IPv6 mobility is an IETF standard that has added roaming capabilities of mobile node (MN). It allows MNs to travel from one network to another without any distraction in communication service. MNs register their current location to home stations and correspondent hosts via a process known as binding update.In IPv6 mobility, return routability protocol (RRP) is a standard procedure for updating the current location of MNs through binding update message to their communicants. However, RRP has several security threats and issues. Subsequently, RRP was integrated with identity-based encryption for improvement of security. Nevertheless, it suffers from some limitations such as inherent key escrow problem, lack of key revocation, high computational load and latency while providing security. Hence, this paper proposes a novel approach called optimised RRP using certificateless public key encryption to address these issues. The proposed protocol is simulated and validated using Automated Validation of Internet Security Protocols and Applications (AVISPA)
– a model checker. Finally, the simulation and numerical results illustrate the extent to which the proposed protocol surpasses the existing method in terms of enhanced security and significant reduction in communication payload with minimised latency.
Distributed data mining has played a vital role in numerous application domains. However, it is widely observed that data mining may pose a privacy threat to individual’s sensitive information. To address privacy problem in distributed association rule mining (a data mining technique), we propose two protocols,which are securely generating global association rules in horizontally distributed databases. The first protocol uses the notion of Elliptic-curve-based Paillier cryptosystem, which helps in achieving the integrity and authenticity of the messages exchanged among involving sites over the insecure communication channel. It offers privacy of individual site’s information against the involving sites and an external adversary. However, the collusion of two sites may affect the privacy of individuals. To address this problem, we incorporate Shamir’s secret sharing scheme in the second protocol. It provides privacy by preventing colluding sites and external adversary attack. We analyse both protocols in terms of fulfilling the privacy-preserving distributed association rule mining requirements.
The growth and use of semantic web has led to a drastic increase in the size, heterogeneity and number of ontologies that are available on the web. Correspondingly, scalable ontology matching algorithms that will eliminate the heterogeneity among large ontologies have become a necessity. Ontology matching algorithms generally do not scale well due to the massive number of complex computations required to achieve matching. One of the methods used to address this problem is the use of partition-based systems to reduce thematching space. In this paper, we propose a new partitioning-based scalable ontology matching system called PSOM2. We have designed a new neighbour-based intra-similarity measure to increase the quality of the clusterset formation for the partition-based ontology matching process. These sets of clusters or sub-ontologies are matched across the input ontologies to identify matchable cluster pairs, based on anchors that are efficiently discovered through a new light-weight linguistic matcher (EI-sub). However, in order to further increase the efficiency of the time-consuming anchor discovery process we have designed a Map Reduce-based EI-sub process where anchors are discovered in distributed and parallel fashion. Experiments on benchmark OAEI(Ontology Alignment Evaluation Initiative) large scale ontologies demonstrate that the new PSOM2 system achieves, on an average, 31% decrease in entropy of the clusters and 54.5% reduction in overall run time. Based on the experimental results, it is evident that the new PSOM2 achieves better quality clusters and a major reduction in execution time, leading to an effective and scalable ontology matching system.
A new set of promising rotation-invariant features based on radon and discrete cosine transform (DCT) is proposed for fingerprint matching. The radon and DCT of a tiny area in the region of core point of fingerprint image is computed. In the proposed method only 34% DCT coefficients are used for feature extraction. Competency of this approach is tested on standard databases, namely FVC2002 and FVC2004. This approach provides 70% genuine acceptance rate (GAR) at *0% false acceptance rate (FAR) and 95% GAR at 10% FAR on rotated and non-rotated databases, respectively. Experimental results prove that the proposedfeature extraction approach is rotation invariant.
Network security has become a concern with the rapid growth and expansion of the Internet. While there are several ways to provide security for communications at the application, transport, or network layers, the data link layer security has not yet been adequately addressed. Dynamic Host Configuration Protocol(DHCP) and Address Resolution Protocol (ARP) are link layer protocols that are essential for network operation. They were designed without any security features. Therefore, they are vulnerable to a number of attacks such asthe rogue DHCP server, DHCP starvation, host impersonation, man-in-the-middle, and denial of service attacks. Vulnerabilities in ARP and DHCP threaten the operation of any network. The existing solutions to secure ARPand DHCP could not mitigate DHCP starvation and host impersonation attacks. This work introduces a new solution to secure ARP and DHCP for preventing and mitigating these LAN attacks. The proposed solution provides integrity and authenticity for ARP and DHCP messages. Security properties and performance of the proposed schemes are investigated and compared to other related schemes.
