless networking. In this paradigm, a small node failure may trigger a collection of rapidly-spreading isolation events called cascade-of-failures. Unlike existing works that studied the outcome of cascading failures from the spatial and probabilistic perspectives, this paper sheds light on the temporal properties of the cascade-of-failures. To do this, we introduce a maximum isolation time that quantifies the steps needed until the last node is isolated by the cascades, and then show that it scales non-monotonically to the fraction of initial survivors (non-failure nodes) and increases logarithmically with the network size. Then, we use the result to further analyze a node resilience metric, which is the likelihood that a node does not become isolated before its social networking session is finished. These findings, which are validated using numerical simulations, provide a temporal perspective of network performance that is valuable in the design of D2D-based SNSs yet still missing in the literature.

File sharing is one of the most promising Proximity Service (ProSe) using device-to-device (D2D) communication technology. As the prospective D2D-enabled file sharing rely on mobile peer discovery and connection in physical proximity, the challenges come from the highly dynamic environment as a consequence of the unpredictable user mobility and the temporal-spatial locality of content popularity. A challenging yet open question is how users could move to fetch target files durably and stably. In this paper, we characterize mobile file sharing in temporal and spatial domains by how long time the service can be active (referred as service lifetime τL), how far a mobile user can move away while keeping ongoing service (referred as service distance∥DL∥), and how fast the ongoing service would be recovered once interruption caused by handover (classified into bundled handover and split handover in this paper) (referred as recover delay HO). Answers to these questions offer a straightforward interpretation of the potentials of D2D communications for file sharing on the move. Both theoretical and simulation results suggest that D2D file sharing is more benefit for popular content among group moving users with longer service lifetime and distance and less delay caused by mobile user handover and peer discovery.

Spectrum monitoring is a prerequisite in dynamic access regulation, policy enforcement, as well as spectrum database establishment. In this paper, we introduce the dimension of geographical space into the spectrum monitoring problem, and studied deployment strategies of multiple monitors, in terms of coverage time and cost. The monitoring problem is modeled as a 3-d continuous sweep coverage problem, whose solution space is then reduced by effectively dividing the spectra-location space, in order to achieve a small coverage time. The cost minimization is then formulated as a Multiple Traveling Salesman problem (MTSP), which is NP-hard. By observing the structure of the strategy space, we propose a solution that attains a reasonable cost, without applying complex optimization algorithms.

To secure a Vehicular Ad-hoc Network (VANET), extensive studies have been conducted on developing authentication infrastructures, and identifying misbehaving vehicles. The effectiveness of such efforts heavily depends on the underlying communication network. However, information exchange in the VANET can be severely delayed because of its highly-dynamic and partially-connected topology. Such delay can be potentially exploited by attackers to cause physical impacts to the transportation system. In this paper, we propose and model a new attack, called vehicle evacuation attack, to investigate how the message delay endangers the trustworthiness in VANETs, and further causes physical impacts to cars on the road. Our study demonstrates that there exists a linear relationship between the delay of message dissemination and the impact of the vehicle evacuation attack, which can be used as a guideline on security, reliability, and safety design in real-world VANETs.

Device-to-device (D2D)-based mobile social networking (MSN) has been touted as a low-power, low-cost alternative to conventional MSN over cellular and WiFi networks. Nevertheless, the open nature of communication channels and the users' social activity that tend to decline over time expose D2D-based MSN to network contractions, which is a consequence of initial dropouts, combined with the jointly unsuccessful information delivery and social contacts. As a result, the network may found some of its users to vanish without knowing the cause of such dropouts. This, however, is a desirable knowledge that can guide towards the design of effective countermeasure schemes. In this paper, we study the problem of deciding whether initial dropouts are social or communication-induced, given the outcome of the network contractions. To do so, we define a minimum utility ratio that quantifies the worst-case impact of network contractions generated by initial dropouts. We derive self-consistent equations for computing the different minimum utility ratios caused by social and communication-based dropouts. We further show that this metric may exhibit different scaling order behaviors, depending on the root cause of initial dropouts. Finally, numerical results show that the proposed self-consistent equations and scaling order analysis can effectively distinguish social and communication-induced network contractions.

Motivated by the massive and increasing number of online messaging service users, the idea of utilizing shortrange device-to-device (D2D) communication has been adapted to the access of instant messaging services on-the-go, introducing a D2D-based messaging service (D2D-Msg) paradigm that promises higher data rate and longer battery life. The quality of message dissemination in such a new paradigm, however, remains largely unknown due to the open nature of the D2D environment. To address this, we define a node survival probability that captures the impact of random and targeted node failures due to the open wireless environment. Further, we define a secondary infection rate R* that measures how fast message propagates initially, and leverage a framework based on probability generating function to analyze R* under random and targeted failures. Numerical results show that the D2D-Msg is more robust against random failure, the targeted node failure favors communication graph with narrow degree distribution, and R* is proportional to the ratio between the number of message-receiving users to all users, which is a good metric for quantifying the D2D-Msg's robustness.

Device-to-device (D2D)-based social networking service (SNS) is an emerging information system that enables users with social ties to exchange multimedia contents through multihop short-range wireless links. In the D2D-based SNS, a random initial node failure may lead to a cascade of failures, which is a series of events in which users become isolated from others over subsequent time instances. Different from previous studies that analyze whether network-wide connectivity can be preserved after a cascade of failures, our study sheds light on the D2D-based SNS's resilience from the perspective of end-user connection experience. In this paper, we first introduce a numerical method for calculating the mean fraction of nodes that are not affected by the cascading failures and the amount of time to reach the end of such sequence of failures. Then, we apply a probabilistic approach to derive the lower and upper bounds of a node resilience metric, which is the likelihood that an end-user will not be isolated during an ongoing social networking session. Our analysis and numerical simulations indicate that, compared to exponentially-distributed session times, user session times with Pareto (heavy-tailed) distribution results in poorer node resilience, which quickly deteriorates when the mean session time is high.

As the explosion of Internet traffic is quickly leading to overloaded cellular network, device-to-device (D2D)-based content sharing is proposed as a method to offload mobile data traffic. The performance of D2D-based content sharing is dramatically affected by the success rate of content fetching from nearby devices and quality of content transmission, which is determined by the geographic distribution of mobile devices, the number of devices having contents in their caches, and the condition of D2D links. Hence, a key problem is how to cache various contents in the limited storage of mobile devices for improving the success rate of content fetching. In this paper, we aim to design a caching policy by considering the joint impact of locality of real-world mobile data traffic and device contact pattern to improve the success rate of content fetching. To do this, we first study the characteristics of network traffic and device contact pattern by analyzing traces from realistic networks. Then, we design a locality-based caching policy and derive the content caching probability and hit ratio through mathematical analysis. Through numerical evaluation and trace-driven simulations, we not only quantify how content popularity, content active lifetime, content size, content bit rate, device storage, transmission rate, and closeness centrality affect the content hit ratio, but also provide comparison on hit ratio and storage cost in different caching policy, which is a strong evidence that the joint impacts from characteristics of content and device are the necessary consideration when to design a caching policy.

As online social networks grow in both size and connectivity, epidemic information dynamics in such networks is attracting considerable research interests, due to its impact on both the network and individuals. This paper studies control of malicious information (virus) epidemic with replicable antidote information, taking topological characteristics of the underlying graph into consideration. Specifically, we analytically relate the extinction time of the virus to the diameter and giant component size of the remaining graph after the initial antidote distribution. With this divide and conquer guideline, topology-based antidote distribution approaches are designed, and then examined through simulations in real world network portions.

Along with the explosive growth of mobile social network (MSN) users and the advent of device-to-device (D2D) communications, D2D-based MSN (D2D-MSN) has become a promising alternative for exchanging multimedia contents on-the-go. Although the complete structure of a D2D-MSN plays a key role in understanding its performance, such knowledge is not readily available due to the difficulty of collecting connectivity information from the vast amount of users. To model the structure, we define a D2D-MSN network that jointly captures the social connectivity over the MSN and the opportunistic D2D contacts among users. A random walk with self loop (RWSL) scheme that quickly converges to its stationary distribution is proposed to collect a subset of D2D-MSN nodes. An estimator is then introduced to obtain an unbiased estimate of the D2D-MSN graph's joint degree distribution, pi, j, from the set of visited nodes, leading to an unbiased RWSL scheme. The resulting estimate of pi, j can be used as a statistic for creating synthetic graph and generating functions for analyzing robustness of D2D-MSN. Numerical results show that the proposed unbiased RWSL converges faster to its stationary distribution, achieves higher joint degree distribution accuracy, and visits less number of nodes, compared to existing graph exploration schemes.

Online Social Networks (OSNs) is taking over television and newspapers, to be the dominant information dissemination option. The growing involvement of individuals create the situation that colliding, even contradicting information coexist and propagate in the same network, which gives rise to an interesting question: how will the conflicting information propagate? To answer this question, the propagation process is described to be an Susceptible-Infected-Cured (SIC) epidemic, and we propose an inference algorithm to study the transient behavior of the competing propagation processes in connected networks. Moreover, we provide an analytic method to derive the conditional infection count distribution for networks with special topologies, as a step further to understand the evolution. A trace collected from the Internet is analyzed to validate our model and methods.

Cascading failures are one of the most devastating forces in power systems, which may be initially triggered by minor physical faults, then spread with Domino-like chain-effect, resulting in large-scale blackout. How to prevent cascading failures becomes imperative, as our daily lives heavily depend on stable and reliable power supply. The next-generation power system, namely Smart Grid, is envisioned to facilitate real-time and distributed control of critical power infrastructures, thus effectively forestalling cascading failures. Although cascading failures have been well investigated in the literature, most studies were confined only in the power operation domain with the assumption that communication is always perfect, which is, however, not true for today's communication networks, where traffic congestion and random delay happen. Therefore, an open question is how to characterize cascading failures in the communication-assisted smart grid? To this end, we take an in-depth inspection of cascading failures in smart grid and reveal the interactions between the power system and the communication network. Our results provide insights into the interactions between physical failure propagation and communication message dissemination. In addition, we show that while ideal communications can undoubtedly help prevent cascading failures, under-achieved communications (i.e., communications with severe delay) can, counter-intuitively, exacerbate cascading failures.

In an era of networks in which any individual is connected with one another, such as Internet of Things (IoT) and Online Social Networks (OSNs), the networks are evolving into complex systems, carrying a huge volume of information that may provoke even more. An interesting, yet challenging question is how such information dissemination evolves, that is, to continue or to stop. Specifically, we aim to find out the aftermath of epidemic spreading via individuals and conflicting information dissemination. From a holistic, networking view, it is impossible to take every aspect into accounts for complex networks toward these questions. Therefore, we establish a Susceptible-Infectious-Cured (SIC) propagation model to examine two simple network topologies, clique and star, in terms of extinction time and half-life time of information under controllable, epidemic dynamics. For a network of size n, both theoretical and numerical results suggest that extinction time and half-life time are O(log n/n) for clique networks, and O(log n) for star networks. More interestingly, given an initial network state I0, the extinction time is constant (O(1)) for cliques, and O(log I0) for stars; while the half-life time is O(log 1/I0) for both clique and star networks, respectively. In addition, we developed a method to estimate the conditional infection count distribution, which indicates the scope of information dissemination.

