With the rapid growth of wearable devices and the Internet of Things (IoT), users increasingly rely on smartwatches and fitness trackers for services in connected environments. These devices collect sensitive physiological and behavioral data, requiring reliable user authentication to ensure secure access by authorized users. However, few-shot scenarios with significant intra-class variability and limited training data make it challenging for models to capture complex feature interactions. In this paper, we propose a Ratio-Enhanced Feature Interaction (RE-FI) framework designed for wearable running data. RE-FI systematically constructs pairwise product and ratio-based cross features to explicitly model second-order interactions and applies information gain–based feature selection to retain the most discriminative features. We provide theoretical justification for these design choices across linear models, tree-based classifiers, and deep neural networks. We validate RE-FI on real-world running data using nine representative classifiers. Results show that RE-FI consistently outperforms the baseline feature set across all classifiers, achieving an average accuracy improvement of 4.2% and up to 6.2% in F1-score. In extreme few-shot settings with only five samples per user, RE-FI maintains robust performance, with Random Forest achieving an average accuracy of 86%. Overall, this work offers a computationally simple feature-interaction solution that is compatible with diverse supervised models on session-level wearable statistical features and provides a principled approach for wearable security and personalized health monitoring in IoT applications.

Machine learning-based Android malware detection (AMD) models require abundant data for training robust app classifiers, creating vulnerability to poisoning attacks. Attackers inject poisoned samples into Android app markets, leading to the insertion of a backdoor into the model upon adoption in the training process. Subsequently, attackers can generate evasive malware by embedding a backdoor trigger in malware samples. Currently, research on backdoor attacks towards AMD has just begun to emerge. Existing attacks produce a fixed trigger and apply it to various malware. Once the trigger is discovered by static analysis methods (e.g., software similarity analysis), however, multiple malware carrying this trigger will be simultaneously exposed. To diversify the trigger, we propose anAPK-SpecificBackdoorAttack algorithm (ASBA), which trains a generative adversarial network to generate a specific trigger for every malware sample. Moreover, ASBA manages to make the generated triggers as different as possible, in order to further reduce the likelihood of malware being collectively captured. Extensive experiments have demonstrated that ASBA achieves a 94.6% average attack success rate (ASR) on three datasets, five feature extraction methods and three classification models. Furthermore, compared to state-of-the-art poisoning attack algorithms, ASBA produces more diverse and more effective triggers.

Recent years have witnessed the proliferation of learning-based Android malware detectors. These detectors can be categorized into three types, String-based, Image-based and Graph-based. Most of them have achieved good detection performance under the ideal setting. In reality, however, detectors often face out-of-distribution samples due to the factors such as code obfuscation, concept drift (e.g., software development technique evolution and new malware category emergence), and adversarial examples (AEs). This problem has attracted increasing attention, but there is a lack of comparative studies that evaluate the existing various types of detectors under these challenging environments. In order to fill this gap, we select 12 representative detectors from three types of detectors, and evaluate them in the challenging scenarios involving code obfuscation, concept drift and AEs, respectively. Experimental results reveal that none of the evaluated detectors can maintain their ideal-setting detection performance, and the performance of different types of detectors varies significantly under various challenging environments. We identify several factors contributing to the performance deterioration of detectors, including the limitations of feature extraction methods and learning models. We also analyze the reasons why the detectors of different types show significant performance differences when facing code obfuscation, concept drift and AEs. Finally, we provide practical suggestions from the perspectives of users and researchers, respectively. We hope our work can help understand the detectors of different types, and provide guidance for enhancing their performance and robustness.

