Load-balancing metrics: Comparison for infrastructure-based wireless networks

Wen, Yean‐Fu; Shen, Jui-Chang

doi:10.1016/j.compeleceng.2013.07.002

Cited by 7 publications

(8 citation statements)

References 24 publications

(36 reference statements)

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“…Traditional routing methods have been discussed and designed for transmission from source to destination using shortest-path algorithms, such as Dijkstra's algorithm and the Bellman-Ford routing algorithm [29]. The main idea of these methods is to identify a routing path with a quality-of-service (QoS) metric, such as transmission bandwidth or delay [30]. However, nodal delay and segment routing [31] are not included as strategies for in-network processing applications, such as monitoring applications of wireless sensor networks (WSNs) [32].…”

Section: Related Workmentioning

confidence: 99%

“…The bandwidth does not reflect the traffic load. Although the traffic load reflects the transmission link status, the nodal status is VOLUME x, 2016 not reflected [30]. Therefore, herein, both transmission delay and nodal processing time were employed as combination metrics for routing to identify the near-optimal routing path and processing node.…”

Section: Related Workmentioning

confidence: 99%

“…The IP camera must detect one type of event in our model. The software-defined network (SDN) [29], [30] Segment routing…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The metric selected depends on the goal of transmission. [30] assumes that inclusion in the network relies on network statuses being periodically collected for routing decisions. We also assumed that background loading for the IP camera must be computed, and thus, the IP cameras tend to offload tasks to other devices, cloudlets, or cloud hosts.…”

Section: Problem Descriptionmentioning

confidence: 99%

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Optimization-Based Offloading and Routing Strategies for Sensor-Enabled Video Surveillance Networks

et al. 2020

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For the Internet of things, having sensors in devices used for video surveillance services, such as cameras, is crucial. The advancement of edge computing technology has enabled high computing capacity and the handling of massive data sets. The concept of cloudlets is employed in edge computing for in-network processing, especially for large-size multimedia data processing. Cloudlets are essential for services with high computing costs. Contrary to traditional cloud computing, data can be offloaded to in-network devices and core clouds, thereby improving the quality of service and enhancing resource utilization. However, the trade-off between network transmissions and nodal processes with delay-aware multimedia traffic has been demonstrated to be an NP-complete problem. The problem is presented as a mathematical formula to maximize the minimal delay gap between the tolerable event delay, sending time, and processing time. The problem is subject to in-network processing node assignment, routing paths, transmission capacities, computing capacities, and the effective service period. The Lagrangian approach was employed to evaluate the method proposed in this study; a near-optimal solution was obtained, and several experiments were performed to demonstrate that the proposed method outperforms existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The IP camera must detect one type of event in our model. The software-defined network (SDN) [29], [30] Segment routing…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

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Optimization-Based Offloading and Routing Strategies for Sensor-Enabled Video Surveillance Networks

et al. 2020

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“…Most studies on QoS consider delay jitter [5], [6], minimum delays [7] or delay jitters as the comparison metrics [8]. However, those metrics of estimating the QoS might not be suitable for various types of MDs.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian-Relaxation-Based Self-Repairing Mechanism for Wi-Fi Networks

2019

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Wi-Fi was developed to support software-defined networks that are crucial for achieving high quality of service for next generation wireless networks. In this software-defined networking technology, network resources are utilized to reduce the effects of several influential factors such as the limited number of nonoverlapping channels, co-channel interference, rapidly changing distribution of customers, and access point (AP) failure. These factors cause irregular network traffic and service unavailability in some users. In this paper, we address the problems causing the AP failure and interference. The studied problems were modeled as linear and nonlinear mathematical programming problems. The Lagrangian relaxation approach, which is a type of divide-and-conquer method, was used to solve the load distribution problems near optimally. A self-repairing heuristic multiple-level load-balancing traffic adjustment was proposed to manage the problems pertaining to the AP failure. The delay difference metric, which is defined as the difference between tolerable delay and transmission delay, was used to evaluate the difference between the upper bound and lower bound of the delay. Moreover, the metric was used to estimate the improvement ratio between the existing methods and the proposed method. Modifiable transmission power ranges (which involve the cell breathing method) and association managements are adjusted to minimize the narrow gap and improve the self-repairing performance. Thus, resources are appropriately allocated to users, and adequate QoS is attained.

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