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This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users. [ABSTRACT FROM AUTHOR]

During the last few years, with the decrease of their sizes and costs, Unmanned Aerial Vehicles (UAVs) became more feasible for their use in general-purpose applications. As an important basis for UAV applications, the Air-to-Ground (A2G) radio propagation channel has gained attention in the channel modeling literature. However, whereas the A2G propagation channel is inherently dynamic (time-varying), the majority of the available models do not consider such a time-variability. This paper proposes a channel multi-path component (MPC) tracking algorithm and shows its ability to analyze the data collected by a real A2G measurement campaign in a suburban environment. Based on the obtained results, a time-varying statistical channel model for A2G communications in realistic suburban scenarios is proposed. The model is able to stochastically characterize parameters related to the birth and time-of-life of the multi-path components (MPCs), as well as their evolution in terms of delay, Doppler frequency or magnitude. Correlation coefficients to relate different channel characteristics are also obtained. Our work shows that the MPCs evolution over time for the UAV A2G channel can be described by simple regular patterns. [ABSTRACT FROM AUTHOR]

Handwriting is an essential skill for developing sensorimotor and intellectual skills in children. Handwriting constitutes a complex activity relying on cognitive, visual-motor, memory and linguistic abilities, and is therefore challenging to master, especially for children with learning difficulties such as those with cognitive, sensorimotor or memory deficits. Recently-emerged haptic guidance systems have a potential to facilitate the acquisition of handwriting skills in both adults and children. In this paper we present a longitudinal experimental study that examined the effects of haptic guidance to improve handwriting skills in children with cognitive and fine motor delays as a function of the handwriting complexity in terms of visual familiarity and haptic difficulty. A haptic-based handwriting training platform that provides haptic guidance along the trajectory of a handwriting task was utilized. 12 children with cognitive and fine motor delays defined in terms of intellectual difficulty (IQ score) and mild motor difficulty in pincer grasp control, participated in the study. Children were divided into two groups, a target group and a control group. The target group completed haptic-guided training and pencil-and-paper test whereas the control group took only the pencil-and-paper test without any training. A total of 32 handwriting tasks was used in the study where 16 tasks were used for training while the entire 32 tasks were completed for evaluation. Results demonstrated that the target group performed significantly better than the control group for handwriting tasks that are visually familiar but haptically difficult (Wilcoxon signed-rank test, p $< $ 0.01). An improvement was also seen in the performance of untrained tasks as well as trained tasks (Spearman’s linear correlation coefficient, 0.667; p $=$ 0.05). In addition to confirming that haptic guidance can significantly improve motor functions, this study revealed a significant effect of task difficulty (visual familiarity and haptic complexity) on the effectiveness of haptic guidance for handwriting skill acquisition for children with cognitive and fine motor delays. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment. [ABSTRACT FROM AUTHOR]

This paper studies low-latency streaming codes for the multi-hop network. The source transmits a sequence of messages to a destination through a chain of relays, and requires the destination to reconstruct each message by its deadline. We assume that each communication link is subjected to a certain maximum number of packet erasures. The case of a single relay (a three-node network) was considered in Fong et al. (2020). A coding scheme known as symbol-wise decode and forward was proposed. In the present work, we propose an alternative scheme that is different from Fong et al. (2020) and still achieves the same rate as in Fong et al. (2020) for the one hop case as the field-size goes to infinity. Furthermore, our proposed scheme naturally generalizes to the case of multiple-relay nodes yielding new achievable rates for this setting. The main difference with Fong et al. (2020) is that our proposed scheme exploits the ability of the relay nodes to adapt the transmission based on the erasures on the previous link. Hence, we refer to our scheme as “state-dependent” and contrast it with the scheme in Fong et al. (2020) that is state-independent. Our scheme requires the relay nodes to append a header to the transmitted packets, and we show that the size of the header does not depend on the field-size of the code. We also derive an upper bound on the maximal streaming rate achievable over a network with an arbitrary number of relays. We show that this upper bound matches our achievable rate in the special case when the maximal number of erasures on the first link is greater than or equal to the maximal number of erasures on each of the following links, and the field size goes to infinity. [ABSTRACT FROM AUTHOR]

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation. [ABSTRACT FROM AUTHOR]

