Availability Of Network Resources Research Articles

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks.

Security paradigms for IoT in telecom networks: Conceptual challenges and solution pathways

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: IoT, Telecom, Networks, Security.

SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: Security, IoT, Telecom, Solutions, Network.

Intrusion Detection System

Our project introduces an Integrated Intrusion Detection System (IDS) designed to bolster network and system security through a multi-faceted approach. This comprehensive IDS seamlessly combined Machine Learning, Anomaly Detection, File Scanning, and DDoS Prevention mechanisms to offer robust defense against diverse cyber threats. At the heart of the system lies the Machine Learning component, which continuously adapts to the evolving threat landscape. By analyzing network traffic and system behavior, it identifies both known and emerging threats in real-time, reducing reliance on traditional signature-based detection methods and allowing organizations to proactively stay ahead of cybercriminals. Anomaly Detection constantly monitors network and system activity, comparing it against established baselines. Any deviations from these baselines trigger alerts, facilitating swift responses to unusual activities that may indicate security breaches. File Scanning is another vital component ensuring data integrity and preventing malware infiltration or data exfiltration. It conducts thorough file analysis, checking for suspicious code, behavior, or unauthorized access, and offers continuous monitoring to detect anomalies in real-time. Additionally, our IDS includes Distributed Denial of Service (DDoS) Prevention mechanisms. By detecting and mitigating DDoS attacks, the system ensures the continuous availability of network resources and services even under intense traffic loads. This integrated approach to intrusion detection and prevention leverages Machine Learning, Honeypots, Anomaly Detection, File Scanning, and DDoS Prevention, empowering organizations to safeguard critical assets, maintain data integrity, and ensure network security in today's dynamic and perilous digital landscape. Our IDS solution serves as a valuable addition to the cybersecurity toolkit for organizations seeking comprehensive security against a wide range of threats.

A Study of Role and Applicability of Data Mining Techniques in Intrusion Detection Systems

Abstract: The swift development of computers technologies changed the way data and information were kept. The risk of this data being exposed to uninvited and unauthorized users arises with this new paradigm of data access. Numerous systems have been created that examine data to look for deviations from a user's or system's typical behavior or look for a known signature in the data. Intrusion Detection Systems (IDS) is the name given to these systems. These systems use a variety of approaches, including machine learning algorithms and statistical methodologies. With the massive rise in the use of network-based services and information sharing on networks, network security has emerged as the fundamental component. The integrity, confidentiality, and availability of computer and network resources are all seriously compromised by intrusion, which also poses a severe risk to network security. Network audit data classification by humans is a costly, time-consuming, and laborious task. An intrusion detection system (IDS) is one tool used to find anomalies and attacks on a network. The network intrusion detection system has made extensive use of data mining techniques to extract valuable information from vast amounts of network data. This work proposes a hybrid model that combines two distinct intrusion detection techniques: anomaly-based and signaturebased. The model is separated into two stages. Systems for detecting intrusions make use of audit data produced by network devices, operating systems, and application software. These sources generate enormous databases that contain tens of millions of records. Data mining, which is the process of extracting meaningful patterns from a sizable amount of information, is used to analyze this data. The presented paper deals with the role and the applicability of data mining techniques in designing and developing the IDS Systems.

EDITORS: Energy-aware Dynamic Task Offloading using Deep Reinforcement Transfer Learning in SDN-enabled Edge Nodes

In mobile edge computing systems, a task offloading approach should balance efficiency, adaptability, trust management, and reliability. This approach aims to maximise resource utilisation, improve user experience, and satisfy application-specific requirements while taking into account the dynamic and limited resource nature of edge environments. Additionally, while offloading tasks, these systems are vulnerable to several attacks and privacy breaches, necessitating edge node trust evaluation. However, not all of these necessary features are present in the offloading methods currently used. This research proposes ‘EDITORS’ (energy-aware dynamic task offloading method utilising Deep Reinforcement Transfer Learning (DRTL) in Software-Defined Network (SDN) enabled edge computing environments), a novel approach aimed at addressing the multifaceted issues associated with offloading in mobile edge computing systems. The primary goal of EDITORS is to design a task offloading system that incorporates trusted edge nodes while prioritising energy efficiency, timeliness, reliability, adaptability, and outperforming existing task offloading methods in terms of the quality of the task offloading plan. This method uses of DRTL agents at edge nodes, which communicate with the SDN controller to learn the most appropriate offloading choices based on network conditions and resource availability. Extensive simulations (six) are conducted which show that the EDITORS significantly increases energy efficiency while preserving low-latency task completion compared to five existing offloading methods (DDLO, DROO, DMRO, DRL without TL and SDN and DRL with SDN). EDITORS includes trust evaluation, trusted edge device prediction using LSTM, and adaptation of newly added devices through transfer learning, unlike other task offloading methods that just concentrate on task offloading.

Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops

We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

SummaryCloud computing (CC) refers to the on‐demand availability of network resources, particularly data storage and processing power, without requiring special or direct administration by users. CC, which just made its debut as a collection of public and private data centers, provides clients with a unified platform throughout the Internet. Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Due to the exponential rise of cloud computing, systems based on cloud computing now require an effective data security mechanism. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption. Encryption algorithms play an important role in information security systems and various cloud computing‐based systems. Current researchers have focused on lightweight cryptography, genetics‐based cryptography, and machine learning (ML) algorithms for security in CC. This review study analyses CC security threats, problems, and solutions that use one or more algorithms. The work discusses several lightweight cryptographies, genetics‐based cryptography and different ML algorithms that are used to overcome cloud security issues, including supervised, unsupervised, semi‐supervised, and reinforcement learning. Moreover, we enlist future research directions to secure CC models.

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

Dynamic Multi-Resource Optimization for Storage Acceleration in Cloud Storage Systems

Demand for using cloud object storage has been increasing in order to efficiently manage a large number of binary large objects (BLOBs), including videos, photos and documents. Although many companies and institutions are currently trying to utilize public cloud object storage services such as AWS Simple Storage Service (S3), most of existing encoding systems for safe storage of data have not been optimized for current cloud object storage architecture. In this article, we propose a novel dynamic extreme erasure encoding algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DexEncoding</i> aiming to maximize the utility of clients where the encoding locations in the cloud storage architecture are dynamically optimized between gateway and storage servers with respect to the time-varying cloud environment. Here, the utility captures the satisfaction of clients for the speed of data storage and fairness among clients. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DexEncoding</i> efficiently resolves resource bottlenecks by adapting to the dynamic network, processing and storage resource availability and storage request. Real measurement-driven simulations demonstrate that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DexEncoding</i> algorithm drastically outperforms that applied in the state-of-the-art object storage systems in a perspective of clients’ satisfaction.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloud-based healthcare systems are discussed in detail.

A SURVEY ON MOBILITY-AWARE AND DELAY-SENSITIVE SERVICE PROVISIONING IN MOBILE EDGE-CLOUD NETWORKS

Mobile edge computing (MEC) has emerged as a viable technique for pushing the cloud frontier to the network edge, enabling network services to be provisioned in close proximity to mobile consumers. Serving customers at the edge of the cloud can minimize service latency, lower operational costs, and increase network resource availability. Along with MEC, network function virtualization (NFV) is a viable strategy for implementing various network service functions as software in cloudlets (servers or clusters of servers). Giving mobile consumers virtualized network services can improve their service experience, simplify network service deployment, and make network resource management easier. However, mobile users roam arbitrarily inside networks, and different users often request various services with varying resource needs and latency requirements. As a result, it is a significant issue to provide dependable and smooth virtualized network services for mobile users in a MEC network while satisfying their specific latency needs, subject to network resource limits. This research focuses on the supply of virtualized network function services for mobile users in MEC while taking user mobility and service latency requirements into mind. We begin by posing two novel optimization issues for user service request admissions, with the goals of maximizing accumulative network utility and accumulative network throughput over a certain time horizon. Then, for the utility maximization issue, we design a constant approximation approach. We also create an online method for the problem of accumulative throughput maximization. Finally, we use experimental simulations to assess the performance of the suggested methods. The results of the experiments show that the proposed algorithms are promising.

A novel hybrid deep learning approach for 5G network traffic control and forecasting

Summary5G is planned to link not just traditional devices such as tablets and smartphones, but also smart devices, smart homes, autonomous vehicles, and industry 4.0 which significantly increases the amount of traffic over the network. Network function virtualization and software defined networks will be used heavily to create scalably and on‐demand 5G architecture using virtual network functions. In this article, we proposed a unique approach to scaling 5G core network resources by predicting traffic load fluctuations using a hybrid model. Most researchers have presented deep learning models to anticipate regular traffic to improve services, however, these recommended models have failed to estimate traffic load during festivals to unexpected changes in traffic conditions. To solve this issue, we introduced CNN+LSTM, a hybrid model that combines CNN, and LSTM to forecast cumulative network traffic across particular intervals to scale up and properly estimate the availability of 5G network resources by leveraging traffic load variations. The suggested model surpasses the other tested deep learning models and existing techniques that forecast the output in both normal and abnormal traffic conditions, according to a comparison of the produced output with existing techniques.

SDFA: A Service-Driven Fragmentation-Aware Resource Allocation in Elastic Optical Networks

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

California Children Presenting to an Emergency Department for Mental Health Emergencies: Trajectories of Care.

