Candidate Nodes Research Articles

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.

An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks

The Influence Maximization (IM) problem aims to identify a small subset of nodes that have the most influence spread in a network. Although it is an NP-hard problem, the continuous increasing size of the social networks leads to a substantially higher computational complexity, and therefore, has motivated numerous researchers to explore better approaches. This paper proposes a multi-criteria decision making (MCDM) based meta-heuristic approach to solve the IM problem in social networks. An MCDM approach is utilized to select candidate nodes by eliminating less influential ones at the preliminary phase which decreases the computation cost. Thereafter, to find the optimal solution, a modified version of Simulated Annealing (SA) with an enhanced search strategy is proposed. The performance of this proposed approach is tested and verified by solving the IM problem on eight real-life social networks of different sizes and types and comparing the results with six benchmark algorithms. The experimental results indicate that the proposed algorithm has a better trade-off between the solution quality and computational run time than the other algorithms.

Dynamically Generated Hierarchical Neural Networks Designed With the Aid of Multiple Support Vector Regressors and PNN Architecture With Probabilistic Selection.

The two issues on dynamically generated hierarchical neural networks such as the sort of basic neurons and how to compose a layer are considered in this article. On the first issue, a variant version of the least-square support vector regression (SVR) is chosen as a basic neuron. Support vector machine (SVM) is a representative classifier which usually shows good classification performance. Along with the SVMs, SVR was introduced to deal with the regression problem. Especially, least-square SVR has the advantages of high learning speed due to the substitution of the inequality constraints by the equality constraint in the formulation of the optimization problem. Based on the least-square SVR, the multiple least-square (MLS) SVR, which is a type of a linear combination of least-square SVRs with fuzzy clustering, is proposed to improve the modeling performance. In addition, a hierarchical neural network, where the MLS SVR is utilized as the generic node instead of the conventional polynomial, is developed. The key issues of hierarchical neural networks, which are generated dynamically layer by layer, are discussed on how to retain the diversity of the nodes located at the same layer according to the increase of the layer. In order to maintain the diversity of the nodes, various selection methods such as truncation selection and roulette wheel selection (RWS) to choose the nodes among candidate nodes are proposed. In addition, in order to reduce the computational overhead to determine all candidates which exhibit all compositions of the input variables, a new implementation method is proposed. From the viewpoint of the diversity of the selected nodes and the computational aspects, it is shown that the proposed method is preferred over the conventional design methodology.

Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks

As a promising technology in the Internet of Underwater Things, underwater sensor networks (UWSNs) have drawn a widespread attention from both academia and industry. However, designing a routing protocol for UWSNs is a great challenge due to high energy consumption and large latency in the underwater environment. This article proposes a Q-learning -based localization-free anypath routing (QLFR) protocol to prolong the lifetime as well as reduce the end-to-end delay for UWSNs. Aiming at optimal routing policies, the Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two reward functions (i.e., depth-related and energy-related rewards) for Q-learning with the objective of reducing latency and extending network lifetime. In addition, a new holding time mechanism for packet forwarding is designed according to the priority of forwarding candidate nodes. Furthermore, mathematical analyses are presented to analyze the performance and computational complexity of the proposed routing protocol. Extensive simulation results demonstrate the superiority performance of the proposed routing protocol in terms of the end-to-end delay and the network lifetime.

Reliability‐oriented optimal planning of charging stations in electricity–transportation coupled networks

To coordinate the increasing application of electric vehicles (EVs), the deployment of charging stations becomes vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve the balanced reliability in both the electricity network and EV trip, an optimal planning method for charging stations in the electricity-transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the quasi-sequential Monte Carlo simulation is presented. This study proposes a novel reliability-oriented multi-objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large-scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method.

Towards an efficient weighted random walk domination

In this paper, we propose and study a new problem called the weighted random walk domination. Given a weighted graph G ( V, E ) and a budget B of the weighted random walk, it aims to find a k -size set S , which can minimize the total costs of the remaining nodes to access S through the weighted random walk, which is bounded by B. This problem is critical to a range of real-world applications, such as advertising in social networks and telecommunication base station selection in wireless sensor networks. We first present a dynamic programming based greedy method (DpSel) as a baseline. DpSel is time-consuming when | V | is huge. Thus, to overcome this drawback, we propose a matrix-based greedy method (MatrixSel), which can reduce the computation cost greatly. To further accelerate MatrixSel, we propose a BoundSel approach to reduce the number of the gain computations in each candidate selection by proactively estimating the upper bound of the marginal gain of the candidate node. Notably, all methods can achieve an approximation ratio of (1 - 1/ e ). Experiments on real datasets have been conducted to verify the efficiency, effectiveness, memory consumption and scalability of our methods.

