arXiv subjects

Tony Q. S. Quek

Publications and source records attributed to Tony Q. S. Quek.

At least 253 records · Page 14Linked to original sources

arXiv2020

Age of Information in Random Access Networks: A Spatiotemporal Study

We investigate the age-of-information (AoI) in the context of random access networks, in which transmitters need to send a sequence of information packets to intended receivers over shared spectrum. We establish an analytical framework that accounts for the key features of a wireless system, including the fading, path loss, network topology, as well as the spatial interactions amongst the queues. A closed-form expression is derived to quantify the network average AoI and its accuracy is verified via simulations. Our analysis unveils several unconventional behaviors of AoI in such a setting. For instance, even when the packet transmissions are scheduled in a last-come first-serve (LCFS) order whereby the newly incoming packets can replace the undelivered ones, the network average AoI may not monotonically decline with respect to the packet arrival rates, if the infrastructure is densely deployed. Moreover, the ALOHA protocol is shown to be instrumental in reducing the AoI when the packet arrival rates are high, yet it cannot contribute to decreasing the AoI in the regime of infrequent packet arrivals.

cs.IT↗

arXiv2020

Coordinated Container Migration and Base Station Handover in Mobile Edge Computing

Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time and hence end-to-end (E2E) delay. However, when users are mobile, such containers need to be migrated to other edge servers located closer to the MUs to keep the E2E delay low. Meanwhile, the mobility of MUs necessitates handover among base stations in order to keep the wireless connections between MUs and base stations uninterrupted. In this paper, we address the joint problem of container migration and base-station handover by proposing a coordinated migration-handover mechanism, with the objective of achieving low E2E delay and minimizing service interruption. The mechanism determines the optimal destinations and time for migration and handover in a coordinated manner, along with a delta checkpoint technique that we propose. We implement a testbed edge computing system with our proposed coordinated migration-handover mechanism, and evaluate the performance using real-world applications implemented with Docker container (an industry-standard). The results demonstrate that our mechanism achieves 30%-40% lower service downtime and 13%-22% lower E2E delay as compared to other mechanisms. Our work is instrumental in offering smooth user experience in mobile edge computing.

cs.NI↗

arXiv2020

Optimal Pricing for Job Offloading in the MEC System with Two Priority Classes

Multi-Access edge computing (MEC) is an emerging paradigm where users offload computationally intensive jobs to the Access Point (AP). Given that the AP's resources are shared by selfish users, pricing is a useful tool for incentivising users to internalize the negative externality of delay they cause to other users. Nevertheless, different users have different negative valuations towards delay as some are more delay sensitive. To serve heterogeneous users, we propose a priority pricing scheme where users can get served first for a higher price. Our goal is to find the prices such that in decision making, users will choose the class and the offloading frequency that jointly maximize social welfare. With the assumption that the AP knows users' profit functions, we derive in semi-closed form the optimal prices. However in practice, the reporting of users's profit information incurs a large signalling overhead. Besides, in reality users might falsely report their private profit information. To overcome this, we further propose a learning-based pricing mechanism where no knowledge of individual user profit functions is required. At equilibrium, the optimal prices and average edge delays are learnt, and users have chosen the correct priority class and offload at the socially optimal frequency.

cs.IT↗

arXiv2020

Effect of Spatial and Temporal Traffic Statistics on the Performance of Wireless Networks

The traffic in wireless networks has become diverse and fluctuating both spatially and temporally due to the emergence of new wireless applications and the complexity of scenarios. The purpose of this paper is to quantitatively analyze the impact of the wireless traffic, which fluctuates both spatially and temporally, on the performance of the wireless networks. Specially, we propose to combine the tools from stochastic geometry and queueing theory to model the spatial and temporal fluctuation of traffic, which to our best knowledge has seldom been evaluated analytically. We derive the spatial and temporal statistics, the total arrival rate, the stability of queues and the delay of users by considering two different spatial properties of traffic, i.e., the uniformly and non-uniformly distributed cases. The numerical results indicate that although the fluctuation of traffic (reflected by the variance of total arrival rate) when the users are clustered is much fiercer than that when the users are uniformly distributed, the unstable probability is smaller. Our work provides a useful reference for the design of wireless networks when the complex spatio-temporal fluctuation of the traffic is considered.

cs.NI↗

arXiv2020

Mobile Data Transactions in Device-to-Device Communication Networks: Pricing and Auction

Device-to-Device (D2D) communication is offering smart phone users a choice to share files with each other without communicating with the cellular network. In this paper, we discuss the behaviors of two characters in the D2D data transaction model from an economic point of view: the data buyers who wish to buy a certain quantity of data, as well as the data sellers who wish to sell data through the D2D network. The optimal price and purchasing strategies are analyzed and deduced based on game theory.

