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Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. The data center assigns a subset of input datasets to each server in an uncoded manner, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired task function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. We first propose an information theoretic converse bound on the randomness size. We then propose secure computing schemes based on two well-known data assignments, namely fractional repetition assignment and cyclic assignment. These schemes are optimal subject to using these assignments. Motivated by the observation of the general limitation of these two schemes on the randomness size, we propose a computing scheme with novel assignment, which strictly outperforms the above two schemes. Some additional optimality results are also obtained. [ABSTRACT FROM AUTHOR]

The special issue will address research advances that enable Device-to-device (D2D) communication in cellular networks. The goal is to report on the most up-to-date contributions in this area. Device-to-device communication must be central to all topics. Authors should refer to the submission rules specified in the Information for Authors section of the JSAC guidelines (www.jsac.ucsd.edu/Guidelines/info.html) to prepare their papers. Papers should be submitted through EDAS (http://www.edas.info). [ABSTRACT FROM AUTHOR]

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks. [ABSTRACT FROM PUBLISHER]

This Special issue on the "Economics of Communication Networks and Systems" brings together state-of-the-art research contributions that address major opportunities and challenges of economic and incentive issues in communication networks and wireless systems, using tools such as game theory, pricing, contract theory, mechanism design, with emphasis on new formulations, novel analytical techniques, and varied applications scenarios. It spotlights important new developments in the emerging area of economics of wired and wireless communication systems, which may fuel further developments and germinate a sense of community among researchers who work on network economics. For this Special issue, we received 90 manuscripts out of which 20 were selected to be included after a careful and thorough three round review process. Every submission received at least three reviews, and was assigned to one of the Editors for a closer look. Papers without consensus from the reviewers were assigned additional reviewers. Papers were selected based on their appropriateness for this Special issue and their technical merit. We had to turn down a number of good papers since they did not meet both of the above criteria. The 20 papers we have selected cover various sub-fields: (i) Network mechanism design, (ii) Social networks and incentives, (iii) Network resource allocation games of medium access, routing and flow control, (iv) Network security games, and (v) Non-cooperative communication theory. [ABSTRACT FROM AUTHOR]

The Mobile Internet connects billions of smart phones and laptops. With this global connectivity, the stage is set for the emergence of: (a)Tactile Internetto deliver haptic experiences to remote users, and (b) flexible and integratedBackhaul/Fronthaulnetworks to support demands of such applications. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The article invites paper submissions for the IEEE Wireless Communications Letters (WCL) on topics including broadband wireless communications, network architectures and protocols, and software radio.

In this special issue, we cover some recent results in the following four emerging areas: 5G cellular systems, big data systems, bio/nano/molecular networks, and smart grids. In the past several years, there are various technologies emerging, which are either directly or indirectly related to communication. Some of them are over the evolution of traditional communication systems, while others are over new systems such as smart grids, molecular networks, and big data systems. [ABSTRACT FROM PUBLISHER]

The article discusses various reports published within the issue, including one by Morteza Esmaeili and Mohammad Gholami on designing low-density parity-check (LDPC) codes, one by Norifumi Kamiya and Eisaku Sasaki on the use of an efficient algorithm for systematic encoding of quasi-cyclic (QC)-LDPC codes created from cyclic maximum distance separable (MDS) codes, and one by Xiaojun Yuan and Li Ping on the constructions of systematic rateless codes based on the Luby transform (LT) code.

The article discusses various reports published within the issue, including one by H. Taoka and colleagues on indoor and field experimental results on achieving 4.92 gigabytes per second (Gbps) in a 100-microhertz (MHz) channel bandwidth in high signal-to-noise ratio (SNR) environments, and another by Yang-Seok Choi and Siavash M. Alamouti on the issue of adaptive switching between diversity based approaches and spatial multiplexing.

This section introduces a series of articles on digital communications.

