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11 November 2018

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services †

,
,
and
1
Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
2
Institute of Computer Science, Masaryk University, 602 00 Brno, Czech Republic
3
Applied Mathematics and Computer Science Laboratory at Cadi Ayyad University, 40000 Marrakech, Morocco
*
Author to whom correspondence should be addressed.
Appl. Sci.2018, 8(11), 2220;https://doi.org/10.3390/app8112220
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Abstract

Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives.

1. Introduction

Internet of Things (IoT) paradigm is a noticeable emerging concept that has succeeded in becoming an integral part of our daily life by enabling any object around us to produce, connect, and transfer data via network technologies (i.e., 4G). The smart city is one of the concepts that use the ability of IoT technologies to build an immediate bridge between intelligent services and citizens [1]. Consequently, the smart city paradigm is currently gaining enormous popularity by giving birth to new efficient development of societies and industries. To support this approach further and improve the urban citizens quality of living, many critical infrastructures related to IoT data services are being considered to enhance and realize the vision of smart cities, such as smart healthcare [2], smart grid [3], smart transportation [4], management of energy [5], surveillance systems [6], or smart building [7]. Thus, the IoT technologies have supported various domains with specific needs to perform daily tasks, enhancing the quality of living, as well as the governance of smart cities.
Meanwhile, most available IoT services have become data-driven, which allows them to achieve better recommendations and predictions of future trends of events in smart cities, such as the recommendation of the best available car parking lot. For that reason, the IoT technologies need the incorporation of several techniques to meet the requirements of data, which include seamless real-time access, sharing, storing, processing, and analyzing data anywhere at any time. Cloud computing is one of the technologies that has been proposed to enrich the features of IoT applications by providing unlimited services represented by X as a service [8,9] (i.e., storage as a service) and designing suitable programming models for smart city IoT applications. Consequently, it is possible for IoT data to pave the way for sustainable development in smart cities and their related technologies.
Despite the ability of cloud platforms to provide sophisticated services, many IoT applications prefer to process data and make decisions locally rather than managing and treating IoT data remotely. Further, the access network to cloud platforms requires efficient and stable use of the bandwidth, which is not applicable to IoT-cloud connectivity which might be unreliable or poor in performance (throughput, latency). Consequently, it is beneficial to use the edge services (i.e., fog services) that are closer to IoT devices to improve the availability and scalability of data at the edge of computing [10]. Due to the advantages of edge computing (i.e., mobility support), IoT devices could access the proximate edge resources directly, adapt their request according to location awareness, process their computing tasks, and get results with minimum latency. However, cloud computing with its unlimited resources remains the best central platform to handle IoT tasks while edge computing is targeted to offer distributed local processing with minimized and predictable latency. That pushes us to ask what helps us to determine the best criteria that are driving the decision between a closer or a remote platform for processing IoT data.
In this paper, we address this challenge with the help of multi-criteria decision analysis (MCDA) [11,12] applied to the context of the edge/cloud paradigm. To this end, we first provide a summary of the most common technologies related to edge computing. Notably, we focus on the coordinated fog-to-cloud platforms since this kind of collaborative system integrates the required preferences for making the IoT devices self-contained and autonomous in real time. Then, we review the utilization of the MCDA methods in edge computing, since the MCDA approaches are very well known as a collection of techniques that offer comparison and comprehension of complex problems, enriching them with different forms of information. Next, we propose a middleware architecture to choose an adequate service that is recommended for processing IoT data. Following that, we describe step by step the application of the fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods to deal with the selection process. Finally, we conclude our work by identifying future perspectives.
The remainder of this article is organized as follows. Section 2 gives a brief overview of the current technologies and projects in Edge of computing. Next, it describes the application of MCDA in Edge environment. In Section 3, we describe the proposed architecture and its features. Then, in Section 4, we move to explain the steps followed to select one service among others by applying fuzzy similarity based on the TOPSIS method with a numerical example. In Section 5, we discuss the experience of comparing the ranking results of different popular fuzzy similarity techniques. Finally, we conclude the article with future perspectives in Section 6.

3. Preliminary Foglet–Middleware Design

In this section, we describe the preliminary architecture and design of our proposed solution, which is a middleware that allows choosing one service from diverse candidates or alternatives belonging to diverse fog and cloud platforms.

