Head Nodes Research Articles

Sewage water management and healthcare monitoring in IoT using Optimized deep residual network

ABSTRACT The Internet of Things (IoT) is termed as the interconnection of different smart objects with respect to devices. In this research, two different application scenarios are considered to show the efficiency of the Deep Residual Network (DRN) through multicast routing. The entities involved in the process are IoT nodes, IoT heads, and base stations (BS). The nodes are allowed to capture the information, and the collected data are routed to BS through the head node. The process of routing is made using the CrowWhale optimisation algorithm that enables to transfer the data packets from IoT nodes to BS. In the sewage water management system, entering sewage water into fresh water is detected by DRN which is trained using an optimisation algorithm. In the healthcare system, heart disease prediction is done using DRN to detect normal and abnormal cases more effectively. The adopted CrowWhale-ETR+DRN offered energy, accuracy and sensitivity as 82.54, 0.967, and 0.978 with 100 nodes for the environmental protection dataset. The energy, accuracy, and sensitivity obtained by the proposed model are 83.232, 0.964, and 0.974 using 100 nodes for the heart disease dataset, respectively.

Utility of UV Signature Mutations in the Diagnostic Assessment of Metastatic Head and Neck Carcinomas of Unknown Primary.

Metastatic carcinoma of unknown primary origin to the head and neck lymph nodes (HNCUP) engenders unique diagnostic considerations. In many cases, the detection of a high-risk human papillomavirus (HR-HPV) unearths an occult oropharyngeal squamous cell carcinoma (SCC). In metastatic HR-HPV-independent carcinomas, other primary sites should be considered, including cutaneous malignancies that can mimic HR-HPV-associated SCC. In this context, ultraviolet (UV) signature mutations, defined as ≥ 60% C→T substitutions with ≥ 5% CC→TT substitutions at dipyrimidine sites, identified in tumors arising on sun exposed areas, are an attractive and underused tool in the setting of metastatic HNCUP. A retrospective review of institutional records focused on cases of HR-HPV negative HNCUP was conducted. All cases were subjected to next generation sequencing analysis to assess UV signature mutations. We identified 14HR-HPV negative metastatic HNCUP to either the cervical or parotid gland lymph nodes, of which, 11 (11/14, 79%) had UV signature mutations, including 4 (4/10, 40%) p16 positive cases. All UV signature mutation positive cases had at least one significant TP53 mutation and greater than 20 unique gene mutations. The management of metastatic cutaneous carcinomas significantly differs from other HNCUP especially metastatic HR-HPV-associated SCC; therefore, the observation of a high percentage of C→T with CC →TT substitutions should be routinely incorporated in next generation sequencing reports of HNCUP. UV mutational signatures testing is a robust diagnostic tool that can be utilized in daily clinical practice.

Trust aware angle based secure routing approach for wireless sensor network

Security in wireless sensor network (WSN) is an important approach in the present context as data breaching is becoming more. The data to be routed from source to destination needs more security as WSN has no specific security approach by default. This paper proposes trust based security in WSN using approach. The secure line is drawn from head node to its cluster end point called as angle to provide the security to the nodes which are transferring the data to the head node. Secure line becomes the trust worth line where mobile agent migrates to all the corresponding nodes which are along or near to the secure lines, collects the data and encrypt them. Finally, the data is sent to sink node from head node using a secure path. The agent paradigm is responsible for creating the angle from head node to cluster boundary. Multiple angles can be created if numbers of nodes are more and deployed at different locations. The result shows that the security provided is much better to combat the intruder involvement to breach data along with better network lifetime and minimum delay than compare to conventional techniques.

Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT

Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for Low­Power and Lossy Networks), Optimization, Routing, Multimedia, Healthcare

Optimal Extent of Neck Dissection for a Head and Neck Lymph Node Metastasis from a Remote Primary Site.

