arXiv subjects

Eryk Dutkiewicz

Publications and source records attributed to Eryk Dutkiewicz.

At least 37 records · Page 2Linked to original sources

arXiv2023

Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach

Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the way for the creation of intelligent human-like avatars. Our approach takes a first step toward the Metaverse systems in which the digital avatars are envisioned to be more intelligent by collecting and analyzing brain signals through cellular networks. In our proposed system, Metaverse users experience Metaverse applications while sending their brain signals via uplink wireless channels in order to create intelligent human-like avatars at the base station. As such, the digital avatars can not only give useful recommendations for the users but also enable the system to create user-driven applications. Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner. To this end, we propose a hybrid training algorithm that utilizes recent advances in deep reinforcement learning to address the problem. Specifically, our hybrid training algorithm contains three deep neural networks cooperating with each other to enable better realization of the mixed decision-making and classification problem. Simulation results show that our proposed framework can jointly address resource allocation for the system and classify brain signals of the users with highly accurate predictions.

eess.SP↗

arXiv2023

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

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: (i) the active-transmit message transmitted by the transmitter using conventional RF signals and (ii) the backscatter message 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.

cs.NI↗

arXiv2023

Defeating Eavesdroppers with Ambient Backscatter Communications

Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter communications in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.

cs.IT↗

arXiv2023

Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wireless resources. Specifically, an FL process can be fused with quantized Binomial mechanism-based updates contributed by multiple users. However, optimizing quantization parameters, communication resources (e.g., transmit power, bandwidth, and quantization bits), and the added noise to guarantee the DP requirement and performance of the learned FL model remains an open and challenging problem. This article aims to jointly optimize the quantization and Binomial mechanism parameters and communication resources to maximize the convergence rate under the constraints of the wireless network and DP requirement. To that end, we first derive a novel DP budget estimation of the FL with quantization/noise that is tighter than the state-of-the-art bound. We then provide a theoretical bound on the convergence rate. This theoretical bound is decomposed into two components, including the variance of the global gradient and the quadratic bias that can be minimized by optimizing the communication resources, and quantization/noise parameters. The resulting optimization turns out to be a Mixed-Integer Non-linear Programming (MINLP) problem. To tackle it, we first transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm to solve the transformed problem with an arbitrary relative error guarantee. Extensive simulations show that under the same wireless resource constraints and DP protection requirements, the proposed approximate algorithm achieves an accuracy close to the accuracy of the conventional FL without quantization/noise. The results can achieve a higher convergence rate while preserving users' privacy.

cs.LG↗

arXiv2023

MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.

cs.CR↗

arXiv2023

Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

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.

cs.NI↗

arXiv2023

MetaSlicing: A Novel Resource Allocation Framework for Metaverse

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.

cs.NI↗

arXiv2022

AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing

Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task's performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS's waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment's dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

cs.NI↗

arXiv2022

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.

cs.LG↗

arXiv2022

When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

cs.NI↗

arXiv2022

In-network Computation for Large-scale Federated Learning over Wireless Edge Networks

Most conventional Federated Learning (FL) models are using a star network topology where all users aggregate their local models at a single server (e.g., a cloud server). That causes significant overhead in terms of both communications and computing at the server, delaying the training process, especially for large scale FL systems with straggling nodes. This paper proposes a novel edge network architecture that enables decentralizing the model aggregation process at the server, thereby significantly reducing the training delay for the whole FL network. Specifically, we design a highly-effective in-network computation protocol (INC) consisting of a user scheduling mechanism, an in-network aggregation process (INA) which is designed for both primal- and primal-dual methods in distributed machine learning problems, and a network routing algorithm. Under the proposed INA, we then formulate a joint routing and resource optimization problem, aiming to minimize the aggregation latency. The problem is NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Simulation results showed that the proposed INC framework can not only help reduce the FL training latency, up to 5.6 times, but also significantly decrease cloud's traffic and computing overhead. This can enable large-scale FL.