In vehicular networks, authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that requires additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently when considering the factor of privacy preservation. In this paper, we propose to offload partial revocation task to network edges to lighten the revocation process in vehicles. Particularly, we design a method to achieve decentralized Global Certificate Revocation List (GCRL) management in network edges using permissioned blockchain technologies. In the method, both tamper-proof property and quick synchronization of the GCRL are guaranteed. Besides, our method can detect illegal revocation behaviours by validation in vehicles. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric, and compare with Proof-of-Work scheme in terms of blockchain synchronization latency performance. [ABSTRACT FROM AUTHOR]

The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines. [ABSTRACT FROM AUTHOR]

Classical scheduling techniques are well-known to underestimate the average project duration, yet they remain widely used in practice due to their simplicity. In this paper, the new Critical Duration Index (CDI) is proposed. This index indirectly allows anticipation for the probability of a project ending late, as well as the average project duration extension compared with a deterministic project duration estimate. The accuracy of two simple regression expressions that use the CDI was tested on two representative data sets of 4,100 artificial and 108 empirical (real) projects. Results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing enhanced forecasting possibilities, calculating the CDI only requires basic scheduling information that is available at the planning stage. It can thus be easily adopted by project managers to improve their project duration estimates over prior deterministic techniques. Classical scheduling techniques like Gantt charts or the critical path method are known to underestimate the project duration. However, they remain widely used in practice due to their simplicity. In this paper, we have proposed the Critical Duration Index (CDI). This index allows anticipation for the probability of a project ending later than the estimate produced with a Gantt chart or the critical path method. It also allows an estimation for how much longer the project might take to be completed, that is, the extension of the delay. The accuracy of two simple regression expressions that use the CDI was tested on two representative artificial and real project data sets. Our results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing one to forecast the probability and extension of a project delay, calculating the CDI only requires basic scheduling information that is available at the planning stage. Hence, it can be easily adopted by project managers to improve their project duration estimates over other deterministic techniques. [ABSTRACT FROM AUTHOR]

In this paper, nonlinear differential equations with a generalized proportional Caputo fractional derivative and finite delay are studied in this paper. The eventual presence of impulses in the equations is considered, and the statement of initial value problems in three cases is defined: namely non-instantaneous impulses, instantaneous impulses and no impulses. The relations between these three cases are discussed. Additionally, some stability properties are investigated. We apply the Mittag–Leffler function which plays a vital role and which gives well-known bounds on the norm of the solutions. The symmetry of this function about a line and the bounds is a property that plays an important role in stability. Several sufficient conditions are presented via appropriate new comparison results and the modified Razumikhin method. The results generalize several known results in the literature. [ABSTRACT FROM AUTHOR]

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure called Cyclic Scheduler Detection (CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under $\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm. [ABSTRACT FROM AUTHOR]

Government environment is the main determinant in attracting private investment into public–private partnership (PPP) projects, especially in developing countries. Red tape, an indicator of a government's efficiency, plays a critical role in private investment in PPP projects. Reasonable levels of red tape can enhance government transparency and promote private investment, while excessive red tape usually represents low governance efficiency and imposes further risk on private investors. This article explores how developing countries’ red tape affects private investment in PPP projects by examining the moderating effect of corruption. Analyzing a database of 308 PPP projects in 111 developing countries, the study reveals an inverted U-shaped relationship between red tape and private investment. Corruption weakens the positive relationship between red tape and private investment at low levels of red tape and mitigates their negative relationship at high levels. The study integrates the inconsistent results of previous research that postulated either a positive or negative relationship between red tape and private investment by proposing a nonlinear model. It also theorizes the moderating effect of corruption based on real management practice and illustrates its mechanism in absorbing private investment in PPP projects. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is emerging as a promising paradigm for network architectures. By migrating network functions from dedicated hardware appliances to software instances running in a virtualized environment, NFV promises to offer a more flexible way to deploy and manage service function chains (SFCs). When deploying these SFCs to users, the network operators require not only the user's demands (e.g., end-to-end delay) to be satisfied, but require the cost of SFC mapping to be minimized (e.g., resource consumption). To fulfill these two goals, in this paper, we have investigated how to realize the delay-aware and resource-efficient SFC mapping in inter-datacenter elastic optical networks. We first formulate an integer linear programming (ILP) model to solve the problem exactly. The main optimization goal in the ILP model is to jointly minimize resource consumption and end-to-end delay to achieve optimal virtual network function placement. Then, a delay-aware and load-balancing mapping algorithm (DALB-MA) is proposed to obtain a near-optimal solution in a reasonable amount of time. Finally, we evaluate the proposed ILP model and heuristic algorithms via extensive simulations. The results indicate that the proposed ILP model and the DALB-MA outperform the benchmarks in terms of block rate, average cost, number of CPUs used, maximum frequency slot index, and delay margin gain. [ABSTRACT FROM AUTHOR]

