Load Balancing in Wireless Networks using SDN-enabled Infrastructure: Traffic Analysis

MengHeang, Heng; Gilani, Syed Mushhad M.; Tang, Hong; Zhao, Guofeng; Abdalla, Hemn Barzan

doi:10.4108/eai.13-7-2017.2270385

Cited by 7 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data transmission process within a chain is designed to ensure that each sensor is able to deliver its data successfully to the CL 19 . It means that even if a non‐CL node fails, the protocol has mechanisms in place to handle it, as proposed in references 20,21. In other words, the protocol is designed to handle node failures and still enable successful data transmission within the chain 22,23 .…”

Section: Proposed Fault Tolerance Mechanismsmentioning

confidence: 99%

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

Jalili,

Alzubi,

Rezaei

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryWireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing in WSNs, with the aim of reducing energy consumption and delay. However, node failures are inevitable due to energy reduction and node mobility. Therefore, fault tolerance techniques must be integrated into routing protocols. A new fault tolerance method has been proposed to prevent early chain failures in WSNs by formulating network availability and reliability using Markov chain analysis. The results indicate that chain‐based routing protocols with one spare node are more reliable than those with two spare nodes.

show abstract

Section: Proposed Fault Tolerance Mechanismsmentioning

confidence: 99%

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

Jalili,

Alzubi,

Rezaei

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…and E TX (M : e) denote the total energy utilized for transferring N bytes of packets at a distance e. Equation ( 4) demonstrates the model of electronic energy, in which E ae indicates the energy utilized during data aggregation. The total energy required for attaining M bytes of packets at distance d is given by Equation (5). Equation ( 6) illustrates the energy necessary for amplification.…”

Section: Energy Modelmentioning

confidence: 99%

“…The goal is to gather information on the environment surrounding network sensors. All environmentally gathered data packets are passed on to a node of network, termed as the base station (BS) [3][4][5]. For further processing, the sensor nodes are generally used in areas where it is impossible or complex for humans to operate.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization algorithm for security aware cluster head selection process to aid hierarchical routing in wireless sensor network

2021

View full text Add to dashboard Cite

In wireless sensor networks, clustering is said to be the most noteworthy technique for increasing the lifetime of network that directly leads a better routing mechanism. This approach involves grouping of sensor nodes to clusters and choosing the appropriate cluster heads for each cluster. In fact, cluster heads gathers data from corresponding nodes in cluster and transmits those aggregated data to base station. However, the major issue in this is the selection of the appropriate cluster head. Till now, many research works have been carried out for solving this issue by considering different constraints. This paper introduces a new cluster-based routing model by selecting the optimal cluster head. Moreover, a novel algorithm known as grey wolf updated whale optimization algorithm is introduced. Here, a new multi-objective function is defined with respect to different constraints like distance, delay, security and energy, respectively. Finally, the performance of security aware clustering with grey wolf updated whale optimization algorithm is evaluated and validated over other conventional works with respect to alive node analysis, throughput and normalized network energy, respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Then, an algorithm is used to derive the optimal association relationship between APs utilizing the global vision of SDN controller. A similar strategy is also proposed in [65][66][67].…”

Section: Load Balancing In Wireless Linksmentioning

confidence: 99%

Intelligent Load Balancing Techniques in Software Defined Networks: A Survey

et al. 2020

View full text Add to dashboard Cite

In the current technology driven era, the use of devices that connect to the internet has increased significantly. Consequently, there has been a significant increase in internet traffic. Some of the challenges that arise from the increased traffic include, but are not limited to, multiple clients on a single server (which can result in denial of service (DoS)), difficulty in network scalability, and poor service availability. One of the solutions proposed in literature, to mitigate these, is the use of multiple servers with a load balancer. Despite their common use, load balancers, have shown to have some disadvantages, like being vendor specific and non-programmable. To address these disadvantages and improve internet traffic, there has been a paradigm shift which resulted in the introduction of software defined networking (SDN). SDN allows for load balancers that are programmable and provides the flexibility for one to design and implement own load balancing strategies. In this survey, we highlight the key elements of SDN and OpenFlow technology and their effect on load balancing. We provide an overview of the various load balancing schemes in SDN. The overview is based on research challenges, existing solutions, and we give possible future research directions. A summary of emulators/mathematical tools commonly used in the design of intelligent load balancing SDN algorithms is provided. Finally, we outline the performance metrics used to evaluate the algorithms.

show abstract

Load Balancing in Wireless Networks using SDN-enabled Infrastructure: Traffic Analysis

Abstract: KEYWORDS

Cited by 7 publications

References 8 publications

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing inWSNs

Hybrid optimization algorithm for security aware cluster head selection process to aid hierarchical routing in wireless sensor network

Intelligent Load Balancing Techniques in Software Defined Networks: A Survey

Contact Info

Product

Resources

About