A botnet in mobile networks is a collection of compromised nodes due to mobile malware, which are able to perform coordinated attacks. Different from Internet botnets, mobile botnets do not need to propagate using centralized infrastructures, but can keep compromising vulnerable nodes in close proximity and evolving organically via data forwarding. Such a distributed mechanism relies heavily on node mobility as well as wireless links, therefore it breaks down the underlying premise in existing epidemic modeling for Internet botnets. In this paper, we adopt a stochastic approach to study the evolution and impact of mobile botnets. We find that node mobility can be a trigger to botnet propagation storms: the average size (i.e., number of compromised nodes) of a botnet increases quadratically over time if the mobility range that each node can reach exceeds a threshold; otherwise, the botnet can only contaminate a limited number of nodes with average size always bounded above. This also reveals that mobile botnets can propagate at the fastest rate of quadratic growth in size, which is substantially slower than the exponential growth of Internet botnets. To measure the denial-of-service impact of a mobile botnet, we define a new metric, called last chipper time, which is the last time that service requests, even partially, can still be processed on time as the botnet keeps propagating and launching attacks. The last chipper time is identified to decrease at most on the order of 1=√B, where B is the network bandwidth. This result reveals that although increasing network bandwidth can help mobile services, it can, at the same time, indeed escalate the risk of services being disrupted by mobile botnets.

There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in cognitive radio networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius a, which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on a, below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.

Android is one of the most popular mobile operating systems nowadays, whose popularity, however, also attracts even more crafty developers to develop malicious softwares, or malwares, to exploit illegitimate means for profit. As a basic countermeasure, Android enforces the permission request scheme, in which an application (App) is required to present to the user the system resources (permissions) it will access, and ask user's approval before installation. However, this approach has been proven ineffective as it delegates the whole responsibility of decision- making to the user, who usually lacks the professional knowledge to comprehend the interpretation of a permission. Alternatively, many current researches focus on identifying potential malwares based on attributes of individual Apps, such as inspecting their source code, which, unfortunately, fall in another extreme which tend to make the decision for the user. Nevertheless, from the user's perspective, a satisfactory solution should be an approach which assists users to make the decision of the App installation on their own, by providing them with lucid reasons and requiring minimum professional knowledge. Based on the observation that the description of an App is the most direct interface to communicate its functionality to the user, in this paper we are motivated to explore the relationship between the description and the requested permissions of an App, and further build a model to predict proper permissions based on its description. Our evaluation with Apps collected from the Google Play Market shows that our prediction can achieve as high as 87% accuracy. In this regard, provide a user has full understanding of the description of an App, our model can act as an effective reminder to the user if the App tries to stealthily request permissions that are inconsistent with its description, which is a major character commonly exploited by malwares.

Smart grid features real-time monitoring and control by integrating advanced communication networks into traditional power grids. This integration, however, makes smart grid vulnerable to cyber attacks, i.e., the anomalies caused by attackers in the communication network can affect ordinary operations of the power grid and result in severe physical damage. To protect smart grid from cyber attacks, many traditional countermeasures, such as message encryption, have been proposed to be directly migrated to fit this system. In this regard, the very first fundamental questions that need to be addressed are how to evaluate and compare the physical impacts of cyber attacks and countermeasures, and whether traditional cyber security countermeasures can result in satisfactory performance in smart grid. Motivated by these questions, we establish a small-scale smart grid prototype, and use both experiments and cross-domain simulations to evaluate and compare the reaction of the power system under cyber attacks, with and without the presence of traditional countermeasures. Our study reveals that traditional countermeasures can not be readily migrated to protect smart grid in particular, and shows that during system emergencies where prompt system reactions are critical, the extra latency caused by message encryption and decryption can result in more than 10 times in the magnitude of voltage collapse. Our work indicates that traditional countermeasures may not fit smart grid, the newly emerging cyber- physical system, which has strict time constraint. Therefore it is essential for researchers to seek solutions to address smart grid specific security threats.

Smart grid is a cyber-physical system that integrates power infrastructures with information technologies. To facilitate efficient information exchange, wireless networks have been proposed to be widely used in the smart grid. However, the jamming attack that constantly broadcasts radio interference is a primary security threat to prevent the deployment of wireless networks in the smart grid. Hence, spread spectrum systems, which provide jamming resilience via multiple frequency and code channels, must be adapted to the smart grid for secure wireless communications, while at the same time providing latency guarantee for control messages. An open question is how to minimize message delay for timely smart grid communication under any potential jamming attack. To address this issue, we provide a paradigm shift from the case-by-case methodology, which is widely used in existing works to investigate well-adopted attack models, to the worst-case methodology, which offers delay performance guarantee for smart grid applications under any attack. We first define a generic jamming process that characterizes a wide range of existing attack models. Then, we show that in all strategies under the generic process, the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of traffic can in fact improve the worst-case delay performance. As a result, we demonstrate a lightweight yet promising system, transmitting adaptive camouflage traffic (TACT), to combat jamming attacks. TACT minimizes the message delay by generating extra traffic called camouflage to balance the network load at the optimum. Experiments show that TACT can decrease the probability that a message is not delivered on time in order of magnitude.

It has been demonstrated that in wireless networks, blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability p and failure connection function g(·), to characterize node failures and their spreading properties, respectively. Then we prove that each blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of blackholes through analysis and simulations.

With the popularity of smartphones and explosion of mobile applications, mobile devices become the prevalent computing platform for convenient communication and rich entertainment. Mobile cloud computing (MCC) is proposed to overcome the limited resources of mobile systems. However, when users access MCC through wireless networks, cellular network is likely to be overloaded and Wi-Fi connectivity is intermittent. Therefore, device-to-device (D2D) communication is exploited as an alternative for MCC. An important issue in exploring D2D communication for MCC is how users can detect and utilize the computing resources on other mobile devices. In this paper, we propose two mobile cloud access schemes: optimal and periodic access schemes, and study the corresponding performance of mobile cloud computing (i.e., mobile cloud size, node's serviceable time percentage, and task success rate). We find that optimally, node's serviceable time percentage and task success rate approach 1. Using more practical periodic access scheme, node's serviceable time percentage and task success rate are determined by the ratio of contact and inter-contact time between two nodes.

Although the research of mobile data networks has gained significant attention lately, little have been done to study the impact of finite network boundary in such networks. To understand how finite cell boundary affects the delay performance, we analyze the delay problem from the packet movement point of view. We first divide the network scenarios into three categories based on the value of the expected packet propagation speed v and derive the upper and lower bounds for their expected packet delay, respectively. Then, we show that the packet delay scales linearly when v0 and v = 0 when the boundary effect is absent. Simulation results verify our analysis and show that the boundary effect in fact increases the delay performance for both v > 0 and v = 0.

A mobile cloudlet is a set of resource-rich mobile devices - referred to as cloudlet nodes - that an initiator mobile device can connect to for services. In this paper, we examine the fundamental mobile cloudlet properties that unfold whether and when a mobile cloudlet can provide mobile application service. Specifically, we investigate the cloudlet size, cloudlet node's lifetime and reachable time. Traces and mathematical analysis demonstrate that 1) the more frequently mobile devices meet, the larger the pool of computing resources an initiator can access; 2) intermittent connection between devices has little adverse effect on the optimal computing performance of mobile cloudlet in the long run; 3) the ratio E(TC)/[E(TI)+E(TC)] indicates the connection likelihood of an initiator and a cloudlet node (i.e., reachability of the cloudlet node), where TCand TIare their contact and inter-contact time. We further derive upper and lower bounds on computing capacity and computing speed of a mobile cloudlet. An initiator can use both bounds to decide whether to offload its task to remote clouds or local mobile cloudlets for better mobile application services.

A botnet in mobile networks is a collection of compromised nodes due to mobile malware, which are able to perform coordinated attacks. Different from Internet botnets, mobile botnets do not need to propagate using centralized infrastructures, but can keep compromising vulnerable nodes in close proximity and evolving organically via data forwarding. Such a distributed mechanism relies heavily on node mobility as well as wireless links, therefore breaks down the underlying premise in existing epidemic modeling for Internet botnets. In this paper, we adopt a stochastic approach to study the evolution and impact of mobile botnets. We find that node mobility can be a trigger to botnet propagation storms: the average size (i.e., number of compromised nodes) of a botnet increases quadratically over time if the mobility range that each node can reach exceeds a threshold; otherwise, the botnet can only contaminate a limited number of nodes with average size always bounded above. This also reveals that mobile botnets can propagate at the fastest rate of quadratic growth in size, which is substantially slower than the exponential growth of Internet botnets. To measure the denial-of-service impact of a mobile botnet, we define a new metric, called last chipper time, which is the last time that service requests, even partially, can still be processed on time as the botnet keeps propagating and launching attacks. The last chipper time is identified to decrease at most on the order of 1/√B, where B is the network bandwidth. This result reveals that although increasing network bandwidth can help with mobile services; at the same time, it can indeed escalate the risk for services being disrupted by mobile botnets.

Smart grid is a cyber-physical system which integrates communication networks into traditional power grid. This integration, however, makes the power grid susceptible to cyber attacks. One of the most distinguished challenges in studying the aftermath of cyber attacks in smart grid lies indata-centricthreats. Even though such attacks are critical to the information network, they will result in much more Domino-like impact than they behave in cyber world. This is because for an information-centric network, distorted or delayed information undermines services and applications. But in power grid, these data-centric attacks may result in instable power systems, and further detrimental impact of power supplies. In this paper, we present Greenbench, a benchmark that is designed to evaluate real-time power grid dynamics in response to data-centric attacks. The simulation results provide several counter-intuitive suggestions to both smart grid security research and deployment.

Recently, wireless networking for emerging cyber-physical systems, in particular the smart grid, has been drawing increasing attention in that it has broad applications for time-critical message delivery among electronic devices on physical infrastructures. However, the shared nature of wireless channels unavoidably exposes the messages in transit to jamming attacks, which broadcast radio interference to affect the network availability of electronic equipments. An important, yet open research question is how to model and detect jamming attacks in such wireless networks, where communication traffic is more time-critical than that in conventional data-service networks, such as cellular and WiFi networks. In this paper, we aim at modeling and detecting jamming attacks against time-critical wireless networks with applications to the smart grid. In contrast to communication networks where packets-oriented metrics, such as packet loss and throughput are used to measure the network performance, we introduce a new metric, message invalidation ratio, to quantify the performance of time-critical applications. Our modeling approach is inspired by the similarity between the behavior of a jammer who attempts to disrupt the delivery of a time-critical message and the behavior of a gambler who intends to win a gambling game. Therefore, by gambling-based modeling and real-time experiments, we find that there exists a phase transition phenomenon for successful time-critical message delivery under a variety of jamming attacks. That is, as the probability that a packet is jammed increases from 0 to 1, the message invalidation ratio first increases slightly, then increases dramatically to 1. Based on analytical and experimental results, we design the Jamming Attack Detection based on Estimation (JADE) scheme to achieve robust jamming detection, and implement JADE in a wireless network for power substations in the smart grid.

