The classic multi-hop wireless network optimization problem has recently re-attracted many attentions due to some emerging applications such as wireless mesh network, space-air ground integrated networks, and 5G/6G integrated access and backhaul systems. The key issue in multi-hop wireless network optimization is the interference management through scheduling. The fundamental NP-hardness of this problem is that there are exponentially many possible independent sets (ISs), but only a small number of them will be scheduled in the optimal solution (termed as the scheduling structure). The existing literature on approximation algorithms are mainly along the direction of searching the ISs in a heuristic manner. With the capability of machine learning (ML) algorithms in supporting big data analytics, in this paper, we propose a two-stage self-supervised learning framework that can explore the scheduling structure from historical optimization instances, and such knowledge is then exploited to solve a new instance with greatly reduced computational overhead. At the exploring stage, we develop dimension reduction techniques for effective scheduling structure classification in a high-dimensional vector space. At the exploiting stage, we design an innovative structure criticalness indicator (SCI)-based IS selection algorithm that can robustly lead to close-to-optimal approximation of the average achievable throughput with roughly constant complexity. Furthermore, we also contribute a geometric canonical representation (GCR) system to equip the ML-assisted optimization framework with a topology-aware applicability. The proposed topology-aware ML (TAML) framework exhibits good generalizability across different network topologies and flow demands.

The era of artificial intelligence generated content (AIGC) has emerged to offer the benefits of personalized content to meet the needs of different users. A majority of generative AI models require computationally intensive training and/or finetuning with large datasets, which make resource-constrained devices, such as mobile edge devices (MEDs) to seldom participate in the training process. In addition, MEDs are not willing to share their private data with a central cloud server to train the models. Distributed machine learning (ML) approaches, such as federated learning (FL) and split learning (SL), offer some benefits in achieving data privacy, as well as model convergence for balanced data distributions. However, in practical settings where the datasets of MEDs are imbalanced, both FL and SL suffer poor performance. This motivates us to propose collaborative split federated learning (CSFL) for AIGC. Our CSFL approach supports resource cognition in which MEDs participate based on their available resources. We demonstrate that CFSL achieves good performance in terms of model convergence speed and training latency, while preserving data privacy when training a denoising diffusion probabilistic model for AIGC.

This study finds and proves that strictly periodic scheduling is optimal in terms of energy efficiency within the context of remote state estimation. We model a problem where the sensor transmits local state estimates over an independent and identically distributed packet dropping channel to a remote estimator. From the angle of energy efficiency, we discover and explain that periodic scheduling can arbitrarily approach optimal scheduling in infinite horizon. Building upon this, we have derived explicit conclusions regarding the optimal periodic scheduling, underscoring that: 1) Under the condition of maximum energy efficiency, the optimal scheduling policy is to enforce a strict periodic scheduling; 2) A concrete expression for the identified strict scheduling period based on the system parameters has been established. The study culminates with a series of numerical simulations that showcase the efficacy of our theoretical findings.

Age of information (AoI) is a crucial metric in modern communication systems, quantifying the information freshness at the receiver side. This study proposes a novel and general approach utilizing stochastic hybrid systems (SHS) for AoI analysis and minimization in carrier sense multiple access (CSMA) networks. Specifically, we consider a practical networking scenario where multiple nodes contend for transmission through a standard CSMA-based medium access control (MAC) protocol, and the tagged node under consideration uses a small transmission buffer for a low AoI. We for the first time develop an SHS-based analytical model for this finite-buffer transmission system over the CSMA MAC. Moreover, we develop a creative method to incorporate the collision probability into the SHS model, with background nodes having heterogeneous traffic arrival rates. This new model enables us to analytically find the optimal sampling rate to minimize the AoI of the tagged node in a wide range of practical networking scenarios. Our analysis reveals insights into buffer size impacts when jointly optimizing throughput and AoI. The SHS model is cast over an 802.11-based MAC to examine the performance, with comparison to ns-based simulation results. The accuracy of the modeling and the efficiency of optimal sampling are convincingly demonstrated.

Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.

Reasoning under conditions of uncertainty is important in many areas where posterior knowledge depends on prior likelihood estimation from data. Distributed computing can provide a leverage for enhancing Bayesian network (BN) structure learning while keeping data private to users. We propose a decentralized learning framework based on distributed computing of BNs in local sites. Our method yields a higher fitness score (FS) for BNs in local sites, preserves local data privacy and significantly reduces the computation cost by 69.09% in comparison with a centralized approach which yields a low FS score, and does not keep user data private.

The research on combining cryptanalysis with deep learning has recently attracted increasing attention. As an ultra-lightweight cipher for IoT environments, SPECK has attracted much attention from researchers for its excellent performance, and there have been some attempts to introduce deep learning into differential cryptanalysis on SPECK. However, existing work often built differential distinguishers based on traditional residual network, whose accuracy and interpretability on other tasks is inferior to that of attention mechanisms. In order to improve model accuracy and to further utilise the deep learning model to analyse the security of SPECK, this paper introduces the attention mechanism into the differential cryptanalysis on SPECK. First of all, by introducing an attention mechanism in the output layer of the residual network, we achieve a higher accuracy than existing works, and confusion matrices prove that the enhancement brought by attention is effective. Furthermore, using the visualization algorithm, we demonstrate the effectiveness of the attention mechanism intuitively and further analyze the features extracted from the ciphertext by deep learning. In addtion, the bit transfers captured from the ciphertexts by the attention mechanism reflects the possible insecurity of the 5, 6 and 7 rounds of SPECK for specific input differentials, and our work again demonstrates the great potential of deep learning for applications in differential cryptanalysis.

Keeping sensor data fresh is desired for Industrial Internet of Things (IIoT), especially, in real-time monitoring applications. However, this may require sensors always in active mode and, thus, incur low energy efficiency. In this article, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel. We study the problem of optimizing the sensor data freshness in terms of Age of Information (AoI) while saving energy by scheduling the sensor to sleep when needed. The problem is formulated as a Markov decision process that takes both AoI and energy consumption into account, to which we theoretically prove that the optimal scheduling policy forms a cyclic sleep–wake pattern. The optimal sleep period is also analyzed. Simulation results demonstrate that the proposed scheduling policy outperforms other existing policies.

Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

A significant challenge in 802.11p based vehicular ad hoc networks (VANETs) is that the cooperative awareness messages (CAMs) tend to experience collisions. In this paper, we propose an adaptive CAM messaging algorithm based on the emerging methodology of the age of information (AoI). Our objective is to minimize an age-penalty function in a trajectory prediction application. In our design, each vehicle will compute a local penalty which serves as an indicator on whether the CAM messaging frequency is appropriate for its mobility status; and at the same time, calculates an appropriate penalty associated with all its neighbors which serves as an indicator regarding the impact of network congestion on the trajectory prediction quality. The aggregated penalty score integrating both the local and neighboring parts will be used to adaptively control the CAM sending frequency. We are to present simulation results demonstrating that our adaptive messaging method can indeed mitigate network congestion while meet the driving safety requirements.

In this paper, we study how to minimize the age of information (AoI) for remote monitoring over a WiFi network, where a tagged node under study needs to deliver the sampling messages to the monitoring application installed at the access point (AP). We consider a very challenging practical scenario where multiple background nodes might incorporate heterogeneous and generic traffic models; all the nodes contend for the transmission channel through the practical IEEE 802.11 based medium access control (MAC) protocol. The existing AoI analyses over distributed MAC protocol are not sufficient for our problem, which are limited to simplified MAC modeling or homogeneous traffic modeling. We propose an AoI optimization algorithm that integrates the AoI queueing analysis with the 802.11 MAC performance analysis. Specifically, we develop an innovative method to address the impact of the MAC channel attention on the message service time of the tagged node and compute the minimal AoI iteratively. Simulation results demonstrate that our algorithm is very accurate and robust crossing a variety of networking scenarios. Moreover, our methods require only local computation and slight probing of the conditional collision probability, making them suitable for practical use.

As a main use case of 5G and Beyond wireless network, the ever-increasing machine type communications (MTC) devices pose critical challenges over MTC network in recent years. It is imperative to support massive MTC devices with limited resources. To this end, Non-orthogonal multiple access (NOMA) based random access network has been deemed as a prospective candidate for MTC network. In this paper, we propose a deep reinforcement learning (RL) based approach for NOMA-based random access network with truncated channel inversion power control. Specifically, each MTC device randomly selects a pre-defined power level with a certain probability for data transmission. Devices are using channel inversion power control yet subject to the upper bound of the transmission power. Due to the stochastic feature of the channel fading and the limited transmission power, devices with different achievable power levels have been categorized as different types of devices. In order to achieve high throughput with considering the fairness between all devices, two objective functions are formulated. One is to maximize the minimum long-term expected throughput of all MTC devices, the other is to maximize the geometric mean of the long-term expected throughput for all MTC devices. A Policy based deep reinforcement learning approach is further applied to tune the transmission probabilities of each device to solve the formulated optimization problems. Extensive simulations are conducted to show the merits of our proposed approach.

Chainweb and some other parallel blockchain systems have recently been proposed, with the objectives of improving the throughput and enhancing the tamper-proof capability. While many security related studies have been conducted for traditional single-chain based blockchain systems, the security aspect of parallel chain systems is yet to be well studied and understood. Our paper presents a systematic study on selfish mining attacks in Chainweb based on mathematical modeling. Specifically, selfish mining is conducted by concentrating the computation power on a subset of parallel chains and operating a proper withholding strategy. We demonstrate how to establish a Markov chain based analytical model with innovative techniques to handle the very large state space. Our Markov chain model is also capable of handling different number of parallel chains. The mathematical analysis brings an insightful, in fact counterintuitive, finding that the attackers need less computation power to harvest additional rewards through withholding when Chainweb contains a larger number of chains; while the common understanding is that the more chains are used, the more tamper-proof the system is. The accuracy of the Markov chain analysis is demonstrated via comparison to the simulation results.

Email Bombing, a kind of denial-of-service (DoS) attack is crippling internet users and is on the rise recently. A particularly notorious type is the Subscription Bombing attack, where a victim user’s inbox is bombarded with a stream of subscription emails at a particular period. This kind of attack helps the perpetrator to hide their real motive in lieu of a barrage of legitimate-looking emails. The main challenge for detecting subscription bombing attacks is that most of the attacking email appears to be legitimate and benign and thus can bypass existing anti-spam filters. In order to shed some light on the direction of detecting the bombing attacks, in this paper we first conduct some reverse engineering study on the Gmail anti-spam mechanism (as the information is not publicly available) and in-depth feature analysis of real-life bombing attack emails. Leveraging the insights from our reverse engineering study and data analysis, we propose a novel layered detection architecture, termed as SubStop, to detect and mitigate subscription bombs. SubStop exploits the statistics of incoming volume, source domain distribution, the correlation among different features, and implements machine learning to achieve effective detection. In specific, we utilize the weighted support vector machine (WSVM) and properly tune the class weights to achieve high accuracy in detecting bombing attacks. Despite the scarcity of public email data sets, we conduct extensive experiments on a real-life subscription bomb attack and real-time attacks using our bombing simulation script (which is facilitated by our reverse engineering findings), on test email accounts. Detailed experimental results show that our proposed architecture is very robust and highly accurate in detecting and mitigating a subscription bombing attack.

Ensuring data freshness is important for Industrial Internet of Things (IIoT). In this article, we consider a typical IIoT application where multiple sensors monitor some time-varying physical processes and report measurements to a central base station through an unreliable wireless channel. In order to save energy, each sensor may switch to sleep mode for a while after successfully transmitting a packet. Since only awake sensors are able to transmit data, we propose a novel function of Age of Information with penalty (or AoI-penalty) to capture the eagerness of the active sensors to provide fresh information. We formulate a new AoI-penalty minimization problem for scheduling the sensors’ transmissions. We theoretically derive a necessary condition for the system’s AoI-penalty to converge to a finite value, and further obtain a lower bound of the AoI-penalty. Moreover, we develop a max-weight-based scheduling policy and theoretically prove that it is the optimal policy when the network is symmetric and the channel is error-free. Simulation results demonstrate that the proposed policy achieves AoI performance near to the lower bound and that with such sleep–wake sensors, the achieved AoI performance is close to that with nonsleeping sensors but at a much lower energy cost.

Data-driven machine learning approaches have been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing works use simplistic network representations that cannot properly encode the topological difference. They are often limited to fixed topology, and the performance is degraded because the learning target does not get sufficient information since the topological information is not well captured.To address this, we leverage the graphical neural network techniques and propose a two-stage topology-aware deep learning (TADL) framework, which trains a graph embedding unit and a link usage prediction module jointly to discover links likely to be used in optimal scheduling. By properly encoding the network structure, it makes input data with varying topology possible, and also provides more informative clues for the learning target.Important techniques are developed to ensure learning efficiency. The performance is evaluated on canonical multi-hop flow problems with diverse network structures, sizes and realistic deployment scenarios. It achieves close-to-optimum solution quality with a significant reduction in computation time without retraining.

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.

The next mobile generation is highly expected since it is supposed to increase the bit rate and reduce latency to allow multiple new services been offered. However, there is a big concern about energy efficiency, since, more base stations must be added, providing amplified coverage and higher spectral efficiency. There have been several algorithms designed to reduce the energy consumption of wireless networks by switching ON/OFF the small cells inside a Macro-cell without risking the bit rate received by the users. However, the resource allocation issues involved, e.g. base station selection and user association are NP-hard in general. In this paper we propose a novel solution, applying machine learning to train two neural networks to predict which base stations are not critical and can start their sleeping mode and also predict the associations between the users and base stations giving a complete solution for optimizing the energy efficiency in wireless networks. The outcome will provide similar results to the mathematical optimization but saving 99% of the time spent.

The efficient scheduling of transmission links in a wireless network with a certain optimization objective and subject to the interference and network flow constraints plays a central role in wireless networking research. As an alternative to traditional mathematical analysis, data-driven learning methods have shown promise in solving difficult problems by extracting knowledge from experiences and inspired applications of machine learning in wireless networking. In this paper, we focus on tackling the fundamental scheduling issue in multi-hop wireless networks with machine learning, facing the great challenges of the involvement of non-differentiable operations and the consideration of variable network topologies. To address these issues, we propose a reinforcement learning-based method to solve a class of network flow problems under the protocol interference model. Learning from experience, the proposed approach develops a strategy to sequentially select optimum subsets of links to transmit simultaneously to maximize the system throughput without causing interference. The model structure is designed in a way that incorporates network topological information to allow a flexible number of network nodes, and allows non-differentiable decision operation to pass informative gradient information. Experiments with synthetic and real-world deployment data demonstrate that the proposed algorithm achieves close-to-optimum performance at a significantly reduced time cost.

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker's extra gain increases monotonically with its computational power.

Wireless optimization involves repeatedly solving difficult optimization problems, and data-driven deep learning techniques have great promise to alleviate this issue through its pattern matching capability: past optimal solutions can be used as the training data in a supervised learning paradigm so that the neural network can generate an approximate solution using a fraction of the computational cost, due to its high representing power and parallel implementation. However, making this approach practical in networking scenarios requires careful, domain-specific consideration, currently lacking in similar works. In this paper, we use deep learning in a wireless network scheduling and routing to predict if subsets of the network links are going to be used, so that the effective problem scale is reduced. A real-world concern is the varying data importance: training samples are not equally important due to class imbalance or different label quality. To compensate for this fact, we develop an adaptive sample weighting scheme which dynamically weights the batch samples in the training process. In addition, we design a novel loss function that uses additional network-layer feature information to improve the solution quality. We also discuss a post-processing step that gives a good threshold value to balance the trade-off between prediction quality and problem scale reduction. By numerical simulations, we demonstrate that these measures improve both the prediction quality and scale reduction when training from data of varied importance.

Non-orthogonal multiple access (NOMA) has been considered as a promising solution for improving the spectrum efficiency of next-generation wireless networks. In this paper, the performance of a p-persistent slotted ALOHA system in support of NOMA transmissions is investigated. Specifically, wireless users can choose to use high or low power for data transmissions with certain probabilities. To achieve the maximum network throughput, an analytical framework is developed to analyze the successful transmission probability of NOMA and long term average throughput of users involved in the non-orthogonal transmissions. The feasible region of the maximum number of concurrent users using high and low power to ensure successful NOMA transmissions are quantified. Based on analysis, an algorithm is proposed to find the optimal transmission probabilities for users to choose high and low power to achieve the maximum system throughput. In addition, the impact of power settings on the network performance is further investigated. Simulations are conducted to validate the analysis.

Recent advances in communications technologies and the proliferation of connected devices have spawned a variety of information-centric Internet-of-Things (IoT) systems, where timely information updating is normally required. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. With the development of IoT applications, it is becoming increasingly common that the application-level services, e.g., status updates, rely on the timely delivery of fresh information from a number of information sources. In this article, we study the application-oriented scheduling for optimizing information freshness in the presence of correlated information sources. To this end, we adopt the concept of Age of Correlated Information (AoCI) and formulate the scheduling problem as an episodic Markov decision process (MDP) problem with an application-oriented policy. Given the complexity of the above problem, we develop a learning-based approach that not only leverages the emerging deep reinforcement learning (DRL) techniques but also exploits diverse-domain knowledge. The numerical results show that the proposed approach achieves better performance in terms of AoCI, compared to some typical baseline methods.

