Node Localization Method Research Articles

An Efficient Localization Method for Mobile Nodes in Wireless Sensor Networks

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.

Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Particle Swarm Optimization and Differential Evolution

Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Particle Swarm Optimization and Differential Evolution

Improvement of Positioning Technology Based on RSSI in ZigBee Networks

In this paper, the node localization methods of ZigBee wireless sensor networks were studied. There are two key issues affecting the positioning accuracy: accuracy of RSSI value and optimization of localization algorithm. For the first issue, the effects of two kinds of environmental disturbance on RSSI values were analyzed, and then RSSI values were pretreated using Kalman filter. For the second, the RSSI-based localization algorithm were introduced in detail, and a new algorithm-triangle centroid localization algorithm based on weighted feature points-was proposed. MATLAB simulation and actual network tests were carried out. The simulation and experimental results all showed that our pretreatment strategy of RSSI and optimization of localization algorithm had great effects on positioning accuracy.

Multi-sensor data fusion methods for indoor localization under collinear ambiguity

Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer–Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR.

Node Localization Method for Wireless Sensor Networks Based on Hybrid Optimization of Differential Evolution and Particle Swarm Algorithm

Regarding the node localization problems for wireless sensor network, a hybrid optimization method was pro- posed accordingly on differential evolution(DE) algorithm and particle swarm optimization(PSO) algorithm. Firstly, the position and velocity of the initial population were randomly generated by PSO, and the fitness function was constructed according to the mean square error of estimated and measured distance between the unknown nodes and their adjacent an- chor node. Secondly, the mutation and selection operation of DE algorithm were executed to find out the optimum posi- tion of the population. Lastly, the current velocities and positions of all particles of the population were updated, and the crossover operation and selection operation of DE algorithm were executed to update the current global optimum position of the whole population. Population global optimum solution of iterative search algorithm is the position coordinate of the unknown node. Simulation results indicate that the proposed localization method has smaller average localization error and higher localization accuracy than that of DE algorithm and PSO algorithm in the same environment.

Three-dimensional WSN Node Localization Method Based on LSSVR Optimized by DE Algorithm

Aiming at the problem that the three-dimensional (3D) node localization precision is affected by the model parameters of least squares support vector regression (LSSVR) node localization method for wireless sensor network, a localization method based on LSSVR optimized by differential evolution (DE) algorithm is proposed in this paper. Firstly, the 3D node localization model is built through LSSVR and the kernel function parameter and the regularization parameter are optimized by DE algorithm. Then, the fitness function of DE algorithm is constructed according to the mean square error of a number of virtual nodes from the predicted position and their actual position, and the global optimal parameters of LSSVR are acquired through limited modeling parameters iterative searching method. Finally, the LSSVR optimized by DE algorithm is used to realize the node localization. The simulation results show that the localization accuracy of the proposed method is superior to that of least square (LS) and LSSVR method. Keywords: Differential evolution algorithm, least square support vector regression, three-dimensional node localization, wireless sensor network.

RSS-Based Source Localization When Path-Loss Model Parameters are Unknown

In this letter we present a novel method for source node localization using received signal strength in situations where path-loss model parameters are unknown. First, a ratio approach is used to eliminate the transmitting power uncertainty, then, combined with a search method for path-loss exponent under certain constraints, linear least squares is utilized to determine the location of the source node. Simulations are used to demonstrate the feasibility and suitability of the proposed method. The simulation results indicate that the method presented outperforms other off-the-shelf source node localization algorithms intended for use when path-loss model parameters are unknown.

Node Localization Algorithm Based on DV-HOP in Wireless Sensor Networks

Node self-positioning is one of the core technologies in wireless sensor networks and is the premise of many applications. It is significant to achieve highly efficient and reliable node localization for event observation, target tracking and efficient routing. The DV-Hop localization algorithm which is one of the Range-Free algorithms is researched deeply. Firstly, algorithmic process, error sources, and the amount of communication and computing for Traditional DV-Hop algorithm are analyzed. For low positioning accuracy in Conventional DV-Hop localization algorithm, an improved DV-Hop localization algorithm based on estimate of average hop distance, Min-Max and position correction is brought forward. The simulation results show that the improved algorithm can improve the positioning accuracy more effectively without increasing hardware consumption in nodes comparing with conventional DV-Hop algorithm. So it is a practical and effective node localization method in wireless sensor networks.

A New Technique That Complements Sentinel Lymph Node Biopsy: Lymph Node Dissection Under the Intercostobrachial Nerves in Early-Stage Breast Cancer

BackgroundArm lymphedema is a common complication after axillary lymph node dissection (ALND), and there is no effective treatment. The clinical significance of sentinel lymph node biopsy (SLNB) is to avoid the risk of arm lymphedema caused by ALND in cases in which the nodes are negative for cancer cells. In developed countries, sentinel lymph node (SLN) localization methods by using combined dye and radioactive tracer techniques predict the lymph node status in early-stage breast cancer with comparable success rates, accuracy, and false-negative rates. In fact, most researchers agree that the combined technique has significantly higher accuracy than marking the sentinel nodes with dye alone. In China, the radioactive tracer technique is mostly used in research but not in clinical surgery, where it is not permitted. The necessity of intercostobrachial nerves (ICBN) preservation is now accepted by the surgeons and has become the standard procedure in such dissections, which reduces postoperative skin numbness and loss of feeling in the upper arm. MethodsIn our study, the data of 177 patients were analyzed to identify the feasibility of such an assignment and potential clinical significance of ICBN in dividing the axillary space. ResultsWe found that the mean value for the number of nodes under the ICBN was enough for accession, the lymphedema caused by axillary lymph nodes dissection under the intercostobrachial nerves (PALND) was similar with SLNB and lower than ALND, and the PALND can make up for SLNB in false negative (SLNs are negative, while non-SLNs are positive). ConclusionALND under the ICBN could be considered a procedure complementary to SLNB in early-stage breast cancer in China, where the radioactive tracer technique is not widely applied.