A New Solution for City Distribution to Achieve Environmental Benefits within the Trend of Green Logistics: A Case Study in China

Green logistics has become a consensus and an important method to achieve sustainable development in industrial activities. However, the traditional direct distribution mode has high carbon emissions, an uncertain delivery time, and a low delivery efficiency. Uncoordinated resource allocations and unreasonable network layouts of terminal distributions have shackled green development within the express delivery industry. Considering the trend of green logistics, this study innovatively proposes a comprehensive and environmentally friendly mode for city distribution based on end crowdsourcing service stations (ECSSs). This study also adopts node centrality indices of complex network theory to evaluate the node importance of existing terminal distribution outlets. The comprehensive weights of the indices are obtained via the three-scale AHP (Analytic Hierarchy Process) and TOPSIS (Technology for Order Preference by Similarity to an Ideal Solution) methods to identify the candidate nodes for ECSSs. Finally, a location model is built to determine the optimal location to establish the ECSSs. A real-world case study was conducted to provide the location scheme of ECSSs in Beijing, China. Environmental benefits as well as economic and social benefits can be substantially achieved through the implementation of the new mode. The results show that carbon emissions can be reduced by 23.79–28.49% for the end of the distribution, 16.27–16.35% for the front-end, and approximately 17% for the entire distribution. Additionally, the loading rate of vans for the front-end of the distribution can be improved by 15.77%.

Reconstruction of Curve Networks from Unorganized Spatial Points

Curve network reconstruction from a set of unorganized points is an important problem in reverse engineering and computer graphics. In this paper, we propose an automatic method to extract curve segments and reconstruct curve networks from unorganized spatial points. Our proposed method divides reconstruction of curve networks into two steps: 1) detecting nodes of curve segments and 2) reconstructing curve segments. For detection of nodes of curve segments, we present a principal component analysis-based algorithm to obtain candidate nodes from unorganized spatial points and a Euclidean distance-based iterative algorithm to remove peripheral nodes and find the actual nodes. For reconstruction of curve segments, we propose an extraction algorithm to obtain the points on each of curve segments. We present quite a number of examples which use our proposed method to reconstruct curve networks from unorganized spatial points. The results demonstrate the effectiveness of our proposed method and its advantages of good automation and high reconstruction efficiency.

Creating Incremental Models of Indoor Environments through Omnidirectional Imaging

In this work, an incremental clustering approach to obtain compact hierarchical models of an environment is developed and evaluated. This process is performed using an omnidirectional vision sensor as the only source of information. The method is structured in two loop closure levels. First, the Node Level Loop Closure process selects the candidate nodes with which the new image can close the loop. Second, the Image Level Loop Closure process detects the most similar image and the node with which the current image closed the loop. The algorithm is based on an incremental clustering framework and leads to a topological model where the images of each zone tend to be clustered in different nodes. In addition, the method evaluates when two nodes are similar and they can be merged in a unique node or when a group of connected images are different enough to the others and they should constitute a new node. To perform the process, omnidirectional images are described with global appearance techniques in order to obtain robust descriptors. The use of such technique in mapping and localization algorithms is less extended than local features description, so this work also evaluates the efficiency in clustering and mapping techniques. The proposed framework is tested with three different public datasets, captured by an omnidirectional vision system mounted on a robot while it traversed three different buildings. This framework is able to build the model incrementally, while the robot explores an unknown environment. Some relevant parameters of the algorithm adapt their value as the robot captures new visual information to fully exploit the features’ space, and the model is updated and/or modified as a consequence. The experimental section shows the robustness and efficiency of the method, comparing it with a batch spectral clustering algorithm.

Query-Specific Deep Embedding of Content-Rich Network.

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations of its neighboring nodes. The GCN output is further fed to a deep encoder-decoder model to convert each node to a Gaussian distribution and then convert back to its node identity. The dissimilarity between the two nodes is measured by the Wasserstein distance between their Gaussian distributions. We define the nodes of the network to be positives if they are relevant to the query node and negative if they are irrelevant. The labeling of the positives/negatives is based on an upper bound and a lower bound of the Wasserstein distances between the candidate nodes and the query nodes. We learn the parameters of CNN, GCN, encoder-decoder model, Gaussian distributions, and the upper bound and lower bounds jointly. The learning problem is modeled as a minimization problem to minimize the losses of node identification, network structure preservation, positive/negative query-specific relevance-guild distance, and model complexity. An iterative algorithm is developed to solve the minimization problem. We conducted experiments over benchmark networks, especially innovation networks, to verify the effectiveness of the proposed method and showed its advantage over the state-of-the-art methods.

