BACKGROUND: Organoid image segmentation is essential for quantitative analysis in disease modeling and drug screening, yet remains highly challenging due to substantial morphological variability and blurred boundaries in organoid images. Existing approaches often struggle to achieve a favorable balance between segmentation accuracy and computational efficiency. RESULTS: In this paper, DeShiftNet, a lightweight segmentation framework, is proposed to extract discriminative features with high accuracy while maintaining low computational overhead. The model incorporates a deformable-shifted encoding strategy that adaptively samples local structures. It also includes a cross-attention-guided decoder for selective multi-scale feature alignment. Furthermore, a deformable multi-scale contextual refinement module enhances boundary coherence and contextual consistency. Extensive experiments on the multi-type OrganoID dataset show that DeShiftNet achieves competitive performance compared with recent segmentation models, while maintaining only 1.78M parameters and 2.65 GFLOPs. Notably, DeShiftNet achieves a Dice score of 0.961 on the Lung subset. CONCLUSION: These results indicate its potential practical value for efficient organoid segmentation in high-throughput experimental workflows.

Personalized Federated Learning (PFL) has become a promising learning paradigm, enabling the training of high-quality personalized models through multiple communication rounds between clients and a central server. However, directly applying traditional PFL in real-world environments where communication is expensive, limited, or infeasible is challenging, as seen in Low Earth Orbit (LEO) satellite constellations, which face severe communication constraints due to their high mobility, limited contact windows. To address these issues, we introduce Federated Oriented Learning (FOL), a novel four-stage one-shot PFL algorithm designed to enhance local model performance by leveraging neighboring models within stringent communication constraints. FOL comprises model pretraining, model collection, model alignment (via fine-tuning, pruning, post fine-tuning, and ensemble refinement), and knowledge distillation stages. We establish two theoretical guarantees on empirical risk discrepancy between student and teacher models and the convergence of the distillation process. Extensive experiments on datasets Wildfire, Hurricane, CIFAR-10, CIFAR-100, and SVHN demonstrate that FOL consistently outperforms state-of-the-art one-shot Federated Learning (OFL) methods; for example, it achieves accuracy improvements of up to 39.24% over the baselines on the Wildfire dataset.

Numerous service providers rely on crowdsourcing from service users, rather than a less-specific, more public group, to provide better services to the users themselves. In the majority of studies on the incentive mechanism for crowdsourcing, however, the users' intrinsic desire/demand for better service, which could have been exploited to enhance their involvement in crowdsourcing, has been largely overlooked. Therefore, conclusions are limited regarding the optimal incentives and the benefits of crowdsourcing on the service quality and thus the market share. In this paper, we study the incentive mechanism for crowdsourcing that combines a financial reward in the form of service price discounts and users' intrinsic demand for better service. Our focus is on leveraging the users dual role, i.e., the interdependence between the service and the users. We show that the dynamic market converges to a unique equilibrium under mild conditions, with the consideration of varying service usage levels and privacy concerns of the users. Besides, counter-intuitively, failure to take into account the users' intrinsic reward leads to too little extrinsic incentive. Moreover, our results showed how the competition reshapes the markets, which cannot be intuitively or trivially predicted without a thorough analysis.

With the immense amount of publicly available data online, many companies and research institute are able to download the online data for free and train the machine learning models which will finally result in products that would enhance our everyday life. While enjoying the advantages of such large amount of free data, people (data providers or data owners) have the concern that their personal data may be crawled without the owner’s consent. This brings out an underlying issue in the context of machine learning that in the current literature and applications, dataset owners (also referred to as "dataset providers" in the following text) can only choose between the two extreme decisions of either to share their data entirely, or not share any of their data at all. Another side of this issue is that the privacy of the dataset to be shared is either completely revealed due to the full disclosure of the dataset, which benefits the potential consumers of the dataset (referred to as dataset user/buyer in the following text); or the dataset is not shared at all which preserves the privacy, but impede the development of new technologies.In this paper, we propose the novel Hide-and-Seek data sharing framework that serves as a middle point between the difficult "share or no share" extreme decisions, which provides a "partial share" option based on the consumers’ needs, and hence is able to protect the partial privacy of the dataset providers while sharing enough amount of data needed for the user to train their models at a desired accuracy. Extensive amount of experiments have been conducted on the CIFAR-10, Street View House Number (SVHN), and the CIFAR-100 datasets. Our experimental results verify the effectiveness of the proposed Hide-and-Seek framework. We also show in the experiments that our framework is able to protect data provider’s privacy without changing the visual patterns of the dataset, and therefore, doesn’t affect the regular usage of the data (such as using it as a profile photo).

In recent years, the proliferation of LEO (Low-Earth Orbit) satellites and the accumulation of space debris have made Near-Earth space more and more crowded, and hence significantly increased the risk of collisions in this space. As a result, precise orbit prediction becomes essential for LEO satellites to avoid collision, maintain the right constellation, and perform normal space operations. Although machine learning(ML)-based satellite orbit prediction methods have been extensively explored, most existing methods are trained on simulated/synthetic orbit data, which essentially assumes a stationary orbit process and cannot reflect the non-stationary dynamic orbit changes in real-world LEO satellite constellations that are caused by satellite flight status adjustment (e.g., for the purpose of collision avoidance). In this study, we propose a novel multi-range (global-local) self-attention transformer-based ML model, the GloLoSAT (Global-Local Probsparse Self-Attention Transformer), and train the model over real-world LEO satellite orbit data crawled from N2YO.com to give more precise orbit prediction in case of a series of orbit adjustments. Theoretical analysis demonstrates our Global-Local Probsparse Self-Attention mechanism can achieve$O(L\log(L))$computational complexity with respect to the input sequence length. Extensive experiments conducted on real satellite orbit tracking datasets demonstrate the efficacy of GloLoSAT in achieving consistent performance improvements across various prediction scenarios compared to their counterparts.

Estimation of wood moisture content (MC) is a fundamental aspect of woodworking, construction, and various other industries that rely on the versatile properties of wood. In this paper, we present mmMC, a novel system that estimates the MC of wood by a single commercial off-the-shelf (COTS) mmWave radar, which provides accurate end-to-end real-time wood moisture measurement results while being non-invasive, portable, and flexible in deployment. The proposed system uses a novel target reflection feature (TRF) to determine the reflectivity of the wood, thereby correlating it with specific MC levels. To robustly and accurately estimate the TRF of the wood from the mmWave signal, a signal processing pipeline is proposed. Specifically, an object detection and signal extraction module is designed to resolve the range of the object and eliminate multipath effects of the original signal. Then, a multiple chirps/antennas signals processing module is proposed to obtain a stable TRF from the extracted signals by using signals from different antennas and chirps. The TRF is associated with the MC of wood by a regression to enable real-time MC estimation. Through extensive real-world experiments, we have demonstrated that the proposed system has high accuracy and reliability.

Many civil structures are at risk of failure due to bolts loosening under shock or vibration. Early detection of looseness is critical for safety. In this research, we utilize mathematical model with motion equations and optimization technique to estimate parameters that can detect looseness. A novel tightness index is introduced to quantify the extent/degree of looseness. The method was validated on wood and steel structures for demonstrating its effectiveness.

