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By incorporating the redefined error monitor function into network design, an error redefinition neural network (ERNN) is proposed to control mobile redundant manipulators to execute the tracking task in this paper. The global asymptotic stability and the strong anti-disturbance capability of the ERNN are proved theoretically. Furthermore, the ERNN can overcome the overshoot and constant disturbance. Meanwhile, the ERNN is input-to-state stable while the bounded time-varying disturbance is considered as the control input.

Mobile operators collect and store the network generated traffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. Firstly, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Secondly, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Thirdly, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art.

Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks.

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with O(n⁴) variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Most real-world networks are time-varying, and many are subject to the stochastic functioning of their nodes and edges. Examples can be seen in the human brain undergoing an epileptic seizure, spontaneous infection and recovery in epidemics, and intermittent functioning of devices in the Internet of Things. Moreover, such networks are becoming increasingly large due to rapid technological advances. However, little has been done to study time-varying, large-scale, stochastic networks (SNs) from a reliability engineering perspective. Toward this goal, this article develops a fault-tolerance model for a type of time-varying network in which nodes (and/or edges) stochastically switch between active and inactive states. It considers fault tolerance from a global connectivity point of view, which has applications in many natural and engineered networks. Specifically, this article presents a Markov chain framework that models the dynamic behavior of nodes and allows for the computation of quantitative measures, including availability and time-to-failure metrics. To accommodate large-scale networks and emphasize global connectivity, this framework utilizes percolation theory, which has recently been of interest in the reliability engineering discipline, to characterize network failure. This article makes several contributions: it proposes a Markov chain framework for computing fault-tolerance metrics that is tractable for large-scale networks, it shows the existence of a phase transition in network availability of a time-varying SN, and it accounts for finite-size effects of percolation in the fault-tolerance model. The proposed methodology is applied to Erdos-Renyi random graphs and a real, large-scale power grid. Experimental results provide insights into network design, maintenance, and failure prevention of time-varying SNs.

Previous video salient object detection (VSOD) approaches have mainly focused on the perspective of network design for achieving performance improvements. However, with the recent slowdown in the development of deep learning techniques, it might become increasingly difficult to anticipate another breakthrough solely via complex networks. Therefore, this paper proposes a universal learning scheme to obtain a further 3% performance improvement for all state-of-the-art (SOTA) VSOD models. The major highlight of our method is that we propose the ‘motion quality’, a new concept for mining video frames from the ‘buffered’ testing video stream for constructing a fine-tuning set. By using our approach, all frames in this set can all well-detect their salient object by the ‘target SOTA model’ — the one we want to improve. Thus, the VSOD results of the mined set, which were previously derived by the target SOTA model, can be directly applied as pseudolearning objectives to fine-tune a completely new spatial model that has been pretrained on the widely used DAVIS-TR set. Since some spatial scenes in the buffered testing video stream are shown, the fine-tuned spatial model can perform very well for the remaining unseen testing frames, outperforming the target SOTA model significantly. Although offline model fine tuning requires additional time costs, the performance gain can still benefit scenarios without speed requirements. Moreover, its semisupervised methodology might have considerable potential to inspire the VSOD community in the future.

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

It is shown that compensation network and coil design collectively affect wireless power transfer efficiency. Based on this fact, a novel design approach of wireless power transfer (WPT) systems is proposed. Unlike majority of existing designs which aim at enhancing power transfer efficiency (PTE) at one rated operating point, the proposed design enhances the overall system PTE. This feature is essential for variable output loads such as batteries, where their internal impedance varies during charging period. To achieve this goal, improving time-weighted average efficiency of WPT system is set as the design criterion. Experimental studies of a 1.2 kW WPT system supplying constant resistive loads and battery loads verify that the proposed design enhances PTE both at rated output load and under wide output load variations.

This paper proposes a method which generates a set of weights to synthesize sum or difference pattern with precisely controlled sidelobe level (SLL), null, and dynamic range ratio (DRR) for arbitrary arrays. Our pattern synthesis approach reduces mutual coupling between the neighboring elements and complexity of the feeding network design. However, the formulated optimization problem is nonconvex due to the nonconvex objective function and fractional DRR constraint. To tackle it, we firstly introduce two sets of auxiliary variables: one for DRR constraint and the other for sidelobe and null constraints. By doing so, we then decompose the original optimization problem into three sets of subproblems characterized by the auxiliary variables and weight variables. To facilitate the subproblem with weight variables reaching its optimum values, we derive an appropriate range of step size. Finally, we iteratively solve these subproblems to obtain the solution to the original problem. Extensive experiments employing non-equispaced linear and rectangular arrays, concentric ring array, and cylinder array, are implemented to demonstrate that the developed approach can accurately control SLLs, null and DRR for arbitrary arrays.