In this paper, we analytically study the performance of opportunistic multi-channel bonding protocol supporting delay-sensitive multimedia services. We consider a multi-channel system shared by IEEE 802.11ac users who can transmit over multiple channels and legacy users who can only transmit over one single channel. By analyzing the channel bonding behavior of IEEE 802.11ac users and the random access of legacy users, bonding probability and successful bonding probability of IEEE 802.11ac users can be derived. Furthermore, the access delays of both legacy and 802.11ac users are analyzed. According to the analytical results, the network capacity which quantifies the maximum number of multimedia flows that can be supported with guaranteed delay is then presented. Additionally, the impacts of different parameters such as traffic data rate on the network capacity are investigated. Our analytical results show that channel bonding is favorable when the secondary channels are underutilized. But channel bonding should be disabled when there are already intense contentions from legacy users. Based on the analytical results, we propose a heuristic bonding policy which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. Extensive simulations have been conducted to validate the analytical results.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

This paper studies resource allocation for a fully sustainable cooperative network, which consists of multiple Internet of Things (IoT) nodes powered by radio-frequency (RF) energy, one relay with renewable energy supplies, and one destination. Specifically, the relay forwards the data received from IoT nodes to the destination and charges the IoT nodes at the same time. A throughput maximization problem is formulated, which takes into consideration the upper bound of transmit power, stochastic energy harvesting (EH) process and channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different parameters. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

The proliferation of smartphone and tablet empowers ubiquitous Device-to-device (D2D) networks. Using a locally handled wireless link between devices provides significant convenience to all use cases of Internet of Things, such as home monitors, garage door, and Google home. Nowadays, digital cameras have also adapted Wi-Fi technology to ease the process of transferring pictures and videos to smartphones and laptops. This paper introduces the D2D file transfer mechanisms between cameras and mobile platforms and presents an in-depth empirical security analysis on the D2D network created by those devices. Cameras and applications are close-sourced black boxes, which makes security investigation considerably challenging. In this paper, the analysis concentrates on the most popular camera brands in the market and our team reveals some critical vulnerabilities. We exploit the discovered flaws to construct a proof-of-concept attack to demonstrate how to steal an image from a camera in a busy Wi-Fi environment. We conclude the paper with improvement suggestions and possible solutions. The experimental setup we have developed could be used for future related research.

The protocol design of device-to-device (D2D) networks have regained research interest in recent years, due to the increasing number of networking devices and the diverse deployment settings. Most of the network optimization tasks are fundamentally difficult NP-hard problems in wireless settings, because managing interference introduces combinatorial complexity. Existing approaches use general heuristic algorithms for the underlying graph problems. While efficient and simple, they are not adaptive to the changing requirement and priorities of the service providers, and make no use of the past data to recognize and exploit the information within. In this paper, we study a representative network optimization task of maximizing the throughput-based system utility through link scheduling in a single-radio, single-channel D2D networks, and propose a learning-based method to leverage past experience to generate a good scheduling policy. We combine the pattern matching capabilities provided from recurrent neural networks (RNN) and the flexibility in changing environment from reinforcement learning (RL). The algorithm is implemented with existing software frameworks and tested with numerical experiments. We find that its overall solution quality is comparable to existing heuristics with various network scales, and report an improved system throughput with significant lower computation time.

Information source sampling and update scheduling have been treated separately in the context of real-time status update for age of information optimization. In this paper, a unified sampling and scheduling (S2) approach is proposed, focusing on decentralized updates in multiaccess wireless networks. To gain some insights, we first analyze an example consisting of two-state Markov sources, showing that when both optimized, the unified approach outperforms the separate approach significantly in terms of status tracking error by capturing the key status variation. We then generalize to source nodes with random-walk state transitions whose scaling limit is Wiener processes, the closed-form Whittle's index with arbitrary status tracking error functions is obtained and indexability established. Furthermore, a mean-field approach is applied to solve for the decentralized status update design explicitly. In addition to simulation results which validate the optimality of the proposed S2scheme and its advantage over the separate approach, a use case of dynamic channel state information (CSI) update is investigated, with CSI generated by a ray-tracing electromagnetic software.

Motivated by the increasingly urgent demands for delivering fresh information, the age-of-information (AoI) has recently been introduced as an important metric for evaluating the timeliness performance of information update systems and has shed light on a number of research studies. Nevertheless, the most common goal of the existing works does not characterize the value of information freshness from the users' perspective. In this paper, we introduce the concept of effective AoI (EAoI) to quantify the freshness of the information users utilize for decision-making. We consider a general request-response model, which captures both proactive information update and timely information delivery, for investigating the scheduling problem with respect to EAoI minimization. By decomposing the scheduling problem into multiple computationally tractable subproblems, we propose request-aware scheduling policies for static and dynamic request models, respectively. The numerical results show that serving users requests proactively can reduce time-average EAoI in both scenarios.

WirelessHART is one of the most widely used communication standards in industrial wireless networks. In order to meet the stringent real-time requirements in industrial applications, WirelessHART incorporates many designs including the time slotted channel hopping mechanism that enables dynamic time scheduling and channel allocation. In this paper, we study the problem of joint transmission scheduling and channel allocation aiming to minimize the end-to-end delay of multiple flows in multihop WirelessHART networks. We propose a new network model based on a multidimensional scheduling space spanned by flow-link-channel-slot tuples. A multidimensional conflict graph is then established to depict the conflict relationships among the tuples. Based on this, the original delay minimization problem is formulated as an integer program, which however is difficult to solve due to its significantly large scale. To this end, we develop an iterative hop-wise scheduling algorithm by transforming the original problem into a series of maximum weighted independent set problems. We derive theoretical analysis on the schedulability and the performance bound of the proposed algorithm. In addition, we show that our results can be easily extended to accommodate more general scenarios. Finally, extensive simulation results are provided to demonstrate the effectiveness of the algorithm.

Radio frequency (RF) energy harvesting is a promising technology that enables self-sustainable wireless Internet of Things (IoT) networks. In this article, we analyze the energy harvesting performance of a Wi-Fi-based IoT network, where a large number of IoT devices are connected via Wi-Fi for both data communication and energy transfer. Applying probability theory and statistical geometry, we first develop an analytical model to study the energy sustainability of wireless IoT devices with Wi-Fi charging, which operate in an active/charging mode and access the channel using carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Based on the analysis, we derive the necessary and sufficient conditions for the AP beaconing frequency and the charging period of IoT devices to ensure that a network with a general random topology is long-term energy sustainable. It is shown that transmission collisions due to random access result in too much energy consumption that makes it difficult to achieve energy sustainability of IoT devices. To maximize the total network throughput while ensuring long-term energy sustainability of Wi-Fi IoT devices, a distributed energy-sustainable throughput-optimal algorithm is proposed for user charging period selection. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed algorithm.

Mobile device-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each other even when commercial Internet access is not available. Because D2D network is expected to be an intrinsic part of the Internet of Things (IoT) and mobile device is the smartest and the most advanced commercial device in everyday usage, the D2D feature and related security protocols it adopts influences the design and implementation of many other IoT devices. While D2D network provides tangible benefits to users, it also raises the security risks of information leaking. This paper presents an in-depth empirical security analysis on mobile D2D network among Android devices. Android apps could establish a mobile D2D network in various ways, including Wi-Fi hotspot, Wi-Fi Direct, and Bluetooth. Those mobile D2D protocols normally take different protection mechanisms, which makes security investigation considerably challenging. In this paper, we focus on most popular apps in the Google Play Store, with aggregated downloads more than 500 million. Our analysis reveals some critical vulnerabilities. The key findings are bi-fold. First, the current mobile D2D network framework enabled by Android has significant flaw of overprivilege issue. Second, we have identified that most data transfer over mobile D2D network is unencrypted. Furthermore, we exploit the identified Android framework flaws to construct three proof-of-concept attacks and we conclude this paper with security lessons and suggestions of possible solutions against the identified security issues.

Future cities are to be not only an intelligent and green living environment but also provide human-centered public services at a lower cost. The Internet of Things (IoT) and artificial intelligence (AI) are two cornerstone technologies enabling the smart city concept, which are fusing into an organic whole in recent years. Some particular joint points where IoT meets AI are intelligent IoT devices, smart sensing boosted by AI, and IoT big data mining with AI. Such “IoT meets AI” trend is already casting significant impact to enable a smart city. Some examples are: smartphones are able to learn the touching pattern of users; home Wi-Fi router can intelligently detect and locate an intruder; vehicles locations and movement information can be exploited for traffic control; video cameras installed in the street can perform face recognition locally.

The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application of ad hoc clouds is to outsource computational intensive problems to nearby cloud agents. Specifically, for the problem of solving a linear algebraic equation (LAE), an outsourcing client assigns each cloud agent a subproblem, and then all involved agents apply a consensus-based algorithm to obtain the correct solution of the LAE in an iterative and distributed manner. However, such a distributed collaboration paradigm suffers from cyber security threats that undermine the confidentiality of the outsourced problem and the integrity of the returned results. In this paper, we identify a number of such security threats in this process, and propose a secure outsourcing scheme which not only preserves the privacy of the LAE parameters and the final solution from the participating agents, but also guarantees the correctness of the final solution. We prove that the proposed scheme has low computation complexity at each agent, and is robust against the identified security attacks. Numerical and simulation results are presented to demonstrate the effectiveness of the proposed method.

To support bandwidth-intensive services such as virtual reality video applications, next generation WLANs will allow users to transmit over multiple channels for high data rate transmissions. In this paper, an analytical model is developed to study the performance of the dynamic channel bonding in IEEE 802.11ac, and non-contiguous channel aggregation in IEEE 802.11ax, with coexisting legacy single channel users. By modeling the transmissions of single channel and multi-channel users with and without channel bonding as a two-level renewal process, the bonding probability of multi-channel users, along with the throughput of different users in each channel, are derived. Our analysis shows that multi-channel users can boost their throughput at the cost of degraded throughput of legacy users. Furthermore, it is shown that 802.11ax provides higher spectrum utilization compared to 802.11ac, while 802.11ac provides a friendlier coexistence with single channel users. Based on the analysis, a heuristic algorithm for primary channel selection is further proposed to maximize the throughput of multi-channel users. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection algorithm.

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency.

In this paper, the joint beamforming design for max-min fair simultaneous wireless information and power transfer (SWIPT) is investigated in a green cloud radio access network (Cloud-RAN) with millimeter wave (mmWave) wireless fronthaul. To achieve a balanced user experience for separately located data receivers (DRs) and energy receivers (ERs) in the network, joint transmit beamforming vectors will be optimized to maximize the minimum data rate among all the DRs, while satisfying each ER with sufficient RF energy at the same time. Then, a two-step iterative algorithm is proposed to solve the original non- convex optimization problem with the fronthaul capacity constraint in an l0-norm form. Specifically, the l0-norm constraint can be approximated by the reweighted l1-norm, from which the optimal max-min data rate and the corresponding joint beamforming vector can be derived via semidefinite relaxation (SDR) and bi-section search. Finally, extensive numerical simulations are performed to verify the superiority of the proposed joint beamforming design to other separate beamforming strategies.

The problem of optimal resource allocation in wireless networks usually involves scheduling of the network independent sets (ISs), of which the number increases exponentially in the network scale. To deal with such large-scale optimization problems, traditional approaches often resort to some heuristics or iterative algorithms for obtaining a relatively small set of ISs to solve the problems, but at the cost of suboptimality or long convergence time. In this paper, we consider wireless network resource allocation in dynamic flow environments, aiming at maximizing the network capacity. We propose a learning-based approach to find ISs based on the dynamic flow demands. Specifically, instead of searching for individual ISs, we propose to learn groups of ISs by using a deep belief network (DBN). We present detailed design of the DBN-based learning method including details in the offline training and online running phases. Simulation results demonstrate that our DBN-based method outperforms existing ones in terms of achieved network capacity and computation time.

Long Term Evolution (LTE) deployment in the unlicensed spectrum is considered a promising solution to overcome spectrum shortage. To ensure fair coexistence among unlicensed users, two Load Based Equipment (LBE) access technologies are introduced in European Telecommunications Standards Institute (ETSI) standard. However, it is not clear whether the two LBE protocols can ensure fair channel access between unlicensed Licensed Assisted Access (LAA) and Wi-Fi users. To this end, renewal theory based analytical models are developed to study the performance of these two LBE random access protocols. Specifically, the throughput performance of Wi-Fi users and LAA users is first derived and compared. Our results show that both options may not achieve throughput fairness among Wi-Fi and LAA users if the key protocol parameters are not fine tuned. Generally option A favors Wi-Fi users, while option B favors LAA users. To improve the fairness performance, channel access parameters in both protocols should be adapted to network conditions in order to achieve the best coexisting performance in terms of both fairness and network throughput. The analysis provides important guidance for the implementation of Listen-before-Talk based access mechanisms in LAA. Extensive simulations using NS-3 are conducted to validate the accuracy of the models.

In this paper, we consider a green cloud radio access network (C-RAN) with simultaneous wireless and power transfer ability. In order to reduce the energy consumed for updating the channel state information (CSI), energy users are divided into two different groups, including the free charge group and the MIMO group. Then a semi-definite programming problem is formulated under the constraints of energy and information transmission requirements. To minimize the total energy consumption, two algorithms are developed to authorize the energy users into two group divisions in single time slot. Then the algorithms are extended to long term scenarios consisting training and long term stages, the CSI of free charge energy users are not required during the long term stage. Simulation and numerical results are presented to demonstrate the efficiency of the proposed algorithms in significantly reducing the energy consumption of C-RAN systems.

Wireless network resource allocation is an important issue for designing Internet of Things systems. In this paper, we consider the problem of wireless network capacity optimization that involves issues such as flow allocation, link scheduling, and power control. We show that it can be decomposed into a linear program and a nonlinear weighted sum-rate maximization problem for power allocation. Unlike most traditional methods that iteratively search the optimal solutions of the nonlinear subproblem, we propose to directly compute approximated solutions based on machine learning techniques. Specifically, the learning systems consist of both support vector machines (SVMs) and deep belief networks (DBNs) that are trained based on offline computed optimal solutions. In the running phase, the SVMs perform classification for each link to decide whether to use maximal transmit power or be turned off. At the same time, the DBNs compute an approximation of the optimal power allocation. The two results are combined to obtain an approximated solution of the nonlinear program. Simulation results demonstrate the effectiveness of the proposed machine learning-based algorithm.

In this paper, we investigate wireless networks consisting of a legitimate transmitter (Alice), a legitimate receiver (Bob), eavesdroppers (Eves), and friendly jammers. Two network scenarios are considered depending on whether Alice and the jammers have the ability to detect the existence of Eves in their vicinity. If they do not have the ability, as a means to enhance the secrecy, Alice transmits artificial noise and each jammer selectively radiates a jamming signal based on the channel gain between the jammer and Bob. On the other hand, when they have the ability, Alice sends a confidential message to Bob if no Eve is detected within its guard zone, and the jammers transmit jamming signals when there exists at least one Eve in their vicinity. We model the spatial distributions of Eves and jammers as $\beta $ -Ginibre point processes, which can characterize repulsion among the nodes and include the Poisson point process (PPP) as a special case. Then, we analyze both the probability that Bob successfully decodes the confidential message and the probability that the message is secure against eavesdropping. Also, we show that our analysis is a generalization of previous works on the networks with PPPs by recovering them from our analytical results.

In this paper, we investigate the access mechanisms of LTE Licensed Assisted Access (LAA) co-existing with Wi-Fi over the unlicensed band. To this end, we first develop an analytical model to study the performance of existing Load Based Equipment (LBE) MAC for Unlicensed Long Term Evolution (U-LTE), identify the fairness issues, and quantify the reservation overhead of the protocol. To maximize the network throughput and ensure fair spectrum sharing of U- LTE and Wi-Fi, we propose a hybrid MAC protocol that combines the best features of LBE MAC and Frame Based Equipment (FBE) MAC. A two-level renewal process-based model is also developed to analyze the throughput performance of the proposed MAC. By jointly optimizing the sleep period and the contention window size of U-LTE, the best co-existing performance in terms of the total network throughput and throughput fairness of U-LTE and Wi-Fi can be achieved, with minimal reservation overhead. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed MAC protocol.

With the explosive growth of mobile applications and high computation burden on each single device, more and more end users demand to offload expensive computing tasks to external sites via job offloading technologies. Due to the fluctuating nature of jobs from end users, traditional cloud computing paradigm, however, has difficulties in accommodating highly dynamic job requests and meeting heterogeneous user requirements. Locating close to mobile users, edge-clouds have the potential to complement the cloud computing platform by acting as an efficient spot to perform users' deadline-sensitive tasks. In this paper, we study the resource allocation problem for accommodating deadline-sensitive jobs in edge-cloud system. We formulate a revenue maximization problem that captures the SLA-oriented property of job execution, and propose an efficient online multi-resource allocation algorithm that achieves low competitive ratio with moderate resource augmentation.

In this paper, we investigate joint beamforming for energy-throughput tradeoff in a sustainable cloud radio access network system, where multiple base stations (BSs) powered by independent renewable energy sources will collaboratively transmit wireless information and energy to the data receiver and the energy receiver simultaneously. In order to obtain the optimal joint beamforming design over a finite time horizon, we formulate an optimization problem to maximize the throughput of the data receiver while guaranteeing sufficient RF charged energy of the energy receiver. Although such problem is non-convex, it can be relaxed into a convex form and upper bounded by the optimal value of the relaxed problem. We further prove tightness of the upper bound by showing the optimal solution to the relaxed problem is rank one. Motivated by the optimal solution, an efficient online algorithm is also proposed for practical implementation. Finally, extensive simulations are performed to verify the superiority of the proposed joint beamforming strategy to other beamforming designs.

