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Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network's nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.
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http://dx.doi.org/10.1038/s41598-024-77686-y | DOI Listing |
Sensors (Basel)
July 2025
Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance.
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April 2025
PRINCE Laboratory Research, ISITcom, University of Sousse, Hammam Sousse, Tunisia.
Improving road safety and easing congestion require effective real-time traffic data analysis and management. A crucial part of intelligent transportation systems, vehicular ad hoc networks (VANETs) deal with issues like inconsistent data from erratic vehicle movements and frequent topology changes. In order to develop a responsive and adaptable network management architecture for VANETs, this study makes use of Software-Defined Networking (SDN).
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April 2025
Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
Routing in Unmanned Aerial Vehicle (UAV) networks is critical for effective data transfer and overall network performance. However, current UAV routing algorithms exhibit high latency, poor route selection, excessive energy consumption, and limited flexibility in changing network topologies. To overcome these limitations, this paper proposes a new routing strategy that uses the Shuffled Frog Leaping Algorithm (SFLA) to improve UAV network routing.
View Article and Find Full Text PDFComput Biol Med
May 2025
College of Computer Science and Engineering, University of Hail, Hail, 81481, Saudi Arabia.
The Medical Internet of Things (MIoTs) encompasses compact, energy-efficient wireless sensor devices designed to monitor patients' body outcomes. Healthcare networks provide constant data monitoring, enabling patients to live independently. Despite advancements in MIoTs, critical issues persist that can affect the Quality of Service (QoS) in the network.
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March 2025
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India.
This research work proposes a Distributed Blockchain-Assisted Secure Data Aggregation (Block-DSD) technique for MANETs, ensuring high security and energy efficiency in disaster management scenarios. A Zone-based Clustering Approach (ZCA) is employed to segment the network into secure zones, with optimal Cluster Heads (CHs) selected using the Artificial Neuro-Fuzzy Inference System (ANFIS). Data aggregation is secured through a Two-Step Secure (STS) method and Elliptic Curve Cryptography (ECC), while optimal routing is achieved using the Improved Elephant Herd Optimization (IEHO) algorithm.
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