Pediatric emergency department (ED) mental health visits are increasing in the United States. At the same time, child/adolescent psychiatric services are limited. This study examines the trajectory of pediatric patients presenting with mental health emergencies to better understand availability of specialty care resources in regional networks. This retrospective cohort study used a California Office of Statewide Health Planning and Development linked ED and Inpatient Discharge Dataset (2005-2015) to study pediatric patients (5-17 years) who presented to an ED with a primary mental health diagnosis. Outcomes were disposition: discharge, admission, or transfer.Patients transferred were further analyzed for disposition. Regression models to identify characteristics associated with disposition were created. There were 384,339 pediatric patients presented for a primary mental health emergency from 2005 to 2015; 287,997 were discharged, 17,564 were admitted, and 78,725 were transferred. Among those not discharged, patients with public (odds ratio [OR], 1.28; P < 0.01) or self-pay insurance (OR, 5.64; P < 0.01), Black (OR, 2.15; P < 0.01), or Native American race (OR, 2.32; P < 0.01), and who presented to rural EDs (OR, 3.10; P < 0.01), nonteaching hospitals (OR, 3.06; P < 0.01), or hospitals in counties without dedicated child/adolescent psychiatric beds (OR, 5.59; P < 0.01) had higher odds of transfer.Among those not discharged from the second hospital, Black patients (OR, 2.47; P < 0.03) and those who were transferred to a teaching hospital (OR, 1.9; P < 0.01) had higher odds of second transfer. Pediatric patients with mental health emergencies experience different trajectories of care. Transfer protocols and regionalized networks may help streamline services and decrease inefficiencies in care.

Combining Resource-Aware Recommendation and Caching in the Era of MEC for Improving the Experience of Video Streaming Users

The coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user quality of experience (QoE). However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user QoE and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE under the constraints of UE capabilities and the availability of network resources. In order to make the problem manageable, we assume that the regular user consumes video content keeping some time interval between them, and this user moves slowly inside the coverage of a base station. We evaluate our proposal using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and cache hit ratio by at least 14% in comparison with the other method.

Admission Control and Virtual Network Embedding in 5G Networks: A Deep Reinforcement-Learning Approach

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

Algorithm for dynamic channel reservation of aggregated data flow

In modern telecommunications systems, dynamic reservation of channel resources for an aggregated data stream is one of the most important tasks, as it allows for efficient use of available resources and provides high-quality service to subscribers. Developing an algorithm for dynamic reservation is a challenging task, as it requires ensuring optimal resource allocation among subscribers during variable network load. This article discusses an approach to developing an algorithm for dynamic reservation of channel resources for an aggregated data stream that provides efficient resource allocation and ensures high-quality service to subscribers. Specifically, approaches to dynamic resource allocation based on network load and approximation and optimization methods that reduce algorithm complexity and ensure its effective operation will be considered. The results of this work can be useful for telecommunications system developers and for research in the field of telecommunications. The article proposes an algorithm for dynamic reservation of channel resources for an aggregated data stream. The implementation of this algorithm is performed on a VPN gateway designed to manage data streams, filter them, and organize priority access to data streams in the VPN tunnel. In the event of a network load, the developed algorithm should first evaluate the network status and, based on the results obtained, perform data stream access control functions, taking into account their priorities. Based on the developed algorithm for dynamic reservation of channel resources for an aggregated real-time data stream, this section establishes a system of criteria for efficient utilization of channel resources for protected corporate multiservice communication networks under conditions of network resource availability for servicing the proposed workload from the access network and network congestion. The approach to prioritizing data streams takes into account user categories and service classes, which, unlike existing tools, allows for the task of prioritized servicing under load conditions and determines the best use of the available channel resources at the transport layer of protected corporate multiservice communication networks.

WEB DDoS Attack Detection Method Based on Semisupervised Learning

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

Resource availability influences global social network properties in Gunnison’s prairie dogs (Cynomys gunnisoni)

Abstract Increasingly we are discovering that the interactions between individuals within social groups can be quite complex and flexible. Social network analysis offers a toolkit to describe and quantify social structure, the patterns we observe, and evaluate the social and environmental factors that shape group dynamics. Here, we used 14 Gunnison’s prairie dogs networks to evaluate how resource availability and network size influenced four global properties of the networks (centralization, clustering, average path length, small word index). Our results suggest a positive correlation between overall network cohesion and resource availability, such that networks became less centralized and cliquish as biomass/m2 availability decreased. We also discovered that network size modulates the link between social interactions and resource availability and is consistent with a more ‘decentralized’ group. This study highlights the importance of how individuals modify social cohesions and network connectedness as a way to reduce intragroup competition under different ecological conditions.