Number Of Nodes In Network Research Articles

Distributed Data Access in Industrial Edge Networks

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Randomized Greedy Sensor Selection: Leveraging Weak Submodularity

We study the problem of estimating a random process from the observations collected by a network of sensors that operate under resource constraints. When the dynamics of the process and sensor observations are described by a state-space model and the resource are unlimited, the conventional Kalman filter provides the minimum mean square error (MMSE) estimates. However, at any given time, restrictions on the available communications bandwidth and computational capabilities and/or power impose a limitation on the number of network nodes, whose observations can be used to compute the estimates. We formulate the problem of selecting the most informative subset of the sensors as a combinatorial problem of maximizing a monotone set function under a uniform matroid constraint. For the MMSE estimation criterion, we show that the maximum elementwise curvature of the objective function satisfies a certain upper-bound constraint and is, therefore, weak submodular. Building upon the work of Mirzasoleiman et al. on submodular maximization, we develop an efficient randomized greedy algorithm for sensor selection and establish guarantees on the estimator's performance in this setting. Extensive simulation results demonstrate the efficacy of the randomized greedy algorithm compared to state-of-the-art greedy and semidefinite programming relaxation methods.

Improved network lifetime to identify unexploited PATH using series cornerstone PATH algorithm in wireless sensor networks

In sensor network lot of difficulties are made for routing, so does not improve the network lifetime. Normal routing consumes more resources; it is not efficient approach for huge number of sensor nodes in network. Sender node not finds the all nodes location in particular time slot. Also heavy load given to the routing path provides unexploited routing path. Proposed Improved Network Lifetime to Identify Unexploited Path (ILIUP) is applied to network which increase lifetime of routing nodes. The main purpose of ILIUP is to discover the efficient path with uninterrupted packet forwarding process. The unexploited paths are detected based on its behavior, sender broadcast data packet continuously from source node to destination node through path that does not handle heavy load or traffic in network. Then develop the series cornerstone path algorithm to obtain series of intermediate node for relaying the data packets frequently, it tracks the each and every node position, easy to assign routing path. It reduces energy consumption, routing overhead, and increase network lifetime, Throughput.

Influence of Interlink Topology on Multilayer Network Robustness

Cascading failures between interdependent multilayer networks are being widely studied, especially the trend of robustness caused by the interlinks between networks. However, few researchers pay attention to the effect of the interlink topology on the robustness of coupled networks, which is a critical interlink factor of multilayer networks. In this study, the method frame of multilayer network experiment simulation is given. Through numerical simulation and actual network simulation, the exhaustive method is used to enumerate all the patterns of interlink topological relations of multilayer networks (three-layer or more). The research verifies that the interlink topology affects the global robustness and that there exists a fragile interlink pattern in the patterns of interlink topologies. The star-like interlink pattern with the most uneven interlink-degree distribution leads to the weakest robustness; the pattern with average interlink-degree distribution reveals good global stability as a loop-like pattern or entire interlink pattern. In addition, the influence of interlink topology is independent. The simulation results are not affected by the network layer number and intraparameters (including the network-generated form, each layer of network node number, and average degree of each layer of network). Thus, ignoring the interlink topology may result in the actual system suddenly becoming vulnerable before the theoretical calculation point. Interlink topology as an independent factor affecting the robustness of multilayer networks should be paid more attention.

Sampling and Inference of Networked Dynamics Using Log-Koopman Nonlinear Graph Fourier Transform

