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

Machine learning-based malware detection is known to be vulnerable to adversarial evasion attacks. The state-of-the-art is that there are no effective defenses against these attacks. As a response to the adversarial malware classification challenge organized by the MIT Lincoln Lab and associated with the AAAI-19 Workshop on Artificial Intelligence for Cyber Security (AICS'2019), we propose six guiding

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 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

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

Community-aware centrality is an emerging research area in network science concerned with the importance of nodes in relation to community structure. Measures are a function of a network's structure and a given partition. Previous approaches extend classical centrality measures to account for community structure with little connection to community detection theory. In contrast, we propose cluster-quality

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

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

The papers in this special section focus on data-driven Internet of Things (IoT) for smart cities. Developing smart cities to better support the growing urban population is a global and complex challenge and involves interdisciplinary fields. Instead of the traditional Internet of Contents in human-to-human and human-to-machine communications, the Internet of Things with communications among massive

The papers in this special section focus on edge computing for Internet of Things (IoT). The IoT is expected to improve the quality of human lives through billions of Internet-based devices that connect the physical and digital worlds. To satisfy the computation and storage requirements of IoT, cloud computing has served as the most important computing infrastructure. However, with the explosion of

The papers in this special section focus on heterogeneous communication networks. These networks, consisting of terrestrial mobile networks, satellite networks, IoT, WiFi, and etc., is a promising technique to meet the challenges in future mobile communications. Heterogeneous network resources are expected to cooperate with each other to support more efficient data transmissions and also provide more

The papers in this special section focus on smart systems and intelligent networking that is powered with Big Data analytics. Smart systems, including Internet of Things (IoT), have emerged to address contemporary economic, societal, and environmental challenges, such as business and production automation, urban sustainability, climate change, healthcare, and globalization. They encompass different

Network services usually chain multiple types of Virtual Network Functions (VNFs) together in a specific order, known as service chain. One important issue of providing network services is reliability, which means that each type of VNF in a service chain acts properly on its function. The effective way to guarantee the reliability is to utilize sufficient redundancy through providing VNF backup instances

Owing to powerful programmability, software defined networking (SDN) is well aligned with the requirements of NFV datacenters. Although the mainstream SDN frameworks can effectively respond to the requests of control plane, the load distribution among controllers is not balanced due to the request dynamics and request diversity. The existing solutions balance the load of control plane by migrating

In this paper, we introduce a rank-one Semi-Definite Programming (SDP) solution method for mobile source localization in sensor networks. The position and velocity of mobile source are jointly estimated using Time Delay (TD) measurements. To obtain the position and velocity of mobile source, a Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint

As multi-hop wireless networks are attracting more attention, the need to evaluate their performance becomes essential. In order to evaluate the performance metrics of multi-hop wireless networks, including sending and receiving rates of a node as well as the collision probability, a model based on Stochastic Reward Nets (SRNs) is proposed. The proposed SRN models a typical node in such networks, considered

In this article, the problems of stabilization and $L_2$ -gain performance analysis for continuous-time networked control systems (NCSs) with quantization effects are addressed. The plant output and control input are quantized respectively by two different dynamic quantizers before being transmitted over networks. In order to deal with the dynamic behavior resulting from the dynamic quantizers, a hybrid

Task caching, based on edge cloud, aims to meet the latency requirements of computation-intensive and data-intensive tasks (such as augmented reality). However, current task caching strategies are generally based on the unrealistic assumption of knowing the pattern of user task requests and ignoring the fact that a task request pattern is more user specific (e.g., the mobility and personalized task

This work deals with the analysis and protocol design problems of the prescribed-time interval bipartite consensus of multi-agent systems on signed and directed graphs. A new distributed protocol with hybrid constant and time-varying feedbacks of local signed error is proposed, whose consensus time period is independent of the specific topology among agents and initial states of all agents. By introducing

Network embedding, which is the task of learning low-dimensional representations of vertices, has attracted increasing attention recently. Evidences have been found that the hidden metric space of many realistic complex networks is hyperbolic. The topology and weight emerge naturally as reflections of the hyperbolic metric property. A common objective of hyperbolic embedding is to maximize the likelihood