In this paper, an analytical model is developed to study the performance of distributed and opportunistic multichannel bonding in IEEE 802.11ac WLANs, with co-existing legacy IEEE 802.11a/b/g users. By modeling the transmissions of legacy users and ac users with and without channel bonding in each channel as a two-level renewal process, the channel bonding probability of ac users in each secondary channel is derived. Based on the bonding probability, the throughput of legacy users and ac users can be analyzed respectively. Our analysis shows that ac users with bonding capabilities achieve higher throughput at the cost of degraded throughput of legacy users. The overall network throughput also decreases due to the increased contention level imposed by ac users in secondary channels. Based on the analysis, we further propose a channel selection scheme for ac users to select the best primary channel, in order to mitigate the contentions in the network and attain the maximal network throughput. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection scheme.

With the development of wireless networks, the scale of network optimization problems is growing correspondingly. While algorithms have been designed to reduce complexity in solving these problems under given size, the approach of directly reducing the size of problem has not received much attention. This motivates us to investigate an innovative approach to reduce problem scale while maintaining the optimality of solution. Through analysis on the optimization solutions, we discover that part of the elements may not be involved in the solution, such as unscheduled links in the flow constrained optimization problem. The observation indicates that it is possible to reduce problem scale without affecting the solution by excluding the unused links from problem formulation. In order to identify the link usage before solving the problem, we exploit deep learning to find the latent relationship between flow information and link usage in optimal solution. Based on this, we further predict whether a link will be scheduled through link evaluation and eliminate unused link from formulation to reduce problem size. Numerical results demonstrate that the proposed method can reduce computation cost by at least 50% without affecting optimality, thus greatly improve the efficiency of solving large scale network optimization problems.

Mobile crowd sensing presents a new sensing paradigm which allows an individual participant with mobile device perform sensing tasks and activities of professional organizations. Privacy is one major concern in the mobile crowd sensing application. In this paper, we first present a fog-assisted mobile crowd sensing architecture, then we propose two privacy-preserving crowd sensing schemes for two categories of crowd sensing applications. The first scheme is based on an additive homomorphic encryption algorithm and allows the service subscriber collect the statistical data without revealing the individual data from each participant. The second scheme is based on a bitwise-XOR homomorphic encryption algorithm and allows the service subscriber collect the accurate data without know which data is from which participant. The proposed schemes can achieve κ-anonymity privacy level for each participant. We also give the performance analysis of the proposed schemes.

Fog computing is a promising architectural pattern to reduce the amount of data that is transferred to the cloud for processing and analysis. In this paper, we study fog-assisted data streaming scenario in which fog nodes at the network edge share their spare resources to help pre-process raw data of applications hosted in the cloud. A distributed resource sharing scheme is presented where the software defined network (SDN) controller dynamically adjusts the volume of application data that will be directed to fog nodes for pre-processing. The SDN controller makes decisions by coordinating fog nodes and cloud platform to collaboratively solve a social welfare maximization problem. Based on a hybrid alternating direction method of multipliers (H-ADMM) algorithm, computation burden for solving the optimization problem is fully distributed to fog nodes, cloud platform and SDN controller, where local variables of fog nodes are updated in parallel. With proper design of message exchange pattern, the communication overhead of the coordination to SDN controller grows smoothly with increasing number of participating fog nodes.

An analytical model is developed to evaluate the network performance of an IEEE 802.11ac Wireless Local Area Network (WLAN) in support of delay sensitive video services over multiple channels. Specifically, the channel bonding probability and the channel access delay of wireless users are analyzed, considering the contentions among legacy and ac users in the same channel and across multiple channels. Based on the analysis, the network capacity region, i.e., the maximum number of traffic flows can be supported with the bounded delay performance in a multi-channel WLAN with and without channel bonding, is then derived. Our analysis shows that channel bonding can greatly improve the network capacity when the channel is under-utilized with a small number of legacy users co-existing with the ac users; yet channel bonding is not always favorable and it may degrade the network capacity when the number of legacy users increases due to the increased contentions in the network. The analysis provides important guidance for effective admission control and channel bonding strategies to guarantee the bonded service delay of realtime applications. Extensive simulations validate the analysis.

LTE over unlicensed spectrum (LTE-U) is one of the promising approaches to further improve LTE network throughput. To maximize the benefit of LTE-U, in this work we study joint resource allocation for LTE over the legacy licensed spectrum and the sharing unlicensed spectrum in a multi-cell scenario. Specifically, we formulate a mixed-integer power-channel allocation problem aiming at maximizing the network throughput, with the constraints of protecting the coexisting Wi-Fi networks and hardware limitation of user equipments in the LTE-U networks. To solve the resource allocation problem efficiently, we exploit delay column generation approach to decompose the original optimization problem and then propose a novel algorithm based KKT conditions. Simulation results show the advantage of LTE-U networking and the effectiveness of the proposed algorithm in terms of convergence speed and network throughput.

In cyber-physical system (CPS) over IEEE 802.11e-based wireless local area networks (WLANs), a misbehaving node can gain significant advantage over other normal nodes in terms of resource sharing by deliberately manipulating its protocol parameters. Due to the random spectrum-access nature of the protocol, it is challenging to detect the misbehaving node accurately and in real-time. Moreover, many existing misbehavior detectors, primarily designed for traditional IEEE 802.11 networks, become inapplicable in IEEE 802.11e networks with heterogeneous network configurations. In this paper, we propose novel real-time and light-weight countermeasures including a hybrid-share misbehavior detector and a packet-dropping-based misbehavior mitigation mechanism for IEEE 802.11e-based CPS. We develop mathematical models for the performance of the proposed detector and mitigation mechanisms. Extensive simulation results show that the proposed mechanisms can achieve a high detection rate and punish a misbehaving node with a high packet dropping rate.

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the L-maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within L hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of L so as to find the optimal L to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal L does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models.

In this paper, we consider a fully sustainable cooperative communication system which consists of multiple source nodes with radio-frequency (RF) energy harvesting capabilities, a half-duplex relay node with renewable energy supplies, and a destination node. Specifically, the relay node is powered by the green energy harvested from renewable sources such as solar or wind, while the source nodes are wirelessly charged by the RF energy from the relay node's forwarding signals to the destination node. An optimal joint time scheduling and power allocation problem is formulated to achieve the maximum system sum-throughput of the users over a finite time horizon. To tackle the formulated NP-hard non-convex mixed integer nonlinear programming problem, we first analyze its upper bound by problem reformulation and relaxation, which can be simplified by the directional water filling algorithm and iteratively solved by sequential parametric convex approximation. We then propose an optimal branch-and-bound framework to solve the formulated problem, and develop an efficient sub-optimal offline algorithm and a heuristic online algorithm to reduce the computational complexity. Finally, extensive simulations are conducted to verify the superiority of the proposed solution and demonstrate that the sub-optimal algorithm approaches the performance upper bound with polynomial time complexity.

In this paper, we analyze the performance of wireless sensor networks using stochastic geometry. In practical networks, since nodes in the networks are not independently placed, there exists a correlation among the locations of the nodes. In order to capture the effect of the correlation, we model the spatial distribution of the nodes as α-Ginibre point processes (GPPs), which reflect the repulsion. It is assumed that each sensor node is associated with the closest gateway and employs a fractional channel inversion power control, which adjusts transmit power based on the contact distance. We first identify the characteristics of the contact distance and transmit power, and then investigate the outage performance of the networks using the derived characteristics. We also examine energy harvesting networks where each sensor harvests energy from radio frequency signals radiated by energy sources and transmits data to its serving gateway when the harvested energy is enough to conduct the fractional channel inversion power control. Since the α-GPP contains the Poisson point process (PPP) as a particular case, our analysis can be interpreted as a generalization of previous works on the networks modeled by PPPs. The accuracy of our analysis is validated through simulation results.

In this paper, an analytical framework is developed to study the performance of opportunistic channel bonding in IEEE 802.11 WLANs. Specifically, we consider a WLAN operating on multiple channels shared by both legacy users and IEEE 802.11ac users with channel bonding capability. By capturing the opportunistic channel bonding from the IEEE 802.11ac users in the primary channel and the random access of legacy users in the secondary channels, we derive the successful channel bonding probability, and the throughput of both legacy and IEEE 802.11ac users. Our analysis shows that with multi-channel bonding, ac users achieves a higher throughput at the cost of reduced throughput of legacy users in the secondary channels. The channel bonding achieves a higher total network throughput only when there is no legacy user in the secondary channels, and the total throughput decreases when legacy users exist due to the increased contentions in the secondary channels. The analysis provides important guidance for the deployment of multi-channel WLANs where ac users should select a proper primary channel to maximize its bonding opportunity and to attain the maximum throughput. Extensive simulations are conducted to validate the analysis.

This paper studies the charging problem of plug-in hybrid electric vehicles (PHEVs) with a game theoretic approach. The interplay between PHEVs and smart micro-grid charging stations are modeled as a multi-leader-multi-flower Stackelberg game. In the game, each PHEV needs to select a charging station for maximum utility given the charging prices from each station; and each charging station needs to adjust its charging price for improved utility based on the charging requests received. An important issue being considered in this paper is that the actual cost for charging into a target energy level depends on the travel distance and traffic conditions between the requesting PHEV and the finally selected charging station. In this paper, we adopt a location aware approach to explicitly incorporate the location- related cost into the utility function in the game model. Note that the distance information, traffic information along the roads, and communications between PHEVs and charging stations are to be obtained or enabled by vehicular ad hoc networks (VANET). We develop the algorithms for charging station selection and price adjustment. Simulation results are presented to demonstrate that the utility improvement of the location-aware model over the location-blind model.

The proliferation of smart phones enables ubiquitous Mobile Ad-hoc Networks (MANETs) where mobile devices communicate with peers over a wireless channel in an ad hoc mode. In this paper, we introduce a novel method to achieve multi-hop communication among open-source, non-rooted Android devices using Wi-Fi Direct Technology, also known as Wi-Fi Peer-to-Peer (P2P). Then we implement a proactive routing protocol in an MANET using multiple off-the-shelf smart phones to enable efficient message delivery over a multi-hop MANET.

As wireless networks are gaining increasing popularity, the network energy efficiency has become a critical issue. In this paper, we focus on energy-efficient networking in a generic multi-radio multi-channel (MR-MC) wireless network where transmission scheduling, transmit power control, radio and channel assignment are coupled together in a multi-dimensional resource space, thus requiring joint optimization and low complexity algorithms. We propose a novel Decomposed Approach For energy-efficient (DAFEE) networking in MR-MC networks, with the objective to minimize network energy consumption while guaranteeing a certain level of performance. In particular, we leverage a multi-dimensional tuple-link based model and a concept of resource allocation pattern to transform the complex optimization problem into a linear programming (LP) problem. The LP problem however has a very large solution space due to the exponentially many possible resource allocation patterns. We then exploit delay column generation and distributed learning techniques to decompose the problem and solve it with an iterative process. Furthermore, we propose a sub-optimal algorithm to speed up the iteration with constant-bounded performance. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

ZigBee has been widely recognized as an important enabling technique for Internet of Things (IoT). However, the ZigBee nodes are normally resource-limited, making the network susceptible to a variety of security threats. This paper closely investigates a severe attack on ZigBee networks termed as ghost, which leverages the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete the energy of the nodes. We show that the impact of ghost is very large and that it can facilitate a variety of threats including denial of service and replay attacks. We highlight that merely deploying a standard suite of advanced security techniques does not necessarily guarantee improved security, but instead might be leveraged by adversaries to cause severe disruption in the network. We propose several recommendations on how to localize and withstand the ghost and other related attacks in ZigBee networks. Extensive simulations are provided to show the impact of the ghost and the performance of the proposed recommendations. Moreover, physical experiments also have been conducted and the observations confirm the severity of the impact by the ghost attack. We believe that the presented work will aid the researchers to improve the security of ZigBee further.

Wireless personal area network (WPAN) is small-ranged network centered at an individual for interconnecting personal devices. For such a network, the bootstrapping mechanism with which the devices establish a secure group key is of critical importance. Most existing bootstrapping mechanisms require out-of-band channels and involve human interactions for authentication. In this paper, we aim to develop a fully automated bootstrapping mechanism with only in-band channels with approvable security. Toward this end, we designed an integrity-guaranteed message (IGM) structure, a self-authenticated key agreement protocol, and a prescheduling mechanism in allusion to the IEEE 802.15.4 standard for WPANs. The IGM structure guarantees that an adversary cannot modify the IGM message without being detected, thus protects the message integrity without the requirement of shared secrets between the sender and the receiver devices. The proposed self-authenticated key agreement protocol utilizes the IGM's integrity guaranteed property, works together with the prescheduling mechanism to achieve message self-authentication, thus protecting the secure bootstrapping process from the node impersonation attack and the man-in-the-middle attack without leveraging any out-of-band channels. We analyze the security performance of the proposed schemes, and show that they can be seamless interoperative with the existing IEEE 802.15.4 standard.

One of the solutions to improve mobile Delay Tolerant Network (DTN) performance is to place additional stationary nodes, called throwboxes, to create a greater number of contact opportunities. In this paper, we study a key optimization problem in a time-evolving throwbox-assisted DTN: throwbox selection, to answer the questions such as “how many active throwboxes do I need?” and “which throwboxes should be activated?” We formally define two throwbox optimization problems: min-throwbox problem and k-throwbox problem for time-evolving DTNs modeled by weighted space-time graphs. We show that min-throwbox problem is NP-hard and propose a set of greedy algorithms which can efficiently provide quality solutions for both challenging problems.

In IEEE 802.11-based networks, it is well-known that selfish nodes can gain significant performance advantage over other normal nodes by manipulating the medium access control (MAC) protocol parameters. There have been many studies on the detection of such misbehavior, though most of them focus on centralized detection with the assistant of an access point in wireless local-area networks (WLANs). In ad hoc networks, due to the complexity introduced by the hidden terminal issues, many existing misbehavior detection schemes rely on the RTS/CTS (request-to-send/clear-to-send) messages to infer the details of MAC layer behavior of the monitored nodes. However, for networks without the RTS/CTS mechanism (e.g., using the basic access mode), those schemes become inapplicable. In this paper, we propose a novel two-step misbehavior detector for IEEE 802.11-based ad hoc networks, based on observations of successful transmissions and channel conditions, which does not require the RTS/CTS messages. The main idea is to check whether the measured performance matches the model-based expectations, in which neighbors exchange data and cooperate to make detection decisions. Our detector sets two barriers to catch the misbehaving nodes: the first step utilizes neighbor- broadcasted information to establish the relationship between channel availability and transmission rate and checks if the relationship matches the theoretical model, while the second step checks whether the model-based throughput meets the observations. We demonstrate the effectiveness of the our detector through simulations.

In this paper, a new MAC protocol for LTE over unlicensed spectrum (LTE-U) is presented that allows friendly co-existence of LTE-U with other unlicensed wireless networks, including Wi-Fi. Specifically, in a time-slotted LTE-U system, LTE- U users can transmit continuously for a period after a successful channel reservation during the spectrum sensing period. Following each LTE transmission period, a certain duration is reserved for asynchronous Wi-Fi transmissions. By adaptively adjusting the periods of LTE transmissions, Wi-Fi transmissions, and spectrum sensing, different levels of Wi-Fi protection can be achieved. Based on the proposed MAC, an analytical model is developed to study the throughput performance of both LTE-U and Wi-Fi, considering the asynchronous transmission nature of Wi-Fi within the time-slotted MAC structure. Impacts of the protocol parameters, i.e., the periods of LTE/Wi-Fi transmissions and spectrum sensing, on the throughput performance of LTE-U and Wi-Fi are also investigated. Extensive simulation results are provided to validate the analysis.

Device-to-device (D2D) communication is the need of the hour in the domain of next generation wireless networking and in the rapidly evolving smartphone network world. D2D technology facilitates mobile users to communicate with each other directly, bypassing the cellular base stations. As a popular D2D technique, WiFi-Direct is also a budding new technology that has the ability to set up wireless communications between a group of smartphones. While single-hop D2D based networks have been promising and energy efficient, multi-hop D2D based networks, though demanded in some emerging applications, are not well studied. In this paper, we elaborate the concept of multihop smartphone networks based on WiFi-Direct and propose an energy efficient cluster-based routing protocol, QGRP, to address the energy issue of increasing importance due to high energy costs of smartphones. Simulations demonstrate that QGRP can save significant amounts of energy compared to the cases without QGRP.

Device-to-device (D2D) communications recently have attracted broad attention owing to its potential ability to improve spectrum and energy efficiency within the existing cellular infrastructure. Lacking sophisticated control, D2D user equipments (DUEs) themselves are not powerful enough to resist eavesdropping or fight against security attacks. This work investigates selection of jamming partners for D2D users to thwart reception by social outcasts in D2D overlay, by exploiting social relationship to improve secrecy rate. We aim to maximize the secrecy rate of worst case, eavesdropping by any outcast, through selecting jammer node while allocating transmit power for both source and jammer. We present a heuristic genetic algorithm based solution to evaluate the problem directly. In addition, we also propose approximated optimization solutions by considering power allocation of upper and lower bounds to simplify the problem, by leveraging the fractional programming (GFP) oriented Dinkelbach-type algorithm. Numerical results show that the proposed schemes can achieve better performance through finding an appropriate partner.