Blockchain based cryptocurrencies have received great attention but long confirmation times of transactions hinder its adoption for micro-payments. Payment network is one of the solutions proposed to address the scalability issue. It transforms the problem into a routing problem by leveraging Hash Time Locked Contracts (HTLC). Payment networks possess unique characteristics to be considered when routing protocol is designed. Channels must stay balanced to have a sustainable network. In this paper, we present a payment network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally utilize multi-point connection for unbalanced payment scenarios. The results show that we can keep the channels in the network more equally balanced compared to minimal fee approach and multiple connections from customers to stores increases the success ratio.

The echelon utilization of retired batteries has attracted more attention as the market share of electric vehicles (EVs) increases steadily year by year. The current battery recycling methods cannot utilize the remaining value of these retired batteries effectively. Studies have shown that the Second-Life Batteries (SLBs) have a much lower cost compared to fresh batteries but retain a certain capacity simultaneously, which means they have a high secondary utilization value. Therefore, this paper proposes a novel multi-stage system planning model including Battery Energy Storage System (BESS). BESS designed in this paper involves not only the use of fresh batteries but also the echelon utilization of SLBs and considers the multiple lifespan cycles based on its State of Health (SOH). Moreover, the proposed planning strategy integrates the electricity and transportation networks in the first stage and evaluates the flexibility of the planning system by comparing the adaptation costs of each system in the second stage. The proposed planning model is verified on the IEEE 33-bus electricity system and the 20-node transportation system. According to the simulation results, the differences in system cost and system operation situations caused by fresh batteries and SLBs are analyzed, verifying the effectiveness and flexibility of echelon utilization of SLBs.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

In telecommunications, network service accessibility as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. Remote connectivity, however, would have benefited from a more inclusive consideration in the existing generations of mobile communications. To remedy this, sustainability and its social impact are being positioned as key drivers of sixth generation's (6G) research and standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions., Comment: This paper has been accepted for publication in IEEE Wireless Communications. 9 pages, 5 figures, 1 table

Internet of thing (IoT) devices are increasingly growing in mobile networks with the ubiquity of various IoT services. They share the same infrastructure with smartphones while having different requirements for communication resources and security defense mechanisms. Distinguishing IoT devices from smartphones has far-reaching implications on effective network design, resource allocation scheme, pricing scheme etc. In this paper, we distinguish between 12,107 IoT devices and 12,693 smartphones in the real world via characterizing their network traffic. The IoT devices fall into five categories, namely locating, monitoring, portable, point of sale (POS), and vehicle. We analyze the device behaviors from network domain, physical domain, and time domain, make comparisons between each kind of IoT devices and smartphones, and design effective features based on the distinguishable network behavior characteristics at packet level, traffic level, and mobility level. Then we train several classifiers based on our feature set to identify different kinds of mobile devices. Specifically, the accuracy of identifying IoT devices from smartphones achieves 95.86%, and the accuracies of distinguishing IoT devices in each category from smartphones are all over 95%. In the trained classifiers, feature importance verifies the discriminability of different network traffic characteristics observed in our multi-domain measurement. Our study reveals the network traffic behavior characteristics for IoT devices, and successfully distinguishes them from smartphones, which paves the way for better network design, resource allocation, pricing scheme, and security defense mechanisms.

In this letter, we investigate the network planning of the heterogeneous aerial networks to maximize the network coverage. Specifically, a joint high-altitude aerial base station (HABS) trajectory and low-altitude aerial base station (LABS) position optimization problem is formulated with the objective to maximize the number of covered users. Based on the feature of the air-to-ground channel, we first obtain the optimal altitude of the LABSs through the bisection method. Then, we propose a joint particle-and-fish swarm algorithm to optimize the horizontal position of the LABS. For the uncovered users, we model the user coverage probability in the HABS and devise a random interval contraction based algorithm to optimize the flying radius of the HABS. Simulation results indicate that our algorithms can fully exploit the mobility of the heterogeneous aerial networks to cover more users.

With the increasing global communication demands and the development of Internet of Things (IoT), extending the connectivity to rural and remote areas has become imperative for future networks. The sixth generation (6G) network is expected to provide heterogeneous services and seamless network coverage for everyone and everything. Combining the advantages of both satellite and terrestrial networks, the integrated satellite-terrestrial network architecture is promising to provide global broadband access for all types of users, which has drawn much attention from both the academia and industry. In this paper, we present a comprehensive survey of the state-of-the-art of integrated satellite-terrestrial networks towards 6G. First, an executive classification and summary of the integration architecture is presented from network design to performance optimization. Then, typical applications of the integrated satellite-terrestrial network are discussed based on the architecture. By considering the unique characteristics of the two networks, main challenges are pointed out when performing integration, such as the long propagation delay, complex link conditions and high dynamics of the network topology. Finally, some promising future techniques are explored from the perspective of the integrated architecture. A detailed survey of the potential integration architectures is of great importance to enable more flexible network design and construction in future 6G networks. This paper will provide valuable guideline on future research and development of integrated satellite-terrestrial networks.