Despite its rarity and limited documentation, therapeutic neck dissection (ND) for cervical lymph node (LN) metastases from distant primary sites is increasingly practiced, potentially enhancing survival rates. However, the optimal ND extent remains unclear. This study aimed to determine the safety of excluding upper neck levels from ND. We retrospectively analyzed 25 patients who underwent ND for cervical LN metastases from remote primary tumors between 2015 and 2021 (12 with primary lung tumors, four with ovary, three with mammary gland, three with esophagus, two with thymus, and one with colon). Assessing clinical characteristics and occult metastasis rates, we observed LN metastases predominantly at levels III and IV. Occult metastases occurred in 14 out of 25 patients, primarily at neck levels III and IV (55.0% and 50.0%, respectively). The five-year disease-specific survival rate for all patients was 44.3%. While no statistically significant impact of occult metastasis on prognosis was confirmed, an association between the postoperative LN ratio and poor prognosis was revealed. Our findings suggest that prophylactic NDs at levels I, II, and Va may not be essential for managing cervical LN metastases from remote primary malignancies. This could lead to a more tailored and less invasive therapeutic strategy.

Evaluating the Elasticity of Metastatic Cervical Lymph Nodes in Head and Neck Squamous Cell Carcinoma Patients Using DWI-based Virtual MR Elastography.

The assessment of metastatic cervical lymph nodes in head and neck squamous cell carcinoma patients is crucial; as such, many studies focusing on non-invasive imaging techniques to evaluate metastatic cervical lymph nodes have been performed. The aim of our study was to assess the usefulness of elasticity values on diffusion weighted imaging (DWI)-based virtual MR elastography in the evaluation of metastatic cervical lymph nodes from head and neck squamous cell carcinoma. Two head and neck radiologists measured the elasticity values of 16 metastatic cervical lymph nodes from head and neck squamous cell carcinoma and 13 benign cervical lymph nodes on DWI-based virtual MR elastography maps. Mean, minimum, maximum, and median elasticity values were evaluated for lymph nodes between the two groups and interobserver agreement in measuring the elasticity was also evaluated. The mean, maximum, and median elasticity values of metastatic cervical lymph nodes were significantly higher than those of benign cervical lymph nodes (P = 0.001, 0.01, and 0.002, respectively). Diagnostic accuracy, sensitivity, and specificity of the mean elasticity were 82.8%, 93.8%, and 69.2%, respectively. Interobserver agreement was excellent for the mean and median elasticity (intraclass correlation coefficients were 0.98 for both). Estimated elasticity values based on DWI-based virtual MR elastography show significant difference between benign and metastatic cervical lymph nodes from head and neck squamous cell carcinoma. While precise modulation of MR sequences and calibration parameters still needs to be established, elasticity values can be useful in differentiating between these lymph nodes.

Detectability and intra-fraction motion of individual elective lymph nodes in head and neck cancer patients on the Magnetic Resonance Image guided linear accelerator

Background and purposeIndividual elective lymph node irradiation instead of elective neck irradiation is a new concept for head-and-neck cancer (HNC) patients developed for the Magnetic Resonance Image guided linear accelerator (MR-linac). To prepare this, the detectability, volume changes and intra-fraction motion of elective lymph nodes on the MR-linac was assessed. Materials and methodsA total of 15 HNC patients underwent diagnostic pre-treatment MRI. Additionally, two MR-linac scans were obtained with a 10-minute time difference in the first week of radiation treatment. Elective lymph node contours inside lymph node levels (Ib-V) were segmented on the pre-treatment MRI and the MR-linac scans and compared on number and maximal transversal diameter. Intra-fraction motion of elective lymph nodes on the MR-linac was estimated using Center of Mass (COM) distances and incremental isotropic expansion of lymph node segmentations. ResultsOf all 679 detected lymph nodes on the pre-treatment MRI, eight lymph nodes were not detectable on the first MR-linac scan and 16 new lymph nodes were detected. Lymph node diameters between the pre-treatment MRI scan and the MR-linac scan varied from −0.19 to + 0.13 mm. COM distances varied from 1.2 to 1.7 mm and lymph node contours had to be expanded with 3 mm. ConclusionsNearly all elective lymph nodes were detectable on the 1.5T MR-linac scan with no major changes in target volumes compared to the pre-treatment MRI. Simulated intra-fraction motion during the MR-linac scans was smaller than the 5-mm margin that will be used in the first elective lymph node radiation treatment.