Device-to-device (D2D) communications enable direct communications and information distribution among closely located devices in wireless networks. Many applications of D2D communications require message dissemination to a group of mobile users at certain locations. The challenges of message dissemination come from highly dynamic network environments due to movements of devices. Existing studies on message dissemination have focused on information propagation speed and latency, but the size of the area affected by message dissemination at time t is also critical to D2D communication applications that heavily depend on message dissemination among users in a geographic region. In this paper, we study the fundamental issues in D2D communications: how far the message dissemination can reach by time t (referred as dissemination distance) and how long the dissemination takes to inform nodes located at distanced (referred to as hitting time), especially in dynamic, intermittently connected networks. We first derive analytic bounds of dissemination distance and hitting time under different dissemination mechanisms, providing the spatial and temporal limits of message dissemination. Analytic results are further validated by simulation results of several corresponding dissemination algorithms. Finally, two application scenarios are provided to illustrate how our results serve as guidelines to choose or design appropriate dissemination methods for different D2D communication applications.

Node failures are unavoidable in wireless networks and an initial failure may further trigger a sequence of related failures, incurring many holes in the network, which can easily result in devastating impact on network performance. To understand the size of these holes is very important to identify solutions to offset their adversarial effects. In this paper, we focus on the size of holes in Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability p and failure connection function g(·), to characterize node failures and their spreading properties, respectively. Then we prove that each hole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of holes.

Smart grid is an emerging cyber-physical system which aims at making power systems more intelligent and efficient. One of the major attributes of smart grid is integration of distributed renewable power resources into the traditional power grid. As a result, traditional centralized control is not always effective in smart grid, and distributed control is essential for flexible energy management. To facilitate distributed control, Intelligent Electronic Devices (IEDs), which are embedded computers equipped on power devices, are interconnected based on the peer-to-peer communication model. An open question is whether such a distributed control mechanism over peer to peer communication is delay-efficient to support time-critical smart grid applications. To answer this question, we establish a micro smart grid, called Green Hub, to measure the delay performance for both distributed and centralized control systems. Our results show that, for computationally intensive applications, the delay performance of the distributed system is worse than that of the centralized control system, mostly due to IEDs' limited capability. In addition, we find that in distributed control systems, the peer to peer communication may cause different behaviors of physical devices in power systems, and consequently deviates their decisions from optimal. Our experimental study reveals the distributed control system in smart grid does not necessarily performs better than the centralized control system for certain applications, and the peer to peer communication in the distributed control system may bring new concerns which did not exist in the centralized control system. A special attention need to be paid on the effectiveness and efficiency aspects when design algorithms/schemes for smart grid.

Vehicular ad hoc network (VANET) is one of the most promising large-scale applications of mobile ad hoc networks. VANET applications are rooted in advanced understanding of communication networks because both control messages and data information need to be disseminated in geographic regions (i.e., Geocast). The challenges come from highly dynamic environments in VANET. Destination nodes in geocast are dynamic over time due to vehicle mobility, which undermines existing results on dissemination latency and information propagation speed with pre-determined destinations. Moreover, the affected area by the dissemination, which is referred to as horizon of message (HOM), is critical in geocast as it determines the latency for the message reaching nodes inside the area of interest (AOI), in which the message is relevant to drivers. Therefore, we characterize the HOM in geocast by how far the message can reach within time t (referred as dissemination distance) and how long the message takes to inform nodes at certain locations (referred as hitting time). Analytic bounds of dissemination distance and hitting time are derived under four types of dissemination mechanisms, which provide insights into the spatial and temporal limits of HOM as well as how the numbers of disseminators and geographic information exchanges affect them. Applying analytic and simulation results to two real applications, we observe that geocast with AOI near the source or high reliability requirement should recruit multiple disseminators while geocast with AOI far from the source need to utilize geographic information for fast message propagation.

It has been demonstrated that in wireless networks, Blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. Therefore, many solutions, such as routing protocols and restoration algorithms, are proposed to deal with Blackholes by identifying alternative paths to bypass these holes such that the effect of Blackholes can be mitigated. These advancements are based on an underlying premise that there exists at least one alternative path in the network. However, such a hypothesis remains an open question. In other words, we do not know whether the network is resilient to Blackholes or whether an alternative path exists. The answer to this question can complement our understanding of designing routing protocols, as well as topology evolution in the presence of random failures. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability p and failure connection function g(·), to characterize node failures and their spreading properties, respectively. Then we prove that each Blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of Blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of Blackholes through analysis and simulations.

The capacity scaling property specifies the change of network throughput when network size increases. It serves as an essential performance metric in large-scale wireless networks. Existing results have been obtained based on the assumption of using a globally planned link transmission schedule in the network, which is however not feasible in large wireless networks due to the scheduling complexity. The gap between the well-known capacity results and the infeasible assumption on link scheduling potentially undermines our understanding of the achievable network capacity. In this paper, we propose the scheduling partition methodology that decomposes a large network into small autonomous scheduling zones and implements a localized scheduling algorithm independently in each partition. We prove the sufficient and the necessary conditions for the scheduling partition approach to achieve the same order of capacity as the widely assumed global scheduling strategy. In comparison to the network dimension $(\sqrt{n})$, scheduling partition size $(\Theta (r(n)))$ is sufficient to obtain the optimal capacity scaling, where $(r(n))$ is the node transmission radius and much smaller than $(\sqrt{n})$. We finally propose a distributed partition protocol and a localized scheduling algorithm as our scheduling solution for maximum capacity in large wireless networks.

Communication networks are an indispensable component in the smart grid power systems by providing the essential information exchange functions among the electrical devices that are located distributively in the grid. In particular, wireless networks will be deployed widely in the smart grid for data collection and remote control purposes. In this paper, we model the smart grid wireless networks and present the communication delay analysis in typical wireless network deployment scenarios in the grid. As the time critical communications are coupled with the power system protections in the smart grid, it is important to understand the delay performance of the smart grid wireless networks. Our results provide the delay bounds that can help design satisfactory wireless networks to meet the demanding communication requirements in the smart grid.

In many Vehicular Ad-hoc Network applications, such as hazard warning and traffic coordination, the message dissemination in unreliable and highly mobile network environment is a key challenge. In order to understand the relationship between dissemination latency and reliability, we analyze the latency of gaining α-reliability that a node correctly receives a message with probability larger than α (0min(α). Besides dissemination reliability requirement α, tmin(α) also depends on node's original distance from the source, node mobility, channel reliability, and traffic flow. Numerical analysis discloses several interesting insights that 1) tmin(α) is dominated by the first attempt to send the message to a destination, 2) node mobility has little impact on tmin(α) in emergency information dissemination, and 3) transmission range and node density greatly affect dissemination latency and reliability.

Vehicular Adhoc Networks (VANETs) aim to improve road safety and convenience through vehicle-to-vehicle and vehicle-to-roadside communications. Traffic information and accident warnings are often disseminated to vehicles in certain areas where driving could be affected by hazardous situations. Such message dissemination with destinations confined in specific geographic regions is referred to as Geo-Dissemination. In this paper, we analyze how far a geo-dissemination can possibly reach over a period of time t (denoted as dissemination distance D(t)), and what is the latency for a message to reach locations that are d distance far from the source (denoted as the stopping time τ). Simulations results of two dissemination methods (stateless opportunistic forwarding and GPS-based message broadcasting) are compared with our analytic results.

Mobile ad hoc networks (MANETs) are self-configuring wireless networks that lack permanent infrastructure and are formed among mobile nodes on demand. Rapid node mobility results in dramatic channel variation, or fading, that degrades MANET performance. Employing channel state information (CSI) at the transmitter can improve the throughput of routing and medium access control (MAC) protocols for mobile ad hoc networks. Several routing algorithms in the literature explicitly incorporate the fading signal strength into the routing metric, thus selecting the routes with strong channel conditions. While these studies show that adaptation to the time-variant channel gain is beneficial in MANETs, they do not address the effect of the outdated fading CSI at the transmitter. For realistic mobile node speeds, the channel gain is rapidly varying, and becomes quickly outdated due the feedback delay. We analyze the link throughput of joint rate adaptation and adaptive relay selection in the presence of imperfect CSI. Moreover, for an 802.11 network that employs geographic opportunistic routing with adaptive rate and relay selection, we propose a novel method to reduce the effect of the feedback delay at the MAC layer in the presence of Rayleigh fading. This method exploits channel reciprocity and fading prediction and does not require significant modification to the existing 802.11 frame structure. Extensive network simulations demonstrate that the proposed approach significantly improves the throughput, delay, and packet delivery ratio for high mobile velocities relative to previously proposed approaches that employ outdated CSI at the transmitter.

Cognitive Radio Networks (CRNs) have become promising network components to improve spectrum utilization efficiency, where secondary (unlicensed) users exploit spectrum opportunistically without interfering with the coexisting primary users. A challenging yet open question is how to ensure that information can be disseminated to the entire CRN, which is a prerequisite to applications of wireless networks. In this paper, we address the connectivity of large multi-channel CRNs. Particularly, we study full connectivity and percolation of secondary networks. The former is the existence of a communication path between any two nodes and the latter is the existence of a large component of secondary users. We find that the sufficient and necessary condition to achieve full connectivity is λ = Θ(log n/πr2Ps), where λ is the density, n is the number and r is the transmission range of secondary users respectively, and Psis the probability that any two secondary users can communicate with each other without interfering with primary users. We further show that the required density for percolation is a constant, and identify an upper bound on λ, above which the network is percolated and a lower bound on λ below which the network is not percolated. Our results provide a theoretical understanding of connectivity in large multi-channel cognitive radio networks.

The smart grid is an emerging cyber-physical system that integrates power infrastructures with information technologies. In the smart grid, wireless networks have been proposed for efficient communications. However, the jamming attack that broadcasts radio interference is a primary security threat to prevent the deployment of wireless networks. Hence, spread spectrum systems with jamming resilience must be adapted to the smart grid to secure wireless communications. There have been extensive works on designing spread spectrum schemes to achieve feasible communication under jamming attacks. Nevertheless, an open question in the smart grid is how to minimize message delay for timely communication in power applications. In this paper, we address this problem in a wireless network with spread spectrum systems for the smart grid. By defining a generic jamming process that characterizes a wide range of existing jamming models, we show that the worst-case message delay is a U-shaped function of network traffic load. This indicates that, interestingly, increasing a fair amount of redundant traffic, called camouflage, can improve the worst-case delay performance. We demonstrate via experiments that transmitting camouflage traffic can decrease the probability that a message is not delivered on time in order of magnitude for smart grid applications.