cs.NI↗

arXiv2020

A Unified Framework for SINR Analysis in Poisson Networks with Traffic Dynamics

We study the performance of wireless links for a class of Poisson networks, in which packets arrive at the transmitters following Bernoulli processes. By combining stochastic geometry with queueing theory, two fundamental measures are analyzed, namely the transmission success probability and the meta distribution of signal-to-interference-plus-noise ratio (SINR). Different from the conventional approaches that assume independent active states across the nodes and use homogeneous point processes to model the locations of interferers, our analysis accounts for the interdependency amongst active states of the transmitters in space and arrives at a non-homogeneous point process for the modeling of interferers' positions, which leads to a more accurate characterization of the SINR. The accuracy of the theoretical results is verified by simulations, and the developed framework is then used to devise design guidelines for the deployment strategies of wireless networks.

cs.IT↗

arXiv2020

Multi-Armed Bandit Based Client Scheduling for Federated Learning

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

cs.IT↗

arXiv2020

Is Blockchain Suitable for Data Freshness? -- Age-of-Information Perspective

Recent advances in blockchain have led to a significant interest in developing blockchain-based applications. While data can be retained in blockchains, the stored values can be deleted or updated. From a user viewpoint that searches for the data, it is unclear whether the discovered data from the blockchain storage is relevant for real-time decision-making process for blockchain-based application. The data freshness issue serves as a critical factor especially in dynamic networks handling real-time information. In general, transactions to renew the data require additional processing time inside the blockchain network, which is called ledger-commitment latency. Due to this problem, some users may receive outdated data. As a result, it is important to investigate if blockchain is suitable for providing real-time data services. In this article, we first describe blockchain-enabled (BCE) networks with Hyperledger Fabric (HLF). Then, we define age of information (AoI) of BCE networks and investigate the influential factors in this AoI. Analysis and experiments are conducted to support our proposed framework. Lastly, we conclude by discussing some future challenges.

cs.DC↗

arXiv2020

Performance Analysis for Multi-Antenna Small Cell Networks with Clustered Dynamic TDD

Small cell networks with dynamic time-division duplex (D-TDD) have emerged as a potential solution to address the asymmetric traffic demands in 5G wireless networks. By allowing the dynamic adjustment of cell-specific UL/DL configuration, D-TDD flexibly allocates percentage of subframes to UL and DL transmissions to accommodate the traffic within each cell. However, the unaligned transmissions bring in extra interference which degrades the potential gain achieved by D-TDD. In this work, we propose an analytical framework to study the performance of multi-antenna small cell networks with clustered D-TDD, where cell clustering is employed to mitigate the interference from opposite transmission direction in neighboring cells. With tools from stochastic geometry, we derive explicit expressions and tractable tight upper bounds for success probability and network throughput. The proposed analytical framework allows to quantify the effect of key system parameters, such as UL/DL configuration, cluster size, antenna number, and SINR threshold. Our results show the superiority of the clustered D-TDD over the traditional D-TDD, and reveal the fact that there exists an optimal cluster size for DL performance, while UL performance always benefits from a larger cluster.

cs.IT↗

arXiv2020

Joint Optimal Software Caching, Computation Offloading and Communications Resource Allocation for Mobile Edge Computing

As software may be used by multiple users, caching popular software at the wireless edge has been considered to save computation and communications resources for mobile edge computing (MEC). However, fetching uncached software from the core network and multicasting popular software to users have so far been ignored. Thus, existing design is incomplete and less practical. In this paper, we propose a joint caching, computation and communications mechanism which involves software fetching, caching and multicasting, as well as task input data uploading, task executing (with non-negligible time duration) and computation result downloading, and mathematically characterize it. Then, we optimize the joint caching, offloading and time allocation policy to minimize the weighted sum energy consumption subject to the caching and deadline constraints. The problem is a challenging two-timescale mixed integer nonlinear programming (MINLP) problem, and is NP-hard in general. We convert it into an equivalent convex MINLP problem by using some appropriate transformations and propose two low-complexity algorithms to obtain suboptimal solutions of the original non-convex MINLP problem. Specifically, the first suboptimal solution is obtained by solving a relaxed convex problem using the consensus alternating direction method of multipliers (ADMM), and then rounding its optimal solution properly. The second suboptimal solution is proposed by obtaining a stationary point of an equivalent difference of convex (DC) problem using the penalty convex-concave procedure (Penalty-CCP) and ADMM. Finally, by numerical results, we show that the proposed solutions outperform existing schemes and reveal their advantages in efficiently utilizing storage, computation and communications resources.

cs.IT↗

arXiv2020

Why is My Secret Leaked? Discovering Vulnerabilities in Device-to-Device File Sharing

The number of active users of Wi-Fi Direct Device-to-Device file sharing applications on Android has exceeded 1.8 billion. Wi-Fi Direct, also known as Wi-Fi P2P, is commonly used for peer-to-peer, high-speed file transfer between mobile devices, as well as a close proximity connection mode for wireless cameras, network printers, TVs and other IoT and mobile devices. For its end users, such type of direct file transfer does not incur cellular data charges. However, despite the popularity of such applications, we observe that the software vendors tend to prioritize the ease of user flow over the security in their implementations, which leads to serious security flaws. We perform a comprehensive security analysis in the context of security and usability and report our findings in the form of 17 Common Vulnerabilities and Exposures (CVE) which have been disclosed to the corresponding vendors. To address the similar flaws at the early stage of the application design, we propose a joint consideration of security and usability for such applications and their protocols that can be visualized in form of a customised User Journey Map (UJM).

