Robust Node Localization Based on Distributed Weighted-multidimensional Scaling in Wireless Sensor Networks

The main goal of this research is concerning protocols to contain in-motion sensor nodes in large-scale topologies, and constitute a fleet wireless sensor network which can assistant in-flight localization and alignment for carrier-based aircraft. In this paper, we abstract network model according to specific conditions of F-WSN, and select performance indicators to evaluate it. A distributed multiple-weight data gathering and aggregation protocol (DMDG) is proposed, which contains all-sided active clustering scheme and realizes long-range realtime communication by tacical data link under a TDMA/CSMA channel sharing mechanism. An analytical paradigm facilitating the most appropriate choice of the next relay is proposed. Simulation results indicate that DMDG scheme can balance the energy consumption and extend the network lifetime notably, compared with existing clustering protocols, especially in sparse node density or anisotropic topologies.

Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. The main goal of this research is concerning protocols to contain in-motion sensor nodes in large-scale topologies, and constitute a fleet wireless sensor network which can assistant in-flight localization and alignment for carrier-based aircraft. In this paper, we abstract network model according to specific conditions of F-WSN, and select performance indicators to evaluate it. A distributed multiple-weight data gathering and aggregation protocol (DMDG) is proposed, which contains all-sided active clustering scheme and realizes long-range realtime communication by tacical data link under a TDMA/CSMA channel sharing mechanism. An analytical paradigm facilitating the most appropriate choice of the next relay is proposed. Experimental results have shown that DMDG scheme can balance the energy consumption and extend the network lifetime notably, outperform LEACH, PEACH, and DEEC in terms of network lifetime and coverage rate, especially in sparse node density or anisotropic topologies.

The effect of localization error in mobile sensor networks is investigated in this paper. Localization is an essential building block in mobile sensor networks, and is achieved through information exchange among the sensors. Sensor deployment algorithms often rely on the Voronoi diagram, which is obtained by using the position information of the neighboring sensors. In the sensor network coverage problem, it is desired to place each sensor in a proper position in its Voronoi cell such that its local coverage increases. On the other hand, it is often assumed that all measurements are sufficiently accurate, while in a practical setup even a small localization error may lead to significant uncertainty in the resultant Voronoi diagram. This paper is concerned with the degrading effect of position measurement error in the sensor network coverage problem. To this end, the effect of localization error on the boundaries of the Voronoi polygons is investigated. Two polygons are obtained for each sensor, and it is shown that the exact Voronoi polygon (corresponding to accurate localization) lies between them. The area between these two polygons is directly related to the size of error. A sensor deployment strategy is presented based on these two polygons, using a quantitative local density function which takes the uncertainty of the Voronoi polygons into account to maximize the local coverage of each sensor.

ABSTRACTWireless sensor networks (WSNs) are broadly employed in lots of applications. WSN is comprised of spatially dispersed sovereign devices enabled with sensors to examine some physical phenomenon. The node localization, which is to be responsive of nodes’ location in the system, is a crucial component of numerous WSN functions and applications. Various localization algorithms have been proposed to precisely locate nodes in WSNs. The localization algorithms are normally categorized into range-based and range-free schemes based on range measurements. The range-based methods use exact computation measure (distance or time) among nodes in the network. So range-based methods need some additional hardware for such computation, thus expensive to be used in practice. Because of hardware constraints of sensor network devices, range-free methods are considered cost efficient alternatives. Though, these methods normally contain higher localization error in comparison with range-based methods. DV-Hop is an usual range-free method that use hop-based evaluation. In this article, we have modified DV-Hop method and applied a meta-heuristic (PSO) technique to overcome the positioning error. The algorithm has been implemented in MATLAB R2015a for the validation of results. The performance of implemented algorithm has been analyzed in terms of localization error, error variance, accuracy, and coverage. The results of modified algorithm confirm that our algorithm decreases the localization error and improved the localization accuracy as compared to DV-Hop and improved DV-Hop (IDV-Hop) algorithms.

Usage of wireless gadgets is increasing day by day in both metropolitan and countryside localities. The network infrastructure does not reach all the localities; especially in rural areas, where the internet availability is inadequate to meet the network requirements. Devices in such rural locations will be out of the network for some period. This non-persistent connectivity problem can be solved by using the moving nodes as a carrier for data transmission. In this work, we classify the vehicular network into sparse, semi-sparse and dense network based on the number of static and moving nodes present in a location. In sparse and semi-sparse network, there won't be any continuous path between the source and destination nodes. In such networks, we can use Delay Tolerant Network (DTN) protocols for data transmission. In our work, we try to find the suitable DTN protocol for the sparse and semi-sparse network topology. The protocols considered in this work are Epidemic, Spray and Wait, and Maxprop and the parameters we analyzed are delivery ratio, average latency and overhead ratio. We simulated these protocols in a vehicular network with heterogeneous characteristics, for the dense, semi-sparse and sparse network topology. The routing protocols performance is analyzed by delivery ratio, average latency and overhead ratio using Opportunistic Network Environment (ONE) simulator for varying the message lifetime and node density.