Cognitive Radio Networks (CRNs) have emerged to become promising network components for exploiting spectrum opportunistically in order that information can be delivered in circumstances otherwise impossible. Challenging yet open questions are how fast and how far a packet can be delivered in such networks, in temporal and spatial domains, respectively. The answers to these questions offer a straightforward interpretation of the potentials of CRNs for time-sensitive applications. To tackle these questions, we define two metrics, dissemination radius ∥ℒ(t)∥ and propagation speed S(d). The former is the maximum Euclidean distance that a packet can reach in time t, and the latter is the speed that a packet transmits between a source and destination at Euclidean distance d apart, which can be used to measure the transmission delay. Further, we determine the sufficient and necessary conditions under which there exist spatial and temporal limits of information dissemination in CRNs. We find that when information cannot be disseminated to the entire network, the limiting dissemination radius is statistically dominated by an exponential distribution, while the limiting information propagation speed approaches to zero. Otherwise, the dissemination radius approaches to infinity and the propagation speed S(d) is no lower than some constant k for large d. The results are validated through simulations.

Cognitive Radio Networks (CRNs), as a phenomenal technique to improve spectrum efficiency for opportunistic communications, become an integral component in the future communication regime. In this paper, we study the end-to-end latency in CRNs because many CRN applications, such as military networks and emergency networks, are either time-sensitive or dependent on delay performance. In particular, we consider a general mobility framework that captures most characteristics of the existing models and accounts for spatial heterogeneity resulting from the scenario that some locations are more likely to be visited by mobile nodes (these can be home in the case of people, or garage in the case of vehicles). By assuming that secondary users are mobile under this general framework, we find that there exists a cutoff point on the mobility radius #, which indicates how far a mobile node can reach in the spatial domain, below which the latency has a heavy-tailed distribution and above which the tail distribution is bounded by some Gamma (light-tailed) distribution. A heavy tail of the latency implies a significant probability that it takes long time to disseminate a message from the source to the destination and thus a light-tailed latency is crucial for time-critical applications. Moreover, as the network grows large, we notice that the latency is asymptotically scalable (linear) with the dissemination distance (e.g., the number of hops or Euclidean distance). Another interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the tail distribution of the latency in finite networks and the linearity of latency in large networks. Our results encourage the CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.

The radio link between a pair of wireless nodes is determined by radio channels, transmission range, node mobility and node-pair distance, which form a set of random factors in multihop wireless networks. The properties of such radio links can be characterized by link lifetime, residual link lifetime and link change rate, which, in fact, have been widely used for network design and performance evaluation. In this paper, we take a new modeling approach that captures the dynamics of radio channels and node movements in small-scale. More specifically, distance transition probability matrix is designed in order to describe the joint effects of dynamic transmission range due to radio channel fading and relative distance of a node-pair resulting from random movements. We find that the PDF of link lifetime can be approximated by an exponential distribution with parameter characterized by the ratio of average node speed \bar{V} to effective transmission range R_e. To further understand the implication of link properties, analytical results are used to investigate the upper bound of network connectivity and the associated network performance is evaluated by extensive simulations.

Backoff misbehavior, in which a wireless node deliberately manipulates its backoff time, can induce significant network problems, such as severe unfairness and denial of service. Although great progress has been made toward the design of countermeasures to backoff misbehavior, little attention has been focused on quantifying the gain of backoff misbehaviors. In this paper, to assess the gain that misbehaving nodes can obtain, we define and study two general classes of backoff misbehavior: continuous misbehavior, which keeps manipulating the backoff time unless it is disabled by countermeasures, and intermittent misbehavior, which tends to evade the detection of countermeasures by performing misbehavior sporadically. Our approach is to introduce a new performance metric, namely order gain, to characterize the performance benefits of misbehaving nodes in comparison to legitimate nodes in CSMA/CA-based wireless networks. We derive the order gains of both continuous and intermittent misbehaviors and further investigate the relation between our metric, order gain, and the throughput gain for a misbehaving node. We show that in IEEE 802.11 networks, the throughput ratio of a backoff misbehaving node to a legitimate node is either bounded above or proportional to the number of legitimate nodes. We use both simulations and experiments to validate our theoretical analysis and to further demonstrate the impact of a wide range of backoff misbehaviors on network performance in CSMA/CA-based wireless networks.

Vehicular Adhoc Networks (VANETs) are dedicated to improve the safety and efficiency of transportation systems through vehicle to vehicle or vehicle to road side communications. VANETs exhibit dynamic topology and intermittent connectivity due to high vehicle mobility. These distinguished features declare a challenging question: how to detect on the fly vehicular networks such that we can explore mobility-assisted message dissemination and topology control in VANETs. As being closely related to network dynamics, vehicle mobility could be explored to uncover network structure. In this paper, we have observed that mobility of vehicle, rather than being random, shows temporal locality (i.e., frequently visiting several communities like home and office), and spatial locality (i.e., velocity constrained by road layout and nearby vehicles). We first examine temporal locality using a campus trace, then measure temporal locality similarity between two vehicles based on the relative entropy of their location preferences. By further incorporating spatial locality similarity, we introduce a new metric, namely dual locality ratio (DLR), which represents the mobility correlation of vehicles. Simulation results show that DLR can effectively identify dynamic vehicular network structures. We also demonstrate applications of DLR for improving performances of data forwarding and clustering in vehicle-to-vehicle networks.

The smart grid is the next-generation power system that incorporates power infrastructures with information technologies. In the smart grid, power devices are interconnected to support a variety of intelligent mechanisms, such as relay protection and demand response. To enable such mechanisms, messages must be delivered in a timely manner via network protocols. A cost-efficient and backward-compatible way for smart grid protocol design is to migrate current protocols in supervisory control and data acquisition (SCADA) systems to the smart grid. However, an open question is whether the performance of SCADA protocols can meet the timing requirements of smart grid applications. To address this issue, we establish a micro smart grid, Green Hub, to measure the delay performance of a predominant SCADA protocol, distributed network protocol 3.0 (DNP3) over TCP/IP. Our results show that although DNP3 over TCP/IP is widely considered as a smart grid communication protocol, it cannot be used in applications with delay constraints smaller than 16ms in Green Hub, such as relay protection. In addition, since DNP3 provides reliability mechanisms similar to TCP, we identify that such an overlapped design induces 50%-80% of the processing delay in embedded power devices. Our results indicate that DNP3 over TCP/IP can be further optimized in terms of delay efficiency, and a lightweight communication protocol is essential for time-critical smart grid applications.

This paper investigates the access time of mobile nodes to infrastructure networks in Mobile Hybrid Networks (MHNs), e.g., sensor-actuator networks, where mobile nodes move around the coexisting infrastructure networks. In such networks, mobile nodes may relay data packets in a hop-by-hop fashion, and eventually deliver packets to the wired networks. Our objective is to study the lower bound of the access time, which implies the minimum time to deliver data packets from low-speed, unreliable wireless networks to the high-speed, reliable infrastructure network. In particular, we propose a theoretical framework to analyze the packet propagation speed in MHNs, and to show that there is a unified lower bound on the access time regardless of data forwarding schemes. When wireless nodes move at an average speed of v and variance σ2towards an AP, and their density λ is small (e.g., the network is sparse and surely disconnected), we prove that the expected access time is lower bounded by (Lb(0)-r)2/σ2packet mobility is unbiased and Lb(0)-r/v for the biased case, where Lb(0) is the distance from the source node to the nearest access point. Here, packet mobility is the mobility of a packet due to both node mobility and data transmissions. We further propose a routing scheme to achieve such theoretical bounds.

Dissemination latency is critical to the applications of cognitive radio networks, which have become an important component of current communication infrastructure. This paper investigates the distribution of dissemination latency in a cognitive radio network where licensed users (primary users) are static and cognitive radio users (secondary users) are moving under general mobility, which provides fundamental understanding of the fastest information delivery that a mobile cognitive radio network is able to accommodate. We show that the dissemination latency depends on the stationary spatial distribution and mobility capability α (characterizing the region that a mobile secondary user can reach) of secondary users. Given any stationary spatial distribution, we find that there exists a critical value on α, below which the latency is heavy-tailed and above which the right tail of the distribution is bounded by some Gamma random variable. Moreover, we find that although the traffic and spatial density of primary users inversely impact the expected latency, they make no influence on the tail distribution of the latency. The results in this paper are validated through numerical studies and can advance our understanding of CR network performance.

In this paper we study the connection availability of wireless networks from the perspective of end users with individual mobility, in contrast to existing studies mainly focusing on network-centric connectivity. Specifically, we evaluate the user connectivity by two metrics: neighbor connection time and neighbor isolation time. The former is the time for a node being connected with at least one neighbor; while the latter is the time for a node being isolated before it is connected to new neighbors. By using the theory of Markov renewal process, we obtain the asymptotic bounds on the expected neighbor connection and isolation times, which are dependent on the node density, transmission range, and average speed.We find that if the network is super-critical, implying the existence of a unique giant (connected) component, both asymptotic bounds scale with the number of nodes in the network; otherwise, expected connection and isolation times are constant.

Ensuring timely delivery of event reports is a critical requirement in mobile sensor networks, as the effectiveness of area surveillance depends largely on the sensor network response delay. However, due to the sensor mobility and the energy limitation, fast report transportation is constrained by the intermittent wireless link connections between sensor nodes. We derive lower and upper analytical bounds to characterize quantitatively the event reporting speed by considering both the sensor mobility and the energy consumption. Our work evaluates the delay performance of mobile sensor networks to meet the prompt reporting requirement in these networks.

Time-critical wireless applications in emerging network systems, such as e-healthcare and smart grids, have been drawing increasing attention in both industry and academia. The broadcast nature of wireless channels unavoidably exposes such applications to jamming attacks. However, existing methods to characterize and detect jamming attacks cannot be applied directly to time-critical networks, whose communication traffic model differs from conventional models. In this paper, we aim at modeling and detecting jamming attacks against time-critical traffic. We introduce a new metric, message invalidation ratio, to quantify the performance of time-critical applications. A key insight that leads to our modeling is that the behavior of a jammer who attempts to disrupt the delivery of a time-critical message can be exactly mapped to the behavior of a gambler who tends to win a gambling game. We show via the gambling-based modeling and real-time experiments that there in general exists a phase transition phenomenon for a time-critical application under jamming attacks: as the probability that a packet is jammed increases from 0 to 1, the message invalidation ratio first increases slightly (even negligibly), then increases dramatically to 1. Based on analytical and experimental results, we further design and implement the JADE (Jamming Attack Detection based on Estimation) system to achieve efficient and robust jamming detection for time-critical wireless networks.

