Communication Delays Research Articles

TPAAS: Trustworthy privacy-preserving anonymous authentication scheme for online trading environment

In recent years with the improvement of information communication technology (ICT) and wireless communication, Online Trading Environment (OTE) has become a popular E-commerce platform to connect sellers and buyers in an efficient way. As, OTE’s are increasing in a wider range, the authentication and verification of entities in OTE network becomes a challenging task. Although, some authentication schemes exist in OTE’s, they have flaws such as account creation delays, authentication delays, communication cost and user privacy. In this work, a trustworthy and secure anonymous authentication scheme is proposed to prevent malicious users to enter into the OTE network. In addition, our proposed scheme provides conditional privacy to users until they maintain a genuine relationship with other entities without compromising. If any dispute occurs, then the system will revoke the access of that particular entity. Moreover, the security and performance analysis in this work concludes that our scheme ensures a secure interface to provide sustainable trading experience to users by taking less computation cost and communication delay when compared to other existing authentication schemes.

First Steps Toward a Participatory Research Program: Early Intervention Perspectives on SLP Services for Children of Diverse Backgrounds.

This research was designed to deepen the understanding of contributing factors to disparities in speech-language pathology (SLP) service delivery to children of diverse backgrounds with language and communication delays, as well as to initiate community connections. We sought to understand the perspectives and experiences of those who provide or oversee SLP services to children with communication disorders in early intervention settings in Washington State. Individual semistructured interviews were completed with six SLPs and three administrators across five organizations. The interviews were designed to understand both individual- and systems-level contributing factors to service delivery challenges. Interviews were analyzed using an iterative coding process with multiple rounds of coding and multiple coders. In addition to synthesizing participants' understandings of, and preparation for, culturally responsive practice, three overarching themes are presented: (1) assets of early intervention as a service delivery model, (2) the distance between families and the practitioners and systems that serve them, and (3) compromised access and quality of services due to a predominantly monolingual-English system. Findings inform the next steps for building community partnerships with early intervention organizations. The long-term goal of this work is to collaboratively develop a participatory research program on equitable service delivery that addresses children's, families', and SLPs' needs.

Maternal alcohol consumption during pregnancy and child development: Role of ADH1B and ALDH2 gene polymorphisms-The Yamanashi Adjunct Study of the Japan Environment and Children's Study.

The role of polymorphisms in genes regulating alcohol metabolism, particularly those modulating the impact of prenatal alcohol exposure on the neurodevelopment of offspring, remains inconclusive. Herein, we aimed to determine the involvement of ADH1B and ALDH2 gene polymorphisms in maternal alcohol consumption during pregnancy and the risk of developmental delay in offspring in a Japanese population. We analyzed 1727 mother-child pairs from the Yamanashi Adjunct Study of the Japan Environment and Children's Study. Maternal alcohol consumption during pregnancy was determined through a mid-pregnancy questionnaire and categorized into three groups: never-drinkers, those who quit drinking in early pregnancy, and current drinkers. Developmental delays in children were assessed in five domains using the Japanese version of the Ages and Stages Questionnaire, Third Edition (J-ASQ-3) at 3 years of age. We conducted a logistic regression analysis to explore the relationship between maternal drinking status during pregnancy and developmental delays in offspring with respect to maternal ADH1B (rs1229984) or ALDH2 (rs671) gene polymorphisms. Children born to mothers who continued alcohol consumption during pregnancy had a higher risk of delayed communication skills at 3 years of age compared with children born to mothers who did not drink alcohol (adjusted odds ratio [OR], 5.82; 95% confidence interval, 1.84-18.38). Analysis by ALDH2 gene polymorphism revealed that alcohol consumption by mothers carrying the wild-type ALDH2 (*1/*1) increased the risk of delayed communication skills at 3 years of age, whereas alcohol consumption by mothers carrying a heterozygotic genotype of ALDH2 (*1/*2) enhanced the risk of developmental delay in all five domains of the J-ASQ-3. The impact of ADH1B gene polymorphism could not be clearly elucidated. Our results suggest that alcohol consumption by pregnant females carrying the deficient variant ALDH2*2 genotype may increase the risk of developmental delay in their offspring.