Discusses various reports published within the issue, including one on progressive linear precoder optimization for multi-input-multi-output packet retransmissions and another on multiple antenna air interface for the asynchronous multiple-access channel.

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically. [ABSTRACT FROM AUTHOR]

Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active learning, and enable continual learning. Bayesian learning provides a principled approach to address all these limitations, at the cost of an increase in computational complexity. This paper studies distributed Bayesian learning in a wireless data center setting encompassing a central server and multiple distributed workers. Prior work on wireless distributed learning has focused exclusively on frequentist learning, and has introduced the idea of leveraging uncoded transmission to enable “over-the-air” computing. Unlike frequentist learning, Bayesian learning aims at evaluating approximations or samples from a global posterior distribution in the model parameter space. This work investigates for the first time the design of distributed one-shot, or “embarrassingly parallel”, Bayesian learning protocols in wireless data centers via consensus Monte Carlo (CMC). Uncoded transmission is introduced not only as a way to implement “over-the-air” computing, but also as a mechanism to deploy channel-driven MC sampling: Rather than treating channel noise as a nuisance to be mitigated, channel-driven sampling utilizes channel noise as an integral part of the MC sampling process. A simple wireless CMC scheme is first proposed that is asymptotically optimal under Gaussian local posteriors. Then, for arbitrary local posteriors, a variational optimization strategy is introduced. Simulation results demonstrate that, if properly accounted for, channel noise can indeed contribute to MC sampling and does not necessarily decrease the accuracy level. [ABSTRACT FROM AUTHOR]

This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimizes feature extraction, source coding, and channel coding in a task-oriented manner, i.e., targeting the downstream inference task rather than data reconstruction. Specifically, we leverage an information bottleneck (IB) framework to formalize a rate-distortion tradeoff between the informativeness of the encoded feature and the inference performance. As the IB optimization is computationally prohibitive for the high-dimensional data, we adopt a variational approximation, namely the variational information bottleneck (VIB), to build a tractable upper bound. To reduce the communication overhead, we leverage a sparsity-inducing distribution as the variational prior for the VIB framework to sparsify the encoded feature vector. Furthermore, considering dynamic channel conditions in practical communication systems, we propose a variable-length feature encoding scheme based on dynamic neural networks to adaptively adjust the activated dimensions of the encoded feature to different channel conditions. Extensive experiments evidence that the proposed task-oriented communication system achieves a better rate-distortion tradeoff than baseline methods and significantly reduces the feature transmission latency in dynamic channel conditions. [ABSTRACT FROM AUTHOR]

Ultra-Reliable and Low Latency Communications (URLLC) is an important use case in 5G NR that targets at 1-ms level delay sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by enhanced Mobile BroadBand (eMBB) service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through modulation and coding scheme (MCS) selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implemented DELUXE through a link-level 5G NR simulator with GPU and MathWorks 5G toolbox. Through extensive experiments, we show that DELUXE can successfully choose MCS for eMBB transmissions to maintain the desired link reliability while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement ($< 125 \mu \text{s}$) in 5G NR. [ABSTRACT FROM AUTHOR]

The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers’ QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer’s encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services. [ABSTRACT FROM AUTHOR]

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients’ private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL’s delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy. [ABSTRACT FROM AUTHOR]

This special issue aimed to bring together research HetNet (Heterogeneous Cellular Network) articles from a wide range of perspectives in different industrial and academic communities. The goals are to advance the understanding of the challenges faced over the next decade, solidify accepted models and metrics, and catalog innovative approaches to design and deploy HetNets. We were delighted to receive a total of 110 submissions to the special issue, and accepted manuscripts have been selected after a rigorous review process. Due to the large volume of submissions, the accepted papers will be published in two back-to-back JSAC special issues. In this second issue, there are 18 accepted papers, which can be broadly categorized under four major research areas: 1) traffic analysis; 2) scheduling, energy efficiency, and cloud aspects; 3) device-to-device communication; and 4) analytically tractable models. [ABSTRACT FROM PUBLISHER]

