Static Nodes Research Articles

Dynamic Hierarchical Optimization for Train-to-Train Communication System

To enhance the operational efficiency of high-speed trains (HSTs), Train-to-Train (T2T) communication has received considerable attention. This paper introduces a T2T cooperative communication model that allows direct information exchange between HSTs, enhancing communication efficiency and system performance. The model incorporates a mix of dynamic and static nodes, and within this framework, we have developed a novel Dynamic Hierarchical Algorithm (DHA) to optimize communication paths. The DHA combines the stability of traditional algorithms with the flexibility of machine learning to adapt to changing network topologies. Furthermore, a communication link quality assessment function is proposed based on stochastic network calculus, which accounts for channel randomness, allowing for a more precise adaptation to the actual channel environment. Simulation results demonstrate that DHA has superior performance in terms of optimization time and effect, particularly in large-scale and highly dynamic network environments. The algorithm’s effectiveness is validated through comparative analysis with traditional and machine learning-based approaches, showing significant improvements in optimization efficiency as the network size and dynamics increase.

Adaptive inverse control for trajectory tracking with dead‐zone nonlinearity under cyberattacks

AbstractControl systems rely heavily on the accuracy and reliability of sensor data; however, the integrity of these data can be compromised through spoofing attacks, leading to significant modelling errors that can render control impractical. In addition, centralized control poses a significant threat to system security. To address these issues, a distributed framework is suggested for a discrete‐time nonlinear system that encounters unknown dead‐zones at its input. The framework uses the inherent resilience of a decentralized peer‐to‐peer network to secure information exchange, eliminating the need for prior knowledge of system dynamics or potential attacks. The proposed framework performs two complex tasks: identifying the nonlinear system and dealing with the unknown nonlinearity at the input in the form of a dead‐zone. An adaptive dead‐zone inverse is used to handle the unknown nonlinearity at the input in the form of a dead‐zone and integrate blockchain technology to secure communication between components. The blockchain component ensures tamper‐proof data transmission and resistance to cyberattacks, providing both detection and defence mechanisms without prior knowledge of system dynamics or potential attacks. The actuator and plant components are matched and synchronized using a private network with static nodes, ensuring deterministic and well‐coordinated communication. Simulation results demonstrate that the proposed framework both with and without blockchain integration, maintains stability and outperforms traditional methods in terms of robustness and accuracy, even when all parts of the framework are adjusted in response to attacks.

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed here by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to determine a predefined number of clusters with their initial cluster heads (CHs) and centroid locations of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree-based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from DDBLACH nodes of clusters to sink of the network. Simulation results indicate the superiority of our proposed scheme over other existing methods in various aspects, such as improving more data packet transmission by 14%-23%, presence of alive nodes and subsequently average network lifetime by 5%-24%.

Exploring the Influence from London Development on London Underground Mobility Through Network Analysis

As cities grows up, people continue to gather in cities, and human activities in cities have become more intense than ever before. These human activities will subtly reshape urban space, causing significant transformation of the urban physical environment and socio-economic environment. The method of big data provides a promising perspective for us to explore the impact of human activities on the urban environment. This study investigates the commuting patterns of London citizens in the subway and explores the impact of human activities on the importance of network nodes in subway stations. We use network analysis to characterize the node’s importance through degree, closeness, and betweenness centrality, and then compare these centralities with the centrality with the human mobility as weight. The result shows that the node’s importance based on centrality characteristics are largely reshaped by human mobility, which in turn drive the government to build new lines to hold the mobility flow. This research is helpful for providing insights for the London subway and urban construction.

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Data Collection and Recharging of Sensor Node by Mobile Sink in Wireless Sensor Network

Abstract: The wireless sensor network (WSN) has limited battery and storage capacity. The main challenge in static sink nodes is the lack of energy and data transfer to the base station (BS). A mobile sink (MS) is an excellent way to handle these difficulties in WSN. The use of an MS solution not only ensures long-term network functionality, but also improves network performance. In this paper, the MS-based data collection and recharging, mobile device is used for data collection from sensor nodes and recharging the sensor nodes throughout the network whenever required. There are two types of MS-based solutions described as (i) Mobile devices for data collection from sensor nodes in the network. (ii) Mobile device for collecting data and recharging sensor nodes from the network. Finally, in this paper, we presented the advantages, disadvantages, and other criteria of both types. Also, a potential framework is presented for data collection and recharging of sensor nodes by MS in WSN. The core contribution of this paper is present the state of art and future roadmap for data collection and recharging of sensor nodes by MS in WSN. The identification of the main open challenges and future direction in this research area are also highlighted and discussed.

