ADMM-Based Sensor Network Localization Using Low-Rank Approximation

There are many algorithms demonstrated effectively for wireless sensor network (WSN). Some approaches aim to provide precise position and others demonstrate identification purpose. Even though there is a growing awareness for Zigbee-based sensor networks applications that an integrated coordination of hardware and software is needed for current applications. Especially, the previous research efforts could not clearly establish the impact of protocols or topology management issues. This paper intends to design a more convenient and efficient localization algorithm for Zigbee wireless sensor networks for the paint-ball game. A RSSI-based algorithm (Received Signal Strength Indicator, RSSI) and fixed nodes topology is developed to illustrate the performance of a novel mixed centroid localization algorithm within the paint-ball game scenarios. The experiment data and algorithm actually help the paint-ball game simulation project. The next step for this project is getting beacons motion figure which may expand this algorithm further applications.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Localization technology is the basis of practical applications of wireless sensor networks (WSNs). An improved localization algorithm for wireless sensor networks was proposed to reduce hop distance error and solve the premature convergence problem for the hybrid localization algorithm of DV-Hop and Bat (BADV-Hop). Based on the vector routing localization mechanism, an optimization method was designed to enhance the average hop distance accuracy. The linear weight strategy was used to correct searching velocity of bats. In addition, tendentious random disturbance was established to optimize location information. Experimental results showed that the localization accuracy of improved algorithm is 13% greater than that of DV-Hop algorithm and 3% greater than that of BADV-Hop algorithm. Moreover, the improved algorithm effectively reduced the maximum iterations and avoided premature convergence without any additional energy consumption compared to BADV-Hop algorithm.

Wireless Sensor Networks (WSNs) are being deployed in a variety of location-aware applications, where the measurement of data is meaningless without accurate location. Many localization algorithms have been proposed in the literature. However, localization in threedimensional (3D) space has not been studied sufficiently. Also, artificial neural networks are not commonly used in localization. In this paper, we present a 3D localization algorithm for wireless sensor networks using artificial neural networks. In the proposed scheme, the idea of the 2D localization using neural network is extended to achieve a 3D localization with simplicity, location accuracy, mobility, and low cost. The simulation results depict the performance and the effectiveness of the proposed approach.

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.

A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

Wireless sensor networks (WSNs) is emerging as a key tool for data acquisition in many applications, and localization algorithms of the sensors have a vital importance due to the intrinsic characteristics of this type of networks. The objective of this paper is to propose an algorithm of range-free localization for WSNs using a mobile robot equipped with a rotary directional antenna. This algorithm, denominated as the azimuthally defined area localization algorithm (ADAL), is executed in each sensor node and is based only on the analysis of the information received from the mobile robot, therefore is energy efficient and contributes to extend the lifetime of the sensor network. Additionally the proposed algorithm has the advantage of being simple and economical. The simulation results show that the proposed algorithm is a practical, effective and accurate method to localize sensor nodes in a WSN.

Wireless Sensor Networks (WSNs) are being deployed in a variety of location-dependent applications, where the measurement of data is meaningless without accurate location. Many range-free algorithms were proposed in order to minimize the extra cost that appears in the ranged-based algorithms. Fuzzy logic provides a simple, fast and cost efficient way that can be used in range-free algorithms. In this paper, we propose a fuzzy-based collaborative localization algorithm for wireless sensor networks. Our proposed algorithm implements a Sugeno-type fuzzy system using a collaborative feedback communication scheme. The proposed algorithm provides more accurate localization estimation without any additional hardware implementation. The simulation results demonstrate the effectiveness of the proposed approach.

In a Wireless Sensor Networks, localization techniques are needed to identify the exact position of an event. In this paper, we proposed Fuzzy Inference System (FIS) based target localization algorithm in a three-dimensional wireless sensor networks. In the proposed scheme, distance between anchor and target nodes are calculated by adding correction factor with hop size of an anchor node. The concept of fuzzy logic-based edge weight calculation is introduced to improve the localization accuracy. Simulation result shows that proposed scheme achieves less localization error and better accuracy as compared with existing localization technique.

In many applications of wireless sensor networks, location is very important information. Conventional location information comes from manual setting or GPS device. However, manual location setting requires huge cost of human time, and GPS location setting requires expensive device cost and large device size. Furthermore, GPS can not operate in indoor environment. Both approaches are not applicable to localization task of wireless sensor networks. In this paper, an accurate and efficient localization algorithm based on multidimensional scaling (AIDS) is proposed in hierarchical network environment. Through localized computation of multidimensional scaling within a cluster, the computation overhead can be distributed to each cluster. On the other hand, MDS- based localization approach requires the estimation of multihop distance. Through restricting estimation of multihop distances within a cluster, the proposed localization algorithm achieves better accuracy and can operate in non-convex network environment. Experimental results reveal that the proposed HMDS localization algorithm outperforms MDSMAP algorithm in terms of accuracy.

Internet of Things (IoT) systems require localization for the nodes within Wireless Sensor Networks (WSNs) for many location-based services. Since thousands of sensor nodes would exist in some networks, having GPS on each node is impractical, not merely due to the hardware cost, but also because of the poor performance indoors. Localization is now recognized as a crucial area for study. The research presented in this paper puts forward a half-measure weighted centroid DV-Hop localization algorithm. The proposed algorithm adjusts the locations of unknown nodes using redundant information obtained from localization equations. Simulation of sensor networks resulted in significant improvements in accuracy and lower error rates in localization estimation, while maintaining low hardware and computational costs.

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

On accuracy of region based localization algorithms for wireless sensor networks

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.