Neural-network Processing Units (NPU), which specializes in the acceleration of deep neural networks (DNN) inference, is of great significance to latency-sensitive areas like robotics or edge service. However, there are few works focusing on the network design for NPU in recent studies. Most of the popular lightweight structures (e.g. MobileNet) are designed with depthwise convolution, which has less computation in theory but is not friendly to existing hardware, and the speed tested on NPU is not always satisfactory. Even under similar FLOPs (the number of multiply-accumulates), vanilla convolution operation is always faster than depthwise one. In this paper, we will propose a novel architecture named Serial and Parallel Group Network (SPGNet), which can capture discriminative multi-scale information and at the same time keep the structure compact. Extensive evaluations have been conducted on different computer vision tasks, e.g. image classification (CIFAR and ImageNet), object detection (PASCAL VOC and MS COCO) and person re-identification (Market-1501 and DukeMTMC-ReID). The experimental results show that our proposed SPGNet can achieve comparable performance with the state-of-the-art networks while the speed is 120% faster than MobileNetV2 under similar FLOPS and over 300% faster than GhostNet with similar accuracy on NPU.

Robust channel models for indoor areas are a crucial part of network planning and are immensely valuable for the small cell and indoor 5G network evolution. As the main input for many resource allocation and network planning problems, the accuracy of the path loss model can improve the overall accuracy of these techniques. Previous measurement campaigns exist for outdoor areas and higher frequencies, however extensive indoor measurements at these frequencies is missing from the literature. Both WLAN and LTE networks use 2.4 GHz and 5 GHz bands. For this work, indoor measurements were carried out in two distinct indoor environments, at two frequencies, and various models were compared. The measurements were made at the Deutsches Museum Bonn and the ICT cubes, an office space at RWTH Aachen University. Both empirical and deterministic models are tested on the data, the free space path loss model, the single and dual slope models with line-of-sight and nonline-of-sight, ray tracing models, and artificial neural network models were all tested and evaluated. Overall, the artificial neural network combined with the free space path loss model proved to be the most robust model which accurately predicted the propagation in the indoor environments, at both frequencies.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this paper, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and energy harvesting. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the CPU frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of user mobility, the EH model, and the channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Optical Wireless Communication (OWC) is being explored for application in the next-generation broadcasting networks, where possessing accurately determined user locations becomes increasingly important. Received signal strength (RSS) Visible Light Positioning (VLP)-based localisation systems aim to deliver these centimetre-level location data at a low cost by featuring but a single photodiode (PD). Maximising the VLP accuracy requires optimising the LED transmitter locations, which is missing currently. An evolutionary optimisation algorithm is proposed to determine the optimal LED locations and the associated positioning error values for various configurations. The sensitivity of the planning on the number of VLP-enabled LEDs, the LEDs' characteristics, the room dimensions and the positioning parameters is investigated. Experimental data, i.e., two datasets with 157(2) measurement points each, serve to validate the simulations.

Industrial Internet-of-Things (IIoT) applications require reliable and efficient wireless communication. Assuming dense wireless sensor networks (WSNs) operating in a harsh environment, a concept of a time-division multiple access (TDMA)-based WSN enriched with electronically steerable parasitic array radiator (ESPAR) antennas is proposed and examined in this work. The utilized antenna provides one omnidirectional and 12 directional radiation patterns that can be electronically switched by the sensor node. We introduce a relay discovery algorithm, which selects those sensor nodes with an ESPAR antenna capable to act as relay. The selection of the relay nodes is based on a certain link quality threshold that algorithm uses as input. The outcome is a reduction in the number of layers or hops with a guaranteed Quality of Service (QoS). To emphasize the physical aspect of the wireless propagation, we introduce the measured antenna radiation patterns and consider two different path-loss propagation models representing blockage-free and blockage-prone industrial environments. A number of network simulations were performed and signal-to-noise ratio (SNR) as a link quality measure was examined with respect to the network density and different measured radiation pattern settings. The main outcomes show a tradeoff between SNR per link and the percentage of nodes that can serve as relays. As a result, we propose network design guidelines that take under consideration the QoS range with respect to SNR together with an optimal number of antenna radiation patterns that should be selected as a tradeoff between latency, energy consumption, and reliability in a network.

The contemporary Network on Chips (NoCs) are becoming intricate in design to serve the high throughput and low latency demands of multicore platforms. The complexity level of interconnect module makes it extremely difficult to ensure the functional correctness at the presilicon verification stage. Hence, post-silicon debug is performed on NoC as a necessary step to capture the escaped network design faults. The traditional store and forward trace-based debug methods encounter the problems of large trace buffer requirement and limited availability of trace communication bandwidth. These constraints become more stringent for short-lived network faults (misroute, packet drop, etc.), which demand more frequent trace collection for their detection. In this regard, we propose WiND, which is wireless-enabled NoC for post-silicon debug. WiND is a robust NoC debug framework that optimally uses the limited trace buffer space and can efficiently speed up the trace communication. The proposed method augments wireless interfaces (WIs) on top of the baseline wired NoC for validation purposes. The wireless medium is utilized for long-range test payload communication to reduce the volume of trace. The WIs are also used for high-speed interchip trace transfer. A modified router architecture is used to enable the trace collection, and to enhance the trace communication. WiND platform is examined with several synthetic and SPLASH-2 benchmark workloads, and compared with the traditional wired platform. An overall improvement of 15%–26% on fault detection and 27%–34% on path reconstruction in the case of different faults is observed for the same trace buffer size.