Mobile networks of the future are predicted to be much denser than today's networks to cater to increasing user demands. In this context, cloud-based radio access networks have garnered significant interest as a cost-effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best of the authors' knowledge, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud-based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10% to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers. [ABSTRACT FROM PUBLISHER]

This paper targets the performance analysis of multiple-input-multiple-output (MIMO) point-to-point single-aperture relay-assisted and MIMO relay-assisted free-space optical (FSO) communications. The objective of the paper is to compare the different FSO diversity methods via an outage probability analysis over gamma-gamma turbulence channels in the case of independent fading among the different apertures of the communicating nodes. Through a well-tailored calculation method, we approximate the gamma-gamma distribution and the gamma-gamma sum-distribution by appropriate gamma distributions. This method resulted in simple closed-form outage probability expressions that are accurate for average-to-large values of the signal-to-noise ratio (SNR) and in asymptotic expressions that are useful for evaluating the diversity gains and coding gains. In particular, we derived the diversity gains of MIMO repetition coding (RC), MIMO transmit laser selection (TLS), MIMO all-active relaying (AR) and MIMO selective-relaying (SR). We prove that MIMO-RC and MIMO-TLS, on one hand, and MIMO-AR and MIMO-SR, on the second hand, achieve the same diversity gain and we evaluate the coding gain of TLS with respect to RC and of SR with respect to AR. By comparing the MIMO and cooperative methods with point receivers, we reach the central result that it is always better, from a diversity gain point of view, to add more apertures to the source and destination rather than adding more relays in their vicinity despite the fact that the fading variance along FSO links decreases with the distance. For receivers that implement aperture averaging, we derive conditions under which MIMO techniques are unconditionally better than cooperative techniques with any network topology. [ABSTRACT FROM PUBLISHER]

Heterogeneous wireless networks (HetNets) provide a means to increase network capacity by introducing small cells and adopting a layered architecture. HetNets allocate resources flexibly through time sharing and cell range expansion/contraction allowing a wide range of possible schedulers. In this paper, we define the capacity of a HetNet down link in terms of the maximum number of downloads per second, which can be achieved for a given offered traffic density. Given this definition we show that the capacity is determined via the solution to a continuous linear program (LP). If the solution is smaller than 1 then there is a scheduler such that the number of mobiles in the network has ergodic properties with finite mean waiting time. If the solution is greater than 1 then no such scheduler exists. These results have clear implications for network planning. The above results continue to hold if a more general class of schedulers is considered. [ABSTRACT FROM AUTHOR]

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with \cal O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of \cal O(NK^2 ) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within $(N-1)K$ nats of the optimal sum-rate. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

A call for papers on privacy and utility preservation for deep packet inspection (DPI), content-aware policy control, network planning and network management and data leak prevention with DPI is presented.

A call for papers on full-duplex wireless networks and communications.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

A call for papers on electrical engineering and telecommunication systems is presented.

Results from information and communication theory have been used with phenomenal success in data storage systems, and have helped computer storage become ubiquitous and cheap. However, currently available solutions have hit a performance wall: existing approaches are designed for simpler channels, and do not match the needs of new storage technologies where the data must be packed as densely as possible on increasingly adverse mediums. Such performance provisioning not only violates fundamental information-theoretic laws but directly increases the cost of a storage system. At the device level, recent advances in emerging data storage technologies, such as non-volatile memories (NVM), bit-patterned media recording (BPMR) and heat-assisted magnetic recording (HAMR) are bound to transform the storage industry. These technologies require the information to be stored and accessed in an asymmetric manner. At the system level, massive distributed storage networks, data centers and cloud storage systems desperately need new coding schemes to improve storage efficiency. The purpose of this issue is to provide a synthesized source of recent research results and to serve as a springboard for future work in this emerging area. We invite both theoretical and applied papers [ABSTRACT FROM AUTHOR]

