A wireless sensor network (WSN) is a one made up of small sensing devices equipped with processors, memory, and short-range wireless communication. Sensor nodes, are autonomous nodes, which include smart dust sensors, motes and so on. They co-operatively monitor physical or environmental conditions and send the sensed data to the sink node. They differ from traditional computer networks due to resource constraints, unbalanced mixture traffic, data redundancy, network dynamics, and energy balance. These kinds of networks support a wide range of applications that have strong requirements to reduce end-to-end delay and losses during data transmissions. When large numbers of sensors are deployed in a sensor field and are active in transmitting the data, there is a possibility of congestion. Congestion may occur due to buffer overflow, channel contention, packet collision, a high data rate, many to one nature, and so on. This leads to packet loss which causes a decrease in throughput and lifetime. Maximum throughput, energy efficiency and minimum error rate can be achieved by minimizing the congestion. A number of quality of service (QoS) techniques has been developed to improve the quality of the network. This article gives an overview of existing QoS techniques and a parametric comparison made with recent developments. This article mainly concentrates on network congestion in WSN environment.

In present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

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