Design of Reinforced Hybrid Fuzzy Rule-Based Neural Networks Driven to Inhomogeneous Neurons and Tournament Selection

In this article, we introduce novel reinforced hybrid fuzzy rule-based neural networks (RHFNNs). This article is concerned with the development of the design methodologies of hybrid fuzzy rule-based model for constructing the network structure and enhancing its predictive abilities through the combination of inhomogeneous neurons [i.e., clustering-based polynomial neurons (CPNs) and polynomial neurons (PNs)] and tournament selection. The key points of the proposed RHFNN are enumerated as follows: The first layer of the proposed network consists of CPNs. CPN can effectively reflect the complex nonlinear structure encountered in the data space, and refine (granulate) it with the help of the clustering algorithm. Two types of CPNs including hard C-means (HCM) clustering-based polynomial neuron (HCPN) and fuzzy C-means (FCM) clustering-based polynomial neuron (FCPN) are designed. According to the type of CPN used in the first layer, RHFNN can be categorized into two types, namely, RHFNN based on HCPN (HRHFNN) and RHFNN based on FCPN (FRHFNN). We use PNs to construct the second and consecutive layers. PN can identify and approximate the nonlinear relationship among system's inputs and outputs. A tournament-based performance selection (TPS) algorithm stemming from evolutionary computation is used for selection of neuron. TPS not only ensures that the candidate nodes have sufficient fitting ability but also enhances the individual diversity in the node set and provides the abilities to generate better prediction nodes. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization is considered to reduce the deviation between coefficients and ameliorate overfitting as well as boost generalization ability. The performance of RHFNN is discussed through a variety of publicly available machine learning datasets. From the experimental results, we conclude that RHFNN achieves the best prediction accuracy on 13 of 15 datasets; the statistical analysis also confirms the superiority of RHFNN.

Cross Layer Based Cooperative Communication Protocol for Improving Network Performance in Underwater Sensor Networks

For underwater sensor networks (USNs), cooperative communications have been introduced to improve network performance with the help of relay nodes. The previous cooperative communications select the best relay node on a hop-by-hop basis. Therefore, they have limitations in improving performance. In order to get better performance, a cooperative communication protocol based on the cross layer is proposed in this paper. The proposed protocol uses the information provided by a routing protocol at the network layer for the erroneous data packet delivery. It selects one with the minimum routing cost among relay candidate nodes. The routing protocol in the selected relay node provides the MAC layer with the address of the next hop node on the path to the sink node. Then, the MAC layer in the selected relay node forwards the erroneous data packet to the next hop node rather than a receiver node. Performance studies are carried out through simulation. Simulation results show that the proposed protocol has about 21.8% lower average delay and about 14.4% lower average number of nodes passed than the previous protocol, regardless of the maximum transmission range.

Pricing influential nodes in online social networks

Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks. For this purpose, an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed. Firstly, the network embedding-based community detection approach is developed, by which the social network is divided into several high-quality communities. Secondly, the solution of influence maximization is composed of the candidate stage and the greedy stage. The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm, and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm. Finally, experimental results demonstrate the superiority of the proposed method compared with existing methods, from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

A software-defined caching scheme for the Internet of Things

Exploiting Content-Centric Networking (CCN) caching capabilities in which contents are cached on intermediate nodes can be beneficial in IoT as it can decrease the latency, reduce required transmission hops, limit traffic load on the content producer and improves availability. This article presents a new scheme for caching the contents in IoT environments. In the proposed method, devices are grouped into clusters where the cluster heads act as the cache controller. Additionally, we consider a global SDN/Cache controller (GSCC), which is responsible for orchestrating cache decisions in the whole IoT network. Such a centrally managed caching system increases the efficiency of resource usage in the IoT network. In the proposed scheme, the decision about caching the content is made in three steps: 1) determining the value of content and make decisions about caching it, 2) determining the candidate cluster, and 3) selecting candidate nodes for caching the content. In each step, several metrics are taken into account, and Multi-Criteria Decision Making (MCDM) approaches such as Analytical Hierarchy Process (AHP) and TOPSIS are used to select the best option based on considered criteria. Simulation results show that our proposed caching method can achieve an average cache hit rate of 72% and decreases the average hop count of content retrievals by 42%. Moreover, our results confirm the superiority of our algorithm over some existing methods in terms of different evaluation metrics.