Deep neural network (DNN) based website fingerprinting (WF) attacks can achieve an attack success rate (ASR) of over 90%, seriously threatening the privacy of Tor users. At present, adversarial example (AE) based defenses have demonstrated great potential to defend against WF attacks. However, existing AE-based defenses require knowing a complete traffic trace for adversarial perturbation calculation, which is unrealistic in practice. Moreover, they may become ineffective once adversarial training (AT) is adopted by attackers. To mitigate these two problems, we propose a defense called ALERT. It generates adversarial perturbations without knowing traffic traces, and can effectively resist AT-aided WF attacks. The key idea of ALERT is to produce universal perturbations that vary from user to user. We conduct extensive experiments to evaluate ALERT. In the closed world, ALERT significantly surpasses four representative WF defenses, including the state-of-the-art (SOTA) defense AWA. Specifically, ALERT reduces the ASR of the SOTA DF attack to 12.68% and uses only 20.13% of communication bandwidth. In the open world, ALERT uses only 19.91% of bandwidth, reduces the True Positive Rate (TPR) of the DF attack to 37.46%, obviously outperforming the other defenses.

With the widespread application of machine learning-based Android malware detection methods, building a high-quality dataset has become increasingly important. Existing large-scale datasets are mostly annotated with VirusTotal by aggregating the decisions of antivirus engines, and most of them indiscriminately accept the decisions of all engines. In reality, however, these engines have different capabilities in detecting malware, especially those that have been obfuscated. Previous research has revealed that code obfuscation degrades the detection performance of these engines to varying degrees. This makes us believe that using all engines indiscriminately is unreasonable for dataset annotation. Therefore, in this paper, we first conduct a data-driven evaluation to confirm the negative effects of code obfuscation on engine-based dataset annotation. To gain a deeper understanding of the reasons behind this phenomenon, we evaluate the availability, effectiveness and robustness of every engine under various code obfuscation techniques. Then we categorize the engines and select a set of obfuscation-robust engines. Finally, we conduct comprehensive experiments to verify the effectiveness of the selected engines for dataset annotation. Our experiments show that when 50% obfuscated samples are mixed into the training set, on the classic malware detectors Drebin and Malscan, using our selected engines can effectively improve detection performance by 15.21% and 19.23%, respectively, compared to using all the engines.

Adversarial patches can mislead object detectors to produce erroneous predictions. To defend against adversarial patches, one can take two types of protections on the model side, including modifying the detector itself (e.g., adversarial training) or attaching a new model in front of the detector. However, the former often deteriorates clean performance of detectors, and the latter may have high deployment costs caused by too many training parameters. Inspired by the phenomenon of "bone hyperplasia" in human bodies, we present a novel model-side adversarial patch defense, called HARP (Hyperplasia based Adversarial Patch defense). Just as bone hyperplasia can enhance bone strength and skeletal stability, the hyperostosia of detectors can also help to resist adversarial patches. Following this idea, HARP chooses to improve adversarial robustness by "growing" lightweight CNN modules (i.e., hyperplasia modules) on the pre-trained object detectors. We conduct extensive experiments on the PASCAL VOC and COCO datasets to compare HARP with the data-side defense JPEG and the model-side defenses adversarial training, SAC and FNC. Experimental results show that HARP provides excellent defense against adversarial patches while maintaining clean performance, outperforming the compared defense methods. Under PGD-based adaptive attacks, HARP surpasses the recently proposed defense method SAC by 12.5% in mean average precision (mAP) on PASCAL VOC, and 13.2% on COCO dataset. In addition, experiments confirm that the increase in model inference time caused by HARP is almost negligible.

Android malware detection has attracted increasing attention due to the rapid growth of mobile malware. However, running an in-cloud Android malware detection system usually incurs high hardware and bandwidth costs. This dilemma motivates us to develop a method to repurpose an in-cloud image-classification neural network to detect Android malware. Given an Android app, the proposed method first embeds its features into an image, skillfully perturbs the feature-embedded image, and then feeds the modified image into the in-cloud image classifier. The classifier's outputs are finally mapped into a malware detection result. In addition, two new techniques (perturbation hiding and group mapping) are proposed to reduce the risk of repurposing behavior being recognized and improve detection performance. Experiments show that our perturbations are usually imperceptible to humans, and our method outperforms both traditional machine learning-based detectors and deep learning-based detectors in detection performance.