Recent years have witnessed the ever-growing interest and adoption of autonomous vehicles (AVs), thanks to the latest advancement in sensing and artificial intelligence (AI) technologies. The LiDAR sensor is adopted by most AV manufacturers for its high precision and high reliability. Unfortunately, LiDARs are susceptible to malicious spoofing attacks, which can lead to severe safety consequences for AVs. Most current work focuses on protecting LiDAR against spoofing attacks by using perception model-level defense methods, whose effectiveness unfortunately depends on the correctness of the LiDAR’s sensing outcome. A spoofer thus can elude from these methods as long as it fabricates points that maintain the right contextual relationship held by the legitimate points. In this paper, we propose to use the signal’s Doppler frequency shift to verify the sender of the signal and detect potential spoofing attacks. To this end, we first thoroughly analyze the working principle of LiDAR and conduct real-world experiments to deeply understand and reveal the vulnerability of LiDAR sensors. We then prove that the Doppler frequency shifts of legitimate and spoofing signals present different characteristics, which can be used to fundamentally protect the LiDAR sensing outcome. For better demonstration purposes, we consider three attack models, including static attacker, moving attacker, and moving attacker with control of both velocity and signal frequency. For each of the models, we first show how the spoofing attack is performed and then present our countermeasures. We then propose a statistical spoofing detection framework to jointly consider the impact of short-term uncertainty in vehicle velocity, which can provide more accurate spoofing detection results in realistic environments. Extensive numerical results are provided in a wide range of settings and road conditions.

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

The development of the Internet of Things (IoT) combined with the emergence of federated learning (FL) makes it possible for mobile edge computing (MEC) to gain insight from physically separated data without violating privacy or burdening communication. Due to the distributed nature of MEC devices, researchers have uncovered that the FL is vulnerable to backdoor attacks, which aim at injecting a subtask into the FL without corrupting the performance of the main task. The backdoor attack achieves high accuracy on both the main task and the backdoor subtask when injected at FL model convergence. However, the effectiveness of the backdoor is weak when injected in early training stage. In this article, we strengthen the early-injected backdoor attack by using information leakage. We show that FL convergence can be expedited if the client’s data set mimics the distribution and gradients of the whole population. Based on this observation, we propose a two-phase backdoor attack, which includes a preliminary phase for the subsequent backdoor attack. Taking advantage of the preliminary phase, the later injected backdoor achieves better effectiveness, as the backdoor effect is less likely to be diluted by normal model updates. Extensive experiments are conducted on the MNIST data set under various data heterogeneity settings to evaluate the effectiveness of the proposed backdoor attack. The results show that the proposed backdoor outperforms existing backdoor attacks in both success rate and longevity, even when defense mechanisms are in place.

Reconfigurable intelligent surface (RIS) offers a new way to provide controllable non line-of-sight (NLoS) propagation paths for millimeter-wave (mmWave) directional communication to overcome the performance degradation caused by line-of-sight blockage. However, current coverage models do not consider the impact of path direction difference on path's availability, which is a crucial property of mmWave directional communication network. In the paper, we propose a new coverage model called$(k,\alpha )$-coverage. A receiver is$(k,\alpha )$-covered if it is covered by at least$k$RISs to have$k$different NLoS path directions and the angular separation between any two adjacent path directions is at least$\alpha$. In this case, when the current communication direction is blocked by an obstacle, other RIS created paths are still likely to be available for transmission, which increases the robustness of mmWave directional communication. To tackle the problem of using the least number of RISs to achieve the$(k,\alpha )$-coverage, we formally define the$(k,\alpha )$-coverage models and propose methods to verify if the target area is$(k,\alpha )$-covered by the given set of RISs. Then, we solve the problem under both deterministic and random RIS deployment schemes. For the deterministic deployment scheme, we derive the optimal$k$-sided regular polygon deployment patterns and use it to achieve area$(k,\alpha )$-coverage. An analytical performance bound on the number of RISs needed is also derived. For the random RIS deployment scheme, we derive the$(k,\alpha )$-coverage probability under uniform and spatial-Poisson RIS distributions. Finally, extensive simulation results are provided to validate our analyses.

As the micro-foundation of the market economy, identifying and preventing various risks are crucial both for the companies themselves and for market regulators, financial institutions and investors. To detect enterprise risks, many studies have constructed and utilized various indicator metrics based on information about each company to assess the level of enterprise risk. However, enterprises are not independent in the market, they form a complex network on the basis of their production activities. The enterprise risk comes not only from the operation of each enterprise itself, but also from the related enterprises in its business network. In addition, the risk detection methods relied on indicator metrics aims to asset a specific or a certain kind of risk faced by enterprises. That is, these methods are incapability of identifying the constantly changing risks caused by the uncertainty of enterprises. To this end, this study aims to (i) construct a network of enterprises based on supply chain relationships to incorporate the contagion effect of related companies, (ii) learn event prototype representations from company action event data via a self-supervised learning method, and capture the time-varying operational states of enterprises using the learned prototypes, (iii) adopt graph neural networks to identify enterprise risks from the perspective of supply chain contagion and comprehensive events. The experimental results on real datasets show that the proposed method is important for predicting and assessing enterprise risks and provides useful information for the decision-making of companies and investors.

The development of the Internet of Things (IoT) combined with the emergence of federated learning (FL) makes it possible for mobile edge computing (MEC) to gain insight from physically separated data without violating privacy or burdening communication and the server. Due to the distributed nature of MEC, researchers have uncovered that the FL is vulnerable to backdoor attacks, which aim at injecting a subtask into the FL without corrupting the performance of the main task. However, the single-shot backdoor attack in the early training stage is weak. In this paper, we strengthen the early-injected single-shot backdoor attack by using information leakage. We show that FL convergence can be expedited if the client's dataset mimics the distribution and gradients of the whole population. Based on this observation, we propose a two-phase backdoor attack, which includes a preliminary phase for the subsequent backdoor attack. Benefiting from the preliminary phase, the later injected backdoor achieves better effectiveness, as the backdoor effect is less likely to be diluted by normal model updates. Numerical experiments show that the proposed backdoor outperforms existing backdoor attacks in both success rate and longevity, even when defense mechanisms are in place.

With the rapid increase of smart devices in the Internet of Things, more and more visible light (VL) systems, e.g., VL communication (VLC) and VL sensing (VLS), are developed on the existing light fixtures to offload the current crowded spectrum, how to guarantee the authenticity of the VL signal in these systems becomes an urgent problem. This is because almost all of today’s light fixtures are unprotected and can be openly accessed by almost anyone and, hence, are susceptible to spoofing attacks. As such, ensuring received VL signals are coming from the authentic transmitters (LEDs), rather than from a spoofer, is the key in ensuring the quality and correctness of the VL communication and sensing outcomes. Existing authentication methods are either not directly applicable to signal-level authentication, or have poor scalability. In this article, by exploiting the intrinsic linear superposition characteristics of VL, we propose VL-Watchdog, a scalable and always-on signal-level spoofing detection framework that is applicable to both VLC and VLS systems. VL-Watchdog is based on redundant orthogonal coding of the transmitted VL, and can be implemented as a small hardware add-on to an existing VL system. A proof-of-concept testbed was developed to verify the feasibility of VL-Watchdog. The effectiveness of the proposed framework was validated through extensive numerical evaluations against a comprehensive set of factors.

