Prolonging network lifetime is one of the most important design objectives in energy-constrained wireless sensor networks (WSNs). Using a mobile instead of a static base station (BS) to reduce or alleviate the non-uniform energy consumption among sensor nodes is an efficient mechanism to prolong the network lifetime. In this paper, we deal with the problem of prolonging network lifetime in data gathering by employing a mobile BS. To achieve that, we devise a novel clustering-based heuristic algorithm for finding a trajectory of the mobile BS that strikes the trade-off between the traffic load among sensor nodes and the tour time constraint of the mobile BS. We also conduct experiments by simulations to evaluate the performance of the proposed algorithm. The experimental results show that the use of clustering in conjunction with a mobile BS for data gathering can prolong network lifetime significantly.

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Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile devices. A mobile device roams sensing fields and collects data from sensors through a short transmission range. Multihop communication is used to improve data gathering by reducing the tour length of the mobile device. In this paper we study the trade-off between energy saving and data gathering latency in wireless sensor networks. In particular, we examine the balance between the relay hop count and the tour length of a mobile Base Station (BS). We propose two heuristic algorithms, Adjacent Tree-Bounded Hop Algorithm (AT-BHA) and Farthest Node First-Bounded Hop Algorithm (FNF-BHA), to reduce energy consumption of sensor nodes. The proposed algorithms select groups of Collection Trees (CTs) and a subset of Collection Location (CL) sensor nodes to buffer and forward data to the mobile BS when it arrives. Each CL node receives sensing data from its CT nodes within bounded hop count. Extensive experiments by simulation are conducted to evaluate the performance of the proposed algorithms against another heuristic. We demonstrate that the proposed algorithms outperform the existing work with the mean of the length of mobile BS tour.

Using a mobile base station (BS) in a wireless sensor network can alleviate nonuniform energy consumption among sensor nodes and accommodate partitioned networks. In the work of Jerew and Liang (2009) we have proposed a novel clustering-based heuristic algorithm for finding a trajectory of the mobile BS that strikes a nontrivial tradeoff between the traffic load among sensor nodes and the tour time constraint of the mobile BS. In this paper, we first show how to choose the number of clusters to ensure there is no packet loss as the BS moves between clusters. We then provide an analytical solution to the problem in terms of the speed of the mobile BS. We also provide analytical estimates of the unavoidable packet loss as the network size increases. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm. The results show that the use of clustering in conjunction with a mobile BS for data gathering can significantly prolong network lifetime and balance energy consumption of sensor nodes.

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Background and Purpose: Convolutional neural network is widely used for image recognition in the medical area at nowadays. However, overall accuracy in predicting lung tumor is low and the processing time is high as the error occurred while reconstructing the CT image. The aim of this work is to increase the overall prediction accuracy along with reducing processing time by using multispace image in pooling layer of convolution neural network. Methodology: The proposed method has the autoencoder system to improve the overall accuracy, and to predict lung cancer by using multispace image in pooling layer of convolution neural network and Adam Algorithm for optimization. First, the CT images were pre-processed by feeding image to the convolution filter and down sampled by using max pooling. Then, features are extracted using the autoencoder model based on convolutional neural network and multispace image reconstruction technique is used to reduce error while reconstructing the image which then results improved accuracy to predict lung nodule. Finally, the reconstructed images are taken as input for SoftMax classifier to classify the CT images. Results: The state-of-art and proposed solutions were processed in Python Tensor Flow and It provides significant increase in accuracy in classification of lung cancer to 99.5 from 98.9 and decrease in processing time from 10 frames/second to 12 seconds/second. Conclusion: The proposed solution provides high classification accuracy along with less processing time compared to the state of art. For future research, large dataset can be implemented, and low pixel image can be processed to evaluate the classification.

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame’s total distortion, ensuring better flow rate allocation and enhancing the video frames’ reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame’s total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.

In a mobile ad hoc network, where nodes are deployed without any wired infrastructure and communicate via multihop wireless links, the network topology is based on the nodes' locations and transmission ranges. The nodes communicate through wireless links, with each node acting as a relay when necessary to allow multihop communications. The network topology can have a major impact on network performance. We consider the impact of number and placement of neighbours on mobile network performance. Specifically, we consider how neighbour node placement affects the network overhead and routing delay. We develop an analytical model, verified by simulations, which shows widely varying performance depending on source node speed and, to a lesser extent, number of neighbour nodes.