Wireless caching is one of the promising technologies to mitigate the traffic burden of cellular networks and the large cost of deploying a higher volume of wired backhaul by introducing caching storage. In the manner of “cutting” wired equipments, all types of vehicles can be readily leveraged as serving access points with caching storage, where their moving nature should be taken into account to improve latency and data throughput. In this paper, we consider a mobility-aware vehicular caching which has a role in offloading delay-tolerable contents from cellular networks. We first clarify the influence of mobility in cellular caching networks, then set the mobility-aware optimization problem of vehicular caching to carry on delay-tolerable contents. Trace-driven numerical results based on rural and urban topographies show that, in presence of individual demand for delay-tolerable contents, the proposed vehicular caching scheme enhances the quality-of-service (QoS) (maximally twofold) relying on the contents delivery being centrally or distributedly controlled.

An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

X communication model with two receivers is introduced to underwater acoustic networks, in which each transmitter sends an independent message to each receiver. Based on distance aligned structure, we propose a scheme, which can perform perfect interference alignment. The feasibility is also illustrated in three dimensional Euclidean space.

This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.

In the evolution of wireless networks such as wireless sensor networks, mobile ad-hoc networks, and delay/disruption tolerant networks, the Store-Carry-Forward (SCF) message relaying paradigm has been commonly featured and studied with much attention. SCF networking is essential for offsetting the deficiencies of intermittent and range limited communication environments because it allows moving wireless communication nodes to act as “mobile relay nodes”. Such relay nodes can store/carry/process messages, wait for a better opportunity for transmission, and finally forward the messages to other nodes. This paper starts with a short overview of SCF routing and then examines two SCF networking scenarios. The first one deals with large content delivery across multiple islands using existing infrastructural transportation networks (e.g., cars and ferries) in which mobility is uncontrollable from an SCF viewpoint. Simulations show how a simple coding technique can improve flooding-based SCF. The other scenario looks at a prototype system of unmanned aerial vehicle (UAV) for high-quality video surveillance from the sky in which mobility is partially controllable from an SCF viewpoint. Three requisite techniques in this scenario are highlighted - fast link setup, millimeter wave communications, and use of multiple links. Through these examples, we discuss the benefits and issues of the practical use of SCF networking-based systems.

Networks-on-Chip (or NoCs, for short) play important roles in modern and future multi-core processors as they are highly related to both performance and power consumption of the entire chip. Up to date, many optimization techniques have been developed to improve NoC's bandwidth, latency and power consumption. But a clear answer to how energy efficiency is affected with these optimization techniques is yet to be found since each of these optimization techniques comes with its own benefits and overheads while there are also too many of them. Thus, here comes the problem of when and how such optimization techniques should be applied. In order to solve this problem, we build a runtime framework to throttle these optimization techniques based on concise performance and energy models. With the help of this framework, we can successfully establish adaptive selections over multiple optimization techniques to further improve performance or energy efficiency of the network at runtime.

State-of-the-art parallel computers, which are growing in parallelism, require a lot of things in their interconnection networks. Although wide spectrum of efforts in research and development for effective and practical interconnection networks are reported, the problem is still open. One of the largest issues is congestion control that intends to maximize the network performance in terms of throughput and latency. Throttling, or injection limitation, is one of the center ideas of congestion control. We have proposed a new class of throttling method, Entropy Throttling, whose foundation is entropy concept of packets. The throttling method is successful in part, however, its potentials are not sufficiently discussed. This paper aims at exploiting capabilities of the Entropy Throttling method via comprehensive evaluation. Major contributions of this paper are to introduce two ideas of hysteresis function and guard time and also to clarify wide performance characteristics in steady and unsteady communication situations. By introducing the new ideas, we extend the Entropy throttling method. The extended methods improve communication performance at most 3.17 times in the best case and 1.47 times in average compared with non-throttling cases in collective communication, while the method can sustain steady communication performance.

This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.

In recent years, applications of neural networks with Clifford algebra have become widespread. Hyperbolic numbers are useful Clifford algebra to deal with hyperbolic geometry. It is difficult when Hopfield neural network is extended to hyperbolic versions, though several models have been proposed. Multistate or continuous hyperbolic Hopfield neural networks are promising models. However, the connection weights and domain of activation function are limited to the right quadrant of hyperbolic plane, and the learning algorithms are restricted. In this work, the connection weights and activation function are extended to the entire hyperbolic plane. In addition, the energy is defined and it is proven that the energy does not increase.

