Fuzzy Approach for Locating Sensors in Industrial Internet of Things

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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science (2018) 000–000 Procedia Computer Science 16000 (2019) 772–777

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The International Workshop on Emerging Networks and Communications The International Workshop on Emerging Networks and Communications (IWENC 2019) (IWENC 2019) November 4-7, 2019, Coimbra, Portugal November 4-7, 2019, Coimbra, Portugal

Fuzzy Fuzzy Approach Approach for for Locating Locating Sensors Sensors in in Industrial Industrial Internet Internet of of Things Things

Sarah El Hamdia,b,∗ , Mustapha Oudaniaa , Abdellah Abouabdellahbb , Anass Sebbaraa Sarah El Hamdia,b,∗, Mustapha Oudani , Abdellah Abouabdellah , Anass Sebbar a International University of Rabat, TICLAB, Morocco

a International University of Rabat, TICLAB, Morocco b ENSA Kenitra ,Ibno Tofail University, MOSIL, Morocco b ENSA Kenitra ,Ibno Tofail University, MOSIL, Morocco

Abstract Abstract Nowadays, in this era of a data driven thinking and reflection, data mining and data analysis are keys to any business survival Nowadays, in this era of a data driven reflection, miningtechnology and data analysis are keys to anya business survival in a competitive conjectural market. Thethinking internetand of things is andata emerging that manages to create path for the new in a competitive conjectural market.system. The internet things istechnology an emerging technology that manages to create path for the new generation of industrial production This of advanced is requirement to the proliferation of aSmart factories, it generation of industrial production system. This of advanced is requirement to the proliferation of resources Smart factories, it represents the best tool to help this new concept plants totechnology organize themselves and optimize the available and their represents the The best purpose tool to help thispaper new concept of plantsatoproposal organizefor themselves and optimize the available resourcesinternet and their consumption. of this is two-pronged; an architectural framework of the industrial of consumption. The purpose formulation of this paperbased is two-pronged; a proposal for an the shop industrial internetinto of things, and a mathematical on fuzzy logic to determine thearchitectural ideal locationframework of sensors of at the floor taking things, and a mathematical formulation based on fuzzy logic to determine the ideal location of sensors at the shop floor taking into consideration several restrictions. consideration several restrictions. c 2019  2018 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © c 2018  The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND This is an open access article under the CC BY-NC-ND license license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility responsibility of ofthe theConference ConferenceProgram Program Chairs. Chairs. Peer-review under responsibility of the Conference Program Chairs. Keywords: I2oT, Architecture, Fuzzy Theory; Keywords: I2oT, Architecture, Fuzzy Theory;

1. Introduction 1. Introduction The Internet of Things (IoT) enabled manufacturing companies to indoctrinate industry 4.0 initiatives, since it is The Internet of of Things (IoT) companies indoctrinate industry 4.0 initiatives, since itauis considered a pillar the back endenabled layer ofmanufacturing the new paradigm [1]. Thetofourth revolution is imagined as an industrial considered a pillar of the back end layer of the new paradigm [1]. The fourth revolution is imagined as an industrial automation, smart manufacturing, smart factories, as it employs artificial intelligence and wireless technology. Industrial tomation, manufacturing, as it aspect, employsitsartificial intelligence wireless technology. Internet ofsmart Things (I2oT) is IoTsmart linkedfactories, to industrial goal is to ensure theand productivity growth, andIndustrial enhance Internet of Things (I2oT) is IoT linked to industrial aspect, its goal is to ensure the productivity growth, and enhance the computing equipment to render them proficient [2]. The concept of IoT was initially introduced by Kevin Ashton the to about renderthe them The concept IoT was initially introduced by at Kevin Ashton as acomputing title to hisequipment presentation useproficient of RFID[2]. technology in theofcompany supply chain in 1999 Procter and as a title to his presentation about the use of RFID technology in the company supply chain in 1999 at Procter and Gamble. The general idea was to capacity computers to sense, observe, and understand the world without the human Gamble. The general idea was to capacity computers to sense, observe, and understand the world without the human ∗ ∗

Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000. Corresponding Tel.: +0-000-000-0000 ; fax: +0-000-000-0000. E-mail address:author. [email protected] E-mail address: [email protected] c 2018 The Authors. Published by Elsevier B.V. 1877-0509  c 2018 1877-0509  Thearticle Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 ©under 2019 Thearticle Authors. Published by Elsevier B.V. This is an open access under the Conference CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review responsibility of the Program Chairs. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs. Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2019.11.012



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El Hamdi et al. / Procedia Computer Science 00 (2018) 000–000 Table 1: Brief Comparison of Reference Architecture.