Networked nonlinear dynamics underpin the complex functionality of many engineering, social, biological, and ecological systems. Monitoring the networked dynamics via the minimum subset of nodes is essential for a variety of scientific and operational purposes. When there is a lack of a explicit model and networked signal space, traditional evolution analysis and non-convex methods are insufficient. An important data-driven state-of-the-art method use the Koopman operator to generate a linear evolution model for a vector-valued observable of original state-space. As a result, one can derive a sampling strategy via the linear evolution property of observable. However, current polynomial Koopman operators result in a large sampling space due to: (i) the large size of polynomial based observables ($O(N^2)$, $N$ number of nodes in network), and (ii) not factoring in the nonlinear dependency between observables. In this work, to achieve linear scaling ($O(N)$) and a small set of sampling nodes, we propose to combine a novel Log-Koopman operator and nonlinear Graph Fourier Transform (NL-GFT) scheme. First, the Log-Koopman operator is able to reduce the size of observables by transforming multiplicative poly-observable to logarithm summation. Second, a nonlinear GFT concept and sampling theory are provided to exploit the nonlinear dependence of observables for Koopman linearized evolution analysis. Combined, the sampling and reconstruction algorithms are designed and demonstrated on two established application areas. The results demonstrate that the proposed Log-Koopman NL-GFT scheme can (i) linearize unknown nonlinear dynamics using $O(N)$ observables, and (ii) achieve lower number of sampling nodes, compared with the state-of-the art polynomial Koopman linear evolution analysis.

Optimal Distributed Leader Election without Decoding

Abstract In the past decades, with the exponential implementation of large-scale wireless networks such as Internet-of-Things, an enormous demand on designing the relative algorithms and protocols has come into our sight. As an important primitive function in wireless communication, the problem of leader election has attracted much attention in research. In this paper, we pursue efficient distributed solutions for leader election in large-scale wireless networks. Specifically, a simple but optimal distributed leader election algorithm in wireless networks with asynchronous wakeup nodes is proposed, under the practical Signal-to-Interference-plus-Noise (SINR) interference-depicting model. Our algorithm is randomized and can elect a leader in an asymptotically optimal time bound of Θ(log n) with high probability, where n is the number of nodes in network. The algorithm does not rely on any information about the network topology, needs only simple node capability to implement, and uses no encoding and decoding functions. Hence, the algorithm is efficient and energy-saving, which makes it possible to be implemented in a wide range of scenarios, and become a building block for many higher-level operations, like consensus, broadcast and so on.

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

Flow routing can achieve fine-grained network performance optimizations by routing distinct packet traffic flows over different network paths. While the centralized control of Software-Defined Networking (SDN) provides a control framework for implementing centralized network optimizations, e.g., optimized flow routing, the implementation of flow routing that is adaptive to varying traffic loads requires complex models. The goal of this study is to pursue a model-free approach that is based on reinforcement learning. We design and evaluate QR-SDN, a classical tabular reinforcement learning approach that directly represents the routing paths of individual flows in its state-action space. Due to the direct representation of flow routes in the QR-SDN state-action space, QR-SDN is the first reinforcement learning SDN routing approach to enable multiple routing paths between a given source (ingress) switch-destination (egress) switch pair while preserving the flow integrity. That is, in QR-SDN, packets of a given flow take the same routing path, while different flows with the same source-destination switch pair may take different routes (in contrast, the recent DRL-TE approach splits a given flow on a per-packet basis incurring high complexity and out-of-order packets). We implemented QR-SDN in a Software-Defined Network (SDN) emulation testbed. Our evaluations demonstrate that the flow-preserving multi-path routing of QR-SDN achieves substantially lower flow latencies than prior routing approaches that determine only a single source-destination route. A limitation of QR-SDN is that the state-action space grows exponentially with the number of network nodes. Addressing the scalability of direct flow routing, e.g., through routing only high-rate flows, is an important direction for future research. The QR-SDN code is made publicly available to support this future research.

How Can Platform Leader Achieve Sustainable Development in Platform-Based Ecosystem?

