Tree-based data aggregation approach in wireless sensor network using fitting functions

Atoui, Ibrahim; Ahmad, Ali Bawasheakh; Medlej, Maguy; Makhoul, Abdallah; Tawbe, Samar; Hijazi, Abbas

doi:10.1109/icdipc.2016.7470808

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…Data aggregation is a technique in which redundant information is removed before processing by the server. Data aggregation is treated as a regression problem and is solved by SVM [92] and reinforcement [93] respectively. Table 5 shows the summary of the literature survey of ML techniques for WSN-IoT.…”

Section: Summary Of Literature Survey Of ML Techniques For Wsn-iotmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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Section: Summary Of Literature Survey Of ML Techniques For Wsn-iotmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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“…Although the data is secured, some insecurity may arise in transmitting data to the wireless channel. 15 This data insecurity is because interference may arise as the number of sensor nodes increases, resulting in encryption failure.…”

Section: Introductionmentioning

confidence: 99%

An improved blockchain based encryption scheme for secure routing in wireless sensor network using machine learning technique

Meena

Sharma

2022

Trans Emerging Tel Tech

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A secure routing scheme plays an integral role in ensuring the secure routing and efficiency of wireless sensor networks (WSNs). Recently, many studies have been undertaken to improve the data aggregation process, data security, and routing security. However, these approaches are highly suffered due to major limitations like time complexity, malicious attacks, and data insecurity. This research aims to develop an improved blockchain based encryption scheme for secure routing in wireless sensor networks using machine learning techniques. An improved artificial quantum beetle swarm neural network approach is proposed to perform the data aggregation among the sensor nodes. The cosine equivalent function and packet density correlation degree approach is emphasized to perform clustering in sensor nodes. The cluster head (CH) is selected based on the similarity of each sensor node. The Kalman filtering technique is introduced to filter out the data from unwanted malicious. This technique helps to filter the data before it is sent to the CH. Blockchain‐based encryption scheme is emphasized for data security by preventing malicious attacks during data transmission. The enhanced differential evaluation based firefly routing protocol is presented to maintain secure routing by choosing the optimal routing for the protected data transmission. Performance metrics such as latency, throughput, routing performance, energy consumption, FPR, blockchain (BC) computational performance, dead sensor count and average hop count are analyzed and compared with existing techniques. In an experimental scenario, the proposed approach achieves a maximum packet delay of 500 slots, throughput of 581 times/s, a latency of 0.31 ms, a computation complexity of 0.6, and a routing complexity of 2081 ms with a BC computation time of 95 ms. The performance of the proposed approach shows a better result than other existing approaches.

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“…is an efficient way to achieve it [3,4,5]. Depending on the operation locations, the existing data bundling schemes (e.g., [4,5,6,7,8,9,10,11,12]) can be classified into in-node, in-network, and hybrid bundling. However, one major disadvantage of employing data bundling schemes is the increase of end-to-end (E2E) delay, which draws much attention in WSN research [13,14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Optimal Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks

Huan

Kim

Lee

et al. 2019

Sensors

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Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture recognition—have been developed, time synchronization accuracy becomes an important requirement for those WSN applications too. Message bundling is considered one of the effective methods to reduce the energy consumption for message transmissions in WSNs, but bundling more messages increases the transmission interval of bundled messages and thereby their end-to-end delays; the end-to-end delays need to be maintained within a certain value for time-sensitive applications like factory monitoring and disaster prevention, while the message transmission interval affects time synchronization accuracy when the bundling includes synchronization messages as well. Taking as an example a novel WSN time synchronization scheme recently proposed for energy efficiency, we investigate an optimal approach for message bundling to reduce the number of message transmissions while maintaining the user-defined requirements on end-to-end delay and time synchronization accuracy. Formulating the optimal message bundling problem as integer linear programming, we compute a set of optimal bundling numbers for the sensor nodes to constrain their link-level delays, thereby achieving and maintaining the required end-to-end delay and synchronization accuracy. Extensive experimental results based on a real WSN testbed using TelosB sensor nodes demonstrate that the proposed optimal bundling could reduce the number of message transmissions about 70% while simultaneously maintaining the required end-to-end delay and time synchronization accuracy.

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Tree-based data aggregation approach in wireless sensor network using fitting functions

Cited by 16 publications

References 14 publications

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

An improved blockchain based encryption scheme for secure routing in wireless sensor network using machine learning technique

Optimal Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks

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