Location Of Nodes Research Articles

Granulomatous lymphadenitis in Taiwan: Unraveling infantile peak and Bacillus Calmette-Guérin lymphadenitis

BackgroundGranulomatous lymphadenitis, a histopathological diagnosis, often indicates infections, such as those caused by mycobacterial and fungal agents. MethodsWe conducted an analysis of 1098 granulomatous lymphadenitis cases, examining age distribution, lymph node locations, and laterality. Molecular detection of Bacillus Calmette-Guérin (BCG) was performed on archived formalin-fixed paraffin-embedded tissue specimens. ResultsOur analysis revealed a bimodal age distribution, notably with a minor peak in infants. These infantile cases predominantly featured axillary involvement, frequently occurring on the left side. Positive rates of BCG identification decreased with age: <1 year, 71%; 1–2 year, 33%; 2–3 year, 13%; 3–4 year, 0%. Remarkably, only one of the 14 cases with molecularly confirmed BCG lymphadenitis had comments regarding BCG in the pathological report. Compared with patients born after 2016 (BCG at 5–8 months), those born before 2016 (BCG at birth) developed BCG lymphadenitis at a wider age range with right skewness (before 2016, 13 ± 11 months [range, 3–33 months] vs. after 2016, 10 ± 2 months [range, 8–13 months]). Four of the 14 BCG-positive cases had congenital heart disease. Seven patients received anti-tuberculosis drugs following surgical excision. No surgical complications were reported. ConclusionsBCG lymphadenitis constitutes a distinctive minor peak within the spectrum of granulomatous lymphadenitis in Taiwan. Pathologists should consider the possibility of BCG infection, especially in cases of infantile axillary, supraclavicular, neck lymphadenopathies on the left side. Moreover, BCG administration at 5–8 months may reduce delayed-onset BCG lymphadenitis.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

SummaryThe underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate.

Felis bee optimization-based localization for energy-efficient communication in IoT-assisted WSN

Localization of WSN sensor nodes gains significance in order to grasp the idea for dead node replacement and re-establishing the network from data loss. In this research, the node localization and the position estimation of the dead nodes during routing are more effectively identified by the Felis bee localization protocol. The node localization is significant for effective communication and data transfer throughout the network with high signal strength. The developed approach assists in identifying the dead nodes and replaces them with less localization error thus, the signal strength of the receiver side is maximum. By utilizing the information on the exact position of the anchor nodes, the unknown location of the dead nodes is estimated by the Felis bee localization protocol. The Felis bee localization protocol enhances the potential of node localization concerning the energy, localization error, and multi-objective function, and attains the minimum RMSE as 0.566 at the 25th round, maximum RSSI as −47.925 dBm as well as the Energy at round 25 as 0.6517 J and the Throughput as 0.2952bps over the simulation area of 10 × 100. Further the established localization reaches the minimum RMSE as 0.673, the maximum Energy as 0.5141 J, maximum throughput as 0.5682,and the maximum RSSI as −53.911 dBm, at the 50th round over simulation area 200 × 200 and surpasses other conventional localization techniques.

ОПТИМІЗАЦІЯ КОНСТРУКЦІЇ ТА РЕЖИМУ РОБОТИ СУШИЛЬНОЇ КАМЕРИ НА ОСНОВІ ЇЇ 3D МОДЕЛІ

This paper focuses on the analysis and optimization of the drying chamber design using SolidWorks Simulation software. One of the main problems that arises in the design of drying chambers is the need to ensure optimal conditions for efficient drying of materials. In this regard, this study determines the optimal design parameters of the drying chamber and its operating mode to ensure uniform temperature distribution in the stack. The methodology includes creating a 3D model of the drying chamber and its main components in SolidWorks, generating a difference mesh, conducting a thermal study in SolidWorks Simulation, and applying Design Study to optimize the design parameters of the drying chamber and its operating modes. The simulation results include the output of the heat flow in the stack and temperature measurements at various points, which allows us to assess the temperature differences and the impact of optimization parameters on them. Temperature measurements at different points were performed using the Prob tool, which takes into account the exact geometry and location of nodes on the tetrahedral elements of the difference grid of the 3D model of the drying chamber used for the calculations. In general, the study consists of applying an integrated approach to the design of drying chambers using SolidWorks and automating the optimization process through the SolidWorks API, for which software has been created. The software also allows users to enter input data for the 3D model design and thermal study parameters for their transfer to SolidWorks. In addition, the software provides the ability to perform calculations directly in SolidWorks Simulation but in the background, so that the results obtained will be displayed in a special program window in a fully automated mode. It is also worth noting that performing calculations without visualizing this process significantly reduces the time to obtain results that can be used to improve the technological processes of drying various materials in drying chambers.

Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA &amp; PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate.

Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques

Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes.

Advances in Connection Techniques for Raw Bamboo Structures—A Review

While bamboo’s sustainability and impressive mechanical properties make it suitable for structural use, its application is hindered by challenges in connection systems. Bamboo’s hollow, thin-walled nature, dimensional variations, and anisotropic properties complicate connection design. Despite numerous studies and proposed connection types, a consensus on preferred bamboo connections remains elusive. Ideal connections for raw bamboo structures should be robust, economical, practical, simple, and easy to assemble. This paper reviews 62 scientific papers from the Scopus database published between 2003 and 2024, along with additional relevant references. It identifies research gaps, recommending further studies on bamboo connections considering factors like species, harvest age, treatment type, and node location. The analysis of failure modes and long-term behavior is essential to anticipate and mitigate risks associated with bamboo connections, ensuring durability, and minimizing maintenance needs. Lastly, developing universally accepted standards and codes for bamboo and bamboo connections is crucial for enabling their widespread adoption in the construction industry.

Construction of the Ecological Security Pattern in Xishuangbanna Tropical Rainforest Based on Circuit Theory

Urban modernization and economic globalization have led to significant changes in traditional natural landscapes. The unregulated and large-scale expansion of rubber plantations in Xishuangbanna has resulted in water and scenic forests being replaced by rubber forests and the complex rainforest ecosystem being replaced by simple artificial forests. This has resulted in increasingly prominent ecological problems such as soil erosion, regional microclimate changes, and sharp declines in biodiversity. The ecological security pattern is an important way to protect regional ecological sustainability. Taking the tropical rainforest in Xishuangbanna as an example, this study identified ecological sources through the evaluation of the importance of ecosystem services, constructed resistance surfaces through ecological sensitivity evaluation, and used circuit theory to simulate ecological processes in heterogeneous landscapes by calculating “electricity” or “resistance”, thereby identifying ecological corridors and key ecological nodes. The results identified 31 ecological source areas, 65 key ecological corridors, 7 potential ecological corridors, 37 ecological pinch points, and 99 ecological barriers. The overall distribution of ecological sources was scattered, with higher density in the northwest and southeast regions. Ecological corridors were distributed along high mountains, and both ecological sources and corridors were mainly composed of forest land. Based on circuit theory, this study filled the gap in the MCR model’s inability to identify the true width of corridors due to ignoring the randomness of biological migration. It determined the spatial range of ecological corridors and the specific locations of ecological nodes and barriers, providing a reference for solving ecological problems in Xishuangbanna, such as “rainforest fragmentation”.

An optimized meta‐heuristic clustering‐based routing scheme for secured wireless sensor networks

SummaryPrivacy and security present significant challenges in wireless sensor networks (WSNs). In order to enhance security, the sensor network is equipped with high throughput. While the importance of both source node (SN) and base station (BS) location privacy and security is acknowledged, recent research has predominantly focused on location privacy. Addressing this gap, the geometric zigzag bidirectional tree effectively tackles privacy and security threats at both the SN and BS locations. Future iterations of WSN are anticipated to integrate additional functionalities to meet diverse requirements in real‐world applications. The absence of robust security constraints leads to an unpredictable sensor network setup. This article aims to reduce energy consumption (EC) while simultaneously enhancing network security and connectivity. To address security challenges in WSN, we propose an optimized meta‐heuristic clustering‐based privacy key‐agreement routing technique. In the suggested system, a gateway‐based network is constructed to devise a key arrangement protocol that promotes privacy during communication. The proposed routing strategy involves forming clusters of sensor nodes ( ), facilitating the efficient selection of cluster heads (CHs) that prioritize nodes with the least modification. This effectively addresses the EC problem. A comprehensive performance evaluation is conducted, considering improvements in energy efficiency, packet delivery ratio (PDR), throughput, end‐to‐end delay ( delay), and EC.

