Physical layer design of wireless sensor network nodes

Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio frequency （RF） parameters and various microcontroller unit （MCU） solutions. An implementation of the WSN node is presented with experimental results and a novel ＂one processor working at two frequencies＂ energy saving strategy. The lifetime estimation issue is analyzed with consideration to the periodical listen required by common WSN media access control （MAC） algorithms. It can be concluded that the startup time of the RF which determines the best sleep time ratio and the shortest backoff slot time of MAC, the RF frequency and modulation methods which determinate the RX and TX current, and the overall energy consumption of the dual frequency MCU SOC （ system on chip） are the most essential factors for the WSN node physical layer design.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Analysis on the Network Node of Konjac Glucomannan Molecular Chain

The dynamic changes of the complex network and the material form and function were actuated by the molecular chains. The interaction behavior between molecular chains was difficult to illuminate because the dynamic changes of macromolecules were observed difficultly by normal spectrum method and the methods to test and evaluate the complex network evolution prediction and intervention are rare. The mathematic model of domino offect of molecular chains was established based on the topological structure of molecular chain aggregation of Konjac glucomannan, and the molecular entanglement mechanism of Konjac glucamannan blends was studied through molecular simulation and knot theory analysis combined with experimental verification. The results suggested that two network models （topological entanglement and solid knot） of Konjac glucomannon blends were formed through hydrogen bond nodes. The topological entanglement was strengthened with the increase of concentration and the form of molecular chains was Gaussian chain which could not allow traverse moving owing to the intermolecular cross and entanglement and the shield of intramolecular interaction. Besides, the structures of Konjac glucomannon blends became more stable due to the solid knot. Both of them were verified by the experimental results. This experimental method simplifies the microscopic description of Konjac glucomannon, and there is important guiding significance of the experimental results for the prediction and control ofpolysaccharides＇ structure and function.

Chain-type wireless sensor network node scheduling strategy

In order to reduce power consumption of sensor nodes and extend network survival time in the wireless sensor network （WSN）, sensor nodes are scheduled in an active or dormant mode. A chain-type WSN is fundamental y different from other types of WSNs, in which the sensor nodes are deployed along elongated geographic areas and form a chain-type network topo-logy structure. This paper investigates the node scheduling prob-lem in the chain-type WSN. Firstly, a node dormant scheduling mode is analyzed theoretical y from geographic coverage, and then three neighboring nodes scheduling criteria are proposed. Sec-ondly, a hybrid coverage scheduling algorithm and dead areas are presented. Final y, node scheduling in mine tunnel WSN with uniform deployment （UD）, non-uniform deployment （NUD） and op-timal distribution point spacing （ODS） is simulated. The results show that the node scheduling with UD and NUD, especial y NUD, can effectively extend the network survival time. Therefore, a strat-egy of adding a few mobile nodes which activate the network in dead areas is proposed, which can further extend the network survival time by balancing the energy consumption of nodes.

Research on Node Classification Based on Joint Weighted Node Vectors

Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.

无线传感器网络基于测距的节点定位算法综述OverviewoftheNodeLocalizationAlgorithmBasedonRangingofWirelessSensorNetworks

基于测距的定位方法对测量的距离信息运用几何知识求解未知节点的位置,常用在定位精度较高的领域,可在误差、能耗、受环境因素影响等方面进行优化。本文对基于测距的无线传感器网络节点定位算法进行详细地分析和比较。

Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges

Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

Neighbor-Based Malicious Node Detection in Wireless Sensor Networks

The primary function of wireless sensor networks is to gather sensor data from the monitored area. Due to faults or malicious nodes, however, the sensor data collected or reported might be wrong. Hence it is important to detect events in the presence of wrong sensor readings and misleading reports. In this paper, we present a neighbor-based malicious node detection scheme for wireless sensor networks. Malicious nodes are modeled as faulty nodes behaving intelligently to lead to an incorrect decision or energy depletion without being easily detected. Each sensor node makes a decision on the fault status of itself and its neighboring nodes based on the sensor readings. Most erroneous readings due to transient faults are corrected by filtering, while nodes with permanent faults are removed using confidence-level evaluation, to improve malicious node detection rate and event detection accuracy. Each node maintains confidence levels of itself and its neighbors, indicating the track records in reporting past events correctly. Computer simulation shows that most of the malicious nodes reporting against their own readings are correctly detected unless they behave similar to the normal nodes. As a result, high event detection accuracy is also maintained while achieving low false alarm rate.

Detecting overlapping communities based on vital nodes in complex networks

Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.

Multiple Node Placement Strategy for Efficient Routing in Wireless Sensor Networks

The advances in recent technology have lead to the development of wireless sensor nodes forming a wireless network, which over the years is used from military application to industry, household, medical etc. The deployment pattern of sensor nodes in Wireless Sensor Network (WSN) is always random for most of the applications. Such technique will lead to ineffective utilization of the network;for example fewer nodes are located at far distance and dense nodes are located at some reason and part of the region may be without the surveillance of any node, where the networks do consume additional energy or even may not transfer the data. The proposed work is intended to develop the optimized network by effective placement of nodes in circular and grid pattern, which we call as uniformity of nodes to be compared with random placement of nodes. Each of the nodes is in optimized positions at uniform distance with neighbors, followed by running a energy efficient routing algorithm that saves an additional energy further to provide connectivity management by connecting all the nodes. Simulation results are compared with the random placement of nodes, the residual energy of a network, lifetime of a network, energy consumption of a network shows a definite improvement for uniform network as that of with the random network.