Deploy Efficiency Driven k-Barrier Construction Scheme Based on Target Circle in Directional Sensor Network

With the increasing demand for security, building strong barrier coverage in directional sensor networks is important for effectively detecting un-authorized intrusions. In this paper, we propose an efficient scheme to form the strong barrier coverage by adding the mobile nodes one by one into the barrier. We first present the concept of target circle which determines the appropriate residence region and working direction of any candidate node to be added. Then we select the optimal relay sensor to be added into the current barrier based on its input-output ratio (barrier weight) which reflects the extension of barrier coverage. This strategy looses the demand of minimal required sensor nodes (maximal gain of each sensor) or maximal lifetime of one single barrier, leading to an augmentation of sensors to be used. Numerical simulation results show that, compared with the available schemes, the proposed method significantly reduces the minimal deploy density required to establish k-barrier, and increases the total service lifetime with a high deploy efficiency.

An Attention Mechanism Based Algorithm of Identifying Vulnerable Spots of the Internet Data Center Power Systems

With the rapid development of Mobile Internet and Big Data in recent years, the demand for data centers is increasing. The number of super large data centers (more than 1000 counters) has increased. This paper focus on the distribution system, who is responsible for providing a continuous, stable and uninterrupted power supply for the data center. In the event of a large-scale power outage, all business in the data center will be interrupted. Therefore, identifying which electrical equipment in the data center paly the key role in a large-scale power outage is extremely important. Considering the computational complexity of the algorithm, the validity of the vulnerable nodes, the intelligence of the algorithm, this paper proposes a two-layer fragile node identification method based on attention mechanism. The algorithm selects the topology structure and electrical parameters of the data center power distribution system, and considers the flow load of the nodes around each electrical device to form an expression vector. After the vulnerability ranking of the candidate node expression vectors, the k most vulnerable nodes are selected. The outline of the two-layer node identification method is as follows, the first layer calculates an alternative node sequence through a variety of graph-based fragile node identification methods, and the second layer re-calibrates the expression vector of each candidate node based on the attention mechanism and then sorts all candidate nodes. Through the simulation test of a real data center power distribution system, we can see that the proposed algorithm in this paper identifies the effectiveness of the vulnerable nodes in the data center power distribution system.

Research on collapsed (α, k)-NP-community on signed graph

In social network, the departure of critical users can lead to the disintegration of the entire community, i.e., lead a large number of other users to drop out. This paper aims to find the critical users in the community and prevent the whole community from disintegrating. Most of the other research work is carried out on the unsigned network. In this paper, we study this problem on a signed network and propose a new community model (α, k)-NP-community. Specifically, the nodes in the community model should satisfy the maximum subgraph with no less than ak good friends (positive neighbor) and no more than k enemy (negative neighbor). We propose an effective algorithm to find (α, k)-NP-community. In order to identify the critical users of (α, k)-NP-community, we propose the problem of collapsed (α, k)-NP-community: given a signed graph G, integers α, k, our goal is to find such nodes in graph G, the departure of the best collapser will lead to the smallest (α, k)-NP-community. We prove that the problem is NP-hard. Finally, we present two lemmas to apply the αKCA algorithm, which effectively reduced the number of candidate nodes and accelerated the search process. The results of extensive experiments on four real datasets demonstrate the effectiveness and efficiency of our algorithms.

A Distributed Mode Reassignment Scheme for Mobile Multihop Wireless Network of FDD Nodes

The use of frequency division duplex (FDD) communication in a multihop wireless network partitions the nodes in two operating modes. Since the FDD nodes of the same mode cannot communicate with each other, it can limit the node connectivity and also lead to network partitioning. In this article, a novel, distributed, low-complexity mode reassignment (MR) scheme is designed for maintaining connectivity among the mobile FDD nodes in a decentralized, multihop wireless (airborne) network. Since it is not practical to know the complete topology of such a network in real time, the proposed scheme is fully distributed, relies only on the local node information, and applied only on those nodes whose connectivity drops below a threshold. Besides maintaining a desired value of connectivity degree for each node while it is moving around, the proposed MR scheme also minimizes the impact of mode change on the existing links and ongoing data transmissions. A Kalman filter based node mobility prediction and link failure prediction scheme is designed to select the best candidate nodes for mode change. The optimality and error control bounds of the proposed MR scheme show that it consistently achieves optimal connectivity for most nodes, while requiring a low number of nodes undergoing the mode change during each MR round.

A multidomain virtual network embedding algorithm based on multiobjective optimization for Internet of Drones architecture in Industry 4.0

SummaryUnmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (a) In order to improve the optimization performance and reduce the computation time, we propose a multidomain virtual network embedding algorithm (MP‐VNE) adopting the centralized hierarchical multidomain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (b) In order to simplify the multiobjective optimization problem, we transform the multiobjective problem into a single‐objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (c) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP‐VNE algorithm has better performance than MC‐VNM, LID‐VNE, and other algorithms in terms of delay, cost and comprehensive indicators.