A selective anatomically based lymph node sampling can replace a side specific pelvic lymphadenectomy in endometrial cancer with failed sentinel node mapping

AimTo evaluate the locations of metastatic pelvic sentinel nodes (SLN) and the proportion of SLNs outside and within defined typical anatomical positions along the upper paracervical lymphatic pathway (UPP). Patients and methodsConsecutive women with endometrial cancer (EC) of all risk groups underwent pelvic SLN-detection using cervically injected indocyanine green (ICG). A strict anatomically based algorithm and definitions of SLNs was adhered to. The positions of ICG-defined SLNs were intraoperatively depicted on an anatomical chart. All SLNs were examined using ultrastaging and immunohistochemistry. The proximal third of the obturator fossa and the interiliac area were defined as typical positions. The parauterine lymphovascular tissue (PULT) was separately removed. The proportions of metastatic SLNs, overall and isolated, typically, and atypically positioned were analyzed per woman. ResultsA median of two (range 1–12) SLN metastases along the UPP including the PULT were found in 162 women. 41 of 162 women (25.3 %) had isolated metastases in the obturator fossa harboring 49.1 % of all SLN metastases. Three women (1,9 %) had isolated PULT metastases. SLN metastases outside typical positions were identified in 28/162 women (17.3 %); isolated metastases were seen in seven women (4.3 %), so 95.7 % of pelvic node positive women had at least one metastatic SLN located at a typical position. ConclusionA selective removal of lymph nodes at typical proximal obturator and interiliac positions and the PULT can replace a full side specific pelvic LND when SLN mapping is unsuccessful. The obturator fossa is the predominant location for metastatic disease.

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

Research of the optimization problem of structure hierarchical communication network with changing its parameters

The article is devoted to the study of the problem of optimizing the hierarchical structure of a multicommodity communication network with discrete flows when changing its important parameters, such as the capacity of network arcs in transport blocks and the size of the transport block for transportation of the discrete small-lot cargo or of the data transmission in a digital communication network. The network has three levels of hierarchy – a backbone, a zonal and an internal and four types of nodes - backbone nodes of the first, second and third types, forming the backbone and zonal levels of the network, and nodes of the fourth type, which subordinate to each backbone node and forming the internal levels of the network. Types of nodes differ from each other in terms of functionality. The main task of the study is to establish how the structure of the backbone network changes (the number and location of backbone nodes of the first, second, and third types), the flow processing and distribution scheme, and the technical and economic indicators of the network's functioning for different values of its parameters. The principles of organization of sorting and distribution of flows in a three-level network and its mathematical model are given. A mathematical model of the optimization problem of the backbone network structure and flow sorting and distribution scheme is formulated. Algorithms for solving the problem are based on the discrete analogue of the local descent method proposed by the authors earlier, when the neighborhoods of the metric space of possible solutions are chosen based on heuristic considerations, taking into account the specifics of the problem being solved. Computer modeling of the problem on a network containing 10 nodes at the specified change in the both parameters is carried out. The modeling was carried out on the example of the transport network of cargo transportation using a computer program that is part of the instrumental software of the Information and Analytical Decision Support System (IA DSS), which is being developed at the Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine. An experimental study of solution the problem showed that the structure of the network is weakly dependent on the change in the carrying capacity of the arcs and the size of the transport block. The proposed computer technology for solving problem when the network parameters are changed allows you to interactively modeling various options of a network, changing the topology, hierarchical structure, flows, parameters and constraints of the model and from the set of the obtained solutions to choose a best option, taking into account the selected a goal function and the accepted constraints; calculate preliminary technical and economic indicators of the network's functioning, estimate the cost of additional resources and plan the amount of investment required for the modernization and construction of its structural elements, which ultimately makes it possible to increase the efficiency functioning of the network by optimizing use of its resources and reducing the operating costs for the processing and transportation of flows.

Black widow optimization for reducing the target uncertainties in localization wireless sensor networks

Abstract Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be utilized for maximizing the achieved accuracy. This problem, defined as the Sensor Selection Problem (SSP), has also been categorized as NP-Hard. While different metaheuristics have been proposed for attaining a near optimal solution in both problems, no approach has considered the two problems simultaneously, thus resulting in suboptimal solutions since the SSP is biased by the actual node distribution once deployed. In this paper, a combined approach of both problems simultaneously is proposed, thus considering the SSP within the NLP. Furthermore, a novel metaheuristic combining the Black Widow Optimization (BWO) algorithm and the Variable Neighbourhood Descent Chains (VND-Chains) local search, denominated as BWO-VND-Chains, is particularly devised for the first time in the author’s best knowledge for the NLP, resulting in a more efficient and robust optimization technique. Finally, a comparison of different metaheuristic algorithms is proposed over an actual urban scenario, considering different sensor selection criteria in order to attain the best methodology and selection technique. Results show that the newly devised algorithm with the SSP criteria optimization achieves mean localization uncertainties up to 19.66 % lower than traditional methodologies.

