The extensive adoption of deep learning, coupled with the exponential growth of data, has raised concerns regarding potential privacy disclosure, particularly through membership inference attacks where adversaries attempt to determine whether specific data samples were used in model training. Differential privacy has emerged as a prominent technique to mitigate these concerns. However, its application often results in degraded model performance and significant utility loss. This paper proposes a dynamic differential privacy mechanism based on feature importance in deep learning (DPFI) to address this issue.Meanwhile, the introduction of superpixel segmentation not only mitigates the trade-off between accuracy and utility but also reduces the high complexity caused by high-dimensional features. The core concept of DPFI is that the noise level for each feature is determined based on its importance to the model. Specifically, we first initialize and train a model with differential privacy. Then, we perform superpixel segmentation on the dataset and apply Shapley Additive Explanations on the segmented images to calculate the feature importance. Next, we propose a noise addition strategy based on the importance of the features and their distribution. The privacy guarantees are rigorously analyzed through Rényi differential privacy. Experiments demonstrate that DPFI outperforms existing methods in terms of both model accuracy and resistance to membership inference attacks.

TheComputing Power Network(CPN) is a distributed system that integrates computing resources to optimize utilization, but ensuringQuality of Service(QoS) is challenging due to high demand and complex heterogeneous connections. Accurate workload prediction is essential for maintaining QoS, yet the diverse and complex user requirements in CPN make prediction difficult. To address this challenge, we propose an ensemble workload prediction model with fluctuation division control for workload prediction in CPN, comprising three key components. First, we use theThree-Way Decision(3WD) approach to partition workload fluctuations, controlling granularity thickness and applying clustering to capture dynamic workload characteristics. Second, we develop tailored prediction methods for each of the three partitioned regions and ensembles them to enhance overall prediction performance. Third, the ensemble prediction method is applied to each region to obtain the final predicted values. The proposed method introduces an innovative fluctuation division control strategy for characteristic mining to capture dynamic workload fluctuation patterns and designs the effective ensemble workload prediction model deal with the problem of non-stationary workload prediction in CPN. Experimental results on trace datasets from Alibaba and Dinda demonstrate that the proposed model improves the higher average prediction accuracy by up to 26.06%$\sim$66.4% than the comparison methods.

This abstract describes the project funded by the IEEE SMCS on Transforming Educational Assets and Materials (TEAM) in Systems, Man, and Cybernetics. The project develops an open course on Cyber Physical Systems (CPS) Foundation and Design for Unmanned Aerial Vehicles (UAVs). The course will be available to the public and serve the need of researchers, students and professionals who are interested in conducting UAVs related research. The open course contains integrated modules on control, communication and networking, computing, and artificial intelligence (AI) to provide trainees a comprehensive knowledge needed for UAVs. The course is self-paced and contains quizzes in each module for help students assess the quality of learning and also allow course designers to evaluate the effectiveness of the course materials for continuous improvement. The open course promotes CPS which is a SMCS technical field. It will also attract students and professionals to the SMC community.

In recent years,Coded Edge Computing(CEC) has been greatly studied as a promising technology to effectively mitigate the impact of stragglers and provide confidentiality in edge collaborative computing. It is crucial to verify the correctness of both intermediate results and the final result especially in untrustable and unreliable edge computing scenarios. However, the existing works on verification in CEC always verify and directly discard the whole incorrect intermediate results. In this paper, we propose thePartial Decode and Compare(PDC) verification scheme, which can fully utilize the correct part in the incorrect intermediate results to reduce the complexity and tolerate more abnormal edge devices. The PDC verification scheme consists of two parts:Final Result Verification(FRV) andAbnormal Edge Device Identification(AEDI). By deeply analyzing the decoding impact of the intermediate results on the final result, the PDC verification scheme divides the intermediate results and final results intosubresult vectors. It decodes, compares, and verifies the final result in units of subresult vectors. In this way, the obtained parts which verified to be correct do not need to participate in the following verification. Therefore, it can significantly reduce the verification overhead including both the number of required decoding rounds and the complexity of each decoding round. Based on the correct final result verified by the PDC verification scheme, we also propose anAbnormal Edge Devices Identificationscheme to identify all abnormal edge devices that return incorrect intermediate results. We then present extensive theoretical analyses and simulation experiments of the PDC verification scheme, which demonstrates that the PDC verification scheme can tolerate a higher ratio of incorrect intermediate results and achieve lower verification overhead than the state-of-the-art verification works. Therefore, the proposed PDC verification scheme enables CEC to provide reliable services in unstable and unreliable edge computing scenarios.

Recently, edge computing has demonstrated increasing potential to provide low-latency computing services. Coded edge computing can not only make full use of the resources of heterogeneous edge computing servers, but also significantly reduce the negative effects of slow computing devices on computing time. Nevertheless, since edge servers may be unreliable or untrustworthy, the user will decode and get incorrect computation results even if it uses one incorrect sub-computation result returned by faulty edge servers. In this paper, for the existing coded edge computing schemes, we focus on the distributed matrix-matrix multiplication and design a general and efficientDecode-and-Compare Verification(DCV) scheme to verify the correctness of computation results and identify faulty edge servers by utilizing the properties of coded computing itself. The DCV scheme contains two components: (1) computation result verification,i.e., obtain the computation result and verify its correctness, and (2) faulty edge server identification,i.e., identify the faulty edge servers by verifying the correctness of returned sub-computation results. For both the independent and collusion faulty edge server models, we conduct solid theoretical analyses on the required decoding rounds, the coding redundancy and the successful verification probability to demonstrate that the correct computation result can be efficiently verified. We also conduct a lot of experiments on the DCV scheme from different aspects and the results show that it achieves much less computation time to get the correct computation result compared with other potential schemes, including homomorphic encryption and local computation.

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although modeling DLC decision-making has been studied for years, the impact of human factors, which is crucial in accurately modelling human DLC decision-making strategies, is largely ignored in the existing literature. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers’ decision-making maneuvers by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model captures the human decision-making strategies and imitates human drivers’ lane-change maneuvers, which can achieve 98.66% prediction accuracy. Moreover, we also analyze the lane-change impact of our model compared with human drivers in terms of improving the safety and speed of traffic.

Accurately predicting the future motions of surrounding traffic agents is critical for the safety of autonomous ve-hicles. Recently, vectorized approaches have dominated the motion prediction community due to their capability of capturing complex interactions in traffic scenes. How-ever, existing methods neglect the symmetries of the prob-lem and suffer from the expensive computational cost, facing the challenge of making real-time multi-agent motion prediction without sacrificing the prediction performance. To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction. By decomposing the problem into local con-text extraction and global interaction modeling, our method can effectively and efficiently model a large number of agents in the scene. Meanwhile, we propose a translation-invariant scene representation and rotation-invariant spa-tial learning modules, which extract features robust to the geometric transformations of the scene and enable the model to make accurate predictions for multiple agents in a single forward pass. Experiments show that HiVT achieves the state-of-the-art performance on the Argoverse motion forecasting benchmark with a small model size and can make fast multi-agent motion prediction.

An Autonomous Driving System (ADS) uses a plethora of sensors and many deep learning based tasks to aid its perception, prediction, motion planning, and vehicle control. To ensure road safety, those tasks should be synchronized and use the latest sensing data, which is challenging since 1) different sensors have different sensing periods, 2) the tasks are interdependent, 3) computing resource is limited. This work is the first that uses Age of Information (AoI) as the performance metric for task scheduling in an ADS. We show that minimizing AoI is equivalent to jointly minimizing the response time and maximizing the throughput. We formally formulate the AoI-centric task scheduling problem. To derive practical scheduling solutions, we extend the formulation and formulate the optimal AoI-centric periodic scheduling problem with a given cycle. A reinforcement learning-based solution is designed accordingly. With experiments simulated according to the Apollo driving system, we compare the scheduling performance of the AoI-centric task scheduling with Apollo’s schedulers from the perspective of AoI, throughput, and worst case response time. The experiment results show that the maximum AoI in the proposed scheduling solution with 4 cores is lower than that in Apollo’s schedulers with 8 cores.

To improve safety in public transportation, a major issue is how to avoid traffic accidents. To this end, a recent report has demonstrated that more than 90% of accidents in the United States were due to drivers’ abnormal behaviors. Relevant to this observation, many recent studies have proposed to use different sensors to monitor drivers’ behaviors and apply learning algorithms to detect abnormal behaviors. Nevertheless, most existing systems are expensive and inconvenient to be deployed or significantly affected by the environment. In this article, we propose and develop a novel and effective solution, namely, SafeDriving, that collects signals from electromyography (EMG) sensors and then utilizes an effective deep-learning model to detect abnormal behaviors in real time. Specifically, we first utilize a wearable EMG sensor that can be attached to a driver’s forearm to collect a large amount of sensing data from human drivers, for which we define five typical abnormal driving behaviors (i.e., fetching forward, picking up, turning the steering wheel sharply, turning back, and touching sunroof) and label each sample accordingly. Next, using the labeled data, we design and train multiple state-of-the-art classifiers to improve the performance of SafeDriving, e.g., convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The extensive experiments demonstrate that GRU can lead to the best performance with an average accuracy of 93.94%. Based on this observation, we further investigate other important factors, such as the binding area of the sensor, the tightness of binding, the duration of the sample, etc. The proposed SafeDriving system provides an effective approach to reliably assess drivers’ driving behaviors with affordable commodity sensors and be further used in public safety.

In smart grids, a major challenge is how to effectively utilize consumers’ energy consumption data while preserving security and privacy. In this article, we tackle this challenging issue and focus on energy theft detection, which is very important for smart grids. Specifically, we note that most existing energy theft detection schemes are centralized, which may be unscalable, and more importantly, may be very difficult to protect data privacy. To address this issue, we propose a novel privacy-preserving federated learning framework for energy theft detection, namely, FedDetect. In our framework, we consider a federated learning system that consists of a data center (DC), a control center (CC), and multiple detection stations. In this system, each detection station (DTS) can only observe data from local consumers, which can use a local differential privacy (LDP) scheme to process their data to preserve privacy. To facilitate the training of the model, we design a secure protocol so that detection stations can send encrypted training parameters to the CC and the DC, which then use homomorphic encryption to calculate the aggregated parameters and return updated model parameters to detection stations. In our study, we prove the security of the proposed protocol with solid security analysis. To detect energy theft, we design a deep learning model based on the state-of-the-art temporal convolutional network (TCN). Finally, we conduct extensive data-driven experiments using a real-energy consumption data set. The experimental results demonstrate that the proposed federated learning framework can achieve high accuracy of detection with a smaller computation overhead.