This paper presents a novel embedding protocol that allows for safe and efficient operation of the Hybrid Autonomous Intersection Management (H-AIM) protocol concurrently with actuated and adaptive signal controllers. The proposed protocol extends H-AIM to allow it to cope with some operational uncertainty that is common in actuated signal controllers. A novel approach for computing safety bounds on signal timing is presented as a way of insuring safety in the face of demand uncertainty. Experimental results show the feasibility and effectiveness of combining H-AIM with actuated controllers for various levels of connected and autonomous vehicle (CAV) market penetration and different combinations of common signal control schemes, namely, adaptive signal timing, fixed signal timing, and signal actuation. The benefits are presented in terms of delay improvement when common actuation protocols are used in conjunction with the H-AIM protocol. In contrast to previous reports, the results presented in this paper suggest that mixtures of turning movement assignments that are more permissive for CAVs and less permissive for human operated vehicles are often detrimental in terms of traffic delay. Nonetheless, when implemented on top of an actuated and adaptive controller, the extended H-AIM protocol is shown to never be detrimental while presenting statistically significant reductions in total delay when more than 15% of the traffic is composed of CAVs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the PID controller design problem of path following for an autonomous ground vehicle (AGV). Firstly, a bicycle model is adopted and a vehicle offset model from the target path is integrated to the bicycle model. The PID controller considers the vehicle longitudinal speed variation for adaption to the road curvature and the stochastic delay induced by the network communication. This is realized by transforming the tuning problem of proportional-integral-derivative (PID) gains for path following into a design problem of a static-output-feedback (SOF) controller for a time-delayed linear parameter varying (LPV) model form. A sufficient condition is adopted to guarantee the stability of the closed-loop system. In order to achieve better tracking performance, we propose a strategy in which the PID gains are piecewise constant and are dependent on the road-curvature ranges. The stability of the switched system is guaranteed via the common Lyapunov function method. Grey wolf optimizer (GWO) is employed to solve the optimization problem with maximum absolute tracking error as the optimization objective and stability condition and actuator dynamics as constraints. Both simulation results based on the CarSim-Simulink joint platform and hardware-in-loop experiment results are used to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism. [ABSTRACT FROM AUTHOR]

The performance of throughput, delay, and their tradeoff in mobile ad hoc networks (MANETs) has been investigated on different assumptions. Nevertheless, few papers consider the supportive infrastructure in MANETs. With the help of supportive infrastructure, i.e., cellular networks, the performance of throughput and delay in MANETs can be improved. The impact of supportive infrastructure on the throughput–delay tradeoff in MANETs is an open research point. In this paper, we are interested in the optimal throughput–delay tradeoff in MANETs with supportive infrastructure. We investigate the supportive infrastructure that only provides transmission pipes between distant nodes. We study the optimal throughput–delay tradeoff in MANETs on different assumptions such as different densities of nodes and base stations (BSs), discrete/continuous mobility models with various velocities, and the fast/slow mobility assumption. We obtain the asymptotically optimal throughput–delay tradeoff and propose transmission policies based on random linear coding (RLC) to achieve the optimal throughput–delay tradeoff asymptotically. From the obtained optimal throughput–delay tradeoff, we find that supportive infrastructure reduces the delay bound from velocity, which is a lower bound of delay caused by nodes' velocity. In particular, if supportive infrastructure can cover the entire network, then the delay bound from velocity vanishes when per-node throughput does not exceed a threshold. Moreover, we find that there also exists a delay bound from the transmission range of the instant transmission. In addition, we observe that, for the optimal throughput–delay tradeoff in the large throughput region, the case in the continuous mobility model outperforms the case in the discrete mobility model, and the case on the slow mobility assumption outperforms the case on the fast mobility assumption. [ABSTRACT FROM PUBLISHER]

