A Modified Differential Evolution with Heuristics Algorithm for Non-convex Optimization on Sensor Network Localization

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Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks

With the high-speed development of wireless radio technology, numerous sensor nodes are integrated into wireless sensor networks, which has promoted plentiful location-based applications that are successfully applied in various fields, such as monitoring natural disasters and post-disaster rescue. Location information is an integral part of wireless sensor networks, without location information, all received data will lose meaning. However, the current localization scheme is based on equipped GPS on every node, which is not cost-efficient and not suitable for large-scale wireless sensor networks and outdoor environments. To address this problem, research scholars have proposed a rangefree localization scheme which only depends on network connectivity. Nevertheless, as the representative range-free localization scheme, Distance Vector-Hop (DV-Hop) localization algorithm demonstrates extremely poor localization accuracy under anisotropic wireless sensor networks. The previous works assumed that the network environment is evenly and uniformly distributed, ignored anisotropic factors in a real setting. Besides, most research academics improved the localization accuracy to a certain degree, but at expense of high communication overhead and computational complexity, which cannot meet the requirements of high-precision applications for anisotropic wireless sensor networks. Hence, finding a fast, accurate, and strong solution to solve the range-free localization problem is still a big challenge. Accordingly, this study aspires to bridge the research gap by exploring a new DV-Hop algorithm to build a fast, costefficient, strong range-free localization scheme. This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. To address the poor localization accuracy problem in irregular C-shaped network topology, it adopts an efficient Grew Wolf Optimizer instead of the least-squares method. The dynamic communication range is introduced to refine hop between anchor nodes, and new parameters are recommended to optimize network protocol to balance energy cost in the initial step. Besides, the weighted coefficient and centroid algorithm is employed to reduce cumulative error by hop count and cut down computational complexity. The developed localization framework is separately validated and evaluated each optimized step under various evaluation criteria, in terms of accuracy, stability, and cost, etc. The results of EGWO-DV-Hop demonstrated superior localization accuracy under both topologies, the average localization error dropped up to 87.79% comparing with basic DV-Hop under C-shaped topology. The developed enhanced DWGWO-DVHop localization algorithm illustrated a favorable result with high accuracy and strong stability. The overall localization error is around 1.5m under C-shaped topology, while the traditional DV-Hop algorithm is large than 20m. Generally, the average localization error went down up to 93.35%, compared with DV-Hop. The localization accuracy and robustness of comparison indicated that the developed DWGWO-DV-Hop algorithm super outperforms the other classical range-free methods. It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks

Regularized Least Square Multi-Hops Localization Algorithm for Wireless Sensor Networks

Abstract: Position awareness is very important for many sensor network applications. However, the use of Global Positioning System receivers to every sensor node is very costly. Therefore, anchor based localization techniques are proposed. The lack of anchors in some Wireless Sensor Networks lead to the appearance of multi-hop localization, which permits to localize nodes even if they are far from anchors. One of the well-known multi-hop localization algorithms is the Distance Vector-Hop algorithm (DV-Hop). Although its simplicity, DV-Hop presents some deficiencies in terms of localization accuracy. Therefore, to deal with this issue, we propose in this paper an improvement of DV-Hop algorithm, called Regularized Least Square DV-Hop Localization Algorithm for multi-hop wireless sensors networks. The proposed solution improves the location accuracy of sensor nodes within their sensing field in both isotropic and anisotropic networks. We used the double Least Square localization method and the statistical filtering optimization strategy, which is the Regularized Least Square method. Simulation results prove that the proposed algorithm outperforms the original DV-Hop algorithm with up to 60%, as well as other related works, in terms of localization accuracy

One Kind of Redundant Reliability Wireless from Network Algorithm Research Based on Advanced Version DV-HOP Route Agreement

Wireless ad-hoc network has occupied important content in the field of broadband wireless network. Traditional wireless ad-hoc network using conventional peer node, without a central controller, multiparty routing technology in the current application of pumping has been a huge success. But this kind of network is widespread shortcomings with poor reliability. In order to solve this problem, we creatively put forward a kind of based on the modified DV - HOP routing protocol the high reliability of the wireless ad-hoc network is proposed. In the protocol algorithm used for heterogeneous network integration technology is terminal technology. Through this algorithm's computer simulation and actual environment performance and function test, proved this algorithm is compared with traditional wireless ad-hoc network routing protocol algorithm has higher reliability, and it can than the current wireless ad-hoc network has an average of 500 hours continuous trouble-free working time up to 600 hours. So you can argue that this algorithm has high practical value and is worth popularizing in the field