광신호 기반의 마이크로 센서 노드 위치 인식 시스템을 위한 파라미터 식별

In this paper, the parameter identification for localization scheme for the optics-based micro sensor node is conducted. We analyzed short measurement range problem which can be occurred in optical based micro sensor node localization method using a time of flight. And we set up the theory for distance and maximum reflected laser power to overcome the problem by identifying hardware parameters like laser power, effective area of MEMS CCR, sensitivity of photodetector, and so on. Experimental results of measurement of maximum reflected laser power were compared with results of the theory. By using the theory, we can identify hardware parameters of localization scheme to measure particular position of the optics-based micro sensor node.

Wireless sensor node localization by multisequence processing

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.

WSN Node Localization Algorithm Based on Adaptive Particle Swarm Optimization

In order to overcome shortcomings of existing range-free wireless sensor network (WSN) node localization methods such as huge computation volume and great effect of node density on localization precision, a WSN localization algorithm based on adaptive particle swarm optimization (APSO) was put forward in combination with particle swarm theory and DV-Hop algorithm. This algorithm improved localization precision by more than 20%, and the effect of node density on localization precision was significantly less than DV-Hop algorithm without any addition of hardware facilities and communication load.

Optimal Nonlinear Estimation for Localization of Wireless Sensor Networks

This paper deals with the problem of sensor localization when the localization error is nonlinearly propagated over the sensor network as occurs in Radio Interferometric Positioning System (RIPS). A noise model that takes the location uncertainties of anchor nodes into account in the node localization process has been derived based on a generic nonlinear measurement model that encapsulates the use of many popular node localization techniques. These include circular, parabolic and hyperbolic methods. This development enables the use of a distributed stochastic estimation method for node localization of wireless sensor networks with nonlinear measurements in the presence of anchor node uncertainties. The effectiveness of the derived equivalent noise model is demonstrated via a simulated progressive localization of a mote network using the RIPS measurements.

Research of Node Localization Algorithm Based on DV-HOP in Wireless Sensor Networks

Node self-positioning is one of the core technologies in wireless sensor networks and is the premise of many applications. It is significant to achieve highly efficient and reliable node localization for event observation, target tracking and efficient routing. The DV-Hop localization algorithm which is one of the Range-Free algorithms is researched deeply. Firstly, algorithmic process, error sources, and the amount of communication and computing for Traditional DV-Hop algorithm are analyzed. For low positioning accuracy in Conventional DV-Hop localization algorithm, an improved DV-Hop localization algorithm based on estimate of average hop distance, Min-Max and position correction is brought forward. The simulation results show that the improved algorithm can improve the positioning accuracy more effectively without increasing hardware consumption in nodes comparing with conventional DV-Hop algorithm. So it is a practical and effective node localization method in wireless sensor networks.

MP-based Algorithm in Node Localization of Wireless Sensor Network

W ireless sensor network (WSN) has attracted more and more research interest b ecause of its various applications in distributed monitoring, communication, and computing . For most applications, developing a distributed practical positioning algorithm is probably the most important and challenging task. As a high-resolution method of parameter estimation , m atrix pencil (MP) algorithm can be used in node localization. W e put forward a novel range-based node localization method for WSN i n th is paper . The new scheme employ s MP algorithm for multi-path time-delay estimation , multilateral localization for absolute positioning and Taylor algorithm or Chan algorithm to solve non-linear equations . T he proposed method has some key superiority over traditional loca lization methods, such as higher positioning accuracy, smaller computational amount, suppression over non-Gaussian noise, energy saving, faster executing, etc. Simulations show that the accuracy of MP algorithm for time of arrival (TOA) estimation is low (ns-level) and non-Gaussian noise can be well inhibited. T he impact of some factors such as signal to noise ratio (SNR) and the number of nodes are also studied in this paper.

Lymphatic drainage imaging of breast cancer in mice by micro-magnetic resonance lymphangiography using a nano-size paramagnetic contrast agent.

The presence of lymph node metastases is an important factor in breast cancer patient prognosis. Therefore, the precise identification of sentinel lymph nodes in these patients is critical. Improving current magnetic resonance (MR) imaging methods using a newly synthesized nano-size paramagnetic molecule, G6, as a contrast agent, provides an attractive means toward attaining this goal. A four-dimensional method of micro-MR lymphangiography using G6 (9 nm/240 kd) was developed to visualize the lymphatic ducts and lymph nodes draining mouse mammary tumors over time. The ability of micro-MR lymphangiography with the G6 contrast agent to visualize lymphatic drainage of normal mouse mammary tissue was compared with that of the conventional MR contrast agent, Gd-[DTPA]-dimeglumine (<1 kd). Lymphatic drainage in spontaneous and xenografted breast tumor models was visualized using the G6 contrast agent. Draining lymphatic ducts and lymph nodes were clearly visualized in the mammary tissue of normal mice and in spontaneous and xenografted breast tumor models after a direct mammary gland or peritumoral injection of G6. Gd-[DTPA]-dime-glumine, by contrast, failed to depict lymphatic flow from the mammary tissue in normal mice using the same method. Micro-MR lymphangiography using the G6 contrast agent revealed the absence of filling in the metastatic foci of affected lymph nodes. The superior temporal and spatial resolution of micro-MR lymphangiography using the contrast agent G6 may facilitate the study of tumor lymphatic drainage and lymphatic metastasis in both experimental animals and clinical medicine. In addition, this may be a powerful new method for sentinel lymph node localization in human breast cancer.