This paper examines the multiple-input multiple-output (MIMO) characteristics of the vehicular MIMO channel at 5.9 GHz by analyzing the correlation of multipath components (MPCs) in the case of high antenna separation and the angular characteristics of the same scenario. The scenario is selected as crossing traffic on urban intersections. The 2 $\times$ 2 channel measurements with a wideband channel sounder are employed for the correlation analysis, and in parallel, the same scenario is simulated with a ray-tracing tool for the analysis of the angular domain. We introduce measures and tools to describe the relevant characteristics and the performance; the limitations of the ray-tracing simulation are also subdued to critical evaluation. The focus of this paper is set on the evaluation of the presented method on one selected experiment (combination of measurement and ray-tracing simulation) rather than on an exhaustive statistical analysis of many scenarios. The combination lies in the higher level of analyzing the MIMO characteristics of the channel using two different methods. The results provide that the MIMO channels show a similar power level in terms of mean MPC power, but the linear correlation stays relatively low. Specifically, discrete MPCs tend to be more highly correlated than diffuse MPCs. Furthermore, the angular spread is highly dynamic but small and substantially increases when both vehicles are near the center of the intersection. Significant power contributions caused by buildings along the roadside are identified. [ABSTRACT FROM AUTHOR]

Time synchronization in wireless sensor networks (WSNs) is a topic that has been attracting the research community in the last decade. Most performance evaluations of the proposed solutions have been limited to theoretical analysis and simulation. They consequently ignored several practical aspects, e.g., packet handling jitters, clock drifting, packet loss, and mote limitations, which affect real implementation on sensor motes. Authors of some pragmatic solutions followed empirical approaches for the evaluation, where the proposed solutions have been implemented on real motes and evaluated in testbed experiments. This paper gives an insight on issues related to the implementation of synchronization protocols in WSN. The challenges related to WSN environment are presented; the importance of real implementation and testbed evaluation are motivated by some experiments we conducted. The most relevant implementations of the literature are then reviewed, discussed, and qualitatively compared. While there are several survey papers that present and compare the protocols from the conception perspectives, as well as others that deal with mathematical and signal processing issues of the estimators, a survey on practical aspects related to the implementation is missing. To our knowledge, this paper is the first one that takes into account the practical aspect of existing solutions. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the impact of information-theoretic secrecy constraint on the capacity and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume n legitimate nodes move according to the fast i.i.d. mobility pattern and each desires to communicate with one randomly selected destination node. There are also n^\nu static eavesdroppers located uniformly in the network and we assume the number of eavesdroppers is much larger than that of legitimate nodes, i.e., \nu>1. We propose a novel simple secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model under a few technical assumptions. Based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint D, we find that the optimal secrecy throughput capacity is \widetilde\Theta(W((D/n))^(2/3)), where W is the data rate of each link. We observe that: 1) the capacity-delay tradeoff is independent of the number of eavesdroppers, which indicates that adding more eavesdroppers will not degenerate the performance of the legitimate network as long as \nu>1; 2) the capacity-delay tradeoff of our paper outperforms the previous result \Theta((1/n\psie)) in refid="ref11"/, where \psie=n^{\nu-1}=\omega(1) is the density of the eavesdroppers. Throughout this paper, for functions f(n) and g(n), we denote f(n)=o(g(n)) if \limn\rightarrow\infty(f(n)/g(n))=0; f(n)=\omega(g(n)) if g(n)=o(f(n)); f(n)=O(g(n)) if there is a positive constant c such that f(n)\leq cg(n) for sufficiently large n; f(n)=\Omega(g(n)) if g(n)=O(f(n)); f(n)=\Theta(g(n)) if both f(n)=O(g(n)) and f(n)=\Omega(g(n)) hold. Besides, the order notation \widetilde\Theta omits the polylogarithmic factors for better readability. [ABSTRACT FROM AUTHOR]