Existing Android malware detection methods are usually hard to simultaneously resist various obfuscation techniques. Therefore, bytecode-based code obfuscation becomes an effective means to circumvent Android malware analysis. Building obfuscation-resilient Android malware analysis methods is a challenging task, due to the fact that various obfuscation techniques have vastly different effects on code and detection features. To mitigate this problem, we propose combining multiple features that are complementary in combating code obfuscation. Accordingly, we develop an obfuscation-resilient Android malware analysis methodCorDroid, based on two new features: Enhanced Sensitive Function Call Graph (E-SFCG) and Opcode-based Markov transition Matrix (OMM). The first describes sensitive function call relationships, while the second reflects transition probabilities among opcodes. Combining E-SFCG and OMM can well characterize the runtime behavior of Android apps from different perspectives, hence increasing the difficulty of misleading malware analysis through using code obfuscation to affect detection features. To evaluate CorDroid, we generate 74,138 obfuscated samples with 14 different obfuscation techniques, and compare CorDroid with the state-of-the-art detection methods (e.g., MaMaDroid, RevealDroid and APIGraph). In terms of average F1-Score, CorDroid is 29.69% higher than MaMaDroid, 21.80% higher than APIGraph, and 9.71% higher than RevealDroid, respectively. Experiments also validate the complementarity between E-SFCG and OMM, and exhibit the high execution efficiency of CorDroid.

Deep neural network (DNN) based website fingerprinting (WF) attacks pose a severe threat to the privacy of Tor users. To overcome this challenge, adversarial perturbation based WF defenses have been recently proposed to fool the classifiers of attackers, through purposefully perturbing the user’s traffic traces. Unfortunately, these defenses significantly deteriorate once the WF attacks are enhanced withadversarial training(AT). AT endows the WF attacks with more powerful website recognition capability, through learning the perturbed traffic traces generated by attackers. To resist the WF attacks enhanced by AT, we develop ablack-boxWF defense, called Acup3. First, Acup3 leveragesmany-to-one website imitationto make the traffic traces associated with different websites look more like each other, increasing the difficulty of website classification. Second, Acup3 generatestrace-agnosticperturbations without accessing traffic traces, making it suitable for practical deployment. Third, Acup3 employsperturbation variationto diversify the traffic traces of different users visiting the same website, making the knowledge learnt from AT less helpful for WF attacks. Therefore, Acup3 is more robust against AT. Experiments demonstrate Acup3 markedly surpasses four representative WF defenses (e.g., Mockingbird and AWA) in defense capability and bandwidth overhead. Facing the state-of-the-art (SOTA) attack Var-CNN enhanced with AT, Acup3 depresses its attack success rate (ASR) from 98% to 24.29% with only 13.95% bandwidth overhead. Compared to the SOTA defense AWA, Acup3 causes a 24.5% larger decrement in ASR of WF attacks, and achieves a more than 100 times faster speed of perturbation generation.

Trajectory prediction aims to forecast agents’ possible future locations considering their observations along with the video context. It is strongly needed by many autonomous platforms like tracking, detection, robot navigation, and self-driving cars. Whether it is agents’ internal personality factors, interactive behaviors with the neighborhood, or the influence of surroundings, they all impact agents’ future planning. However, many previous methods model and predict agents’ behaviors with the same strategy or feature distribution, making them challenging to make predictions with sufficient style differences. This paper proposes the Multi-Style Network (MSN), which utilizes style proposal and stylized prediction using two sub-networks, to provide multi-style predictions in a novel categorical way adaptively. The proposed network contains a series of style channels, and each channel is bound to a unique and specific behavior style. We use agents’ end-point plannings and their interaction context as the basis for the behavior classification, so as to adaptively learn multiple diverse behavior styles through these channels. Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel. Experiments show that the proposed MSN outperforms current state-of-the-art methods up to 10% quantitatively on two widely used datasets, and presents better multi-style characteristics qualitatively.

