Modelling and Analysis of Multi-objective Service Selection Scheme in IoT-Cloud Environment
01 Jan 2018-pp 63-77
TL;DR: An assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) and FBuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) which helps the users to select an optimal cloud where uncertainty and subjectivity are parameterized using triangular fuzzy members and is handled by using linguistic values is introduced.
Abstract: Internet of Things (IoT) is a heterogeneous ubiquitous network based upon modern computational intelligent techniques. A large scale IoT environment composed of thousands distributed entities and a number of multimedia smart devices. In recent years, due to the improvement of popularity and capability of smart mobile devices, Mobile Cloud Computing (MCC) gains a considerable attention in Internet of Things (IoT) environment. As there are variety of clouds that provides same services, it becomes quite difficult for users to choose an ideal cloud from a variety of clouds for migrating computationally intensive applications. So, selecting the optimal cloud among multiple alternatives which saves resource availability and execution time is a Multi-Criteria Decision Making (MCDM) issue. This chapter introduces an assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) which helps the users to select an optimal cloud where uncertainty and subjectivity are parameterized using triangular fuzzy members and is handled by using linguistic values. The proposed computational intelligence decision making model enables decision makers to better understand the whole evaluation process and thus provides more accuracy, systematic and efficient decision support tool.
Citations
More filters
TL;DR: A new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin detectors based on Internet of things (IoT) by using multi-criteria decision-making (MCDM).
Abstract: This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin detectors based on Internet of things (IoT) by using multi-criteria decision-making (MCDM). The novelty of this work is in the use of an evaluation matrix for the performance evaluation of real-time skin detectors that are based on IoT. Nevertheless, an issue with the performance evaluation of real-time skin detector approaches is the determination of sensible criteria for performance metrics and the trade-off amongst them on the basis of different colour spaces. An experiment was conducted on the basis of three phases. In the first phase, a real-time camera based on cloud IoT was used to gather different caption images. The second phase could be divided into two stages. In the first stage, a skin detection approach was developed by applying multi-agent learning based on different colour spaces. This stage aimed to create a decision matrix of various colour spaces and three groups of criteria (i.e. reliability, time complexity and error rate within a dataset) for testing and evaluating the developed skin detection approaches. In the second stage, Pearson rules were utilised to calculate the correlation between the criteria in order to make sure, either needs to use all of the criteria in decision matrix and the criteria facts that affect the behaviour of each criterion, in order to make sure that use all the criteria in evaluation as multidimensional measurements or not. In the third phase, the MCDM method was used by integrating between a technique in order of preference by similarity to the ideal solution and multi-layer analytic hierarchy process to benchmark numerous real-time IoT skin detection approaches based on the performed decision matrix from the second phase. Three groups of findings were obtained. Firstly, (1) statistically significant differences were found between the criteria that emphasise the need to use all of the criteria in evaluation. (2) The behaviour of the criteria in all scenarios was affected by the distribution of threshold values for each criterion based on the different colour spaces used. Therefore, the differences in the behaviour of criteria that highlight the use of the criteria in evaluation were included as multidimensional measurements. Secondly, an overall comparison of external and internal aggregation values in selecting the best colour space, namely the normalised RGB at the sixth threshold, was discussed. Thirdly, (1) the YIQ colour space had the lowest value and was the worst case, whereas the normalised RGB had the highest value and was the most recommended of all spaces. (2) The lowest threshold was obtained at 0.5, whereas the best value was 0.9.
63 citations
Cites methods from "Modelling and Analysis of Multi-obj..."
...10 indicates a reasonable comparison [98, 135]....
[...]
...This method is based on the values of the decision matrix created in the previous phase [98]....
[...]
TL;DR: This work proposes a framework that makes use of a multi-criteria decision making (MCDM) as a combination of known approaches under the names Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for conducting the selection process where QoS parameters of various component of IoT act as criteria.
Abstract: With the proliferation of a number of IoT-based service providers in the market, it would be difficult to select a suitable IoT service as per the requirement among the vast pool of available services showing similar capabilities. Quality-of-service (QoS) parameters that define a service may be used for doing an appropriate selection. Here we consider IoT as the composition of its three possible components: Things, communication entity and computing entity, and description of an IoT may include QoS parameters for each of these components. We propose a framework that makes use of a multi-criteria decision making (MCDM) as a combination of known approaches under the names Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for conducting the selection process where QoS parameters of various component of IoT act as criteria. We demonstrate the effectiveness of the proposed approach along with the sensitivity analysis for showing the robustness of the proposed framework.