Modeling interactions among vehicles is critical in improving the efficiency and safety of autonomous driving since complex interactions are ubiquitous in many traffic scenarios. To model interactions under different traffic scenarios, most existing works consider interaction information implicitly in their specific tasks with hand-crafted features and predefined maneuvers. Extracting interaction representation, which can be commonly used among different downstream tasks, is not explored. In this article, we propose a general and novel graph self-attention network (GSAN) to learn the spatial–temporal interaction representation among vehicles by a framework consisting of pretraining and fine-tuning. Specifically, in the pretraining step, we construct the GSAN module based on a graph self-attention layer and a gated recurrent unit layer, and use trajectory autoregression to learn the interaction information among vehicles. In the fine-tuning step, we propose two different adaptation schemes to utilize the learned interaction information in various downstream tasks and fine-tune the entire model with only a few steps. To illustrate the effectiveness and generality of our spatial–temporal interaction model, we conduct extensive experiments on two typical interaction-related tasks, namely, lane-changing classification and trajectory prediction. The experiment results demonstrate that our approach significantly outperforms the state-of-the-art solutions of these two tasks. We also visualize the impact of surrounding vehicles on the ego vehicle in different interaction scenes. The visualization offers an intuitive explanation on how our model captures the dynamic changing interactions among vehicles and makes good predictions in various interaction-related tasks.

In recent years, many deep learning models have been adopted in autonomous driving. At the same time, these models introduce new vulnerabilities that may compromise the safety of autonomous vehicles. Specifically, recent studies have demonstrated that adversarial attacks can cause a significant decline in detection precision of deep learning-based 3-D object detection models. Although driving safety is the ultimate concern for autonomous driving, there is no comprehensive study on the linkage between the performance of deep learning models and the driving safety of autonomous vehicles under adversarial attacks. In this article, we investigate the impact of two primary types of adversarial attacks, perturbation attacks, and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models. In particular, we consider two state-of-the-art models in vision-based 3-D object detection: 1) Stereo R-CNN and 2) DSGN. To evaluate driving safety, we propose an end-to-end evaluation framework with a set of driving safety performance metrics. By analyzing the results of our extensive evaluation experiments, we find that: 1) the attack’s impact on the driving safety of autonomous vehicles and the attack’s impact on the precision of 3-D object detectors are decoupled and 2) the DSGN model demonstrates stronger robustness to adversarial attacks than the Stereo R-CNN model. In addition, we further investigate the causes behind the two findings with an ablation study. The findings of this article provide a new perspective to evaluate adversarial attacks and guide the selection of deep learning models in autonomous driving.

In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficienttask allocationalgorithm to obtain a set of selected edge devices and the number of coded vectors allocated to them. Using the task allocation results, we then designsecure coded computingschemes, for two cases, (1) with redundant computation and (2) without redundant computation, all of which satisfy the availability and security conditions. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.

Sampling-based motion planning (SBMP) is a major algorithmic trajectory planning approach in autonomous driving given its high efficiency and outstanding performance in practice. However, driving safety still calls for further refinement of SBMP. In this article we organically integrate algorithmic motion planning with learning models to improve SBMP in highway traffic scenarios from the following two perspectives. First, given the number of points to be sampled, we develop a new model to sample “important” points for SBMP by predicting the intention of surrounding vehicles and learning the distribution of human drivers’ trajectory. Second, we empirically study the relationship between the number of sample points and the environment, which is largely ignored in conventional SBMP. Then, we provide a guideline to select the appropriate number of points to be sampled under different scenarios to guarantee efficiency. The simulation experiments are conducted based on the vehicle trajectory dataset NGSIM. The results show that the proposed sampling strategy outperforms existing sampling strategies in terms of the computing time, traveling time, and smoothness of the trajectory.

Federated learning (FL) is a promising machine learning paradigm because it allows distributed edge devices to collaboratively train a model without sharing their raw data. In practice, a major challenge to FL is that edge devices are heterogeneous, so slow devices may compromise the convergence of model training. To address such a challenge, several recent studies have suggested different solutions, in which a promising scheme is to utilize coded computing to facilitate the training of linear models. Nevertheless, the existing coded FL (CFL) scheme is limited by a fixed coding redundancy parameter, and a weight matrix used in the existing design may introduce unnecessary errors. In this paper, we tackle these issues and propose a novel framework, namely CFL-HC, to facilitate CFL in heterogeneous computing scenarios. In our framework, we consider a computing system consisting of a central server and multiple computing devices with original or coded datasets. Then we specify an expected number of input-output pairs that are used in one round. Within such a framework, we formulate an optimization problem to find the best deadline of each training round and the optimal size of the computing task allocated to each computing device. We then design a two-step optimization scheme to obtain the optimal solution. To evaluate the proposed framework, we develop a real CFL system using the message passing interface platform. Based on this system, we conduct numerical experiments, which demonstrate the advantages of the proposed framework, in terms of both accuracy and convergence speed.

Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However, offloading a computation task from a mobile device to other mobile devices is a challenging task due to frequent topology changes and link failures because of node mobility, unstable and unknown communication environments, and the heterogeneous nature of these devices. To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.

Recently, edge computing (EC) has attracted wide attention as a novel and promising computing mode with high real-time and low-latency characteristics. However, users' privacy and the limited resources have become major concerns in the implementation of EC because edge devices are usually heterogeneous and untrustworthy. Although many related works have protected the user's privacy, they did not take the storage resource limitation of heterogeneous edge devices into consideration and their schemes may cause high communication load. In this paper, we propose PCHEC, a Private Coded computation scheme for Heterogeneous Edge Computing, to protect the user's privacy and minimize the communication load. Specifically, PCHEC first gives a storage allocation scheme to minimize the communication load in EC where the heterogeneous edge devices have different storage limits. Secondly, PCHEC utilizes linear coding to mix the target data with other information for the protection of the user's privacy. To evaluate the efficiency of PCHEC, we make theoretically analysis and conduct extensive simulations. The experiments show PCHEC effectively reduces the communication load by up to 70% compared with other schemes.

In recent years, edge computing (EC), as an emerging technology, has been widely used in various industries. It can meet the needs of industries in real-time business, application intelligence, security and privacy protection. However, edge devices may not always be trustworthy in the edge computing environment. Moreover, traditional edge computing systems have ignored the fact that the computation capability of user device can also be used. In this paper, we propose the Minimum Computation Latency Secure Edge Computing (MCLSEC) scheme to minimize computation latency and provide the security of computing data by utilizing linear coding and the resources of both edge devices and user device. Specifically, we consider the matrix multiplication as a computation task, which is an important module in many application operations, such as machine learning, big data analysis, etc. We firstly theoretically analyze the total computation latency of edge devices and user device in the coded edge computing. We then give the design of the MCLSEC scheme, which includes of the coding scheme and the task allocation scheme. Moreover, we also give theoretical analysis to show the proposed MCLSEC scheme is secure and optimal. Finally, we conduct extensive simulation experiments to show the effectiveness of the proposed scheme. Compared with the existing schemes, MCLSEC scheme significantly reduces the computation latency of edge computing while ensuring data confidentiality.

For autonomous driving, an essential task is to detect surrounding objects accurately. To this end, most existing systems use optical devices, including cameras and light detection and ranging (LiDAR) sensors, to collect environment data in real time. In recent years, many researchers have developed advanced machine learning models to detect surrounding objects. Nevertheless, the aforementioned optical devices are vulnerable to optical signal attacks, which could compromise the accuracy of object detection. To address this critical issue, we propose a framework to detect and identify sensors that are under attack. Specifically, we first develop a new technique to detect attacks on a system that consists of three sensors. Our main idea is to: 1) use data from three sensors to obtain two versions of depth maps (i.e., disparity) and 2) detect attacks by analyzing the distribution of disparity errors. In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method. Based on the detection scheme, we further develop an identification model that is capable of identifying up to n-2 attacked sensors in a system with one LiDAR and n cameras. We prove the correctness of our identification scheme and conduct experiments to show the accuracy of our identification method. Finally, we investigate the overall sensitivity of our framework.

Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.

A group of connected and autonomous vehicles (CAVs) with common interests can drive in a cooperative manner, namely cooperative driving, which has been verified to significantly improve road safety, traffic efficiency and environmental sustainability. A more general scenario that various types of cooperative driving applications such as truck platooning and vehicle clustering, will coexist on roads in the foreseeable future. To support such multiple cooperative drivings, it is critical to design an efficient message dissemination scheduling in a shared communication channel. Most ongoing research suggests using the time-division multiple access (TDMA) method on top of IEEE 802.11p as a potential remedy. However, TDMA requires time synchronization and is not flexible, especially in the multiple cooperative drivings scenario where the beacon frequency needs to be updated and the number of cooperative drivings changes to meet the time-varying traffic conditions. In this paper, we focus on the study of the message dissemination protocol for platooning, a typical and well-known cooperative driving pattern. Specifically, we proposed a hybrid message dissemination protocol which aims at guaranteeing the reliable delivery of beacon messages for a multi-platooning system. We first adopt a TDMA-based medium access method for intra-platoon communication to improve the reliability and efficiency of beacon dissemination. We then present a token-passing medium access method for inter-platoon communication, which maps platoons into a token ring to schedule their beacon transmission time. We conduct extensive numerical experiments to validate the effectiveness of our protocol.

Edge computing is a promising technology that can fulfill the requirements of latency-critical and computation-intensive applications. To further enhance the performance, coded edge computing has emerged because it can optimally utilize edge devices to speed up the computation. In this paper, we tackle a major security issue in coded edge computing: how to verify the correctness of results and identify attackers. Specifically, we propose an efficient verification scheme, namely Decode-and-Compare (DC), by leveraging both the coding redundancy of edge devices and the properties of linear coding itself. To design the DC scheme, we conduct a solid theoretical analysis to show the required coding redundancy, the expected number of decoding operations, and the tradeoff between them. To evaluate the performance of DC, we conduct extensive simulation experiments and the results confirm that the DC scheme can outperform existing solutions, such as homomorphic encryption and computing locally at the user device.

Modeling the interactions among vehicles has been considered essential in improving efficiency and safety in autonomous driving, since the real traffic scenarios, such as merging lanes, intersection, and lane change, are full of complex interactions. In the literature, interaction is considered implicitly in individual tasks, which makes it hard to extract the interactions for other related downstream tasks. In this paper, we propose a novel Graph Self-Attention Network (GSAN) to quickly capture and quantify the influence of interactions among vehicles from historical trajectories, which can be used as a tool to introduce the impact of interactions into different downstream tasks and further analyze the dominating features affecting the interactions among vehicles. We conduct experiments on the trajectory prediction task as one example to illustrate how to use the spatial-temporal interaction vector to improve the performance of interaction related tasks. The experiment results demonstrate that the GSAN module outperforms the state-of-the-art solutions in terms of the trajectory prediction accuracy. Also, we visualize the effects from all surrounding vehicles on the ego vehicle by heat maps using the trained attention values from the GSAN module.

In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.

