Presents the table of contents for this issue of the publication.

Presents the introductory editorial for this issue of the publication.

The energy harvesting cognitive wireless sensor network (EHCWSN) introduces energy harvesting technology and cognitive radio technology into the traditional wireless sensor network (WSN), which significantly prolongs the working life of the sensor node and effectively alleviates the congestion problem of the unlicensed spectrum. Due to the uncertainty of the energy harvesting process and the behavior

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design incentive and coordination mechanisms between the customers and the supplier to lower the capacity cost. This paper proposes a novel scheme based on flexible contracts

This paper presents an event-triggered cluster consensus scheme for heterogeneous nonlinear second-order multi-agent systems (MASs) subject to cyber attacks (i.e., aperiodic denial-of-service (DoS) attacks), actuator faults and integral quadratic constraints (IQCs) under directed communication topology containing a directed spanning tree. Based on local communication, an event-triggered adaptive fault-tolerant

This paper produces an efficient semidefinite programming (SDP) solution for community detection that incorporates non-graph data, which in this context is known as side information. SDP is an efficient solution for standard community detection on graphs. We formulate a semi-definite relaxation for the maximum likelihood estimation of node labels, subject to observing both graph and non-graph data

Presents corrections to the above named paper.

This paper investigates the combined economic environmental dispatch problem of Microgrids in a distributed manner. In order to optimize the competing agminated operation cost and environmental impact objectives simultaneously, a distributed method which combines the distributed consensus based algorithm with dynamic weights is proposed to assign the energy among generation units, energy storage units

The risk of severe illness and mortality from COVID-19 significantly increases with age. As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19. By taking advantage of network theory, we develop an age-stratified epidemic model for COVID-19 in complex contact networks. Specifically, we present an extension of standard

In this paper, the partial-nodes-based (PNB) state estimation issue is investigated for a class of discrete-time complex networks with constrained bit rate and bounded noises. Measurements from only a fraction of nodes in a complex network are acquired and used for state estimation. The communication between sensor nodes and estimators is accomplished over a wireless digital communication network with

The rapid development of internet of vehicles (IoV) has recently led to the emergence of diverse intelligent vehicular applications such as automatic driving, auto navigation, and advanced driver assistance, etc. However, the current vehicular communication framework, such as vehicle-to-vehicle (V2V), vehicle-to-cloud (V2C), and vehicle-to-roadside infrastructure (V2I), still remain challenging in

Network behaviors such as power control and transmission duration allocation significantly affect network performance. The existing related works explored network performance improvement by balancing radio resources between backhaul and access links of each millimeter wave (mmWave) small cell in the fifth generation (5G) cellular systems, but they only focused on network throughput instead of network

Node embedding aims to encode network nodes as a set of low-dimensional vectors while preserving certain structural properties of the network. In recent years, extensive studies have been conducted to preserve network communities, i.e., structural proximity of network nodes. However, few of them have focused on preserving the structural equivalence of network nodes, which describes the similarity of

This paper investigates the observability of discrete-time heterogeneous multi-agent systems (MASs) under an absolute protocol with switching topology and fixed topology, respectively, in which the agents are first-order and second-order integrator agents. By properly decomposing the adjacency matrix of such system into sub-matrices and selecting an appropriate output vector measured by the leaders

To investigate the impact of the strategic operation of grid-level energy storage systems on wholesale electricity markets, we study a Cournot competition model where each merchant storage owner seeks to maximize its own profit and the social planner makes the centralized economic dispatch decisions to maximize the social welfare. We establish the existence and uniqueness of the resulting (Cournot)

Due to the additive property of most machine learning objective functions, the training can be distributed to multiple machines. Distributed machine learning is an efficient way to deal with the rapid growth of data volume at the cost of extra inter-machine communication. One common implementation is the parameter server system which contains two types of nodes: worker nodes, which are used for calculating

We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server.

This paper studies a nonsmooth resource allocation problem with network resource constraints and local set constraints, where the interaction graphs among agents are generally strongly connected digraphs. First, we design a centralized continuous-time proximal gradient algorithm, where each agent uses the global Lagrangian multipliers and the global values of constraint functions. For the case that

Network theory is a useful framework for studying interconnected systems of interacting entities. Many networked systems evolve continuously in time, but most existing methods for the analysis of time-dependent networks rely on discrete or discretized time. In this paper, we propose an approach for studying networks that evolve in continuous time by distinguishing between interactions , which we model

As a tool to infer the internal state of a network that cannot be measured directly, network tomography has been extensively studied under the assumption that the measurements truthfully reflect the end-to-end performance of measurement paths, which makes the resulting solutions vulnerable to manipulated measurements. In this work, we investigate the impact of manipulated measurements via a recently

State estimation and sensor selection problems for nonlinear networks and systems are ubiquitous problems that are important for the control, monitoring, analysis, and prediction of a large number of engineered and physical systems. Sensor selection problems are extensively studied for linear networks. However, less attention has been dedicated to networks with nonlinear dynamics. Furthermore, widely