In this work an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model the periodic performance of some multi-input single-output (MISO) processes, namely: brewery operations (case study 1) and soap production (case study 2) processes. Two ANFIS models were developed to model the performance of the two processes under study. The results of the study show that for brewery operations, ANFIS model 2 with a correlation coefficient of 0.9972, as against 0.9956 for ANFIS model 1, had a better correlation than anequivalent MAMDANI fuzzy model. On the order hand, for soap production process, ANFIS model 1 had better correlation with an equivalent MAMDANI model. Generally, there is a general agreement among the models onthe periodic performance of the processes. Thus, all the models show that for the brewery, the best performance was in the period 2010–2011 and the period 2008–2009 was the worst. Similarly, for the soap production process, the best performance was in 2011 and the worst in 2012. The results show that a combination of transfer function and ANFIS could be used effectively to model process performance.
In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers’ service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by an exact method is quite difficult and sometimes impossible if number of constraints is large enough (i.e., NP-hard), and solution time of a VRPHTW grows exponentially. As the size of the problem grows, a near optimal solution can be found using a heuristic method. A hierarchical approach consisting of two stages as
‘‘cluster-first route-second’’ is proposed. In the first stage, customers are assigned to vehicles using three different clustering algorithms (i.e., K-means, K-medoids and DBSCAN). In the second stage, a VRPHTW is solved using a MILP. The main contribution of the article is that the proposed hierarchical approach enables us to deal with a large size real problem and to solve it in a short time using the exact method. Finally, the proposed approach is employed on a supermarket chain. An instance of the algorithm is demonstrated to illustrate the applicability of the proposed approach and the results obtained are highly favourable.
Land cover change detection has been a topic of active research in the remote sensing community. Due to enormous amount of data available from satellites, it has attracted the attention of data mining researchers to search a new direction for solution. The Terra Moderate Resolution Imaging Spectrometer(MODIS) vegetation index (EVI/NDVI) data products are used for land cover change detection. These data products are associated with various challenges such as seasonality of data, spatio-temporal correlation, missing values, poor quality measurement, high resolution and high dimensional data. The land cover change detection has often been performed by comparing two or more satellite snapshot images acquired on different dates. The image comparison techniques have a number of limitations. The data mining technique addresses many challenges such as missing value and poor quality measurements present in the data set, by performing the pre-processing of data. Furthermore, the data mining approaches are capable of handling large data sets and also usesome of the inherent characteristics of spatio-temporal data; hence, they can be applied to increasingly immense data set. This paper stretches in detail various data mining algorithms for land cover change detection and each algorithm’s advantages and limitations. Also, an empirical study of some existing land cover change detection algorithms and results have been presented in this paper.
Conventionally, two AC side current sensors are needed in vector control of grid side converter for AC–DC bidirectional power conversion. The present paper proposes a technique where the control can be achieved with the use of only one AC side current sensor. The control principle utilises the information of unsensedsecond current sensor for its estimation, which is embedded and readily available in conventional control technique itself. In the proposed method, the grid side d–q axis reference currents of the current controllers are used for estimation of b-axis component of grid current, while the a-axis component of grid current is calculated by one AC side current sensor. Effect of voltage unbalance on the control is also studied in this paper. The proposed control is validated with detailed simulation and experimental observations for both steady-state andtransient conditions. The proposed control gives satisfactory performance.
A simple modified version of neuro-fuzzy controller (NFC) method based on single-input, reduced membership function in conjunction with an intuitive flux–speed decoupled feedback linearization (FBL) approach of induction motor (IM) model is presented in this paper. The proposed NFC with FBL remarkablysuppresses the torque and speed ripple and shows improved performance. Further, the modified NFC is tuned by genetic algorithm (GA) approach for optimal performance of FBL-based IM drive. Moreover, the GA searchesthe optimal parameters of the simplified NFC in order to ensure the global convergence of error. The proposed simplified NFC integrates the concept of fuzzy logic and neural network structure like a conventional NFC, butit has the advantages of simplicity and improved computational efficiency over the conventional NFC as the single input introduced here is an error (speed and torque) instead of two inputs, error and change in error, as in the conventional NFC. This structure makes the proposed NFC robust and simple as compared with conventional NFC and thus, can be easily applied to real-time industry application. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. Theeffectiveness of the proposed method using FBL of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using the proposed simple NFC as compared to the conventional NFC; rather, it showssuperior performance over PI-controller-based drive.