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, DNA sequencing, and other laboratory procedures in biochemistry. A recent generation of biochips uses a micro-electrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and seamlessly integrates microelectronics and microfluidics using CMOS technology and a TSMC fabrication process. To ensure that bioassays are carried out on MEDA biochips efficiently, high-level synthesis algorithms have recently been proposed. However, as in the case of conventional DMFBs, microelectrodes are likely to fail when they are heavily utilized, and previous methods fail to consider reliability issues. In this article, we first present a new microelectrode cell (MC) design such that the droplet-sensing operation can be enabled/disabled for individual MCs. Next, “partial update” and “partial sensing” operations are presented based on an IEEE Std. 1687 IJTAG network design. Finally, wear-leveling synthesis method is proposed to ensure uniform utilization of MCs on MEDA. A comprehensive set of simulation results demonstrate the effectiveness of the proposed hardware design and design automation methods.

Effective network planning improves performance in the radio-frequency identification (RFID) system. This article proposes an optimal deployment of phased array reader antennas for RFID network planning (RNP). For practical considerations, the RNP problem is analyzed and formulated based on a multipath propagation model, where each reader is equipped with a phased array antenna. The gains and radiation directions of the antennas are adjusted by voltage states instead of gestures, reducing the cumbersome antennas redeployment. An indicator called amplitude fluctuation under narrowband (AFN) is proposed to reflect the disturbance of frequency selective fading caused by the multipath effect. To effectively address the RNP problem, an improved chicken swarm optimization algorithm with two targeted strategies is developed. Simulation and experiment comparisons with the existing algorithms demonstrate the superiority of the proposed approach.

Recent advances in networking and computing technologies have led to an increased interest in considering computation and communication in a joint and distributed mode according to the computing in the network paradigm. Furthermore, the emergence of the sixth generation (6G) networks is profiling the ever more challenging applications' requirements imposed by the new era service classes. In order to guarantee efficient implementation and availability to the upcoming applications, characterized by stringent quality of experience requirements, an accurate network design and delay analysis is becoming mandatory to pursue efficient 6G network dimensioning and enable computing in the network-based applications. Toward this goal, this article proposes a suitable end-to-end delay performance analysis and reliability evaluation in the case of a 6G network offering virtual reality services, which is considered one of the most challenging technologies for the coming new era service classes. More in depth, the aim of the article is the formulation of the stochastic end-to-end delay bound by applying supermartingale envelopes in order to allow accurate VR reliability prediction in relation to the number of users linked to the same computing node with a specified service profile. The goodness and effectiveness of the proposed approach is validated by providing comparison with simulation results and analytical predictions derived by resorting to the use of the classical Markov queueing theory.

This article studies the cross-layer network planning that tries to combine flexible Ethernet (FlexE) and elastic optical networks (EONs), for FlexE-over-EONs. We focus our investigation on the most challenging setting, i.e. , the FlexE-over-EONs based on the FlexE-aware architecture, and consider both single-hop and multi-hop scenarios for the cross-layer planning. For the single-hop scenario, we assume that all the client flows are routed over end-to-end lightpaths in the EON. We formulate a mixed integer linear programming (MILP) model for this problem, transform it into the class constrained bin packing problem (CCBP), and leverage the primal-dual interior-point (PDIP) method to propose a polynomial-time approximation algorithm for it. Then, for the multi-hop scenario, we use a more realistic assumption that each client flow can be routed over multiple lightpaths in the EON. We show that after solving the virtual topology design, the cross-layer planning in this scenario can be transformed into that in the single-hop scenario. Therefore, an integer linear programming (ILP) model is formulated to tackle the virtual topology design, and we design a polynomial-time approximation algorithm for it by modifying the well-known branch-and-bond method. To evaluate the performance of our two-step method for the multi-hop scenario, we also propose a heuristic algorithm. Extensive simulations verify that regarding large-scale cross-layer planning for FlexE-over-EONs, our approximation algorithms are significantly more time-efficient than the ILP/MILP models, and their solutions have bounded gaps to the optimal ones and are much better than those of the heuristic.