By offloading computation-intensive and latency-sensitive tasks from mobile devices to the mobile edge server, mobile edge computing (MEC) has been considered as a promising technology in 5 G and beyond to reduce task delay and energy consumption. This paper considers an optimization framework of computation offloading in a MEC system with multiple devices. We aim to minimize the energy consumption

Online social networks, such as Facebook and Twitter, enable users to share their shopping/travel experiences with their friends. However the influence on users’ decision-making on next visit/buy has sparse research exposure, by accurately modeling long-term user behaviors from historical data. The existing methods do not fully take advantage of the underlying social networks to model user interests

Water distribution networks are designed and managed to provide adequate water flow to all users. Unexpected events, such as prolonged dry spells or cyber-physical attacks, can affect this status quo . Under water scarcity conditions, users located in high-pressure zones have a competitive advantage at the expense of the other customers, which, if exercised freely, can reduce the overall social welfare

The growing popularity of Online Social Networks (OSN) have prompted an increasing number of companies to promote their brands and products through social media. This paper presents a topological potential scheme for predicting influential nodes from large scale OSNs to support more intelligent brand communication. We first construct a weighted network model for the users and their relationships extracted

Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction

The agent dynamics in a multi-agent system subject to both actuator saturation and unknown nonuniform input delay impose difficulty in the protocol design for all agents achieving state synchronization, although solutions can be found for agents with actuator saturation and for agents with input delay. In this paper, we design a low-gain based protocol for each agent such that the protocol output (agents’

This paper is devoted to investigating the exponential synchronization problem on a class of coupled heterogeneous neural networks with hybrid time-varying delays. Since the heterogeneity of neural networks, the quasi-synchronization rather than complete synchronization is studied. Consider that the controller would suffer from impulsive disturbances, the distributed pinning control strategy is introduced

With the advancements of wireless network transmission technology, 2D video is hard to satisfy people's requirement for multimedia. Therefore, the high-definition 3D video that can bring a whole new viewing experience is starting to enter people's vision. However, when a tremendously large number of users play 3D video, it puts enormous computational pressure on the cloud server, which incurs high

Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without

The world-changing blockchain technique provides a novel method to establish a secure, trusted, and decentralized system for solving the security and personal privacy problems in the Internet of Things (IoT) applications. As the mining process in blockchain requires high computational power, the lightweight IoT devices need to purchase computational resources from edge servers and thus can offload

Advanced communication technologies (e.g., 5G) probably elicit a complete change of network and its applications. For example, a growing number of services begin shifting from central clouds to vast mobile devices, as the hybrid use of cloud and fog computing technologies can provide enhanced quality of service and efficient utilization of resources. However, to design such complex hybrid cloud-fog

We develop a group-based continuous-time Markov general epidemic modeling (GroupGEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic

Temporary fork is a fundamental phenomenon in many blockchains with proof of work, and the analysis of temporary fork has recently drawn great attention. Different from existing efforts that focus on the blockchain system factors such as block size, network propagation delay or block generation speed, in this paper we explore a new key dimension of computing power from the miners’ perspective. Specifically

The heterogeneous networks which collaborate among Space, Air, Ground, and Sea (SAGS) networks significantly promote the development of the Internet of Things (IoT). Billions of IoT devices in SAGS networks generate massive data to support various applications. We argue that it is crucial to construct a Collaboration Trust Interconnections System (CTIS) to provide the ubiquitous SAGS network accessibility

Despite highly accurate intrusion detection schemes based on machine learning (ML) reported in the literature, changes in network traffic behavior quickly yield low accuracy rates. An intrusion detection model update is not easily feasible due to the enormous amount of network traffic to be processed in near real-time for high-speed networks, in particular, under big data settings. In this paper, we

In Software Defined Networks (SDNs), flows usually request to be processed by a service chain (an ordered set of virtualized network services). SDN-enabled networks manage the routing and processing of flows by a large number of associated finer-granularity rules. These rules are maintained by switches in their local hardware such as Ternary Content Addressable Memories (TCAMs), which support high-speed