3.1. General Description of the Proposed Foglet

In this section, we propose a foglet as a middleware that aims at performing the interaction between the customers and fog-to-cloud providers (Figure 1). When a user delegates a task to fog-to-cloud platforms, it sends a request to the foglet, where, the handler module ensures the communication between the end-client and the foglet by intercepting a request and sending back the results. Then, the request is processed by the selection service manager that is responsible for the management of data in this architecture. In fact, this module will first check the available services that belong to centralized and decentralized platforms. To do that, it sends a notification to the service repository that contains a list of available QoSs related to the edge and cloud services. Then, a list of edge-cloud candidates is selected according to the user’s request and comes back to the selection service manager. Following this, the menu of selected services is sent to the module of rank services for evaluation of the quality of services according to defined criteria. At that point, the Fuzzy-TOPSIS MCDA methods will be applied to choose one alternative (or service) as an optimal solution. To do that, the MCDA methods will evaluate the alternatives in terms of criteria. Then, they will select a more appropriate service according to the request, which will be redirected by the scheduler to a selected fog/cloud service. After sending back the results, the scheduler, which ensures the communication between the foglet and services, will intercept the response and redirect it to the handler module that will return it to the end-user.
Figure 1. Foglet architecture.

3.2. Selection of Criteria

IoT technologies are becoming part of our everyday lives. For that reason, many works [55] have focused on improving connectivity and communication among numerous IoT devices as well as making their systems self-contained and autonomous by collecting, generating, and extracting accurate and timely information. To enhance further this vision, the benefits of the fog and cloud services are used effectively and efficiently for ensuring the sustainability of IoT services. However, the selection of the adequate services is a crucial task since it is based on a set of quantitative and qualitative parameters belonging to the characteristics of the fog-to-cloud platforms. This coordinated paradigm adopts two models, which are: the decentralized model that offers quick, even real-time, interaction and location-based services to closer smart objects, and the centralized model that is perfect at batch processing and provides the unlimited proper services necessary for the treatment of IoT data (Figure 2). Therefore, the choice of criteria for establishing preference relations between fog and cloud services depends on understanding the nature of the collaborative model.
Figure 2. Advantages of selecting decentralized and centralized services.
In this paper, we identify and analyze the criteria that have a direct influence on the selection phase and express clearly the most important characteristics of fog and cloud platforms. We consider ten criteria (Table 2), which are: usability, scalability, adaptability, performance, proximity, mobility, cost, network, availability, and security to be considered for this problem of fog/cloud service selection.
Table 2. Criteria.

3.3. Ranking Fog/Cloud Services

On having the architecture of the proposed solution and selection criteria, the next step is to devise the ranking that will be used within the Rank of services component of the foglet architecture (Figure 1). This section is devoted to the description of the process of rating the alternatives, while Section 4 guides the reader through a specific computation of the ranking tables, together with the validation of the results.
Based on Table 1 and our previous study [11] in which we described how the most certain and uncertain MCDA methods enhance the offloading process in mobile cloud computing as well as the selection of cloud services in general, we found that the TOPSIS is the most proposed ranking approach in the literature, since it is one of the famous fundamental multi-criteria decision-making ranking approaches in MCDA. The main features of TOPSIS [56] are chosen as the alternatives that simultaneously have the shortest distance from the ideal solution and the farthest distance from the anti-ideal solution. Also, among the selected works, we notice that the fuzzy method is prevalently used in mobile cloud offloading since this method is characterized by using linguistic variables to describe fuzzy terms that are then mapped to numerical variables. Moreover, it deals effectively with uncertain and imprecise information to solve real-world problems in different domains including cloud computing. Furthermore, the success of fuzzy and TOPSIS methods is measured by solving several MCDA problems, such as management of research and development projects as a portfolio of investments [57]. Accordingly, in this paper, we focus on using the benefits of these MCDA methods to solve the selection of services in the fog-to-cloud environment. Note that for understanding the space and time complexity of the solution, existing works on MCDAs can be consulted [12,56,58].
To do that, our computational procedure of the ranking consists of two parts. Part 1 applies fuzzy similarity [58] to deal with the imprecise or vague nature of linguistic assessment through a trapezoidal fuzzy number (Figure 3). Then, we calculate the fuzzy weight of each criterion identified for the decision problem. Part 2 applies the fuzzy weight of each criterion for the TOPSIS based model to rate and select the best candidate based on the score of each alternative (Figure 4). In the next section, the computation is demonstrated on a numerical example via MATLAB (see Appendix A).
Figure 3. A generalized trapezoidal fuzzy.
Figure 4. Flowchart of the service selection based on Fuzzy similarity TOPSIS.