Architecture

Details

RAMI

- Started in Germany as a result of Industry 4.0 emergence. - Dedicated to IoT standards of Smart Factories.

IIRA

- Founded by the Industrial Internet Consortium lead by Cisco in US. - Contribute to a larger consideration to Industry.

IoT-A

- Most spread and basic one. - Delivers an accurate architecture based on information perspectives.

Focus - Life Cycle of the product. - IIoT Value stream. - Business - Monitoring of operations - Optimization Generic of the informatics.

intervention [3]. The integration of the IoT technology needs new design principles and personalized features, since the technology will re-orient business models, will change the role of the customer, opens the opportunity of tracking every object through its whole cycle life, generates information which may be critical and in real time [4]. This paper is organized as the follows. In Section 2, we will describe the different reference architectures on I2oT. Then in Section 3, we will briefly describe the work done in these 30 years on fuzzy logic and the interest of this approach to solve the problem of sensor location layout. The Sink Location and Routing Problem (SLRP), as introduced in the literature is presented in Section 4. We introduce and solve the SLRP with fuzzy restrictions on the number of located sinks in Section 5 and we give some concluding remarks in the last section. 2. Industrial IOT architectures I2oT integration success depends on the business models, network valorization and creation of an adequate IoT ecosystem. The ecosystem is a set of assets that facilitates interoperability, and implementation of the technology; To simplify the development of the solution, some reference architecture were agreed upon based on a common framework and concepts [5]. Given the fact that the recent proliferation of smart devices has resulted in extravagant generation of data and services different IoT architectures have been recently proposed [6]. Furthermore, in an I2oT architecture, having an ERP is an important step to achieve an efficient industrial system [7, 8]. Three main architectures whose objective is interoperability, simplified development and easy implementation, will be briefly compared in Table 1. The first architecture is labeled RAMI, abbreviation of Reference Architecture Model Industry, clearly linked to industry 4.0, it is an architecture that adds manufacturing and logistics details to IoT technology. The second architecture IIRA stands for Industrial Internet Reference Architecture; it is endowed with a strong focus to industry, with a layer dedicated to operational technology. The third architecture IoT-A, its focal point is detailed view of the technological aspects of IoT, and machine to machine communication. It is important to note that despite their differences, all three architectures have management and security aspects across all their layers. The basic premise of the internet of things, Fig.1 is that the different arranged sensors and other devices can be governed, manipulated and interrogated via the internet by human actors, or by programs that reflect the behavior and desires of the user [9]. Perception layer is where the devices are located, to ensure the real time collect of data from the physical world, then communicate with other devices or share it via the network, as cited in Figure 2. Gateway Layer eases and favors the communication of information supplied by the previous layer via the use of cutting edge technologies such Wi-Fi. There is a possibility that this layer may refine and assemble the data. Application Layer is where Big Data Analytics operates; usually the data is stored in cloud, which may be risky especially for sensitive data [10]. The common idea of the proposed architecture, Figure 3, based on our reading, starts with the generation of massive amount of data from I2oT devices, devices such as sensors that can be installed not only at the manufacturing plant but also at strategic partners locals and shared equipment such as suppliers and service providers. Then the data is processed by using big data analytics, filtered while following a standard model thus shared through the platform with the edge of the cloud,

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Fig. 1: I2oT and Shop Floor Assets link.

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Fig. 2: Common Three Layers.

where the monitoring and decision making process is engaged by the manager of the plant consequently providing support.