In recent years, we have witnessed fast‐growing unmanned aerial systems (UAS) based applications. To better facilitate these applications, many efforts have been made to enhance the capability of UAS from various aspects, including communications, control and networking, and so on. Nevertheless, most of these studies neglect the computation aspect. Recently, the UAS‐enabled mobile edge computing (MEC) has attracted increasing research attention, which utilises UAS with onboard computing capability to provide on‐demand computing services for mobile users. However, existing research on UAS‐enabled MEC remains at the theory stage and how to design a UAS platform with advanced onboard computing capability has not been addressed. In this study, the authors aim to fill this research gap and design an open UAS‐based airborne computing platform with advanced onboard computing capability. This platform was designed from three aspects: hardware, software, and applications. In particular, feasible computing hardware to perform UAS onboard computing is first considered and a prototype is then designed. To enhance the flexibility and programmability of the platform, two key virtualisation techniques are then investigated. Finally, they test the performance of their prototype by executing real UAS onboard computing tasks, the results of which verify the feasibility and potentials of the proposed airborne computing platform.

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS , which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.

In cloud computing, it is crucial to maintain service continuity, while power outage is one of the most common and serious threats. To improve the resilience of cloud against power outage, a service provider usually deploys emergency energy supply (e.g., UPSs and generators) in a data center. When a power outage at a data center happens, the cloud service provider needs to make the operation decision on which subset of VMs to keep running and which servers to host such VMs to minimize its loss (or maximize its profit) using the emergency energy supply while the selected VMs are running in the affected data center until they are finished, migrated to other data centers, or normal power supply of the affected data center has been restored. No prior research has theoretically studied such a cloud service continuity problem under power outage. In this paper, we tackle this challenge and investigate the cloud service continuity problem. Specifically, we consider that a profit is associated with maintaining the continuity of a service, denoted as service continuity profit. Based on that we first formulate an optimization problem that aims to maximize the total profit subject to energy constrains. After showing the hardness of the problem, we focus on the design of approximation algorithms for solving the problem, where we consider two practical cases. In the first one with sufficient number of servers for re-provisioning, we develop a constant approximation algorithm of which the worst-case performance approaches the optimal solution within a constant factor (≈4.5-6.4). In the second one, we consider the general case with limited number of servers, and we develop an approximation algorithm with an approximation ratio of around 5.7-8. By combining these two algorithms together, we can achieve both good worst-case performance and average performance. Simulation results demonstrate the efficiency in terms of maximizing the service continuity profit of the proposed algorithms.

In recent years, edge computing has attracted increasing attention for its capability of facilitating delay-sensitive applications. In the implementation of edge computing, however, data confidentiality has been raised as a major concern because edge devices may be untrustable. In this paper, we propose a design of secure and efficient edge computing by linear coding. In general, linear coding can achieve data confidentiality by adding random information to the original data before they are distributed to edge devices. To this end, it is important to carefully design code such that the user can successfully decode the final result while achieving security requirements. Meanwhile, task allocation, which selects a set of edge devices to participate in a computation task, affects not only the total resource consumption, including computation, storage, and communication, but also coding design. In this paper, we study task allocation and coding design, two highly-coupled problems in secure coded edge computing, in a unified framework. In particular, we take matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task, and study optimal task allocation and coding design to minimize resource consumption while achieving information-theoretic security.

Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.

The success of machine learning in domains such as computer vision and computer games has triggered a surge of interest in applying machine learning in computer networks. This paper tries to answer a broadly-debated question: can we improve the performance of intradomain routing, one of the most fundamental blocks in the Internet, with reinforcement learning (RL)? Due to the complex network traffic conditions and the large action space in routing, it is difficult to give a definite answer for existing RL-based routing solutions. To gain an in-depth understanding on the challenges of RL-based routing, we systematically classify different RL-based routing solutions and investigate the performance of several representative approaches, in terms of scalability, stability, robustness, and convergence. With the lessons learned in evaluating various RL-based routing solutions, we propose two methods, called supervised Q-network routing (SQR) and discrete link weight-based routing (DLWR), which boost the performance of RL-based routing and outperform the de facto shortest path intradomain routing.

To protect user privacy, many smartphone systems adopt the permission-based mechanism in which a user can evaluate the risk of requests for private information from a mobile app before installing it. However, recent studies show that the permission based mechanism is vulnerable to application collusion attacks because two apps, which appear to be harmless individually, can establish a covert channel and use it to leak confidential information. Consequently, people have designed some covert channel detection schemes, by checking abnormal status of the phone. In this paper, we point out that existing covert channel detection schemes may fail to detect a new type of collusion attacks referred as user-behavior-based covert channels. We implement three covert channels on Android smartphones. Our work sets a new alarm for the security issue of using smartphones. We then study the countermeasures to this new type of covert channels. Instead of trying to directly detect the proposed new type of covert channels, we propose two mitigation solutions to reduce the effectiveness of such covert channels. The mitigation solutions are also valid to other existing sensor-based side channels and/or covert channels on the phone.

The continuous demand for content-rich multimedia is pushing for high-speed and secure transmission approaches. In recent years, linear network coding (LNC) has been shown to be a promising technology to improve network throughput, transmission reliability, and information security. In this paper, we study the optimal transmission topology construction and LNC design for a secure multiple-source multicast to deliver the same content with integral link rates, which can be equivalent to the secure multicast problem with a virtual source, i.e., the integer secure virtual-source multicast (ISVM) problem. The objectives of the ISVM problem include the following: 1) satisfy the weakly secure requirements, 2) maximize the secure multicast rate (SMR), and 3) minimize the transmission cost when the SMR is maximized. First, we analyze the necessary and sufficient condition that there exist a transmission topology with integral link rates and a secure LNC that can achieve a given SMR$R$. Then, we model the ISVM problem as an integer linear programming based on the theoretical analysis and design an efficient transmission topology construction algorithm to solve the ISVM problem by utilizing the Lagrangian relaxation and subgradient method. We also analyze the size of finite field required to construct thedeterministic LNCfor a secure virtual-source multicast and the probability that the virtual-source multicast is weakly secure when usingrandom LNCin the ISVM problem. Finally, we design upper and lower bounds for the ISVM problem and conduct extensive simulations to compare the performance of the proposed algorithms with these two bounds.

With the advances of Internet of Things technologies, individual vehicles can now exchange information to improve traffic safety, and some vehicles can further improve safety and efficiency by coordinating their mobility via cooperative driving. To facilitate these applications, many studies have been focused on the design of inter-vehicle message dissemination protocols. However, most existing designs either assume individual driving pattern or consider cooperative driving only. Moreover, few of them fully exploit infrastructures, such as cameras, sensors, and road-side units. In this paper, we address the design of message dissemination that supports heterogeneous driving patterns. Specifically, we first propose an infrastructure-assisted message dissemination framework that can utilize the capability of infrastructures. We then present a novel beacon scheduling algorithm that aims at guaranteeing the timely and reliable delivery of both periodic beacon messages for cooperative driving and event-triggered safety messages for individual driving. To evaluate the performance of the protocol, we develop both theoretical analysis and simulation experiments. Extensive numerical results confirm the effectiveness of the proposed protocol.

In recent years, many cross-VM covert channels have been discovered in cloud computing, causing serious security concerns. For such covert channels, some mitigation schemes have been proposed, but usually one mitigation scheme aims at a specific covert channel, which may be inefficient in defending against potential new attacks. In this paper, we propose a generic solution to mitigate the risk of a broad class of timing-based cross-VM covert channels. The design is motivated by our finding that the capacity of most timing-based cross-VM covert channels highly depends on the co-run probability among VMs, where the co-run probability depends not only on how VMs are assigned to servers, but also how VMs are scheduled on a single server, which is related to managing the vCPUs assigned to each VM. We find that the VM co-run probability can be reduced when the number of vCPUs increases, but it also causes extra system overhead in resource utilization. In this paper, we propose a generic VM provisioning and VM scheduling solution to jointly minimize the co-run probability among VMs, meanwhile, maintaining high resource utilization. We experimentally demonstrate that the proposed scheduling algorithm can mitigate the risk of timing-based cross-VM covert channel with lower system overhead. We also conduct simulation of VM provisioning which shows that the proposed solution can achieve the balance between high resource utilization and low risk of information leakage caused by cross-VM covert channels.

For many emerging mobile broadband services and applications, the source and destination are located in the same local region. Consequently, it is very important to design access networks to facilitate efficient local data exchange. In the past few years, most existing studies focus on either the wired or wireless domains. In this paper, we aim to exploit both the wired and wireless domains. Specifically, we consider a Fiber-Wireless access network in which a passive optical network (PON) connects densely deployed base stations. In such a scenario, we propose a novel access scheme, namely, NCDA, where the main idea is to utilize both network coding and device association. To understand the potentials of NCDA, we first formulate a mixed integer nonlinear programming (MINLP) to minimize the weighted number of packet transmissions (WNT), which is related to both the system capacity and energy consumption. We then theoretically analyze the tight upper bounds of the minimal WNT in the PON, which helps us to approximate the original problem by a mixed integer linear programming (MILP). Next, we develop efficient algorithms based on linear programming relaxation to solve the optimal NCDA problem. To validate our design, we conduct extensive simulation experiments, which demonstrate the impact of important network parameters and the promising potentials of the proposed scheme.

As a major driving force for adopting cloud computing, continuous cost reduction has been constantly pursued by cloud users. For a group of users with heterogeneous cloud resource demands, it may be possible for them to buy resources in a collaborative way in order to save the purchase cost, which is known as group-buying in business. While group-buying can benefit cloud users in principle, the question is how to design an implementation scheme to support group-buying on the cloud market. In this paper, we address the question by studying a coalition formation game, aiming to design a way under which the users can form stable coalitions for group-buying. It turns out that group-buying on the cloud market is challenging in that most popular solution concepts may fail to constitute stable coalitions. In order to sustain group-buying for cloud services, we propose a new solution concept, contractually group stable, which is an extension of an existing concept in the literature. We show that this new solution concept can guarantee the existence of stable coalitions, making group-buying always possible on the cloud market. We also develop computing algorithms for solving the coalition formation game under our concept. Computational experiments show that our concept can bring in substantial cost reduction for cloud users.

With the successful deployment of the fourth-generation cellular networks around the world, long-term evolution (LTE) and LTE-advanced (LTE-A) have become key technologies to enable Internet of Things (IoT) applications. To accommodate various streaming data of IoT applications, LTE/LTE-A standards have defined several quality-of-service (QoS) classes for different traffic characteristics, in terms of traffic bit-rate, tolerable delay, and packet loss rate. Moreover, to meet the stringent power-saving requirements for IoT devices, LTE/LTE-A standards also have defined the discontinuous reception/transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep when no data need to be received or transmitted from/to the evolved Node B (eNodeB).