Exploiting multiple radio interfaces over multiple channels and using directional antennas are promising technologies to enhance the performance of wireless networks. However, in such a multi-dimensional network resource space, assignment of radios and channels and configuration of antenna directions are coupled, making the complexity for network capacity optimization dramatically increase. Existing work has considered either multi-radio multi-channel (MRMC) networks or networks with directional antennas (DA); however, there lacks a generic framework for such complex MRMC-DA wireless networks. In this paper, we employ the tuple concept to define Link- Radio-Antenna-Channel tuple links, which are then utilized as building blocks to construct a multi-dimensional conflict graph (MDCG) of the MRMC-DA network. The MDCG model facilitates mapping the original MRMC-DA network into a simple virtual single-radio single-channel network, on which the capacity optimization problem can be formulated as a linear program. To circumvent searching the exponentially many independent sets, we apply the delayed column generation method to design our algorithm. Simulations demonstrate the performance of the proposed method and analyze the different effects of the numbers of channels, radios and antenna choices.

Hybrid wireless networks are networks that are composed of both ad hoc transmissions and cellular transmissions. Many existing works have analyzed the capacity of hybrid wireless networks. By assuming the uniform traffic model that a source node would select a random node as the destination, the network capacity is a function of number of nodes and number of base stations. Nevertheless, the real network traffic pattern is related to the social behaviors of users. In this work, we study the capacity of hybrid wireless networks with the social traffic model under the L-maximum-hop routing policy. If two nodes are within L hops away, packets will be transmitted in the ad hoc mode; otherwise, packets are transmitted through the base stations. To our best knowledge, we are the first to study this problem and develop the capacity as a function of number of nodes, number of stations, traffic model parameters, and L.

Exploiting direct transmissions between geographically close mobile users without passing through the base stations, device‐to‐device (D2D) communications contribute significant improvement to the spectral efficiency of cellular networks. In D2D‐assisted cellular networks, the social interaction of mobile users is an important property that will affect the practical performance and should be seriously accounted in the network resource allocation, which is yet to be fully explored. In this study, the authors investigate the social interactions for D2D transmissions and develop a contact time model to characterise the D2D links. A D2D link can be considered for resource allocation only when the two users encounter and their contact time is enough long to complete a meaningful transmission. They formulate and compare both sociality‐blind and sociality‐aware optimisation problems for resource allocation in D2D‐assisted cellular networks. Extensive numerical results are presented, validating that the sociality‐aware resource allocation can achieve higher performance than that of the sociality‐blind approach.

The greedy maximal scheduling (GMS) and maximal scheduling (MS) algorithms are well-known low-complexity scheduling policies with guaranteed capacity region in the context of single-radio single-channel (SR-SC) wireless networks. However, how to design maximal scheduling algorithms for multiradio multichannel (MR-MC) wireless networks and the associated capacity analysis are not well understood yet. In this paper, we develop a new model by transforming an MR-MC network node to multiple node-radio-channel (NRC) tuples. Such a framework facilitates the derivation of a tuple-based back-pressure algorithm for throughput-optimal control in MR-MC wireless networks and enables the tuple-based GMS and MS scheduling as low-complexity approximation algorithms with guaranteed performance. An important existing work on GMS and MS for MR-MC networks is that of Lin and Rasool (IEEE/ACM Trans. Networking, vol. 17, no. 6, 1874-1887, Dec. 2009), where link-based algorithms are developed. Compared to the link-based algorithms, the tuple-based modeling has significant advantages in enabling a fully decomposable cross-layer control framework. Another theoretical contribution in this paper is that we, for the first time, extend the local-pooling factor analysis to study the capacity efficiency ratio of the tuple-based GMS in MR-MC networks and obtain a lower bound that is much tighter than those known in the literature. Moreover, we analyze the communications and computation overhead in implementing the distributed MS algorithm and present simulation results to demonstrate the performance of the tuple-based maximal scheduling algorithms.

The IEEE 802.15.4 standard, which supports low-cost communications, has been applied in a variety of wireless networks. Developing accurate analytical models for the IEEE 802.15.4 medium access control (MAC) protocol is critical for the design and performance evaluation of such networks. Periodic traffic is a common traffic pattern generated in many practical application scenarios, for which most existing analytical models assuming either saturated or random network traffic patterns become inapplicable. In this paper, we develop an accurate and scalable analytical model to analyze the IEEE 802.15.4 MAC protocol with the periodic traffic. Our model can accurately capture the protocol stochastic behavior in each period in scenarios such as with or without retransmissions and with single clear channel assessment (CCA) or double CCAs. Extensive simulations are conducted to validate the proposed model by both transient and aggregate performance evaluations, and the results show that the model captures MAC behavior with periodic traffic accurately. We also discuss about extending the proposed model to account for heterogeneous scenarios and the hidden node problem.

The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance.

The Enhanced Distributed Channel Access (EDCA) specification in the IEEE 802.11e standard supports heterogeneous backoff parameters and arbitration inter-frame space (AIFS), which makes a selfish node easy to manipulate these parameters and misbehave. In this case, the network-wide fairness cannot be achieved any longer. Many existing misbehavior detectors, primarily designed for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous network configuration. In this paper, we propose a novel real-time hybrid-share (HS) misbehavior detector for IEEE 802.11e based wireless local area networks (WLANs). The detector keeps updating its state based on every successful transmission and makes detection decisions by comparing its state with a threshold. We develop mathematical analysis of the detector performance in terms of both false positive rate and average detection rate. Numerical results show that the proposed detector can effectively detect both contention window based and AIFS based misbehavior with only a short detection window.

Recent advances in Delay Tolerant Networks (DTNs) have overcome limitations in connectivity by relying on intermittent contacts between mobile nodes to deliver packets. However, lack of rich contact opportunities still causes poor delivery ratio and long delay of DTN routing. One of the solutions to improve mobile DTN performance is to place additional stationary nodes, called throwboxes, to create a greater number of contact opportunities. In this paper, we study a key optimization problem in a time-evolving throwbox-assisted DTN: k-throwbox placement problem, to answer "where should I put my k throwboxes to optimize the performance?". We model a time-evolving DTN as a weighted space-time graph which includes both spacial and temporal information. We prove that k-throwbox placement problem is NP-hard and propose a set of greedy algorithms which can efficiently provide quality solutions. One of the proposed algorithms can guarantee an (1 - 1/e) approximation for the k-throwbox placement problem. Simulation results based on random time-evolving DTNs and real life DTN traces demonstrate the efficiency of the proposed methods.

Multi-radio multi-channel (MR-MC) networks contribute significant enhancement in the network throughput by exploiting multiple radio interfaces and non-overlapping channels. While throughput optimization is one of the main targets in allocating resource in MR-MC networks, recently, the network energy efficiency is becoming a more and more important concern. Although turning on more radios and exploiting more channels for communication is always beneficial to network capacity, they may not be necessarily desirable from an energy efficiency perspective. The relationship between these two often conflicting objectives has not been well-studied in many existing works. In this paper, we investigate the problem of optimizing energy efficiency under full capacity operation in MR-MC networks and analyze the optimal choices of numbers of radios and channels. We provide detailed problem formulation and solution procedures. In particular, for homogeneous commodity networks, we derive a theoretical upper bound of the optimal energy efficiency and analyze the conditions under which such optimality can be achieved. Numerical results demonstrate that the achieved optimal energy efficiency is close to the theoretical upper bound.

Recent studies show that the 60GHz wireless technology could help resolving the hotspot issue in data center networks (DCNs). However, transmissions over 60GHz suffer from limitations of short transmission range and blockage, which makes it a new challenge how to appropriately establish wireless links in the DCN. In this paper, we propose to integrate wireless links into the DCN with the wireless neighborway scheme. Each top-of-rack switch (ToR) has multiple 60GHz wireless links connecting to its neighboring ToRs, which are termed as neighborways. The elephant flow from the sending ToR can be partially offloaded through neighborways, which are then delivered to the ToRs around the receiving ToR through wired links of the DCN, and finally converged to the receiving ToR. The fundamental challenge for the design is how to prevent the offloaded traffics from forming new hotspots in the network fabric. To this end, we developed a wireless network planning solution with corresponding IP address assignment and traffic engineering scheme, which can leverage the potential underutilized wired links in the DCN and meanwhile avoid forming new hotspots. The simulation results show that the proposed scheme can notably relieve the hotspots in the DCN.

With the rapid growth of smartphone and tablet users, Device-to-Device (D2D) communications have become an attractive solution for enhancing the performance of traditional cellular networks. However, relevant security issues involved in D2D communications have not been addressed yet. In this paper, we investigate the security requirements and challenges for D2D communications, and present a secure and efficient key agreement protocol, which enables two mobile devices to establish a shared secret key for D2D communications without prior knowledge. Our approach is based on the Diffie-Hellman key agreement protocol and commitment schemes. Compared to previous work, our proposed protocol introduces less communication and computation overhead. We present the design details and security analysis of the proposed protocol. We also integrate our proposed protocol into the existing Wi-Fi Direct protocol, and implement it using Android smartphones.

This paper proposes a joint cooperative relaying and jamming scheme based on distributed beamforming to enhance the secrecy rate of a wireless channel in the presence of an eavesdropper. Specifically, we consider the scenario that a source node transmits messages to a destination with the help of multiple cooperative relays, where a relay might be compromised to become an eavesdropper as an inside attacker. Our joint relaying and jamming approach is to assign some relay nodes to act as jammers to interfere the eavesdropper, while the remaining ones continue relaying information to the destination. We propose a protocol to implement the joint cooperative relaying and jamming. Our protocol further considers the particular challenge brought by the inside attacker: it may know the jamming signal and can use it to remove the interference from the jammers. The issue of phase and frequency synchronization in the distributed beamforming is also taken into account. A mixed integer programming problem is formulated to maximize the secrecy rate with the proposed joint relaying and jamming, by which the optimal role assignment to a relay and the associated optimal power assignment can be solved. Numerical results are presented to demonstrate the efficiency of the proposed joint relaying and jamming in improving the secrecy rate, with comparison to existing approaches.

We study how to achieve optimal network capacity in the most energy-efficient manner over a general large-scale wireless network, say, a multi-hop multi-radio multi-channel (MR-MC) network. We develop a multi-objective optimization framework for computing the resource allocation that leads to optimal network capacity with minimal energy consumption. Our framework is based on a linear programming multi-commodity flow (MCF) formulation augmented with scheduling constraints over multi-dimensional conflict graph (MDCG). The optimization problem however involves finding all independent sets (ISs), which is NP-hard in general. Novel delayed column generation (DCG) based algorithms are developed to effectively solve the optimization problem. The DCG-based algorithms have significant advantages of low computation overhead and achieving high energy efficiency, compared to the common heuristic algorithm that randomly searches a large number of ISs to use. Extensive numerical results demonstrate the energy efficiency improvement by the proposed energy-efficient optimization techniques, over a wide range of networking scenarios.

The main-thread approach for optimizing the throughput capacity over a multihop wireless network is to apply a multi-commodity flow (MCF) formulation, augmented with a scheduling constraint derived from the conflict graph associated with the network. A fundamental issue with the conflict graph based MCF formulation is that finding all independent sets (ISs) for scheduling is NP-hard in general. If we express the MCF formulation in a matrix format, the constraint matrix will contain a very large number of columns, with each IS being associated with one column. According to the linear programming theorem, such a type of problem can be addressed with the delayed column generation (DCG) method. Unfortunately, applications of the DCG in wireless networks have not received much attention. To the best of knowledge, none of the existing work conducted theoretical studies of the performance of DCG in wireless networks. In this paper, we study the DCG method in the context of a general network flow problem. With a protocol interference model, we rigorously prove that searching an entering column in the DCG operation is equivalent to a maximum weighted independent set (MWIS) problem. A prominent theoretical contribution of this paper is the theorem that: if an MWIS approximation algorithm with the approximation ratio β (<1) is applied in the DCG method, the maximum flow solved will be at least β of the optimal solution. Furthermore, the DCG method is also applied to the multi-radio multi-channel (MR-MC) networks. With extensive numerical results comparing to the existing methods, we show that the DCG method achieves the most preferred tradeoff between computation complexity and network capacity and maintains good scalability when addressing large-scale networks, particularly in the complex MR-MC context.

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

In this paper, we develop a scalable destination-oriented multicast (DOM) protocol for computer networks where the routers have enhanced intelligence to process packets. The basic idea of DOM is that each multicast data packet carries explicit destinations information, instead of an implicit group address, to facilitate the data delivery. Based on such destinations information, each router can compute necessary multicast copies and next-hop interfaces. A fundamental issue in DOM is to constrain the bandwidth overhead due to explicit addressing, which is tackled with a Bloom-filter based design. Our design incorporates the reverse path forwarding (RPF) concept and the BGP routing information, so that DOM can work efficiently in practical networking scenarios especially with asymmetric inter-domain routing. A critical issue in Bloom-filter based design is the issue of forwarding loop due to false positives. We propose an accurate tree branch pruning scheme, which equips the DOM the capability to completely and efficiently remove the false-positive forwarding loop. Furthermore, we study how the DOM can be deployed in an incremental manner over a network, in which only a small fraction of the routers have DOM-aware intelligence while others are legacy routers. We present extensive simulation results over a practical topology to demonstrate the performance of DOM, with comparison to the traditional IP multicast and the free riding multicast (FRM) protocols.

The session initiation protocol (SIP) is widely used for controlling multimedia communication sessions over the Internet Protocol (IP). Effectively detecting a flooding attack to the SIP proxy server is critical to ensure robust multimedia communications over the Internet. The existing flooding detection schemes are inefficient in detecting low-rate flooding from dynamic background traffic, or may even totally fail when flooding is launched in a multi-attribute manner by simultaneously manipulating different types of SIP messages. In this paper, we develop an online detection scheme for SIP flooding attacks, by integrating a novel three-dimensional sketch design with the Hellinger distance (HD) detection technique. In our sketch design, each SIP attribute is associated with a two-dimensional sketch hash table, which summarizes the incoming SIP messages into a probability distribution over the sketch table. The evolution of the probability distribution can then be monitored through HD analysis for flooding attack detection. Our three-dimensional design offers the benefit of high detection accuracy even for low-rate flooding, robust performance under multi-attribute flooding, and the capability of selectively discarding the offending SIP messages to prevent the attacks from bringing damages to the network. Furthermore, we design a scheme to control the distribution of the normal traffic over the sketch. Such a design ensures our detection scheme's effectiveness even under the severe distributed denial of service (DDoS) scenario, where attackers can flood over all the sketch table entries. In this paper, we not only theoretically analyze the performance of the proposed detection techniques, but also resort to extensive computer simulations to thoroughly examine the performance.

The distributed nature of the CSMA/CA-based wireless protocols, for example, the IEEE 802.11 distributed coordinated function (DCF), allows malicious nodes to deliberately manipulate their backoff parameters and, thus, unfairly gain a large share of the network throughput. In this paper, we first design a real-time backoff misbehavior detector, termed as the fair share detector (FS detector), which exploits the nonparametric cumulative sum (CUSUM) test to quickly find a selfish malicious node without any a priori knowledge of the statistics of the selfish misbehavior. While most of the existing schemes for selfish misbehavior detection depend on heuristic parameter configuration and experimental performance evaluation, we develop a Markov chain-based analytical model to systematically study the performance of the FS detector in real-time backoff misbehavior detection. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay, and missed detection ratio under a detection delay constraint. We present thorough simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the developed FS detector.

In this paper, we give a global perspective of multicast capacity and delay analysis in Mobile Ad Hoc Networks (MANETs). Specifically, we consider four node mobility models: (1) two-dimensional i.i.d. mobility, (2) two-dimensional hybrid random walk, (3) one-dimensional i.i.d. mobility, and (4) one-dimensional hybrid random walk. Two mobility time-scales are investigated in this paper: (i) fast mobility where node mobility is at the same time-scale as data transmissions and (ii) slow mobility where node mobility is assumed to occur at a much slower time-scale than data transmissions. Given a delay constraint$D$, we first characterize the optimal multicast capacity for each of the eight types of mobility models, and then we develop a scheme that can achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic factor. In addition, we also study heterogeneous networks with infrastructure support.

A complete flow statistics report should include both flow size (the number of packets in a flow) counting and flow volume (the number of bytes in a flow) counting. Although previous studies have contributed a lot to the flow size counting problem, it is still a great challenge to well support the flow volume statistics due to the demanding requirements on both memory size and memory bandwidth in monitoring device. In this paper, we propose a DIScount COunting (DISCO) method, which is designed for both flow size and flow bytes counting. For each incoming packet of length l, DISCO increases the corresponding counter assigned to the flow with an increment that is less than l. With an elaborate design on the counter update rule and the inverse estimation, DISCO saves memory consumption while providing an accurate unbiased estimator. The method is evaluated thoroughly under theoretical analysis and simulations with synthetic and real traces. The results demonstrate that DISCO is more accurate than related work given the same counter sizes. DISCO is also implemented on the network processor Intel IXP2850 for a performance test. Using only one microengine (ME) in IXP2850, the throughput can reach up to 11.1 Gb/s under a traditional traffic pattern. The throughput increases to 39 Gb/s when employing four MEs.