The high directionality of mmWave communication makes its line-of-sight (LoS) path susceptible to blockage when the user is moving. Most existing solutions have very stringent requirements on the antennas of the transmitter and the receiver, which are hardly met by today's consumer-level commercial off-the-shelf (COTS) mmWave products. In reality, a COTS device uses low-resolution wide-beam antennas, and hence cannot support the aforementioned methods for NLoS beam switching in response to the LoS blockage. In this paper, we develop a new method to support high-resolution mmWave multi-path channel resolving based on coarse-grained wide-beam phased array antennas. We design a novel real-time beam-switching algorithm that allows COTS devices to estimate the location and reflection coefficient of the dominant reflectors. Whenever the current LoS is blocked, our algorithm can compute in real-time the best alternative beam direction based on estimated reflectors to establish a strong NLoS link. We implemented the proposed algorithm on a COTS mmWave device and evaluated the system's performance on the physical and transport layer. Our experiments demonstrate the effectiveness of our algorithm on estimating dominant reflectors and calculating strong alternative beam directions, and its efficacy in providing robust connections for COTS mmWave devices.

As more and more visible light communication (VLC) and visible light sensing (VLS) systems are mounted on today’s light fixtures, how to guarantee the authenticity of the visible light (VL) signal in these systems becomes an urgent problem. This is because almost all of today’s light fixtures are unprotected and can be openly accessed by almost anyone, and hence are subject to tampering and substitution attacks. In this paper, by exploiting the intrinsic linear superposition characteristics of visible light, we propose VL-Watchdog, a scalable and always-on signal-level spoofing detection framework that is applicable to both VLC and VLS systems. VL-Watchdog is based on redundant orthogonal encoding of the transmitted visible light, and can be implemented as a small hardware add-on to an existing VL system. The effectiveness of the proposed framework was validated through extensive numerical evaluations against a comprehensive set of factors.

Global precoding is an effective way to suppress interference in cell-free massive MIMO systems. However, it requires all access points (APs) to upload their local instantaneous channel state information (CSI) to a central processor via capacity-constrained fronthaul links, consuming significant bandwidth resources. Such overhead may become unaffordable in an ultra-dense network (UDN) in future 5G systems, due to the large number of APs and the frequent CSI uploads required to combat the fast-changing state of the high-frequency channels. In order to address this issue, we propose a novel bandwidth-efficient global zero-forcing precoding strategy for downlink transmission in cell-free massive MIMO systems. By exploiting the physical structure of Rician fading channels, we propose a novel model-based CSI compression mechanism, which decomposes a channel matrix into a line-of-sight (LoS) and a non-line-of-sight (NLoS) components, and then compresses them using a model-based method and a singular-value-decomposition (SVD)-based method, respectively. We also present two optimization-based algorithms to obtain the phase information of the LoS component of the channel, which is then used by the proposed channel matrix decomposition. The simulation results demonstrate the efficiency of the proposed precoding strategy on reducing the upload overhead and improving the bandwidth efficiency.

Although millimeter wave (mmWave) is a promising technology in 5G communication, its severe path attenuation and susceptibility to line-of-sight (LOS) blockage result in much more unpredictable outages than traditional technologies. This special propagation property raises a significant challenge to the mobility management in mmWave cellular networks. Since conventional handover policies purely rely on the measurement of signal strength, they would cause a large number of unnecessary handovers due to the frequent short-term LOS blockage by obstacles, imposing high signaling and energy overhead. In this paper, we propose two novel handover mechanisms to reduce unnecessary handovers by carefully deciding the next base station (BS) a user should handover to, so that the new user-BS connection after the handover can last as long as possible. Without prior knowledge of user’s mobility and environment, the proposed handover mechanisms exploit the empirical distribution of user’s post-handover trajectory and LOS blockage, learned online through a multi-armed bandit (MAB) framework. Depending on the contexts extracted from RSS information, two different MAB problems for handover are formulated, which focus on spatial and space-time contexts, respectively, The results of numerical simulations demonstrate that the proposed contextual handover mechanisms significantly outperform existing counterparts on reducing handovers in all simulated scenarios.

Ample evidence has confirmed the importance of information in security. While much research on security game has assumed the attackers' limited capabilities to obtain target information, few studies consider the possibility that the information can be acquired from a data broker, not to mention exploring the attackers' profit-seeking behaviors in the shrouded underground society. This paper studies the role of information in the security problem when the target information is sold by a data broker to multiple attackers. We formulate a novel multi-stage game model to characterize both the cooperative and competitive interactions of the data broker and attackers. The attackers' competition with correlated purchasing and attacking decisions is modeled as a two-stage stochastic model, and the bargaining process between the data broker and the attackers is analyzed in a Stackelberg game. The study contributes to the literature by exploring the behaviors of the attackers with labor specialization, and providing quantitative measures of information value from an economic perspective. The proposed frameworks characterize both the attackers' competitive equilibrium solutions and the data broker's pricing strategies under different market parameters. We also show how factors such as the quality of information, the heterogeneity in attackers' utilities, and their cooperative purchasing strategy would have an impact on the results.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS is a bargaining mechanism whose terms and conditions must be carefully determined based on mutual benefits in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator with limited resources and uncertain demands, and a more resourceful operator with excessive capacity. The backup reservation agreement requires the local operator (say, operator A) to reserve a certain amount of resources (e.g., spectrum) for future sharing from the resource-owning operator (say, operator B). In return, operator B guarantees availability of its reserved resources to meet the need of operator A. We characterize the bargaining between the operators in terms of the optimal reservation prices and quantities with and without consideration of their competitions in market share, respectively. The conditions under which competing operators have incentive to cooperate are explored. The impact of competition intensity and redundant capacity on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation improves both resource utilization and profits of operators, with the potential to support higher target service levels for end users. We also find that, under certain conditions, operator B may still have the incentive to share its resources even at the risk of impinging on its own users.

While visible light communication (VLC) is expected to have a wide range of applications in the near future, the security vulnerabilities of this technology have not been well understood so far. In particular, due to the extremely short wavelength of visible light, the VLC channel presents several unique characteristics than its radio frequency counterparts, which impose new features on the VLC security. Taking a physical-layer security perspective, this paper studies the intrinsic secrecy capacity of VLC as induced by its special channel characteristics. Different from existing models that only consider the specular reflection in the VLC channel, a modified Monte Carlo ray tracing model is proposed to account for both the specular and the diffusive reflections, which is unique to VLC. A deep neural network model is also proposed to describe the spatial VLC channel response based on a limited number of channel response samples calculated from the ray tracing model. Based on these models the upper and the lower bounds of the VLC secrecy capacity are derived, which allow us to evaluate the VLC communication confidentiality against a comprehensive set of factors, including the locations of the transmitter, receiver, and eavesdropper, the VLC channel bandwidth, the ratio between the specular and diffusive reflections, and the reflection coefficient. Our results reveal that due to the different types of reflections, the VLC system becomes more vulnerable at specific locations where strong reflections exist.