Previous correlation-based wideband multiple-input–multiple-output (MIMO) channel models have been limited to non-line-of-sight (NLoS) scenarios where simple antenna correlation measurements are sufficient to describe the correlated Gaussian process representing the fading statistics. However, in the presence of line-of-sight (LoS) or specular reflections, simple antenna correlation measurements unfortunately do not lead to accurate fading characterizations. In this paper, we propose a trend-stationary correlation-based analytical model for wideband LoS and NLoS MIMO channels and design and validate a wideband multitone MIMO channel sounder and estimator to, respectively, measure channel impulse responses (CIRs) and estimate channel parameters for this model. Joint maximum-likelihood (ML) estimation of the LoS component and the NLoS complex correlation matrix is presented. In addition to enabling the estimation of the path correlation matrix of a wireless channel in the presence of LoS components, the modeling approach also provides tools to evaluate the validity of the assumption of uncorrelated scattering (US), even in LoS environments, which is commonly used in channel modeling. A more general model that does not assume US is also presented, which can likewise be estimated using the proposed estimator. To validate the channel sounding/model estimator techniques, we employ channel sounding waveforms and collect snapshots from the outputs of a state-of-the-art hardware MIMO channel emulator capable of emulating multipath channels under the US assumption. Receiver signal processing and channel parameter estimation are performed on the collected data, and the results are compared with the emulated channel parameters. The work is unique in that it provides a correlation-based multipath MIMO channel modeling approach that addresses LoS environments, it describes corresponding estimation techniques to extract the full complex correlation matrix, and the model estimators are backed using channel sounding with emulation-based validation. [ABSTRACT FROM PUBLISHER]

In this paper, we rigorously analyze an HIV-1 infection model with CTL immune response and three time delays which represent the latent period, virus production period and immune response delay, respectively. We begin this model with proving the positivity and bound-edness of the solution. For this model, the basic reproduction number R0 and the immune reproduction number R1 are identified. Moreover, we have shown that the model has three e-quilibria, namely the infection-free equilibrium E0, the infectious equilibrium without immune response E1 and the infectious equilibrium with immune response E2. By applying uctuation lemma and Lyapunov functionals, we have demonstrated that the global stability of E0 and E1 are only related to R0 and R1. The local stability of the third equilibrium is obtained under four situations. Further, we give the conditions for the existence of Hopf bifurcation. Finally, some numerical simulations are carried out for illustrating the theoretical results. [ABSTRACT FROM AUTHOR]

This paper deals with the speed synchronization control of a connected vehicle subject to a replay attack. A large number of replay attack signals are injected into controller area network (CAN) through external network, which greatly reduces the real-time control performance of a connected integrated motor-transmission (IMT) system. In order to ensure the performance of an IMT speed tracking system under large random message delays, a robust reset controller combined with a delay-robust speed synchronization controller satisfying energy-to-peak performance is designed in this paper. The uncertain impact caused by a replay attack is described by large random network delays which are modeled by polytopic inclusion. Then, a dynamic output-feedback controller considering the uncertainty caused by attack-delays is proposed for online speed tracking. Moreover, a robust reset controller is designed to obtain comparatively better transient response in the case of large attack-delays. In this control strategy, once the reset condition is triggered, the after-reset value calculated by linear matrix inequalities (LMIs) would replace the dynamic state vector. Finally, the effectiveness of the proposed controller is verified by comparing it with model predictive control (MPC), existing PD control considering delays and energy-to-peak robust control in terms of performance. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of asynchronous communications on stationary solutions for discrete-time multiagent systems with a fixed topology. The asynchrony in this context implies that there is no global clock to synchronize the update time in each distributed agent and also each agent updates its own state asynchronously without waiting for values received from neighboring agents. It has been noted without much attention that asynchronous stationary solutions may not be unique and disagree with a synchronous stationary value. In some applications, this discrepancy may cause serious consequences, and thus it is critical to analyze the effect of asynchrony on stationary solutions. In this paper, we develop necessary and sufficient conditions for the uniqueness of stationary solutions for discrete-time multiagent systems. In the case of nonunique stationary solutions, the normalized expected error between synchronous and asynchronous stationary solutions is also analyzed. To validate the proposed methods, we provide three example problems: 1) containment control; 2) fixed-point iteration; and 3) distributed averaging formulated in a discrete-time setting. [ABSTRACT FROM AUTHOR]

