The Implementation of Identity Based Routing Protocol in IOT Environment

Identity routing itself deals with the addresses that refers to only the identity of the host and not its location. Internet of things on the other hand is a concept that allows the connection of device via internet as well as communication of this devices with the owner. Information is transferred back and forth between devices and their respective owner. Singling out a specific device and establishing communication is on the basis of routing principles. The implementation of this concept especially in our everyday life would mean digitalizing ones home to better function more efficiently at work. Keywords:Routing, Identity based, Internet of Things, Address, Topology, Communication, Connection, Information, Devices and Host. DOI: 10.7176/CEIS/10-2-03 Publication date:March 31st 2019

A New Sparse Representation Algorithm for 3D Human Pose Estimation

This paper addresses the problem of recovering 3D human pose from single 2D images using Sparse Representation. While recent Sparse Representation (SR) based 3D human pose estimation methods have attained promising results estimating human poses from single images, their performance depends on the availability of large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but it is relatively easy to acquire a large amount of unlabeled data. Moreover, all SR based 3D pose estimation methods only consider the information of the input feature space and they cannot utilize the information of the pose space. In this paper, we propose a new framework based on sparse representation for 3D human pose estimation which uses both the labeled and unlabeled data. Furthermore, the proposed method can exploit the information of the pose space to improve the pose estimation accuracy. Experimental results show that the performance of the proposed method is significantly better than the state of the art 3D human pose estimation methods

A Combination Method of Centrality Measures and Biological Properties to Improve Detection of Protein Complexes in Weighted PPI Networks

Introduction: In protein-protein interaction networks (PPINs), a complex is a group of proteins that allows a biological process to take place. The correct identification of complexes can help better understanding of the function of cells used for therapeutic purposes, such as drug discoveries. One of the common methods for identifying complexes in the PPINs is clustering, but this study aimed to identify a new method for more accurate identification of complexes. Method: In this study, Yeast and Human PPINs were investigated. The Yeast datasets, called DIP, MIPS, and Krogan, contain 4930 nodes and 17201 interactions, 4564 nodes and 15175 interactions, and 2675 nodes and 7084 interactions, respectively. The Human dataset contains 37437 interactions. The proposed and well-known methods have been implemented on datasets to identify protein complexes. Predicted complexes were compared with the CYC2008 and CORUM benchmark datasets. The evaluation criteria showed that the proposed method predicts PPINs with higher efficiency. Results: In this study, a new method of the core-attachment methods was used to detect protein complexes enjoying high efficiency in the detection. The more precise the detection method is, the more correct we can identify the proteins involved in biological process. According to the evaluation criteria, the proposed method showed a significant improvement in the detection method compared to the other methods. Conclusion: According to the results, the proposed method can identify a sufficient number of protein complexes, among the highest biological significance in functional cooperation with proteins

Modeling Timed Elaborate Requirements in Service-Oriented

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Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

Abstract Some types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets. Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results

Making real-time systems fault tolerant: a specification-based approach

501-509To make an event-triggered real-time system safe in application layer, this study presents a specification-based run-timeverification (RV) and fault tolerance approach in following steps: i) System is isolated from its environment by modeling interactionbetween them; ii) Considering safety requirements violation, observation-verification-tolerance rules are systematicallyobtained; and iii) Rules are weaved into control software (called software instrumentation) by an automatic way. For effectiveness,proposed approach is applied to classic and real-time Railroad Crossing Control System (RCCS)

Query processing optimization in broadcasting XML data in mobile communications

Todays, XML as a de facto standard is used to broadcast data over mobile wireless networks. In these networks, mobile clients send their XML queries over a wireless broadcast channel and recieve their desired XML data from the channel. However, downloading the whole XML data by a mobile device is a challenge since the mobile devices used by clients are small battery powered devices with limited resources. To meet this challenge, the XML data should be indexed in such a way that the desired XML data can be found easily and only such data can be downloaded instead of the whole XML data by the mobile clients. Several indexing methods are proposed to selectively access the XML data over an XML stream. However, the existing indexing methods cause an increase in the size of XML stream by including some extra information over the XML stream. In this paper, a new XML stream structure is proposed to disseminate the XML data over a broadcast channel by grouping and summarizing the structural information of XML nodes. By summarizing such information, the size of XML stream can be reduced and therefore, the latency of retrieving the desired XML data over a wirless broadcast channel can be reduced. The proposed XML stream structure also contains indexes in order to skip from the irrelevant parts over the XML stream. It therefore can reduce the energy consumption of mobile devices in downloading the results of XML queries. In addition, our proposed XML stream structure can process different types of XML queries and experimental results showed that it improves the performace of XML query processing over the XML data stream compared to the existing research works in terms of access and tuning times

A Multi-Objective Optimization Model for Data-Intensive Workflow Scheduling in Data Grids

The concept of workflow is used for modeling many of the data-intensive scientific applications executed on data grids. A Workflow is a series of interdependent tasks during which data is processed by different tasks. Scheduling the workflows in the grids is the process of assigning tasks to appropriate resources with the aim of achieving goals such as reducing workflow completion time while considering the data dependencies between the tasks. Data access time, processing time, and waiting time together constitute task completion time in the grids. Workflow scheduling aims to optimize these parameters in such a way that the workflow completion time decreases, and the system efficiency improves. In this paper, a scheduling model based on multi-objective optimization is proposed for scheduling data-intensive workflows in data grids. The scheduling model aims to optimize data communication cost, waiting time, and tasks processing time while considering data dependencies between the tasks. The model defines the data communication cost in terms of data transfer time in various communications between nodes (intra-and inter-cluster communications). This study uses four different Multi-Objective Evolutionary Algorithms (MOEAs) as well as Random Search (RS) algorithm to implement the proposed scheduling model. Convenient coding mechanisms for representing chromosomes, compatible crossover and mutation operators were also designed. Simulation results of the proposed scheduling model using different optimization algorithms are presented. The results are then assessed and compared based on different quality indicators