Collective Cooperation Among Base Stations to Sustain Wireless Infrastructure

Teng, Rui

doi:10.1109/tsusc.2020.3035475

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Assessing the cascading robustness of scale-free WSNs was accomplished in [21,22,23] by the use of the probability generating function, which was constructed from the exponential-degree cascading failure model. Researchers in [24,25] investigated the cascading resilience of three different WSN topologies by using the degree values of sensor nodes as an indication of their load (i.e., small-world topology, scale-free topology, and random topology). They came to the conclusion that the number of failures that cascaded from one another correlated positively with the power law exponent of scale-free networks.…”

Section: Literature Reviewmentioning

confidence: 99%

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Kuthe,

Khandelwal

2024

Preprint

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One of the best methods for increasing energy efficiency in large-scale wireless networks is sleep scheduling. The scalability of existing sleep scheduling algorithms is limited in real-time applications due to their higher complexity or lack of coordination. Furthermore, these models' routing performance needs to be adjusted for various use scenarios. This essay suggests designing a low complexity sleep-scheduling model with iterative bioinspired learning for better routing efficiency in the context of diverse networks in order to integrate these qualities. At first, the suggested model gathers progressively larger data sets on node location, energy usage, fault frequency, and additional quality of service measures. These parameter sets are used to train a set of scalar nodes (nodes with low efficiency levels), that decide initial sleep scheduling cycles for high-efficiency multimedia nodes. The sleep schedules are co-ordinated by multimedia nodes during their inter-node communications, which assists the scalar nodes to iteratively improve sleep-and-wake cycles. These cycles are decided by a Bacterial Foraging Optimization (BFO) process, which assists in improving energy efficiency without compromising on QoS performance levels. The BFO process is cascaded with a fault-tolerant Grey Wolf Particle Swarm Optimizer (GWPSO), that evaluates high-efficiency routing paths that can provide low delay, low energy consumption, with lower probability of faults. Due to these operations, the proposed model is able to reduce communication delay by 4.9%, and energy needed for communication by 8.5%, while improving throughput by 3.4%, and consistency of routing-performance by 1.9% when compared with existing routing techniques. This performance was observed to be consistent even under node-level faults for heterogenous network scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Kuthe,

Khandelwal

2024

Preprint

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Delay-Tolerant Distributed Algorithms for Decision-Making in Vehicular Networks

Chen

Qin

Hong

et al. 2023

Asia Pac. J. Oper. Res.

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Learning a fast global model that describes the observed phenomenon well is a crucial goal in the inherently distributed Vehicular Networks. This global model is further used for decision-making, which is especially important for some safety-related applications (i.e., the altering of accident and warning of traffic jam). Most existing works have ignored the network overhead caused by synchronizing with neighbors, which inevitably delays the time for agents to stabilize. In this paper, we focus on developing an asynchronous distributed clustering algorithm to learn the global model, where cluster models, rather than raw data points, are shared and updated. Empirical experiments on a message delay simulator show the efficiency of our methods, with a reduced convergence time, declined network overhead and improved accuracy (relative to the standard solution). This algorithm is further improved by introducing a tolerant delay. Compared to the algorithm without delay, the performance is improved significantly in terms of convergence time (by as much as 47%) and network overhead (by around 53%) if the underlying network is geometric or regular.

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Collective Cooperation Among Base Stations to Sustain Wireless Infrastructure

Cited by 2 publications

References 37 publications

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Delay-Tolerant Distributed Algorithms for Decision-Making in Vehicular Networks

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