This paper is devoted to the consensus problem of networked nonlinear agents with multiple self-delays and time-varying coupling. We first establish a generalized Halanay’s inequality based on comparison theorem, and then convert the consensus problem into a stability problem of retarded differential equation with time-varying coefficients, from which sufficient conditions for consensus are derived. Our results manifest that the time-average of coupling strength over intervals of certain length, together with the underlying topology and the values of self-delays, plays a crucial role in guaranteeing consensus. Based on the theoretical analysis, an estimation of the largest admissible delay is also available. This paper provides a general framework for achieving consensus in agents with time-varying coupling, such as coupling with external perturbations, intermittent control in on–off fashion, or pulse-modulated coupling strength, etc. Furthermore, useful criteria are given for various applications which have also been verified with numerical simulations. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to play a vital role in 6G wireless systems and beyond due to the vast available bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial statistical channel model for mmWave and sub-THz frequencies based on extensive radio propagation measurements at 28 and 140 GHz conducted in an indoor office environment from 2014 to 2020. Omnidirectional and directional path loss models and channel statistics such as the number of time clusters, cluster delays, and cluster powers were derived from over 15,000 measured power delay profiles. The resulting channel statistics show that the number of time clusters follows a Poisson distribution and the number of subpaths within each cluster follows a composite exponential distribution for both LOS and NLOS environments at 28 and 140 GHz. This paper proposes a unified indoor statistical channel model for mmWave and sub-Terahertz frequencies following the mathematical framework of the previous outdoor NYUSIM channel models. A corresponding indoor channel simulator is developed, which can recreate 3-D omnidirectional, directional, and multiple input multiple output (MIMO) channels for arbitrary mmWave and sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna beamwidth. The presented statistical channel model and simulator will guide future air-interface, beamforming, and transceiver designs for 6G and beyond. [ABSTRACT FROM AUTHOR]

Currently healthcare data packets do not get any special priority while routing through the Internet of Things (IoT) networks. These data packets flow through routers using conventional QoS process which does not guarantee that a patient’s critical health data traveling in congested IoT network will actually be routed to doctors office on time. This leaves the remote medical treatment at risk with possible threat to patients’ lives. In this paper, we studied the current healthcare packet routing process in IoT and its performance issues in congested IoT networks, then proposed a sensor driven solution to prioritize healthcare data routing in congested IoT networks. In our proposed system, we add a healthcare data identifier in IP packet header at the sensor level, modify QoS software at the network router level, and provide the highest priority to healthcare data packet routing based on the healthcare data identifier seen at router QoS. A prototype has been built and tested by using TI Launchpads. Test data shows that healthcare packets with an identifier can route to doctors at 80% less latency than healthcare packets routed without an identifier. Additionally, the proposed system saves medical diagnostic cost, and more importantly, reduces the risk of losing human lives. A shorter version of this analysis has also been presented to IEEE conference. The detail analysis in this paper opens up multiple avenues for further research. [ABSTRACT FROM AUTHOR]

Delay and lag deteriorate path tracking accuracy and system stability. If not properly compensated, they can cause instability or limit the driving speed of autonomous vehicles. This paper presents a preview steering control design considering both communication delay and steering lag to achieve accurate, smooth, and computationally efficient path tracking for highly automated vehicles. Two strategies are adopted for the delay: forward state predictor and delay augmentation. The steering lag is approximated by a first-order lag system. The path tracking problem with delay and lag is solved by the preview control theory. The resulted controller is in an analytical form and is computationally efficient for online implementation. We also analyze the system stability and closed-loop responses in both the time and frequency domain. The control is implemented on an automated vehicle platform and tested inside Mcity and on open roads. Experimental results showed lower tracking errors and significantly improved stability margin compared to the controls ignoring delay and lag. [ABSTRACT FROM AUTHOR]

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies. [ABSTRACT FROM AUTHOR]