Because of the flexibility and convenience brought by cloud computing, it has been adopted in many applications. To preserve the privacy of cloud data, data owner often encrypts all sensitive data files, which makes the keyword search application based on plaintext a very challenging task. Therefore, several privacy-preserving keyword search algorithms have been developed recently, and most of these works support only single-data-owner settings. However, there are always more than one data owners in real applications, which are much more complex and challenging than single-owner scenario. To support multi-owner keyword search, those prior search algorithms need to be repeated several times, because each data owner intends to encrypt his own files with a unique private-key separately. It is absolutely not an efficient way. In this paper, we propose a privacy and integrity-preserving multi-owner ranked-keyword search scheme termed PIMRS. In the PIMRS, we exploit an asymmetric scalar-product encryption function based on the TF × IDF rule to preserve data privacy and to obtain more precise search results. Furthermore, a circular bi-direction-linked list based scheme is proposed to preserve the integrity of search results, which also enables the misbehaviors of cloud server to be detected. The security analysis of PIMRS shows its privacy and integrity property, and extensive experiments based on real-world data set confirm the high efficiency of proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.

The continuous growth of media-rich content calls for more efficient and secure methods for content delivery. In this paper, we will address the optimallinear network coding(LNC) design forsecure unicast streamingagainst passive attacks, under the requirement ofinformation theoretical security. The objectives include 1) satisfying the information theoretical security requirement, 2) maximizing the transmission rate of a unicast stream, 3) minimizing the number of additional random symbols, and 4) minimizing the total bandwidth cost of content delivery. To fulfill the first three objectives, we formulate aninformation theoretically secure unicast streaming(ITSUS) problem, and then solve it by transforming it to a maximum network flow problem with node-capacity constraints. Based on the solution of the ITSUS problem, we develop an efficient algorithm that can find the optimal transmission topology with minimum bandwidth cost in a polynomial amount of time. With the optimal transmission topology, we investigate the design of bothdeterministicLNC and random LNC. For thedeterministicLNC design, we not only prove that it achieves the four objectives but also analyze the size of required finite field. Moreover, for the random LNC design, we analyze the probability that a random LNC scheme satisfies the information theoretical security requirement. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

To efficiently provide cloud storage services, most providers implement data deduplication schemes so as to reduce storage and network bandwidth consumption. Due to its broad application, many security issues about data deduplication have been investigated, such as data security, user privacy, etc. Nevertheless, we note that the threat of establishing covert channel over cloud storage has not been fully investigated. In particular, existing studies only demonstrate the potential of a single-bit channel, in which a sender can upload one of the two predefined files for a receiver to infer the information of "0" and "1". In this paper, we design a more powerful deduplicationbased covert channel that can be used to transmit a complete message. Specifically, the key features of our design include: (1) a synchronization scheme that can establish a covert channel between a sender and a receiver, and (2) a novel coding scheme that allows each file to represent multiple bits in the message. To evaluate the proposed design, we implement the covert channel and conduct extensive experiments in different cloud storage systems. Our work highlights a more severe security threat in cloud storage services.

Heterogeneous network (HetNet) is a promising technology to improve the capacity of future generations of cellular network, in which a mobile station can be served by multiple base stations (BSs) with different scales of coverage range, including short range low power nodes (LPNs). In HetNet, a major challenge is how to provide guaranteed quality-of-service (QoS) for all users. To address this issue, we investigate a practical scenario in which the massive multiple-input multiple-output (MIMO) technology is adopted by the cooperation of one macro-cell BS and several LPNs. Furthermore, we provide a lightweight channel state information (CSI) acquisition scheme for the implementation. Numerical simulation results demonstrate that the signal-to-interference-and noise ratio (SINR) of intended users in LPNs covered with small cells can be significantly increased by this proposed massive MIMO precoding scheme, whereas oppressing the impact on neighboring victim users.

Covert channel is a classical threat to cyber security because it aims to transfer data between entities that are not allowed to exchange information. To enhance the security of cyber systems, many covert channels have been identified and investigated, in which IP-timing covert channel is one of the important risks because IP is the dominating communication protocol for computer networks. However, despite the potential risks, existing IP-timing covert channels seem to be less significant because most of them carry information by arbitrary inter-packet delays, which leads to low transmission rates and can be easily detected. In this paper, we identify a novel IP-timing covert channel that can significantly increase the transmission rate. Specifically, we propose a new framework for IP-timing covert channel, where the main idea is to use the routes to carry information. Based on the framework, we present the detailed designs for IP-timing covert channels based on TCP and UDP, in which we develop new technique to reduce the channel error rate. To evaluate the performance of the proposed covert channels, we also implement them in realistic systems and conduct extensive experiments. The experimental results show that the proposed IP-timing covert channel achieves 15 times higher rate than existing channels with less than 0.54% error rate. This study shows that the risk of IP-timing channel can be more serious than expected, which requires more sophisticated countermeasures.

To protect user privacy, many smartphone systems, such as Android and Windows Phone, adopt the permission-based mechanism in which a user can evaluate the request of private information by a mobile app before installing it. However, recent studies show that the permission-based mechanism is vulnerable to application colluding attacks because two apps, which appear to be harmless individually, can establish a covert channel and use it to leak confidential information. In general, existing known covert channels usually work in a way that one app can modify the status of a system component, while the other can read the status. Even though several covert channel detection schemes have been proposed recently to fight against this type of covert channels, we point out that such designed covert channel detection schemes are not sufficient. In this paper, we demonstrate the possibility of establishing novel covert channels that work in quite different ways, in which one app (e.g., a game) can be designed deliberately such that the user will be induced to voluntarily modify the status of a system component (e.g., a motion sensor), while the other app can read the status of the system component. To validate our design, we implement three covert channels on Android. Our experiments show that these channels can bypass existing detection schemes. Moreover, we also measure the achievable throughput, error rate, and energy consumption in devices. The results demonstrate that our covert channels can achieve a transmission with high accuracy and low energy consumption. Our work sets a new alarm for the security issue of using smartphones.

The increasing demand for media-rich content has driven many efforts to redesign the Internet architecture. As one of the major candidates, information-centric network (ICN) has attracted significant attention, where in-network cache is a key component in different ICN architectures. In this paper, we propose a novel framework for optimal cache management in ICNs which jointly considers caching strategy and content routing. Specifically, we propose a cache management framework for ICNs based on software-defined networking (SDN) where a controller is responsible for determining the optimal caching strategy and content routing via linear network coding (LNC). Under the proposed cache management framework, we formally formulate the problem of minimizing the network bandwidth cost by jointly considering caching strategy and content routing with LNC. We develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs. We further develop a lower bound of the problem and conduct extensive experiments to compare the performance of the NCCM algorithm with the lower bound. Simulation results validate the effectiveness of the NCCM algorithm and framework.

One critical issue for vehicular safety applications is how to timely and reliably disseminate kinetic information, known as beacon, among vehicles. In this paper, we try to improve the beacon dissemination performance in vehicular ad hoc networks (VANETs) especially in drastic disturbance scenarios. To this end, a decentralized beacon dissemination control scheme (DBDCS) is proposed from the cyber-physical system perspective, where both the vehicle dynamics and VANET behaviors are jointly considered. In the envisioned scheme, the control channel interval for beacon dissemination can be adaptively adjusted based on both the current local traffic dynamics and the networking situation. Numerical results show that the proposed scheme can significantly improve the beacon dissemination performance especially in disturbance scenarios.

In mobile ad hoc networks MANET, deploying a small number of mobile relays can greatly improve the throughput, delay, and security performance. However, in such a hybrid MANET, it is challenging to determine the optimal locations of mobile relays. In this paper, we study a mobile relay placement problem to maximize the network throughput of hybrid MANET with secure network coding capability. Specifically, we first study the maximal throughput of a hybrid MANET, in which the position of each mobile relay is known. For such a special case, we model the maximal throughput problem as a linear programming problem. On the basis of the understanding of this problem, we then formulate the optimal relay placement problem as an integer linear programming problem. Because integer linear programming is too complex to solve for a large MANET, we propose an efficient near-optimal approximation algorithm based on linear programming-relaxation. Finally, we conduct extensive simulation experiments, which demonstrate the effectiveness of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In this paper, we address the energy‐efficient connectivity problem of awireless sensor network(WSN) that consists of (1) staticsensor nodesthat have a short communication range and limited energy level, and (2)relay nodesthat have a long communication range and unlimited power supply, and that can be added or relocated arbitrarily. For such a WSN, existing studies have been focused on the design of efficient approximation algorithms to minimize the number of relay nodes. By contrast, we propose a unified backbone construction framework that can be performed in a centralized manner with two objectives: (1) to minimize the number of nodes in the backbone and (2) to maximize the lifetime of the network. To solve such a challenging problem, we formulate three subproblems: (1)partial dominating set with energy threshold(PDSET); (2)partial dominating set with largest residual energy(PDSLE); and (3)minimum relay node placement(MRNP). For these three subproblems, we develop polynomial‐time algorithms. We also prove that our algorithm for PDSLE is optimal, and our algorithm for the PDSET and MRNP problems have small approximation ratios. Numerical results show that the proposed framework can significantly improve energy efficiency and reduce backbone size. Copyright © 2012 John Wiley & Sons, Ltd.

In mobile ad hoc networks (MANETs), it is important to understand the throughput-delay trade-off (TD trade-off) problem in large-scale scenarios. In the literature, the TD tradeoff problem has been studied extensively and many of them are based on the independent and identically distributed (i.i.d.) mobility model, in which each node can randomly move to any place in the network, after every time slot. Although the i.i.d. model has been widely used, it cannot fully represent MANETs in which nodes change positions less frequently. To characterize such MANETs, in this paper, we propose a generalized i.i.d. (g.i.i.d.) mobility model, in which each node moves once after every 1/f (0 < f ≤ 1) time slots, and remains static between two moves. To investigate the TD trade-off under the g.i.i.d. model, we develop a novel multi-relay multi-hop (MRMH) scheme that exploits the opportunities of multi-hop transmissions when the network is static. Furthermore, to enable the multi-hop transmissions, we construct a new percolation highway system, which has not been used in the TD trade-off analysis for MANETs. Using the proposed MRMH scheme, we develop and prove constructive bounds for throughput and delay in MANETs with different scales of f. Our constructive bound is asymptotically optimal for f = 1 (i.e., the i.i.d. model).

To protect user privacy in wireless mesh networks (WMNs), it is important to address two major challenges, namely: flow untraceability and movement untraceability, which prevent malicious attackers from deducing the flow paths and the movement tracks of mobile devices. For these two privacy requirements, most existing approaches rely on encrypting the whole packet, appending random padding, and applying random delay for each message at every intermediate node, resulting in significant computational and communication overheads. Recently, linear network coding (LNC) has been introduced as an alternative but the global encoding vectors (GEVs) of coded messages have to be encrypted so as to conceal the relationships between the incoming and outgoing messages. In this paper, we aim to explore the potential of LNC to ensure the flow untraceability and movement untraceability. Specifically, we first determine the necessary and sufficient condition, with which the two privacy requirements can be achieved without encrypting either GEVs or message contents. We then design a deterministic untraceable LNC (ULNC) scheme to provide flow untraceability and movement untraceability when the sufficient and necessary condition is satisfied. Finally, we discuss the effectiveness of the proposed ULNC scheme against traffic analysis attacks in WMNs.