New Trust Based Smart Data Forwarding Mechanism in Vehicular Delay Tolerant Network

Vehicular Delay Tolerant Network (VDTN) is the growing field with a considerable possibility to handle future wireless application’s requirements. Using vehicles for the data communication purpose can be contemplated as a substitute for the wired and wireless systems. This paper proposes a trust-based data forwarding mechanism for Vehicular Delay Tolerant Network (VDTN). Data forwarding in VDTN requires every vehicular or stationary node to participate in data forwarding. But some time malicious nodes show non-cooperative behaviour in data forwarding. Therefore, malicious nodes must be identified specifically to accelerate the data forwarding. A threshold based social skeleton membership process along with new trust-based data forwarding mechanism is proposed in this paper. We reveal that the social skeleton members perform better in data forwarding in terms of data delivery ratio, data delay and data overhead. Simulation results demonstrate that the trust-based data forwarding improves the data delivery ratio with trust-based data forwarding approach. After simulation a comparative analysis of two different scenarios is presented with epidemic routing, prophet routing and spray and wait routing.

A WLAN-oriented distributed hierarchical parallel simulation method

To solve the problem that the efficiency decreases with the increase of terminal nodes in wireless local area network(WLAN) distributed parallel simulation, a WLAN-oriented distributed hierarchical parallel simulation method is proposed. Firstly, considering the WLAN star network topology, the process of the simulated access point (AP) is denoted as the main process, which is responsible for time synchronization with the other processes of the simulated station (STA) nodes in the WLAN network. Secondly, the processes of all simulated STAs are then evenly divided into groups, and the group leader is responsible for the synchronization of processes within the group. Then, after the main process broadcasts the simulation start event, the group leader process firstly collects the end message of the group member's terminal node, and then reports to the main process. A three-layer hierarchical structure is constructed, consisting of the main process layer, the group leader process layer and the group member process layer. Finally, the time gain factor of the hierarchical simulation method is analyzed and obtained in closed-form expression, under the different computational loads. Simulation results show that comparing to the existing non-hierarchical simulation method, when the average computational load is 1.2x time unit and the nodes number is 100, the gain of the proposed method can reach 50%.

An Efficient Approach for Localizing Sensor Nodes in 2D Wireless Sensor Networks Using Whale Optimization-Based Naked Mole Rat Algorithm

Localization has emerged as an important and critical component of research in Wireless Sensor Networks (WSNs). WSN is a network of numerous sensors distributed across broad areas of the world to conduct numerous activities, including sensing the data and transferring it to various devices. Most applications, like animal tracking, object monitoring, and innumerable resources put in the interior as well as outdoor locations, need to identify the position of the occurring incident. The primary objective of localization is to identify the locality of sensor nodes installed in a network so that the location of a particular event can be traced. Different optimization approaches are observed in the work for solving the localization challenge in WSN and assigning the apt positions to undiscovered sensor nodes. This research employs the approach of localizing sensor nodes in a 2D platform utilizing an exclusive static anchor node and virtual anchors to detect dynamic target nodes by projecting these six virtual anchors hexagonally at different orientations and then optimizing the estimated target node co-ordinates employing Whale Optimization-based Naked Mole Rat Algorithm (WONMRA). Moreover, the effectiveness of a variety of optimization strategies employed for localization is compared to the WONMRA strategy concerning localization error and the number of nodes being localized, and it has been investigated that the average error in localization is 0.1999 according to WONMRA and is less than all other optimization techniques.

Smart Data Harvesting in Cache-Enabled MANETs: UAVs, Future Position Prediction, and Autonomous Path Planning

The task of gathering data from nodes within mobile ad-hoc wireless sensor networks presents an enduring challenge. Conventional strategies employ customized routing protocols tailored to these environments, with research focused on refining them for improved efficiency in terms of throughput and energy utilization. However, these elements are interconnected, and enhancements in one often come at the expense of the other. An alternative data collection approach involves the use of Unmanned Aerial Vehicles (UAVs). In contrast to traditional methods, UAVs directly collect data from mobile nodes, bypassing the need for routing. While existing research predominantly addresses static nodes, this paper proposes an evolutionary based, innovative path selection approach based on future position prediction of caching enabled mobile ad-hoc wireless sensor network nodes for UAV data collection, aimed at maximizing node encounters and gathering the most valuable information in a single trip. The proposed technique is evaluated for different movement models and parameter configurations.