BPSK modulated GPS L1 CA signal is the most widely used GNSS signal to date, and the first path detection (FPD) of the conventional GPS L1 CA signals is the most challenging problem to ensure reliable GPS positioning in multipath environments. In this paper, we propose an FPD network (FPDN) based on multi-layer perceptron (MLP)-Mixer to extract the first path from the discrete autocorrelation function (ACF) output accurately with low computational cost. In addition, the proposed FPDN is useful in practice because it is robust to noise and achieves a high FPD performance without any prior assumption on the number of total incoming multipath, which is required for conventional signal processing-based FPD techniques. We compare the performance of the proposed FPDN to that of diverse conventional techniques, such as techniques based on narrow correlator, super-resolution, and some widely used CNNs such as VGGNet, ResNet, and U-Net, through simulations and field tests. As demonstrated, the proposed FPDN outperforms all of the compared FPD techniques in terms of the computational cost and accuracy for wide range of carrier-to-noise (C/N0) ratios. [ABSTRACT FROM AUTHOR]

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

The Smart City contexts and the adoption of Industry 4.0 are being upgraded with the latest technologies throughout all systems. New data are continuously collected in huge amounts; therefore, an efficient data aggregation scheduling scheme is highly demanded. This paper addresses the minimum time aggregation scheduling problem in duty-cycled sensor networks. Existing solutions schedule the sensory data through predefined routing structures, which limit the utilization of diverse active time slots of sensors. We propose a period-driven pipeline scheduling approach, namely PDA, that simultaneously grows the aggregation tree and assigns a transmission schedule for each node being added to the tree. Particularly, this process is performed in a top-down manner. In each iteration, corresponding to a time slot, PDA uses a multi-level ranking strategy to schedule several $\langle sender, receiver \rangle$ 〈 s e n d e r , r e c e i v e r 〉 pairs, so that in a working period ahead, the possibility to pipeline as many transmissions as possible is high. Intensive simulation results show that the proposed scheme notably works better than the best known recent algorithms by having up to 35 percent shorter aggregation time, as well as having a significantly improved network throughput and time utilization. [ABSTRACT FROM AUTHOR]

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other’s entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX’s parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100 percent, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols. [ABSTRACT FROM AUTHOR]

In this paper, a new game-theoretic multi-layer computing resource and network resource allocation algorithm is proposed for computation offloading in Multi-access Edge Computing (MEC) networks. We establish a new multi-layer system model for Data Center Operators and develop resource allocation relation between the MEC data center operators and users. We also investigate the matching relationship between the cloudlets and users. Based on the proposed multi-layer allocation algorithm, a joint optimization framework is delivered to realize the allocation of computing resources and communication channels in MEC. Simulation results show that the proposed algorithm provides effective solution for the problem of cloudlets allocation in MEC networks, and can significantly improve the computing and communication capabilities in the presence of user heterogeneity. [ABSTRACT FROM AUTHOR]

In this paper, hardware investigations show the effects of the hidden and exposed node problems in visible light communication (VLC) networks. Furthermore, the request to send/clear to send (RTS/CTS) mechanism, as a physical layer-independent solution, is used for solving the hidden node problem in VLC networks. A VLC hardware system, called VLCIoT, which was designed and implemented in our laboratory based on PHY I of the IEEE 802.15.7 standard, is used for the physical layer. The medium access control layer of the IEEE 802.15.7 is considered for the network. We implement the multiple access protocol of the IEEE 802.15.7 and the RTS/CTS mechanism on the microcontroller of each device using the C programming language. Goodput, message loss ratio, fairness, average delay, energy efficiency, network load, frame size, number of hidden nodes, and number of contending nodes are the evaluated parameters. The results show that for the IEEE 802.15.7 increase of the hidden nodes decreases the goodput and energy efficiency and increases the message loss ratio and the average delay. But, using the RTS/CTS mechanism, the hidden nodes do not affect the goodput, the message loss ratio, the energy efficiency, and the average delay because this mechanism solves the hidden node problem. The results show that the RTS/CTS mechanism increases the saturation goodput by 300%, decreases the message loss ratio by 94%, decreases the average delay by 50%, and increases the energy efficiency by 300%. The results show that at higher network load, higher data frame size, and more contending nodes, the performance of the RTS/CTS mechanism is better. Also, in symmetric networks, in which all nodes are hidden, or no hidden node exists in the network, fairness is better than the asymmetric networks, in which some nodes are hidden while others are not [ABSTRACT FROM AUTHOR]

