Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network

Panja, Ayan Kumar; Ghosh, Arka

doi:10.1007/978-981-15-2125-6_11

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…The weight of the individual base learners are obtained as;

W (italicBi) = \frac{Accuracy_CV (C_{i})}{\sum_{i = 1}^{count_b} Accuracy_CV (C_{i})}

here B i is the base learner. Algorithm 2 depicts the majority voting procedure, which is deployed onto every edge drone to collect data and predict performance prediction and routing (Mukherjee, Panja, & Dey, 2020; Panja & Ghosh, 2020) in a real‐time manner.…”

Section: Aerial Edge Computingmentioning

confidence: 99%

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An intelligent edge enabled 6G‐flying ad‐hoc network ecosystem for precision agriculture

Mukherjee

Panja

Dey³

et al. 2022

Expert Systems

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Unmanned aerial vehicle based precision agriculture is a predominant research area. The modern flying ad‐hoc network leverages the advanced low latency vehicular communication and intelligent computing paradigms that help the ecosystem to grow up to the next level. In this work, we propose an ecosystem for precision agriculture that leverages the use of the opportunistic MQTT protocol in an edge‐enabled intelligent drone network for sensing and performing crop prediction using an intelligent ensemble machine learning model. The proposed approach leverages the edge computing system that requires low energy devices and also exploits the ultra‐low latency opportunistic message transfer methodology. The experimental results show the maximum of 0.9 message delivery ratio and a minimum of 600 ms latency is achieved by opportunistic MQTT protocol in an ultra‐low latency sparse network scenario. A weighted ensemble model is deployed onto the edge enabled devices or the drones. An accuracy of 96.5% is achieved in predicting the type of crops that can be grown in the soil about the selected area of interest.

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“…The weight of the individual base learners are obtained as;

W (italicBi) = \frac{Accuracy_CV (C_{i})}{\sum_{i = 1}^{count_b} Accuracy_CV (C_{i})}

Section: Aerial Edge Computingmentioning

confidence: 99%

“…performance prediction and routing(Mukherjee, Panja, & Dey, 2020;Panja & Ghosh, 2020) in a real-time manner. A block diagram depicting the model built with train and test set separately is shown in Figure 5.…”

mentioning

confidence: 99%

An intelligent edge enabled 6G‐flying ad‐hoc network ecosystem for precision agriculture

Mukherjee

Panja

Dey³

et al. 2022

Expert Systems

Self Cite

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“…There is a chance that data packets will be streamed across the network in this case. A y a n P a n j a and G h o s h [109] achieve load balancing in a WSN using standard techniques such as Breadth First Search and Dijkstra's algorithm, which finds the shortest path to route data packets. The central principle of load balancing is to reduce hot spots in order to extend the network's life.…”

Section: Load Balancingmentioning

confidence: 99%

“…M u h a m m a d A d i l et al [111] develop the Energy Gauge Node (EGN) strategy to reduce energy consumption by making efficient use of sensor nodes and resolving load-balancing issues. [109] 2020 Finding shortest path Needs to lower hot spots in the network Heuristic Load Balancing Algorithm [110] 2020 Non-linear programming based on network topology Based on transmission power Load balancing using Energy Gauge Nodes [111] 2020 Energy-balanced joint routing and Asynchronous service cycles Depends completely on the deployment of EGN nodes Lightweight load-balanced and authentication scheme [112] 2021 Based on security authentication and deployment Control packets overhead EGN nodes are also installed in the WSNs of ordinary nodes because these nodes have a large battery capacity, a high computing power, a reasonable communication speed, and sufficient storage space. The EGN nodes in the network are in charge of disposing of node waste energy and transferring information on the network's Hop set.…”

Section: Load Balancingmentioning

confidence: 99%

Role of Clustering, Routing Protocols, MAC protocols and Load Balancing in Wireless Sensor Networks: An Energy-Efficiency Perspective

Munusamy¹,

Vijayan²,

Ezhilarasi³

2021

Cybernetics and Information Technologies

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Wireless networks play an important role in science, including medicine, agriculture, the military, geography, and so on. The main issue with a network of wireless sensors is how to manage resource utilization to extend its lifetime. This paper investigates the various aspects of increased energy usage that may improve network life. Variables related to energy consumption and various performance metrics are investigated in terms of energy efficiency. To investigate how the network’s energy usage can be managed, a quick overview of clustering protocols, routing protocols, MAC protocols, and load balancing protocols is conducted. This paper can provide researchers with an idea of the various parameters that influence energy consumption and what methodologies could be adapted by each parameter to conserve energy, thereby extending the network’s lifetime.

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Qualitative Survey on Sensor Node Deployment, Load Balancing and Energy Utilization in Sensor Network

Cited by 2 publications

References 26 publications

An intelligent edge enabled 6G‐flying ad‐hoc network ecosystem for precision agriculture

An intelligent edge enabled 6G‐flying ad‐hoc network ecosystem for precision agriculture

Role of Clustering, Routing Protocols, MAC protocols and Load Balancing in Wireless Sensor Networks: An Energy-Efficiency Perspective

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