Parametric models of video quality are designed for service and network planning as well as video quality monitoring. They are widely applied in a broad range of applications especially when video streams are encrypted or even unavailable at all. Designing metrics in these models remains challenge due to limited available information. In this paper, a spatio-temporal resolution-adaptive parametric (STRAP) model is proposed to evaluate the quality of video streaming services considering the spatial and temporal resolutions. This work serves as a follow-up study for ITU Rec. P.1203.1 that we were previously involved. The relationship between the content complexity and the spatial and temporal resolutions are analyzed and incorporated into the proposed model. Moreover, the effect of video up/down-scaling in display devices on the perceived video quality is further taken into consideration. Experimental results showed that the proposed model can be used as a reliable indicator for video streaming providers to improve their services performance.

Ambient temperature is closely related to human health. Maintaining a comfortable and stable temperature is crucial for the treatment, rehabilitation and daily e-health care of patients, where the Internet of Things has been widely applied for this purpose. However, the existing research always separates the network design from temperature monitoring and control. In order to provide better e-health services, we design a solution to minimize the cost of energy consumption and delay. This work discusses a cyber-physical design for an indoor temperature monitoring system using a wireless sensor network. All sensors dynamically adjust the sleep/wake duty cycles and select the optimal anycast routing according to the sensed temperature. In addition, IoT-enabled heating, ventilation, and air conditioner (HVAC) systems for indoor temperature control have attracted unprecedented attention. HVAC can be connected to the Internet for weather and time-varying electricity price data. This work discusses the problem of minimizing the total energy cost of the HVAC system while keeping the indoor temperature within a prefixed range. The key idea is to leverage the time-varying electricity prices and do precooling/preheating using HVAC. The simulation results show that our temperature monitoring and control algorithm outperforms other heuristic schemes.

Lane marking detection is a fundamental task, which serves as an important prerequisite for automatic driving or driver-assistance systems. However, the complex and uncontrollable driving road environment as well as the discontinuous lane marking appearance make this task challenging. In this work, a novel deep neural network architecture is presented to detect lane markings in a complex environment by analyzing their structure information. There are two contributions to the network design. Firstly, a semantic-guided channel attention (SGCA) module is developed to select the low-level features of a deep convolutional neural network by taking the high-level features as the guidance. Secondly, a pyramid deformable convolution (PDC) module is formulated to enlarge the receptive fields and to capture the complex structures of lane markings by applying deformable convolutions on multiple feature maps with different scales. Hence, our network can better reduce false detection and enhance lane marking structures simultaneously. The experimental results on three benchmark datasets for lane marking detection show that our method outperforms other methods on all the benchmark datasets.

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and could trigger a high asset risk. Thus, system operators have the incentive to postpone network reinforcement, and ‘wait and see’ whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties based on a combined cumulant and Gram-Charlier expansion method. The waiting cost is quantified by the options’ cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty and avoid hastily investment.

This article proposes a network design model to minimize the worst-case network congestion against multiple link failures, where open shortest path first link weights are determined at the beginning of network operation. In the proposed model, which is called the preventive start-time optimization model against multiple link failures (PSO-M), the number of multiple link failure patterns to support is restricted by introducing a probabilistic constraint called probabilistic guarantee . If the total probability of non-connected failure patterns does not exceed a specified probability, PSO-M provides a feasible solution of link weights. Otherwise, no feasible solution can be obtained. We introduce an extended model of PSO-M, called PSO-M with link reinforcement (PSO-MLR), where links are reinforced under a budget constraint. Link reinforcement in PSO-MLR has two purposes: maintaining network connectivity and reducing the worst-case congestion ratio. Numerical results show that PSO-M offers lower worst-case congestion ratios than the start-time optimization model, where link weights are obtained against the non-failure pattern assuming that multiple link failures are possible. The superiority of PSO-M strengthens as the average node degree of the network increases. Given a fixed budget, PSO-MLR allows the worst-case congestion ratio to be varied within a specific range. PSO-MLR can support a part of non-connected failure patterns to determine link weights, and so is a valuable enhancement of PSO-M.

Locations of mobile agents are often requisite information for wireless applications such as sensor networks and Internet of Things (IoT). As the network size increases, verifying the localizability of all nodes in a network quickly becomes intractable. In this article, we turn to analyzing the unbounded localizability of infinite stochastic networks under sequential localization methods. Specifically, we prove the existence of the phase transition on the probability of localizing an unbounded subnetwork from a bounded initial anchor set in Poisson point process networks. The phase transition occurs when the node intensity of the network reaches a critical intensity, which is determined by the adopted sequential localization method. Furthermore, we develop a simulation method to obtain tight upper and lower bounds of the critical intensity for two-dimensional (2-D) networks with high confidence, and provide the numerical bounds under several typical sequential localization methods. We also show by simulation that the percentage of localizable nodes increases rapidly near the critical intensity, which provides guidelines for network design and deployment.