Optimal interference mitigation with deep learningbased channel access in wireless body area networks

SummaryWireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

Experimental Design of Wireless Sensor Network Simulation Teaching Based on OBE Concept

In response to the problem that students cannot effectively master the theoretical knowledge of wireless sensor networks, this study proposes an outcome-based education (OBE) guided teaching method and designs a simulation experiment on wireless sensor networks using the NS3 simulation platform. In the experiment, access point (AP) and station (STA) nodes are set up to present different network environments, and the transmission layer protocols (TCP and UDP) are changed to analyze different network performance. Through the analysis of experimental results, students can gain a vivid understanding of the basic knowledge of wireless sensor networks and achieve the expected teaching goals. This teaching method helps improve students' understanding and application ability of wireless sensor network theory, and promotes their learning and research in related fields.

RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks

RSSI-based optimization of static and mobile node combinations for dynamic node localization in wireless sensor networks

Temporally-aware node embeddings for evolving networks topologies

Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. Aiming at fill the gap regarding the inability of static methods to capture evolving processes on dynamic networks, we propose a biased random walk method named Evolving Node Embedding (EVNE). EVNE leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. It learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines by up to 20% in a downstream node classification task. EVNE’s embeddings achieve better performance than others, based on experiments with four classifiers and five datasets. In addition, we present seven variations of our model to show the impact of each of EVNE’s mechanisms.

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

JCOG2002: A randomized phase III study of thoracic radiotherapy for extensive stage small cell lung cancer.

TPS8132 Background: Consolidative thoracic radiotherapy (cTRT) has shown a marginal improvement in overall survival (OS) and progression-free survival (PFS) for extensive-stage small-cell cancer (ES-SCLC). Consequently, ASTRO and NCCN guidelines conditionally recommend the use of cTRT. Despite the recent establishment of anti-PD-L1 antibody (aPD-L1) combined with platinum-doublet chemotherapy as the standard first-line treatment for ES-SCLC, the impact of cTRT in the era of immunotherapy remains uncertain. Methods: The JCOG2002 study, a randomized, multicenter phase 3 trial, initiated in October 2021 to assess the superiority of additional cTRT in terms of OS for ES-SCLC following induction aPD-L1 plus platinum doublet chemotherapy. Eligibility patients must meet criteria for the first registration including ES-SCLC, no prior radiation or chemotherapy history, age 20 or older, ECOG performance status 0 or 1, and adequate organ function. Induction treatment involves atezolizumab + carboplatin + etoposide or durvalumab + cisplatin / carboplatin + etoposide. Responding patients proceed to the second registration, randomization (1:1 between 30 Gy in 10 fractions of cTRT plus aPD-L1 maintenance therapy and maintenance therapy only). The cTRT targets metastatic lymph nodes in stations #1-7 and ipsilateral stations #10-12 identified at diagnosis. The adjustment factors include response to induction treatment (CR/PR versus SD), presence of brain metastasis (yes versus no), participating institutions, and aPD-L1 type (atezolizumab versus durvalumab). The primary endpoint is OS, with secondary endpoints being PFS and safety. The study aims to enroll 240 randomized patients, with 330 in the first registration, providing 80 % power at a one-sided 5 % significance level to detect a hazard ratio of 0.69 with 3-year accrual period and 1-year follow-up. As of February 6, 2024, 239 patients initiated induction treatment, and 121 have been randomized. This trial is registered at Japan Registry of Clinical Trials as jRCTs031210393 (https://jrct.niph.go.jp/detail/jRCTs031210393). Clinical trial information: jRCTs031210393 .

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Abstract Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.

Channel utilization of media access control protocols for underwater acoustic networks with propagation delay and mobilitya).

This paper investigates the impact of mobility on underwater acoustic communication networks in which the propagation delay is comparable to or larger than the packet duration. An underwater acoustic wireless network, consisting of static and mobile nodes, is studied for its link-layer channel utilization. Synchronous and asynchronous media access control (MAC) protocols are employed with ALOHA, TDMA (time-division multiple access), and artificial intelligence (AI) agent nodes. The simulation results of a multi-node network show that the asynchronous MAC protocols achieve up to 6.66× higher channel utilization than synchronous protocols by allowing time slots to be shorter than the maximum propagation delay among nodes and permitting asynchronous transmission time. The high mobility of a few mobile nodes also favors asynchronous protocols and increases the overall channel utilization. However, node mobility causes more difficulties for the AI node to learn the environment, which may be ineffective to achieve higher gains in channel utilization.