cs.IT↗

arXiv2022

An Effective Framework of Private Ethereum Blockchain Networks for Smart Grid

A smart grid is an important application in Industry 4.0 with a lot of new technologies and equipment working together. Hence, sensitive data stored in the smart grid is vulnerable to malicious modification and theft. This paper proposes a framework to build a smart grid based on a highly effective private Ethereum network. Our framework provides a real smart grid that includes modern hardware and a smart contract to secure data in the blockchain network. To obtain high throughput but a low uncle rate, the difficulty calculation method used in the mining process of the Ethereum consensus mechanism is modified to adapt to the practical smart grid setup. The performance in terms of throughput and latency are evaluated by simulation and verified by the real smart grid setup. The enhanced private Ethereum-based smart grid has significantly better performance than the public one. Moreover, this framework can be applied to any system used to store data in the Ethereum network.

cs.CR↗

arXiv2021

MetaChain: A Novel Blockchain-based Framework for Metaverse Applications

Metaverse has recently attracted paramount attention due to its potential for future Internet. However, to fully realize such potential, Metaverse applications have to overcome various challenges such as massive resource demands, interoperability among applications, and security and privacy concerns. In this paper, we propose MetaChain, a novel blockchain-based framework to address emerging challenges for the development of Metaverse applications. In particular, by utilizing the smart contract mechanism, MetaChain can effectively manage and automate complex interactions among the Metaverse Service Provider (MSP) and the Metaverse users (MUs). In addition, to allow the MSP to efficiently allocate its resources for Metaverse applications and MUs' demands, we design a novel sharding scheme to improve the underlying blockchain's scalability. Moreover, to leverage MUs' resources as well as to attract more MUs to support Metaverse operations, we develop an incentive mechanism using the Stackelberg game theory that rewards MUs' contributions to the Metaverse. Through numerical experiments, we clearly show the impacts of the MUs' behaviors and how the incentive mechanism can attract more MUs and resources to the Metaverse.

cs.CR↗

arXiv2021

Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services

In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, taking into account limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on the MUs' provided information/features. To mitigate straggling problems with privacy-awareness, each selected MU can then encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, each selected MU can propose a contract to the MAP according to its expected trainable local data and privacy-protected encrypted data. To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging FL-based multiple utility functions. These utility functions account for the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Then, we transform the problem into an equivalent low-complexity problem and develop a light-weight iterative algorithm to effectively find the optimal solutions. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing the network's social welfare, i.e., total utility of all participating entities, up to 114% under the privacy cost consideration compared with those of baseline methods.

cs.NI↗

arXiv2021

Defeating Super-Reactive Jammers With Deception Strategy: Modeling, Signal Detection, and Performance Analysis

This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter's activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals' dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.

cs.NI↗

arXiv2021

Enabling Large-Scale Federated Learning over Wireless Edge Networks

Major bottlenecks of large-scale Federated Learning(FL) networks are the high costs for communication and computation. This is due to the fact that most of current FL frameworks only consider a star network topology where all local trained models are aggregated at a single server (e.g., a cloud server). This causes significant overhead at the server when the number of users are huge and local models' sizes are large. This paper proposes a novel edge network architecture which decentralizes the model aggregation process at the server, thereby significantly reducing the aggregation latency of the whole network. In this architecture, we propose a highly-effective in-network computation protocol consisting of two components. First, an in-network aggregation process is designed so that the majority of aggregation computations can be offloaded from cloud server to edge nodes. Second, a joint routing and resource allocation optimization problem is formulated to minimize the aggregation latency for the whole system at every learning round. The problem turns out to be NP-hard, and thus we propose a polynomial time routing algorithm which can achieve near optimal performance with a theoretical bound. Numerical results show that our proposed framework can dramatically reduce the network latency, up to 4.6 times. Furthermore, this framework can significantly decrease cloud's traffic and computing overhead by a factor of K/M, where K is the number of users and M is the number of edge nodes, in comparison with conventional baselines.

cs.IT↗

arXiv2021

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to next-generation networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

cs.LG↗

arXiv2021

Secure Swarm UAV-assisted Communications with Cooperative Friendly Jamming

This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol which allows the UAVs to collaborate to harvest wireless energy, relay information, and jam the eavesdropper. To evaluate the secrecy rate, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation and to show the effectiveness of the proposed framework in terms of SOP as compared with the conventional amplify-and-forward relaying system. Using the derived SOP, one can obtain engineering insights to optimize the energy harvesting time and the number of UAVs in the swarm to achieve a given secrecy protection level. The analytical SOP derived in this work can also be helpful in future UAV secure-communications optimizations (e.g., trajectory, locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP.

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