Dissemination latency and speed are central to the applications of cognitive radio networks, which have become an important component of current communication infrastructure. In this paper, we investigate the distributions and limits of information dissemination latency and speed in a cognitive radio network where licensed users (primary users) are static and cognitive radio users (secondary users) are mobile. We show that the dissemination latency depends on the stationary spatial distribution and mobility capability α (characterizing the region that a mobile secondary user can reach) of secondary users. Given any stationary spatial distribution, we find that there exists a critical value on α, below which the latency and speed are heavy-tailed and above which the right tails of their distribution are bounded by Gamma random variables. We further show that as the network grows to infinity, the latency asymptotically scales linearly with the “distance” (characterized by transmission hops or Euclidean distance) between the source and the destination. Our results are validated through simulations.

Energy efficient communication is a critical research problem in large-scale multihop wireless networks because of the limited energy supplies from batteries. We investigate in this paper the minimum energy required to fulfill various information delivery goals that correspond to the major communication paradigms in large wireless networks. We characterize the minimum energy requirement in two steps. We first derive the lower bounds on the energy consumption for all the possible solutions that deliver the information as required. We then design routing schemes that accomplish the information delivery tasks by using an amount of energy comparable to the lower bounds. Our work provides the fundamental understandings of energy needs and the efficient solutions for energy usages in major communication scenarios, which contribute to the rational dimensioning and wise utilization of the energy resources in large wireless networks.

This paper investigates the speed limit of information propagation in large-scale multihop wireless networks, which provides fundamental understanding of the fastest information transportation and delivery that a wireless network is able to accommodate. We show that there exists a unified speed upper bound for broadcast and unicast communications in large-scale wireless networks. When network connectivity is considered, this speed bound is a function of node density. If the network noise is constant, the bound is a constant when node density exceeds a threshold; if the network noise is an increasing function of node density, the bound decreases to zero when node density approaches infinity. As achieving the speed bound places strict requirements on node locations, we also quantify the gap between the actual achieved speed and the desired bound in random networks in which the relay nodes are not located as desired. We find that the gap converges to zero exponentially as node density increases to infinity.

Cognitive Radio (CR) networks have become an important component of the modern communication infrastructure due to their capability of improving spectrum usage efficiency by exploiting channels opportunistically. In CR networks, the network topology changes very frequently because of the temporarily available channels and dynamic transmitting parameters (e.g. transmission power and transmitting frequency), which may even result in network disconnectivity from time to time. Hence an interesting and open question is that: are there bounds on end-to-end delay between a source-destination pair with Euclidean distance d apart in such networks? These bounds are required for time-critical applications. This paper first investigates the nature of topology-incurred end-to-end delay in large multi-channel CR networks and then identifies the conditions under which the asymptotic topology-incurred delay scales linearly with the Euclidean distance (d); that is, the conditions under which the end-to-end delay is bounded. The results in this paper are validated through extensive simulations and can advance our understanding of CR network performance.

This paper presents a new algorithm that can be readily applied to solve all-terminal network reliability optimization problem of a wireless network in a fading environment. The optimization problem solved considers finding the optimal topological layout of links at which the all-terminal network reliability is maximized by controlling the nodes' transmission powers. To that end, a link probabilistic model is developed to relate fading, attenuation, interference and nodes' transmission powers to link reliability. Then, the proposed algorithm utilized this probabilistic model to control nodes' transmission power to maximize links reliabilities and hence all-terminal network reliability. The proposed algorithm is based on two major steps that use a global stochastic optimization technique, Cross Entropy (CE) to generate the optimal network topology and control nodes transmission powers such that all-terminal network reliability is maximized. An illustrative example is used to illustrate the proposed algorithm.

In this paper, we address the problem of quantifying the impact of backoff misbehaving nodes in IEEE 802.11 networks. We propose two performance metrics, throughput gain ratio and throughput degradation ratio to quantify the performance gain of misbehaving nodes over legitimate nodes and the performance loss of legitimate nodes due to backoff misbehavior, respectively. We use asymptotic analysis to derive both throughput gain ratio and throughput degradation ratio in an IEEE 802.11 network in the presence of multiple misbehaving nodes. We show that, in general, the throughput gain ratio increases linearly with the number of legitimate nodes, and the throughput degradation ratio increases linearly with the number of misbehaving nodes. Finally, we use ns-2 simulations to validate our analytical results.

Traffic overloading failures are infectious due to the correlations in their occurrence. When a node fails at excessive load, the re-routed traffic may fail other nodes in the network. We present in this paper a mathematical characterization of the resilience of large wireless networks to the traffic overloading failures. We show that as the number of failed nodes increases, the network transits from a failure-resistant phase to a failure-prone phase at certain time. We derive an upper bound on this phase transition time to provide an estimation of the maximum network resilience to traffic overloading failures.

Abstract-Many real systems are hybrid networks which include infrastructure nodes in multi-hop wireless networks, such as sinks in sensor networks and mesh routers in mesh networks. However, we have very little understanding of network connectivity in such networks. Therefore, in this paper, we consider hybrid networks denoted by H(α, β) with ad hoc nodes and base stations and prove how base stations can improve the connectivity of ad hoc nodes in subcritical phase, that is, the ad hoc node density, λαis lower than the critical density λαc. We first study the impact of density of base stations, λβon the connectivity, and find that with the existence of a positive density of base stations which have the same transmission range as ad hoc nodes, i.e., λβ> 0, the number of connected ad hoc nodes is Θ(n) with probability nearly 1, where n is the number of ad hoc nodes. However, the size of connected ad hoc component scales linearly with λβwith probability nearly 1 when λβis lower than c1(λα). We then study the impact of transmission range of base stations, τβon the connectivity, and find the additional benefit of enlarging rβto enhance the connectivity of ad hoc nodes.

Backoff misbehavior, in which a wireless node deliberately manipulates its backoff time, can induce significant network problems, such as severe unfairness and denial-of-service. Although great progress has been made towards the design of countermeasures to backoff misbehavior, little attention has been focused on quantifying the gain of backoff misbehaviors. In this paper, we define and study two general classes of backoff misbehavior to assess the gain that misbehaving nodes can obtain. The first class, called continuous misbehavior, keeps manipulating the backoff time unless it is disabled by countermeasures. The second class is referred to as intermittent misbehavior, which tends to evade the detection by countermeasures by performing misbehavior sporadically. Our approach is to introduce a new performance metric, namely order gain, which is to characterize the performance benefits of misbehaving nodes in comparison to legitimate nodes. Through analytical studies, simulations, and experiments, we demonstrate the impact of a wide range of backoff misbehaviors on network performance with respect to the number of users in CSMA/CA-based wireless networks.

Correlated failures pose a great challenge for the normal functioning of large wireless networks, because an initial local failure may trigger a global sequence of related failures. Given their potentially devastating impact, we characterize the spread of correlated failures in this paper, which lays the foundation for evaluating and improving the failure resilience of existing wireless networks. We model the failure contagiousness as two generic functions: the failure impact radius distribution function fr(x) and the failure connection function g(x). By using the percolation theory, we determine the respective characteristic regimes of fr(x) and g(x) in which correlated failures will and will not percolate in the network. As our model represents various failure scenarios, the results are generally applicable in understanding the spread of a wide range of correlated failures.

Many real systems are hybrid networks which include infrastructure nodes in multi-hop wireless networks, such as sinks in sensor networks and mesh routers in mesh networks. However, we have very little understanding of network connectivity in such networks. Therefore, in this paper, we consider hybrid networks denoted by H(¿, ß) with ad hoc nodes and base stations and prove how base stations can improve the connectivity of ad hoc nodes in subcritical phase, that is, the ad hoc node density, ¿¿is lower than the critical density ¿¿c. We find that with the existence of a positive density of base stations, i.e., the density of base stations ¿ß> 0 which have the same transmission range as ad hoc nodes, the number of connected ad hoc nodes is ¿(n) with probability nearly 1, where n is the number of ad hoc nodes. However, the size of connected ad hoc component scales linearly with ¿ßwhen it is lower than c1(¿¿) with probability nearly 1, which demonstrates a tremendous benefit of using base stations to enhance the connectivity of ad hoc nodes. Further, we study a hybrid network architecture that makes a significant connectivity improvement with transmission range rßlarger than r¿for ad hoc nodes. Therefore, our results provide a theoretical understanding of to what extent ad hoc nodes can benefit from base stations in multi-hop wireless networks.

Network survivability is the ability of a network to stay connected under failures and attacks, which is a fundamental issue to the design and performance evaluation of wireless ad hoc networks. In this paper, we focus on the analysis of network survivability in the presence of node misbehaviors and failures. First, we propose a novel semi-Markov process model to characterize the evolution of node behaviors. As an immediate application of the proposed model, we investigate the problem of node isolation where the effects of denial-of-service (DoS) attacks are considered. Then, we present the derivation of network survivability and obtain the lower and upper bounds on the topological survivability for k-connected networks. We find that the network survivability degrades very quickly with the increasing likelihood of node misbehaviors, depending on the requirements of disjoint outgoing paths or network connectivity. Moreover, DoS attacks have a significant impact on the network survivability, especially in dense networks. Finally, we validate the proposed model and analytical result by simulations and numerical analysis, showing the effects of node misbehaviors on both topological survivability and network performance.

This paper examines the performance of wireless mesh networks (WMNs) under the impact of path-based denial of service (DoS) attacks. Specifically, we study the factors that are conducive to path-based DoS attacks, while focusing external interferences, medium errors, and physical diversity. We setup a wireless mesh testbed and configure a set of experiments to gather measurements and assess the effects of different factors. We find that the impact of external interferences and medium errors on network performance is exacerbated when path-based DoS attacks are carried out. Another interesting observation is that a far attacker can lead to an increased performance degradation than a close-by attacker due to physical diversity. Further, we discuss a simple strategy to counter path-based DoS attacks, which has potential for reducing the impact of the attack significantly.

This work studies the capacity limits of Cognitive Radio (CR) networks with a hybrid relay scheme in which PRimary (PR) nodes dominate the spectrum usage, while coordinating with secondary or CR nodes in forwarding packets. This is in contrast to previous efforts that are focused on the analysis with cooperative relay scheme where only secondary (CR) nodes participate in relay operation. Intuitively, it is expected that the hybrid scheme can improve network capacity because primary nodes have higher power and transmission capacity compared to CR nodes and data transmission with hybrid relay scheme can be much faster and more reliable. Therefore, we aim to investigate such potential benefits with respect to cooperative relay. However, we find that given the total data rate W, number of usable channels ¿C¿ and the number of CR nodes n in a network, the achievable capacity of CR networks with hybrid relay is of the order of ¿(W/¿C¿n). This result is much lower than the capacity of cooperative relay, which is of the same order as the capacity of multi-channel wireless networks (e.g., multi-channel multi-radio (MC-MR) networks), ¿(W 1/(¿n log n)). It is plausible that PR nodes can potentially become bottlenecks when they are used as high-priority relay nodes in the hybrid scheme. Thus, our study suggests that for delay-sensitive applications such as realtime traffic, hybrid relay is a good option; otherwise, cooperative relay is preferred.