Recently, a novel 3D wireless mechanism based on 60 GHz band has been proposed to mitigate the job completion time (JCT) in Data Center Networks (DCNs), where signals bounce off DC ceilings to establish wireless connections. The 3D scheme could alleviate hotspots in DCNs with flexible multigigabit wireless links, which bypasses the line-of-sight limitation of 60 GHz wireless links. However, the novel wireless transmission mechanism incurs significant change in the traditional interference model for wireless networks, and the theoretical analysis tool for such hybrid networks is still unavailable. This paper presents a theoretical framework for such 3D DCNs, where the entire network is first transformed from 3D to 2D by remodeling the 3D interference effects. The transformed graph is then processed with a multi-dimensional conflict graph methodology, where the wired and wireless sub-graphs induced by the DCN topology are jointly analyzed. The last but not least, the processed graph is modeled as a minimum job completion time (MJCT) problem, where the optimal traffic engineering, channel allocation and scheduling schemes in the original DCN can be obtained. Simulation results are presented to demonstrate the delay performance of our proposed approach.

The main focus of this paper is to show theoretically that power is a crucial factor in multi-radio multi-channel (MR-MC) wireless networks and hence by judiciously leveraging the power, one can realize a considerable gain on the capacity for MR-MC wireless networks. Such a capacity gain through power enhancement is revealed by our new insights of a co-channel enlarging effect. In particular, when the number of available channels (c) in a network is larger than that necessary for enabling the maximum set of simultaneous transmissions (c̃), allocating transmissions to those additional c-c̃ channels could enlarge the distance between the co-channel transmissions; the larger co-channel distance then allows a higher transmission power for higher link capacity. The finding of this paper specifically indicate that by exploiting the co-channel enlarging effect with power, one can realize the following gain on the capacity for MR-MC wireless networks: (i) In the channel-constraint region (c̃minto Pminc/c̃α/2, then a gain of Θ(log(c/c̃)α/2) is achieved; (ii) In the power-constraint region (c ≥ nφ/2), if each node sends at the maximum power level, Pmax= Pmin.nKor Pmin.2nφ/2, depending on the power availability at a node, then a gain of Θ(log n) or Θ(n) is achieved, respectively.

The open and distributed nature of the IEEE 802.11 based wireless networks provides selfish users the opportunity to to gain an unfair share of the network throughput by manipulating the protocol parameters, say, using a smaller contention window. In this paper, we propose an adaptive approach for real-time detection of such selfish misbehavior. An adaptive detector is necessary in practice, as it needs to deal with different misbehaving scenarios where the number of selfish users and the contention windows exploited by each selfish user are different. In this paper, we first design a basic misbehavior detector based on the non-parametric cumulative sum (CUSUM) test. While the basic detector can be modeled with a Markov chain, we further resort to the Markov decision process (MDP) technique to enhance the basic detector to an adaptive design. In particular, we develop a novel reward function based on which the optimal policy of the MDP can be determined. The optimal policy indicates how the adaptive detector should operate at each state. Another important feature of our detector is that it enables an effective iterative method to detect multiple misbehaving nodes. We present thorough simulation results to confirm the accuracy of our analysis, and demonstrate the efficiency of the adaptive detector compared to a static solution.

In this paper, we propose a secure cooperative data downloading framework for paid services in vehicular ad hoc networks (VANETs). In our framework, vehicles download data when they pass by a road side unit (RSU) and then share the data after they travel out of the RSU's coverage. A fundamental issue of cooperative data downloading is how vehicles effectively share data with each other. We develop an application layer data sharing protocol which coordinates the vehicles to relay data for sharing according to their positions. Such coordinated sharing can avoid collisions in the medium access control (MAC) layer and the hidden terminal issue in multi-hop transmissions. A salient feature of the proposed sharing protocol is that it can guarantee the receipt of the requested data file for each applicant vehicle passing a road side unit. Moreover, we also address security and privacy issues in the process of data downloading and sharing, ensuring applicants' exclusive access to the applied data and privacy of the vehicles involved in the application. We carry out NS2 simulations to thoroughly examine the performance of the proposed cooperative downloading protocol implemented over an 802.11p based VANETs.

Private BitTorrent (PT) is a new trend in Peer-to-Peer file sharing system, which provides high incentives for its users to seed after download by maintaining an upload-to-download ratio in the tracker for each registered community member. From the data we collected from six active PT sites, we discover that the population of both users and contents in any single PT site is much less than the public BitTorrent, and the intersection of content sets in different PTs is quite small. Based on this observation, we propose a content sharing/distribution framework among PTs (named CrossPT), as well as its sharing mechanism. In addition, we investigate the sharing strategy of the PT participants in CrossPT using game theory and the fetch strategy by modeling the scenario to a Neighbor Selection Problem (NSP). We prove NSP to be NP-complete and propose a heuristic algorithm to solve it. The evaluations with the input of crawled data from six PT sites demonstrate the efficiency of our mechanism. The content sizes of the six PT sites can be increased by 113.95%-438.46% with CrossPT. Also, the content distribution process can be done in less than one second, excluding the delivery time of the content itself.

This paper studies how to select a path with the minimum expected end-to-end delay (EED) in a multiradio multichannel (MR-MC) wireless mesh network. While the existing studies mainly focus on the packet transmission delay due to medium access control (MAC), our new EED metric further takes into account the queuing delay at the MAC layer. In particular, in the MR-MC context, we develop a generic iterative approach to compute the multiradio achievable bandwidth (MRAB) for a path, taking the impact of inter-/intraflow interference and space/channel diversity into consideration. The MRAB is then combined with the EED to form the metric weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient is defined to quantitatively represent the channel diversity for a given path. Moreover, we design and implement a distributed WEED-based routing protocol for MR-MC wireless networks by extending the well-known AODV protocol. Extensive simulation results are presented to demonstrate the performance of EED/WEED-based routing, with comparison to some existing well-known routing metrics.

Channel zapping time is a critical quality of experience (QoE) metric for IP-based video delivery systems such as IPTV. An interesting zapping acceleration scheme based on time-shifted subchannels (TSS) was recently proposed, which can ensure a zapping delay bound as well as maintain the picture quality during zapping. However, the behaviors of the TSS-based scheme have not been fully studied yet. Furthermore, the existing TSS-based implementation adopts the traditional IP multicast, which is not scalable for a large-scale distributed system. Corresponding to such issues, this paper makes contributions in two aspects. First, we resort to theoretical analysis to understand the fundamental properties of the TSS-based service model. We show that there exists an optimal subchannel data rate which minimizes the redundant traffic transmitted over subchannels. Moreover, we reveal a start-up effect, where the existing operation pattern in the TSS-based model could violate the zapping delay bound. With a solution proposed to resolve the start-up effect, we rigorously prove that a zapping delay bound equal to the subchannel time shift is guaranteed by the updated TSS-based model. Second, we propose a destination-oriented-multicast (DOM) assisted zapping acceleration (DAZA) scheme for a scalable TSS-based implementation, where a subscriber can seamlessly migrate from a subchannel to the main channel after zapping without any control message exchange over the network. Moreover, the subchannel selection in DAZA is independent of the zapping request signaling delay, resulting in improved robustness and reduced messaging overhead in a distributed environment. We implement DAZA in ns-2 and multicast an MPEG-4 video stream over a practical network topology. Extensive simulation results are presented to demonstrate the validity of our analysis and DAZA scheme.

While the spectrum resource of modern society is more and more insufficient, Cognitive Radio, which allows the Secondary Users (unlicensed users, SU) to access the licensed spectrum, is a promising solution to make the utilization of spectrum resource more efficient. Among many different paradigms of cognitive radio, market-driven spectrum trading has been proved to be an efficient way to deal with Cognitive Radio problems. In this paper, we consider the problem of spectrum trading with single primary user (PU) who has multiple spectra selling his idle spectra to multiple SUs in multiple types. Since there is only one PU, so it is a monopoly market, in which the PU sets the prices, powers and time for the spectrum he sells, just as a monopolist. SUs as customers choose the spectrum with exact price, power and time to buy. We model it as a two-dimensional power-time-price contract which is much different from the usual contract because the time could either be a strategy that an SU could decide to choose itself or a type which is not decided by SUs. We first discuss the situation in which the time is set as the strategy and we will prove that it can derive a feasible contract with some conditions. Then we will discuss the second situation in which the time is set as a type. In this situation, because the SU has two kinds of types, so it's difficult to make it become a feasible contract, however we will provide a solution to deal with this problem.

Safety provision is a crucial application for the vehicular ad hoc networks (VANETs). In the VANETs, vehicles periodically broadcast their geographic information. Therefore, collisions can be avoided. In this paper, we address the issue of large computation overhead caused by the safety message authentication. A cooperative message authentication protocol(CMAP) is presented to alleviate vehicles' computation burden. In the protocol, because vehicles share their verification results with each other in a cooperative way, the number of safety messages that each vehicle needs to verify will be reduced greatly. A new research issue of the protocol is how to select verifiers in the city road scenario. Thus, we propose three verifiers selection algorithms, n-nearest method, most-even distributed method and the compound method for the CMAP. Based on the simulation results from NS2, comparisons between these three methods and the existing work are given at the end of the paper to demonstrate the performance of our protocol.

In cognitive radio networks, secondary users (SUs) are considered as selfish spectrum users, thus how to maximize the spectrum efficiency by these selfish users becomes an endless research topic. Recent studies mostly focus on the competition analysis between SUs using economic mechanism, such as game theory and auction, but the spectrum owner can hardly increase the spectrum efficiency directly when SUs apply the distributed manner. In this paper, we consider the slotted uplink scenario where several SUs have data transmitted to secondary access point (AP) under distributed random access manner. The AP decides how to divide its spectrum which maximizes the whole throughput, then SUs select the channels which would maximize their own profit. Our results show that SUs' channel selection process leads to a Nash Equilibrium, and the AP derives the proper number of channels based on the properties of NE. Moreover, we derive a rule for AP to decide which SUs should access the spectrum and lead to the increment in the whole throughput.

How much information can one transmit over a randomly distributed ad hoc network of n secondary devices, overlaid with m primary devices? Such a network model is referred to as cognitive wireless network (CWN) and our paper addresses the above question by characterizing its throughput capacity. Although a handful of research efforts related to throughput capacity exist in the area of CWNs, most of these solutions under-explore the capacity analysis. Their analysis particularly indicates that secondary devices can realize only a less or no gain on throughput capacity, in comparison to classical ad hoc networks, when the primary devices are densely placed in the network. Our detailed investigation shows that this unsatisfying capacity figure is due to the unrealistic assumptions and inefficient allocation of wireless spectrum for secondary devices, while formulating the capacity analysis. By resolving the issues in existing research efforts, we enhance the throughput capacity of secondary devices in CWN and at the heart of our analysis lies a novel spectrum usage policy known as γ-distance spectrum usage policy for secondary devices. In contrast to classical ad hoc networks, our results stipulate that when primary devices are densely placed in the network with an i.i.d inactive period of Poff, cognitive wireless network can realize an aggregate throughput capacity of WPoffγ√(m)off/γ√(n/log n) under the constraint Poffγ√(m)off/γ>; 1. This is in fact an interesting result which claims that by judiciously allocating the traffic of secondary devices in the licensed and unlicensed spectrum bands, one can enhance the throughput capacity of cognitive wireless networks.

A mobile hotspot in a vehicular environment consists of a group of end users that move as a whole in a public transit. This work analyzes the performance of video service in mobile hotspots based on heterogeneous wireless technologies. We jointly consider the contention-based random access of the WLAN for the link layer, adaptive modulation and coding of the WWAN link at the physical layer, and batch packet arrivals of video traffic at the application layer. Taking into account the highly varying WWAN link due to vehicle mobility and multipath fading, we analyze the delay and loss performance of video service at the packet level. The numerical examples demonstrate the effects of link bandwidth, vehicle mobility, and channel fading.

In this paper, we study the throughput-optimal control in the multi-radio multi-channel (MR-MC) wireless networks, which is particularly challenging due to the coupled link scheduling and channel/radio assignment. This paper has threefold contributions: 1) We develop a new model by transforming a network node into multiple node-radio-channel (NRC) tuples. Such modeling facilitates the development of a tuple-based back pressure algorithm, the solution of which can jointly solve the link scheduling, routing and channel/radio assignment in the MRMC network. 2) The tuple-based model enables the extensions of some well-known algorithms, e.g., greedy maximal scheduling and maximal scheduling, to MR-MC networks with guaranteed performance. We provide stability and capacity efficiency ratio analysis to the tuple-based scheduling algorithms. 3) The tuple-based framework facilitates a decomposable cross-layer formulation that enhances the delay performance of throughput-optimal control by integrating the link-layer scheduling with the network-layer path selection, where both hop-count and queuing delay are considered. Simulation results are presented to demonstrate the capacity region and delay performance of the proposed methodology, with comparison to the existing approach [3].

In our previous work [9], we investigated the capacity and delay of a static hybrid wireless network, consisting of n static wireless nodes overlaid with a cellular architecture of m base stations. By employing a more practical and simple routing policy, we proved that each wireless node can be realized with a throughput that scales sublinearly or linearly with m. This was in fact a significant result as opposed to prior works on hybrid wireless networks which claims that if m grows slower than some threshold, the benefit of augmenting those base stations to the pure ad hoc network is insignificant. Albeit our novel approach can render improved benefits in terms of capacity and delay as opposed to prior efforts, the analysis shows that one requires a large deployment cost in order to achieve a Θ(1) capacity. Existing research efforts also indicate that for pure mobile ad hoc networks, a capacity of Θ(1) can be achieved by exploiting the mobility of the nodes, at the expense of very high end-to-end delay. This larger delay, nevertheless, stems from the assumption of global mobility, where nodes move around the entire network. In this paper, by leveraging a more practical and restricted mobility model, we investigate the capacity and delay of our hybrid wireless network design with n mobile nodes and m base stations, termed as mobile hybrid wireless network. Interestingly, our results show that each node can be realized with a capacity of Θ(1), while keeping the average end-to-end delay smaller by a factor of m than the pure mobile ad hoc networks.

As voice over IP (VoIP) increasingly gains popularity, traffic anomalies such as the SIP flooding attacks are also emerging and becoming into a major threat to the technology. Thus, detecting and preventing such anomalies is critical to ensure an effective VoIP system. The existing flooding detection schemes are inefficient in detecting low-rate flooding from dynamic background traffic, or may even totally fail when flooding is launched in a multi-attribute manner by simultaneously manipulating different types of SIP messages. In this paper, we develop an online scheme to detect and subsequently prevent the flooding attacks, by integrating a novel three-dimensional sketch design with the Hellinger distance (HD) detection technique. The sketch data structure summarizes the incoming SIP messages into a compact and constant-size data set based on which a separate probability distribution can be established for each SIP attribute. The HD monitors the evolution of the probability distributions and detects flooding attacks when abnormal variations are observed. The three-dimensional design equips our scheme with the advantages of high detection accuracy even for low-rate flooding, robust performance under multi-attribute flooding, and the capability of selectively discarding the offending SIP messages to prevent the attacks. Moreover, we develop an estimation freeze mechanism to protect the detection threshold from being polluted by attacks. Not only do we theoretically analyze the performance of the proposed detection and prevention techniques, but also resort to extensive simulations to thoroughly examine the performance.

Recently, several Bloom filter based multicast schemes have been proposed, in which multicast routing information is carried with an in-packet Bloom filter. Since routers have no need to maintain forwarding states on a per-group basis, the Bloom filter based multicast protocols have desirable scalability. However, a critical issue is that these schemes may incur forwarding loops due to the false positive inherent in the Bloom filter. Existing solutions can only conditionally mitigate the probability of the forwarding loop, instead of fully preventing such events which (once occurred) will cause severe damage to the network. In this paper, we resolve this issue in the context of a destination-oriented multicast (DOM) scheme, a Bloom filter based multicast protocol carrying destinations IP addresses with the in-packet Bloom filter. With a theoretical analysis of the loop issue in DOM context developed, we reveal that the DOM design natively supports automatical elimination of permanent forwarding loops in all cases except a subtle one termed as conservation of bits. Based on the conclusion, we derive a probability upper bound on the loop occurrence in DOM. Furthermore, we propose an accurate tree branch pruning scheme, which equips the DOM the capability to completely and efficiently remove the false-positive forwarding loop. We present simulation results over a practical topology to demonstrate the performance of the loop mitigating DOM, with comparison to a representative Bloom filter based multicast scheme FRM and traditional IP multicast.

Sampling technology has been widely deployed in network measurement systems to control memory consumption and processing overhead. However, most of the existing methods suffer from large errors for the estimation of small-size flows. To address this problem, we propose an adaptive non-linear sampling (ANLS) method for flow size estimation. Instead of statically pre-configuring the sampling rate, ANLS dynamically adjusts the sampling rate for each flow according to the value of a corresponding counter. A smaller sampling rate is utilized when the counter value is large, while a larger sampling rate is employed for a smaller counter. In this paper, the unbiased flow size estimation, the relative error, and the required counter size are studied through theoretical analysis and experimental evaluations. The analysis and experiments demonstrate that ANLS can significantly improve the estimation accuracy (particularly for small-size flows), and save memory consumption, while maintaining processing overhead comparable to existing methods. Moreover, we validate the design of ANLS by implementing an FPGA-based prototype, which is capable of measuring traffic throughput up to 26.5 Gbps.

In this paper, we propose a security protocol to detect sybil attacks for position based applications in privacy preserved vehicular ad hoc networks (VANETs). Vehicles in our protocol identify sybil attacks locally in a cooperative way by examining the rationality of vehicles' positions to their own neighbors. The attack detection utilizes the characteristics of communication and vehicles' GPS positions which are included in the periodically broadcasted safety related messages. No extra hardware and little communication and computation overhead will be introduced to vehicles. Therefore, our protocol is very light weighted and suitable for real applications. Moreover, a smart attacker scenario in which a malicious vehicle may adjust its communication range to avoid detection and the malicious vehicles' collusion scenario are also considered. Simulation results based on NS2 are presented to demonstrate the performance of the proposed protocol.