have helped computer storage become ubiquitous and cheap. However, currently available solutions have hit a performance wall: existing approaches are designed for simpler channels, and do not match the needs of new storage technologies where the data must be packed as densely as possible on increasingly adverse mediums. Such performance provisioning not only violates fundamental information-theoretic laws but directly increases the cost of a storage system. At the device level, recent advances in emerging data storage technologies, such as non-volatile memories (NVM), bit-patterned media recording (BPMR) and heat-assisted magnetic recording (HAMR) are bound to transform the storage industry. These technologies require the information to be stored and accessed in an asymmetric manner. At the system level, massive distributed storage networks, data centers and cloud storage systems desperately need new coding schemes to improve storage efficiency. The purpose of this issue is to provide a synthesized source of recent research results and to serve as a springboard for future work in this emerging area. We invite both theoretical and applied papers. Original, previously unpublished research articles will be considered. Authors should follow the IEEE JSAC Information for Authors: http://www.jsac.ucsd.edu/Guidelines/info.html Prospective authors are requested to submit their manuscripts according to the following timetable (via EDAS or by sending the manuscript to email: JSAC13storage@gmail.com). [ABSTRACT FROM AUTHOR]

The IEEE Communications Society, the IEEE Signal Processing Society, and the IEEE Vehicular Technology Society are pleased to announce the launch of a new journal: IEEE WIRELESS COMMUNICATIONS LETTERS (WCL), in 2012. WCL will publish timely, novel and high-quality recent results on Wireless Communications in letter format, i.e., 4 pages maximum. IEEE WIRELESS COMMUNICATIONS LETTERS topics are listed. Authors are invited to submit their manuscript to this new journal via the WCL Manuscript Central Web Site: http://mc.manuscriptcentral.com/wcl Letters should be concisely written and no longer than the 4-page limit in IEEE standard print format (10- point single-spaced double-column version) using the IEEE style files. Authors are required to upload the manuscript in double-spaced format as a PDF file and the double-column version of the manuscript at the time of submission. Authors should upload PDF versions of all items in the reference list which are not publicly available, such as unpublished (submitted) papers. [ABSTRACT FROM AUTHOR]

In this paper, we consider user location privacy in mobile edge clouds (MECs). MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users. Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell). We consider using chaff services to defend against such an eavesdropper, with a focus on strategies to control the chaffs. Assuming the eavesdropper performs maximum likelihood detection, we consider both heuristic strategies that mimic the user’s mobility and optimized strategies designed to minimize the detection or tracking accuracy. We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper’s tracking accuracy to zero when the user’s mobility is sufficiently random. We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy. The efficacy of our solutions is verified through both synthetic and trace-driven simulations. [ABSTRACT FROM PUBLISHER]

Software-defined networks (SDNs) introduce interesting new opportunities in how network routes can be defined, verified, and changed over time. Despite the logically-centralized perspective offered, however, an SDN still needs to be considered a distributed system: rule updates communicated from the controller to the individual switches traverse an asynchronous network and may arrive out-of-order. This can lead to (temporary or permanent) inconsistencies and triggered much research over the last years. We, in this paper, initiate the study of algorithms for consistent network updates in “timed SDNs”—SDNs in which individual node updates can be scheduled at specific times. While technology enabling tightly synchronized SDNs is emerging, the resulting algorithmic problems have not been studied yet. This paper presents, implements and evaluates Chronus, a system which provides provably congestion- and loop-free network updates, while avoiding the flow table space headroom required by existing two-phase update approaches. We formulate the minimum update time problem as an optimization program and propose two polynomial-time algorithms which lie at the heart of Chronus: a decision algorithm to check feasibility and a greedy algorithm to find a good update sequence. Extensive experiments on Mininet and numerical simulations show that Chronus can substantially reduce transient congestion and save over 60% of the rules compared with the state of the art. [ABSTRACT FROM PUBLISHER]