New Dv-Distance Algorithm Based on Path for Wireless Sensor Network

Localization is one of the significant field of research in wireless sensor networks (WSNs). It broadly refers to estimating the location of unidentified nodes in a WSN using anchor nodes (nodes whose location is known). Localization, along with the mobility of nodes in the network, makes this field more potent for practical purposes. Therefore, in this paper, we propose an algorithm for localization in WSNs using a mobile anchor and subordinate nodes (LWMS). In the proposed algorithm, we use a mobile anchor node to localize all the other unidentified nodes in the network. The mobile anchor node localizes the nodes in its path using RSSI technique, and these localized nodes are called the static anchor nodes. Using these static anchor nodes, the algorithm localizes all other nodes around them. The nodes localised using static anchor nodes are called subordinate anchor nodes. There are still some nodes left in the network to be localized due to holes or a small percentage of anchor nodes. Further, these subordinate nodes are used to localize all the remaining nodes in the network. We have also used a method of solving the system of distance equations, which reduces the propagation of error. The simulation results corroborate that LWMS performs better than the other two algorithms used for comparison.

In wireless sensor networks (WSN) high-accuracy localization is crucial for both of WNS management and many other numerous location-based applications. Only a subset of nodes in a WSN is deployed as anchor nodes with their locations a priori known to localize unknown sensor nodes. The accuracy of the estimated position depends on the number of anchor nodes. Obviously, increasing the number or ratio of anchors will undoubtedly increase the localization accuracy. However, it severely constrains the flexibility of WSN deployment while impacting costs and energy. This paper aims to drastically reduce anchor number or ratio of anchor in WSN deployment and ensures a good trade-off for localization accuracy. Hence, this work presents an approach to decrease the number of anchor nodes without compromising localization accuracy. Assuming a random string WSN topology, the results in terms of anchor rates and localization accuracy are presented and show significant reduction in anchor deployment rates from 32% to 2%.

Estimating the location of sensor nodes in wireless sensor networks is a fundamental requirement in a variety of sensing applications. In large scale dense deployments where the area covered by sensor nodes is very large, it is impossible to localize all sensor nodes using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate sensor node positions. In some deployments it is required to maintain the anchor nodes at the edge of the simulated area. In previous work, we introduced a new localization scheme that uses distance measurements to localize sensor nodes using a collinear and non-collinear mobile anchor nodes placed at the edge of the sensed area. A Kalman Filter was then used to improve the location accuracy for each node. In this scheme each SN estimated its location from two independent directions then use such information to improve localization accuracy. In this paper, we extend the work to use side localization using hop measurements and fixed anchor node. We also compare the performance of using side localization for both hop and distance measurement. Through simulation we show that side localization using distance and hop measurements outperform DV-Hop and DV-Distance, which are mainstream localization protocols. The weighted mean hop measurement gives higher localization accuracy than using using distance measurement. However, if Kalman Filter is used distance measurement gives better localization accuracy.

Geotagging of sensor data in a wireless sensor network is important in many applications. It may not always be possible to record locations of the sensor nodes during deployment. Localization techniques provide the node location information, however most of these techniques rely on the neighboring nodes for localization. Thus it is essential that the information from neighbors be trustworthy and/or the localization techniques be robust to some of the nodes turning malicious. In this paper, we address the scenario where the malicious node(s) attempt to disrupt the localization process of a target node in an uncoordinated manner. We propose a secure localization technique, called the weighted least square (WLS) localization, in a network with one or more compromised anchor nodes (nodes with known positions are referred to as anchor nodes). The WLS technique assigns larger weights to the anchor nodes that are closer to the target node and is shown to offer significant advantages over existing techniques. The Cramer-Rao lower bound (CRLB) on the root mean square error (RMSE) of the position estimate for the uncoordinated attack is also derived. The proposed technique is shown to provide better localization accuracy than existing algorithms.

Mobility makes node localization more difficult and influences localization accuracy greatly for Wireless Sensor Network (WSN). Static localization algorithms cannot perform well in mobile WSN. As a range-free localization algorithm for mobile WSN, Monte Carlo Localization Boxed algorithm (MCB) localizes the unknown node by exploiting the node’s mobility and calculating the mean value of current location samples. In MCB algorithm, current location samples are selected in sample boxes randomly and purposelessly. When the anchor nodes’ density of mobile WSN is small and after several iterative times, the samples would degrade rapidly to cause low localization accuracy. To address those problems, Grey Model Prediction based Mont Carlo Localization Boxed Algorithm (GMMCB) is proposed in this paper. In predication phase of GMMCB, several previous time intervals’ location samples are used to predict the current samples by using the method of Grey Model Prediction. More accurate samples are predicted to improve localization accuracy and reduce iteration times of resampling to save precious energy of sensor nodes. The simulation compares the localization effectiveness of GMMCB, MCB and Centroid algorithms to demonstrate the good performance of GMMCB.

