Energy optimization in low-power wide area networks by using heuristic techniques

Ahmed, Zeinab E.; Saeed, Rashid A.; Mukherjee, Amitava; Ghorpade, Sheetal N.

doi:10.1016/b978-0-12-818880-4.00011-9

Cited by 39 publications

(16 citation statements)

References 43 publications

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“…We observed that, higher fairness index in intersite means that, the system attempts to maintain the UE throughput similar for all the IoT nodes in the network regardless of their locations relative to eNB and received SINR [33][34][35]. According to the fairness index for intraand intersites, a higher number of users gives less fairness index in the two cases of intersite and intrasite CoMP, because the competition between users becomes tenser.…”

Section: Ue Wideband Sinrmentioning

confidence: 94%

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Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Mukhtar

Saeed

Mokhtar

et al. 2022

Wireless Communications and Mobile Computing

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Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

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Section: Ue Wideband Sinrmentioning

confidence: 94%

“…where the first summation in the above equation represents the useful received signal from the L′ cooperating transmits power (TPs), the second summation represents the interference signals, and N is the noise power. Then, the SINR for the UE using JT-CoMP can be written as [33]…”

Section: The Methodologymentioning

confidence: 99%

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Mukhtar

Saeed

Mokhtar

et al. 2022

Wireless Communications and Mobile Computing

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“…The cycles of ACO are based on local and global searches. As a result, local ants have the capacity to migrate in the direction of a latent area, bringing the optimum solution according to the probability of transition related to the area [31].…”

Section: The Optimization Modelmentioning

confidence: 99%

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

2022

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In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are simultaneously minimized by the rotation and alternation of operative teams between those tasks with higher and lower values of metabolic energy required. We also verified that tasks with lower ergonomic-load requirements influence the reorganization of the task sequence by lowering the overall value of metabolic energy, which is reflected in the reduction of the ergonomic load.

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“…ACO represents an intelligent algorithm for path planning (Dai et al, 2019;Jovanovic, Tuba & Voß, 2016;Wang, Lin & Wang, 2016). It has a strong calculative mechanism (Ahmed et al, 2020). Generally, it is used for optimization by updating the pheromone trails and orienting the ants around the search space by which each ant generates a new fitness function to be used for generating an overall global fitness.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Qawqzeh

Alharbi

Jaradat

et al. 2021

PeerJ Computer Science

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Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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Energy optimization in low-power wide area networks by using heuristic techniques

Cited by 39 publications

References 43 publications

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

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