Existing works designed for the multi-radio multi-channel (MR-MC) wireless network mainly rely on the orthogonal or non-overlapping channels. But in reality, the limited number of non-overlapping channels is a major issue when the network is dense. In this paper, we study the impact of partially overlapping channels (POC) on the network capacity through computing the maximum achievable capacity in the MR-MC context. We first extend our tool of multi-dimensional conflict graph (MDCG) [4] to the weighted MDCG (WMDCG) under the physical interference model, where the weight of each edge accurately indicates the amount of interference. Such a tool facilitates the theoretical analysis considering the POC. We then develop a computing methodology to calculate the optimal achievable network capacity with POC by formulating a linear programming (LP) multi-commodity flow (MCF) problem, augmented by the interference constraints based on the WMDCG. The numerical results demonstrate the effectiveness of our computing methodology.

Denial of Service (DoS) attacks such as the SIP flooding pose great threats to normal operations of VoIP networks, and can bear various forms to elude detection. In this paper, we address the stealthy SIP flooding attack, where intelligent attackers deliberately increase the flooding rates in a slow pace. As the attack only gradually influences the traffic, it can effectively be disguised from previous SIP flooding detection methods. In order to identify the stealthy attack in its early stage for timely response, we propose a detection scheme based on the signal processing technique wavelet, which is able to quickly expose the changes induced by the attack. In particular, we monitor the percentage of energy corresponding to the detail signal obtained from the wavelet analysis as an indication of the attack. Also, considering the scalability of the proposed scheme, we resort to the sketch technique, which can summarize the traffic observations to a fixed-size hash table to provide raw traffic signals for the wavelet analysis regardless how many users exist in the VoIP network. We validate the performance of the proposed scheme through computer simulation and demonstrate its ability to quickly and accurately detect the attacks.

How to design the next-generation Internet is an open technique issue. One of the mainstream ideas is to enhance network routers with application-oriented intelligence. For example, firewalls,Web proxies/caches, mobile gateways, and multicast capable nodes are equipments with application-oriented intelligence for security/performance enhancement. However, there is no systematic study on what intelligence should be incorporated into the router and what the fundamental benefit of the application-oriented networking is. This paper presents a generic application-oriented networking (GAON) simulation framework compatible with the Network Simulator ns-2 to facilitate the research in the area. With GAON, developers can conveniently enhance the ns-2 nodes with customized functionalities, and seamlessly incorporate them into the regular ns-2 system. GAON provides a generic scenario control interface, through which ns- 2 users can flexibly load/unload customized GAON processing agents on network nodes. The regular ns-2 node structure is extended, where a GAON agent classifier is set up to dispatch GAON traffic to correct GAON agents. Moreover, a unified interface to the ns-2 built-in routing table is developed to facilitate GAON agents forwarding packets. Two multicast protocols are implemented to demonstrate the validation of GAON, with the simulation results presented.

It is very challenging to compute the capacity region of a multi-radio multi-channel (MR-MC) network, which involves complex resource contention including the co-channel interferences and radio interface contentions. In this paper, we study the local sufficient rate constraints that can be constructed at each network node in a distributed manner to ensure a feasible flow allocation for the MR-MC network. The analysis of capacity region with the rate constraints is facilitated by our tool of multi-dimensional conflict graph (MDCG), which systematically describes all kinds of conflict relationships in an MR-MC network. Specially, we establish two types of local sufficient constraints, the neighborhood constraint and the sufficient clique constraint, respectively; and both types can ensure a constant portion of the optimal capacity region, termed as capacity efficiency ratio. The capacity efficiency ratios associated with the neighborhood constraint and the sufficient clique constraint are related to the analysis of the interference degree and the imperfection ratio of an MDCG, respectively. A specific challenge is that methodology computing the interference degree and the imperfection ratio of single-radio single-channel (SR-SC) networks could not be directly extended to the MR-MC context, because MR-MC network has disruptively different geometric properties compared to the SR-SC network: In an MR-MC network, the geometric closeness does not necessarily imply interference due to possible parallel transmissions over different radios and channels. The fundamental contributions of this paper are the theoretical studies of the interference degree and the imperfection ratio of an MDCG, revealing how such graphical characteristics are related to those in the SR-SC context under the impact of the MR-MC geometric property. We also present extensive numerical results to demonstrate the effectiveness of the proposed local sufficient constraints in ensuring a larger capacity region compared to the well-known results in.

How much information can one send through a random ad hoc network of n nodes, if overlaid with a cellular architecture of m base stations? This network model is commonly referred to as hybrid wireless networks and our paper analyzes the above question by characterizing its throughput capacity. Although several research efforts related to throughput capacity exist in the area of hybrid wireless networks, most of these solutions under-explore the capacity analysis. Their results particularly indicate that one can realize only a less than log or no gain on capacity, as compared to pure ad hoc networks, when m scales slower than some threshold. This unsatisfying capacity gain is due to the fact that the base stations were not properly exploited while formulating the capacity analysis. Moreover, these research efforts also assume an one-hop wireless uplink between a node and its associated base station. Nevertheless, with those power-constrained wireless nodes, this assumption clearly indicates an unrealistic scenario. In this paper, we establish the bounds on capacity and delay by resolving the issues in existing efforts and at the heart of our analysis lies a simple routing policy known as same cell routing policy. Our findings particularly stipulate that whether m = O(n/log n) or Ω(n/log n), each node can realize a throughput that scales, sublinearly or linearly, with m. This is in fact a significant result as opposed to previous efforts which claims that if m grows slower than some threshold, the benefit of augmenting those base stations to the original ad hoc network is insignificant. Our analysis also shows that for a maximum per node throughput Λ(n, m), the average end-to-end delay in a hybrid network can be bounded by Θ(Λ(n, m)n/m), which has an inverse relationship to m.

The distributed nature of the CSMA/CA based wireless protocols, e.g., the IEEE 802.11 distributed coordinated function (DCF), allows malicious nodes to deliberately manipulate their backoff parameters and thus unfairly gain a large share of the network throughput. The non-parametric cumulative sum (CUSUM) test is a promising method for real-time misbehavior detection due to its ability to quickly find abrupt changes in a process without any a priori knowledge of the statistics of the change occurrences. While most of the existing schemes for selfish behavior detection depend on heuristic parameter configuration and experimental performance evaluation, we develop a Markov chain based analytical model to systematically study the CUSUM based scheme for real-time detection of the backoff misbehavior. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay and missed detection ratio under a detection delay constraint. Moreover, we find that the short-term fairness issue of the 802.11 DCF impacts the transition probabilities of the Markov model and thus the detection accuracy. We develop a shuffle scheme to mitigate the short-term fairness impact on the sample series, and investigate the proper shuffle period (in terms of observation windows) that can maintain the randomness in each node's backoff behavior while resolving the short-term fairness issue. We present simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the developed real-time detection scheme.

This paper studies maximum multiflow (MMF) and maximum concurrent multiflow (MCMF) in muliti-channel multi-radio multihop wireless networks under the 802.11 interference model or the protocol interference model. We introduce a fine-grained network representation of multi-channel multi-radio multihop wireless networks and present some essential topological properties of its associated conflict graph. By exploiting these properties, we develop practical polynomial approximation algorithms for MMF and MCMF with constant approximation bounds regardless of the number of channels and radios. Under the 802.11 interference model, their approximation bounds are at most 20 in general and at most 8 with uniform interference radii; under the protocol interference model, if the interference radius of each node is at least c times its communication radius, their approximation bounds are at most 2 (⌈π/ arcsin c-1/2c⌉ + 1). In addition, we also prove that if the number of channels is bounded by a constant (which is typical in practical networks), both MMF and MCMF admit a polynomial-time approximation scheme under the 802.11 interference model or under the protocol interference model with some additional mild conditions.

In this paper, we give a global perspective of multicast capacity and delay analysis in Mobile Ad-hoc Networks (MANETs). Specifically, we consider two node mobility models: (1) two-dimensional i.i.d. mobility, (2) one-dimensional i.i.d. mobility. Two mobility time-scales are included in this paper: (i) Fast mobility where node mobility is at the same time-scale as data transmissions; (ii) Slow mobility where node mobility is assumed to occur at a much slower time-scale than data transmissions. Given a delay constraint D, we first characterize the optimal multicast capacity for each of the four mobility models, and then we develop a scheme that can achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic factor. Our study can be further extended to two-dimensional/one-dimensional hybrid random walk fast/slow mobility models and heterogeneous networks.

In this paper, we propose a distributed key management framework based on group signature to provision privacy in vehicular ad hoc networks (VANETs). Distributed key management is expected to facilitate the revocation of malicious vehicles, maintenance of the system, and heterogeneous security policies, compared with the centralized key management assumed by the existing group signature schemes. In our framework, each road side unit (RSU) acts as the key distributor for the group, where a new issue incurred is that the semi-trust RSUs may be compromised. Thus, we develop security protocols for the scheme which are able to detect compromised RSUs and their colluding malicious vehicles. Moreover, we address the issue of large computation overhead due to the group signature implementation. A practical cooperative message authentication protocol is thus proposed to alleviate the verification burden, where each vehicle just needs to verify a small amount of messages. Details of possible attacks and the corresponding solutions are discussed. We further develop a medium access control (MAC) layer analytical model and carry out NS2 simulations to examine the key distribution delay and missed detection ratio of malicious messages, with the proposed key management framework being implemented over 802.11 based VANETs.

Energy efficiency is of great importance for the wireless sensor network (WSN). A popular way to save energy is to construct clusters for data aggregation and forwarding. This paper studies the distributed cluster algorithm to improve the energy consumption efficiency. We observe that the cluster head has to lie within the transmission range of the base station (sink node) and the distance between cluster head and base station is critical for the energy consumption performance, we propose a hybrid relative distance based clustering scheme (HRDCS), which considers the relative distance to the base station and the residual energy level during the cluster head selection stage. Consequently we could better balance the chance of being cluster head for all nodes. Simulation results show that our scheme is able to result in longer network lifetime than the well-known protocol LEACH.

In this paper, we propose a Decode-and-Forward Network Coded (DFNC) multi-user cooperation scheme for orthogonal uplink channels in a wireless network. The network consists of m peers having independent information transmitting to a common destination. Each peer transmits its own data in the first time phase and serves as a relay in the second time phase to transmit the network coded information combining its own and others. Assuming block fading with independent fading coefficients, we evaluate the outage probability performance of the proposed scheme using high-SNR approximation. Particularly, we develop an upper bound on outage probability of DFNC, and compare it with Space-Time-Coded (STC) cooperation protocol and Repetition-based (REP) cooperation protocol. From theoretical analysis and numerical results, we show that the outage performance of proposed scheme outperforms both STC and REP at least in high-SNR region, regardless of total number of cooperating peers and provides diversity order of m + 1 in contrast with diversity order of m provided by STC and REP.

Cooperative Peer-to-Peer (P2P) information repair has been proposed to mitigate the packet loss among mobile peers during the 3G Cellular Base Station (BS) broadcast. Then network coding based P2P information exchange algorithms have been proposed to further improve the network performance, e.g., PIE algorithm and DNC-CPR algorithm. In this paper, we propose a connected dominating set (CDS) based P2P information repair (PPIR) protocol via random linear network coding, to alleviate the congestion and burden of BS's downlink channels. Our PPIR protocol consists of two phases: intra-cluster information exchange and inter-cluster information exchange. Dividing network into clusters enables our protocol to work on a sparse network environment and deeply reduces the impact of transmission collisions. Therefore, our PPIR protocol reduces the repair latency and improve the network performance. Furthermore, our protocol is capable of identifying the lost packets for the whole network and being aware of the repair process completion. Simulation results validate the effectiveness and efficiency of our PPIR protocol compared with the DNC-CPR algorithm.

One of the crucial challenges in practical wireless networks is how to provide robust communication over fading channels. Recently, cooperative communications have emerged as a promising approach to achieve spatial diversity and thereby reduce the negative effects of fading on wireless channels. Nevertheless, a few existing works indicate that deploying cooperative relays in large-scale wireless networks can lead to an elevated level of interference which in turn leads to degraded throughput and higher packet losses. It is also well-known that the use of multiple frequency channels can mitigate the wireless interference to a greater extent and thus improve the overall performance of a wireless network. Therefore, it is interesting and important to design and evaluate protocols that can provide the combined potential of both cooperation communication and multiple channels. In this paper, we propose a novel cooperative multi-channel MAC protocol that integrates the capabilities of both multiple channels and cooperative communications at the MAC layer to improve the performance of wireless networks. The performance improvement of our proposed solution is further evaluated by extensive simulations.

The Session Initiation Protocol (SIP) has been widely used in VoIP for session control and management. As the basic SIP specifications do not require the proxy servers to track the states of established sessions, an extension header field "Session-Expires" has been proposed for SIP to allow the proxy server to hold resources for established sessions just within the specified periods. In this paper, we identify a novel denial of service (DoS) attack utilizing this SIP extension to drain resources of the proxy servers in wireless VoIP. In particular, by deliberately setting a large value of the "Session-Expires'' header and then physically disconnecting from the wireless network, attackers can repeatedly hold resources of the proxy server as long as they want. Also, the low-volume nature of the attack allows it to avoid being detected by existing volume-based intrusion detection systems. As a counter-measure, we propose a robust detection scheme based on the statistical Anderson-Darling test. The key insight that leads to the scheme is the changed statistical property of the header values induced by the attack. We validate the performance through computer simulation. The scheme shows its ability to detect the attack and is even more effective when applied against the distributed denial of service (DDoS) attack.

Most multicast routing protocols construct reverse shortest path trees (SPTs) to deliver shared data. However, the use of the reverse SPT presents a challenge in the inter-domain routing environment, as the path from the source to a receiver could be asymmetric to the one used to go from the receiver to the source. A possible approach is to utilize the round-trip joining message but it incurs the demerit of long joining delay. In this paper, we propose a BGP-view based fast-join (BFJ) mechanism, where the receiver domain border router leverages the BGP routing information in the border router of the source domain to identify an efficient joining path. The initial joining message can then be delivered using source routing along the identified path to quickly construct the reverse SPT even in asymmetric routing environment. Subsequent joining messages temporarily label corresponding interfaces at intermediate routers so that requested data packets are steered to the subscriber as soon as possible. The NS2 simulation results show that the proposed BFJ scheme is more efficient than the approach of round-trip joining message even under the favored condition of the latter.

In this paper, we study how to compute the optimal capacity planning in a multi-radio multi-channel (MR-MC) wireless network, that is, to find solutions for a set of coupled problems including channel assignment, scheduling, and routing, with the objective to optimize network capacity. The current state of the art mainly resorts to formulation of a mixed integer programming problem, which is NP-hard in general, and then computes an approximate solution to such a problem. We develop a novel concept of multi-dimensional conflict graph (MDCG) in this paper. Based on MDCG, the optimal capacity planning can be modeled as a linear programming (LP) multi-commodity flow (MCF) problem, augmented with constraints derived from the MDCG. The MDCG-based MCF solution will provide not only the maximum throughput or utility, but also the optimal channel assignment, scheduling and routing to achieve it. Moreover, the MDCG-based optimal capacity planning can exploit dynamic channel swapping, which is difficult to achieve for those existing heuristic algorithms. Numerical results are presented to demonstrate the efficiency of the MDCG-based capacity planning, with comparison to the well-known heuristic algorithm presented in [1].

Network coding is known as a promising approach to improve wireless network throughput. However, it is only applicable when different flows are routed through a certain coding structure. One of the fundamental issues is to accurately identify the coding structures and optimally utilize the coding nodes. In this paper, we formally establish general coding conditions to identify multiple coding nodes along a path, while most of the existing approaches may ignore the coding opportunities or bring in potential negative impact on other flows. The general coding condition is further augmented with a flow selection mechanism, which determines the set of flows to be coded at a coding node. Not only native packet but also encoded packet could be involved in the selection mechanism. Combination of the proposed coding conditions and the flow selection mechanism ensures that all the flows involved in coding can be properly decoded at the destinations. We implement the proposed coding conditions and flow selection mechanism over the AODV protocol, and efficiency of the proposed techniques is demonstrated with NS2 simulations, compared to the existing coding conditions.

In this paper, we propose a secure data downloading protocol with privacy preservation in vehicular ad hoc networks (VANETs). In the data downloading application, vehicles send requests, such as where is the nearest gas station, at a road side unit (RSU) and receive the corresponding responses from application servers via current or the following RSUs. It would be easy for eavesdroppers to get vehicles' private information if semi-trust RSUs are compromised because both request and response messages are forwarded by them. Therefore, we develop a protocol which enables vehicles to download data securely from RSUs with their privacy under protection even when one or multiple RSUs are compromised. Our protocol guarantees vehicles exclusive access to their requested data while eavesdroppers can not obtain any private information of the vehicles. Possible attacks and the corresponding solutions as well as privacy evaluation are discussed to demonstrate the performance of the proposed protocol.

The existence of channel variations (or fading) is one of the crucial challenges that affects the capacity of wireless networks. Recently, cooperative communications have emerged as a promising approach to achieve spatial diversity and thereby reduce the negative effects of fading on wireless channels. On the other hand, in addition to channel variations, it is well-known that interference among concurrent transmissions also severely limits the network capacity particularly in multi-hop settings. Recent studies indicate that the use of multiple channels can reduce the wireless interference and thus greatly improve the overall network capacity. In this paper, we propose a model termed as CoopMC which employs multiple channels in cooperative networks to mitigate the impact of interference. This work primarily investigates the capacity of CoopMC in multi-hop settings and derive the asymptotic capacity bounds under random placements of nodes. Our analysis reveals the important insights on when a network can benefit from cooperative communications and how multi-channel networking can further improve the network capacity.