To efficiently archive and query data in wireless sensor networks (WSNs), distributed storage systems, and multisink schemes have been proposed recently. However, such distributed access cannot be fully supported and exploited by existing routing protocols in a large-scale WSN. In this paper, we will address this challenging issue and propose a distributed geographic $(K)$-anycast routing (GKAR) protocol for WSNs, which can efficiently route data from a source sensor to any $(K)$ destinations (e.g., storage nodes or sinks). To guarantee $(K)$-delivery, an iterative approach is adopted in GKAR where in each round, GKAR will determine not only the next hops at each node, but also a set of potential destinations for every next hop node to reach. Efficient algorithms are designed to determine the selection of the next hops and destination set division at each intermediate node. We analyze the complexity of GKAR in each round and we also theoretically analyze the expected number of rounds required to guarantee $(K)$-delivery. Simulation results demonstrate the superiority of the GKAP scheme in reducing the total duration and the communication overhead for finding $(K)$ destinations, by comparing with the existing schemes, e.g., $(K 1)$-anycast [10].

In this paper, we will address the modeling and optimal design of linear network coding (LNC) for secure unicast with multiple streams between the same source and destination pair. The objectives include 1) satisfying the weakly secure requirements, 2) maximizing the transmission data rate, and 3) minimizing the size of the finite field. To fulfill the first two objectives, we formulate a secure unicast routing problem and prove that it is equivalent to a constrained link-disjoint path problem. Based on this fact, we develop an efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. With the given topology, we investigate the design of both weakly secure deterministic LNC and weakly secure random LNC. In the designs of deterministic LNC and random LNC, we prove that the required size of the finite field decreases with the decrease of the number of intermediate nodes in the topology. Therefore, to meet the third objective, we formulate a problem to minimize the number of intermediate nodes. We prove that this problem is NP-Complete and develop an approximation algorithm to solve it. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

In most existing wireless networks, end users obtain data content from the wired network, typically, the Internet. In this manner, virtually all of their traffic must go through a few access points, which implies that the capacity of wireless network is limited by the aggregated transmission data rate of these access points. To fully exploit the capability of wireless network, we envision that future wireless networks shall be able to provide data content within themselves. In this paper, we address the behavior of such networks from a theoretical perspective. Specifically, we consider that multicast is used for distributed content delivery, and we investigate the asymptotic upper bound of the throughput capacity for distributed content delivery in large-scale wireless ad hoc networks (DCD-WANET). Our analysis shows how the upper bound of throughput capacity is affected by the geometric size of the network, the number of data items, the popularity of the data content, and the number of storage nodes that contain those data items. In particular, our theoretical results show that, if the number of storage nodes exceed a critical threshold, the upper bound grows with the number of storage nodes, according to a power-law where the scaling exponent depends on the popularity of data items. We also provide the data item placement strategy to achieve the upper bound of throughput capacity for DCD-WANET.

In this paper, we investigate the throughput capacity of large-scale wireless networks, in which three network-assisted coding schemes are considered: (1) multi-point-to-point coding (MPPC); (2) MPPC based network coding (NC); and (3) MPPC based physical-layer network coding (PLNC). This study is based on the generalized physical model, in which the transmission rate depends on the signal to noise and interference ratio (SINR). Such a model has not been used to analyze the behaviors of large-scale wireless networks with the aforementioned coding schemes. To understand the capacity gains of these schemes, we develop constructive lower bounds for one-dimensional (1D) and two-dimensional (2D) networks with size factor w, in which we construct novel wireless highway systems. This study shows that, compared to point-to-point coding (PPC), MPPC can improve the scaling law of network capacity when w exceeds a certain scale. In addition, this study reveals that MPPC based NC and PLNC can improve the capacity by constant factors. Specifically, NC can always obtain a gain of 2 in both 1D and 2D networks. On the other hand, the gain of PLNC can be larger than 2 in 1D networks, and can be up to 2 in 2D networks, depending on w, transmission power, noise, and path-loss of propagation.

For future wireless networks, a major challenge is how to efficiently support applications and services to end-users. Currently, most existing solutions attempt to address this issue on the application layer. In this paper, we take a different approach and we present a unified routing algorithm to efficiently enable applications in a Heterogeneous Wireless Mesh Network (HWMN). The algorithm builds upon service-oriented architecture (SOA) concepts, and more importantly, it provides confidential and reliable communication links over the mesh. Three techniques are implemented: key management, key shifting, and node relations. Such an approach provides a guideline to design secure and service-oriented HWMNs.

In this paper, we study the optimal design of linear network coding (LNC) for secure unicast against passive attacks, under the requirement of information theoretical security (ITS). The objectives of our optimal LNC design include (1) satisfying the ITS requirement, (2) maximizing the transmission rate of a unicast stream, and (3) minimizing the number of additional random symbols. We first formulate the problem that maximizes the secure transmission rate under the requirement of ITS, which is then transformed to a constrained maximum network flow problem.We devise an efficient algorithm that can find the optimal transmission topology. Based on the transmission topology, we then design a deterministic LNC which satisfies the aforementioned objectives and provide a constructive upper bound of the size of the finite field. In addition, we also study the potential of random LNC and derive the low bound of the probability that a random LNC is information theoretically secure.

Flow untraceability is one critical requirement for anonymous communication with network coding, which prevents malicious attackers with wiretapping and traffic analysis abilities from relating the senders to the receivers, using linear dependency of the received packets. There have recently been proposals advocating encryptions on the Global Encoding Vectors (GEV) of network coding to thwart such attacks [1], [2]. Nevertheless, there has been no exploration of the capability of networking coding itself, to constitute more efficient and effective algorithms which guarantee anonymity. In this paper, we design a novel, simple, and effective linear network coding mechanism (ALNCode) to achieve flow untraceability in a communication network with multiple unicast flows. With solid theoretical analysis, we first show that linear network coding (LNC) can be applied to thwart traffic analysis attacks without the need of encrypting GEVs. Our key idea is to mix multiple flows at their intersection nodes by generating downstream GEVs from the common basis of upstream GEVs belonging to multiple flows, in order to hide the correlation of upstream and downstream GEVs in each flow. We then design a deterministic LNC scheme to implement our idea, by which the downstream GEVs produced are guaranteed to obfuscate their correlation with the corresponding upstream GEVs. We also give extensive theoretical analysis on the intersection probability of GEV bases and the influential factors to the effectiveness of our scheme, as well as the algorithm complexity to support its efficiency.

Network coding has been proved as an effective way to enhance the throughput of the multihop wireless ad hoc networks in both unicast and multicast traffics. However, in a random network topology with non-coding-aware routing protocols, the performance enhancement may be limited because the packet routing scheme does not take advantage of the possibility of network coding. In this paper, we propose a Backbone ROuting with Network Coding (BRONC) scheme over a multihop wireless network, which combines the benefits of both backbone routing and network coding techniques. With backbone-based routing, all packets are forced to be transmitted over a constructed backbone. Because of the characteristics of bi-directional traffic flow and pre-specified routes in backbone routing, the possibility of coding packets at intermediate nodes can be substantially increased, and thus the benefit of network coding is fully exploited. Simulation results show that BRONC improves the coding opportunity significantly, and it outperforms the existing opportunistic coding schemes in terms of throughput, packet delivery ratio and transmission overhead.

In recent years network coding has attracted significant attention in telecommunication. The benefits of network coding to a communication network include the increased throughput as well as secure data transmission. The purpose of this work is to design secure linear network coding against wiretap attack. The problem is to maximize the transmission data rate of multiple unicast streams between a pair of source and destination nodes, under the condition of satisfying the weakly secure requirements. Different from most existing research on network coding that designs the network coding scheme based on a given network topology, we will consider the integrated network topology design and network coding design. Such an integrated approach has not been reported by other researchers. In this paper, we formally introduce the problem, prove the problem is computational intractable, and then develop efficient heuristic algorithms. We first try to find the transmission topology that is suitable for network coding. Based on the topology, we design linear network coding scheme that is weakly secure. We conduct simulations to show that the proposed algorithms can achieve good performance.

Minimizing the worst-case playback delay (WPD) in VoD services is both critical and challenging. Given a fixed amount of bandwidth for broadcasting and patching, there is no prior work on determining the minimum WPD, let alone guaranteeing it. In this work, we propose novel schemes that leverage the unique properties of a TDM-based Passive Optical Network (PON) by performing rebroadcasting and patching at its Optical Network Unit (ONUs). For a given bandwidth available for VoD services in the PON, we derive the minimum worst-case playback delay (WPD), and also design optimal patch scheduling algorithm as well as ONU rebroadcast and patching channel assignment to guarantee such minimum WPD. Numerical results confirm the superiority of the proposed schemes over the existing ones in terms of both worst-case and average performance.

Linear network coding is a promising technology that can maximize the throughput capacity of communication network. Despite this salient feature, there are still many challenges to be addressed, and security is clearly one of the most important challenges. In this paper, we will address the design of secure linear network coding. Specifically, we will investigate the network coding design that can both satisfy the weakly secure requirements and maximize the transmission data rate of multiple unicast streams between the same source and destination pair, which has not been addressed in the literature. In our study, we first prove that the secure unicast routing problem is equivalent to a constrained link-disjoint path problem. We then develop efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. Based on the topology, we design deterministic linear network code that is weakly secure and can be constructed at the source node. And finally, we investigate the potential of random linear code for weakly secure unicast and prove the low bound of the probability that a random linear code is weakly secure.

In the past few years, the capacity of multicast traffic in large-scale random wireless networks has attracted considerable attention because of the fundamental importance of network capacity and the multicast applications. However, there are still significant gaps between the upper bounds and the constructive lower bounds. In this paper, we develop a novel percolation highway system with which the constructive lower bounds can arbitrarily approach the upper bounds.

In the past few years, wireless mesh networks (WMNs) have drawn significant attention from academia and industry as a fast, easy, and inexpensive solution for broadband wireless access. In WMNs, it is important to support video communications in an efficient way. To address this issue, this paper studies the multipath routing for multiple description (MD) video delivery over IEEE 802.11 based WMN. Specifically, we first design a framework to transmit MD video over WMNs through multiple paths; we then investigate the technical challenges encountered. In our proposed framework, multipath routing relies on the maximally disjoint paths to achieve good traffic engineering performance. However, video applications usually have strict delay requirements, which make it difficult to find multiple qualified paths with the least joints. To overcome this problem, we develop an enhanced version of Guaranteed-Rate (GR) packet scheduling algorithm, namely virtual reserved rate GR (VRR-GR), to shorten the packet delay of video communications in multiservice network environment. Simulation study shows that our proposed approach can reduce the latency of video delivery and achieve desirable traffic engineering performance in multipath routing environment.

