Security and key management in IoT‐based wireless sensor networks: An authentication protocol using symmetric key

Wireless sensor networks (WSN) consist of hundreds of miniature sensor nodes to sense various events in the surrounding environment and report back to the base station. Sensor networks are at the base of internet of things (IoT) and smart computing applications where a function is performed as a result of sensed event or information. However, in resource‐limited WSN authenticating a remote user is a vital security concern. Recently, researchers put forth various authentication protocols to address different security issues. Gope et al presented a protocol claiming resistance against known attacks. A thorough analysis of their protocol shows that it is vulnerable to user traceability, stolen verifier, and denial of service (DoS) attacks. In this article, an enhanced symmetric key‐based authentication protocol for IoT‐based WSN has been presented. The proposed protocol has the ability to counter user traceability, stolen verifier, and DoS attacks. Furthermore, the proposed protocol has been simulated and verified using Proverif and BAN logic. The proposed protocol has the same communication cost as the baseline protocol; however, in computation cost, it has 52.63% efficiency as compared with the baseline protocol.     

Discovering and tracking query oriented active online social groups in dynamic information network

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.

     

Continuous microwave assisted flow synthesis and characterization of calcium deficient hydroxyapatite nanorods

Synthetic calcium deficient hydroxyapatite (CDHA) nanorods (<100?nm) were rapidly prepared with the help of a new continuous microwave assisted flow synthesis (CMFS) reactor in 5?min only from aqueous solution of calcium hydroxide and orthophosphoric acid at pH 8.5. The effect of various reaction parameters like, pH, concentration, temperature, residence time, degree of crystallinity and particle surface area were studied in detail. The phase purity, particle size and morphology of the powder samples were characterised by techniques such as X-ray diffraction analysis, transmission electron microscopy, scanning electron microscopy and FTIR and Raman spectroscopy. With the help of X-ray photoelectron spectroscopy, the chemical analysis was completed. Measurements were taken into account to estimate the particle size following the dynamic light scattering. The results showed that the employed synthesis procedure offered an efficient and economical route to achieve high quality nano-sized products with suitable size and low level of impurities.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.     

An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.

     

High resolution target localization using rotating linear array radar

In this paper, an algorithm is proposed for detection and joint estimation of parameters of multiple targets using rotating antenna array. This paper is sequel to our previous work addressing a two element antenna array only. Joint estimation of number of targets, the targets’ range, Doppler and their directions of arrivals is carried while the effects of antenna rotation are compensated for a multi element linear array. The effectiveness of the algorithm is verified by the simulation results carried out for an eight element array. The proposed algorithm is able to resolve targets with same range and Doppler. The computations of the proposed algorithm are calculated and comparison is also given with other algorithms.

     

Sustainable Framework for Smart Transportation System: A Case Study of Karachi

In this paper, a framework of smart transportation system is proposed, aiming to address the transportation problem in Karachi city. In modern day world, the mega cities and urban areas are on the edge of transformation into smart cities. With the advancement of engineering and technology, smart cities are designed to integrate and utilize these scientific innovations to provide smart solutions and social innovations for sustainable infrastructure, thus they are able to provide its resident highest quality of life by utilizing its resources effectively. One of the major application of smart cities is the Smart Transportation System, which provides safer, quick, environment friendly service to the residents. Thus, this study highlights the current traffic situation of Karachi and propose a framework to transform it into a smart transportation system. In order to have a smart transportation system, it is necessary to have in-depth knowledge and information about the city dynamics and its traffic related issues. Therefore, this study also highlights current traffic situation of Karachi, its road conditions and capacity, vehicles condition, alternate mean of transport (other than road-based system) and its present condition, and finally proposes a framework to develop a smart transportation system while keeping in mind the aforesaid traffic problems.

     

Compact and High Speed Architectures of KASUMI Block Cipher

Wireless Personal Communications - This paper proposes highly compact and high speed hardware architectures of 64-bit KASUMI block cipher for wide range of wireless applications. A novel...     

An effective method for classification with missing values

Classification is one of the most important tasks in machine learning with a huge number of real-life applications. In many practical classification problems, the available information for making object classification is partial or incomplete because some attribute values can be missing due to various reasons. These missing values can significantly affect the efficacy of the classification model. So it is crucial to develop effective techniques to impute these missing values. A number of methods have been introduced for solving classification problem with missing values. However they have various problems. So, we introduce an effective method for imputing missing values using the correlation among the attributes. Other methods which consider correlation for imputing missing values works better either for categorical or numeric data, or designed for a particular application only. Moreover they will not work if all the records have at least one missing attribute. Our method, Model based Missing value Imputation using Correlation (MMIC), can effectively impute both categorical and numeric data. It uses an effective model based technique for filling the missing values attribute wise and reusing then effectively using the model. Extensive performance analyzes show that our proposed approach achieves high performance in imputing missing values and thus increases the efficacy of the classifier. The experimental results also show that our method outperforms various existing methods for handling missing data in classification.     

Preparation and characterization of PPRC/EPDM thermoplastic vulcanizates: A comparative study

Thermoplastic vulcanizates (TPVs) of polypropylene random copolymer (PPRC) with ethylene–propylene diene monomer rubber (EPDM) were prepared through in situ and ex situ compatibilization techniques. Silanized silica as nanofiller was incorporated in the ex situ compatibilized TPVs. Mechanical properties were measured for different formulated TPVs with increasing loading of EPDM from 10 to 30 phr. A comparative data is generated to discuss the effect of two compatibilization techniques and addition of silica filler in the TPVs. The degradation studies were also carried out to check the stability of the blends under harsh ultraviolet environment. The in situ compatibilized TPVs provided better overall mechanical properties. Moreover, it is worth mentioning that the properties of ex situ compatiblized TPVs were enhanced by incorporation of silanized silica filler. Silanized silica filler helped in reducing the die swell significantly. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46726.