Physical Layer Deterministic Network Coding Using PUM Turbo Codes over AWGN Channel, N Nodes through a Base Station Scenario

Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less transmitted data that carry the same amount of information. NC research work over the physical layer and the upper layers are popular and needed to be more investigated. In this paper, we propose a practical system of large-number of connected multi-source network coding (LMSNC), at the physical layer that exploits the broadcast nature of the wireless channel, using the practical and bandwidth-efficient schemes decode-and-forward (DF) and then compare it with Amplify and Forward (AF). The theoretical analysis and the simulation results show the effect of the noise when it cumulates in AF system and how DF is solving this severe default. Moreover, we consider the MSNC for Small-number of connected sources (SMSNC) and the two-way communication setup where two users exchange their information over an intermediate network node (ideally called Base Station), as two reference cases to compare with. With SMSNC, the number of necessary downlink transmissions from the intermediate node to the users is reduced, and thus the throughput is increased. Simulation results obtained using high-performance non-binary turbo codes, based on Partial Unit Memory (PUM) codes (4, 2, 1, 4) and (8, 4, 3, 8);confirm that combining PUM Turbo Code (PUMTC) and NC in the proposed MSNC setup gives almost the same BER performance as that for SMSNC at the small number of processing steps mainly when PUMTC (8, 4, 3, 8) is performed, which is required to retrieve the received coded messages. In the scenario of AF, combining packets results to cumulate the noise, which justifies the reason we decided to increase the number of transmitted coded messages in the network, i.e., the BER performance improves when sending extra coded messages. Finally, the possibility for a trade-off among BER, data rate and the number of transmitted coded messages is shown for LMSNC through graphics and simulation results.

Malicious Node Detection Using Confidence Level Evaluation in a Grid-Based Wireless Sensor Network

In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.

A Malicious and Malfunctioning Node Detection Scheme for Wireless Sensor Networks

Wireless sensor networks are often used to monitor physical and environmental conditions in various regions where human access is limited. Due to limited resources and deployment in hostile environment, they are vulnerable to faults and malicious attacks. The sensor nodes affected or compromised can send erroneous data or misleading reports to base station. Hence identifying malicious and faulty nodes in an accurate and timely manner is important to provide reliable functioning of the networks. In this paper, we present a malicious and malfunctioning node detection scheme using dual-weighted trust evaluation in a hierarchical sensor network. Malicious nodes are effectively detected in the presence of natural faults and noise without sacrificing fault-free nodes. Simulation results show that the proposed scheme outperforms some existing schemes in terms of mis-detection rate and event detection accuracy, while maintaining comparable performance in malicious node detection rate and false alarm rate.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Rapid identifying high-influence nodes in complex networks

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method（RIM）to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered（SIR） model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness,and eigenvector centrality methods.

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks （WSNs）. In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter （KF） is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter （EKF） is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation （MLE） and traditional KF algorithm.

Reliable Network Design Problem under Node Failure with Benders Decomposition

The design of telecommunication network with capacity constraints of links, routers and ports of routers is considered in this paper. Specially, we limit each demand flow traversed through a pre-specified maximal number of links (called hops) under node failure scenarios in IP layer network. Such a design must be the most cost-effective and ensure that feasible flows continue to exist even when any relay node of the network fails. We propose a reliable mixed-integer programming (MIP) model with multi-scenario constraints to optimally design a minimum-cost survivable IP network that continues to support a good communication under any node failure scenario. Then we transform the MIP model into many single scenario models, that is, simplified MIPs, nonlinear programming (NLP) models and MIP models under Benders decomposition Then we transform the MIP model into many single scenario models, that is, simplified MIPs, nonlinear programming (NLP) models and MIP models under Benders decomposition. Three heuristic methods are proposed to solve these models including branch-and-bound algorithm, global algorithm for NLP, and heuristic algorithm based on benders decomposition. We mainly study the application of Benders decomposition method, where dual model and bounding procedures are given for each MIP model under Benders decomposition at each scenario. The results of our computational experiments validate the effectiveness of the proposed models and algorithms.

On Energy-Efficient Node Deployment in Wireless Sensor Networks

In wireless sensor networks, sensor nodes collect local data and transfer to the base station often relayed by other nodes. If deploying sensor nodes evenly, sensor nodes nearer to the base station will consume more energy and use up their energy faster that reduces system lifetime. By analyzing energy consumption, a density formula of deploying nodes is proposed. The ratio of whole energy of sensor nodes to energy consumption speed of sensor nodes in every area can get consistent if deploying nodes by the density formula, therefore system lifetime is prolonged. Analysis and simulation results show that when communication dominates whole energy consumption and the monitored region is big compared with radio range of sensor node, system lifetime under this scheme can be 3R/(2t) times of that under deploying nodes evenly, where R is radius of the monitored region and t is radio range of sensor node.