Video on demand (VoD) is one of the most important services for many network operators that deploy and operate optical access networks. It is crucial to design next generation optical access networks that can guarantee a high quality VoD service. In this paper, we address this challenging issue and focus on the worst-case playback delay (WPD), which cannot be guaranteed by Internet-based video streaming, and has not been well addressed previously in optical access networks. Specifically, we first propose an integrated Gigabit Passive Optical Network (GPON) and Wavelength Division Multiplexing PON (WDM PON) architecture. With the proposed architecture, an optical line terminal (OLT) can broadcast popular videos through GPON and deliver other videos through WDM-PON, while the optical network units (ONUs) can conduct patching for their end users. We then elaborate on two minimum-WPD schemes. In the first one, we assume that the video broadcast schedule is fixed at the OLT and develop an optimal patching scheme at each ONU such that the WPD is minimized. In the second one, we consider coordinated OLT broadcast scheduling and ONU patching. A heuristic algorithm which can achieve near-optimal WPD is proposed for coordinated OLT broadcast scheduling and ONU patching. Simulation results confirm the superiority of the proposed schemes over the existing ones in terms of both worstcase and average delay performance.

Network coding is a promising technology that can effectively improve the efficiency and capacity of multihop wireless networks by exploiting the broadcast nature of the wireless medium. However, current packet routing schemes do not take advantage of the network coding, and the benefits of network coding have not been fully utilized. To improve the performance gain of network coding, in this paper, we apply network coding over a wireless backbone and investigate the performance of this approach from a theoretical perspective. Our analysis shows that, compared to network coding over ad hoc networks with traditional routing schemes, network coding over the backbone structure exhibits significant advantages. This is because all packets are transmitted over the constructed backbone with pre-specified routes, and consequently the opportunity for coding packets at intermediate nodes can be substantially improved. To further enhance the performance, we also present an optimized link scheduling protocol for network coding over a wireless backbone. The performance results show that with proposed approach, the coding gain can achieve the theoretical bound in some scenarios.

While original network coding is proposed over the data link layer, recent work suggests that it can also be implemented on the physical layer. In fact it is more natural for wireless networks because of its omnidirectional transmission. In this paper, we investigate the symbol-error-rate (SER) for binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK), but the approaches can be generalized to other constellation schemes. The closed-form SER results are derived for physical layer network coding over AWGN channels. The theoretical analysis is also validated by numerical simulation.

In this paper, we discuss the channel coding design to support the two-phase physical-layer network coding (PLNC) under a typical three-node network coding scenario. We prove, even two source nodes could use different channel codes, the error correction capability at the relay node is bounded by that of the weaker one, i.e. the code with a smaller minimum distance. Therefore, we propose to use the same channel code at two source nodes, which can also provide potential to simplify the joint PLNC-channel decoding procedure (PLNC-DEC) at the relay node. Moreover, we propose a channel coding scheme based on linear convolutional codes to relax the strict synchronization requirement, which is difficult to achieve in some real applications. The proposed coding scheme is robust to the synchronization error between two source nodes and enables the relay node to perform the PLNC-DEC with only one Viterbi decoding procedure. Compared to the three-phase network coding scheme, the proposed scheme reduces the decoding/demodulation complexity of the relay node almost by half and achieves significant throughput gain.

In this paper, we propose a backbone construction scheme over heterogeneous ad hoc networks, where the network nodes have different characteristics such as communication capacity, processing power and energy resource. Most of the wireless backbone construction techniques focus on minimizing the number of backbone nodes. In our proposed scheme, we not only minimize the backbone size, but also take the characteristics of nodes into account when building a backbone. In the scheme, the more capable nodes have higher probability to serve as backbone nodes and provide a wireless highway over which end-to-end communication can take place. The proposed scheme includes two major steps, which can be solved by formulating as a Dominating Set (DS) problem and a Steiner Tree Problem with Minimum Number of Sterner Points (STP-MSP) respectively. We focus on the two subproblems and present a number of polynomial time approximation algorithms. Simulation results show that the proposed scheme achieves higher average backbone node performance, while has approximately the same backbone size comparing with other schemes.

Physical-layer network coding (PLNC) is a novel wireless communication technology, in which multiple transmitters can send signals on the same channel to the same receiver at the same time. Our previous studies have revealed that PLNC can substantially improve the throughput performance of the whole network. In this paper, we address the security performance of PLNC. In particular, we investigate the symbol error performance of a potential eavesdropper in the PLNC system. Extensive simulation studies show that PLNC can provide security means against passive eavesdroppers.

Throughput capacity of a random wireless network has been studied extensively in the literature. Most existing studies were based on the assumption that each transmission involves only one transmitter in order to avoid interference. However, recent studies on physical-layer network coding (PLNC) have shown that such an assumption can be relaxed to improve throughput performance of a wireless network. In PLNC, signals from different senders can be transmitted to the same receiver in the same channel simultaneously. In this paper, we investigate the impact of PLNC on throughput capacity of a random wireless network. Our study reveals that, although PLNC scheme does not change the scaling law, it can improve throughput capacity by a fixed factor. Specifically, for a one-dimensional network, we observe that PLNC can eliminate the effect of interference in some scenarios. A tighter capacity bound is derived for a two-dimensional network. In addition, we also show achievable lower bounds for random wireless networks with network coding and PLNC.

In this paper, we address the topology control issue for future wireless mesh networks (WMNs). In particular, we propose a novel topology control scheme that attempts to maximize the overall throughput in the network with random unicast traffic demands. The main idea of the scheme is to establish multiple semi-permanent wireless highways, each of which can convey the traffic for nodes along the highways. To evaluate the performance of the proposed scheme, we conduct theoretical analysis, which demonstrates that viable solutions for highways do exist with high probability. The theoretical analysis also proves the optimality. Within the new topology control framework, we demonstrate that advanced technologies, including network coding and physical- layer network coding (PLNC), can be applied to substantially improve the throughput capacity of the network.

Vehicular ad hoc networking is an important component of Intelligent Transportation Systems. The main benefit of vehicular ad hoc network (VANET) communication is seen in active safety systems that increase passenger safety by exchanging warning messages between vehicles. Other applications and private services are also permitted in order to lower the cost and to encourage VANET deployment and adoption. Dedicated Short Range Communications (DSRC) is a key enabling technology for VANET applications and services. There are many challenges that must be addressed before VANETs can be successfully deployed. Among these challenges is designing of security mechanisms to secure VANETs against abuse, and designing of efficient medium access control (MAC) protocols so that safety related and other application messages can be timely and reliably disseminated through VANETs. In this paper we propose a secure MAC protocol for VANETs, with different message priorities for different types of applications to access DSRC channels. Our simulations and analysis show that the proposed MAC protocol can provide secure communications while guarantee the reliability and latency requirements of safety related DSRC applications for VANETs.

Since the pioneer work by Gupta and Kumar, the throughput capacity of random wireless networks has been studied extensively in the literature. Nevertheless, most existing studies are based on the assumption that each node can receive at most one transmission at a time. However, several recent studies have shown that such a constraint can be relaxed. Particularly, with physical-layer network coding, one node can receive more than one transmission from different transmitters simultaneously. In this paper, we investigate the impact of physical-layer network coding on the throughput capacity of random wireless networks. Our analysis show that the physical-layer network coding scheme can improve the throughput capacity but cannot change the scaling law. Specifically, for one-dimensional random wireless network, our analysis provides the capacity of network with physical-layer network coding. For two-dimensional random wireless networks, we derive tighter capacity bounds for existing transmission schemes, as well as the bounds for physical-layer network coding.

In wireless mesh network (WMN), it is important to provide an efficient handoff scheme, due to the frequent user mobility. To address this issue, we propose a mobile agent (MA) based handoff approach, where each mesh client has a MA residing on its registered mesh router. To guarantee quality of service (QoS) and achieve differentiated priorities during the handoff, we develop a proportional threshold structured optimal effective bandwidth policy for call admission control (CAC) on the mesh router. Simulation study shows that our proposed CAC scheme can obtain satisfying tradeoff between differentiated priorities and statistical effective bandwidth in WMN handoff environment.

In this paper, we propose a distributed topology control (DTC) and the associated inter-layer interfacing architecture for efficient channel-interface resource allocation in multi-channel multi-radio (MCMR) mesh networks. The proposed solution is (i) routing agnostic but traffic adaptive; (ii) it fully achieves channel multiplexing over multiple interfaces; (iii) its well-defined over-the-air signaling mechanism can be incorporated with various distributed topology optimization algorithms; and (iv) it is fairly PHY/MAC-agnostic and can be integrated with various mesh access technologies.

In this paper, we focus on the problem of effectively supporting a large number of small group multicasts in optical burst switched (OBS) networks. We first propose a multicast scheme for small group multicast in OBS networks. To reduce burst loss due to potential burst contentions, a deflection routing scheme for multicast is proposed. We then develop an analytical model to evaluate the performance of the multicast scheme and the deflection routing scheme. To the best of our knowledge, this is the first analytical model proposed for the problem of multicast with deflection routing. The model is very general in the sense that it can handle unicast traffic, multicast traffic, and the mixture of unicast and multicast traffic, with or without deflection routing. The analytical model is verified through simulations. Numerical results show that the analytical model is accurate, and that, under low or medium network load, deflection routing for multicasting can significantly reduce burst loss while slightly increasing burst delay.

Abstract Despite the rapid advance in networking technologies, detection of network anomalies at high‐speed switches/routers is still far from maturity. To push the frontier, two major technologies need to be addressed. The first one is efficient feature‐extraction algorithms/hardware that can match a line rate in the order of Gb/second; the second one is fast and effective anomaly detection schemes. In this paper, we focus on design of efficient data structure and algorithms for feature extraction. Specifically, we propose a novel data structure that extracts the so‐called two‐directional (2D) matching features, which are shown to be effective indicators of network anomalies. Our key idea is to use a Bloom filter array (BFA) to trade‐off a small amount of accuracy in feature extraction, for much less space and time complexity, so that our data structure can catch up with a line rate in the order of Gb/second. Different from the existing work, our data structure has the following properties: (1) it dynamic Bloom filter, (2) combination of a it sliding window with Bloom filter, and (3) using an insertion–removal pair to enhance Bloom filter with a removal operation. Our analysis and simulation demonstrate that the proposed data structure has a better space/time trade‐off than conventional algorithms. For example, for a fixed time complexity, the conventional algorithm (i.e., hash table [1—8]) requires a memory of 1.01 Gbits while our data structure requires a memory of only 62.9 Mbits, at the cost of losing 1% accuracy in feature extraction. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we investigate optimal key management design for secure and survivable heterogeneous wireless sensor networks (HWSNs). In particular, we formulate the key management problem as a multi‐objective optimization problem, in which the cost of the sensor network, and the security and survivability metrics of the sensor network are taken into account. To solve the multi‐objective optimization model, we develop a genetic algorithm (GA)‐based approach that can efficiently obtain near‐optimal solutions. We show the performance of our scheme through extensive numerical results. With a small amount of powerful sensor nodes in HWSN, we can balance the cost of the sensor network and the resilience of the sensor network with the required security constraint in different hostile environment. Copyright © 2008 John Wiley & Sons, Ltd.