Preoperative repeat endoscopy in colorectal cancer (CRC) patients is considered by many to be an integral component of surgical planning. Little is known, however, about the utility of re-endoscopy. A retrospective review of 342 consecutive patients undergoing elective surgical resection for CRC from January 2008 to December 2011 was performed. Patients were included if the initial endoscopist was different than the operating surgeon. A localization error was recorded if the final tumor location identified during surgery was in a different anatomical segment than that identified by endoscopy. The Chi-squared test was used to compare categorical variables. An error rate with a 95% confidence interval was obtained using the exact binomial distribution. 298 patients were identified, 118 (39.6%) of whom also underwent a preoperative re-endoscopy by the operating surgeon or partner. Nineteen patients had incorrect tumor localization at initial endoscopy, equivalent to a 6.4% error rate (95% CI 3.88-9.78). In comparison, there were two localization errors on re-endoscopy, 1.69% (95% CI 0.21-6.00). Re-endoscopy was found to be protective against localization errors (P < 0.05), correcting 10 of the 12 errors made at the initial endoscopy. The sensitivity of re-endoscopy as a diagnostic tool to detect errors was 83% with a corresponding specificity of 100%. The overall accuracy of re-endoscopy in preventing endoscopic localization errors was 92% (95% CI 81-100). There is a small but important localization error rate in preoperative endoscopic evaluation of colorectal tumors. Re-endoscopy appears to be safe and may potentially identify and correct these errors and help with preoperative planning at the expense of delaying surgery. Further research is necessary to find ways to improve localization and identify which patients would benefit from re-endoscopy.

Communication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Accurate localization of nodes in a sensor network is a crucial step before the sensor network can be utilized for various applications. This paper proposes a successive estimation method to self-localize the sensor nodes using time of arrival (TOA) measurements, through the aid of a few anchor nodes whose positions are known <emphasis emphasistype="italic">a priori</emphasis>. The successive nature of the proposed algorithm makes it attractive for distributed computation in a resource constrained environment. The proposed technique uses subsets of TOAs to obtain coarse sensor node estimates. Depending on available computation and transmission resources, the node positions are refined to improve their accuracies. The solution of the proposed node localization algorithm is algebraic and closed-form, which simplifies computation and avoids possible local convergence or divergence problems as in the traditional iterative approaches. A main benefit of the proposed algorithm is that although it is closed-form and performs successive estimation of the node locations, it is able to reach the CramÉr–Rao lower bound (CRLB) accuracy under white Gaussian measurement noise with sufficient SNR. This is confirmed by the theoretical analysis and corroborated by simulations. </para>

Accurately determining the location of mobile wireless sensor nodes in real-time is essential for many purposes. This paper proposes a new and efficient algorithm for localization of mobile node(s) within a WSN. The proposed algorithm, called iterative CCA-MAP (iCCA-MAP), is based on the CCA-MAP algorithm which applies an efficient nonlinear data mapping technique. The latter has been shown to perform extremely well for localizing stationary nodes in WSNs. Simulation results show that the localization error results for both CCA-MAP and iCCA-MAP are similar. However, the computational time required for obtaining location results using the iterative CCA-MAP scheme is far smaller than that of the original CCA-MAP. The advantage of the proposed algorithm is that it can provide the mobile node's location information at near real-time, allowing for it to be applied at a much higher frequency in order to provide up-to-date estimations of the mobile node's position, which can result in a lower localization error.

Due to the infrastructure-less, dynamic and broadcast nature of radio transmissions, communications in mobile ad hoc networks (MANETs) are susceptible to malicious traffic analysis. After traffic analysis, an attacker determines a target node and conducts an intensive attack against it, called target-oriented attack. The traffic analysis and the target-oriented attacks are known as quite severe problems in MANETs, including position-based routing protocols, with respect to the degradation of both throughput and security of the routing. Also position information of routing nodes is very sensitive data in MANETs where even nodes not knowing either other establish a network temporarily. Therefore we propose a new position-based routing protocol which keeps routing nodes anonymous, thereby preventing possible traffic analysis. To this end, a time variant temporary identifier temp ID is computed from time and position of a node and used for keeping the node anonymous. Only the position of a destination node is required for the route discovery, and temp ID is used for establishing the route for sending data: a receiver hand shake scheme is designed for determining the next hop on-demand with use of the temp ID. We evaluate the level of anonymity and performance of our scheme. The analysis shows that the proposed scheme ensures the anonymity of both route and nodes and the robustness against the target-oriented attack and several others. Also our scheme is applicable to networks with any density of nodes.

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Localization of sensor nodes is one of the key issues in wireless sensor networks. Next to the ability, to assign a phenomenon to a position, localization is a precondition for sensor network algorithms like geographic clustering and routing. A simple approach for coarse grained localization is centroid localization (CL), which was firstly presented by Bulusu et al. and assumes regularly arranged beacons. The localization accuracy was improved by various centroid-based algorithms, which use approximate distances to improve location estimation through weighting beacons in range, e.g. weighted centroid localization (WCL). Nevertheless, all these approaches have in common an increased localization error near network borders. In this work, we investigate this error and present two strategies to reduce the localization error of border area nodes.

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN. The major aim of the MSRO-NLT technique is to determine the positioning of the unknown nodes in the WSN. Since the traditional search and rescue optimization (SRO) algorithm suffers from the local optima problem with an increase in number of iterations, MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique. The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm. In order to validate the effective node localization performance of the MSRO-NLT algorithm, a set of simulations were performed to highlight the supremacy of the presented model. A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Performance modeling and analysis of void-handling methodologies in underwater wireless sensor networks