Turbulent water jet impingement on surfaces has several applications in cleaning processes and heat transfer equipment. This work aims to find the effect of variation in inlet jet Reynolds number on variation wall shear stress and pressure on surfaces encountered in equipment used in food processing industries, particularly in the dishwasher domain. Computational fluid dynamics simulation of turbulent water jet for Re [6000 is performed in ANSYS Fluent. Simulations are run using the volume of fluid Eulerian multi-phase model with thestandard k-e turbulence modeling. Discretisation is carried out by the implicit unsteady solver scheme, and the SIMPLE algorithm is chosen to solve the set of equations. Shear stress at Re = 6300 is validated with experimental results. There is drastic variation in the static pressure and wall shear stress with Reynolds number. Critical jet exit velocity required for effective cleaning of flat plate is found to be 3 m/s.
In this paper, a systematic analysis of different methods of d-ferrite estimation is carried out based on the well-known relationship between d-ferrite content and hot cracking in stainless steels. Additionally, the influence of certain chemical requirements on d-ferrite stabilization and their relationship to hot cracking isevaluated by the application of a deterministic algorithm based on stringency levels. The results obtained from the application of the stringency level method and prediction diagrams permit the selection of the best option among materials according to their standardized specifications. In addition, the advantage of this integrated method is that we can evaluate particular elemental influences and assign relative weightings to create a database for materials selection.
This study investigated the cutting performance of coated CC6050 and uncoated CC650 mixed ceramics in hard turning of hardened steel. The cutting performance was mainly evaluated by cutting force components and tool wear. The planning of experiments was based on Taguchi’s L36 orthogonal array. Theresponse surface methodology and analysis of variance were used to check the validity of multiple linear regression models and to determine the significant parameter affecting the cutting force components. Tool wear progressions and, hence, tool life, different tool wear forms and wear mechanisms observed for tools coated with TiN and uncoated mixed ceramics are presented along with the images captured by digital and electron microscope. Experimental observations indicate higher tool life with uncoated ceramic tools, which showsencouraging potential of these tools to hard turning of AISI H11 (50 HRC). Finally, tool performance indices are based on units which characterise machined cutting force components and wear when hard turning.
This paper presents the experimental investigations carried out on hand lay-up prototype multicellular glass fibre reinforced polymer (GFRP) composite bridge deck panels under static and fatigue loading. Various sustainability aspects with regard to GFRP structural members were discussed. The aspects include(i) social development; (ii) environmental protection; and (iii) economic development. The GFRP material properties were evaluated by using (i) micromechanics; (ii) simplified composite micromechanics equations(Chamis); (iii) carpet plots; and (iv) equations proposed by Tsai–Hahn. GFRP members with various cross sections were tested to decide the better performance under flexural loading and found that GFRP with hollowsection performs better. For the optimised cross-section dimensions, six multi-cellular GFRP composite bridge deck panels of size 1250 mm 9 333 mm 9 150 mm (l 9 b 9 d) were fabricated by hand lay-up process and tested for static and fatigue loading. It was observed from the experiment that during testing the bridge deck panel, no load shedding was observed even though the resin started cracking. At ultimate load, there was a loud cracking sound and the specimen load shedding occurred suddenly. Factor of safety for load and deflection wascomputed. From the fatigue experiments, it is observed that the percentage reduction in stiffness is approximately 12% for 500,000 cycles.
In closed loop control of PV systems it is important to model the small signal variation of PV panel array output with ambient conditions, namely irradiation and temperature. Changes in these conditions act as a disturbance to the system, but this disturbance needs to be reflected in terms of the quantity being controlled,which can be the PV panel current or the real power. In this work a linearised model is derived to relate the change in system input, namely: irradiance and temperature, with its output, namely: array current and power. The proposed model is experimentally verified with tests run on PV panels, when they are subjected to varying irradiation and temperature conditions in the laboratory. The experimental results confirm the accuracy of the linearised PV panel model.