Key management has become a challenging issue in the design and deployment of secure wireless sensor networks. A common assumption in most existing distributed key management schemes is that all sensor nodes have the same capability. However, recent research works have suggested that connectivity and lifetime of a sensor network can be substantially improved if some nodes are given greater power and transmission capability. Therefore, how to exploit those heterogeneity features in design of a good distributed key management scheme has become an important issue. This paper proposes a unified framework for distributed key management schemes in heterogeneous wireless sensor networks. Analytical models are developed to evaluate its performance in terms of connectivity, reliability, and resilience. Extensive simulation results show that, even with a small number of heterogeneous nodes, the performance of a wireless sensor network can be improved substantially. It is also shown that our analytical models can be used to accurately predict the performance of wireless sensor networks under varying conditions.

Wireless mesh network (WMN) has recently emerged as a promising technology for next-generation wireless networking. In WMNs, many important applications, such as mobile TV and video/audio conferencing, require the support of multicast communication with quality-of-service (QoS) guarantee. In this paper, we address the QoS multicast routing issue in WMNs. Specifically, we propose a novel network graph preprocessing approach to enable traffic engineering and enhance the performance of QoS multicast routing algorithms. In this approach, we employ prioritized admission control scheme and develop a utility-constrained optimal priority gain policy. Extensive simulation results show that our approach can significantly improve the performance of QoS multicast routing in WMNs.

In wireless ad-hoc networks, a major challenge is how to provide robust and efficient communication. To achieve this goal, cooperative communication and network coding have been proven to be effective. In the literature, most existing studies focus on the performance of the two schemes separately. In our study, we will investigate the performance of system that combines them tightly together. In particular, we will propose a unified two-way traffic model that can characterize the features of both of them. Based on this model, we develop a new cooperative network coding scheme to further improve the system throughput. Both decode-and-forward and amplify-and-forward techniques are discussed for the two-way traffic model. Numerical results show that the new scheme can significantly improve the performance over traditional schemes.

In this paper, we propose a novel retransmission scheme that can substantially increase the throughput of wireless local area networks (WLANs). The main idea of the scheme is to enable cooperative communication in the medium access control (MAC) layer such that one node can retransmit messages for its neighboring nodes if the delivery of those messages failed previously due to transmission error. In addition, we also redesign the transmission and acknowledgement policy of the existing IEEE 802.11 protocol in that 1) an upper layer packet will be partitioned into blocks, and 2) the receiver of a message can acknowledge individual blocks through negative acknowledgement (NACK). To evaluate the performance of the proposed scheme, we conduct extensive simulation study under various conditions, such as number of nodes, size of packet, and bit error rate. Simulation results show that, the proposed scheme can significantly increase the throughput performance of WLANs under these conditions, and an optimal block size can lead to the highest throughput in each specific scenario.

In this paper, we study the design of future wireless ad hoc networks. Particularly, we consider that the future wireless ad hoc networks shall be able to efficiently provide connectivity and support a variety of quality of service requirements, such as bandwidth and delay requirements. To address these issues, we propose a novel framework for the design of future wireless ad hoc networks. The key idea of this framework is to establish an overlay hypernetwork, which consists of multiple hyperchannels, each of which can provide connectivity for multiple nodes in the hyperchannel. By comparison, a typical tunnel in existing overlay network schemes can only provide connectivity for two end nodes. Within the framework, we also develop advanced network coding design that can further improve the performance of the network in terms of throughput and delay.

With the advances in wireless communication technologies, wireless sensor networks (WSNs) are becoming more and more attractive because they can provide services that are not possible or not feasible before. In this paper, we address the design issues of an important type of WSN, i.e., WSNs that enable environmental monitoring applications. We first provide an overview and analysis for our ongoing research project about the WSN for coastal area acoustic monitoring. Based on the analysis, we then propose a novel framework that can be used to guide the design of future WSNs that provide environmental monitoring services. The focus of the framework is the network layer design. In our framework, we consider that 1) the future WSN shall be heterogeneous, 2) the network layer design shall better meet the requirements of applications and services, 3) the network layer design shall be able to utilize advanced wireless communication technologies, and 4) the network layer can provide the monitoring functionality.

In this paper, we investigate optimal key management design for secure and survivable heterogeneous wireless sensor networks. In particular, we formulate the key management problem as a multi-objective optimization problem, in which the cost of the sensor network, and the security and survivability metrics of the sensor network are taken into account. To solve the multi-objective optimization model, we develop a genetic algorithm (GA) based approach that can efficiently obtain near- optimal solutions. We show the performance of our scheme through extensive numerical results. With a small amount of powerful sensor nodes in heterogeneous wireless sensor network, we can balance the cost of the sensor network and the resilience of the sensor network with the required security constraint in different hostile environment.

In recent years, grid computing applications are becoming more and more important to the scientific and business communities. For many grid applications that require the exchange of a huge amount of data, optical networks are the nature choices because they can provide tremendous bandwidth. In our previous study, we have shown that anycast routing scheme can efficiently support many emerging high-performance grid applications in OBS networks. In this paper, we first design a label-based control framework for anycast routing in all-optical networks. We then develop an analytical model to evaluate the performance of the proposed anycast scheme. Extensive simulation and numerical results show that our analysis is rather accurate. Moreover, both simulation and analysis demonstrate that the anycast scheme can significantly improve the performance of grid applications, in terms of both loss and delay performance.

WiMAX (Worldwide Interoperability for Microwave Access) is a promising technology for last-mile broadband Internet access. In multiservice WiMAX networks, call admission control (CAC) plays a critical role. In this paper, we address CAC problem from the perspectives of both WiMAX service providers and subscribers. Specifically, we formulate CAC as an optimization problem, in which the demands of service providers and subscribers are taken into account. To solve the optimization problem, we develop a utility and fairness constrained greedy revenue algorithm. Simulation results show that the proposed approach has satisfying performance.

In this paper, we present a comprehensive study on the design of secure and survivable wireless sensor networks (WSNs). Our goal is to develop a framework that provides both security and survivability features that are crucial to applications in WSNs, which are vulnerable to physical and network based security attacks, accidents, and failures. To achieve such a goal, we first examine the security requirements and survivability requirements. We then propose an architecture for security and survivability in WSNs with heterogeneous sensor nodes. To understand the interactions between survivability and security, we also design and analyze a key management scheme. The experiment results show that 1) a good design can improve both security and survivability of WSNs; and 2) in some situation, there is a trade-off between security and survivability.

Distributed lightpath provisioning is expected to play a key role in next-generation WDM optical networks. A major challenge in distributed lightpath provisioning is the potentially significant degradation of network blocking performance caused by outdated link-state information, occurring especially under traffic with short average durations of connections. To address this problem, various parallel reservation schemes have been proposed, with the common feature of applying multiple capacity-search and/or reservation operations executed simultaneously. In this paper, we evaluate the performance of these distributed parallel reservation schemes in wavelength-routed networks. Specifically, we develop general yet accurate analytical models to provide insights into the behavior of the different schemes. We also conduct extensive simulation study. Numerical results show that by using simple parallel reservation schemes, in particular through multiple reservations on several different routes, blocking probabilities caused by outdated link-state information can be drastically lowered and network performance can be significantly improved. The tradeoff between control traffic loads and network blocking performance is also evaluated.

In this paper, we propose a novel downlink resource management framework for multiservice WiMAX (worldwide interoperability for microwave access) networks. Our framework consists of two major components: adaptive power allocation (APA) and call admission control (CAC). We formulate each of them as an optimization problem, where the demands of both WiMAX service providers and subscribers are taken into account. To solve the optimization problems, we develop a fairness-constrained greedy revenue algorithm for downlink APA optimization and a utility-constrained greedy approximation algorithm for downlink CAC optimization. Our simulation results show that, when combining the APA and CAC optimization methods together, the proposed resource management framework can meet the expectations of both service providers and subscribers

In this paper, we focus on the problem of effectively supporting a large number of small group multicasts in optical burst switched (OBS) networks. We first propose a multicast scheme for small group multicast in OBS networks. To reduce burst loss due to potential burst contentions, a deflection routing scheme for multicast is proposed. We then develop a generic analytical model to evaluate the performance of the multicast scheme and the deflection routing scheme. To the best of our knowledge, this is the first analytical model proposed for the problem of multicast with deflection routing. The analytical model is verified through simulations. Numerical results show that the analytical model is accurate and that deflection routing can significantly reduce burst loss probability while slightly increasing burst delay.

In IEEE 802.16 mesh networks, a major issue is the performance of coordinated distributed scheduling schemes. To evaluate these schemes, most existing studies in the literature are based on the assumption that the control messages can be transmitted without collision in the extended neighborhood (2-hop or 3-hop). However, in practice, such kind of quasi-interference model might not hold. In this paper, we investigate the performance of coordinated distributed scheduling in IEEE 802.16 mesh networks under realistic, non-quasi-interference model, in which control messages can be interfered. Extensive simulation studies have been conducted to evaluate the performance of the scheduling algorithms in terms of reception collision ratio, etc. We observe that, in realistic scenarios, the collision ratio of control messages can be as high as 20% for 2-hop extended neighborhood, and about 7% for 3-hop extended neighborhood, which is significant. To deal with the collision problems, we then study how to select scheduling parameters. Our studies show that, an appropriate configuration of parameters such as XmtHoldoffExponent may alleviate the collision ratio and hence can potentially improve the overall scheduling performance.

Despite the rapid advance in networking technologies, detection of network anomalies at high-speed switches/routers is still far from maturity. To push the frontier, two major technologies need to be addressed. The first one is efficient feature-extraction algorithms/hardware that can match a line rate in the order of Gb/s; the second one is fast and effective anomaly detection schemes. In this paper, we focus on design of efficient data structure and algorithms for feature extraction. Specifically, we propose a novel data structure that extracts so-called two-directional (2D) matching features, which are shown to be effective indicators of network anomalies. Our key idea is to use a Bloom filter array to trade off a small amount of accuracy in feature extraction, for much less space and time complexity, so that our data structure can catch up with a line rate in the order of Gb/s. Different from the existing work, our data structure has the following properties: 1) dynamic Bloom filter, 2) combination of a sliding window with the Bloom filter, and 3) using an insertion-removal pair to enhance the Bloom filter with a removal operation. Our analysis and simulation demonstrate that the proposed data structure has a better space/time trade-off than conventional algorithms. For example, for a fixed time complexity, the conventional algorithm (i.e., hash table [1]) requires a memory of 1.01G bits while our data structure requires a memory of only 62.9M bits, at the cost of losing 1% accuracy in feature extraction.

In this paper, we propose a novel nonlinear transform scheme to reduce the Peak-to-Average Power Ratio (PAPR) in multi-carrier modulation (MCM) systems. The key idea of the proposed scheme is to transform the original MCM signals such that the amplitude or the power of the companded signals follows uniform distribution. In this manner, the proposed scheme can effectively reduce the PAPR for different modulation formats and sub-carrier sizes without increasing the system complexity and signal bandwidth. Extensive simulation results show that the proposed schemes can significantly improve the performance of MCM systems.