A basic task in network passive measurement is collecting flow statistics information for network state characterization. With the continuous increase of Internet link speed and the number of flows, flow statistics has become a great challenge due to the demanding requirements on both memory size and memory bandwidth in measurement devices. In this paper, we propose a DIScount COunting (DISCO) method, which is designed for both flow size and flow volume counting. For each incoming packet of length l, DISCO increases the corresponding counter assigned to the flow with an increment that is less than l. With an elaborate design on the counter update rule and the inverse estimation, DISCO saves memory consumption while providing an accurate unbiased estimator. The method is evaluated thoroughly under theoretical analysis and simulations with synthetic and real traces. The results demonstrate that DISCO is more accurate than related work given the same counter size. DISCO is also implemented on network processor Intel IXP2850 for performance test. Using only one MicroEngine (ME) in IXP2850, the throughput can reach up to 11.1Gbps under a traditional traffic pattern, and it increases almost linearly with the number of MEs employed.

Optimal capacity analysis in multi-radio multi-channel wireless networks by nature incurs the formulation of a mixed integer programming, which is NP-hard in general. The current state of the art mainly resorts to heuristic algorithms to obtain an approximate solution. In this paper, we propose a novel concept of multi-dimensional conflict graph (MDCG). Based on MDCG, the capacity optimization issue can be accurately modeled as a linear programming (LP) multi-commodity flow (MCF) problem, augmented with maximal independent set (MIS) constraints. The MDCG-based solution will provide not only the maximum throughput or utility, but also the optimal configurations on routing, channel assignment, and scheduling. Moreover, the MDCG-based optimal capacity planning can exploit dynamic channel swapping, which is difficult to achieve for those existing heuristic algorithms. A particular challenge associated with the MDCG-based capacity analysis is to search exponentially many possible MISs. We theoretically show that in fact only a small set of critical MISs, termed as critical MIS set, will be scheduled in the optimal resource allocation. We then develop a polynomial computing method, based on a novel scheduling index ordering (SIO) concept, to search the critical MIS set. Extensive numerical results are presented to demonstrate the efficiency of the MDCG-based resource allocation compared to well-known heuristic algorithm presented in, and the efficiency of SIO-based MIS computing compared to the widely adopted random algorithm for searching MISs.

Transmission power plays a crucial role in the design and performance of wireless networks. The issue is therefore complex since an increase in transmission power implies that a high quality signal is received at the receiver and hence an increase in channel capacity. Conversely, due to the shared nature of the wireless medium an increase in transmission power also implies high interference in the surrounding region and hence a quadratic reduction in the capacity of wireless networks. Recent literatures indicate that employing multiple channels can mitigate the negative effects of wireless interference and thus greatly improve the overall network capacity. Therefore, it is worth investigating the effect of exploiting power on the capacity of multi-channel multi-radio (MC-MR) wireless networks. Specifically, in this paper we address the following questions: (a) Can we maximize the capacity of MC-MR wireless networks by exploiting power? (b) Under what criteria can we increase the transmission power of the nodes in a MC-MR network? When n nodes each with m half-duplex interfaces are optimally deployed in a torus of unit area, traffic patterns are optimally assigned, each transmission's range is optimally chosen and in the presence of c channels, we show that in contrast to the setting where nodes transmit at minimum power level Po the transport capacity, measured in bit-meters per second, of MC-MR network exploiting power is increased by Θ(cmin) in region cmin0(c/cmin)α/2and P0nα/2respectively-where cminis the minimum number of channels required to achieve conflict-free transmissions in a network. Our analysis also sheds light into several insights that designers may want to consider to improve the performance of energy-efficient bandwidth-constrained wireless networks.

This paper studies the maximum throughput that can be supported by a given wireless mesh backhaul network, over a practical CSMA/CA medium access control (MAC) protocol. We resort to the multi-commodity flow (MCF) formulation, augmented with the conflict-graph constraints, to jointly compute the maximum throughput and the associated optimal network dimensioning; while use a novel approach to take into account the collision overhead in the distributed CSMA/CA MAC. Such overhead has been ignored by the existing MCF-based capacity studies, which assume impractical centralized scheduling and result in aggressive network dimensioning, unachievable over the CSMA/CA MAC. We develop a generic method to integrate the CSMA/CA MAC analysis with the MCF formulation for optimal network capacity analysis, and derive both an upper bound and a lower bound of the network throughput over a practical CSMA/CA protocol. To the best of our knowledge, this paper is the first rigorous theoretical study of the achievable capacity over a multi-hop CSMA/CA based wireless network.

In this paper, we study the delay performance of wireless network considering different resource allocation schemes with single-hop traffic. Existing works studying the delay performance only consider a given resource allocation scheme, either multi-channel system (sharing bandwidth) or time slotted system (sharing time). The fundamental question ignored is which type of resource allocation scheme produces better delay performance with different network configurations, such as number of commodities, traffic statistics. We investigate the impact of different resource allocation schemes on the delay performance. A new arrival mode is designed for the time slotted system to reduce the average delay. We also construct the delay lower bound taking the perfect scheduling policy and queue management into account. We get four important conclusions from the numerical results: 1) the new arrival mode produces better delay performance than the regular mode, and it is immune to the change of time slot length. 2) time slotted system has better delay performance than multi-channel system, and almost achieves the lower bound, 3) the scalability of the multi-channel system is not good, since the delay will be very large with a large number of commodity flows. While time slotted system is scalable with a converging delay value with the infinite number of commodities. 4) both the multi-channel system and time slotted system are sensitive to the difference between arrival rate and service rate, which means that the delay is large when arrival rate is close to service rate.

The open and distributed nature of the IEEE 802.11 based wireless network makes it easy for selfish nodes to gain unfair share on the networks by manipulating the protocol parameters. In this paper, we address the detection of such selfish behavior. The two main challenges associated with the detection problem are the unknown selfish behavior strategy and real-time detection of the behavior. While the two challenges are correlated for efficient detection, existing solutions can not address both of them well at the same time. In our work, we propose a new observation method monitoring the number of successful transmissions of the tagged node. This enables us to capture the short-term dynamic of the traffic behavior which is crucial for real-time detection. Integrating our the observation method with the CUSUM test, we develop a detection scheme to deal with both the challenges without any modification to the existing protocols. Moreover, we utilize a discrete Markov chain based model to characterize the behavior of the CUSUM test statistic, which enables us to quantitatively analyze the tunable parameters in the scheme for guaranteed detection performance. The performance of the proposed scheme is validated through ns-2 simulation. We show that the scheme is capable of quickly and accurately detecting the selfish behavior without knowledge of the selfish strategy.

In this paper, we consider a special case of denial of service (DoS) attack in wireless mesh networks (WMNs) known as selective forwarding attack (a.k.a gray hole attacks). With such an attack, a misbehaving mesh router just forwards a subset of the packets it receives but drops the others. While most of the existing studies on selective forwarding attacks focus on attack detection under the assumption of an error-free wireless channel, we consider a more practical and challenging scenario that packet dropping may be due to an attack, or normal loss events such as medium access collision or bad channel quality. Specifically, we develop a channel aware detection (CAD) algorithm that can effectively identify the selective forwarding misbehavior from the normal channel losses. The CAD algorithm is based on two strategies, channel estimation and traffic monitoring. If the monitored loss rate at certain hops exceeds the estimated normal loss rate, those nodes involved will be identified as attackers. Moreover, we carry out analytical studies to determine the optimal detection thresholds that minimize the summation of false alarm and missed detection probabilities. We also compare our CAD approach with some existing solutions, through extensive computer simulations, to demonstrate the efficiency of discriminating selective forwarding attacks from normal channel losses.

In the new generation of mobile communication systems, high data transmission rate and low bit error rate has been common requirements. However the issue of signal fading in a multi-path environment still stands as a major problem. Using antenna diversity techniques can overcome the problem. In this paper, we thoroughly study the antenna diversity performance on a mobile terminal through theoretical analysis, computer simulations and practical prototype measurements. Specifically, a genetic methodology is developed to accurately evaluate the diversity gain achieved in the mobile terminal from both power level approach and correlation approach. We demonstrate that it is possible and beneficial to implement antenna diversity on terminal side, and high diversity gain can be achieved when the antennas are placed in the terminals properly. Moreover, improvement of the diversity gain can be obtained from the implementation of the matching networks.

Integrated optical and wireless networks are considered as the next generation access networks because of the potential benefits from both technologies. In this paper, we consider the integrated BS/ONU (IBO) placement problem in the hybrid EPON-WiMAX system to efficiently utilize network resources as well as reduce the system cost. We propose a modified clustering algorithm (MCA) to obtain the near-optimal result that minimizes the number of IBOs needed to support all wireless relay BSs residing in the wireless part of the integrated system, while satisfying several necessary quality-of-service (QoS) constraints, e.g., hop count, cluster size, etc. In contrast to the existing related work, our MCA approach forms the clusters starting from the network edge towards its center and the construction of clusters is not only based on the "greedy" idea but also considers the load balance. Simulation and numerical results show that the number of IBOs generated from our algorithm is always less than or equal to the best results known in the current literature, but our algorithm can achieve better network performance in terms of the reduced average transmission delay, lowered link failure probability and more balanced load.

Gray hole attacks (a.k.a selective forwarding attacks) are a special case of denial of service (DoS) attack, where a misbehaving mesh router just forwards a subset of the packets it receives but drops the others. In wireless networks, it is particularly hard to detect the presence of such attackers because a packet loss over the wireless link can be due to bad channel quality, medium access collisions, or intentional dropping. In contrast to existing studies, we propose a more practical algorithm known as channel aware detection (CAD) that adopts two strategies, hop-by-hop loss observation and traffic overhearing, to detect the mesh nodes subject to the attack. We derive the optimal detection thresholds by analyzing the false alarm and missed detection probabilities of CAD. We also compare our approach to existing solutions and demonstrate that CAD detects the attackers effectively even in harsh channel conditions.

The Voice over IP (VoIP) application utilizes the Internet to provide voice service; thus it is susceptible to various security issues common on the IP networks, such as the flooding attack. Moreover, VoIP uses the Session Initiation Protocol (SIP) for session control and management. The transactional nature of SIP makes flooding attack an even severer threat, which can consequentially lead to denial of service (DoS). In this paper, we develop an efficient online SIP flooding detection scheme by integrating the sketch technique with Hellinger distance (HD) based detection. The sketch data structure can summarize the SIP call generating process into a fixed set of data for developing a probability model. The HD technique, combined with on-line traffic estimation, can efficiently identify attacks by monitoring the distance between current traffic distribution and the estimated distribution based on history information. Compared to the original HD detection system, our technique achieves the advantages of higher accuracy, flexibility to deal with multi-attribute attacks and DDoS attacks, and the ability to track the period of attack. Computer simulation results are presented to demonstrate the performance of the proposed technique.

Adaptive resource allocation including the allocation of subcarrier, bit and power in orthogonal frequency division multiple access (OFDMA) system, can significantly improve spectral efficiency, increase capacity, and reduce power consumption. The resource allocation in an OFDMA system is normally modeled as a combinatorial optimization problem involving a nonlinear objective with nonlinear constraints. This leads to high computation complexity that is only solvable online. In this paper we first develop a nonlinear optimization model of OFDMA resource allocation that incorporates not only the throughput constraint but also the delay constraint. We then find an equivalent linear formulation to this optimization problem to facilitate analysis. Moreover, we develop a heuristic approximation algorithm to provide a close solution to the equivalent linear programming problem. The heuristic algorithm is efficient enough for on-line adaptive resource allocation. Simulation results show that the heuristic solution can achieve almost same capacity but with less computation complexity.

This paper presents a protocol-independent approach to reveal a new insight into the performance of carrier sense multiple access with collision avoidance (CSMA/CA) protocols: the family of CSMA/CA protocols, independent of implementation details, share the same optimal operation point where the maximum protocol capacity is achieved. The protocol- independent analysis is inspired by the concept of virtual time slot. At the timescale of virtual-slot, all the CSMA/CA protocols show the same behavior pattern and, therefore, a generic virtual- slot based S-G (VS S-G) analysis is developed to compute the optimal operation point. The accuracy of the VS S-G analysis is benchmarked against the precise protocol-specific analysis, in particular, for the 802.11 distributed coordination function (DCF) and the 802.15.4 contention access period (CAP). Furthermore, this paper discusses how to integrate the network-layer queueing analysis with the VS S-G analysis at the medium access control (MAC) layer to form a generic cross-layer framework for call- level network capacity analysis.

This paper studies how to select a path with the minimum cost in terms of expected end-to-end delay (EED) in a multi-radio wireless mesh network. Different from the previous efforts, the new EED metric takes the queuing delay into account, since the end-to-end delay consists of not only the transmission delay over the wireless links but also the queuing delay in the buffer. In addition to minimizing the end-to-end delay, the EED metric implies the concept of load balancing. We develop EED- based routing protocols for both single-channel and multi-channel wireless mesh networks. In particular for the multi-radio multichannel case, we develop a generic iterative approach to calculate a multi-radio achievable bandwidth (MRAB) for a path, taking the impacts of inter/intra-flow interference and space/channel diversity into account. The MRAB is then integrated with EED to form the metric of weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient can be defined to quantitatively represent the channel diversity along a given path. Both numerical analysis and simulation studies are presented to validate the performance of the routing protocol based on the EED/WEED metric, with comparison to some well- known routing metrics.

This paper develops an efficient and scalable multicast scheme for high-quality multimedia distribution. The traditional IP multicast, a pure network-layer solution, is bandwidth efficient in data delivery but not scalable in managing the multicast tree. The more recent overlay multicast establishes the data-dissemination structure at the application layer; however, it induces redundant traffic at the network layer. We propose an application-oriented multicast (AOM) protocol, which exploits the application-network cross-layer design. With AOM, each packet carries explicit destinations information, instead of an implicit group address, to facilitate the multicast data delivery; each router leverages the unicast IP routing table to determine necessary multicast copies and next-hop interfaces. In our design, all the multicast membership and addressing information traversing the network is encoded with bloom filters for low storage and bandwidth overhead. We theoretically prove that the AOM service model is loop-free and incurs no redundant traffic. The false positive performance of the bloom filter implementation is also analyzed. Moreover, we show that the AOM protocol is a generic design, applicable for both intra-domain and inter-domain scenarios with either symmetric or asymmetric routing.

The group signature based security scheme is a promising approach to provision privacy in vehicular ad hoc networks (VANETs). In this paper, we propose a novel distributed key management scheme for group signature based VANETs, which is expected to considerably facilitate the revocation of malicious vehicles, location privacy protection, heterogenous security policies, and maintenance of the system, compared with the centralized key management assumed by the existing group signature schemes. The distributed nature of the proposed scheme is that the road side units (RSUs) will be responsible for distributing group private keys in a localized manner. A brand- new issue induced by the distributed scheme is that the semi-trust RSUs may be compromised. So we develop security protocols for the distributed key management, which are capable of identifying the compromised RSUs and their collusion with the malicious vehicles if any. Details of possible attacks and the corresponding solutions are discussed to demonstrate the performance of the proposed security protocols.

In this paper, we address the routing issue in a wireless mesh network, where each node is stationary and equipped with one or more radios. Specifically, we propose a new path metric called multi-hop effective bandwidth (MHEB), which provides a generic approach to calculate the achievable bandwidth along a path, taking the impacts of inter/intra-flow interference and space/channel diversity into account. We also present a new routing protocol based on the MHEB metric, which achieves interference-aware and load-balancing routing at the same time. Performance of the MHEB based routing in improving the network throughput is validated through computer simulations, compared with the existing popular routing metrics.

This paper proposes an efficient and scalable multicast scheme based on the concept of application-oriented networking (AON). The traditional IP multicast is bandwidth efficient but suffers from the scalability problem. The overlay multicast, proposed in recent decade, manages a data-dissemination tree at the application layer, and only utilizes unicasts among pairs of hosts; the overlay approach, however, usually incurs a considerable amount of redundant traffic. The essence of AON is to integrate application intelligence into the network. For AON-based multicasting, each packet will carry necessary explicit addressing information, instead of an implicit class-D group address, to facilitate the multicast data delivery. Each AON router will leverage the unicast IP routing table to compute necessary multicast copies and next-hop interfaces. The proposed AON multicast eliminates the need for constructing and maintaining the network-layer multicast routing table, while its bandwidth efficiency is very close to that of the IP multicast.

Sampling technology has been widely deployed in measurement systems to control memory consumption and processing overhead. However, most of the existing sampling methods suffer from large estimation errors in analyzing small-size flows. To address the problem, we propose a novel adaptive non-linear sampling (ANLS) method for passive measurement. Instead of statically configuring the sampling rate, ANLS dynamically adjusts the sampling rate for a flow depending on the number of packets having been counted. We provide the generic principles guiding the selection of sampling function for sampling rate adjustment. Moreover, we derive the unbiased flow size estimation, the bound of the relative error, and the bound of required counter size for ANLS. The performance of ANLS is thoroughly studied through theoretic analysis and experiments under synthetic/real network data traces, with comparison to several related sampling methods. The results demonstrate that the proposed ANLS can significantly improve the estimation accuracy, particularly for small-size flows, while maintain a memory and processing overhead comparable to existing methods.