Fig. 3: Proposed Architecture

3. Proposed mathematical model 3.1. Overview on fuzzy mathematical programming Nowadays, we still rely on building mathematical models for certain and uncertain situations of systems precisely in engineering field. Considering the uncertain situations roots of fuzziness, the probability theory is no longer sufficient; as a matter of fact a new concept emerged thanks to Zadeh in 1965, Fuzzy Logic to help with decision making in fuzzy environment [13]. On the groundwork of this concept, Fuzzy sets; the optimal decision is defined by the maximum of membership function to a fuzzy decision. Many researchers followed in Zadeh footsteps, per example Kaufmann in 1988, he worked on identifying a critical path of an activity by using the triangular fuzzy numbers. DePorter in 1990, used fuzzy linear programming to optimize a project completion time. In 1994, Hapke, introduces the fuzzy project scheduling to allocate resources among various activities in a software [14]. According to Zimmerman, fuzzy set theory is essential for operational research when it comes to modeling fuzzy relationships or fuzzy problems [15]. Fuzzy set theory presents a language for capturing inaccurate factors related to demand. All the works of the mentioned researchers point to a single conclusion: when the demand is only defined in linguistic terms, the use of fuzzy models must be considered a priority. Since Zadeh Work, Fuzzy set theory has become a strong tool to cope with incomplete and uncertain situations [16]. This theory is now put to use to respond to problems emanating from various fields like engineering, business, health science and more. Nevertheless, mathematical programming is still one of the areas to which fuzzy set theories is heavily applied [15]. With the last decade technological advances, an array of these sensors can be tangled or structured in many I2oT featured applications that do not request attended interventions; this is the concept of Wireless sensor networks which involve small nodes with sensing, computation, and wireless communications capabilities [17].

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3.2. Proposed fuzzy mathematical model In this section we present The Sink Location and Routing Problem (SLRP) as formulated by [18] as a p-median: Let be the following parameters: gi jk : the energy consumption to transmit a unit data flow in the path between a sensor type k at point i and a sink at point j. Let be the following decisions variables: xi jk : a binary decision variable equal 1 if a sensor type k at point i is assigned to a sink at point j, 0 otherwise. y j : a binary variable equal 1 if a sink is located at point j, 0 otherwise  Min gi jk xi jk (1) i∈S k∈K j∈N

Subject to:  xi jk = 1, ∀i ∈ S , k ∈ Ki

(2)

j∈N

xi jk ≤ y j , ∀i ∈ S , k ∈ Ki , j ∈ N  yj = p

(4)

xi jk , y j ∈ {0, 1} , ∀i ∈ S , k ∈ Ki , j ∈ N

(5)

(3)

j∈N

The objective function (1) minimises the energy consumption to transmit data flows between all located sensors and sinks. The constraint (2) ensures to assign a sensor to one sink. The constraint (3) guarantees to assign sensors to located sinks. The constraint (4) ensures to locate the desired number of sinks. 4. Fuzzy restriction on the number of located sinks In most cases, the number of sinks to be located is fixed and well-known in advance. But, for practical considerations, we seek for an enough level of coverage with minimum p located sinks or we tolerate redundant coverage with a high number of p covering sinks. This may be expressed by stating that the number of needed sinks to be located will be in the interval [pmaxmin ] where pmin is the minimum number of sinks to guarantee a sufficient level of sinks coverage while pmax is a sufficient number of sinks to achieve a high level coverage by sinks. Thereby, the corresponding Fuzzy Sink location and Routing Problem (FSLRP) model is: Min (1) Subject to: (2) − (3) − (5) 

yj =f p

(6)

j∈N

In the equation (6), the symbol = f denotes the fuzzy equality whose meaning was previously explained. This equation may be replaced by the two following fuzzy inequalities:  yj ≥f p (7) j∈N

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 j∈N

yj ≤f p

5

(8)

Since now, there is no direct methods to solve fuzzy models. A successful method to deal with this kind of problems is to transform it into a sequence of classical ones (called crisp model). After that, the resulted crisp programs are solved either by exact (simplex, Branch and Bound, etc) or using heuristics (Tabu search, Genetic Algorithms, GRASP, etc). The used approach in the current paper is the Parametric Approach introduced in [19]. This method is a two-phases approach. First, the fuzzy problem is transformed into a set of crisp problems usingα − cuts, where the parameter α ∈ [0, 1] represents, in our case, the desired level of coverage by sinks. Thereby, for each value of the parameter α, a classical α − S LRP is obtained. Second, each classical program is solved to optimality using well-known exact algorithms (or heuristics). We obtain a set of solutions of the fuzzy model by gathering the solving results of crisp models corresponding to different values of the parameter . To use the α − cuts method to solve the fuzzy model, we  assume that the constraint (4) may be violated up the values p + ∆r and p − ∆l . Let be P = j∈N y j , then the two constraints (7) and (8) may be defined respectively by the two membership functions as follows:   1 if P ≤ p     1 − P−p µr (P) =  (9) ∆r if p ≤ P ≤ p + ∆r     0 if P > P + ∆r   1 if P ≥ p     1 − p−P µl (P) =  (10) ∆l if p − ∆l ≤ P ≤ p     0 if P < P − ∆l We introduce the corresponding α − cuts as follows: 