Given many known results on wireless network capacity, practical and optimal capacity utilization remains an open question. The existing link scheduling schemes in the literature are not applicable in large wireless networks because of their global operations for topology collection and transmission synchronization. As network size increases, global operations become infeasible to implement. We propose in this paper a localized link scheduling solution for achieving order optimal network capacity. Our method eliminates the global operations and improves significantly the practicality of scheduling implementation. As the cost, localized scheduling reduces the network capacity utilization. However, we prove that the reduction can be bounded by a constant factor. From the scaling order point of view, we hence provide a practical scheduling approach to optimize the network utilization.

The capacity scaling property specifies the changes in network throughput when network size increases and serves as an essential performance evaluation metric for large-scale wireless networks. Existing results have been obtained based on the implicit assumption of negligible overhead in acquiring the network topology and synchronizing the link transmissions. In large networks, however, global topology collection and global link synchronization are infeasible with both the centralized and the distributed link scheduling schemes. This gap between the well-known capacity results and the impractical assumption on link scheduling potentially undermines our understanding of the achievable network capacity. Therefore, the following question remains open: can localized scheduling algorithms achieve the same order of capacity as their global counterpart? In this paper, we propose the scheduling partition methodology by decomposing a large network into many small autonomous scheduling zones, in which localized scheduling algorithms are implemented independently from one another. We prove that any localized scheduling algorithm that satisfies a set of sufficient and necessary conditions can yield the same order of capacity as the widely assumed global scheduling strategy. In comparison to the network dimension √n, scheduling partition sizes Θ(√log n) and Θ(1) are sufficient for optimal capacity scaling in the random and the arbitrary node placement models respectively. We finally propose an example localized link scheduling algorithm to verify the capacity achieved by scheduling partition. Our results thus provide guidelines on the scheduling algorithm design toward maximum capacity scaling in large-scale wireless networks.

The performance of a wireless multimedia sensor network (WMSN) is tightly coupled with the pose of individual multimedia sensors. In particular, orientation of an individual multimedia sensor (direction of its sensing unit) is of great importance for the sensor network applications in order to capture the entire image of the field. In this paper, we study the problem of self-orientation in a wireless multimedia sensor network, that is finding the most beneficial pose of multimedia sensors to maximize multimedia coverage with occlusion-free viewpoints. We first propose a distributed algorithm to detect a node's multimedia coverage and then determine its orientation, while minimizing the effect of occlusions and total overlapping regions in the sensing field. Our approach enables multimedia sensor nodes to compute their directional coverage, provisioning self-configurable sensor orientations in an efficient way. Simulations show that using distributed messaging and self-orientation having occlusion-free viewpoints significantly increase the multimedia coverage.

In battery-constrained large-scale sensor networks, nodes are prone to random failures due to various reasons, such as energy depletion and hostile environment. Random failures can substantially impact the communication connectivity, which in turn impairs the sensing coverage. Redeploying additional sensors is one effective way to maintain the connectivity; however, it may be infeasible and costly to replace failed sensors one by one. When should a redeployment be conducted is an interesting and important question in designing resilient sensor networks. In this paper, we tackle this problem by investigating the devolution process of large-scale sensor networks. We first define a new metric called the first partition time, which is the first time that a network starts to discomposes to multiple isolated small components. Then we analyze the devolution process in a geometric random graph from a percolation-based connectivity perspective and obtain the condition under which the graph is not percolated. Finally, we find out that the lower bound of the first partition time depends on the node lifetime distribution and should be of the order between log (log n) and (log n)1/rhofor rho > 1. This result provides a theoretical upper bound of the latest time that a redeployment has to be carried out.

The timeliness of packet delivery is an important performance measure in wireless networks, especially when urgent messages need to be transported through a network. This paper investigates the fastest packet transportation in light-loaded wireless networks. We show that the end-to-end packet delay depends largely on the locations of the relay nodes that forward the packet and there exists a shortest-delay path theoretically. We also propose a routing algorithm to locate a fast relay path in actual networks to achieve the near-shortest packet delay.

We study link properties over dynamic radio channels based on analytical models and simulations. Specifically, channel variability and mobility are investigated through two quantities: effective transmission range and node-pair distance, respectively. We find that the PDF of link lifetime can be approximated by exponential distribution with parameter characterized by the ratio of average node speed to effective transmission range. Moreover, we show that average link lifetime for slower mobile nodes is mainly determined by radio channel characteristics, whereas for faster mobile nodes, it is dominated by node mobility. Through analysis and simulations, we find that the impacting factors on residual link lifetime are in the decreasing order of average node speed, effective transmission range, and node-pair distance on the fly. We further present the implication and application of link properties to path lifetime, network connectivity, and routing performance.

This paper investigates the speed limit of information propagation in large wireless networks, which provides fundamental understanding of the fastest information transportation and delivery that a wireless network is able to accommodate. We show that there is a unified speed upper bound for broadcast and unicast communications in large wireless networks. When network connectivity and successful packet delivery are considered, this speed upper bound is a function of node density. As this bound is unreachable with finite node density, we also quantify the gap between the actually achieved speed and the desired upper bound, which converges to zero exponentially as the node density increases to infinity.

In this paper, we study the critical phase transition time of large-scale wireless multi-hop networks when the network topology experiences a partition due to increasing random node failures. We first define two new metrics, namely the last connection time and first partition time. The former is the last time that the network keeps a majority of surviving nodes connected in a single giant component; while the latter is the first time that the remaining surviving nodes are partitioned into multiple small components. Then we analyze the devolution process in a geometric random graph of n nodes based on percolation-theory connectivity and obtain the sufficient condition under which the graph is percolated. Based on the percolation condition, the last connection time and first partition time are found to be on the same order. Particularly, when the survival function of node lifetime is exponential, they are on the order of log(log n); while if the survival function is Pareto, the order is (log n)1/ρ, where ρ is the shape parameter of Pareto distribution. Finally, simulation results confirm that the last connection time and first partition time serve as the lower and upper bounds of the critical phase transition time, respectively. Further, an interesting result is that the network with heavy-tailed survival functions is no more resilient to random failures than the network with light-tailed ones, in terms of critical phase transition time, if the expected node lifetimes are identical.

Understanding link statistics in mobile ad hoc networks (MANETs) is essential to design adaptive routing protocols and achieve desired network performance. While much attention has been given to the node mobility impacts, little has been done to investigate the influence of dynamic channel fadings and the joint effects of the interactions among radio channels, transmission range, node mobility and node-pair distance on link statistics. In this paper, we investigate the link stability and availability by using a distance transition probability matrix of a relative distance between two nodes. Our analysis takes the node effective transmission range Reinto account. The relative node movement is based on the Semi-Markov Smooth (SMS) mobility model [1] which captures the smooth node speed (V) transition and the radio channel variations in a small time- scale. We show that the PDF of link lifetime in MANETs can be effectively approximated by the exponential distribution characterized by the parameter V/Re. Moreover, we And that the impacting factors on residual link lifetime are in the decreasing order of node speed, transmission range, node-pair distance. The analytical results are validated by extensive simulations.

It is necessary, though very challenging, to integrate available security technologies in a coherent manner to provide a multi-level security management in an ad hoc mobile network. This paper proposes and analyzes a policy-based network security management mechanism that consists of responsive/preemptive defensive strategies and a "Ripple Effect" security policy activating mechanism. This scheme activates different security policy modes and security levels to provide network protections efficiently.

To understand how node mobility and Byzantine node failures affect connectivity of wireless multi-hop networks, this paper investigates resilience of geometric random graphs to lifetime-based node failure and derives the expected connection time before an end-user is isolated from the graph. Different from previous analytical studies, which mainly focused on the so called critical transmission range, our study sheds light on the resilience analysis from the perspective of end-user's connection experiences. In the paper, we first introduce a simple but general node behavior model by a semi-Markov process. Then we apply the theory of renewal process to the degree of a generic node and analyze the stochastic property of node connection time. At last, we provide the probability that the node isolation event occurs within any end-user's lifetime and a close-form approximation of the network resilience. Our analysis and numeric simulation results indicate that networks with heavy-tailed lifetime (such as Weibull) distributions provides no improvement than those with light-tailed (e.g., Exponential) distributions in terms of longer expected connection lifetime for any end-user. Further, node mobility has more significant impact than lifetime does.

In hierarchical mobile ad hoc networks, the architectural stability is a key factor in determining the network performance. There are many solutions proposed to construct stable clusters, none of which has however revealed the maximum stability attainable in the mobile environments. In this paper, we define two metrics to measure the stability of hierarchical networks: the cluster lifetime and the inter-cluster link lifetime. We model and analyze the maximum of these two lifetimes with consideration of node mobility. The analytical understanding of maximum stability provides a guideline for the clustering and routing protocol design to optimize network performance.

Most of the existing techniques for network reliability evaluation are based on assumptions that all the nodes are perfect and the communication links are static and irreplaceable, However, these assumptions are not applicable for mobile ad hoc networks because of the rapid changes in connectivity and link characteristics due to nodes' mobility. Reliability computations in mobile ad hoc networks should consider the failures of nodes and links in addition to the dynamic of network connectivity caused by nodes' mobility. In this paper, we consider the computation of the two-terminal reliability in ad hoc networks by extending the algorithm proposed by Rai et al. to handle imperfect nodes and the dynamic network connectivity. The effect of nodes failure rates and the mobility pattern on the two-terminal reliability are presented.

This paper presents a new transport solution for WSNs addressing bidirectional end-to-end event and query reliability. The authors aim to reduce the reliable transport overhead while guaranteeing the reliability to deliver all events and queries in WSN applications. The proposed lightweight solution can achieve desired event reliability in conjunction with query reliability by operating with the least possible number of messages, and using a small subset of coordinators which are responsible of loss detection and recovery. Coordinators are selected using a distributed, low-cost algorithm with adaptive path discovery and maintenance features to utilize the cost of retransmissions and energy consumption. Simulation results show that, using such a coordination of sensors, significant savings on communication costs for event and query reliability are attainable while minimizing packet loss, energy consumption, and end-to-end delay.

In this paper, the problem of finding an optimal coverage set by effectively eliminating redundant nodes with guaranteed connectivity without using centralized control and accurate location information was addressed. Using a fully distributed approach, an effective redundant node elimination method that considers even the smallest overlapping regions was proposed to establish a coverage set. Further, an extension scheme is presented that finds the minimum number of sensors among the coverage set, where the network connectivity is guaranteed. The simulation results were presented to illustrate achievable coverage set while preserving connectivity, and energy saving to verify the approaches.

The network resilience has been studied as a fault tolerance measure in wired networks for decades; however, little effort has been made to analyze the resilience of wireless multi-hop networks, especially in the presence of misbehaving nodes. In this work, we study such a problem: whether there exists an overlay achieving "strong" resilience when misbehaving nodes are present in the underlying wireless multi-hop network. To address this problem, we first introduce two new metrics, k-connected survivability and resilience capacity. The former metric is used to measure the network connectivity probabilistically; while the latter one is used to evaluate the ability of accommodating misbehaving nodes deterministically. We then derive an approximate representation of the k-connected survivability, and provide the close-form representations of resilience capacity for k = 1 and k = 2 and a heuristic algorithm to calculate it when k ges 3. Finally, based on our analytical results, we prove that an overlay can achieve the derived resilience by satisfying three conditions: (i) containing all and only cooperative nodes of the original network; (ii) keeping the minimum cooperative degree at least k; (iii) having Theta (log2N) neighbors of each node in average.

