Data Traffic Management Based on Compression and MDL Techniques for Smart Agriculture in IoT

Al-Qurabat, Ali Kadhum M.; Mohammed, Zahraa A.; Hussein, Zahraa

doi:10.1007/s11277-021-08563-4

Cited by 58 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several ways in which the Internet of Things (IoT) may be used as a kind of philanthropy, from healthcare to transportation to industrial automation 7 . IoT sensor nodes are built on the IoT network with restricted power, memory, and computing capabilities 8 . Physical media is managed such that losses are minimized 9 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Srivastava¹,

Paulus²

2022

Concurrency and Computation

View full text Add to dashboard Cite

Nowadays, the wireless sensor network (WSN) with IoT is intended to monitor real-world physical or environmental phenomena in a number of applications, including foreign areas such as health and habitat monitoring. The WSN-IoT network generates huge volume of data, which has to be processed and accessed by the remote users. Due to this large volume of data generation and resource constraint ability make achieving optimal cluster based routing in WSN-IoT. The location of the sensor nodes significantly affects the accuracy of the information collected, which determines the quality of service provided by the application system. WSN can have multiple conflicts, which can create different coverage holes. These holes will break the existing overlap or connection and affect the required operation of the networks. Therefore, it is essential to find and repair the coverage holes to ensure the full functioning of the WSN as a motivation of this study. In this paper, we suggest a novel Coverage Hole aware Optimal Cluster based Routing (CHOCR) scheme for WSN-IoT. First, we propose Modified Lichtenberg optimization (MLO) algorithm for balanced clustering which improve the performance of coverage hole. Second, we develop a linear equilibrium optimization based decision making (LEO-DM) technique to subtract trust value of each IoT node using multiple restraints in cluster and consider the highest trusted node is act as cluster head (CH). After that, a hybrid deep recurrent neural network (HD-RNN) is developed for intermediate node selection to frame the routing between two nodes.Finally, we simulate our proposed CHOCR scheme on the NS3.26 simulator. According to energy consumption, network longevity, number of nodes that are still alive and packet delivery ratio, packet loss ratio and throughput, end-to-end latency and delay of our proposed CHOCR routing system, we compare it to other current routing schemes.

show abstract

Section: Introductionmentioning

confidence: 99%

“…7 IoT sensor nodes are built on the IoT network with restricted power, memory, and computing capabilities. 8 Physical media is managed such that losses are minimized. 9 Wireless Sensor Networks (WSN) nodes are able to process, store, and communicate with other nodes on the network.…”

mentioning

confidence: 99%

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Srivastava¹,

Paulus²

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Management of data traffic has been proposed in [21] for compression and minimum description length (MDL). The goal of the proposed is to reduce the energy consumption of each node and prolong the wireless sensor network's (WSN's) lifespan while maintaining acceptable data accuracy.…”

Section: Introductionmentioning

confidence: 99%

Design an efficient internet of things data compression for healthcare applications

Kahdim

Manaa²

2022

Bulletin EEI

View full text Add to dashboard Cite

The internet of things (IoT) is an ecosystem of connected objects that are accessible and available through the internet. This "thing" in the IoT could be a sensor such as a heart monitor, temperature, and oxygen rate in the blood. These sensors produce huge amounts of information that lead to congestion and an effect on bandwidth in the IoT network. In this paper, the proposed system is based on the Zstandard compression algorithm to compress the sensor data to minimize the amount of data transmitted from the IoT level to the fog level and decrease network overloading. The proposed system was evaluated using compression ratio, throughput, and latency time for healthcare applications. The result showed better calculation through decreased response time and increased throughput for transmitted data compared with the case of non-compressed data. It showed the compression data ratio about 70% of orignial data, maximum number of IoT sensor reads as 100, throughput is 85.43 B/ms, and fog processing delay is 6.25 ms.

show abstract

“…Accordingly, it occurs through unique addressing models that help the node to communicate with others and to cooperate with neighbors for achieving their goals 1 . The IoT appliances in various fields are industrial automation, mobile health, smart buildings, medical aids, assistance to elderly, intelligent energy management, agriculture, 2–4 and smart education 5,6 . IoT is one of the most innovative concepts in the modern era, with its effects being seen in every aspect of human endeavor and offering more potential for smart life.…”

Section: Introductionmentioning

confidence: 99%

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

Ramkumar¹,

Paul

Maram

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.

show abstract

Data Traffic Management Based on Compression and MDL Techniques for Smart Agriculture in IoT

Cited by 58 publications

References 34 publications

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

Design an efficient internet of things data compression for healthcare applications

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

Contact Info

Product

Resources

About