µr (P) ≥ α, ∀α ∈ [0, 1] µl (P) ≥ α, ∀α ∈ [0, 1]

In this case, the constraints (7) and (8) becomes:  y j ≤ p + ∆r (1 − α)

(11)

j∈N

 j∈N

y j ≥ p − ∆l (1 − α)

(12)

Finally, the α − S LRP models are given as:  gi jk xi jk Min i∈S k∈K j∈N

S ub ject to :  xi jk = 1, ∀i ∈ S , k ∈ Ki j∈N

(13)

xi jk ≤ y j , ∀i ∈ S , k ∈ Ki , j ∈ N  y j ≤ p + ∆r (1 − α), ∀α ∈ [0, 1] j∈N

 j∈N

y j ≥ p − ∆l (1 − α), ∀α ∈ [0, 1]

xi jk , y j ∈ {0, 1} , ∀i ∈ S , k ∈ Ki , j ∈ N

(14)

The above model is solved to optimality using CPLEX solver. We present, in the Fig. 4, below the variation of the objective function according to different values of the parameter α from 0. 1 to 1 with 0. 1 step.

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Fig. 4: Visualization of the Variation

5. Conclusion In this paper, we made an overview of Industrial Internet of Things Architecture (I2oT), optimization and scheduling techniques in I2oT and fuzzy methods on mathematical programming. We proposed a fuzzy version of a wellknown linear program studied in the literature modeling the location of wireless sensors. As perspectives, we envision to conduct extensive numerical experiments to solve larger instances using heuristics algorithms. References [1] Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion. [2] Roos, G. (2016). Design-based innovation for manufacturing firm success in high-cost operating environments. She Ji: The Journal of Design, Economics, and Innovation, 2(1), 5-28. [3] Ashton, K. (2009). That internet of things thing. RFID journal, 22(7), 97-114. [4] M. Spring, L. Araujo, (2017), Product biographies in servitization and the circular economy - Industrial Marketing Management Elsevier. [5] Lin, S. W., Miller, B., Durand, J., Joshi, R., Didier, P., Chigani, A., ... & King, A. (2015). Industrial internet reference architecture. Industrial Internet Consortium (IIC), Tech. Rep. [6] S. Krco, B. Pokric, and F. Carrez, (2014), Designing IoT architecture(s): A European perspective, 2014 IEEE World Forum Internet Things, WF-IoT, pp. 7984, 2014. [7] S.El Hamdi, M. Oudani ,A. Abouabdellah (2020) Moroccos Readiness to Industry 4.0. In: Bouhlel M., Rovetta S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. [8] S. El Hamdi., A. Abouabdellah, M. Oudani, (2018, October), Disposition of Moroccan SME Manufacturers to Industry 4.0 with the Implementation of ERP as a First Step. In Sixth International Conference on Enterprise Systems (ES) (pp. 116-122). IEEE. [9] G. Bloom et al. (2018), Design patterns for the industrial Internet of Things, 14th IEEE International Workshop on Factory Communication Systems (WFCS) [10] R. Khan, S. U Khan, R. Zaheer, and S. Khan, (2012), Future Internet : The Internet of Things architecture, possible applications and key challenges, 10th IEEE International Conference on Frontiers of Information Technology. [11] S. G. Taylor; S. F. Bolander, (1991), Process Flow Scheduling Principles, Production and Inventory Management Journal; Alexandria Vol. 32, N 1. [12] EM Goldratt, RE Fox, G Grasman, (1986) - North river press Croton-on-Hudson. [13] L.A. Zadeh, (1965), Fuzzy Sets, Information and Control, Vol.8, 338/353. [14] A. L. Guiffrida, R. Nagi, (1998), fuzzy set theory application in production management research: a literature survey, Journal of Intelligent Manufacturing. [15] H.J. Zimmermann, (1985), Applications of fuzzy set theory to mathematical programming, Information science, Elsevier. [16] L.A.Zadeh, (1978), Fuzzy set as a basis for a theory possibility, Fuzzy sets system, vol.1, pp.3-28. [17] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci , (2002), Wireless sensor networks: a survey, Computer Networks 38 393422. [18] E. Gney, N. Aras, . K .Altnel, & C. Ersoy, (2012). Efficient solution techniques for the integrated coverage, sink location and routing problem in wireless sensor networks. Computers & Operations Research, 39(7), 1530-1539. [19] J. L. Verdegay, (1982). Fuzzy mathematical programming. Fuzzy information and decision processes, 231, 237.