Distance Vector-Hop (DV-Hop) localization is a distributed, hop by hop positioning algorithm. Anchor nodes generate the packets of nodes position information to calculate the number of hops between the beacon nodes and the anchor nodes, and these packets are flooded in the respective Wireless Sensor Networks (WSN). The DV-Hop algorithm obtains the distance information from the unknown nodes to the anchor nodes through the network topology calculation rather than the radio wave signals measurement which are used in the range-based algorithm. However, flooding information from all anchors to all beacon nodes will become too expensive for large networks (i.e., cost, energy), even with low anchor fraction. The region of nodes flooding through the network needs to be confined within logical constraints to produce better accuracy to reduce the localization error while reducing the cost. This research proposes and develops an enhanced algorithm that can improve the region area for sensor nodes placement within the specific localization area. This research aims to produce a longer network lifetime by reducing the energy used of the sensor nodes and at the same time minimize the localization error. This research describes the flooding control of the node's placement for the localization of the DV-Hop algorithm. The placement of nodes is limited inside the region of the anchor nodes. The simulation environment setup comes with parameters such as the radius, area, number of nodes, types of nodes, the anchor proportion, and the value of the regions. The result shows an improvement for the localization error, the power consumption, and the accuracy of the nodes positioning.

Sensor positioning is a fundamental block in many location-dependent applications of wireless sensor networks. Although the main objective in localization is primarily enhancing the positioning accuracy, the importance of energy consumption and localization accuracy poses new challenges. The localization is usually assisted with some self-known position sensors called anchor nodes. In this letter, optimal power allocation for the anchor nodes in a sense of minimizing the energy consumption considering estimation errors is investigated. To have a better estimation of the relative distance between the anchor and unknown nodes using received signal strength indicator (RSSI), average energy of the received beacon is introduced as a new decision metric. Based on this, a squared position error bound as an accuracy parameter is derived, and an optimization problem is proposed to maximize the localization performance. More specifically, the optimal power allocation policy is first derived for the case that the anchor nodes estimate their own locations with no error. Since there are unavoidable errors in the positions of the anchor nodes, the optimization problem is then modified by including uncertainty in the positions of the anchor nodes. The results show that a substantial reduction in power consumption can be achieved by optimal allocation of the transmission power.

Node localization remains a pivotal research challenge in Wireless Sensor Networks (WSN). The DV-Hop method, which estimates the positions of unknown nodes using anchor nodes and multi-hop forwarding, significantly lacks localization accuracy. To address this deficiency, this paper introduces an improved Grey Wolf Optimizer with a communication strategy (IGWO-CS). To alleviate the constraints imposed by the convergence factor in the conventional GWO algorithm, a Logistic chaotic mapping has been integrated. Moreover, two novel communication strategies, namely the Adaptive Weight Mechanism and Lévy Flight, have been implemented. The Adaptive Weight Mechanism dynamically adjusts positions during the weight update process, while the pronounced randomness and the mechanisms of Lévy Flight steps enhance information exchange among wolves, thereby improving convergence speed. Comprehensive experimental analyses on established benchmark functions have validated the efficacy of the IGWO-CS method, illustrating its competitiveness with other advanced algorithms such as the Ranking-based Adaptive Cuckoo Search (RACS) and Modified Particle Swarm Optimization (MPSO). The IGWO-CS method outperformed in 17 out of 23 benchmark functions. In simulation experiments, it achieved standard deviations of normalized errors and distance errors at 1.15E-04 and 2.31E-03, respectively. Quantitative evaluations confirm that the IGWO-CS method surpasses the RACS and MPSO in localization accuracy within WSN, demonstrating its substantial superiority.

Node localization in a wireless sensor network (WSN) seeks to compute the positions of unknown nodes (regular node) using known nodes(anchor nodes). The accuracy with which a WSN is located can have a substantial influence on its performance. Most standard range-free localization techniques disregard anisotropic considerations in real-time wireless sensor applications, resulting in low localization accuracy. To increase localization accuracy in the target area, bio-inspired algorithms will aid in the optimization of expected placements of unknown sensor nodes. The reliable anchor pairing approach and the salps swarm algorithm (SSA) in traditional range-free localization methods like the DV-Hop method are used to mitigate anisotropic factors. Particle swarm optimization (PSO) and grey wolf optimization (GWO) and other well-known optimization methods are compared to the proposed algorithm under different WSN topologies.