Millimeter wave (mmWave) is a promising technology in 5G communication due to its abundant bandwidth re- source. However, its severe path attenuation and vulnerability to line-of-sight (LOS) blockage result in much more unpredictable outages than traditional technologies. This special propagation property raises a significant challenge to the mobility management in mmWave cellular networks. In particular, conventional handover policies purely rely on the measurement of signal strength. If being applied directly in mmWave cellular networks, they would cause a large number of unnecessary handovers due to the frequent short-term LOS blockage by obstacles, imposing high signaling and energy overhead on the network. In this paper, we propose a novel handover mechanism to reduce unnecessary handovers in a mmWave cellular network by carefully deciding the next base station (BS) a user should handover to, so that the new user-BS connection after the handover can last as long as possible. Clearly, making such an optimal decision requires some knowledge on the users post-handover mobility trajectory and LOS blockage, whose realization cannot be assumed at the moment of handover. The proposed handover mechanism addresses this challenge by exploiting the empirical distribution of users post-handover trajectory and LOS blockage, learned online through a multi-armed bandit framework, with the intention to maximize the expectation of the user-BS connection time after each handover. The results of numerical experiments demonstrate that the proposed handover policy outperforms existing counterparts on reducing handovers, especially in the scenarios where users mobility follows regular patterns.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. To collaborate with competitors, NIS is a bargain whose terms and conditions need to be carefully determined to guarantee profitability in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator of limited resources and uncertain demands, and one resourceful operator with potentially redundant capacity. The backup reservation agreement requires the local operator (say, operator A) to pay a fixed reservation fee to the resource-owning operator (say, operator B) at fixed time intervals. In return, the operator B guarantees availability of its resource (e.g., spectrum) up to a predetermined level. In such a way, a certain amount of backup resource capacity is reserved for future use under high traffic demand. We characterize the bargaining between the operators in terms of the optimal reservation prices and resource reservation quantities w/o considerations of the competitions between operators in market share. The conditions under which the competitive operators will cooperate are explored. The impacts of competition intensity, redundant capacity, and demand uncertainty on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation leads to both higher resource utilization and profits for operators, as well as higher service levels for end users. We also find that, under certain conditions, operator B will share its resources with operator A even at the risk of impinging on its own users, and the impact of competition intensity on the sharing decisions is highly dependent on the amount of potential redundant capacity.

This paper presents HC-LBR routing, the first location-based routing (LBR) mechanism that simultaneously retains high communication efficiency and privacy for Internet of Things (IoT). Existing LBR schemes were originally designed for wireless sensor networks (WSNs). Although they offer attractive efficiency and scalability, their privacy performance presents a vulnerability when being used in IoT. This is because, unlike a conventional WSN where all nodes are owned by the same user, an IoT network has an open architecture in which nodes owned by different users are mixed and work together. Therefore, when LBR is directly used in an IoT network, the sharing of location information among alien nodes allows one user to peek into the communication privacy of other users. HC-LBR overcomes this privacy weakness by computing efficient geographic routes directly based on Hilbert-Curve-encrypted location information. HC-LBR consists of two components: A Kademlia-tree-based routing algorithm that supports efficient geographic routing in HC encrypted space, and Rand-Mix, a lightweight traffic mixer that uses multiple traffic flows to enhance the privacy of HC routing. Our simulation-based evaluation verifies the high efficiency and privacy of the proposed methods.

Mobile cloud storage (MCS) is being extensively used nowadays to provide data access services to various mobile platforms such as smart phones and tablets. For cross-platform mobile apps, MCS is a foundation for sharing and accessing user data as well as supporting seamless user experience in a mobile cloud computing environment. However, the mobile usage of smart phones or tablets is quite different from legacy desktop computers, in the sense that each user has his/her own mobile usage pattern. Therefore, it is challenging to design an efficient MCS that is optimized for individual users. In this paper, we investigate a distributed MCS system whose performance is optimized by exploiting the fine-grained context information of every mobile user. In this distributed system, lightweight storage servers are deployed pervasively, such that data can be stored closer to its user. We systematically optimize the data access efficiency of such a distributed MCS by exploiting three types of user context information: mobility pattern, network condition, and data access pattern. We propose two optimization formulations: a centralized one based on mixed-integer linear programming (MILP), and a distributed one based on stable matching. We then develop solutions to both formulations. Comprehensive simulations are performed to evaluate the effectiveness of the proposed solutions by comparing them against their counterparts under various network and context conditions.

With the rapid growth of mobile cloud computing, participatory sensing emerges as a new paradigm to explore our physical world at an unprecedented ne granularity by recruiting the pervasive sensor-enabled smart phones. While extensive optimization has been performed in the literature to coordinate the sensing activity of the cloud-based sensing server (or platform) and the participating smart phones so as to maximize the efficiency of participatory sensing, the privacy issue in the optimization has been largely overlooked. In this paper, we propose a novel privacy-preserving optimization framework that allows both the cloud-based platform and mobile users to share data for the formulation and solution of the optimization, but without revealing sensitive information that may lead to privacy leakage of each other. Our method is built upon a privacy-preserving version of the well-known NP-hard weighted set-coverage problem. To accommodate privacy requirements in this framework, our solution uses a modified bloom filter along with a Dife-Hellman-type exchange protocol among all participants for data aggregation, sharing, and presentation. Through extensive simulation we evaluate the privacy strength of the proposed approach and also verify its effectiveness and low overhead.

We improve data access efficiency in mobile cloud storage by exploring users' context information. Specifically, we optimize data reading and writing on mobile devices from/to distributed cloud storage according to network condition, user mobility pattern, and data access preference. We propose a Reed-Solomon erasure code based context-aware distributed storage system. Then, a mixed integer linear programming (MILP) is proposed to optimize the system efficiency. The proposed method is verified through various simulation scenarios.

Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We are especially interested in the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads. To reduce the computation overhead of the baseline scheme, a packet-block-based mechanism is also proposed, which allows one to trade detection accuracy for lower computation complexity. Through extensive simulations, we verify that the proposed mechanisms achieve significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

Location-based services (LBSs) have raised serious privacy concerns in the society, due to the possibility of leaking a mobile user's location information in enabling location-dependent services. While existing location-privacy studies are mainly focused on preventing the leakage of a user's location in accessing the LBS server, the possible privacy leakage in the calculation of the user's location, i.e., the localization, has been largely ignored. Such a privacy leakage stems from the fact that a localization algorithm typically takes the location of anchors (reference points for localization) as input, and generates the target's location as output. As such, the location of anchors and target could be leaked to others. An adversary could further utilize the leakage of anchor's locations to attack the localization infrastructure and undermine the accurate estimation of the target's location. To address this issue, in this paper, we study the multi-lateral privacy-preserving localization problem, whereby the location of a target is calculated without the need of revealing anchors' location, and the knowledge of the localization outcome, i.e., the target's location, is strictly limited to the target itself. To fully protect the user's privacy, our study protects not only the user's exact location information (the geo-coordinates), but also any side information that may lead to a coarse estimate of the location. We formulate the problem as a secure least-squared-error (LSE) estimation for an overdetermined linear system and develop three privacy-preserving solutions by leveraging combinations of information-hiding and homomorphic encryption. These solutions provide different levels of protection for location-side information and resilience to node collusion and have the advantage of being able to trade a user's privacy requirements for better computation and communication efficiency. Through numerical results, we verify the significant efficiency improvement of the proposed schemes over existing multiparty secure LSE algorithms.

Advances in wearable devices and pervasive computing provide unprecedented opportunity for ubiquitous realtime e-Healthcare and patient monitoring by placing wirelessly connected sensors in, on, and around the body of patients. Due to the privacy-sensitive and mission-critical nature of these wireless body area sensor networks (WBASNs), as well as the desire to use them for long-time uninterrupted monitoring of patients' vital physiological signals, the privacy/security and energy efficiency of WBASNs are of primary concerns. In this paper, we propose a novel In-network AES Equivalent (IAE) mechanism to protect the security/privacy and maintain good energy efficiency for WBASNs at the same time. IAE achieves this goal by outsourcing part of the energy-consuming cryptographic operation to other deliberately-selected peer sensor nodes so as to balance the energy consumption of the entire network. An analytical model is proposed to characterize the computation and communication energy consumption of IAE, based on which we optimize the outsourcing under given security constraints. Through extensive simulations, we verify the effectiveness and efficiency of the proposed mechanism in prolonging the network lifetime under given security requirements.

Location based services (LBSs) have raised serious privacy concerns in the society, due to the possibility of leaking a mobile user's location information in enabling location-dependent services. While existing location-privacy studies are mainly focused on preventing the leakage of user's location in accessing the LBS server, the possible privacy leakage during the localization process has been largely ignored. Such a privacy leakage stems from the fact that a localization algorithm typically takes the location of anchors (i.e., reference points for localization) as input, and generates the target's location as output. As such, the location of anchors, and consequently the target's location, could be leaked to others. An adversary could further utilize the leakage of anchor's locations to attack the localization infrastructure and undermine the accurate estimation of the target's location. To address this issue, in this paper, we study the multi-lateral privacy preserving localization problem, whereby the location of a target is calculated without the need of revealing anchors' location, and the knowledge of the localization outcome is strictly limited to the target itself. To fully protect user's privacy, our study protects not only the user's exact location information (the geo-coordinates), but also any side information that may lead to a coarse estimate of the location. Three privacy-preserving localization solutions are developed by leveraging combinations of information hiding and homomorphic encryption. These solutions provide different levels of protection for location side information and resilience to node collusion, and have the advantage of being able to trade user's privacy requirements for better computation/communication efficiency.

Wireless networks which would connect vehicles via the Internet to a location based service, LBS, also would expose vehicles to online surveillance. In circumstances when spatial cloaking is not effective, such as when continuous precise location is required, LBSs may be designed so that users relay dummy queries through other vehicles to camouflage true locations. This paper introduces PARROTS, Position Altered Requests Relayed Over Time and Space, a privacy protocol which protects LBS users' location information from LBS administrators even (1) when the LBS requires continuous precise location data in a vehicular ad hoc network, (2) when LBS administrators collude with administrators of vehicular wireless access points (a.k.a. roadside units, or RSUs), and (3) when precise location data might be deanonymized using map databases linking vehicle positions with vehicle owners' home/work addresses and geographic coordinates. Defense against deanonymization requires concealment of endpoints, the effectiveness of which depends on the density of LBS users and the endpoint protection zone size. Simulations using realistic vehicle traffic mobility models varying endpoint protection zone sizes measure improvements in privacy protection.

In this paper, we study the quality-of-service (QoS) support for realtime traffic in cognitive radio (CR) networks when spectrum availability and quality is not known a priori. A resource-constrained CR relies on sequential channel sensing and probing to resolve spectrum uncertainty and search for good transmission opportunities in real time. We are interested in maximizing the effective throughput the CR can achieve with a desired confidence (success probability) under a spectrum access delay constraint. This quantity can be interpreted as the QoS-compliant (e.g., min-rate and delay) capacity of the CR link under the uncertain spectrum environment. The optimization is formulated as a finite-horizon optimal stopping problem under the objective of maximizing a given percentile of the rate of return at the stopping time. This formulation cannot be directly solved by classical optimal stopping theory, because the latter only supports a mean-reward objective function. A novel transformation is developed to convert the problem into solving a series of sub problems, each of which optimizes a transformed mean-reward of the original problem and therefore can be solved using classical optimal stopping method. We prove the monotonicity of the sub problems, based on which we develop a fast algorithm to efficiently find the unique solution to the original problem. To account for different MAC mechanisms used in practice, our analysis considers both non-recall and recall channel decision strategies. Extensive simulations are performed to verify the effectiveness and significance of the optimization. We show that significant gains (e.g., over 30%) on the QoS-compliant capacity can be achieved by the proposed algorithm when compared with the counterparts.

A futures market is proposed for spectrum market in order to manage the financial risk in spectrum trade and discovering future price. The similarities between the spectrum market and electricity market are pointed out. The model of spot market price in the spectrum market is discussed using a set of real activity measurements of cellular base stations. The characteristics of price are analyzed using Black-Scholes model. Based on the models, the option pricing strategies, which determine the price of futures options, and hedging policies, which determine the investment in the futures markets in order to reduce the financial risk, are discussed analytically. Both strategies are tested using the measurement data.

In this paper, we study the quality-of-service (QoS) support for realtime traffic in cognitive radio (CR) networks when spectrum availability and quality is not known a priori. A resource-constrained CR relies on sequential channel sensing and probing to resolve spectrum uncertainty and search for good transmission opportunities in real time. We are interested in maximizing the effective throughput the CR can achieve with a desired confidence (success probability) under a delay constraint. This quantity can be interpreted as the QoS-compliant capacity of the CR link under the uncertain spectrum environment. The optimization is formulated as a finite-horizon optimal stopping problem under the objective of maximizing a given percentile of the rate of return at the stopping time. This formulation cannot be directly solved by classical optimal stopping theory, because the latter only supports a mean-reward objective function. A novel transformation is developed to convert the problem into solving a series of sub problems, each of which optimizes a transformed mean-reward of the original problem and therefore can be solved using classical optimal stopping method. We prove the monotonicity of the sub problems, based on which we develop a fast algorithm to efficiently find the unique solution to the original problem. Extensive simulations are performed to verify the effectiveness and significance of the optimization. We show that significant gains (e.g., over 30%) on the QoS-compliant capacity can be achieved by the proposed algorithm when compared with the counterparts.

In a multi-hop wireless ad hoc network, packet losses are attributed to harsh channel conditions and intentional packet discard by malicious nodes. In this paper, while observing a sequence of packet losses, we are interested in determining whether losses are due to link errors only, or due to the combined effect of link errors and malicious drop. We are especially interested in insider's attacks, whereby a malicious node that is part of the route exploits its knowledge of the communication context to selectively drop a small number of packets that are critical to network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This architecture is privacy preserving, collusion proof, and incurs low communication and storage overheads. Through extensive simulations, we verify that the proposed mechanism achieves significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

In this paper, we explore the economic aspects of routing/relaying in a profit-driven opportunistic spectrum access (OSA) network. In this network, primary users lease their licensed spectrum to secondary radio (SR) providers, who in turn provide opportunistic routing/relaying service to end-users if this service is profitable, i.e., if the payment offered by the end-user (a.k.a. the price) exceeds the SR's relaying spectrum cost. This cost is considered private information known only to SRs. Therefore, the end-user has to rely on costs reported by SRs to determine his routing and payment strategy. The challenge comes from the selfish nature of SRs; an SR may exaggerate his cost to achieve greater profit. To give incentive to an SR to report the true cost, the payment must typically be higher than the actual cost. However, from the end-user's perspective, “overpayment” should be avoided as much as possible. Therefore, we are interested in the “optimal” route selection and payment determination mechanism that minimizes the price of the selected route while simultaneously guaranteeing truthful cost reporting by SRs. We formulate this problem as finding the least-priced path (LPP), and we investigate it without and with link capacity constraints. In the former case, polynomial-time algorithm is developed to find LPP and calculate its truthful price. In the latter case, we show that calculating the truthful price of the LPP is in general computationally infeasible. Consequently, we consider a suboptimal but computationally feasible approximate solution, which we refer to as truthful low-priced path (LOPP) routing. A polynomial-time algorithm is proposed to find the LOPP and efficiently calculate its truthful price. A payment materialization algorithm is also developed to guarantee truthful capacity reporting by SRs. The effectiveness of our algorithms in terms of price saving is verified through extensive simulations.

We study the problem of finding the least-priced path (LPP) between a source and a destination in opportunistic spectrum access (OSA) networks. This problem is motivated by economic considerations, whereby spectrum opportunities are sold/leased to secondary radios (SRs). This incurs a communication cost, e.g., for traffic relaying. As the beneficiary of these services, the end user must compensate the service-providing SRs for their spectrum cost. To give an incentive (i.e., profit) for SRs to report their true cost, typically the payment to a SR should be higher than the actual cost. However, from an end user's perspective, unnecessary overpayment should be avoided. So we are interested in the optimal route selection and payment determination mechanism that minimizes the price tag of the selected route and at the same time guarantees truthful cost reports from SRs. This setup is in contrast to the conventional truthful least-cost path (LCP) problem, where the interest is to find the minimum-cost route. The LPP problem is investigated with and without capacity constraints at individual SRs. For both cases, our algorithmic solutions can be executed in polynomial time. The effectiveness of our algorithms in terms of price saving is verified through extensive simulations.

In this paper, we are interested in cognitive radio networks (CRNs) whose operation does not rely on channel sensing. A spectrum server is responsible for collecting spectrum availability and location information from primary radio networks (PRNs), and broadcasting this information to cognitive radios. By subscribing to this broadcast, a CR knows about the spectrum opportunities without sensing channels. Spectrum opportunity under this paradigm presents a multi-level structure that generalizes the well-known channel-sensing-based binary structure. This multi-level structure reflects a microscopic spectrum opportunity for CRs, and can be exploited to increase the CRN throughput. Under this structure, we study efficient spectrum access in a multi-CR environment, with the objective of maximizing the network-wide utilization of spectrum opportunity. The difficulty of our problem comes from the fact that different CRs may decide the same channel to be available, but at different levels. Therefore, channel access needs to be carefully coordinated. Both centralized and distributed solutions are provided, supporting different modes of operation. Numerical results verify the accuracy of our algorithms and the significant gain achieved by the multi-level framework.

Under the current opportunistic spectrum access (OSA) paradigm, a common belief is that a cognitive radio (CR) can use a channel only when this channel is not being used by any neighboring primary radio (PR). Therefore, the existence of a spectrum opportunity hinges on the absence of active cochannel PRs in a macroscopic region. In this paper, we propose the concept of microscopic spectrum opportunity and show that CRs can still utilize this type of opportunities without interfering with active cochannel PRs, even when these PRs are close to them. As a result, a channel may at the same time present different levels of availability to different CRs. Channel access needs to be carefully coordinated between these CRs to avoid collisions, and more importantly, ensure efficient utilization of the spectrum opportunity from a network's standpoint. In this paper, we formulate the coordinated channel access as a joint power/rate control and channel assignment optimization problem, with the objective of maximizing the sum-rate achieved by the cognitive radio network (CRN). We develop both centralized and distributed algorithms to solve this problem. Our simulation results show that even when accounting for the implementation overhead, significant throughput gain is achieved under our designs.

Compromised node and denial of service are two key attacks in wireless sensor networks (WSNs). In this paper, we study data delivery mechanisms that can with high probability circumvent black holes formed by these attacks. We argue that classic multipath routing approaches are vulnerable to such attacks, mainly due to their deterministic nature. So once the adversary acquires the routing algorithm, it can compute the same routes known to the source, hence, making all information sent over these routes vulnerable to its attacks. In this paper, we develop mechanisms that generate randomized multipath routes. Under our designs, the routes taken by the ¿ shares¿ of different packets change over time. So even if the routing algorithm becomes known to the adversary, the adversary still cannot pinpoint the routes traversed by each packet. Besides randomness, the generated routes are also highly dispersive and energy efficient, making them quite capable of circumventing black holes. We analytically investigate the security and energy performance of the proposed schemes. We also formulate an optimization problem to minimize the end-to-end energy consumption under given security constraints. Extensive simulations are conducted to verify the validity of our mechanisms.

In a cognitive radio network (CRN), spectrum opportunities should be efficiently utilized through careful coordination between cognitive radio (CR) users. In this paper, we formulate the coordinated channel access as a joint power/rate control and channel assignment optimization problem, with the objective of maximizing the sum-rate achieved by all CRs over all channels. The problem is formulated under a generalized multi-level spectrum opportunity framework, which reflects the microscopic spatial opportunity available to CRs. A centralized polynomial-time approximate algorithm to the problem is developed. We prove the algorithm's correctness and show its accuracy through numerical examples.

Compromised-node and denial-of-service are two key attacks in wireless sensor networks (WSNs). In this paper, we study routing mechanisms that circumvent (bypass) black holes formed by these attacks. We argue that existing multi-path routing approaches are vulnerable to such attacks, mainly due to their deterministic nature. So once an adversary acquires the routing algorithm, it can compute the same routes known to the source, and hence endanger all information sent over these routes. In this paper, we develop mechanisms that generate randomized multi-path routes. Under our design, the routes taken by the "shares" of different packets change over time. So even if the routing algorithm becomes known to the adversary, the adversary still cannot pinpoint the routes traversed by each packet. Besides randomness, the routes generated by our mechanisms are also highly dispersive and energy-efficient, making them quite capable of bypassing black holes at low energy cost. Extensive simulations are conducted to verify the validity of our mechanisms.