In this paper, we propose a novel scheme to improve the performance of wireless local area networks (WLANs) in error-prone environment. The key idea of the scheme is to partition a frame into several blocks, and then to retransmit only the blocks that encountered errors in the previous transmission. To evaluate the performance of the scheme, we also develop an analytical model to analyze the saturated throughput performance. Extensive simulation and analysis results show that, 1) the proposed scheme can significantly improve the throughput performance of WLANs in error-prone conditions; 2) an optimum block size can lead to the maximum saturated throughput; 3) with an appropriate block size, the throughput can increase with the increase of the size of a frame, even in error-prone environment; and 4) the analytical results are highly accurate.

In the past few years, wireless local area networks (WLANs) have been widely deployed, where the most important standard is IEEE 802.11. To support high data rate applications in the next generation WLANs, a common approach is to aggregate multiple upper layer packets into one large frame in the MAC layer. However, with the increase of frame size, the frame error rate will also be increased in error-prone wireless environments. Therefore, existing frame retransmission scheme may not be efficient since the entire frame will be retransmitted. In this paper, we study a retransmission scheme that is suitable for the aggregation-based MAC protocol, in which only the packets that encounter transmission errors will be retransmitted. The main contribution of our study is to develop an analytical model for evaluating the saturated throughput performance of the MAC protocol. Extensive simulation and analytical results show that, our model is highly accurate and the proposed retransmission scheme can significantly improve the throughput performance in error-prone wireless channels.

Key management is a major challenge in the design and deployment of secure wireless sensor networks. A common assumption in most distributed key management schemes is that all sensor nodes have the same capability. However, recent research work has shown that the connectivity and lifetime of the sensor network can be substantially improved if a small number of sensor nodes have more energy capacity and transmission capability. Therefore, how to utilize these heterogeneity features to design a good distributed key management scheme has become an important issue and needs to be explored. In this paper, we propose a framework for key management schemes in distributed wireless sensor networks with heterogeneous sensor nodes. We show by simulations analysis that, with a small number of heterogeneous nodes, the wireless sensor network can achieve higher key connectivity and higher resilience

Ultra-wideband (UWB) is a promising technology that can support high data rate communication for future Wireless Personal Area Networks (WPANs). To provide high throughput in UWB networks, we proposed a general framework for CSMA/CA based MAC protocol previously [17]. In this framework, multiple upper layer packets can be assembled into a single burst frame at the MAC layer, which can significantly improve the throughput performance by reducing overheads. Nevertheless, the burst assembly procedure may introduce extra packet delay, which is undesirable for some applications. In this paper, we address the performance issue in the burst-frame-based MAC protocol. In particular, we develop an analytical model to evaluate the delay performance of the burst-frame-based MAC protocol under unsaturated conditions. Our delay analysis is unique in that we consider the end-to-end packet delay, which is the duration from the epoch that a packet enters the queue at the MAC layer of the transmitter side to the epoch that the packet is successfully received at the receiver side. The analytical results give excellent agreement with the simulation results, which represents the accuracy of our analytical model. The results also provide important guideline on how to set the parameters of the burst assembly policy. Based on these results, we develop an efficient adaptive burst assembly policy so as to optimize the throughput and delay performance of the burst-frame-based CSMA/CA protocol.

Packet contention is a major issue in asynchronous optical packet switching networks. Optical buffering, which is implemented by fiber delay lines (FDLs), is fundamental to many optical switch implementations for resolving contention. Most existing optical buffering implementations are output-based and require a huge amount of FDLs as well as larger switch sizes, which impose extra cost on the overall system. In this paper, we consider a shared optical buffering architecture which can reduce the buffer size at a switch. We propose an analytical model to evaluate the packet loss probability and the average delay For shared buffers at a single switch. We then compare the performance of output buffers to shared buffers under different granularities of FDLs. We observe that, by choosing an appropriate granularity, the shared buffering scheme can significantly reduce packet loss with much smaller switch sizes and fewer FDLs than the output buffering architecture. The accuracy of the analytical model is also confirmed by extensive simulation.

IEEE 802.11 is the most important standard for wireless local area networks (WLANs). In IEEE 802.11, the fundamental medium access control (MAC) scheme is distributed coordination function (DCF), whose performance has been studied analytically in the literature. However, to the best of the authors' knowledge, there is no accurate model that takes into account both the incoming traffic loads and the effect of bit transmission errors, which, in addition to collision, can also result in unsuccessful packet delivery. In this paper, we address this issue and provide a new analytical model to evaluate the performance of DCF in binary symmetric channels (BSCs). In our study, we consider the impact of different factors together, including the binary exponential backoff mechanism in DCF, various incoming traffic loads, distribution of incoming packet size, queueing system at the MAC layer, and the packet transmission errors, which has never been done before. Extensive simulation and analysis results show that our analytical model can accurately predict the delay and throughput performance of IEEE 802.11 DCF under different traffic and transmission error conditions.

Ultra-wideband (UWB) communication is becoming an important technology for future wireless personal area networks (WPANs). A critical challenge in high data rate UWB system design is that a receiver usually needs tens of micro-seconds or even tens of milliseconds to synchronize with the transmitted signals, known as the timing acquisition problem. Such a long synchronization time will cause significant overhead, since the data rate of UWB systems is expected to be very high. To address the overhead problem, we previously proposed a general framework for MAC protocols in high data rate UWB networks. In this framework, a node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. In this paper, we analyze the unsaturated throughput performance of a burst-frame-based MAC protocol within the framework. Numerical results from the analytical method give excellent agreement with the simulation results, indicating the accuracy of our analytical method.

Packet contention is a major issue in asynchronous optical packet and burst switching networks. Optical buffering, which is implemented by fiber delay lines (FDLs), is fundamental to many optical switch implementations for resolving contention. Most existing optical buffering implementations are output-based and require a huge amount of FDLs as well as larger switch sizes, which impose extra cost on the overall system. In this paper, we consider shared optical buffering which can reduce the buffer size at a switch. Since no previous study is available to analyze the performance of asynchronous architectures with shared buffers, we propose an analytical model to evaluate the packet loss probability and the average delay for shared buffers at a single switch. We then compare the performance of output buffers to shared buffers under different granularities of FDLs. We observe that, by choosing an appropriate granularity, the shared buffering scheme can significantly reduce packet loss with much smaller switch sizes and fewer FDLs than the output buffering architecture. The accuracy of the analytical model is also confirmed by extensive simulation.

A critical challenge in ultra-wideband (UWB) system design is that a receiver usually needs tens of microseconds or even tens of milliseconds to synchronize with transmitted signals; this is known as the timing acquisition problem. Such a long synchronization time causes significant overhead, since the data rate of UWB systems is expected to be very high. We address the timing acquisition problem at the medium access control (MAC) layer, and propose a general framework for medium access control in UWB systems; in this framework, a transmitting node can aggregate multiple upper-layer packets into a larger burst frame at the MAC layer. Furthermore, we design a MAC protocol based on the framework, and analyze its saturation throughput performance. Compared to sending each upper-layer packet individually, which is a typical situation in exiting MAC protocols, the proposed MAC can drastically reduce the synchronization overhead. Numerical and simulation results show that the proposed MAC can significantly improve the performance of UWB networks, in terms of both throughput and end-to-end delay.

In this correspondence, we present systematic and closed-form constructions of complex orthogonal space-time block codes from complex orthogonal designs of rates (k+1)/2k for 2k-1 or 2k transmit antennas for any positive integer k.

In this paper, we analyze the blocking probability of distributed lightpath establishment in wavelength-routed WDM networks by studying the two basic methods: destination-initiated reservation (DIR) and source-initiated reservation (SIR). We discuss three basic types of connection blocking: 1) blocking due to insufficient network capacity; 2) blocking due to outdated information; and 3) blocking due to over-reservation. It is shown that the proposed models are highly accurate for both the DIR and the SIR methods, in both the regular and irregular network topologies, under the whole range of traffic loads.

In this paper, we provide a new analytical model for evaluating the blocking performance of dynamic lightpath establishment in multifiber wavelength-routed networks. By adopting the simple link-independent model together with the wavelength correlation assumptions, we manage to achieve a good balance between analytical accuracy and computational complexity. Extensive numerical results show that the proposed model can quickly produce accurate analytical results under different traffic loads and in different networks.

In this paper, we propose a framework for providing differentiated contention resolution in photonic packet-switched networks by exploiting recirculation buffering and deflection routing. We develop an analytical model to evaluate the packet loss probability and the end-to-end delay for different buffering and deflection routing schemes, and we investigate the effectiveness of the control schemes in providing differentiated loss and delay. The accuracy of the analytical model is confirmed by simulation.

This paper presents a closed form designs of complex orthogonal space-time block codes of rates (k + I)/(2k) for 2k-1 or 2k transmit antennas

In this work, we propose a new distributed signaling scheme, within the GMPLS framework for establishing lightpaths in wavelength-routed networks with sparse wavelength conversion. Analytical models are developed to evaluate the performance of the proposed scheme. Theoretical and simulation results show that compared to the classic schemed designed primarily for networks with no wavelength conversion, the proposed signaling scheme can achieve much lower blocking probability.

Distributed wavelength provisioning is becoming one of the most important technologies for supporting the next-generation wavelength-routed networks. In this paper we analyze the behavior of wavelength-routed networks with partial wavelength conversion capabilities (i.e., where wavelength conversion is available at only a subset of network nodes) when using distributed wavelength provisioning. Simulation results show that the proposed models are highly accurate for different network topologies under various traffic loads.

A problem of many distributed lightpath provisioning schemes is wavelength contention, which occurs when a connection request attempts to reserve a wavelength channel that is no longer available. This situation results from the lack of updated global link-state information at every node. In networks with highly dynamic traffic loads, wavelength contention may seriously degrade the network performance. To overcome this problem, we propose a new framework for distributed signaling and introduce a class of schemes referred to as intermediate-node initiated reservation. In the new scheme, reservations may be initiated at any set of nodes along the route; in contrast, reservations can only be initiated by the destination node in the classic destination initiated reservation (DIR) scheme. As a result, the possibility of having outdated information due to propagation delay is significantly lowered. Specifically, we consider two schemes within this framework, for networks with no wavelength conversion and for networks with sparse wavelength conversion, respectively. Theoretical and simulation results show that, compared with the classic DIR scheme, the new schemes can significantly improve the network blocking performance. The accuracy of the analytical models is also confirmed by extensive numerical simulations.

In this paper, we analyze the blocking probability of dynamic lightpath establishment in wavelength-routed networks. By using the destination-initiated reservation (DIR) method as a case study, we analyze traffic blocking occurring due to insufficient network capacity as well as traffic blocking caused by outdated information. Simulation results show the proposed models to be highly accurate.