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundancy requirements on the measurement and communication resources of the network. In this paper, we take a step towards alleviating such strict requirements by exploring two complementary directions: (i) making a small subset of the nodes immune to attacks, or "trusted", and (ii) incorporating diversity into the network. We define graph-theoretic constructs that formally capture the notions of redundancy, diversity, and trust. Based on these constructs, we develop a resilient estimation algorithm and demonstrate that even relatively sparse networks that either exhibit node-diversity, or contain a small subset of trusted nodes, can be just as resilient to adversarial attacks as more dense networks. Finally, given a finite budget for network design, we focus on characterizing the complexity of (i) selecting a set of trusted nodes, and (ii) allocating diversity, so as to achieve a desired level of robustness. We establish that, unfortunately, each of these problems is NP-complete.

The current pandemic crisis has highlighted the utmost importance of communication networks. The dramatic development of teleworking imposed by strict confinement measures, the need to access educational materials from home, the likely degradation of social relationships are among the impacts of the pandemic that inevitably affect the operation and the performance of networks. The design and the operation of tomorrow’s networks will have to learn from the outbreak that exacerbates the critical need for massive digital inclusion, robust and resilient infrastructures, as well as dynamically adaptive networking to better cope with various, possibly degraded, network access conditions. Of course, the virus is not the only reason to rethink network design: current 5G deployments are among the networking technologies that encourage the emergence of new services, such as immersive services based upon Augmented Reality/Virtual Reality techniques that are pretty demanding in terms of QoS. This industry vision paper provides a network operator’s perspective on some of the forthcoming networking challenges. It explores a few technical options that are likely to be instrumental in the conception and the delivery of optimized, automated and protective networks, for the benefits of the end-user, regardless of his/her connectivity capabilities, whether he/she is in motion or not, and regardless of the nature of the service or contents he/she needs to access.

Bistatic backscatter communication is emerged as a promising technique to significantly enlarge the lifetime of Internet of Things (IoT) network due to its inherently low-power passive component. However, the effective communication range is limited to only several meters. This article studies the tag circuit shunt network, and propose three modes, namely series mode, parallel mode, and mixed mode, to adjust circuit load impedance of the tag to extend the communication range as well as address the integrated circuit (IC) power supply problem. Specifically, we formulate the bit error rate (BER) minimization problems for the three modes by changing the reflection coefficients, subject to power supply constraint. The resulting problems are shown to be nonconvex fractional optimization problems, which are hard to be solved optimally in general. We first obtain a globally optimal solution to the series mode problem by exploiting the hidden monotonic structure based on monotonic optimization theory. Subsequently, we propose a low-complexity iterative suboptimal algorithm for the three modes based on the successive convex approximation (SCA) techniques. Numerical results show that when the direct link is available, the mixed mode outperforms the parallel mode and series mode, and can adaptively adjust the reflection coefficient to satisfy the requirement of IC power supply. In contrast, when the direct link is unavailable, the series mode is the best choice in terms of IC power supply. In addition, traditional on-off keying modulation is shown to be suitable for a low IC power supply, whereas a shunt network is necessary for high of power supply. Furthermore, the performance of SCA-based method closely approaches the optimal solution while with much lower complexity.

Immediately after the occurrence of a natural disaster, communication infrastructures used in daily life become temporarily unavailable. Under such a situation, intermittently connected mobile ad hoc networks (MANETs) play an important role in providing post-disaster networking. While previous studies on such networks have mainly focused on one-to-one messaging applications, the importance of monitoring applications has become increasingly important in recent years. For monitoring applications, the key performance measure is given by the freshness of the information, rather than the traditional delay characteristics. In this paper, we present a mathematical analysis of the age of information (AoI) for intermittently connected MANETs, which captures the information freshness of monitoring applications. We further investigate basic principles in the network design based on the analytical results obtained. In particular, we discuss the AoI-energy tradeoff from different perspectives of source and relay nodes.

Indoor localization via radio-frequency identification (RFID) carries critical importance due to its high accuracy and low hardware requirement. The positions of reader antennas can affect the positioning accuracy and coverage in RFID network. In this letter, we present a novel RFID network planning (RNP) approach to optimize the deployment of reader antennas for accurate 3-D location. First, the 3-D antenna radiation mode of a passive UHF RFID system is set up. Then, the received signal strength (RSS) and path loss characteristics (PLC) are analyzed and a restricted genetic algorithm (RGA) is developed to obtain the optimal solution of the RNP via maximizing the total reward function with constraints. Finally, the convolutional neural network (CNN) and weighted $K$ -nearest neighbor (WKNN) algorithms are respectively used to verify the localization effect. For comparison, the Cramer-Rao lower bound (CRLB) is also derived. The experimental results show that the proposed approach can improve the positioning accuracy as well as the coverage, and enhance the anti-noise capability of the location system.

