DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization.

The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs a DV-Hop algorithm based on multi-objective salp swarm optimization. Firstly, hop counts in the DV-Hop algorithm are subdivided, and the average hop distance is corrected based on the minimum mean-square error criterion and weighting. Secondly, the traditional single-objective optimization model is transformed into a multi-objective optimization model. Then, in the third stage of DV-Hop, the improved multi-objective salp swarm algorithm is used to estimate the node coordinates. Finally, the proposed algorithm is compared with three improved DV-Hop algorithms in two topologies. Compared with DV-Hop, The localization errors of the proposed algorithm are reduced by 50.79% and 56.79% in the two topology environments with different communication radii. The localization errors of different node numbers are decreased by 38.27% and 56.79%. The maximum reductions in localization errors are 38.44% and 56.79% for different anchor node numbers. Based on different regions, the maximum reductions in localization errors are 56.75% and 56.79%. The simulation results show that the accuracy of the proposed algorithm is better than that of DV-Hop, GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop algorithms.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

A Quantum Annealing Bat Algorithm for Node Localization in Wireless Sensor Networks.

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%.

Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks.

The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).

Early reflector localization improves the accuracy of localization and excision of a previously positive axillary lymph node following neoadjuvant chemotherapy in patients with breast cancer.

PURPOSE: Clipped axillary lymph node (CALN) localization after neoadjuvant chemotherapy (NAC) for axillary node positive breast cancer can be difficult due to significant shrinkage or disappearance of the CALN after NAC. This study compares wire localization to a radar-based localization system utilizing a reflector that can be placed before or during NAC, in the months before definitive surgery, to facilitate accurate localization and excision of the CALN. METHODS: Between 2016 and 2019, women with T0-4 N1-3 M0 breast cancer who underwent NAC followed by axillary surgery with planned excision of a biopsy positive or clinically suspicious axillary node via wire or reflector localization were identified. A retrospective chart review was performed comparing successful localization and CALN retrieval by each localization technique. RESULTS: Ninety-nine patients met inclusion criteria. Forty-two patients underwent wire localization while 57 patients underwent reflector localization of the CALN. Successful identification of the CALN by wire or reflector was equivalent (83.3% vs 84.2%, respectively). Twenty-two reflectors placed before or during early/mid NAC (early placement) had 100% successful CALN localization and retrieval in the OR. Placement of wire or reflector localization devices within 8 weeks of surgery (late placement) only resulted in 79.2% localization success (p = .02). CONCLUSION: This study suggests a benefit of axillary lymph node reflector placement in the early NAC setting. Early reflector placement allows for more accurate excision of the CALN during axillary surgery after NAC as compared to placement of localization wires or reflectors in the few weeks prior to surgery.

Node-Positive Patients Treated with Neoadjuvant Chemotherapy Can Be Spared Axillary Lymph Node Dissection with Wireless Non-Radioactive Localizers.

BACKGROUND: Targeted axillary dissection (TAD) involves sentinel lymph node biopsy (SLNB) and excision of a biopsy-proven node marked by a clip. This study evaluates the feasibility of non-radioactive wireless localizers for targeted excision of clipped axillary lymph nodes. METHODS: We identified biopsy-proven, node-positive breast cancer patients treated with neoadjuvant therapy (NAT) and TAD from 2016 to 2020, and included those with a clipped node localized using SAVI SCOUT, Magseed, or RFID Tag. Primary outcome measures were (1) successful localization (ultrasound or mammographic-guided placement < 10 mm from target), and (2) retrieval of the clipped node during TAD, documented by specimen radiography or gross visualization. Secondary outcomes included rates of completion axillary lymph node dissection (cALND) and complications. RESULTS: Overall, 57 patients were included; 1 (1.8%) patient had no clip visible at the time of localization, and no radiographic confirmation of clip placement at the time of biopsy, and was therefore excluded. In the remaining 56 patients, localization was successful in 53 (94.6%) patients and the clipped node was retrieved during TAD in 51 (91.1%) patients. Twenty-three of 27 (85.2%) ypN0 patients were spared cALND; 3 (11.1%) patients had cALND for failed clipped node retrieval during TAD, and 1 (3.7%) for false-positive frozen section. In patients with TAD alone, the rates of axillary seroma and infection were 20.0% and 8.6%, respectively. CONCLUSIONS: Wireless non-radioactive localizers are feasible for axillary localization after NAT, with high success rates of retrieving clipped nodes. The lack of signal decay is an advantage of these devices, allowing flexibility in timing of placement.

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks.

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

Impact of Locally Administered Carboxydextran-Coated Super-Paramagnetic Iron Nanoparticles on Cellular Immune Function.

Interstitially administered iron oxide particles are currently used for interoperative localization of sentinel lymph nodes (LNs) in cancer staging. Several studies have described concerns regarding the cellular accumulation of iron oxide nanoparticles relating them to phenotype and function deregulation of macrophages, impairing their ability to mount an appropriate immune response once an insult is present. This study aims to address what phenotypic and functional changes occur during lymphatic transit and accumulation of these particles. Data show that 60 nm carboxydextran-coated iron nanoparticles use a noncellular mechanism to reach the draining LNs and that their accumulation in macrophages induces transient phenotypic and functional changes. Nevertheless, macrophages recover their baseline levels of response within 7 days, and are still able to mount an appropriate response to bacterially induced inflammation.

Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology.

Groundwater is an important source of human activities, agriculture and industry. Underwater Acoustic Sensor Networks (UASNs) is one of the important technologies for marine environmental monitoring. Therefore, it is of great significance to study the node self- localization technology of underwater acoustic sensor network. This paper mainly studies the node localization algorithm based on range-free. In order to save cost and energy consumption, only a small number of sensing nodes in sensor networks usually know their own location. How to locate all nodes accurately through these few nodes is the focus of our research. In this paper, combined with the compressive sensing algorithm, a range-free node localization algorithm based on node hop information is proposed. Aiming at the problem that connection information collected by the algorithm is an integer, the hop is modified to further improve the localization performance. The simulation analysis shows that the improved algorithm is effective to improve the localization accuracy without additional cost and energy consumption compared with the traditional method.

ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments.

Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust ℓ 2 , 1 -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by ℓ 2 , 1 -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.

Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks.

Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight.

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.

An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks.

Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.

Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes.

Node localization is an essential requirement in the increasing prevalence of wireless sensor networks applications. As the most commonly used localization algorithm, the DV_Distance algorithm is more sensitive to ranging error, and also has lower localization accuracy. Therefore, this paper proposes a novel difference DV_Distance localization algorithm using correction coefficients of unknown nodes. Taking account of the fact that correction coefficients of unknown nodes should be different, the proposed method has employed the correction model based on unknown nodes. Some correction coefficients for different direction anchor nodes can be indirectly calculated using the known difference of actual Euclidean distance and corresponding accumulated hop distance between anchor nodes, and then the weighting factors for the correction coefficients of different direction anchor nodes are also computed according to their actual contribution degree, so as to make sure that the corrected distances from unknown nodes to anchor nodes, modified by the final correction coefficient, are closer to the actual distances. At last, the positions of unknown nodes can be calculated using multilateral distance measurement. The simulation results demonstrate that the proposed approach is a localization algorithm with easier implementation, and it not only has better performance on localization accuracy than existing DV_Distance localization algorithm, but also improves the localization stability under the same experimental conditions.

Localization with Graph Diffusion Property.

Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%.

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring.

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals' smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.

A hybrid TOA-fingerprinting based localization of mobile nodes using UWB signaling for Non-line-of-sight conditions.

Recently, Impulse Radio Ultra Wideband (IR-UWB) signaling has become popular for providing precise location accuracy for mobile and wireless sensor node localization in the indoor environment due to its large bandwidth and high time resolution while providing ultra-high transmission capacity. However, the Non-line-of-sight (NLOS) error mitigation has considerable importance in localization of wireless nodes. In order to mitigate NLOS errors in indoor localization this paper proposes and investigates a novel approach which creates a hybrid combination of channel impulse response (CIR)-based fingerprinting (FP) positioning and an iterative Time of Arrival (TOA) real time positioning method using Ultra Wideband (UWB) signaling. Besides, to reduce the calculation complexities in FP method, this paper also introduces a unique idea for the arrangement of reference nodes (or tags) to create a fingerprinting database. The simulation results confirm that the proposed hybrid method yields better positioning accuracies and is much more robust in NLOS error mitigation than TOA only and FP only and a conventional iterative positioning method.

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics.

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

Lymph node wire localization post-chemotherapy: Towards improving the false negative sentinel lymph node biopsy rate in breast cancer patients.

PURPOSE: To evaluate whether the disease status of the pre-neoadjuvant chemotherapy (NAC) core biopsied lymph node (preNACBxLN) in patients with node positive breast cancer corresponds to nodal status of all surgically retrieved lymph nodes (LNs) post-NAC and whether wire localization of this LN is feasible. MATERIALS AND METHODS: HIPPA compliant IRB approved retrospective study including breast cancer patients (a.) with preNACBxLN confirmed metastases, (b.) who received NAC, and (c.) underwent wire localization of the preNACBxLN. Electronic medical records were reviewed. Fisher's exact test was used to compare differences in residual disease post-NAC among breast cancer subtypes. RESULTS: 28 women with node positive breast cancer underwent ultrasound guided wire localization of the preNACBxLN, without complication. There was no evidence of residual nodal disease for 16 patients, with mean 4.4 (median 4) LNs resected. 12 patients had residual nodal metastases, with mean 9.2 (median 7) LNs resected and mean 2.3 (median 2) LNs with tumor involvement. 11 patients had metastases detected within the localized LN. One patient had micrometastasis in a sentinel LN, despite no residual disease in the preNACBxLN. Patients with luminal A/B breast cancer more often had residual nodal metastases (86%) at pathology, as compared to patients with HER2+ (20%) and Triple Negative breast cancer (50%), though not quite achieving statistical significance (p=0.055). CONCLUSION: Ultrasound guided wire localization of the preNACBxLN is feasible and may improve detection of residual tumor in patients post-NAC.