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm.

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network's status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs' capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

Secured Routing Protocol for Improving the Energy Efficiency in WSN Applications

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML‐Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision‐making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end‐to‐end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG. When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

Clustering Based Energy Efficient Routing for Wireless Sensor Network Using Particle Swarm Optimization

The clustering strategy is the most effective and efficient way to preserve energy in the Wireless Sensor Network (WSN). However, the cluster heads in the hierarchical clustering approach use the majority of the energy that is required to carry out the operations. These operations include receiving the data from the sensor nodes, aggregating it, and then eventually transmitting it to the base station. When choosing the appropriate cluster head, you can play a significant part in reducing the amount of energy that is consumed by the WSN and, as a result, extending its lifespan. A technique for the selection of energy-efficient cluster heads that is based on the particle swarm optimization method is proposed in this study (PSO-EECH). For the method that has been proposed to measure the amount of energy used, we need to take into account the cluster distance, the distance between each sensor node and the nodes that are nearby, and the amount of residual energy that is left in sensor nodes. The aforementioned structure is also capable of doing cluster building, in which the non-cluster head node can follow its CH based on the determined weight function. The proposed PSO-EECH approach has been put through extensive testing, and the results have shown that it possesses a high degree of accuracy in every scenario. The outputs of the proposed algorithm are compared with those of other clustering-based algorithms already in existence, and the conclusions of this comparison have reported that our method outperforms the other existing methods.

T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma

BackgroundThis study seeks to assess the utility of T1 and T2 mapping in distinguishing metastatic lymph nodes from reactive lymphadenopathy in patients with head and neck squamous cell carcinoma (HNSCC), using diffusion-weighted imaging (DWI) as a comparison.MethodsBetween July 2017 and November 2019, 46 HNSCC patients underwent neck MRI inclusive of T1 and T2 mapping and DWI. Quantitative measurements derived from preoperative T1 and T2 mapping and DWI of metastatic and non-metastatic lymph nodes were compared using independent samples t-test or Mann–Whitney U test. Receiver operating characteristic curves and the DeLong test were employed to determine the most effective diagnostic methodology.ResultsWe examined a total of 122 lymph nodes, 45 (36.9%) of which were metastatic proven by pathology. Mean T2 values for metastatic lymph nodes were significantly lower than those for benign lymph nodes (p < 0.001). Conversely, metastatic lymph nodes exhibited significantly higher apparent diffusion coefficient (ADC) and standard deviation of T1 values (T1SD) (p < 0.001). T2 generated a significantly higher area under the curve (AUC) of 0.890 (0.826–0.954) compared to T1SD (0.711 [0.613–0.809]) and ADC (0.660 [0.562–0.758]) (p = 0.007 and p < 0.001). Combining T2, T1SD, ADC, and lymph node size achieved an AUC of 0.929 (0.875–0.983), which did not significantly enhance diagnostic performance over using T2 alone (p = 0.089).ConclusionsThe application of T1 and T2 mapping is feasible in differentiating metastatic from non-metastatic lymph nodes in HNSCC and can improve diagnostic efficacy compared to DWI.

Smart Grid Simulation with LEACH (Low-Energy Adaptive Clustering Hierarchy) Clustering

Power industry applications for smart grid are quite promising. In recent years, network life extension has attracted a lot of study attention. By examining the business needs of the Smart grid, this study enhances the wireless sensor network LEACH algorithm. To ensure the rationality of the cluster head selection, the remaining energy and node density are first taken into consideration. The cluster radius of various sizes is then constructed while clustering using a non-uniform clustering process. To achieve the goal of balancing the energy load, data is transmitted to the base station using the multi-hop mode, and the distance between the cluster head nodes and the remaining energy are utilised as the selection criteria for the next-hop relay node. In order to provide the computational intelligence required to create the Smart Grid, this paper discusses the future of the electric energy system, covering every aspect from power generation to substations, distribution, and customer feedback loops.