In this paper, we investigate the degrees of freedom (DoF) of a MIMO cellular interfering network (CIN) with L (L≥3) cells and K users per cell. Previous works established the DoF upper bound of LK(M+N)/(LK+1) for the MIMO CIN by analyzing the interference alignment (IA) feasibility, where M and N denote the number of antennas at each base station (BS) and each user, respectively. However, there is still a gap between the DoF upper bound and the achievable DoF in existing designs. To address this problem, we propose two linear IA schemes without symbol extensions to jointly design transmit and receive beamforming matrices to align and eliminate interference. In the two schemes, the transmit beamforming vectors are allocated to different cluster structures so that the inter-cell interference (ICI) data streams from different ICI channels are aligned. The first scheme, named fixed cluster structure (FCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of fixed dimension and can achieve the DoF upper bound under some system configurations. The second scheme, named dynamic cluster structure IA (DCS-IA) scheme, allocates ICI beamforming vectors to the cluster structures of dynamic dimension and can get a tradeoff between the number of antennas at BSs and users so that ICI alignment can be applied under various system configurations. Through theoretical analysis and numerical simulations, we verify that the DoF upper bound can be achieved by using the FCS-IA scheme. Furthermore, we show that the proposed schemes can provide significant performance gain over the time division multiple access (TDMA) scheme in terms of DoF. From the perspective of DoF, it is shown that the proposed schemes are more effective than the conventional IA schemes for the MIMO CIN.

Spatial stochastic models have been much used for performance analysis of wireless communication networks. This is due to the fact that the performance of wireless networks depends on the spatial configuration of wireless nodes and the irregularity of node locations in a real wireless network can be captured by a spatial point process. Most works on such spatial stochastic models of wireless networks have adopted homogeneous Poisson point processes as the models of wireless node locations. While this adoption makes the models analytically tractable, it assumes that the wireless nodes are located independently of each other and their spatial correlation is ignored. Recently, the authors have proposed to adopt the Ginibre point process — one of the determinantal point processes — as the deployment models of base stations (BSs) in cellular networks. The determinantal point processes constitute a class of repulsive point processes and have been attracting attention due to their mathematically interesting properties and efficient simulation methods. In this tutorial, we provide a brief guide to the Ginibre point process and its variant, α-Ginibre point process, as the models of BS deployments in cellular networks and show some existing results on the performance analysis of cellular network models with α-Ginibre deployed BSs. The authors hope the readers to use such point processes as a tool for analyzing various problems arising in future cellular networks.

How to manage the video streaming in future networks is becoming a more and more challenging issue. Recent studies on vehicular networks depict a new picture of the next generation Intelligent Transport System (ITS), with high level road safety and more comfortable driving experience. To cope with the heterogeneous network development for the next generation cellular network, centralized medium control is promising to be employed upon Road Side Unit (RSU). To accommodate the QoS constraints posed by video services in vehicular networks, the scalable video coding (SVC) scheme in H.264/AVC standard family offers spatial and temporal scalabilities in the video dissemination. In this paper, we target the resource allocation and layer selection problem for the multi-user video streaming over highway scenario, by employing SVC coding scheme for the video contents. We propose a Resource Allocation and Layer Selection (RALS) algorithm, which explicitly takes account of the utility value of each Group Of Picture (GOP) among all the vehicular users. Simulation results show that our proposed RALS algorithm outperforms the comparison schemes in typical scenarios.

A number of parallel applications run on a high-performance computing (HPC) system simultaneously. Job mapping and scheduling become crucial to improve system utilization, because fragmentation prevents an incoming job from being assigned even if there are enough compute nodes unused. Wireless supercomputers and datacenters with free-space optical (FSO) terminals have been proposed to replace the conventional wired interconnection so that a diverse application workload can be better supported by changing their network topologies. In this study we firstly present an efficient job mapping by swapping the endpoints of FSO links in a wireless HPC system. Our evaluation shows that an FSO-equipped wireless HPC system can achieve shorter average queuing length and queuing time for all the dispatched user jobs. Secondly, we consider the use of a more complicated and enhanced scheduling algorithm, which can further improve the system utilization over different host networks, as well as the average response time for all the dispatched user jobs. Finally, we present the performance advantages of the proposed wireless HPC system under more practical assumptions such as different cabinet capacities and diverse subtopology packings.

