ODMAC++: An IoT communication manager based on energy harvesting prediction

Perez, Samuel; Fuertes, Juan Antonio Cordero; Coupechoux, Marceau

doi:10.1109/pimrc.2017.8292360

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Researchers have attributed the frequency of mcr genes in swine to the use of colistin sulfate for growth promotion or to control infections caused by E. coli in pigs, such as post-weaning diarrhea and edema disease [ 5 , 22 , 23 ]. In the DR, 36 colistin-containing products are approved for veterinary use in poultry, cattle, goats, sheep, pigs, cats, or dogs by the Ministry of Agriculture [ 24 ]. However, there are no known official animal antibiotic regulations, and antibiotic use and sales data still need to be included.…”

Section: Discussionmentioning

confidence: 99%

First Known Report of mcr-Harboring Enterobacteriaceae in the Dominican Republic

Perdomo

Webb

Bugarel

et al. 2023

IJERPH

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Colistin is a last-resort antibiotic used to treat infections caused by multidrug-resistant Gram-negative bacteria. People with a history of travel to the Dominican Republic have become sick with pathogenic bacteria carrying the mobile colistin resistance gene, mcr-1, during and after traveling. This investigation aimed to identify mcr genes in Enterobacteriaceae isolated from food animal sources in the Dominican Republic. Three hundred and eleven samples were tested, from which 1354 bacterial isolates were obtained. Real-time PCR tests showed that 70.7% (220 out of 311) of the samples and 3.2% (44 out of 1354) of the isolates tested positive for the mcr gene. All RT-PCR presumptive mcr-positive isolates (n = 44) and a subset (n = 133) of RT-PCR presumptive mcr-negative isolates were subjected to whole-genome sequencing. WGS analysis showed that 39 isolates carried the mcr gene, with 37 confirmed as positive through RT-PCR and two as negative. Further, all of the mcr-positive genomes were identified as Escherichia coli and all contained a IncX4 plasmid replicon. Resistant determinants for other antibiotics important for human health were found in almost all isolates carrying mcr genes.

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Section: Discussionmentioning

confidence: 99%

First Known Report of mcr-Harboring Enterobacteriaceae in the Dominican Republic

Perdomo

Webb

Bugarel

et al. 2023

IJERPH

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“…Real world applications make use of IoT devices but are limited due to energy consumed, and also it will affect the Nutritious Level. The EH will reduce the value of coverage area and then provide techniques to renew the energy (Perez et al, 2017). Each home will have an SP installed for communication in an effective way.…”

Section: Lifetime Ratio Improvement Techniquementioning

confidence: 99%

Lifetime ratio improvement technique using special fixed sensing points in wireless sensor network

Niranjan¹,

Priyatham

2021

IJPCC

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Purpose The purpose of this paper is to improve the lifetime ratio of wireless sensor networks for maintaining the battery level at a desired point for better improvement of network health. Design/methodology/approach Sensor point network (SPN) is used for variety of applications like weather check, tracking of undesirable vehicles and delivery of data to end points. The proposed special high health sensing point (SHHSP) scheme will overcome several limitations of existing game theory approaches with respect to delay, health and overall throughput. Findings The simulation results of the proposed SHHSP scheme confirms the excellence over the existing works examined with respect to delay, hops, energy consumed, nutrition SP, harmful SP, throughput and overhead. Practical implications It is proposed for a smart communication system in IoT, where in the communication between the sensing point network to its neighbouring sensing network is carried out by selection of SHHSP, this is implemented by using the remaining energy and distance vector with respect to control station. The system is applicable to weather check and can also be used in tracking of vehicles in a vehicle ad hoc networks. Originality/value It is subsidized to the IoT system and vehicle-to-vehicle communication system where in the safety is of utmost concern. The system is concentrated on the battery concern of SPN in a pool of SPNs.

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“…Supervised and Unsupervised Learning (i) Predicting the availability of energy sources [17] (ii) EH-based communication management [33,34] (iii) Real-time insights into energy usage pattern [35] Reinforcement Learning (i) Energy consumption planning [36] (ii) learning an optimal charging path for mobile chargers [37] (iii) Adaptive Power Management [38] (iv) IoT battery management techniques [39] (v) Autonomous Management of Energy-Harvesting IoT Nodes [40] Metaheuristics (i) Energy consumption scheduling [41] (ii) Optimizing operations of Mobile chargers [42] Fuzzy Model (i) Power management using a fuzzy controller [43] (ii) Energy consumption monitoring and control [44] Probabilistic Model (i) Data based probability models of energy production in EH-IoT nodes [45] (ii) Sleep scheduling [20] (iii) Event Prediction [21] (iv) Maximizing the average sensing rate [46]…”

Section: Ai Technique Usage For Smart Energy Managementmentioning

confidence: 99%

Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

Osifeko

Hancke

Abu‐Mahfouz

2020

JSAN

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Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

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ODMAC++: An IoT communication manager based on energy harvesting prediction

Cited by 4 publications

References 6 publications

First Known Report of mcr-Harboring Enterobacteriaceae in the Dominican Republic

First Known Report of mcr-Harboring Enterobacteriaceae in the Dominican Republic

Lifetime ratio improvement technique using special fixed sensing points in wireless sensor network

Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

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