This paper studies the Laplacian spectrum and the average effective resistance of (large) graphs that are sampled from graphons. Broadly speaking, our main finding is that the Laplacian eigenvalues of a large dense graph can be effectively approximated by using the degree function of the corresponding graphon. More specifically, we show how to approximate the distribution of the Laplacian eigenvalues

Nowadays, existing public key infrastructures (PKIs) certificate authentication suffers from many security failures. Trusted certificate authorities (CAs) can issue a valid certificate for any domain name. Although CA is supposed to be trusted by a client if the certificate issued to the client links to the chain of trust (e.g., root CA or subordinate CA). By compromising any of the latter (e.g., root

The Domain Name System (DNS) plays a crucial role in the Internet. However, it is vulnerable to many attacks such as the cache poisoning attack and DDoS attack. Though some countermeasures have been proposed, they still have some limitations. In this paper, we propose B-DNS, a blockchain-based domain name system, which can provide a secure and efficient DNS service. B-DNS fills up two shortcomings

Fifth generation (5G) wireless networksare characterized by applying the software defined networking (SDN) and network function virtualization (NFV) concepts to provide higher reliability and scalability, and lower latency for advent data-hungry services. In the initial version of SDN based 5G, a centralized architecture for SDN controller has been proposed, which inherently cannot fulfill those requirements

Detecting communities in time-evolving/dynamic networks is an important operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. In this paper, we present a new technique called Delta-Screening (or simply, $\Delta$ -screening) for updating

Since the traditional mobile edge computing (MEC) server is fixed at the edge of the network, it is likely to cause severe propagation loss between the edge server and users. However, ground users can be served when the MEC server is integrated into the unmanned aerial vehicle (UAV) base station (BS). Nevertheless, there are three main factors, including the limited system radio resources, the finite

In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory. Inspired by uniform hypergraphs, we propose a new tensor-based multilinear dynamical system representation, and derive a Kalman-rank-like condition to determine the minimum number of control nodes (MCN) needed to achieve controllability of even uniform hypergraphs. We present an efficient

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications

Presents the table of contents for this issue of the publication.

Presents the introductory editorial for this issue of the publication.

Selfish mining strategy allows miners to gain unfair advantage and excess revenue in Bitcoin network, but it also disrupts the normal operation of the network. In this work, we analyze the impact of selfish behavior on the Bitcoin network through a number of performance indicators such as network connectivity, block arrival rate, node response time, and block delivery time for selfish and honest blocks

With the increasing diversity of Distributed Denial-of-Service (DDoS) attacks, it is becoming extremely challenging to design a fully protected network. For instance, Stealthy Link Flooding Attack (SLFA) is a variant of DDoS attacks that strives to block access to a target area by flooding a small set of links, and it is shown that it can bypass traditional DDoS defense mechanisms. One potential solution

LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of

Based on previous research on the finite-time output synchronization of coupled neural networks (CNNs), this paper proposes the novel concept of fixed-time output synchronization and fixed-time $H_{\infty }$ output synchronization, and extends the corresponding results to coupled reaction-diffusion neural networks (CRDNNs). In addition, the output delayed coupling and discontinuous activations are

This paper mainly discusses the passivity and synchronization for a coupled neural networks with multi-weights (CNNMWs) by virtue of devised proportional-integral-derivative (PID) controllers. On one side, several passivity criteria for the CNNMWs are put forward on the basis of suitable Lyapunov functional, inequality techniques and devised proportional-derivative (PD) controller, and the output-strict

We consider the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of equal workers. We assume that objects are endowed with intrinsic qualities and that the probability with which an object is preferred to another depends only on the difference between the qualities of the two competitors. We propose a class of non-adaptive ranking algorithms that

Many efforts have been devoted to understanding how human behaviors induced by the awareness of disease influence the epidemic dynamics in multiplex networks. The awareness and virus spreading is mostly studied with the unaware-aware-unaware-susceptible-infected-susceptible (UAU-SIS) model over a given and fixed (static) network. However, the role of the spatial-temporal properties of multiplex networks

This paper studies the multi-cascade influence maximization problem, which explores the strategies for launching one information cascade in a social network in which multiple cascades can spread concurrently. We leverage the concept of resolution function for defining the correlations between different cascades and propose the independent multi-cascade model. Based on the proposed model, we study the

Due to the rapid development of information and communication technologies with several emerging computing paradigms, such as ubiquitous computing, social computing, and mobile computing, modeling of information diffusion becomes an increasingly significant issue in the big data era. In this study, we focus on a periodic-aware intelligent prediction method based on a comprehensive modeling of user

Given a fixed total budget and a predefined cost model, the budgeted influence maximization problem aims to find a subset of nodes to maximize the influence spread in social networks while its cost should be no more than the fixed total budget. In this paper, we propose a local-global influence indicator based constrained evolutionary algorithm, named IICEA, to solve the budgeted influence maximization

This article focuses on $H_{\infty }$ load frequency control (LFC) of multi-area power systems with cyber-attacks and communication delays by a memory-event-triggered mechanism (METM). The dynamics of power system components, such as governor, turbine and power generator, are imitated by some circuit systems. To save the precious network bandwidth, a METM is constructed to decide which measured data