Adaptive squirrel coyote optimization-based secured energy efficient routing technique for large scale WSN with multiple sink nodes

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node.

Cluster-based fusion detection of soft and hard decisions for underwater non-cooperative targets

Underwater target detection performs the essential contribution in marine exploration. Unfortunately, the little energy available in the underwater environment makes detection difficult. To address the aforementioned obstacles, this paper proposed a detection scheme based on soft and hard decision fusion. The detection scheme consists of two stages: network topology control and the fusion of local and global detection. The first stage, an improved low energy adaptive clustering hierarchy (LEACH) algorithm is introduced into the underwater sensor network to choose the most suitable cluster heads and to introduce relay nodes to establish multi-hop routes between clusters, effectively balancing node energy consumption and extending network lifetime. With the network topology, the generalized likelihood ratio rule (GLRT) is employed by each sensor to detect the underwater non-cooperative target and make the corresponding decision during the second phase. Specifically, the member nodes make the multi-bit local soft decision, and then the cluster head node fuses the decision information from each member nodes to make the one-bit hard decision and transmits it to the fusion center, and finally the fusion center makes the global decision, such that the tradeoff between detection accuracy and energy consumption may be balanced by generating the soft and hard decision fusion technique. Simulations show that the strategy maintains detection performance extremely comparable to that of centralized and conventional multi-bit soft decisions while eliminating communication overhead.

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).

Path Planning with Multiple UAVs Considering the Sensing Range and Improved K-Means Clustering in WSNs

Recently, an Unmanned Aerial Vehicle (UAV)-based Wireless Sensor Network (WSN) for data collection was proposed. Multiple UAVs are more effective than a single UAV in wide WSNs. However, in this scenario, many factors must be considered, such as collision avoidance, the appropriate flight path, and the task time. Therefore, it is important to effectively divide the mission areas of the UAVs. In this paper, we propose an improved k-means clustering algorithm that effectively distributes sensors with various densities and fairly assigns mission areas to UAVs with comparable performance. The proposed algorithm distributes mission areas more effectively than conventional methods using cluster head selection and improved k-means clustering. In addition, a postprocessing procedure for reducing the path length during UAV path planning for each mission area is important. Thus, a waypoint refinement algorithm that considers the sensing ranges of the sensor node and the UAV is proposed to effectively improve the flight path of the UAV. The task completion time is determined by evaluating how the UAV collects data through communication with the cluster head node. The simulation results show that the mission area distribution by the improved k-means clustering algorithm and postprocessing by the waypoint refinement algorithm improve the performance and the UAV flight path during data collection.

Free-Breathing StarVIBE Sequence for the Detection of Extranodal Extension in Head and Neck Cancer: An Image Quality and Diagnostic Performance Study.

(1) Background: This study aims to evaluate the image quality of abnormal cervical lymph nodes in head and neck cancer and the diagnostic performance of detecting extranodal extension (ENE) using free-breathing StarVIBE. (2) Methods: In this retrospective analysis, 80 consecutive head and neck cancer patients underwent StarVIBE before neck dissection at an academic center. Image quality was compared with conventional VIBE available for 28 of these patients. A total of 73 suspicious metastatic lymph nodes from 40 patients were found based on morphology and enhancement pattern on StarVIBE. Sensitivity (SN), specificity (SP), and odds ratios were calculated for each MR feature from StarVIBE to predict pathologic ENE. (3) Results: StarVIBE showed significantly superior image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for enlarged lymph nodes compared to VIBE. The MR findings of "invading adjacent planes" (SN, 0.54; SP, 1.00) and "matted nodes" (SN, 0.72; SP, 0.89) emerged as notable observations. The highest diagnostic performance was attained by combining these two features (SN, 0.93; SP, 0.89). (4) Conclusions: This study confirms that StarVIBE offers superior image quality for abnormal lymph nodes compared to VIBE, and it can accurately diagnose ENE by utilizing a composite MR criterion in head and neck cancer.