Commercial competition paradigm is changing from competitive advantage which based internal resources to the eco-advantage which from ecosystem consisted of internal and external value network under the background of the Internet economy. Enterprises can’t effectively face the complex and fierce market competition environment which relied solely on internal heterogeneous resources or core competencies, so many firms adopt platform strategy and build a business ecosystem to be out of this dilemma. It could get eco-advantage for platform leaders to leverage the external resources and ability from the business ecosystem. How can platform leaders gain sustainable development depending on the superiority niche in the platform ecosystem? This paper will answer this question by taking JD as an example which is a typical B2C Internet platform. The results of the study are as follows: First, the platform firm is the leader and creator in the early establishment of the platform-based ecosystem, it’s the formational basis of eco-advantages comes from enough quantity and high quality of platform user. User install and demand are not only the important orientations for platform enterprises producing and managing but also the power source for forming eco-advantages and sustainable development. Second, the sustainable development of the platform leader is related to the size and strength of the network effect. The more network effects, the more conducive to get eco-advantages and sustainable development for platform leaders. Third, the platform-based ecosystem is a value network consisted of a large number of network nodes which is a symbiosis relationship by competing and cooperating. The cross-border integration and collaborative development of multi-agent in the ecosystem are beneficial to promoting platform leaders to form ecological advantages and achieve sustainable development. Forth, platform leader, which is the leading species and key species determined the fate of species in the ecosystem, provides a place for value co-creation of multi-species. It is the process of multi-agent value co-creation based on the platform for platform leaders to obtain sustainable development, which is also the result of internal innovation and external open innovation. This study is of great significance to enrich the research of platform enterprise theory and guide the sustainable development of platform enterprise.

Optimisation method of MAC protocol based on SVM neural network in VANET

This paper analyses the function expression of the optimal minimum competition window which integrates the network node number, average collision data frame length and sending rate of data frame. At the same time, the proposed optimised method of MAC protocol combines with the SVN neural network which can memory communication environment which include node density and mobile velocity. Each terminal node in the network runs proposed MAC protocol optimisation algorithm based on this function expression to adaptive adjust their minimum competition window and back off the optimal value to improve the network performance. The simulation results show that the effort of optimised algorithm in the Ad Hoc system is limited for unsaturated business VANET; but high accuracy and effect of the optimised algorithm in aspects of throughput and transmission delay has improved significantly for saturated business of VANET.

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.

Swarm Self-Security Intelligence System of Radioactive Materials

The loss and theft of radioactive material is essentially an illegal movement. In order to identify illegal movements, concept of swarm self-security Intelligence of radioactive materials which endows radioactive materials with ability to perceive and respond to danger by associated relationship between multiple radioactive materials (containers) constructed in some way is proposed. The network formed by security intelligent electronic devices installed on radioactive material containers as nodes constitutes the associated relationship. When a node leaves network due to illegal movement, it will lose contact with other nodes. The remaining nodes detect change of total number of nodes in network, and make appropriate delays and response measures. This paper proposes implementation architecture of a decentralized network, with direct communication between nodes. Illegal movement discrimination logic runs on each node of entire network system which can be equivalently regarded as a multi-core parallel computer. Interaction information between nodes is performed according to a standardized information table. Each node can plug in and out of network in silent state. In alert state, each network node can use total number of neighbors' out-of-connection status and whether name of adjacent node changes as a criterion for illegal movement.

Decoupled scheduling in Store-and-Forward OCS networks

By introducing storage into Optical Circuit Switched (OCS) networks and storing delay-tolerant requests until off-peak hours, the networks can have better performance. Since scheduling a data transfer in SnF networks has to deal with the spatial and temporal arrangement of the request, it is much more complicated than the routing-alone problem. In our prior research, we proposed TS-MLG, a holistic approach to solve this problem, but the computational and spatial complexity of the approach is high, limiting its use in large scale networks. In this paper, we propose a decoupled solution and specific algorithms for both sub-problems. The solutions can reduce spatial and computational complexity from O(N2*L2) to O(N*L) with marginal sacrifice on network performance, in which N and L are respectively the number of network nodes and state changes in the network. Simulation results show that in a moderate sized network with frequent state changes, the computing time of request scheduling can be reduced by 600 times. We conclude that the decoupled SnF scheduling has the potential of analyzing SnF in large networks with frequent state changes.