23 citations
TL;DR: This paper proposes a new methodology and comprehensive criteria to help organizations to take an educated decision by applying multi-criteria analysis and stands out from previous work by including not only technical aspects, but economic and social criteria, providing a full view of the problem analyzed.
Abstract: Industry 4.0 is having a great impact in all smart efforts. This is not a single product but is composed of several technologies, one of them being Industrial Internet of Things (IIoT). Currently, there are very varied implementation options offered by several companies, and this imposes a new challenge to companies that want to implement IIoT in their processes. This challenge suggests using multi-criteria analysis to make a repeatable and justified decision, requiring a set of alternatives and criteria. This paper proposes a new methodology and comprehensive criteria to help organizations to take an educated decision by applying multi-criteria analysis. Here, we suggest a new original use of PROMETHEE-II with a full example from weight calculation up to IIoT platform selection, showing this methodology as an effective study for other organizations interested in selecting an IIoT platform. The criteria proposed stands out from previous work by including not only technical aspects, but economic and social criteria, providing a full view of the problem analyzed. A case of study was used to prove this proposed methodology and finds the minimum subset to reach the best possible ranking.
20 citations
TL;DR: A new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making is proposed.
Abstract: IoT is getting popular as it makes human life comfortable. The industry giants such as IBM, Microsoft, Cisco and Amazon have started offering IoT assistance in form of services. Numerous IoT applications exist today with different roles to play in day-to-day life. Because of application diversity and a good number of IoT service providers, it is difficult for IoT users to select the best one as per the requirement and expected quality of service, QoS. To address this, QoS metrics related to major IoT components, i.e., communication, computing and things, are designed to assess the alternative services. IoT users can express their requirements regarding QoS, while service providers exhibit their offerings. Because of three major IoT components, service selection is considered as multi-criteria group decision-making (MCGDM) problem. This work proposes a new MCGDM framework to rank the IoT services that considers rank reversal problem, judgments of decision makers in linguistic term and the uncertainty and risk-attitudinal characteristics in human decision-making. The proposed framework is validated by comparing it with an existing MCGDM model. A case study on IoT health-care application is provided besides the sensitivity analysis to demonstrate the effectiveness of the proposed framework.
18 citations
Cites background from "Modelling and Analysis of Multi-obj..."
...Both [16, 17] considered the IoT cloud services, whereas poor communication services may hamper the cloud services....
[...]
...[17] also considered cloud services selection as an important challenge in IoT and designed it as MCDM problem as in [16]....
[...]
TL;DR: The service selection mechanisms in the IoT are classified into centralized, decentralized, and hybrid classes and the detailed evaluation of these techniques brings a good suggestion for further studies.
Abstract: Internet of Things (IoT) describes embedded devices (things) with Internet-based connectivity, enabling them to receive and send data through the communication network. In addition, it provides infrastructure to let things have interacted with each other and people. This advantage of the IoT can increase reliability, sustainability, and efficiency by enhanced information access fashion. This technology can be used in various fields, such as environmental monitoring, home, and building automation and smart networks. Furthermore, the main aim of Service-Oriented Architecture (SOA) is to select the best services among a pool of services. These services can be selected statically or dynamically regarding the service functionalities and performance limitations. Since the performance of complex services is very important in many distributed domains. This study aims to systematically review the service selection mechanisms in the IoT. The service selection mechanisms are classified into centralized, decentralized, and hybrid classes. Also, the detailed evaluation of these techniques brings a good suggestion for further studies.
16 citations
Cites background from "Modelling and Analysis of Multi-obj..."
...[19] Lecture Notes on Data Engineering and Communications Technologies...
[...]
...[19] have discussed the integration of the IoT and cloud computing to achieve efficient computation offloading process....
[...]
...[19] • Fuzzy analytic hierarchy process • Good efficiency...
[...]
References
More filters
Book•
[...]
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
01 Jan 1985
TL;DR: Analytic Hierarchy Process (AHP) as mentioned in this paper is a systematic procedure for representing the elements of any problem hierarchically, which organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pairwise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy.
