Efficient data handling of wireless sensor network for real time landslide monitoring system using fuzzy technique

Sumathi, M.; Anitha, G.; Sridhar, N.

doi:10.1109/iccpct.2017.8074343

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…In other words, the various harvesting devices produce usable electric energy depending on changes in the energy resource they focus on, and the extent to which they do this is unique to each device. 14 In this respect, to avoid the risk of energy depletion, it is necessary to monitor the surrounding environment continuously because it affects the amount of energy that can be harvested. One previous study collected data such as average temperature, humidity, solar time, and solar radiation to predict the solar energy potential of an energy harvesting system.…”

Section: Related Workmentioning

confidence: 99%

Adaptive operating mode management model for efficient energy harvesting systems

Choi

Koo

Park

2020

International Journal of Distributed Sensor Networks

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Energy harvesting technology is becoming popular concerning efficient use of Internet of Things devices, which collect energy present in nature and use it to power themselves. Although the technology is eco-friendly, it is dependent on the vagaries of the surrounding environment; the amount of energy produced is sensitive to the weather and terrain, and intermittent power threatens the system’s stability. Thus, it is essential to collect data that can determine the circumstances of the surrounding environment. Furthermore, these systems should be designed efficiently for continuous energy harvesting. This efficiency can vary depending on the system’s configuration. Core voltage levels and frequencies typically influence efficiency. To maximize system efficiency, power management with an appropriate combination of controllable factors is necessary. We design an energy harvesting system for real-time data acquisition. We propose a methodology to guide the optimal operating power stage considering various adjustable factors for efficient operation. Also, we propose an adaptive operating power mode management model, which involves selecting the optimal operating power step and the transition to a low-power mode (LPM) during idle time. The proposed model was applied to an actual energy harvesting system to demonstrate its effectiveness and facilitated the operation of the harvesting system at low power.

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Section: Related Workmentioning

confidence: 99%

Adaptive operating mode management model for efficient energy harvesting systems

Choi

Koo

Park

2020

International Journal of Distributed Sensor Networks

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“…For bumpy topology, other novel techniques are used, including borderline detection Hammoudeh et al, (2017), the prediction of landslide Sumathi et al, (2019), prediction of instantaneous rise in quantity of nodes in a particular region of the network Eckerstorfer et al, (2016) as well as innovative data aggregation scheme. For the scenarios stated above, there is a need for remote sensing as most of these network areas cannot be accessed by humans.…”

Section: Introductionmentioning

confidence: 99%

An improved energy-efficient data clustering in UAV-Aided Wireless Sensor Networks for uneven topology

Griffin Siji

2023

Dicov

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It is not uncommon to see sensor nodes deployed in an uneven or hilly terrain.This can be found in many parts of Nigeria. In the same vain, sensor nodes may be deployed in very hostile areas such as in northern parts of Nigeria where insurgents are heavily present. In areas such as those stated above, the use of unmanned aerial vehicles can be used for energy-efficient data collection from the scattered sensor nodes. Unmanned Aerial Vehicles operating at low altitudes can be used to lower the energy consumption of the wireless sensor network by using an intelligent data collection methodology to distribute the UAVs for data collection from the nodes. This paper proposes an energy-efficient and optimized data aggregation (EEODA) scheme in UAV-assisted wireless sensor network for hilly and uneven terrain is designed, using UAVs as data collection points. This can be achieved through the following steps, firstly, a distributed clustering algorithm based on reinforcement learning was proposed to organize the wireless sensor nodes, secondly, a mono-objective simulated annealing search method will be used to efficiently distribute the UAVs for optimum collection of data from the various cluster heads in the network, thirdly the city section mobility model will be used to compute the optimum position for the UAVs to each of the cluster heads in the network. Simulation results show that EEODA scheme proposed in this paper outperforms the EFDC, the closest-performing algorithm to it with an average of 12%. It also outperforms the other two compared algorithms, LEACH with UAV and HEED with UAV with between 17% and 36%, respectively with performance metrics such as energy consumption of nodes, scalability, delay in data aggregation and collection, control overhead and number of dead nodes in each round of clustering.

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“…The deployment of WSNs in the hilly or unreachable areas include vital reasons like border monitoring [10], landslide prediction [11], avalanche prediction [12], and important scientific data collection. In such application scenarios, remote monitoring is important as all of the scenarios raise a threat against human life or national security.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Nazib

Moh

2021

IEEE Access

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Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

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Efficient data handling of wireless sensor network for real time landslide monitoring system using fuzzy technique

Cited by 12 publications

References 28 publications

Adaptive operating mode management model for efficient energy harvesting systems

Adaptive operating mode management model for efficient energy harvesting systems

An improved energy-efficient data clustering in UAV-Aided Wireless Sensor Networks for uneven topology

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

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