MLMAC-HEAP: A Multi-Layer MAC Protocol for Wireless Sensor Networks Powered by Ambient Energy Harvesting

Limited availability of energy is the most crucial constraint of wireless sensor networks (WSN). Most power consuming part of WSN node is transreceiver and operation of transreciever to access medium is controlled by the medium access control (MAC) sublayer of the open systems interconnection (OSI) model. In WSN, till now, most of the present MAC protocols have addressed issues related to power saving. Because of energy constraints, performance is often not a focused design parameter in these MAC protocols. However, recent advances in energy harvesting technology have made it possible to obtain energy from the environment to solve energy limitations. So in this paper, a new multi layer based MAC protocol, MLMAC-HEAP is designed for solar energy harvesting criterion considering performance as the mainly focused parameter. Performance is illustrated in terms of throughput. This paper evaluates the performance of MLMAC-HEAP as a function of energy harvesting rate and number of nodes in network and results are compared and shown to outperform well known MAC protocols. Then, the paper analyses the protocol for various traffic models. Based on application-level requirements, optimization is also done for number of layers.

Target Control of Directed Networks based on Network Flow Problems

Target control of directed networks, which aims to control only a target subset instead of the entire set of nodes in large natural and technological networks, is an outstanding challenge faced in various real world applications. We address one fundamental issue regarding this challenge, i.e., for a given target subset, how to allocate a minimum number of control sources which provide input signals to the network nodes. This issue remains open in general networks with loops. We show that the issue is essentially a path cover problem and can be further converted into a maximum network flow problem. A method termed `Maximum Flow based Target Path-cover' (MFTP) with complexity $O(|V|^{1/2}|E|)$ in which $|V|$ and $|E|$ denote the number of network nodes and edges is proposed. It is also rigorously proven to provide the minimum number of control sources on arbitrary directed networks, whether loops exist or not. We anticipate that this work would serve wide applications in target control of real-life networks, as well as counter control of various complex systems which may contribute to enhancing system robustness and resilience.

A Secure Random Key Distribution Scheme Against Node Replication Attacks in Industrial Wireless Sensor Systems

With the wide deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary are greatly threatening the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended, since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution (SRKD) scheme, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when the number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, the SRKD scheme also has good storage and communication efficiency.

Reliability analysis of complex brain networks based on chaotic time series

The human brain is an organic which whole consisting of hundreds of billions of neurons, and the neural network is the information path of the brain for processing information. Network characteristics will change along with network topology or node change, and the change may lead to brain lesions. Using EEG to construct brain network can effectively discover the internal mechanism of improvement in human brain, but the traditional method of constructing brain network is difficult to reflect the network complexity because of the small number of network nodes. In this paper, a new algorithm of brain network construction based on chaotic time series is proposed. The method estimates neuronal dynamic equations and neuronal networks topology by using only noisy time series. By analyzing the network constructed based on phase space reconstruction of Lorenz system, network characteristics that the average path length increase as embedding dimension, and the cluster coefficient on the contrary are found. The topology and statistical characteristics of the network constructed from neuronal chaotic bursting can reflect time-evolution of time series. Through analyzing the real epileptic EEG signals dataset using the proposed complex networks construction method, we found that epileptic seizure is different from seizure-free intervals in network topology and statistical characteristics. Epileptic EEG of before, during and after a tonic-clonic seizure indicate that cluster coefficients of the sliding local networks significantly increased during a seizure, so the result can provide clonic seizure prediction. The proposed method of complex network topology estimation and complex network construction of time series can depict the dynamic characteristics of brain network, and provide a new idea for the treatment of mental illness.

Integer programming formulations for the shared multicast tree problem

We study the shared multicast tree (SMT) problem in wireless networks. To support a multicast session between a set of network nodes, SMT aims to establish a wireless connection between them, such that the total energy consumption is minimized. All destinations of the multicast message must be connected, while non-destinations are optional nodes that can be used to relay messages. The objective function reflecting power consumption distinguishes SMT clearly from the traditional minimum Steiner tree problem. We develop two integer programming formulations for SMT. Both models are subsequently extended and strengthened. Theorems on relations between the LP bounds corresponding to the models are stated and proved. As the number of variables in the strongest formulations is a polynomial of degree four in the number of network nodes, the models are impractical for computing lower bounds in instances beyond a fairly small size, and therefore a constraint generation scheme is developed. Results from computational experiments with the models demonstrate good promise of the approaches taken.