In this paper, we present the creation and characterization of an open source Internet Protocol Multimedia Subsystem (IMS) test bed. We built this test bed with the intention and motivation of doing research related to the performance of its various functional components as they cooperate to provide

In this paper, we propose a simple yet accurate analytical model for the slotted non-persistent carrier sense multiple access protocol with binary exponential backoff, as specified in the medium access control (MAC) protocol of the IEEE 802.15.4 standard for the contention access period. The model is based on a three-level renewal process, which leads to a general analytical framework applicable to the protocol variants of either single or double sensing, in a saturated or unsaturated case, under a general traffic arrival distribution and with various backoff policies. The analytical model can be used to obtain some important performance metrics, such as MAC throughput and average frame service time. The accuracy of the analytical model is demonstrated by extensive simulation results. The applicability of this model to the performance analysis of other slotted MAC protocols is also briefly discussed.

Distributed prioritized channel access mechanisms have been adopted by the IEEE 802.11e enhanced distributed channel access (EDCA) and the Multiband OFDM Alliance prioritized channel access (PCA) to support service differentiation. In this paper, we propose a novel analytical model for performance study of such mechanisms. The proposed model gives the average frame service time first and then the per station and network normalized throughput, which makes it applicable to both saturated and unsaturated stations. Furthermore, the model is especially helpful in understanding the different effects of the same prioritizing mechanisms in saturated and unsaturated conditions. To the best of our knowledge, there is no similar work reported in the open literature. The accuracy of the analytical model is demonstrated by extensive simulation.

The current trends in service consolidation over Internet Protocol (IP) also stimulates the integration of the industrial automation system with the information technology (IT) infrastructure for more efficient information access and more cost-effective production and management. Field buses have been the de facto communication standard in industrial automation, but mostly based on manufacture-specific protocols. Thus, the interoperability between the manufacturer-specific field bus systems and the external operating environment is the critical factor in enabling the networked industrial automation systems. However, most of the existing field bus integration solutions lack either flexibility or scalability. In this paper, we propose a service-oriented architecture (SOA) based field bus integration architecture (SOAFBIA), where each field bus system is encapsulated with optional interface, manageability interface, and semantic descriptions in a standard format to facilitate interoperability. Moreover, a resource agent is proposed as an enhanced service broker, which implements not only the standard service registry functionality in SOA, but also the resource management functions including admission control, service scheduling, and load balancing.

The packet-pair dispersion techniques are the most common probing-based approach to measuring the bottleneck capacity of a path. In practice, the dispersion measurement, and therefore the bandwidth estimation, could be seriously distorted by the cross traffic queuing between or in front of the probe packet pair. Almost all the existing packet-pair techniques depend on heuristic filtering methods to find a final capacity estimate. In this paper, we take a different perspective to exploit the cross- traffic effect. We develop a queueing model to describe the output packet-pair dispersions interfered by the cross traffic, based on which a new measurement technique to estimate the available bandwidth is derived. Another important contribution is that we for the first time reveal that the statistics of the cross traffic, e.g. the marginal distribution and the autocovariance function of the arrival process, can also be inferred from the stochastic behavior of the output packet dispersions. Efficiency of the proposed available bandwidth estimation and traffic characterization techniques are demonstrated by computer simulations.

This paper presents an edge-based Internet traffic characterization approach. Our objective is to estimate the marginal distribution and the correlation structure of the packet arrival process at a queue, by sending probe packet pairs with a specific dispersion to sample the traffic. The aggregate work load process inferred from the output dispersions is a compound process of the packet arrival process and the packet size distribution. We show that the packet arrival marginal distribution and the packet size distribution can be decoupled by using the probability generating function; given one of the distributions, the other can then be estimated. We use the fact that the Internet packet size follows a known multi-modal distribution. Moreover, multiple series of packet pairs with different input dispersions can be used to estimate the packet arrival process at different time scales, and therefore to estimate the Hurst parameter, which characterizes the long-range dependence, by generating the variance-time plot. While the traffic characterization techniques are developed and validated in a single-queue context, we indicate how the techniques can be applied to a black box system for end-to-end quality of service provisioning along a multi-hop path.

In this paper, we demonstrate that traffic modeling with the fractional Brownian motion (FBM) process is an efficient tool for end-to-end performance analysis over a network provisioning differentiated services (DiffServ). The FBM process is a parsimonious model involving only three parameters to describe the Internet traffic showing the property of self-similarity or long-range dependence (LRD). As a foundation for network-wide performance analysis, the FBM modeling can significantly facilitate the single-hop performance analysis. While accurate FBM based queueing analysis for an infinite/finite first-in-first-out (FIFO) buffer is available in the existing literature, we develop a generic FBM based analysis for multiclass single-hop analysis where both inter-buffer priority and intra-buffer priority are used for service differentiation. Moreover, we present both theoretical and simulation studies to reveal the preservation of the self-similarity, when the traffic process is multiplexed or randomly split, or goes through a queueing system. It is such self-similar preservation that enables the concatenation of FBM based single-hop analysis into a network-wide performance analysis.

This paper presents a novel, simple yet accurate analytical model for the IEEE 802.15.4 contention access period MAC protocol. The model is based on a three-level renewal process, which leads to a general analytical framework applicable for the protocol variants of either single or double sensing, in a saturated or unsaturated case, under a general traffic arrival distribution. The analytical model is used to obtain some important performance metrics, such as MAC throughput and average frame service time. The accuracy of the analytical model is demonstrated by extensive simulation results.

This paper presents an analytical approach to determining the maximum number of on/off voice flows that can be supported over a wireless local area network (WLAN), under a quality of service (QoS) constraint the authors consider multiclass distributed coordination function (DCF) based medium access control (MAC) that can provision service differentiation via contention window (CW) differentiation. Each on/off voice flow specifies a stochastic delay bound at the network layer as the QoS requirement. The downlink voice flows are multiplexed at the access point (AP) to alleviate the MAC congestion, where the AP is assigned a smaller CW compared to that of the mobile nodes to guarantee the aggregate downlink throughput. There are six-fold contributions in this paper: 1) a nonsaturated multiclass DCF model is developed; 2) a cross-layer framework is proposed, which integrates the network-layer queueing analysis with the multiclass DCF MAC modeling; 3) the channel busyness ratio control is included in the framework to guarantee the analysis accuracy; 4) the framework is exploited for statistical multiplexing gain analysis, network capacity planning, contention window optimization, and voice traffic rate design; 5) a head-of-line outage dropping (HOD) scheme is integrated with the AP traffic multiplexing to further improve the MAC channel utilization; 6) performance of the proposed cross-layer analysis and the associated applications are validated by extensive computer simulations.

This paper proposes an approximate yet accurate approach to calculate the loss probabilities in a finite size partitioned buffer system, for the achievement of a quantitative assured service in differentiated services networks. The input is modeled as a fractional Brownian motion (FBM) process including$J$classes of traffic with different packet loss requirements. A first-in first-out (FIFO) buffer partitioned with$J-1$thresholds is used to provide$J$loss priorities. Heuristic expressions of the loss probabilities for all the$J$classes are derived and validated by computer simulations. The proposed loss calculation technique is then extended to a general input process by using the recently proposed traffic substitution technique, where both long-range dependent (LRD) and short-range dependent (SRD) input sources are equivalent to a properly parameterized FBM. We also apply the loss calculation to admission control, where the partition thresholds are optimally configured for quality of service guarantee and maximal resource utilization. Computer simulation results demonstrate that resource allocation based on the accurate finite buffer loss analysis results in much more efficient resource utilization than that based on the classic large-buffer overflow approximation.

This paper proposes an approximate yet accurate approach to calculate the loss probabilities in a finite size partitioned buffer system for the achievement of a quantitative assured service in differentiated services networks. The input is modeled as a fractional Brownian motion (FBM) process including J classes of traffic with different packet loss requirements. A first-in first- out buffer partitioned with J-1 thresholds is used to provide J loss priorities. Heuristic expressions of the loss probabilities for all the J classes are derived, and validated by computer simulations. The proposed loss calculation technique is then extended to a general input process by using the recently proposed traffic substitution technique, where both long-range dependent and short-range dependent input sources are equivalent to a properly parameterized FBM. We also apply the loss calculation to admission control, where the partition thresholds are optimally configured for quality of service guarantee and maximal resource utilization. Computer simulation results demonstrate that resource allocation based on the accurate finite buffer loss analysis results in much more efficient resource utilization than that based on the classic large-buffer overflow approximation.

In this paper, we study voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). With the ubiquitous coverage of the cellular network and the disjoint deployment of WLANs in hot-spot areas, the integrated system has a two-tier overlaying structure. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cells and WLANs. A simple admission scheme is proposed in this study to analyze the dependence of resource utilization and the impact of user mobility and traffic characteristics on admission parameters. Both admission control and rate control are considered to limit the input traffic to the WLAN, so that the WLAN operates in its most efficient states and effectively complements the cellular network. The call blocking/dropping probabilities and data call throughput are evaluated for effective and accurate derivation of the admission parameters. It is observed that the utilization varies with the configuration of admission parameters, which properly distributes the voice and data traffic load to the cells and WLANs. Mobility and traffic variability have a significant impact on the selection of the admission parameters.

This paper presents an analytical model for evaluating the capability of wireless LANs (WLANs) to provision quantitative quality of service (QoS) guarantees. We consider a distributed medium access control (MAC) with class differentiation, where mobile nodes belonging to different classes may have heterogeneous traffic arrival processes or different contention windows. With on/off inputs, our analysis shows that the WLAN admission region under the QoS constraint can be significantly improved, when the statistical multiplexing effect is taken into account. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the proper selection of contention windows plays an important role in improving the WLAN QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved in our analytical model.

In this paper, we study the voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). To maximize the overall resource utilization of the integrated system, complementary quality of service (QoS) support capabilities of the two networks are exploited to serve voice and data traffic. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cellular cells and WLANs. In this study, a generalized admission scheme is analyzed to investigate the dependence of resource utilization on admission parameters, which vary with user mobility and traffic variability. By applying an effective QoS evaluation approach, the admission parameters can be determined using a search algorithm.

This paper presents an analytical model for evaluating the statistical multiplexing effect, admission region, and contention window design in multiclass wireless LANs (WLANs). We consider a distributed medium access control (MAC) which provisions service differentiation via contention window differentiation, where mobile nodes belonging to different service classes have different quality of service (QoS) requirements. With bursty input traffic, we show that the WLAN admission region under the QoS constraint can be significantly improved by exploiting the statistical multiplexing gain. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the selection of contention windows plays an important role in improving the WLAN's QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved from our analytical model. The analysis accuracy and the resource utilization improvement are demonstrated by extensive numerical results.

In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in a heterogeneous WLAN. Quality of Service in a DCF based heterogeneous WLAN is a challenging task due to the lack of centralized scheduling capability. The proposed scheme can maximize the total channel throughput and provide the service differentiation among different traffic stations. This is achieved by updating the transmission probability for each station in a timely manner based on the real-time network measurements. The simulation results show that the scheme can quickly adapt the station transmission probabilities to the desirable values in order to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment.

With the explosively increasing demand of multimedia applications in wireless local area networks (WLAN), Quality of Service (QoS) provisioning has become an important issue. IEEE 802.11 WLAN is the most popular WLAN technology today. In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in WLAN. The proposed scheme can maximize the total channel throughput and also can provide the service differentiation to multiple traffic classes. This is achieved by updating the transmission probability for each station in a timely manner based on the network measurements. The simulation results show the scheme can quickly adapt the station transmission probabilities to the desirable values to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment. The simulation results also match well with the theoretical analysis.

This paper proposes efficient resource allocation techniques for a domain-based wireless/wireline interworking architecture. Resource allocation is driven by the service level agreement (SLA). Each wireless domain can freely choose its internal resource management schemes to guarantee the customer access SLA (CASLA), while the border-crossing traffic is served by a DiffServ/MPLS core network according to the transit domain SLA (TRSLA). Specifically, we propose an engineered priority scheme for a cellular wireless domain, where the CASLA for each service class is met with efficient resource utilization and the interdomain TRSLA bandwidth requirement can be obtained conveniently. In the transit domain, the traffic load fluctuation from upstream access domains is tackled with an inter-TRSLA resource sharing technique, where the spare capacity from underloaded TRSLAs can be exploited by the overloaded TRSLAs to improve resource utilization.

This paper presents an efficient resource sharing scheme for a network supporting multiple service level agreements (SLAs). Specifically, an overloaded SLA can borrow bandwidth from those underloaded SLAs based on a call level service differentiation concept. While flows admitted with the SLA nominal capacity are considered as in profile flows, flows admitted with borrowed bandwidth are tagged as out profile flows and may be preempted later when the original bandwidth owner needs to claim back the resources. Such preemption is considered as the quality of service (QoS) differentiation between the in profile and out profile flows. Through the implementation design and computer simulations, we show that high resource utilization and SLA compliance can be simultaneously achieved by bandwidth borrowing and call level differentiation.

In the differentiated services model, provisioning quantitative assured services is a challenging topic, as it requires loss probability calculation for a partitioned buffer. In this paper, we study such a loss analysis problem with self-similar input traffic, which has never been studied in the open literature. The input is modeled as a fractional Brownian motion process including J classes of traffic. Each class has its unique requirement on packet loss probability. A first-in-first-out buffer partitioned with J-1 thresholds is used to provide J loss priorities. Heuristic expressions of the loss probabilities for all the J classes are derived, and simulation results demonstrate that the heuristic expressions provide an accurate estimate for all the loss probabilities over the entire buffer range.

This paper proposes a call admission control (CAC) policy for a cellular system supporting voice and data services, and providing a higher priority to handoff calls than to new calls. A procedure for searching the optimal admission region is given. The traffic flow is characterized by a three-dimensional (3-D) birth-death model, which captures the complex interaction between the on/off voice and best-effort data traffic sharing the total resources without partition. To reduce complexity, the 3-D model is simplified to an exact (approximate) 2-D model for voice (data). The mathematical expressions are then derived for the performance measures and for the minimal amount of resources required for quality-of-service (QoS) provisioning. Numerical results demonstrate that: 1) the proposed CAC policy performs well in terms of QoS satisfaction and resource utilization; 2) the approximate 2-D model for data traffic can achieve a high accuracy in the traffic flow characterization; and 3) the admission regions obtained by the proposed search method agree very well with those obtained by numerically solving the mathematical equations. Furthermore, computer simulation results demonstrate that the impact of lognormal distributed data file size is not significant, and may be compensated by conservatively applying the Markovian analysis results.

We investigate via computer simulations the statistical multiplexing and admission control for multiclass Markovian sources in a buffer partitioned with J - I thresholds to provide the J loss priorities. Through heuristic conjecture and numerical analysis, the effective bandwidth concept has been extended to the partitioned buffer system, where the traffic is generated by multiclass Markov-modulated fluid sources [Y. Cheng et al., Nov. 2002]. In this paper, the packet loss probabilities in a partitioned buffer system are estimated by computer simulations. The simulation results verify that the effective bandwidth proposed in [Y. Cheng et al., Nov. 2002] can be used for efficient resource allocation while satisfying the quality of service (QoS) requirements. We use importance sampling whenever applicable to improve the simulation accuracy.

We investigate the statistical multiplexing and admission control for a partitioned buffer, where the traffic is generated by multiclass Markov-modulated fluid sources. Each of the sources has J (>1) classes at each state. The quality of service (QoS) is described by the packet loss probability for each class. The buffer is partitioned with J-1 thresholds to provide the J loss priorities. Extending the effective bandwidth concept to such a buffer system is a challenging topic. We find the minimal effective bandwidth in the asymptotic regime of large buffers and small loss probabilities by optimally setting the partition thresholds. The minimal effective bandwidth achieves efficient resource utilization and can be used to do admission control for heterogeneous multiclass Markovian sources in an additive way. The buffer partition thresholds are dynamically adjusted according to the input traffic load to guarantee QoS. Numerical analysis and simulation results verify the QoS satisfaction and the obvious improvement of resource utilization compared with previously published results, when the minimal effective bandwidth is used for resource allocation with the proposed dynamic buffer partitioning techniques.

We investigate the statistical multiplexing and admission control for a partitioned buffer, where the traffic is generated by multiclass Markov-modulated fluid sources. Each of the sources has J (> 1) QoS classes at each state. The QoS is described by the packet loss probability for each class. The buffer is partitioned with J - 1 thresholds to provide the J loss priorities. In the asymptotic regime of large buffers and small loss probabilities, the effective bandwidth is defined and derived based on fluid model analysis and buffer partitioning optimization, which is the minimal channel capacity required to serve a multiclass Markovian source while guaranteeing the QoS requirements of all the classes. For heterogeneous multiclass Markovian sources, numerical studies demonstrate that the proposed effective bandwidth can be used for admission control in an additive way.

In this paper, we propose an algorithm for optimal buffer partitioning which requires the minimal channel capacity to satisfy the quality of service (QoS) requirements of input traffic. The traffic is generated by a Markov-modulated fluid source and has J (larger than 1) QoS classes at each state. The QoS is described by a packet loss probability requirement for each class. The buffer is partitioned with J-1 thresholds to provide the J loss priorities, thus the J classes of service. Traffic is admitted or rejected based on the buffer occupancy and its service class. We also present an approach for the buffer partitioning for heterogeneous Markov-modulated sources. Numerical results demonstrate that the proposed algorithm achieves a higher resource utilization efficiency than previously published results.