This letter presents a small-scale wireless distributed cooperative secure-communication network (WDCSN) design using a novel spectral graph FIR filter to achieve desired secrecy capacity at the intended receiver in the presence of single and multiple eavesdroppers. An undirected weighted graph $\mathcal {G} (V, \mathcal {E}, W)$ , with relays, eavesdroppers, source, and destination are as nodes ( $V$ ), and circularly symmetric complex Gaussian random variables as edge weights ( $W$ ) are considered to represent the WDCSN. A graph Laplacian is used as a graph shift operator to design the proposed filter. Filter coefficients are calculated by using the least squared error as a criterion. Simulations are conducted for a two-way WDCSN to achieve the desired secrecy capacity using different graph structures considered for WDCSN. The proposed filter design's performance is quantified by using the secrecy outage probability metric. The results show that desired secrecy capacity with SOP of $10^{-2}$ is achievable using the proposed methodology, irrespective of graph structures with variable complexity. An SOP performance gain of $(60\!-\!80) \%$ is achieved over the SOP reported in the literature.

The 2-D CMOS process technology scaling may have reached its pinnacle, yet it is not feasible to manufacture all computing elements at lower technological nodes. This has opened a new branch of chip designing that allows chiplets on different technological nodes to be integrated into a single package using interposers, the passive interconnection mediums. However, establishing a high-frequency communication over an entirely passive layer is one of the significant design challenges of 2.5-D systems. In this article, we present a robust clocking architecture for a 2.5-D system consisting of 64 processor cores. This clocking scheme consists of two major components, namely, interposer clocking and on-chiplet clocking. The interposer clocking consists of clocks used to achieve global synchronicity and clocks for interchiplet communication established using the AIB protocol. We synthesized these clocking components using commercial EDA tools and analyzed them using standard tools, on-chip, and package models. We also compare these results against a 2-D design of the same benchmark and another 2.5-D clocking architecture. Our experiments show that the absolute clock power is up to 16% less, and the ratio of clock power to system power is up to 4% less in the 2.5-D design than its 2-D counterpart.

Convolutional neural networks (CNNs) are the backbones of deep learning paradigms for numerous vision tasks. Early advancements in CNN architectures are primarily driven by human expertise and by elaborate design processes. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: 1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario and 2) the search process requires vast computational resources in most approaches. To overcome these limitations, we propose an evolutionary algorithm for searching neural architectures under multiple objectives, such as classification performance and floating point operations (FLOPs). The proposed method addresses the first shortcoming by populating a set of architectures to approximate the entire Pareto frontier through genetic operations that recombine and modify architectural components progressively. Our approach improves computational efficiency by carefully down-scaling the architectures during the search as well as reinforcing the patterns commonly shared among past successful architectures through Bayesian model learning. The integration of these two main contributions allows an efficient design of architectures that are competitive and in most cases outperform both manually and automatically designed architectures on benchmark image classification datasets: CIFAR, ImageNet, and human chest X-ray. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature.

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy.

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models ). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline.

Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network planning. Predicting IIoT network traffic is significantly difficult because its frequently updated topology and diversified services lead to irregular network traffic fluctuations. Motivated by these observations, we proposed a reinforcement learning-based mechanism in this article. We modeled the network traffic prediction problem as a Markov decision process, and then, predicted network traffic by Monte Carlo $Q$ -learning. Furthermore, we addressed the real-time requirement of the proposed mechanism and we proposed a residual-based dictionary learning algorithm to improve the complexity of Monte Carlo $Q$ -learning. Finally, the effectiveness of our mechanism was evaluated using the real network traffic.

In this paper, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

To alleviate the backhaul congestion in future hybrid heterogenous networks, this paper investigates the potential benefits of implementing storages in small base stations (SBSs) operating at frequency range 2 (FR2) bands that co-exist with a tier of massive multiple input multiple output (MIMO) macro BSs (MBSs) operating at frequency range 1 (FR1) bands. We develop a unified analytical framework and derive theoretical bounds for such a cache-enabled FR1–FR2 hybrid network under limited backhaul scenario to analyze the exact and approximate latency, average success probability of file delivery, and average data rate considering two open-access user association policies: i) location-based and ii) content-based association. Numerical results demonstrate that wireless edge caching (WEC) can improve the performance of hybrid wireless networks, albeit certain trade-offs, e.g., increasing cache-enabled SBSs cannot always improve the network performance and there exists an optimal SBS density that provides the best latency and throughput performance. Furthermore, we compare the performance of the network with respect to other key network design parameters such as cache size, content popularity, backhaul capacity, and blockages for both the user associations. Our results show that latency under content-based user association is less than that of location-based user association, and although the difference in the average rates under the two user associations is not obvious, content-based association can extricate more backhaul capacity and thus reduce installation cost significantly.

Single frequency networks (SFN) are being widely adopted for different communication services. SFNs employ transmitters operating at the same frequency band and the locations of transmitter stations must be selected to improve coverage while under different constrains. In general, choosing the transmitters locations to vouch the coverage in a region of interest demands experience and expertise on network design. In this article, relevant network design aspects are mapped into a heuristic method for optimizing the location allocation of SFN transmitters. This results in an iterative algorithm which obtains the number of transmitters and their positions to achieve coverage in a region of interest (the so-called location allocation problem). The algorithm is tested in a particular region of interest in the city of Queimados in Rio de Janeiro, Brazil, demonstrating its adequate performance.