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a synthesis model for high-dimensional DT from a small number of training samples. In this article, we propose a novel DT synthesis method, which makes full use of similarity as prior knowledge to address this issue. Our method is based on the proposed kernel similarity embedding, which can not only mitigate the high dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationships. Specifically, we first put forward two hypotheses that are essential for the DT model to generate new frames using similarity correlations. Then, we integrate kernel learning and the extreme learning machine into a unified synthesis model to learn kernel similarity embeddings for representing DTs. Extensive experiments on DT videos collected from the Internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embeddings can provide discriminative representations for DTs. Further, our method can preserve the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with higher speed and lower computation than the current state-of-the-art methods. The code and more synthesis videos are available at our project pagehttps://shiming-chen.github.io/Similarity-page/Similarit.html.

In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e.g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i.e., model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.

Malware detection techniques achieve great success with deeper insight into the semantics of malware. Among existing detection techniques, function call graph (FCG) based methods achieve promising performance due to their prominent representations of malware's functionalities. Meanwhile, recent adversarial attacks not only perturb feature vectors to deceive classifiers (i.e., feature-space attacks) but also investigate how to generate real evasive malware (i.e., problem-space attacks). However, existing problem-space attacks are limited due to their inconsistent transformations between feature space and problem space.

Adversarial examples pose severe threats to Android malware detection because they can render the machine learning based detection systems useless. How to effectively detect Android malware under various adversarial example attacks becomes an essential but very challenging issue. Existing adversarial example defense mechanisms usually rely heavily on the instances or the knowledge of adversarial examples, and thus their usability and effectiveness are significantly limited because they often cannot resist the unseen-type adversarial examples. In this paper, we propose a novel robust Android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. More precisely, our approach employs a new VAE (variational autoencoder) and an MLP (multi-layer perceptron) to detect malware, and combines their detection outcomes to make the final decision. In particular, we share a feature extraction network between the VAE and the MLP to reduce model complexity and design a new loss function to disentangle the features of different classes, hence improving detection performance. Extensive experiments confirm our model’s advantage in accuracy and robustness. Our method outperforms 11 state-of-the-art robust Android malware detection models when resisting 7 kinds of adversarial example attacks.

Android malware poses severe threats to users, hence raising an urgent demand for malware detection. In-cloud Android malware detection often suffers privacy leakage and communication overheads. Therefore, this article focuses on on-device Android malware detection. At present, on-device malware detectors are usually trained on servers and then transplanted to mobile devices (e.g., smartphones). In practice, on-device training is particularly important due to the demand for offline updates. Because mobile devices are limited in resource, however, on-device training is hard to implement, especially for those high-complexity malware detectors. To overcome this challenge, we design a lightweight on-device Android malware detector, based on the recently proposed broad learning method. Our detector mainly uses one-shot computation for model training. Hence it can be fully or incrementally trained directly on mobile devices. As far as detection accuracy is concerned, our detector outperforms the shallow learning-based models, including support vector machine (SVM) and AdaBoost, and approaches the deep learning-based models multilayer perceptron (MLP) and convolutional neural network (CNN). Moreover, our detector is more robust to adversarial examples than the existing detectors, and its robustness can be further improved through on-device model retraining. Finally, its advantages are confirmed by extensive experiments, and its practicality is demonstrated through runtime evaluation on smartphones.

In cognitive radio networks, self-interested secondary users (SUs) desire to maximize their own throughput. They compete with each other for transmit time once the absence of primary users (PUs) is detected. To satisfy the requirement of PU protection, on the other hand, they have to form some coalitions and cooperate to conduct spectrum sensing. Such dilemma of SUs between competition and cooperation motivates us to study two interesting issues: 1) how to appropriately form some coalitions for cooperative spectrum sensing (CSS) and 2) how to share transmit time among SUs. We jointly consider these two issues, and propose a noncooperative game model with 2-D strategies. The first dimension determines coalition formation, and the second indicates transmit time allocation. Considering the complexity of solving this game, we decompose the game into two more tractable ones: one deals with the formation of CSS coalitions, and the other focuses on the allocation of transmit time. We characterize the Nash equilibria (NEs) of both games, and show that the combination of these two NEs corresponds to the NE of the original game. We also develop a distributed algorithm to achieve a desirable NE of the original game. When this NE is achieved, the SUs obtain a Dhp-stable coalition structure and a fair transmit time allocation. Numerical results verify our analyses, and demonstrate the effectiveness of our algorithm.