Listed Companies’ Networks and Firms’ Auditing Behaviors: Evidence from Textual Analysis on Annual Report

China is a highly relationship-based economy. The widely-used relationship-based transactions form the structure of the capital market and the behaviors of information intermediaries. Theoretically, the company’s networks have an impact on auditing behaviors in three ways. First, the company’s networks increase the difficulty of evaluating the company’s specific investments. Second, the members within the same network repeat certain transactions indefinitely. Third, the company’s networks also make a broader scope of its stakeholders, which may increases the externality of its transactions. However, the company’s networks also provide more information sources for the auditors, which increase the informativeness of the financial report. Thus, this paper predicts that the company’s networks would increase the audit fee and earnings quality. Empirically, this paper investigates how the company’ networks affect auditing behaviors by using textual analysis on annual reports of all the Shanghai and Shenzhen A share listed companies from 2001 to 2012. We measure the networks of each company by using the number of network nodes from each company’s financial report with textual analysis. The empirical results show that the company’s networks increase the audit fee. The fee premiums are even larger for the company’s local networks and cross-industry networks. However, we don’t find any results of the network coefficient on earnings quality. In addition, further analysis shows that earnings quality is higher when the company has larger local networks and larger intra-industry networks. This finding suggests that it is difficult for auditors to understand the cross-province and cross-industry networks of the company. Further cross-sectional analysis shows that the effects of the company’s networks on fee premium and earnings quality are larger with more transparent information environment including more open local market, larger size of auditing firms, and more analyst following. Overall, this paper provides some empirical evidence to the relational contract theory from an auditing perspective, by using the data of the company’s networks to investigate how relational contracts affect auditing behaviors. This paper also has potential implications for the network study, as it offers some new mythologies to measure the company’s networks by using textual analysis.

Computing Coding Solutions for Opportunistic Network Coding Used in Wireless Mesh Networks

Wireless mesh networks represent a key architecture on which several communication systems are relaying. Implementations of these networks which apply contention-based user access control and classical routing techniques suffer throughput limitations and scale poorly with the number of network nodes. One of the solutions proposed to overcome the throughput limitations and improve scalability is the use of network coding (NC) techniques. This paper proposes three algorithms for computing the coding solutions in the network’s nodes when opportunistic NC techniques are employed. The proposed algorithms, applicable in fixed or nomadic networks, rely on the availability of the topology information in the relay nodes which perform NC coding and are strongly related to the transmission buffer management and neighbor nodes selection algorithms. The first two algorithms have lower complexity and the potential to be used in time constrained applications, while the third algorithm is a brute force like one, which can exploit most of the coding opportunities offered by the mesh architecture. Computer simulations performed on test scenarios with large number of nodes and data flows show that all proposed algorithms can achieve significant throughput improvements compared to the uncoded transmissions.

Equivalence between dropout and data augmentation: A mathematical check

The great achievements of deep learning can be attributed to its tremendous power of feature representation, where the representation ability comes from the nonlinear activation function and the large number of network nodes. However, deep neural networks suffer from serious issues such as slow convergence, and dropout is an outstanding method to improve the network’s generalization ability and test performance. Many explanations have been given for why dropout works so well, among which the equivalence between dropout and data augmentation is a newly proposed and stimulating explanation. In this article, we discuss the exact conditions for this equivalence to hold. Our main result guarantees that the equivalence relation almost surely holds if the dimension of the input space is equal to or higher than that of the output space. Furthermore, if the commonly used rectified linear unit activation function is replaced by some newly proposed activation function whose value lies in R, then our results can be extended to multilayer neural networks. For comparison, some counterexamples are given for the inequivalent case. Finally, a series of experiments on the MNIST dataset are conducted to illustrate and help understand the theoretical results.