Established in self-organized mode between mobile terminals (MT), mobile Ad Hoc networks are characterized by a fast change of network topology, limited power dissipation of network node, limited network bandwidth and poor security of the network. Therefore, this paper proposes an efficient one round certificateless authenticated group key agreement (OR-CLAGKA) protocol to satisfy the security demand of mobile Ad Hoc networks. Based on elliptic curve public key cryptography (ECC), OR-CLAGKA protocol utilizes the assumption of elliptic curve discrete logarithm problems (ECDLP) to guarantee its security. In contrast with those certificateless authenticated group key agreement (GKA) protocols, OR-CLAGKA protocol can reduce protocol data interaction between group users and it is based on efficient ECC public key infrastructure without calculating bilinear pairings, which involves negligible computational overhead. Thus, it is particularly suitable to deploy OR-CLAGKA protocol on MT devices because of its limited computation capacity and power consumption. Also, under the premise of keeping the forward and backward security, OR-CLAGKA protocol has achieved appropriate optimization to improve the performance of Ad Hoc networks in terms of frequent communication interrupt and reconnection. In addition, it has reduced executive overheads of key agreement protocol to make the protocol more suitable for mobile Ad Hoc network applications.

In data center networks, Quantized Congestion Notification (QCN) has been ratified as the standard congestion management mechanism in the link layer. Since QCN nonlinearly switches between the rate increase and decrease stages, it is very difficult to understand QCN in depth and provide theoretical guidelines on setting the buffer size of the QCN switch. This paper gives an explicit formula for the boundary of buffer occupancy of the QCN switch. Specifically, based on the fluid model of QCN, we first derive the uniformly asymptotic stability condition of the QCN system. Then, under the condition that QCN is uniformly asymptotically stable, we analyze the buffer occupancy of the QCN switch theoretically and show that the classic rule-of-thumb for buffer sizing is not suitable for QCN. Finally, simulations validate the accuracy of our theoretical results.

Among various training concepts for speaker adaptation, Speaker Adaptive Training (SAT) has been successfully applied to a standard Hidden Markov Model (HMM) speech recognizer, whose state is associated with Gaussian Mixture Models (GMMs). On the other hand, focusing on the high discriminative power of Deep Neural Networks (DNNs), a new type of speech recognizer structure, which combines DNNs and HMMs, has been vigorously investigated in the speaker adaptation research field. Along these two lines, it is natural to conceive of further improvement to a DNN-HMM recognizer by employing the training concept of SAT. In this paper, we propose a novel speaker adaptation scheme that applies SAT to a DNN-HMM recognizer. Our SAT scheme allocates a Speaker Dependent (SD) module to one of the intermediate layers of DNN, treats its remaining layers as a Speaker Independent (SI) module, and jointly trains the SD and SI modules while switching the SD module in a speaker-by-speaker manner. We implement the scheme using a DNN-HMM recognizer, whose DNN has seven layers, and elaborate its utility over TED Talks corpus data. Our experimental results show that in the supervised adaptation scenario, our Speaker-Adapted (SA) SAT-based recognizer reduces the word error rate of the baseline SI recognizer and the lowest word error rate of the SA SI recognizer by 8.4% and 0.7%, respectively, and by 6.4% and 0.6% in the unsupervised adaptation scenario. The error reductions gained by our SA-SAT-based recognizers proved to be significant by statistical testing. The results also show that our SAT-based adaptation outperforms, regardless of the SD module layer selection, its counterpart SI-based adaptation, and that the inner layers of DNN seem more suitable for SD module allocation than the outer layers.

This paper proposes a new quantization framework on activation function of deep neural networks (DNN). We implement fixed-point DNN by quantizing the activations into powers-of-two integers. The costly multiplication operations in using DNN can be replaced with low-cost bit-shifts to massively save computations. Thus, applying DNN-based speech recognition on embedded systems becomes much easier. Experiments show that the proposed method leads to no performance degradation.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

A digital map is a simple dynamical system that is related to various digital dynamical systems including cellular automata, dynamic binary neural networks, and digital spiking neurons. Depending on parameters and initial condition, the map can exhibit various periodic orbits and transient phenomena to them. In order to analyze the dynamics, we present two simple feature quantities. The first and second quantities characterize the plentifulness of the periodic phenomena and the deviation of the transient phenomena, respectively. Using the two feature quantities, we construct the steady-versus-transient plot that is useful in the visualization and consideration of various digital dynamical systems. As a first step, we demonstrate analysis results for an example of the digital maps based on analog bifurcating neuron models.