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Recently, the variable channel-width (VW) scheme was proposed to improve the performance of WLANs. Cooperative channel allocation has been studied in some existing literature under the assumption that the traffic demands of cooperative access points (APs) are constant. In fact, the traffic demands may vary when the corresponding stations change their AP association decisions. Hence, this work jointly considers the channel allocation and AP association, aims to maximize the system performance in terms of throughput and fairness. The problem is formulated as a constrained Integer Non-Linear Programming (INLP) problem, which is NP-hard. Two penalty functions are introduced to relax the constraints, and a discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the problem. The simulation results show that our algorithm can improve the performance by about 20% compared to the fixed traffic scheme.

Channel allocation is a crucial concern in variable-width wireless local area networks. This work aims to obtain the stable and fair nonoverlapped variable-width channel allocation for selfish access points (APs). In the scenario of single collision domain, the channel allocation problem reduces to a channel-width allocation problem, which can be formulated as a noncooperative game. The Nash equilibrium (NE) of the game corresponds to a desired channel-width allocation. A distributed algorithm is developed to achieve the NE channel-width allocation that globally maximizes the network utility. A punishment-based cooperation self-enforcement mechanism is further proposed to ensure that the APs obey the proposed scheme. In the scenario of multiple collision domains, the channel allocation problem is formulated as a constrained game. Penalty functions are introduced to relax the constraints and the game is converted into a generalized ordinal potential game. Based on the best response and randomized escape, a distributed iterative algorithm is designed to achieve a desired NE channel allocation. Finally, computer simulations are conducted to validate the effectiveness and practicality of the proposed schemes.

In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) is usually performed periodically due to the uncertain activity of primary users (PUs). Considering the overhead in performing CSS, a selfish secondary user (SU) may not always participate in CSS. Instead, it elaborately selects a frequency (or number of times) for CSS participation to maximize its interest. A fusion center then schedules it to conduct CSS in appropriate periods. This paper investigates the interactive decision on the CSS participation frequency under sensing performance and quality of service (QoS) requirements. The problem is formulated as a noncooperative game, where Nash Equilibrium (NE) corresponds to the desired frequency selection outcome. Since the strategy sets of SUs are coupled, obtaining directly the NE requires explicit coordination among SUs, which is unrealistic in practice. Alternatively, we decompose the game into a lower-level uncoupled game and a higher-level optimization problem. A distributed hierarchical iterative algorithm (DHIA) is then proposed to obtain the desired frequency selection outcome without requiring explicit coordination. Furthermore, the uncertain sensing performance of SUs and the fairness issue are also considered. Finally, numerical results validate the effectiveness of the proposed scheme.

In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called secondary user) may not be able to sense two or more channels simultaneously. As different channels may have different primary user activities and time-varying channel qualities, an important task is to select which channels to sense and access for a given time period so that the available spectrum left by the primary users can be fully utilized by the secondary user. In this paper, we propose an optimal sensing channel selection policy based on partially observable Markov decision process (POMDP). The proposed policy takes the time-varying channel state into consideration and intends to optimally exploit spectrum resources for the secondary user. In addition to selecting optimal channel to sense, we also derive the optimal sensing time which leads to maximized throughput of the secondary user.

Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.

This paper investigates non-overlapping variable-width channel allocation for cooperative access points (APs) in multiple collision domains with the goal of maximizing the total capacity of wireless local network (WLAN). Due to the complexity of finding an optimal allocation, this paper considers it from a game-theoretic perspective. First, the problem of variable-width channel allocation is formulated as an identical interest game and the existence of pure Nash Equilibrium (NE) is investigated. Then a decentralized learning-based total capacity maximization algorithm (LTCMA) is designed for APs to achieve an optimal allocation. To analyze the fairness property of the optimal allocation, a game-theoretic fairness analysis model is developed. With this model, this paper shows that the fairness is usually acceptable for a WLAN in which every client is rational and free to associate itself with any APs. Finally, the numerical results verify the effectiveness of LTCMA and the fairness of the optimal allocation.