Understanding the impacts of node mobility on topology dynamics is essential to design a mobility resilient ad hoc network. Current research showed that mobility models can heavily affect the study of network topology due to different mobility patterns (Santi, 2005). In this paper, topology dynamics based on the smooth model (Zhao and Wang, 2006) was analyzed, because it generates smooth, microscopic nodal movements, has no speed decay problem, and maintains a uniform spatial node distribution. Specifically, two topology metrics: expected link lifetime and expected link change rate were studied by using a distance transition probability matrix P. By this means, the link existence based on the present distance between a pair of nodes and their relative speed were predicted. The analytical results of topology dynamics are validated by extensive simulations. In addition, by combining graph theory and queuing theory, the topology metrics expected link lifetime and expected link change rate was applied to formulate the upper bound connectivity of a mobile ad hoc network.

Traditional call admission control (CAC) schemes only consider call-level performance and are mainly designed for circuit-switched wireless network. Since future wireless communications will become packet-switched systems, the packet-level features could be explored to improve the system performance. This is especially true when the TCP-type of elastic applications are running over such packet-switched wireless networks, as the elasticity of TCP applications has more tolerance toward the throughput/delay variation than non-elastic traffic does. In order to efficiently utilize the system resource from an admission control perspective, we propose a TCP-aware CAC scheme to regulate the packet-level dynamics of TCP flows. We analyze the system performance under realistic scenarios in which (i) the call holding time for non-elastic traffic like voice is independent of system states and (ii) the call holding time for TCP type of traffic depends on the system state, i.e., on the TCP flow's transmission rate. Extensive simulations are presented under different scenarios to show that the proposed scheme can effectively improve the system performance in terms of call blocking probability, call-level throughput (call/min) and link utilization, in accordance with our theoretical results.

Existing random mobility models have their limitations such as speed decay and sharp turn which have been demonstrated by the previous studies. More importantly, mobility models need to mimic the movements that abide by the physical law for accurate analysis and simulations of mobile networks. Therefore, in this paper, we propose a novel mobility model, semi-Markov smooth (SMS) model. Each SMS movement includes three consecutive phases: speed up phase, middle smooth phase, and slow down phase. Thus, the entire motion in the SMS model is smooth and consistent with the moving behaviors in real environment. Through steady state analysis, we demonstrate that SMS model has no average speed decay problem and always maintains a uniform spatial node distribution. The analytical results are validated by extensive simulation experiments. In addition, we compare the simulation results on link lifetime and percentage of node degree with random waypoint model, Gauss-Markov model and the proposed SMS model.

In this paper, we present a two-tiered scheduling scheme that provides effective energy conservation in wireless sensor networks. The effectiveness of this scheme relies on dynamically updated two-tiered scheduling architecture. We aim to prolong network lifetime, while preserving the major requirements of wireless sensor networks: coverage and connectivity. In this approach, sensors are periodically scheduled to sleep in two phases using weighted greedy algorithms. First, we establish a coverage-tier by selecting a set of sensors that covers the sensing field in order to provide fully monitoring of entire field. Sensors that are not selected for the coverage-tier, are put into sleep immediately. Then, a second tier, called connectivity-tier, is formed on top of the coverage-tier to forward the data traffic to sink node. Thus sensors, essential to coverage-tier but not in connectivity-tier may periodically sleep and become active only for sending new sensing measurement and receiving query from the sink to preserve coverage. By this way, we may allow more nodes to sleep with different sleeping behaviors, i.e., continuous sleep or periodic sleep/active. Moreover, fair energy consumption among sensors is achieved by periodically rotating the coverage and connectivity tiers. Through extensive simulations in ns2, we demonstrate that the two-tier scheduling can reduce average energy consumption up to 40% while balancing the residual energy of sensors.

Mobile ad hoc networks are vulnerable to malicious attacks and failures due to their unique features, such as node mobility and dynamic network topology. The design and evaluation of routing protocols and topology control require sound analysis on network connectivity and node behaviors. However, little work has been done on how node misbehaviors affect network connectivity. Modeling and analysis of node misbehavior involves many challenges such as multiple failures caused by selfishness, mobility, and potential Denial of Service attacks. Thus, we propose a novel model to characterize node misbehaviors based on a semi-Markov process. In particular, we analyze the impact of node misbehavior on network connectivity in a mobile ad hoc network stochastically. Numerical results based on analysis and simulations are provided to demonstrate the effectiveness of our approach and results.

Traditional call admission control (CAC) schemes only consider call-level performance and are believed to be sufficient for the circuit-switched wireless network. Since the future wireless network will become packet-switched, the packet-level performance should not be ignored. This is especially true when the TCP-type of applications are running over such packet-switched wireless networks, because TCP congestion control algorithm will exhaust all the available resource until packet loss occurs. In order to regulate the TCP applications to friendly coexist with other types of services, we propose a TCP-aware CAC scheme. We analyze the system performance under the scenario that call holding time is independent of the system state. Simulations results for different performance metrics are presented to show that the proposed scheme can effectively improve the system performance in terms of call blocking probability, call-level throughput (call/min) and the link utilization

In this paper, we present a new energy-efficient bandwidth allocation scheme for wireless networks. First of all, we investigate the intrinsic relationship between the energy consumption and transmission rates of mobile terminals, in which transmission rate is determined through channel allocations. Then, we propose two schemes for connection admission control: victim selection algorithm (VSA) and beneficiary selection algorithm (BSA) with the intent to reduce energy consumption of each terminal. Moreover, we introduce an adjustment algorithm to statistically meet the demands for quality of service (QoS) during the resource allocation. The performance of the proposed schemes is evaluated with respect to energy consumption rate of each successfully transmitted bit, throughput and call blocking probabilities. An extensive analysis and simulation study is conducted for Poisson and self-similar, multi-class traffic.

Abstract The advances in wireless networks and IP technologies has brought ubiquitous access to all‐IP information into reality. For wireless IP services, IP‐based handoff is a critical issue to the performance of application‐level services. Although mobile IP (MIP) and its extensions, as network layer solutions, have been proposed asde factostandard, transmission throughput degradation due to packet loss, registration delay, and transport layer blocking are unavoidable because of MIP handoff mechanisms. In this paper, we evaluate the performance of a transport layer handoff approach, mobile stream control transmission protocol (mSCTP), and compare it with that of a network layer solution, MIP. mSCTP is based on stream control transmission protocol (SCTP), which is the third general purpose transport layer protocol from IETF. We investigate the use of mSCTP forseamless vertical handoffwithout any change in IP protocol stack by its multi‐homing feature and dynamic address reconfiguration (DAR) extension. We evaluate the performance of mSCTP and MIP by introducing handoff delay, end‐to‐end transmission throughput, and packet loss, and verify our observations by a simulation study of the two protocols in UMTS/802.11b integrated networks using NS‐2 network simulator. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we study and quantify the impact of the most widely used security protocols, such as 802.1x, EAP, IPSEC, SSL and RADIUS, in wireless local area networks (WLANs). Based on the measurements in a wireless network testbed, we present quantitative, realistic findings with regards to both security functions as well as network performance. First, we describe experimental setup including system configuration and protocol stack. Then, we consider a variety of individual and hybrid security policies in order to capture the impact of security services at different network layers. Moreover, depending upon mobile nodes' current location, user mobility is categorized into non-roaming and roaming scenarios. In addition, we define several performance metrics such as authentication time, authentication messages, response time, throughput to measure the overhead associated with security policies on system performance. Comprehensive experimental measurements and analysis are provided for TCP/UDP traffic streams and network variations to demonstrate the impact of security protocols in WLANs.

Wireless networks require strong security mechanisms due to their open medium. However, security effects system performance, and therefore impacts quality of service (QoS) of communications. To analyze the impact of security on system performance, we conduct a detailed experimental study on a wireless IP testbed with security at different layers. We study their impact on different types of data streams such as TCP and UDP with regard to authentication time and cryptographic overhead. Specifically, we experiment with the most widely used security protocols such as WEP, IPsec, 802.1x with RADIUS, and SSL. We classify security protocols into individual and hybrid policies. Then, a new metric, relative security index, is introduced to analyze security strength and overhead tradeoffs quantitatively. Our results demonstrate that the stronger the security, the more signaling and delay overhead; whereas, the overhead does not necessarily increase monotonically with the security strength. Also, we notice that authentication time is a more significant factor than cryptographic cost regarding their contributions towards QoS degradation in wireless networks.

Performance degradation due to routing overhead is a serious impediment to fulfilling quality of service (QoS) in mobile ad hoc networks (MANETs). Therefore, analyzing the impact of routing overhead in a real-time environment becomes critical to developing efficient routing protocols and provisioning network performance. We develop a statistical-analytic approach to studying the impact of the routing overhead on delay and throughput in a real-time MANET testbed. The approach helps us in deriving statistical models of delay and throughput which, in turn, enables us to analyze the behavior of routing protocols beyond the scenarios configured in the testbed. In addition, we conduct a simple analysis of measuring network bandwidth consumed by routing overhead in various environments. Although optimized link state routing (OLSR) and ad-hoc on-demand distance vector (AODV) routing protocols are investigated as case studies in this paper, our approach and findings are applicable to other routing protocols as well.

Classification of sensor nodes can be used as a technique for conserving energy and prolonging the lifetime of a wireless sensor network (WSN). In this paper, we present a new algorithm of lightweight and dynamic classification. By this algorithm, energy consumption is reduced while providing a full coverage, which is an important network parameter in WSNs. Moreover, node classification is adaptive to topology changes and has no constraint on routing protocols and hardware. Based on sensors residual energy, they are classified as essential and non-essential and rotated dynamically. Essential nodes send their measurements to the sink, whereas, non-essential ones do not send new data and receive queries from the sink. This reduces transmitting and receiving energy of non-essential nodes and regulates data traffic. Further, our mechanism may provide location-based tunable redundancy, e.g., if redundant data is needed from a specific region, the sink may query the corresponding essential nodes to activate non-essential ones in that region. We analyze the complexity and energy consumption for the scenario where nodes are randomly deployed in a given region. Analysis, supported by extensive simulation in ns2, shows that energy consumption due to communications can be reduced in proportional to the ratio of essential nodes and fairly distributed among sensors by rotation.