cs.CR↗

arXiv2020

Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff. Our demo is also available online: https://rebrand.ly/91a71

cs.LG↗

arXiv2020

Energy-Aware Offloading in Time-Sensitive Networks with Mobile Edge Computing

Mobile Edge Computing (MEC) enables rich services in close proximity to the end users to provide high quality of experience (QoE) and contributes to energy conservation compared with local computing, but results in increased communication latency. In this paper, we investigate how to jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the orignal problem into three subproblems named as offloading selection (PO ), transmission power optimization (PT ), and subcarriers and computing resource allocation (PS ), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO , employ the equivalent parametric convex programming to cope with the objective function which is in the form of sum of ratios in PT , and deal with PS by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate that the proposed algorithm outperforms the existing schemes.

cs.NI↗

arXiv2020

AoI and Energy Consumption Oriented Dynamic Status Updating in Caching Enabled IoT Networks

Caching has been regarded as a promising technique to alleviate energy consumption of sensors in Internet of Things (IoT) networks by responding to users' requests with the data packets stored in the edge caching node (ECN). For real-time applications in caching enabled IoT networks, it is essential to develop dynamic status update strategies to strike a balance between the information freshness experienced by users and energy consumed by the sensor, which, however, is not well addressed. In this paper, we first depict the evolution of information freshness, in terms of age of information (AoI), at each user. Then, we formulate a dynamic status update optimization problem to minimize the expectation of a long term accumulative cost, which jointly considers the users' AoI and sensor's energy consumption. To solve this problem, a Markov Decision Process (MDP) is formulated to cast the status updating procedure, and a model-free reinforcement learning algorithm is proposed, with which the challenge brought by the unknown of the formulated MDP's dynamics can be addressed. Finally, simulations are conducted to validate the convergence of our proposed algorithm and its effectiveness compared with the zero-wait baseline policy.

cs.IT↗

arXiv2020

Enhancing Physical Layer Security of Random Caching in Large-Scale Multi-Antenna Heterogeneous Wireless Networks

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions.

cs.IT↗

arXiv2020

On Safeguarding Privacy and Security in the Framework of Federated Learning

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding data leakage from the UEs, thereby preserving privacy and security to some extend. However, even if raw data are not disclosed from UEs, individual's private information can still be extracted by some recently discovered attacks in the FL architecture. In this work, we analyze the privacy and security issues in FL, and raise several challenges on preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to illustrate the discussed issues and possible solutions.

cs.NI↗

arXiv2020

Multicast eMBB and Bursty URLLC Service Multiplexing in a CoMP-Enabled RAN

This paper is concerned with slicing a radio access network (RAN) for simultaneously serving two typical 5G and beyond use cases, i.e., enhanced mobile broadband (eMBB) and ultra-reliable and low latency communications (URLLC). Although many researches have been conducted to tackle this issue, few of them have considered the impact of bursty URLLC. The bursty characteristic of URLLC traffic may significantly increase the difficulty of RAN slicing on the aspect of ensuring a ultra-low packet blocking probability. To reduce the packet blocking probability, we re-visit the structure of physical resource blocks (PRBs) orchestrated for bursty URLLC traffic in the time-frequency plane based on our theoretical results. Meanwhile, we formulate the problem of slicing a RAN enabling coordinated multi-point (CoMP) transmissions for multicast eMBB and bursty URLLC service multiplexing as a multi-timescale optimization problem. The goal of this problem is to maximize multicast eMBB and bursty URLLC slice utilities, subject to physical resource constraints. To mitigate this thorny multi-timescale problem, we transform it into multiple single timescale problems by exploring the fundamental principle of a sample average approximation (SAA) technique. Next, an iterative algorithm with provable performance guarantees is developed to obtain solutions to these single timescale problems and aggregate the obtained solutions into those of the multi-timescale problem. We also design a prototype for the CoMP-enabled RAN slicing system incorporating with multicast eMBB and bursty URLLC traffic and compare the proposed iterative algorithm with the state-of-the-art algorithm to verify the effectiveness of the algorithm.

cs.NI↗

arXiv2020

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers' geographic locations, is not well understood. In this paper, we leverage a spatiotemporal model, which allows one to characterize the age of information (AoI) from a joint queueing-geometry perspective, for the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we also derive accurate and tractable expressions for the peak AoI. Numerical results reveal that: i) the packet arrival rate directly affects the service process due to queueing interactions, ii) the proposed scheme can adapt to traffic variations and largely reduce the peak AoI, and iii) the proposed scheme scales well as the network grows in size. This is done by adaptively adjusting the radio access probability at each transmitter to the change of the ambient environment.

cs.IT↗