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic transmission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all channels and then pick the best among them, due to the potentially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid collisions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR's average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that addresses all the above considerations is derived by formulating the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to conduct a "second-round" optimization over the operational parameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone.

A multi-channel parallel transmission protocol is proposed for the medium access control in cognitive radio networks (CRNs). This protocol contains two key elements: multi-channel assignment and multi-channel contention. For an incoming flow-based connection request, the minimum number of parallel channels are assigned to satisfy the rate and interference mask constraints. For the contention of the assigned channels, our protocol provides an extension of the single-channel RTS- CTS-DATA-ACK handshaking of the IEEE 802.11 scheme. The proposed MAC coherently integrates optimization results into a practical implementation. Through numerical examples, we verify that our protocol provides lower connection blocking probability and higher system throughput for CRNs than its single-channel counterpart.

For CDMA-based WSNs, we quantitatively investigate and compare the optimal energy efficiencies and control complexities for three different power/time control (PTC) schemes: PTC with independent transmission power and time (PTC-IPT), PTC with unified transmission time (PTC-UT), and PTC with unified spreading gain (PTC-USG). These schemes provide different degrees of control and require different amounts of overhead. Under each scheme, the minimization of system's energy consumption is formulated as a non-convex optimization problem. The optimal transmission power and time are derived analytically through a variable-decoupling approach. The analytical nature of our results makes it feasible to compare the performance in closed form. Numerical examples and simulations are provided to validate our analysis.

We investigate the energy efficiency in a wireless sensor networks that implements a non-persistent CSMA MAC protocol with adaptive MQAM modulation at the physical layer. The system throughput is estimated based on the number of received ACK packets. The backoff probability at the MAC layer and the modulation order at the physical layer are jointly adapted according to the traffic dynamics, leading to improved system energy efficiency while satisfying a given constraint on the packet retransmission delay. Through numerical examples and simulations, we verify the significant energy-efficiency improvements achieved by this joint optimization compared to the backoff- probability-only and the modulation-order-only adaptations.

Adaptive rate/power control schemes have great po- tential to increase the throughput of wireless CDMA networks. In this paper, we investigate the additional gains achieved through adaptation of the orthogonal modulation (OM) order .W e show that adaptive orthogonal modulation (AOM) can significantly increase network throughput while simultaneously reducing the per-bit energy consumption (compared to variable-rate, variable-power, fixed-order OM schemes). We study the problem of joint rate/power control in AOM under two different objective functions: minimizing the maximum service time and maximizing the sum of users rates. For the first objective function, we show that the optimization problem can be formulated as a generalized geometric program (GGP), which can be transformed into a nonlinear convex problem and solved optimally and efficiently. In the case of the second objective function, we obtain a lower bound on the performance gain of AOM over fixed-order OM schemes. Unlike previous works on adaptive transmission, which have focused mainly on cellular networks, ours is applicable to both ad hoc and cellular networks. Numerical results indicate that relative to a variable-rate, variable- power fixed-order OM scheme, the proposed AOM scheme achieves significant throughput and energy gains.

Recent research in wireless code-division multiple-access systems has shown that adaptive rate/power control can considerably increase network throughput relative to systems that use only power or rate control. In this paper, we consider joint power/rate optimization in the context of orthogonal modulation (OM) and investigate the additional performance gains achieved through adaptation of the OM order. We show that such adaptation can significantly increase network throughput, while simultaneously reducing the per-bit energy consumption relative to fixed-order modulation systems. The optimization is carried out under two different objective functions: minimizing the maximum service time and maximizing the sum of user rates. For the first objective function, we prove that the optimization problem can be formulated as a generalized geometric program (GGP). We then show how this GGP can be transformed into a nonlinear convex program, which can be solved optimally and efficiently. For the second objective function, we obtain a lower bound on the performance gain of adaptive OM (AOM) over fixed-modulation systems. Numerical results indicate that relative to an optimal joint rate/power control fixed-order modulation scheme, the proposed AOM scheme achieves significant throughput and energy gains.

In this paper, we address the problem of minimizing energy consumption in a CDMA-based wireless sensor network (WSN). A comprehensive energy consumption model is proposed, which accounts for both the transmit and circuit energies. Energy consumption is minimized by jointly optimizing the transmit power and transmission time for each active node in the network. The problem is formulated as a non-convex optimization. Numerical as well as closed-form approximate solutions are provided. For the numerical solution, we show that the formulation can be transformed into a convex geometric programming (GP), for which fast algorithms, such as interior point method, can be applied. For the closed-form solution, we prove that the joint power/time optimization can be decoupled into two sequential sub-problems: optimization of transmit power with transmission time serving as a parameter, and then optimization of the transmission time. We show that the first sub-problem is a linear program while the second one can be well approximated as a convex programming problem. Taking advantage of these analytical results, we further derive the per-bit energy efficiency. Our results are verified through numerical examples and simulations

We consider a wireless sensor network in which sensors are grouped into clusters, each with its own cluster head (CH). Each CH collects data from sensors in its cluster and relays them to a sink node directly or through other CHs. The coverage time of the network is defined as the time until one of the CHs runs out of battery, resulting in an incomplete coverage of the sensing region. We study the maximization of coverage time by balancing the power consumption of different CHs. Using a Rayleigh fading channel model for inter-cluster communications, we provide optimal power allocation strategies that guarantee (in a probabilistic sense) an upper bound on the end-to-end (inter-CH) path reliability. Our allocation strategies account for the interaction between routing and clustering by considering the impacts of intra- and inter-cluster traffic at each CH. Two mechanisms are proposed for achieving balanced power consumption: the routing-aware optimal cluster planning and the clustering-aware optimal random relay. For both mechanisms, the problem is formulated as a signomial optimization, which can be efficiently solved using generalized geometric programming. Numerical examples and simulations are used to validate our analysis and study the performance of the proposed schemes.

In this paper, we propose the computation-effective and capacity-near-optimal maximum total Eigenmode gains (MTEG) principle for the transmit antenna selection in the MIMO downlink of a cellular communication system. An equivalent channel matrix (ECM) is proposed to characterize the quasi-static MIMO channel with Rayleigh fading, the co-channel interference, and the additive white Gaussian noise. In accordance with MTEG principle, we prove the transmit antennas should be selected according to the descending order of the norms of their corresponding column vectors in ECM. By numerical examples, we verify the capacity efficiency of the proposed scheme. It is shown that although the least computation complexity is required by MTEG scheme, it provides larger capacity than other existing simplified near-optimal antenna selection scheme and is more close to the optimal exhaustive search in capacity achievements.