Thibang Diphatlha: a sequential multiple assignment randomized trial designed to increase timely adoption of cervical cancer treatment in Botswana.

Delays and missed opportunities for timely treatment contribute significantly to stark inequities in cervical cancer mortality in low- and middle-income countries (LMICs) compared to high-income countries. The vast majority (approximately 90%) of new cases and deaths occur in LMICs, particularly those with high rates of HIV such as Botswana. To date, most of the implementation and cancer control research in Botswana and other LMICs has focused on cancer prevention and screening, with limited focus on cancer treatment. As such, there is a critical need to identify effective strategies to ensure timely care, and to understand contextual factors that shape the response to strategies. Without this fundamental knowledge, cervical cancer will remain a public health crisis in Botswana and other LMICs. To help fill this known gap, this study tests the effectiveness of adaptive strategies on timely treatment adoption using a hybrid (type III) Sequential Multiple Assignment Randomized Trial (SMART) design and evaluate contextual mechanisms contributing to the success or failure of each adaptive strategy. The adaptive strategies are designed to target contextual determinants identified in our prior work, including delayed communication of results to patients, individual and structural barriers to accessing treatment, and suboptimal care coordination between referring and cancer treatment clinics, and are supported by systematic evidence of the effectiveness of nudge strategies in clinical care. The primary implementation outcome is adoption, defined as the initiation of treatment within 90days. Secondary outcomes include fidelity, reach, acceptability, implementation costs, and cancer and HIV-related clinical outcomes. The rationale for the study is that enhancing coordination, communication, and navigation through centralized outreach will both increase timely treatment adoption and be scalable and sustainable after the project is completed. This innovative study seeks to decrease cervical cancer mortality in LMICs by developing and implementing effective and sustainable strategies that can be sustained and adapted to other contexts. Additionally, this study seeks to advance the long-term impact of global implementation science through strong and sustained partnerships in Botswana and other LMICs. ClinicalTrials.govNCT05952141. Registered on July 11, 2023. https://clinicaltrials.gov/study/NCT05952141 PROTOCOL VERSION AND DATE: Version 1 (September 28, 2024).

Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays

A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays

Hopf bifurcation control of memristor-based fractional delayed tri-diagonal bidirectional associative memory neural networks under various controllers

In this paper, authors present a memristor-based fractional order system of tri-diagonal bidirectional associative memory neural networks (TdBAMNNs) incorporating leakage and communication delays. The existence and uniqueness theorem is established for the memristor-based fractional-order TdBAMNNs system. Analysis of Hopf bifurcation anti-control is conducted using various feedback controllers by exploring single-parameter and double-parameter approaches. A study of the stability switching curves is undertaken to determine the direction of stability transitions in the system. The theoretical results and graphical illustrations are validated through numerical simulations to exhibit their feasibility. Comparative analysis of the system’s dynamical behavior under different controllers is performed. This paper comprehensively explores the proposed memristor-based system, proving its theoretical underpinnings, and analyzing stability through bifurcation control strategies and comparative assessments under different control scenarios.

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

OA31 Vitamin C deficiency presenting as juvenile idiopathic arthritis in an autistic child: a case study