4. Ranking Procedure Applied

Let us assume the following setup that will be used as the basis of the ranking demonstration performed in this section. First, consider four alternatives A1, A2, A3, and A4 as services operated on fog and cloud platforms. These could be for instance the available candidates that could reach a satisfactory agreement between the providers and end-users with specific needs. Then consider a committee of three decision makers D1, D2, and D3, formed to select the best alternative. We assume also the priority of importance ranked as: Availability(C1) > Adaptability(C2) > Usability(C3) > Performance(C4) > Cost(C5) > Scalability(C6) > Proximity(C7) > Mobility(C8) > Network(C9) > Security(C10) when choosing the best fog or cloud service. However, the priority of these ten criteria can be varied in other cases, for instance to reflect different preferences of different stakeholders. The whole computational procedure with a numerical example can be summarized as follows:
  • Form a committee of decision-makers (Table 5); the fuzzy rating of each decision-maker can be represented as trapezoidal fuzzy numbers (Figure 3); the linguistic terms for ranking the alternatives and evaluation criteria are shown in Table 3 and Table 4 successively.
    Table 3. Linguistic terms for ranking the criteria.
    Table 4. Linguistic terms for ranking the alternatives.
  • Present the importance weights of the criteria determined by the formed decision-makers D1, D2, and D3 (Table 5).
    Table 5. Importance weight of criteria from three decision-makers.
  • Rate the four alternatives by the formed decision-makers under the criteria (Table 6).
    Table 6. Ratings of the four alternatives by three decision-makers under ten criteria.
  • Convert the linguistic variables shown in Table 5 and Table 6 into trapezoidal fuzzy numbers to form a fuzzy decision matrix (Table 7).
    Table 7. Fuzzy decision matrix.
  • Form the aggregated fuzzy decision matrix (Table 8).
    Table 8. Aggregated fuzzy decision matrix.
  • Form the normalized aggregated fuzzy decision matrix (Table 9) and weighted normalized fuzzy decision matrix (Table 10).
    Table 9. Normalized aggregated fuzzy decision matrix.
    Table 10. Weighted normalized fuzzy decision matrix.
  • Determine the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) (Table 11).
    Table 11. Fuzzy positive and negative ideal solutions (FPIS & FNIS).
  • Calculate the similarity of each alternative from the fuzzy positive ideal solution (see Appendix A).
  • Rank the alternatives according to the average similarity value (Table 12).
    Table 12. Fuzzy-TOPSIS Results.
  • In this numerical example, the ranking order to the four alternatives is A1 > A2 > A4 > A3. Therefore, the alternative A1 is the best service to process the user’s request.

5. Evaluation and Discussion

To evaluate the ranking computed by our approach (based on the technique form [58]), this section compares the ranking of the alternatives to two other popular techniques [59,60] (see Appendix B and Appendix C) that also employ the fuzzy similarity method for solving given MCDA problems by capturing imprecision and inaccurate definition of a decision problem. Also, this section lists all the algorithms here for easy adoption by other research teams. For the evaluation, we adopted the linguistic variables for ranking the alternatives and evaluation criteria as shown in Table 3 and Table 4 successively, and we repeated the same calculation steps described in the previous section for [59,60]. Then, we conducted MATLAB simulation to test the results and compare them with [58] (see Appendix B and Appendix C). As shown in Figure 5, all the applied methods select A1 as the best alternative for processing the user request. However, the ranking order of the alternatives is not identical because of the difference in the Fuzzy positive ideal solution and closeness coefficient calculation (Figure 6 and Figure 7). Yet, the ranking of the candidates is overall well aligned.
Figure 5. Ranking the alternatives according to [58,59,60].
Figure 6. Calculation of Fuzzy positive ideal solution according to [58,59,60].
Figure 7. Calculation of closeness coefficient according to [58,59,60].
Now consider a use case, where a user does not want to use the best-selected alternative due to its high price, for example, and wants to select the second-best alternative according to the proposed ranking list. In this case, A2 would be the selected candidate according to [58,60], while A3 would be selected by [59] (Figure 5). Yet, in this dilemma situation, how could the provider of services identify and determine the best-ranking list of the alternatives and, then, recommend another service to the end-client? and what will be the criteria to select the right one? In this case, it would be very interesting to provide more detailed guidance on the selection among distinct MCDA approaches, which would be an interesting topic for further study that could examine the uncertainty and imprecision issues in multicriteria decision-making for determining the best-ranking list of alternatives.
In fact, the precision of results based on the application of an optimal cloud (or edge) service is the key to successful IoT indoor and outdoor applications. To do that, the selection of cloud or edge services-based MCDA approaches is an essential step that could aid millions of people, who are using IoT devices, for tracking their daily activities. Besides, it could help the providers of services to collect IoT data adequately and process it to extract useful information for improving and monitoring local conditions of IoT applications. Particularly, by 2025, it will be more challenging to process the collected IoT data, since IoT will remarkably exceed 100 billion connected devices [61]. Consequently, this big IoT innovation will create many significant challenges such as how to ensure the quality aspects of IoT data (i.e., selection, verification, and validation of data), save the battery of sensors, optimize routing and prediction of nodes, etc. To face these issues, the providers of services must now be experts not only in the development of applications for IoT paradigm but also in the investigation of the user’s feedback for the available services that would be accounted for effective utilization of cloud/edge services. In this context, the MCDA approaches could help the providers of services to understand and clarify the users’ preferences, consider each decision criterion, and support communication between them. However, the current shift from service orientation to user orientation in recent innovations, like in the smart city domain, will keep introducing decision-support challenges (i.e., a lack of connection between criteria scales [62]) in designing recommendation services in the edge and cloud environments, stemming from diverging recommendations by different approaches. Accordingly, more attention needs to be paid to MCDA best practices since every progress will be of high value for the areas of academia, research, and industry.