In this paper, we study the influence of decoding order on the capacity of multimedia DS-CDMA systems with imperfect successive interference cancellation. In contrast to previous studies, cancellation errors are assumed to be different for different users in this work. For any given decoding order, we derive the necessary power allocation that guarantees the QoS of the multimedia traffic. Based on this result, we prove that instead of by descending order of data rate as suggested in some literature, the system capacity is maximized by decoding users according to the ascending order of cancellation errors. We also prove that this capacity-optimal decoding order makes total residual interference minimum at the same time. Out results are verified by numerical example.

In this paper, we study the throughput optimization of data traffic for a power-constrained voice/data CDMA system by the scheduling of the data users. It is found that under a given received power budget and the constraints of transmission powers, the throughput of data traffic is maximized by selecting simultaneous data users and allocating powers according to the descending order of their received power capabilities, which is defined as the product between the transmission power limit and the channel gain. Based on this principle, a novel dynamic and channel adaptive scheduling is proposed to enhance the throughput performance of data traffic. The validity of the proposed scheme and its robustness to channel estimation error is verified by comparing with the conventional fair-sharing scheme and round-robin scheme via power simulation.

A key problem under imperfect power control in multimedia DS-CDMA networks is how to guarantee the differentiated outage probabilities of different traffic classes resulted from the uncertainty of received powers. In addition, in order to utilize the scarce wireless resource efficiently, as many users as possible should be admitted into the network while providing guaranteed quality-of-service support for them. In this work, a call admission control scheme, differentiated outage probabilities CAC or DOP-CAC, is proposed to achieve the above goals for imperfectly power controlled multimedia CDMA networks. Two important features of CDMA system are considered in our scheme: one is the power multiplexing among bursty traffics and the other is the power allocation scheme employed at the physical layer. The validation and efficiency of DOP-CAC are verified by numerical examples.

In this paper, we propose the capacity-near-optimal and computation-effective maximum total eigenmode gains (MTKG) principle for the transmit antenna selection in the MIMO downlink of a cellular communication system. An equivalent channel matrix (ECM) is proposed to characterize the quasi-static MlMO channel with Rayleigh fading, the co-channel interference, and the additive white Gaussian noise. In accordance with MTEG principle, we prove the transmit antennas should be selected according to the descending order of the norms of their corresponding column vectors in ECM. By numerical examples, the capacity efficiency of the proposed scheme is verified.

This paper develops a finite-capacity single-vacation queueing model with close-down/setup times and batch Markovian arrival process (BMAP) for the statistic behavior of the typical ARQ schemes (Stop-and-Wait, Go-Back-N, Selective Repeat) at transmitter buffer. We obtain the queue length distribution, the average waiting time of arbitrary data frame and the server utilization ratio of the transmitter for all the ARQ strategies by model analysis. Through numerical examples, the influences of the batch arrival process and the link frame transmission error process on system performance are investigated. Numerical results show that an appropriate trade-off between the users service-of-quality (e.g. average waiting time) and the system efficiency (e.g. server utilization ratio) has to be considered in the design of implementation value for the close-down time.

A closed-form capacity quasi-optimal power allocation scheme is presented for the uplink of multimedia code-division multiple-access (CDMA) systems with randomized received signal-to-interference ratio (SIR) resulted from the errors of power control. The optimality in capacity comes from that this scheme provides class-dependent SIR margins subject to the constraint of differentiated outage requirements. The statistics of signal under imperfect power control is modeled as lognormal random variable. The objective of capacity maximization is formulated as the minimization of total average received powers since the capacity of a CDMA system is interference limited. Under this model, we first derive the necessary conditions that a capacity-optimal power allocation should satisfy. By using conservative bounds, we provide a closed-form approximate solution to this optimization problem. This approximate solution provides nearly the same admissible region for multimedia traffic under imperfect power control as the accurate solution (the optimal one) does. The closed-form quasi-optimal power allocation scheme proposed in this paper is just based on this approximate solution. By numerical example we verify our analysis and show that great capacity gain (e.g., 92% as a maximum in the example) can be achieved by our scheme over its counterpart.

Abstract In a practical voice/data CDMA network, the constraint of transmission powers makes it necessary to transmit multiple data users in parallel in order to fully utilize system resource (power). How to choose those data users that should be transmitted simultaneously and allocate appropriate powers among them remains an open issue. In this paper, we prove that it is optimal in the sense of maximum data throughput to select data users and allocate powers according to the descending order of their indexes of received power capability (IRPC). Here, IRPC is defined as the product of the transmission power upper bound and the channel gain. based on this principle, a channel‐adaptive scheduling scheme, partially descending IRPC (PDI) scheduling, is proposed to achieve efficient throughput performance for data traffic while maintaining certain fairness among different users. The efficiency and fairness of the PDI scheduling are verified by comparing with the conventional fair‐sharing scheme and round‐robin scheme through computer simulations. Numerical results also reveal the robustness of the new scheme to the channel estimation errors. Copyright © 2003 John Wiley & Sons, Ltd.

This work studies the optimal power allocation scheme that maximizes the uplink capacity of cellular multimedia CDMA networks under imperfect power control. We derive the conditions that such an optimal power allocation scheme should satisfy and suggest a closed-form approximate solution (a quasi-optimal power allocation scheme) to the question based on conservative bounds. By numerical examples we verify our analysis and show that great capacity gain is achieved by our new quasi-optimal scheme under imperfect power control over the referred scheme, e.g., 92% as a maximum in the example.

We study the transmission rate (R) and bit-energy-to-interference ratio (E/sub b//I/sub 0/) needed to maintain the desired QoS requirement for a given traffic while maximizing the user capacity in multimedia CDMA networks. Closed form functions among transmission rate, bit error rate (or equivalently E/sub b//I/sub 0/), QoS requirements, and traffic characteristics are derived. Based on the dependence between R and E/sub b//I/sub 0/, we propose a capacity-optimal determination of (R, E/sub b//I/sub 0/) for a given traffic and QoS requirement. By numerical example we show that a higher user capacity is achieved by our scheme compared to those which ignore the dependence between R and E/sub b//I/sub 0/.

A key problem under imperfect power control in multimedia DS-CDMA networks is how to guarantee the differentiated outage probabilities of different traffic classes resulted by the uncertainty of received powers. In addition, in order to utilize the scarce wireless resource efficiently, as many users as possible should be admitted into the network while providing guaranteed quality-of-service support for them. In this work, a call admission control scheme, differentiated outage probabilities CAC or DOP-CAC, is proposed to achieve the above goals for imperfectly power controlled multimedia CDMA networks. Two important features of CDMA system are considered in our scheme: one is the power multiplexing among bursty traffic and the other is the power allocation scheme employed at the physical layer. The validation and efficiency of DOP-CAC are verified by numerical examples.

By using Gaussian approximation, the optimal number of simultaneous transmissions which maximizes system throughput in multi-code CDMA networks is derived as a function of system parameters including the processing gain, the packet length, and the correctable bit number. Based on this optimal number of simultaneous transmissions and the queue length of each user, a dynamic rate assignment scheme is proposed to support data users with different rate requirements while improving the system resource utilization efficiency. By numerical example the efficiency of the proposed scheme is verified.