The main benefit of selecting a suitable node as cluster head (CH) in clustering for wireless mobile sensor networks (MWSNs) is to prolong the network lifetime. But the safe selection of CH is a challenging task by taking security into account. Mostly CH selection algorithms in MWSN do not consider security when selecting CH. We have proposed secure CH selection algorithm by calculating weight of each node to deal with secure selection using minimum energy consumption. The weight of node is a combination of different metrics including trust metric (behaviors of sensor node) which promotes a secure decision of a CH selection; in terms of this, the node will never be a malicious one. The trust metric is definitive and permits the proposed clustering algorithm to keep away from any malignant node in the area to select a CH, even if the rest of the parameters are in its favor. Other metrics of node include waiting time, connectivity degree, and distance among nodes. The selection of CHs is completed utilizing weights of member nodes. The preparatory outcomes acquired through simulation exhibit the adequacy of our proposed scheme as far as average rate of avoiding malicious node as a CH, energy efficiency, and some other performance parameters are concerned.

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

Due to the technological advancement in cellular networks, massive data traffic appends to the existing digital technologies. These emerging digital technologies face quality of service (QoS) challenges, particularly when it comes to maintaining the tradeoffs between customers and service providers. The cellular service providers are trying to meet the needs of end users by handling four substantially different types of data, i.e., Real Time, Video, Audio, and Text, with each type having its own requirements. To achieves an efficient QoS, different incentive-based algorithms were proposed. However, these schemes do not ensure a fair distribution of profit among the mobile network operator and access points. Furthermore, these schemes do not provide efficient QoS to the end user and cannot ensure a fair distribution of channels in crowning time. We propose an incentive-based scheme using game theory and two-stage Stackelberg approach for integrated data, offloading the decision-making process in a heterogeneous network. A single mobile base station and some integrated access points in a crowded metropolitan area are modeled in our proposed scheme. This station offers an economic incentive based on traffic types, and access points compete with each other to earn incentives for offload traffic. A mathematical game is derived to analyze the real-world scenario through simulation. The experimental method is applied to validate the numerical outcomes by comparing the results with other models.