Abstract Introduction Considered rare in the UK, we present a case of scurvy presenting as juvenile idiopathic arthritis (JIA). Scurvy is caused by vitamin C deficiency; the incidence is reported as 0.48 cases per 100,000 children under 16 years of age (2015–2017) in the UK. Scurvy is associated with gingival hypertrophy, petechiae, and musculoskeletal pain. The musculoskeletal manifestations and systemic features can present as JIA. This case highlights the importance of considering nutritional deficiencies in the differential diagnosis of paediatric patients presenting with symptoms similar to JIA to avoid misdiagnosis and ensure appropriate management. Case description A seven-year-old child with autism presented to the A/E with a week’s history of limping. Hip X-rays and blood tests were normal, so he was discharged. A week later, he returned with worsening symptoms, including swelling of the right knee. Blood tests and ultrasounds of bilateral hips and knees were normal, and he was once again discharged. On Day 14 of the illness, he was readmitted with increased right knee swelling and fever. Blood tests showed elevated inflammatory markers (ESR 26 mm/hr, CRP 33 mg/L). He was commenced on IV ceftriaxone for suspected osteomyelitis, but continued to experience low-grade fevers and developed swelling in the opposite knee, right ankle and mid-foot. On Day 22 of his illness, he was transferred to the Evelina and was found to have polyarthritis, reduced mobility, gingival hypertrophy and fever. Bloods revealed dropping counts, Hb 74 g/L, WBC 1.9 x 10^9/L and neutrophils 0.5 x 10^9/L, along with rising ferritin (96 -> 301 µg/L), dropping ESR (63 -> 17 mm/hr), and elevated LDH (428 U/L). Immunophenotyping and blood films were negative, leading to a provisional diagnosis of juvenile idiopathic arthritis (JIA) with evolving macrophage activation syndrome (MAS). He was started on anakinra and IV methylprednisolone. A full-body MRI revealed extensive metaphyseal signal abnormalities and mild soft-tissue oedema without active synovitis or joint effusion. The differentials included CRMO, lymphoproliferative disease, and scurvy. By this time, the lab Vitamin C level returned as extremely low at < 2.8 (ref 26-84 µmol/L). Following vitamin C supplementation and dietary support, his full blood count, ferritin, ESR and CRP normalised. He was gradually weaned off anakinra and steroids. His joint swelling, fever, and pain resolved, and he began to remobilise. The patient was discharged under the care of community paediatrics, a dietitian, and the physiotherapy team. Discussion Scurvy results from a severe deficiency of vitamin C in the diet. It is less common than other nutritional deficiencies, often leading to delayed diagnosis, particularly in developed countries. Children with unusual eating habits, global developmental delay or social communication disorders are at higher risk of developing this condition. This was an intriguing case of polyarthritis associated with scurvy. Vitamin C is essential for the synthesis of collagen, carnitine and certain neurotransmitters. It plays a critical role in immune function and the absorption of iron from the gut. Prolonged dietary insufficiency of vitamin C can lead to impaired collagen synthesis. In approximately 80% of instances, scurvy presents with musculoskeletal symptoms such as joint pain, muscle pain, haemarthrosis, and muscle haematomas. Gingival hypertrophy in a child with significant autism was the trigger to investigate vitamin C levels in our patient with polyarthritis. The eventual diagnosis of scurvy was confirmed by low vitamin C levels combined with the MRI findings. This case underscores the importance of considering nutritional deficiencies in patients with complex presentations and highlights the need for a thorough dietary history. Key learning points • Importance of dietary history: The primary learning point from this case is the critical importance of obtaining a comprehensive dietary history in paediatric patients, particularly those with atypical eating habits or developmental delay/social communication disorders. • Importance of a thorough clinical examination: The gingival hypertrophy in the case was the giveaway to check vitamin C levels. • Differential diagnosis of musculoskeletal symptoms: This case highlights the necessity of including nutritional deficiencies, such as scurvy, in the differential diagnosis of musculoskeletal symptoms like joint pain and swelling. • Role of a multidisciplinary approach: The successful management of this case underscores the importance of a multidisciplinary approach. Involving specialists from rheumatology, orthopaedics, nutrition, and general paediatrics was essential in reaching the correct diagnosis and providing comprehensive care. This collaborative effort ensured that all aspects of the patient’s condition were addressed. • Early intervention and treatment: Early intervention with vitamin C supplementation led to a rapid resolution of symptoms and normalisation of inflammatory markers. This case demonstrates the effectiveness of timely treatment in preventing long-term complications and improving patient outcomes. It also highlights the importance of considering vitamin supplementation in patients at risk of dietary deficiencies. The WHO recommends all children under 5 are supplemented with multivitamins.