6. Conclusions

Internet of Things (IoT) is a promising technology that uses self-adaptation and self-organization to support the growth of services in smart cities. Recently, the fog-to-cloud paradigm has started to emerge as a means to facilitate the communication among various IoT sensor nodes, supporting the adaptation of services based on the current context of IoT devices. As the selection of the best fitting services and their configuration is a difficult task, this paper proposes a middleware to facilitate the selection of services in fog-to-cloud platforms to maximize their benefits for the end user. In the work, we employed the MCDA algorithm based on fuzzy similarity and TOPSIS methods to deal with incomplete and imprecise information and introduced a technique for ranking of candidates during the selection process in the face of missing information.
In the future, we aim at further improving the prototypical implementation with more complex demonstration of the efficiency of the proposed solution. Moreover, we plan to analyze the complexity of the MCDA technique used in this paper compared to the other MCDA methods like the VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) method, and study in which situations the recommended ranking by different techniques diverges and why. Also, we will include the identification of additional criteria which may link further the fog and cloud services, increase the ranking score of MCDA methods, and ensure the sustainability and reliability of services in smart cities.

Funding

The work was supported from ERDF/ESF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Similarity of Fuzzy Positive Ideal Solution Using MATLAB

function Similarity =fuzzySimilarity (A,B,n)
% n: number of criteria
%A: Weighted normalized fuzzy decision matrix
%B: fuzzy positive ideal solution
for i=1:n
P_A=sqrt((A(1)-A(2))^2+A(5)^2)+sqrt((A(3)-A(4))^2+A(5)^2)+(A(3)-A(2))+(A(4)-A(1));
P_B=sqrt((B(1)-B(2))^2+B(5)^2)+sqrt((B(3)-B(4))^2+B(5)^2)+(B(3)-B(2))+(B(4)-B(1));
minmax=(min(P_A,P_B)+min(A(5),B(5)))/(max(P_A,P_B)+max(A(5),B(5)));
S1=1-sum(abs(A(1:4)-B(1:4)))/4;
Similarity (i)=S1*minmax;
End

Appendix B. Similarity of Fuzzy Positive Ideal Solution Using MATLAB

function Similarity =fuzzySimilarity (A,B,n)
% n: number of criteria
%A: Weighted normalized fuzzy decision matrix
%B: fuzzy positive ideal solution
for i=1:n
PA=sqrt((A(1)-A(2))^2+A(5)^2)+sqrt((A(3)-A(4))^2+A(5)^2)+(A(3)-A(2))+(A(4)-A(1));
PB=sqrt((B(1)-B(2))^2+B(5)^2)+sqrt((B(3)-B(4))^2+B(5)^2)+(B(3)-B(2))+(B(4)-B(1));
  aA=1/2*(A(5)*(A(3)-A(2)+A(4)-A(1)));
  aB=1/2*(B(5)*(B(3)-B(2)+B(4)-B(1)));
temp=(min(PA,PB)/max(PA,PB))*(min(aA,aB) + min(A(5),B(5)))/(max(aA,aB) + max(A(5),B(5)));
Similarity (i)=(1-(sum(abs(A(1:4)-B(1:4))))/4)*temp;
end

Appendix C. Similarity of Fuzzy Positive Ideal Solution Using MATLAB

function Similarity =fuzzySimilarity (A,B,n)
% n: number of criteria
%A: Weighted normalized fuzzy decision matrix
%B: fuzzy positive ideal solution
for i=1:n
if A(1)==A(4)
  Ya=A(5)/2;
else  
Ya=A(5)*((A(3)-A(2))/(A(4)-A(1))+2);
end
if B(1)==B(4)
 Yb=B(5)/2;
else
  Yb=B(5)*((B(3)-B(2))/(B(4)-B(1))+2);
end
Xa=(Ya*(A(3)+A(2))+(A(4)+A(1))*(A(5)-Ya))/2*A(5);
Xb=(Yb*(B(3)+B(2))+(B(4)+B(1))*(B(5)-Yb))/2*B(5);
Sa=A(4)-A(1);
Sb=B(4)-B(1);
if (Sa+Sb)/2==0
  sasb=0;
else
  sasb=1;
end
temp=(1-abs(Xa-Xb))^(sasb)*min(Ya,Yb)/max(Ya,Yb);
S1=1-sum(abs(A(1:4)-B(1:4)))/4;
Simil4=S1*temp;
Similarity (i)=S1*temp;
end

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