With the increasing demand for secure and high-quality communications in public access wireless IP networks, it is very important to have an in-depth understanding of the relationship between the security and quality of service (QoS). In wireless networks, authentication can provide secure communications by preventing unauthorized usage and negotiating the credentials for data transmission. Nevertheless, it induces heavy overhead to data transmission, further deteriorating overall system performance. Thus, we analyze the impact of authentication on the security and QoS quantitatively in this paper. First, we introduce a system model based on a challenge/response authentication, which is widely used in many mobile environments. Then, a concept of security level is proposed to describe the protection of communications according to the nature of security, i.e., information secrecy, data integrity, and resource availability. By taking traffic and mobility patterns into account, our approach establishes a direct and quantitative connection between the security and QoS through the authentication. Finally, numerical results are provided to demonstrate the impact of security levels, mobility and traffic patterns on overall system performance in terms of authentication delay and call dropping probability.

Battery capacity of mobile terminals and radio bandwidth are both limited and precious resources in wireless networks. In this paper, we present a thorough performance study of energy-based admission control scheme to make the best use of these two resources for effective mobile communications. In order to reduce energy consumption of each terminal, we introduce a victim selection algorithm (VSA) and a beneficiary selection algorithm (BSA) for acquiring bandwidth and releasing bandwidth, respectively. To avoid potential compromise of quality of service (QoS) due to the concern of energy consumption in connection admission control, we further propose an adjustment algorithm to statistically meet the demands for QoS. The performance of the proposed schemes is evaluated with respect to energy consumption rate of each successfully transmitted bit, throughput and call blocking probabilities for a variety of traffic such as Poisson and self-similar, multi-class traffic.

Abstract The successful deployment of wireless local area networks (WLAN) for high speed data transmission and cellular systems for wide coverage and global roaming has emerged to be a complementary platform for wireless data communications. In order to fully exploit potentials in 3G/WLAN integration, authentication of roaming users crossing different networks, must be coupled with mobility management, which is a challenging, yet not resolved issue. The focus of this paper is on state‐of‐art solutions to Wi‐Fi and cellular networks based on IP infrastructure. Moreover, we introduce a new authentication architecture for fast authentication during inter‐networking handoff and large‐scale heterogeneous networks. We show that the new architecture can reduce authentication latency significantly and be adaptive to user mobility and traffic. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper, we present a dynamic security association control scheme to improve system performance during authentication in wireless networks. First, a new architecture composed of licensed authentication centers (LACs) for inter-domain authentication is introduced, in which security associations (SAs) are created and modified on demand, thus reducing the number of SAs and dynamically adjusting the lifetime of an SA. Then, a dynamic SA (DSA) control scheme is developed to determine an optimal threshold time for DSAs by using a utility function for maximizing the bandwidth efficiency. Simulation results reveal the effectiveness of the proposed scheme in terms of the improvement in authentication latency, bandwidth efficiency, and the number of SAs.

Traditional call admission control (CAC) schemes focus on only two parameters: call blocking probability and handoff dropping probability, although energy conservation is important and closely related to admission control. In this paper, we present an adaptive CAC to take a third parameter, energy consumption, into account as well. In order to reveal the relationship between CAC and energy consumption, energy consumption rate (ECR) is introduced. Based on a key observation of ECR that the same bandwidth variation of different mobile users may cause different rates of energy consumption, two algorithms are proposed, victim selection algorithm (VSA) and beneficiary selection algorithm (BSA), to reduce energy consumption. The performance of the proposed scheme for a multi-class services model is analyzed, and simulation results demonstrate that the lowest energy consumption is achieved by our scheme with improvement in blocking/dropping probabilities.

Authentication can provide security by preventing unauthorized usage and negotiating the credentials for secure communications. Nevertheless, it induces heavy overhead to communications, further deteriorating the quality of service (QoS). Therefore, analyzing the QoS and security impact of authentication becomes critical to developing efficient authentication schemes. We first introduce a system model for the analysis of challenge/response authentication in wireless networks. Then, we evaluate authentication cost, delay, and call dropping probability for different security levels. By considering traffic and mobility patterns, we show the numerical results to illustrate the impact of authentication on security and system performance.

We propose a cost-aware control scheme to realize efficient authentication in wireless networks based on the user density, mobility, and traffic patterns of roaming users. First, a mechanism to establish and break a direct security association (SA) between two networks is introduced. Then, an authentication cost function is developed to measure the cost for the proposed scheme. We further investigate an optimal condition to minimize the authentication cost, which decides the optimal number of inter-domain authentication requests to establish direct SAs between networks. Finally, we analyze the optimal condition based on user density, mobility, and traffic patterns. Compared to the authentication without control scheme, the proposed scheme reduces the authentication cost greatly with the increasing number of hops, residence time of mobile users, and the arrival rate of inter-domain authentication requests.

A new negotiation protocol is proposed to reduce network resources waste for real-time multimedia services over wireless networks. Existing negotiation protocols for wireless communications can be classified into two categories: network-oriented negotiation and application-oriented negotiation. Currently, the research on the two categories are separated. This causes inefficient resource utilization, especially for real-time services, which have stringent quality of service (QoS) requirements. In this paper, a new negotiation protocol is presented, which combines application-oriented and network-oriented negotiation to achieve higher resource utilization. We design a protocol architecture and negotiation messages for three scenarios. Simulation results show that our protocol achieves higher bandwidth efficiency and shorter negotiation delay.

User mobility profile (UMP) is a combination of historic records and predictive patterns of mobile terminals, which serve as fundamental information for mobility management and enhancement of quality of service (QoS) in wireless multimedia networks. In this paper, a UMP framework is developed for estimating service patterns and tracking mobile users, including descriptions of location, mobility, and service requirements. For each mobile user, the service requirement is estimated using a mean-square error method. Moreover, a new mobility model is designed to characterize not only stochastic behaviors, but historical records and predictive future locations of mobile users as well. Therefore, our approach incorporates aggregate history and current system parameters to acquire UMP. In particular, an adaptive algorithm is designed to predict the future positions of mobile terminals in terms of location probabilities based on moving directions and residence time in a cell. Simulation results are shown to indicate that the proposed schemes are effective on mobility and resource management by evaluating blocking/dropping probabilities and location tracking costs in wireless networks.

This paper presents a new scheme to estimate the user mobility by incorporating the aggregate history of mobile users and system parameters. With this approach, each user's position within the location area is differentiated by zone partition for more accurate prediction. In order to provide the flexibility of tradeoff between quality demand and computation complexity, the estimation is adjusted dynamically according to the constraint of prediction order. Then an adaptive algorithm is developed to predict the future position of mobile terminals in terms of location probabilities, while considering each terminal's movement direction, residence time, and path information. Simulation results demonstrate that the signaling cost for location tracking under delay bound is greatly reduced based on the estimated user mobility pattern.

Global wireless networks enable mobile users to communicate regardless of their locations. One of the most important issues is location management in a highly dynamic environment because mobile users may roam between different wireless systems, network operators, and geographical regions. A location-tracking mechanism is introduced that consists of intersystem location updates and intersystem paging. Intersystem update is implemented by using the concept of boundary location area, which is determined by a dynamic location update policy in which the velocity and the quality of service are taken into account on a per-user basis. Also, intersystem paging is based on the concept of a boundary location register, which is used to maintain the records of mobile users crossing the boundary of systems. This mechanism not only reduces location-tracking costs, but also significantly decreases call-loss rates and average-paging delays. The performance evaluation of the proposed schemes is provided to demonstrate their effectiveness in multitier personal communication systems.

An efficient signaling protocol for mobility application part (MAP) is essential to mobility support when mobile terminals roam between different networks in next generation wireless systems such as IMT-2000. In this paper, a new signaling protocol is proposed to reduce the overhead caused by mobility management, alleviating network load and consumption of network resources. Moreover, the new protocol effectively reduces the latency of call delivery and call loss rate due to crossing wireless systems with different standards and signaling protocols. Instead of performing location registration after a mobile user arrives at the new system, the mobile user is required to update its location information prior to its reaching the boundary of two systems. Results in this study demonstrate that the new protocol yields significant benefits in terms of reducing signaling costs, delays, and call loss rates.

In next-generation wireless systems, one of the major features that is different from the current personal communication service systems is the seamless global roaming. The mobile subscribers will be allowed to move freely across different networks while maintaining their quality of service for a variety of applications. To meet this demand, the signaling protocol of mobility management must be designed, supporting location registration and call delivery for roaming users who move beyond their home network. A new signaling protocol is proposed, emphasizing the active location registration for ongoing services during the mobile subscribers' movement. Another important goal of this new protocol is to reduce the overhead caused by mobility management so that the signaling traffic load and consumption of network resources can be reduced. The new protocol efficiently reduces the latency of call delivery and call loss rate due to crossing wireless systems with different standards or signaling protocols. The numerical results reveal that the proposed protocol is effective in improving the overall system performance.

A novel paging scheme under delay bounds is proposed for personal communication systems. This paging scheme is independent of the location probability distributions of the mobile users and satisfies the delay bounds, while minimizing the amount of bandwidth used for locating a mobile user. The proposed paging scheme includes the optimal partition algorithm and paging procedure with respect to paging costs and average delays. The numerical results demonstrate that the proposed scheme is very effective in the minimization of the paging costs for location probability conditions such as uniform and non-uniform distributions.

Global wireless networks enable mobile users to communicate regardless of their locations. One of the most important issues is location management in a highly dynamic environment because mobile users may roam between different wireless networks, network operators, and geographical regions. In this paper, a location tracking mechanism is introduced, which consists of intersystem location updates using the concept of boundary location area (BLA) and paging using the concept of boundary location register (BLR). The BLA is determined by a dynamic location update policy in which the velocity and the quality of service (QoS) are taken into account on a per-user basis. The BLR is used to maintain the records of mobile users crossing the boundary of networks. This mechanism not only reduces location tracking costs but also significantly decreases call loss rates and average paging delays. The performance evaluation of the proposed schemes is provided to demonstrate their effectiveness in multitier wireless networks.

New paging schemes are presented for locating mobile users in wireless networks. Paging costs and delay bounds are considered since the paging cost is associated with bandwidth utilization and delay bounds influence call setup time. In general, location tracking schemes require intensive computation to search for a mobile terminal in current PCS networks. To reduce the paging cost, three new paging schemes-reverse, semi-reverse and uniform-are introduced to provide a simple way of partitioning the service areas and accordingly minimizing the paging cost based on each mobile terminal's location probability distribution. Numerical results demonstrate that our approaches significantly reduce the paging cost for various location probability distributions such as uniform, truncated discrete Gaussian, and irregular distributions.

This paper describes current and proposed protocols for mobility management for public land mobile network (PLMN)-based networks, mobile Internet protocol (IP) wireless asynchronous transfer mode (ATM) and satellite networks. The integration of these networks will be discussed in the context of the next evolutionary step of wireless communication networks. First, a review is provided of location management algorithms for personal communication systems (PCS) implemented over a PLMN network. The latest protocol changes for location registration and handoff are investigated for mobile IP followed by a discussion of proposed protocols for wireless ATM and satellite networks. Finally, an outline of open problems to be addressed by the next generation of wireless network service is discussed.