The fixed channelization structure used by IEEE 802.11-based WLANs constrains the total capacity and leads to unfairness. The concept of variable-width channels is recently proposed to overcome these drawbacks. To investigate the problem of the non-overlapping variable-width channel allocation for selfish access points (APs) in a WLAN, we model it as a non- cooperative game, we aim to investigate two fundamental issues on it in this paper: 1) Are there some fair and system-optimal Nash equilibrium (NE) allocations? 2) How to achieve one of these desirable allocations if they exist? At first, the existence of fair and system-optimal Nash equilibria in this game is proved. Then, a simple protocol to achieve one of these desirable NE allocations is proposed. Considering the implementation issues, a punishment-based method and a transfer-based self-enforcing truth-telling method are proposed for single-stage and multistage game scenarios respectively. The numerical results show the effectiveness of our approaches.

Spectrum sensing is important for cognitive radios to utilize the idle spectrum opportunities, and recently cooperation schemes have been introduced to enhance spectrum sensing in specific areas. However, when a mobile cognitive node roams among heterogenous wireless network, it will be difficult to catch the changes of primary user's behavior, or to setup the cooperation relationship with local network nodes in a short time. In this paper, an self-learning spectrum sensing framework is proposed, which can enable the single mobile cognitive node to work in unknown wireless environment. When the wireless environment changes, the main sensing parameters (such as decision threshold, sampling frequency) could be adapted to optimum in the self- earning process. One adaptive algorithm is proposed to find the optimal decision threshold in energy detection sensing method. Simulation results show that, the proposed scheme could converge to optimal sensing parameters in spatial and temporal varying environment.

Cooperation can improve the performance of spectrum sensing. However, the sensing overhead is generally increasing with the number of cooperating users as more data needs to be reported to the fusion center. Most existing works assume a general time frame structure in which spectrum observing and sensing results reporting are conducted sequentially. We argue that this frame structure is inefficient, since the time consumed by reporting contributes little to the performance of spectrum sensing. In this paper, we propose a pipelined spectrum sensing framework, in which spectrum observing is conducted concurrently with results reporting in a pipelined way. By making use of the reporting time for sensing, the new framework provides a much wider observing window for spectrum measurement, which results in a performance improvement of spectrum sensing. Besides, we also present a multi-threaded sequential probability ratio test method (MTSPRT) which is very suitable for the pipelined framework as the data fusion technique. The MTSPRT method can improve the sensing speed significantly. Numerical results indicate that our pipelined sensing scheme incorporating with MTSPRT shows a better performance than the cooperative sensing based on the general frame structure.

Cooperative transmission technique is now widely considered as a promising approach to combat fading and achieve energy efficiency in wireless networks. In this paper we focus on the routing problem in energy-constrained wireless sensor networks (WSNs), of which a cooperative MISO-based routing strategy is adopted. We first analyze the physical layer energy consumption model of cooperative transmission in the scenarios of one hop and hop-to-hop for energy-efficient routing in order to prolong the network lifetime. Based on this analysis, we disclose how the energy-efficient network routing problem is tightly related to the inter-cluster MISO node and hop-to-hop relay node selection. As we noticed, the problem of energy-efficient cooperative routing is NP-hard innately which is difficult to implement in a totally distributive approach. Due to these analysis, a feasible algorithm with minimum cost is thus proposed. In the simulation part, we prove that our protocol can prolong the network lifetime tremendously when choose appropriate transmission parameters. Moreover, as an example, we simulate a typical network scenario which indicates our protocol is more energy efficient when comparing with vMIMO scheme in our previous work.