Abstract: This chapter provides an overview of Analytic Hierarchy Process (AHP), which is a systematic procedure for representing the elements of any problem hierarchically. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pair-wise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy. These judgments are then translated to numbers. The AHP includes procedures and principles used to synthesize the many judgments to derive priorities among criteria and subsequently for alternative solutions. It is useful to note that the numbers thus obtained are ratio scale estimates and correspond to so-called hard numbers. Problem solving is a process of setting priorities in steps. One step decides on the most important elements of a problem, another on how best to repair, replace, test, and evaluate the elements, and another on how to implement the solution and measure performance.
16,547 citations
TL;DR: The rating of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers and a vertex method is proposed to calculate the distance between two triangular fuzzyNumbers.
3,109 citations
Book•
01 Jan 1996
TL;DR: The Analytic Network Process is particularly useful to project the future of a group or company considering all the influences and risks: economic, social, political, technological, environmental, and others.
Abstract: Decision Making with Dependence and Feedback: The Analytic Network Process : the Organization and Prioritization of Complexity #370 pages #2001 #Thomas L. Saaty #Rws Publications, 2001 #0962031798, 9780962031793 #This book shows how to make decisions when alternatives depend on criteria, but also the criteria depend on the alternatives. It shows how to cope with dependence between different groups of people, goals and criteria. The Analytic Network Process is particularly useful to project the future of a group or company considering all the influences and risks: economic, social, political, technological, environmental, and others. Accompanying ANP software is under development. file download xaxotav.pdf
3,095 citations
Book•
01 Jan 1979
TL;DR: On MADM Methods Classification.
Abstract: I. Introduction.- II. Basic Concepts and Foundations.- 1. Definitions.- 1.1 Terms for MCDM Environment.- 1.2 MCDM Solutions.- 2. Models for MADM.- 2.1 Noncompensatory Model.- 2.2 Compensatory Model.- 3. Transformation of Attributes.- 3.1 Quantification of Fuzzy Attributes.- 3.2 Normalization.- 4. Fuzzy Decision Rules.- 4.1 Definition of Fuzzy Set.- 4.2 Some Basic Operations of Fuzzy Sets.- 5. Methods for Assessing Weight.- 5.1 Eigenvector Method.- 5.2 Weighted Least Square Method.- 5.3 Entropy Method.- 5.4 Linmap.- III. Methods for Multiple Attribute Decision Making.- 1. Methods for No Preference Information Given.- 1.1.1 Dominance.- 1.1.2 Maximin.- 1.1.3 Maximax.- 2. Methods for Information on Attribute Given.- 2.1 Methods for Standard Level of Attribute Given.- 2.1.1 Conjunctive Method (Satisficing Method).- 2.1.2 Disjunctive Method.- 2.2 Methods for Ordinal Preference of Attribute Given.- 2.2.1 Lexicographic Method.- 2.2.2 Elimination By Aspects.- 2.2.3 Permutation Method.- 2.3 Methods for Cardinal Preference of Attribute Given.- 2.3.1 Linear Assignment Method.- 2.3.2 Simple Additive Weighting Method.- 2.3.3 Hierarchical Additive Weighting Method.- 2.3.4 ELECTRE Method.- 2.3.5 TOPSIS.- 2.4 Methods for Marginal Rate of Substitution of Attributes Given.- 2.4.1 Hierarchical Tradeoffs.- 3. Methods for Information on Alternative Given.- 3.1 Methods for Pairwise Preference Given.- 3.1.1 LINMAP.- 3.1.2 Interactive Simple Additive Weighting Method.- 3.2 Method for Pairwise Proximity Given.- 3.2.1 Multidimensional Scaling with Ideal Point.- IV. Applications.- 1. Commodity Selection.- 2. Facility Location (Siting) Selection.- 3. Personnel Selection.- 4. Project Selection.- 4.1 Environmental Planning.- 4.2 Land Use Planning.- 4.3 R & D Project.- 4.4 Water Resources Planning.- 4.5 Miscellaneous.- 5. Public Facility Selection.- V. Concluding Remarks.- On MADM Methods Classification.- On Applications of MADM.- On Multiple Objective Decision Making (MODM) Methods.- On Multiattribute Utility Theory (MAUT).- A Choice Rule for MADM Methods.- A Unified Approach to MADM.- On Future Study.- VI. Bibliography.- Books, Monographs, and Conference Proceedings.- Journal Articles, Technical Reports, and Theses.
2,380 citations