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area. [ABSTRACT FROM AUTHOR]

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes “all-direction” lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time. [ABSTRACT FROM AUTHOR]

With the emerging connected-vehicle technologies and smart roadways, the need for intelligent adaptive traffic signal controls (ATSC) is more than ever before. This paper first proposes an Accumulated Exponentially Weighted Waiting Time-based Adaptive Traffic Signal Control (AEWWT-ATSC) model to calculate priorities of roadways for signal scheduling. As the size of the traffic network grows, it adds great complexities and challenges to computational efficiencies. Considering this, we propose a novel Distributed Multi-agent Reinforcement Learning (DMARL) with a graph decomposition approach for large-scale ATSC problems. The decomposition clusters intersections by the level of connectivity (LoC), defined by the average residual capacities (ARC) between connected intersections, enabling us to train subgraphs instead of the entire network in a synchronized way. The problem is formulated as a Markov Decision Process (MDP), and the Double Dueling Deep Q Network with Prioritized Experience Replay is utilized to solve it. Under the optimal policy, the agents can select the optimal signal durations to minimize the waiting time and queue size. In evaluation, we show the superiority of the AEWWT-ATSC based RL methods in different densities and demonstrate the DMARL with a graph decomposition approach on a large graph in Manhattan, NYC. The approach is generic and can be extended to various types of use cases. [ABSTRACT FROM AUTHOR]

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s [ABSTRACT FROM AUTHOR]

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains, as machinery and other components not to be restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that upon interference FoReCo reduces the trajectory error by more than a 34.35% in both simulation, and experimentation. We also show that FoReCo is sufficiently lightweight to be deployed in existing hardware. [ABSTRACT FROM AUTHOR]

With the integration of Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), service providers are able to provide low-latency services to mobile users for profit. In this paper, we study the online service placement and request assignment problem in an MEC network, where service requests arrive one by one without the knowledge of future arrivals, and each arrived request demands a specific service with a tolerable service delay requirement with the aim to maximize the profit of the service provider, through admitting as many service requests as possible for a given monitoring period. This optimization objective is achieved by assigning service requests to appropriate cloudlets in the MEC network, pre-installing service instances into cloudlets to shorten service delays, and accommodating new services by revoking some idle service instances from cloudlets due to limited computing resources in MEC networks. In this paper, we first show that the problem is NP-hard. We then devise an efficient deep reinforcement learning algorithm for the online service placement and request assignment problem that consists of a deep reinforcement learning-based prediction mechanism for dynamic service placement, followed by a dynamic request assignment procedure to assign requests to cloudlets. We finally evaluate the performance of the proposed algorithms by conducting experiments through simulations. Simulation results demonstrate that the proposed algorithm is promising, improving performance by 46.8% compared with that of the comparison algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service’s characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%. [ABSTRACT FROM AUTHOR]

Deterministic Networking (DetNet) has recently attracted much attention. It aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation are proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve better performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an effective and novel multi-agent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The new joint reward and internal rewards mechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes. [ABSTRACT FROM AUTHOR]

Although 5G networks have enabled mobile users to get a better experience, task scheduling remains challenging for massive Internet of Things (IoT) devices in remote areas. This paper investigates the task scheduling problem for delay-aware IoT applications in civil aircraft-augmented space-air-ground integrated networks (CAA-SAGIN), where the normalized sky access platforms (SAPs) can collect and forward the terrestrial tasks. Specifically, we first propose an access control scheme for a non-preemptive priority queuing system and a transmission control scheme with cross-layer optimization. Secondly, considering the uncertain distribution of the transmission numbers and generated data, we formulate a robust two-stage stochastic optimization problem of delay minimization. With the proposed robust task scheduling with risk aversion (RTS-RA) algorithm, the original problem can be decomposed into two subproblems, which can be further transformed into tractable semi-definite program (SDP) problems respectively. Simulation results show that the cross-layer optimization scheme can achieve a good tradeoff between delay and throughput. Also, the RTS-RA algorithm outperforms the exiting offloading schemes in terms of end-to-end delay, transmitted data, and energy consumption with lower computational complexity. [ABSTRACT FROM AUTHOR]