In cognitive radio networks, secondary users (SUs) opportunistically exploit the spectrum unutilized by primary users (PUs). In this paper, we study the secondary communication where secondary transmitters and receivers have different available spectrum. Considering the spectrum diversity and the space distance between different PUs, we introduce cognitive relay node into the secondary communication and propose a novel Cooperative Relay Scheme (CRS) to increase the SINR at secondary receivers. A novel Opportunistic Sharing Scheme (OSS) is also proposed for the secondary transmitters to share the spectrum of relay nodes. We model it with a non-cooperative game, and study the performance of competition of SUs. The Nash equilibrium and Pareto efficiency of this game is presented. Simulations show that CRS can increase SINR at secondary receivers under proper configurations.

In cognitive radio networks, power control is necessary to not only decrease the interference among the secondary users (SUs), but also avoid negative impact to the primary users (PUs). Prevalent research works on power control are mainly focus on maximizing SINR as the QoS requirement of SUs under the interference power constraint for PUs. We note that besides achieving a high SINR to guarantee reliable data transmissions, SUs also require to support heterogenous services with different transmission rates. In order to provide flexible transmission rates to each SU, efficient use of networks radio resource requires transmission rate control in addition to transmit power control. In this paper, we consider the problem of joint power and rate control for SUs in cognitive radio network by using non-cooperative game theory. We study how to jointly allocate optimal transmit power and transmission rate given certain QoS requirement of SUs. We analysis of existence, uniqueness and Pareto efficiency of Nash equilibrium for our game. The performance of our proposed joint power and rate control algorithm is investigated by numeral results.

This paper presents a novel backoff algorithm in CSMA/CA-based Medium Access Control (MAC) protocols for clustered sensor networks. We first show that every node should have the same value of Contention Window (CW) in a cluster by formulating resource allocation as a utility maximization optimal problem, then assume all nodes have the same CW and gain the relation between the optimal value of CW and the number of nodes by maximizing the total network utility with constrains of minimizing collision probability. The result is a new retransmission algorithm that uses an optimal shared CW that is easy to implement and results in fewer collisions than binary exponential backoff algorithm. The proposed scheme can decrease delay and improve throughput, moreover, it is also energy-efficiency for clustered sensor networks, simulation results validate our conclusion.

Wireless sensor networks have limited energy resource and therefore energy-efficient protocols are required to minimize the energy consumption. The virtual MIMO technique is considered as one of the new solutions to solve this problem. In this paper, in order to maximize the network lifetime, we propose a virtual MIMO transmission scheme coupled with multi-hop transmission. In our cross-layer design, the transmission rate, the number of clusters and the number of virtual antenna nodes are jointly optimized. Unlike existing work that is mostly based on Alamouti scheme, the proposed transmission scheme does not require transmitter-side cooperation (joint STBC encoding/decoding), making it more suitable for application in real wireless sensor networks. Simulation results show significant energy savings and lifetime maximization of the network.

Next Generation wireless networks are evolving towards all-data system which are expected to support a variety of application services with diverse transmission rates. Meanwhile, since most of the mobile terminals in wireless networks are battery-powered, to use energy efficiently, each terminal needs to transmit just enough power to achieve the desired transmission rate without causing excessive interference in the network. In this paper, a game-theoretic framework is used to study the joint power and rate control problem on the energy efficiency of wireless data network. A energy-efficient non-cooperative joint power and rate control game is thus introduced in which each user seeks to choose its possible transmit power and transmission rate in order to maximize its own utility while satisfying its target SINR as quality-of service (QoS) requirement. The utility function here we adopt is especially suitable for energy-constrained networks. We introduce pricing of transmit power into the utility function which not only improves the overall system performance, but also obtains Pareto Improvement when compared to the game with no pricing. The existence, uniqueness, best-response strategies and Pareto efficiency of Nash Equilibrium for the proposed game are proved. Based on these analysis, we present a distributive joint power and rate control algorithm. In the simulation part, we investigate the best pricing factor and compare our proposed algorithm with alternative algorithms developed by using game theory.