Wireless sensor network (WSN) is usually deployed in harsh environments for collecting and delivering the data to the remotely located base station. Due to the recent achievements in WSN, tiny and expensive sensors are used with capability of sensing large information and to propagate over longer distances. In range-based localization algorithms, anchor node whose location is known plays an important role. In order to resolve the challenges of traditional localization algorithms, an anchor-based node localization algorithm is proposed for WSN. The distance between anchor or known node and unknown node is measured by optimizing anchors and creating database of optimized anchors. Using trilateration method unknown nodes are located and are used as new anchors which will reduce localization algorithm’s dependency on anchors. The objective of the proposed work is to maximize the life time of the network by minimizing the energy consumption. From the simulation results, we show that the proposed algorithm increases lifetime of wireless sensor network.

The velocity parameter in mobile Wireless Sensor Networks (WSNs) is a critical factor in anchor nodes distribution. However, most of the previous schemes use the random velocity to transmit anchor nodes as in the waypoint mobility model, which produces a considerable overlap between anchor nodes without improving the localization accuracy. In this paper, we improve such model by controlling the anchor node velocity. In the proposed scheme (EDAL), the anchor node velocity is a function of the overlap degree between anchor nodes and number of anchor node in the neighbor. Thus, EDAL can distribute the anchor nodes efficiently to improve the localization accuracy and expand the coverage area simultaneously. We evaluate the EDAL performance through extensive simulation experiments.

Wireless sensor networks (WSN) usage in numerous applications that increased need has risen to explore more efficient and effective manner. Localization is an important issue that can bring efficiency to WSNs. Localization depicts to calculation of the actual location of unknown nodes in network. This paper focuses on the comparison and analysis of Multi-Layer Perceptron Neural Networks (MLPNN) and Radial Basis Function Neural Networks (RBFNN) for the development of localization framework in WSNs. Using Received Signal Strength Indicator (RSSI) determination of the location a static sensor node on 100 100 m 2 grids from three anchor nodes with fixed positions. The simulation results effectually signify that MLPNN has better localization accuracy as compared to RBFNN.

In this paper, we propose a novel model of three points named TP for location estimation in wireless sensor networks (WSNs) with random deployment of anchor nodes. In this model, we select three anchor nodes which have the strongest received signal strength (RSS) for location estimation, the centroid algorithm and the method of intersection of judgment are used to estimate the location of unknown nodes. To further exploit three nearest intersection points in TP, the enhanced TP (ETP) is proposed. The simulation results show that the proposed models outperform MMSE and BML in terms of the localization accuracy for WSNs. Moreover, the localization accuracy of the proposed models in scenario 2 with random deployment of anchor nodes are better than in scenario 1 with planned deployment of anchor nodes. Additionally, compared with MMSE and BML, ETP and TP can reduce the environmental impact on location estimation.

To overcome the disadvantages of the positioning technologies by fuzzy theory in Wireless Sensor Networks (WSNs), positioning algorithms based on information fusion are presented in this article. The fuzzy theory is used to deal with the randomness and fuzziness in the WSNs. And the information fusion is introduced to improve the location accuracy. If the collinearity of the anchor nodes is larger, the misjudged reference nodes may be caused. They are removed by using clustering method. The algorithms in this paper can enhance the location accuracies compared with using the fuzzy theory and alleviate the effect of the RSSI (Received Signal Strength Indication) measure errors. Moreover, the algorithms avoid the high complexity of computation and the requirement of more anchor nodes. Simulation results indicate that the algorithms are more precise, robust as well as with good suitability.

The localization of sensor nodes is a significant issue in wireless sensor networks (WSNs) because many applications cannot provide services without geolocation data, especially during disaster management. In recent years, a promising unknown-nodes positioning method has been developed that localizes unknown nodes, employing a GPS-enabled mobile anchor node moving in the network, and broadcasting its location information periodically to assist localization. In contrast to most studies on path planning that assume infinite energy of the mobile anchor node, the anchor node in this study, consumes different amounts of energy during phases of startup, turning, and uniform motion considering the aftermath of disasters. To enable a trade-off between location accuracy and energy consumption, a path-planning algorithm combining a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) and SCAN algorithm (SLMAT) is proposed. SLMAT ensures that each unknown node is covered by a regular triangle formed by beacons. Furthermore, the number of corners along the planned path is reduced to save the energy of the mobile anchor node. In addition, a series of experiments have been conducted to evaluate the performance of the SLMAT algorithm. Simulation results indicate that SLMAT outperforms SCAN, LMAT, HILBERT, and Z-curve in terms of localization accuracy and energy consumption.