With the development of technology and computer systems, web services are used to develop business processes. Since a web service only performs a simple operation, web services composition has become important to respond to these business processes. In recent times, the number of existing web services has grown increasingly; therefore, similar services are presented increasingly. These similar web services are discriminated based on the various quality of service (QoS) parameters. These quality parameters include cost, execution time, availability, and reliability. In order to have the best QoS, each user should select a subset of services that presents best quality parameters. On the other hand, due to huge number of services, selecting web services for composition is an NP-hard optimization problem. This paper presents an efficient method for solving this problem using biogeography-based optimization (BBO). BBO is a very simple algorithm with few control parameters and effective exploit. The proposed method offers promising solutions to this problem. Evaluation and simulation results indicate efficiency and feasibility of the proposed algorithm. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Bian G.-B

Bozorgi S.M

Suraki M.Y

Hosseinabadi A.A.R

Shareh M.B.

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.

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VIT University

Soft Computing

A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

Fulltext

Sensors

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs. © 2005-2012 IEEE.

IEEE Transactions on Industrial Informatics

Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

Symmetry

A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines

In wireless sensor networks (WSNs), nodes have limited energy and cannot be recharged. In order to tackle this problem, clustering methods are employed to optimize energy consumption, gather data and also enhance the effective lifetime of the network. In spite of the clustering methods advantages, there are still some important challenges such as choosing a sensor as a cluster head (CH), which has a significant effect in energy efficiency. In clustering phase, nodes are divided into some clusters and then some nodes, named CH, are selected to be the head of each cluster. In typical clustered WSNs, nodes sense the field and send the sensed data to the CH, then, after gathering and aggregating data, CH transmits them to the Base Station. Node clustering in WSNs has many advantages, such as scalability, energy efficiency, and reducing routing delay. In this paper, several clustering methods are studied to demonstrate advantages and disadvantages of them. Among them, some methods deal with homogenous network, whereas some deals with heterogeneous. In this paper, homogenous and heterogeneous methods of clustering are specifically investigated and compared to each other. © 2017, Springer Science+Business Media, LLC.

Journal	Data powered by TypesetSoft Computing
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1432-7643
Open Access	0