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Capture and reconstruction of the topology of undirected graphs from partial coordinates: a matrix completion based approach

2017 Spring.Includes bibliographical references.With the advancement in science and technology, new types of complex networks have become common place across varied domains such as computer networks, Internet, bio-technological studies, sociology, and condensed matter physics. The surge of interest in research towards graphs and topology can be attributed to important applications such as graph representation of words in computational linguistics, identification of terrorists for national security, studying complicated atomic structures, and modeling connectivity in condensed matter physics. Well-known social networks, Facebook, and twitter, have millions of users, while the science citation index is a repository of millions of records and citations. These examples indicate the importance of efficient techniques for measuring, characterizing and mining large and complex networks. Often analysis of graph attributes to understand the graph topology and embedded properties on these complex graphs becomes difficult due to causes such need to process huge data volumes, lack of compressed representation forms and lack of complete information. Due to improper or inadequate acquiring processes, inaccessibility, etc., often we end up with partial graph representational data. Thus there is immense significance in being able to extract this missing information from the available data. Therefore obtaining the topology of a graph, such as a communication network or a social network from incomplete information is our research focus. Specifically, this research addresses the problem of capturing and reconstructing the topology of a network from a small set of path length measurements. An accurate solution for this problem also provides means of describing graphs with a compressed representation. A technique to obtain the topology from only a partial set of information about network paths is presented. Specifically, we demonstrate the capture of the network topology from a small set of measurements corresponding to a) shortest hop distances of nodes with respect to small set of nodes called as anchors, or b) a set of pairwise hop distances between random node pairs. These two measurement sets can be related to the Distance matrix D, a common representation of the topology, where an entry contains the shortest hop distance between two nodes. In an anchor based method, the shortest hop distances of nodes to a set of M anchors constitute what is known as a Virtual Coordinate (VC) matrix. This is a submatrix of columns of D corresponding to the anchor nodes. Random pairwise measurements correspond to a random subset of elements of D. The proposed technique depends on a low rank matrix completion method based on extended Robust Principal Component Analysis to extract the unknown elements. The application of the principles of matrix completion relies on the conjecture that many natural data sets are inherently low dimensional and thus corresponding matrix is relatively low ranked. We demonstrate that this is applicable to D of many large-scale networks as well. Thus we are able to use results from the theory of matrix completion for capturing the topology. Two important types of graphs have been used for evaluation of the proposed technique, namely, Wireless Sensor Network (WSN) graphs and social network graphs. For WSN examples, we use the Topology Preserving Map (TPM), which is a homeomorphic representation of the original layout, to evaluate the effectiveness of the technique from partial sets of entries of VC matrix. A double centering based approach is used to evaluate the TPMs from VCs, in comparison with the existing non-centered approach. Results are presented for both random anchors and nodes that are farthest apart on the boundaries. The idea of obtaining topology is extended towards social network link prediction. The significance of this result lies in the fact that with increasing privacy concerns, obtaining the data in the form of VC matrix or as hop distance matrix becomes difficult. This approach of predicting the unknown entries of a matrix provides a novel approach for social network link predictions, and is supported by the fact that the distance matrices of most real world networks are naturally low ranked. The accuracy of the proposed techniques is evaluated using 4 different WSN and 3 different social networks. Two 2D and two 3D networks have been used for WSNs with the number of nodes ranging from 500 to 1600. We are able to obtain accurate TPMs for both random anchors and extreme anchors with only 20% to 40% of VC matrix entries. The mean error quantifies the error introduced in TPMs due to unknown entries. The results indicate that even with 80% of entries missing, the mean error is around 35% to 45%. The Facebook, Collaboration and Enron Email sub networks, with 744, 4158, 3892 nodes respectively, have been used for social network capture. The results obtained are very promising. With 80% of information missing in the hop-distance matrix, a maximum error of only around 6% is incurred. The error in prediction of hop distance is less than 0.5 hops. This has also opened up the idea of compressed representation of networks by its VC matrix

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) ispreferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of theunknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though theoptimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linearclassifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability

Analysis of synchronous localization systems for UAVs urban applications

[EN] Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.S