A wireless rechargeable sensor network was proposed to extend the lifetime of the wireless sensor network. In this article, a charger is combined together with a self-propelled vehicle to provide a more flexible result of charger deployment. The dynamic chargers path selection problem is defined and mapped into the traveling salesman problem. Four metaheuristic algorithms for Internet-of-Things (IoT) applications are designed, and the higher fitness value between the charging path and the number of dead IoT devices is achieved. However, metaheuristic approaches may spend more time on searching solutions so that many IoT devices overuse limited power and fail to be charged for a long time, leading to power exhaustion. In this article, the edge computing technique is applied to accelerate the obtainment of charging paths with the well-defined edge/centralized unit switching. Moreover, to assure the calculated path trustworthy and will not be tampered with, the blockchain technology is adopted. The proposed architecture maintains high-level information credibility while transmitting the information of charging paths within the cloud and edge. The simulation results showed that the proposed method is capable of achieving better charging efficiency and less deployment cost.

Despite the potential benefits of Internet of Underwater Things, a number of issues hinder its realization, including the need for communication reliability and cost-effectiveness. This article aims to optimize network design to implement cost-effective underwater acoustic sensor networks (UASNs) with 3D topology while supporting diverse communication quality of service (QoS) requirements. First, we present an analytical framework based on a queueing system that evaluates communication performances of UASNs, wherein each underwater sensor distributed within a 3D space under the sea surface performs fountain code (FC)-based automatic repeat request (ARQ) transmissions under the slotted-Aloha medium access control protocol. Under the proposed framework, we evaluate communication performances given in terms of successful FC-based ARQ transmission probability and the average queueing delay of an underwater sensor. When evaluating the performances, we formulate the service time of each underwater sensor as a function of network parameters, i.e., the density of data sink and amount of redundancy for FC-based ARQ transmission, before solving a function for accurate service time, such that each sensor can be represented by an M/G/1 queue. Further, our analysis can formulate an optimization problem that aims at minimizing total cost incurred to install and operate 3D UASNs, without compromising two communication QoS requirements. To solve this problem, we propose a recursive algorithm to approach an optimal solution in reasonable time. Numerical evaluations demonstrate the validity of the proposed algorithm.

In cellular networks, a deep knowledge of the traffic demand pattern in each cell is essential in network planning and optimization tasks. However, a precise forecast of the traffic time series per cell is hard to achieve, due to the noise originated by abnormal local events. In particular, mass social events (e.g., concerts, conventions, sport events…) have a strong impact on traffic demand. In this paper, a data-driven model to estimate the impact of local events on cellular traffic is presented. The model is trained with a large dataset of geotagged social events taken from public event databases and hourly traffic data from a live Long Term Evolution (LTE) network. The resulting model is combined with a traffic forecast module based on a multi-task deep-learning architecture to predict the hourly traffic series with scheduled mass events. Model assessment is performed over a real dataset created with geolocated social event information collected from public event directories and hourly cell traffic measurements during two months in a LTE network. Results show that the addition of the proposed model significantly improves traffic forecasts in the presence of massive events.

In the modern technological world, wireless communication has taken a massive leap from the conventional communication system to a new radio communication network. The novel concept of Fifth Generation (5G) cellular networks brings a combination of a diversified set of devices and machines with great improvement in a unique way compared to previous technologies. To broaden the user’s experience, 5G technology provides the opportunity to meet the people’s potential necessities for efficient communication. Specifically, researchers have designed a network of small cells with unfamiliar technologies that have never been introduced before. The new network design is an amalgamation of various schemes such as Heterogeneous Network (HetNet), Device-to-Device (D2D) communication, Internet of Things (IoT), Relay Node (RN), Beamforming, Massive Multiple Input Multiple Output (M-MIMO), millimeter-wave (mm-wave), and so on. Also, enhancement in predecessor’s techniques is required so that new radio is compatible with a traditional network. However, the disparate technological models’ design and concurrent practice have created an unacceptable intervention in each other’s signals. These vulnerable interferences have significantly degraded the overall network performance. This review article scrutinizes the issues of interferences observed and studied in different structures and techniques of the 5G and beyond network. The study focuses on the various interference effect in HetNet, RN, D2D, and IoT. Furthermore, as an in-depth literature review, we discuss various types of interferences related to each method by studying the state-of-the-art relevant research in the literature. To provide new insight into interference issue management for the next-generation network, we address and explore various relevant topics in each section that help make the system more robust. Overall, this review article’s goal is to guide all the stakeholders, including students, operators, engineers, and researchers, aiming to explore this promising research theme, comprehend interferences and their types, and related techniques to mitigate them. We also state methodologies proposed by the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP) and present the promising and feasible research directions toward this challenging topic for the realization of 5G and beyond network.