GTLP: A graph transformer based link prediction framework with unified feature pre-processing

In the realm of graph representation learning, Graph Neural Networks (GNNs) have demonstrated exceptional efficacy across diverse tasks. Typically, GNNs employ message-passing schemes to disseminate node features along graph structures, culminating in learned graph representations. However, their heavy reliance on smoothed node features over graph structures, coupled with limited expressiveness in the presence of node attributes, often constrains link prediction performance. To surmount this challenge, we propose GTLP, a Graph Transformer based link prediction framework. GTLP integrates unsupervised GNNs and structure encoding, enabling a holistic consideration of both topological structures and node features. This approach preserves critical node location and role information, enhancing the model’s expressiveness. By introducing the Graph Transformer model, GTLP adeptly incorporates neighbor information, refining embedding quality and bolstering the model’s learning and generalization capabilities. Notably, our method exhibits superior scalability, accommodating diverse techniques for information extraction, embedding learning, and sampling. Experimental results underscore GTLP’s state-of-the-art performance, outpacing various baselines across five real-world datasets.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Routing networking technology based on improved ant colony algorithm in space-air-ground integrated network

Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites; instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through specialized nodes. This approach alleviates the burden on ordinary nodes within the terrestrial communication network, thereby enhancing the overall network performance. In this protocol, specialized nodes hold a higher forwarding priority than regular nodes. When a source node needs to transmit data, it enters the route discovery phase, utilizing its own type, location, and energy information as heuristic data to calculate forwarding probabilities. Subsequently, it broadcasts route request (RREQ) messages to find the path. Upon receiving the RREQ message, the destination node sends an RREP message for updating information elements and selects the optimal path based on these information elements. Compared to AODV, AC-AODV shows significant improvements in performance metrics such as transmission latency, throughput, energy conversion rate, and packet loss rate.

ENHANCING URBAN PATHFINDING FOR PEDESTRIANS THROUGH FUSION OF MLS AND HMLS DATA

Abstract. Pedestrian pathfinding is crucial for enhancing pedestrian mobility in urban environments. In this research a method to generate navigation graphs based on Mobile Laser Scanning (MLS) and Handheld Laser Scanning (HMLS) data fusion is developed. The input data comprises a 2-kilometer urban street network that integrates both MLS and HMLS data, effectively mitigating sidewalk occlusions caused mainly by parked vehicles. The proposed method encompasses the following steps: (1) Deep Learning semantic segmentation of urban ground elements and navigation-related obstacles, (2) Static vs. Dynamic object differentiation to replicate conditions of unobstructed passage. (3) Sidewalk enrichment with inclination, preservation status, and width. (4) Physical accessibility estimation between sidewalks and crosswalks incorporating curb information. And (5) navigation graph computation based on the enhanced sidewalk data, crosswalks, with accurate node location and connections. Routes were calculated within a Geographical Information System, and results ensure that pedestrians can navigate in urban environments with precision and efficiency.

Angle-Based Distributed Node Localizability and Localization

Angle-Based Distributed Node Localizability and Localization

A Novel Coverage Optimization Scheme Based on Enhanced Marine Predator Algorithm for Urban Sensing Systems

Ensuring the efficient operation of urban sensing systems under emergency conditions represents a current and significant challenge. To address the typical problem of low network coverage due to random deployment of wireless sensor network nodes in urban sensing systems, a novel coverage optimization scheme with an enhanced Marine Predator Algorithm (EMPA) is proposed. For a given target network monitoring area, a joint node sensing model based on Boolean sensing is constructed with the environmental information of the monitoring area as input, and then the model is optimized using EMPA for several iterations to obtain the optimized maximum network coverage and its corresponding optimal node location deployment scheme. Based on the standard MPA, firstly, the Tent chaotic map is introduced to initialize the population to maintain the population diversity of the algorithm; secondly, the adaptive jump step factor is introduced in the jump phase to improve the ability of the algorithm to jump out of the local optimum; finally, the double-subgroup location update strategy is introduced before the end of each iteration of the algorithm, which can better balance the local and global search and further enhance the algorithm’s optimal search capability. The algorithm is compared with four unimodal and multimodal benchmark test functions and applied to three network coverage optimization scenarios in urban sensing systems. The results show that the EMPA algorithm can efficiently optimize the network nodes’ uniform distribution, enhance average network coverage, and save a certain level of node deployment costs for urban sensing systems.