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Gain-scheduled filter design with different time delays for T-S fuzzy systems

In this article, the authors study a problem of parameter-varying systems that gain-scheduled [Formula: see text] filtering T-S fuzzy problems by appearing of time-varying delays. Our paper also aims to establish filters that deal with communication delays. Traditional studies often study delay-free filters, but these filters have a more general form. In addition, a nonlinear fractional transformation structure has been incorporated into this filter, which allows its parameters to vary. The authors implement fuzzy Lyapunov–Krasovskii functionals used to intend the delayed fuzzy filters of this study, and delay-dependent circumstances are obtained for stability and performance analysis. The solution of the presented linear matrix inequalities (LMIs) provides the filter parameters. This method is finally justified with an example that illustrates its effectiveness.

Lightweight Flow‐Based Policy Enforcement for SDN‐Based Multi‐Domain Communication

ABSTRACTAlthough software‐defined networking (SDN) is commonly employed for intra‐domain communication, inter‐domain communication still heavily relies on conventional routing methods, specifically BGP‐based routers. The BGP router plays a crucial role in managing control and data planes, but this traditional approach hinders the exploitation of SDN advantages. Previous studies demonstrated the use of BGP for inter‐domain and end‐to‐end communication. This paper advocates for the adoption of a fully SDN‐based data plane packet switching strategy through the introduction of LPEES, a lightweight policy framework tailored for SDN‐based inter‐domain communication. LPEES strategically confines BGP's functionality to the control plane, preserving SDN benefits. Evaluation results confirm the effectiveness of LPEES compared to the BGP routing approach, as measured by throughput and various network quality of service (QoS) metrics. Additionally, LPEES streamlines inter‐domain communication by utilizing a trust‐based routing policy approach that can establish trust between communicating domains. The presented solution's main advantage is that it loosens the burden on the administrator by requiring less human interference to check the inter‐domain communication security and privacy. Our evaluations show LPEES outperform the BGP‐based in terms of throughput as LPEES achieves a 27 Gbps versus 22 Gbps in the traditional approach. Based on our experiments, LPEES also enhances the communication delay by an average of 17% compared to the traditional BGP‐based approach.

A Fair MAC Protocol Based on Dual Bandwidth Allocation Iterations in Underwater Acoustic Sensor Networks

ABSTRACTMost existing passive allocation‐based media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) overlook the fairness issue in channel bandwidth allocation, resulting in certain nodes occupying channel resources for extended periods, thereby leading to low channel utilization. To address this issue, this paper proposes a fair MAC protocol based on dual bandwidth allocation iterations (FBA‐MAC). Firstly, by limiting the maximum number of packets and the maximum number of available time slots that the host node can receive in each communication cycle, the protocol reduces collisions and collisions more effectively, thus controlling the communication delay. Secondly, in the process of channel bandwidth allocation, a dual bandwidth allocation iterative method was used to allocate appropriate channel bandwidth for each member node according to its communication requirements and the generation time of its generated data packets. Finally, in order to minimize the time interval of each packet arriving at the host node to reduce the communication delay, the transmission scheduling scheme was adaptively adjusted by combining the results of channel bandwidth allocation to maximize the channel utilization. Simulation results indicate that compared to the other four MAC protocols, FBA‐MAC demonstrates significant advantages in terms of fairness (10% higher), achieving higher network throughput (10% higher) and shorter end‐to‐end delay. Therefore, FBA‐MAC exhibits superior adaptability and efficiency in dynamic network environments.

Dynamic clustering based risk aware congestion control technique for vehicular network

In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Robust state agreement in switching networks of nonlinear agents with measurement noises and communication delays

Robust state agreement in switching networks of nonlinear agents with measurement noises and communication delays

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.