Network Function Virtualization (NFV) is changing the way we implement the network functions from expensive hardware to software middleboxes, called Virtual Network Functions (VNFs). Flows always request to be processed by several middleboxes in a specific order, which is known as a service chain. Most researches study the middlebox placement problemand few of them pay attention to the flow scheduling

The rapid growth of digital images motivates organizations and individuals to outsource image storage and computation to the cloud. However, the defenseless upload will raise the risk of privacy leakage while the simple encryption would impede the efficient usage of data. In this paper, we propose a privacy-preserving image retrieval scheme, in which the images are encrypted but similar images to a

The ability to modify the structure of network systems offers great opportunities to enhance their operation, improve their efficiency, and increase their resilience against failures and attacks. This paper focuses on the edge modification problem, i.e., improving network controllability by adding and/or re-weighting interconnections while keeping the actuation structure fixed. We consider a network

Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue

Symmetries are an essential feature of complex networks as they regulate how the graph collective dynamics organizes into clustered states. We here show how to control network symmetries, and how to enforce patterned states of synchronization with nodes clustered in a desired way. Our approach consists of perturbing the original network connectivity, either by adding new edges or by adding/removing

In the past decade, influence maximization becomes one of the fundamental problems in online social networks. It has popular applications such as viral marketing and rumor blocking. This problem asks for some influential users to maximize the expected followers. Unlike traditional influence maximization, we discuss the problem of influence towards a special target user in this paper. We define the

Orientation noises in sensors always spontaneously occur in the distributed process of information transmission or measurement, e.g., aperture radar, ultrasonic and laser sensors. This kind of noises may result in the unalignment of any pair of agents communicated with each other. In this paper, the formation control problem is investigated, where the orientation noises are modeled by some random sequences

This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered

Existing resilient consensus algorithms are mainly developed based on the mean subsequence reduced (MSR) method, which relies on the assumption that there exist at most $f$ malicious agents in the entire network or each neighborhood (i.e., $f$ -total or $f$ -local model). However, in some practical cases, it may be impossible to estimate an appropriate upper bound on the number of malicious agents

Channel reassignment is to assign again on the assigned channel resources in order to use the channel resources more efficiently. Channel reassignment in the Software-Defined Networking (SDN) based Internet of Things (SDN-IoT) is a promising paradigm to improve the communication performance of the network, since it allows software-defined routers (SDRs) with the help of SDN controller to appropriately

Extracting information from real-world large networks is a key challenge nowadays. For instance, computing a node centrality may become unfeasible depending on the intended centrality due to its computational cost. One solution is to develop fast methods capable of approximating network centralities. Here, we propose an approach for efficiently approximating node centralities for large networks using

The rapid development of Internet of Things (IoT) can promote the establishment of the smart connected world by using numerous devices and sensors. Collecting the data perceived by sensing devices in a fast and energy-saving style is critical for building a stable network in IoT. In this paper, we propose an Early Message Ahead Join Adaptive Data Aggregation (E-ADA) scheme for IoT. Firstly, an advance

Graph classification, which aims to identify the category labels of graphs, plays a significant role in drug classification, toxicity detection, protein analysis etc. However, the limitation of scale in the benchmark datasets makes it easy for graph classification models to fall into over-fitting and undergeneralization. To improve this, we introduce data augmentation on graphs (i.e. graph augmentation)

This paper investigates the finite-time synchronization issue for semi-Markov jump inertial neural networks, in which the sampled-data control is employed to alleviate the burden of the limited communication bandwidth. Due to the existence of inertial item, the semi-Markov jump inertial neural networks as hybrid neural systems, are depicted with second-order derivatives for the first time, which can

We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have

In this paper, we propose strategies and develop solutions for a network service provider (NSP) to cost-effectively provision and manage a large number of network slices. Specifically, we propose a novel framework, namely, network slice bundling, in which (1) an NSP can allocate resources and create multiple network slice bundles in advance, (2) a network slice request can be quickly instantiated in

This paper investigates output synchronization of heterogenous time-delay discrete-time multiagent systems via output couplings. Firstly, sufficient conditions are provided to guarantee dissipativity for an individual agent modeled by the descriptor dynamics. Fully distributed consensus protocol based on the outputs of the neighboring agents is proposed to show that the individual dissipativity leads