Numerous tiny sensors are restricted with energy for the wireless sensor networks since most of them are deployed in harsh environments, and thus it is impossible for battery re-change. Therefore, energy efficiency becomes a significant requirement for routing protocol design. Recent research introduces data fusion to conserve energy; however, many of them do not present a concrete scheme for the fusion process. Emerging machine learning technology provides a novel direction for data fusion and makes it more available and intelligent. In this article, we present an intelligent data gathering schema with data fusion called IDGS-DF. In IDGS-DF, we adopt a neural network to conduct data fusion to improve network performance. First, we partition the whole sensor fields into several subdomains by virtual grids. Then cluster heads are selected according to the score of nodes and data fusion is conducted in CHs using a pretrained neural network. Finally, a mobile agent is adopted to gather information along a predefined path. Plenty of experiments are conducted to demonstrate that our schema can efficiently conserve energy and enhance the lifetime of the network. © The Author(s) 2019.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Wang J

Gao Y

Liu W

Kim H.-J.

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

International Journal of Distributed Sensor Networks

Wireless sensor networks (WSN) have deeply influenced the working and living styles of human beings. Information security and privacy for WSN is particularly crucial. Cryptographic algorithms are extensively exploited in WSN applications to ensure the security. They are usually implemented in specific chips to achieve high data throughout with less computational resources. Cryptographic hardware should be rigidly tested to guarantee the correctness of encryption operation. Scan design improves significantly the test quality of chips and thus is widely used in semiconductor industry. Nevertheless, scan design provides a backdoor for attackers to deduce the cipher key of a cryptographic core. To protect the security of the cryptographic system we first present a secure scan architecture, in which an automatic test control circuitry is inserted to isolate the cipher key in test mode and clear the sensitive information at mode switching. Then, the weaknesses of this architecture are analyzed and an enhanced scheme using concept of test authorization is proposed. If the correct authorization key is applied within the specific time, the normal test can be performed. Otherwise, only secure scan test can be performed. The enhanced scan scheme ensures the security of cryptographic chips while remaining the advantages of scan design.

Sensors

Securing Cryptographic Chips against Scan-Based Attacks in Wireless Sensor Network Applications

The smart grid solves the growing load demand of electrical customers through two-way real-time communication of electricity supply and demand sides and home energy management system (HEMS). However, these technical features also bring network security risks to the real-time price signal of the smart grid. The real-time price attack (RTPA) can maliciously raise the real-time price in smart meter, resulting in an increase in electrical customers load demand, causing the extensive damage to the power transmission lines due to overload. In this paper, we based on the behavioral relationship between load demand of electrical customers and real-time price of electricity suppliers (ES), defined the game relationship between RTPA, ES, and electrical customers, established a price elasticity of electricity demand (PEED) model, and proposed a defensive strategy of real-time price attack based on multiperson zero-determinant strategy (MPZDS). The experimental results show that the combination of MPZDS to some extent cut the expected load demand of electrical customers and protect the safety of power transmission lines.

Security and Communication Networks

Research on Defensive Strategy of Real-Time Price Attack Based on Multiperson Zero-Determinant

A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.

An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks

For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution algorithm based on dual-channel convolutional neural networks (DCCNN). The novel structure of the network model was divided into a deep channel and a shallow channel. The deep channel was used to extract the detailed texture information from the original image, while the shallow channel was mainly used to recover the overall outline of the original image. Firstly, the residual block was adjusted in the feature extraction stage, and the nonlinear mapping ability of the network was enhanced. The feature mapping dimension was reduced, and the effective features of the image were obtained. In the up-sampling stage, the parameters of the deconvolutional kernel were adjusted, and high-frequency signal loss was decreased. The high-resolution feature space could be rebuilt recursively using long-term and short-term memory blocks during the reconstruction stage, further enhancing the recovery of texture information. Secondly, the convolutional kernel was adjusted in the shallow channel to reduce the parameters, ensuring that the overall outline of the image was restored and that the network converged rapidly. Finally, the dual-channel loss function was jointly optimized to enhance the feature-fitting ability in order to obtain the final high-resolution image output. Using the improved algorithm, the network converged more rapidly, the image edge and texture reconstruction effect were obviously improved, and the Peak Signal-to-Noise Ratio (PSNR) and structural similarity were also superior to those of other solutions.

Applied Sciences

Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy. © 2018 by the authors.

Journal	Data powered by TypesetInternational Journal of Distributed Sensor Networks
Publisher	Data powered by TypesetSAGE Publications
ISSN	1550-1477
Open Access	Yes