The current blockchain deployment solutions rely on a continuous connectivity network. Unfortunately, the delay tolerant network does not meet this condition. Therefore, we construct a novel blockchain transaction framework with discrete token negotiation called DTNB, which can be deployed on the delay tolerant network to provide transaction services. Specifically, we present the structure of add-chains

Crowdsensing has been recognized as an innovative data gathering paradigm. For efficient management of workers and data collection, the physical area is often divided into multiple subareas where multiple crowdsensing tasks are involved in single subareas and workers in a subarea complete tasks independently and simultaneously. Previous studies only focused on planning task execution of individual

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination

Many engineered and natural systems are modeled as networks of coupled systems. Synchronization is one of their crucial and well-studied behaviors. Uniform coupling strength has been the benchmark practice in the majority of the literature. This paper considers nonuniform coupling strength, and a modified approach to the problem of synchronizability optimization, enabling a reduction to a spectral

A microgrid scenario that consists of electric vehicle (EV) station, combined heat and power (CHP) co-generation, main power grid, and external natural gas station, as well as thermal energy storage water tank is considered in this paper. By jointly considering the uncertainty of real-time electricity price, electricity and heat demands and EVs’ behaviours, the EVs’ batteries are used as energy storage

Electronic healthcare (e-health) networks are increasingly popular, particular during pandemics such as COVID-19. This reinforces the importance of ensuring security and privacy for data-in-transit. One such solution is steganography-based schemes that utilize biological signals (e.g., ECG) as cover signals to preserve the privacy of patient personal information without affecting the diagnostic features

Dual connectivity (DC) has been emphasized in both LTE and 5G networks to utilize a secondary evolved NodeB (SeNB) connected to the master evolved NodeB (MeNB) that can simultaneously serve the user equipment (UE) to improve the per-user throughput as well as the mobility support. However, the non-ideal X2 link between the MeNB and SeNB and the dynamic channel condition can cause severe out-of-order

Controller area network (CAN) is a communication protocol that provides reliable and productive transmission between in-vehicle nodes continuously. CAN bus protocol is broadly utilized standard channel to deliver sequential communications between electronic control units (ECUs) due to simple and reliable in-vehicle communication. Existing studies report how easily an attack can be performed on the

Traffic engineering is one of the importantparts in a datacenter. Software defined networking (SDN) opens opportunities for easier traffic engineering. Functionalities of an SDN switch are determined by the forwarding rules installed on it. Because of the limited space on SDN switches, it is important to keep the number of rules as small as possible. If the number of rules in a switch is higher than

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

The detectability and distinguishability analysis for stochastic block model (SBM) have been very important topics in community detection. Detectability refers to the ability for weak recovery of communities in a graph, and distinguishability refers to the ability to distinguish the generating model of a given network from SBM and Erdos-Renyi (E-R) model. Numerous prior arts have focused on networks

In temporal ordered clustering , given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal {C}_1 \prec \cdots \prec \mathcal {C}_K$ such that for $i< j$ , nodes in cluster $\mathcal {C}_i$ arrived before nodes in cluster $\mathcal {C}_j$ , with $K$ being a data-driven parameter and not known upfront

In this paper, an $H_{\infty }$ estimation approach is given for an array of coupled stochastic complex networks with intermittent nonlinearity switching. A set of binary random variables are adopted to characterize the intermittent switching behavior of the involved nonlinearities. To effectively alleviate data collisions and save energy, the Round-Robin protocol is utilized to curb network congestions

Recently, crowdsensing revolutionizes sensing paradigm in Internet of Things (IoTs). However, a practical incentive mechanism which works for time-varying scenario and fairly incentivizes users to participate in crowdsensing is less studied. In this paper, we propose an incentive mechanism for crowdsensing under continuous and time-varying scenario using three-stage Stackelberg game. In such a scenario

Per-flow connection state monitoring is crucial for detecting malicious traffic or anomalies in networks. The monitoring is extremely challenging in high-speed networks, and would involve high computation and memory costs. We propose a novel stateful Bloom filter (stateBF) to enable a highly compact, low-overhead, and accurate flow-state storage service for the monitoring of the per-flow connection

An active area of research in computational science is the design of algorithms for solving the subgraph matching problem to find copies of a given template graph in a larger world graph. Prior works have largely addressed single-channel networks using a variety of approaches. We present a suite of filtering methods for subgraph isomorphisms for multiplex networks (with different types of edges between

Network embedding aims to map a complex network into a low-dimensional vector space while maximally preserving the properties of the original network. An attributed network is a typical real-world network that models the relationships and attributes of real-world entities. Its analysis is of great significance in many applications. However, most such networks are incomplete with partially-known attributes

The papers in this special section focus on cognitive software defined networks and applications. Next generation networks (NGNs) are xpected to utilize internal and external sources of data through information and wireless communication techniques. Particularly, the demand for autonomic network management, orchestrations and optimization is as intense as ever, even though significant research has