Energy-aware redundancy-aware clustering in wireless sensor networks using Spined Loach Searching Optimization

Wireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient-based technique needs to be implemented in data transmission. The most widely used methods are clustering-based data transmission and energy efficient routing detection. In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bio-inspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced.     

Secure CLS and CL-AS schemes designed for VANETs

The Journal of Supercomputing - Vehicular Ad hoc Network (VANET) is a part of intelligent transport system facing the problem of limited bandwidth. Certificateless aggregate signature (CL-AS)...     

Android malware detection based on system call sequences and LSTM

As Android-based mobile devices become increasingly popular, malware detection on Android is very crucial nowadays. In this paper, a novel detection method based on deep learning is proposed to distinguish malware from trusted applications. Considering there is some semantic information in system call sequences as the natural language, we treat one system call sequence as a sentence in the language and construct a classifier based on the Long Short-Term Memory (LSTM) language model. In the classifier, at first two LSTM models are trained respectively by the system call sequences from malware and those from benign applications. Then according to these models, two similarity scores are computed. Finally, the classifier determines whether the application under analysis is malicious or trusted by the greater score. Thorough experiments show that our approach can achieve high efficiency and reach high recall of 96.6% with low false positive rate of 9.3%, which is better than the other methods.

     

Fluoranthene-2,3- and -1,5-diones are novel products from the bacterial transformation of fluoranthene

Fluoranthene is one of the predominant compounds found in soils and sediments contaminated with polycyclic aromatic hydrocarbons (PAH). Four bacterial strains isolated from PAH-contaminated soils transformed fluoranthene to a number of products during growth on phenanthrene, including the novel metabolites fluoranthene-2,3-dione (F23Q) and fluoranthene-1,5-dione (F15Q). Given the known toxicity and mutagenicity of F23Q, we focused on characterizing this metabolite with respect to its effects on the metabolism of other PAH. The yield of F23Q from fluoranthene ranged from 2% for Sphingomonas yanoikuyae R1 to greater than 20% for Pseudomonas stutzeri P16 and Bacillus cereus P21. None of the strains appeared capable of metabolizing F23Q any further. F23Q strongly inhibited phenanthrene removal by strain R1 but had a negligible to minor effect on phenanthrene degradation by the other organisms. At a concentration of 6.8 microM, F23Q also substantially inhibited the mineralization of benz[a]anthracene, benzo[a]pyrene (BaP), and chrysene by strain R1 as well as BaP mineralization by Pseudomonas saccharophila P15. Inhibition of BaP mineralization by strain P15 was still evident at an F23Q concentration of 0.68 microM. The inhibition of strain R1 by F23Q was explained in part by a cytotoxic effect, but results with strain P15 indicate that other mechanisms of inhibition occur. These findings suggest that quinones such as F23Q and F15Q have the potential to accumulate in PAH-contaminated systems and can inhibit the degradation of other PAH.     

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid’s coverage rate and energy consumption. Finally, each sensor nodes’ sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption