Supporting availability evaluation of a smart building monitoring system aided by fog computing

Electronics LettersVolume 58, Issue 12 p. 471-473 LetterOpen Access Supporting availability evaluation of a smart building monitoring system aided by fog computing Francisco Airton Silva, Francisco Airton Silva orcid.org/0000-0002-8211-6060 Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorIure Fé, Iure Fé Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorCarlos Brito, Carlos Brito Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorGabriel Araújo, Gabriel Araújo Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorLeonel Feitosa, Leonel Feitosa Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorEunmi Choi, Eunmi Choi School of Software, College of Computer Science, Kookmin University, Seoul, South KoreaSearch for more papers by this authorDugki Min, Corresponding Author Dugki Min dkmin@konkuk.ac.kr Department of Computer Science and Engineering, College of Engineering, Konkuk University, Seoul, South Korea Email: anhnt2407@konkuk.ac.kr; dkmin@konkuk.ac.krSearch for more papers by this authorTuan Anh Nguyen, Corresponding Author Tuan Anh Nguyen anhnt2407@konkuk.ac.kr orcid.org/0000-0002-9123-3584 Konkuk Aerospace Design-Airworthiness Research Institute, Konkuk University, Seoul, South Korea Email: anhnt2407@konkuk.ac.kr; dkmin@konkuk.ac.krSearch for more papers by this author Francisco Airton Silva, Francisco Airton Silva orcid.org/0000-0002-8211-6060 Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorIure Fé, Iure Fé Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorCarlos Brito, Carlos Brito Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorGabriel Araújo, Gabriel Araújo Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorLeonel Feitosa, Leonel Feitosa Applied Research to Distributed Systems Lab, Federal University of Piauí, Teresina, BrazilSearch for more papers by this authorEunmi Choi, Eunmi Choi School of Software, College of Computer Science, Kookmin University, Seoul, South KoreaSearch for more papers by this authorDugki Min, Corresponding Author Dugki Min dkmin@konkuk.ac.kr Department of Computer Science and Engineering, College of Engineering, Konkuk University, Seoul, South Korea Email: anhnt2407@konkuk.ac.kr; dkmin@konkuk.ac.krSearch for more papers by this authorTuan Anh Nguyen, Corresponding Author Tuan Anh Nguyen anhnt2407@konkuk.ac.kr orcid.org/0000-0002-9123-3584 Konkuk Aerospace Design-Airworthiness Research Institute, Konkuk University, Seoul, South Korea Email: anhnt2407@konkuk.ac.kr; dkmin@konkuk.ac.krSearch for more papers by this author First published: 20 April 2022 https://doi.org/10.1049/ell2.12502AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Internet of Things (IoT) is increasingly present in people's lives, mainly to provide security and well-being. In homes and buildings, IoT sensors can monitor various factors such as temperature and the smoke presence. In case of danger, some action can be activated. However, a sensor network is a complex and fault-prone architecture. Evaluating an IoT architecture in this context is essential. Thus, the objective of this paper is twofold: (i) suggest a continuous time Markov chain (CTMC) model for a smart building monitoring system as a starting point; (ii) based on the results of a sensitivity analysis, an expanded CTMC with improved system availability and shorter outage periods is proposed. The proposed solution reduced the downtime by 98% compared with the baseline architecture. Systems administrators can use our models to evaluate their solutions. Introduction The Internet of Things (IoT) was created to enable ubiquitous connectivity between different “things” [1]. The number of IoT devices worldwide is predicted to nearly double from 8.74 billion in 2020 to over 16.44 billion in 2025 [2]. IoT devices can be used, for example, to automate activities in smart homes [3], traffic of vehicular networks [4], and health monitoring of elderly people in their homes [5]. Therefore, an IoT system provides services to smart scenarios in different contexts, efficiently managing hardware, software, and communication resources to reduce costs in specific domains. Identifying abnormal behaviour in an environment monitored using IoT has been useful in homes and smart buildings. From the concept of smart homes, the IoT could be extended to a broader context, smart buildings. Intelligent building management requires more sophisticated computing infrastructures to meet the high demand for data generation. Using cloud computing is sometimes impossible due to the distant placement of IoT devices. Fog computing has emerged to bring services closer to the data source [6]. Therefore, the computational power of fog computing can be used to process this high data demand more efficiently. The central research problem is that such addition of processing layers in IoT systems architecture requires performance and availability assessments from the earliest stages of development. However, evaluations with real experiments can be costly because there are many configuration possibilities, especially in smart buildings, where there are usually several floors with several rooms. Unnecessary expenses can be made to obtain resources that will not be used. Analytical models (e.g., queuing nets, Petri nets, Markov chains) can be a solution in this context, allowing predictions based on probabilistic calculations in most cases [7-13]. Some recent works explored performance evaluation of IoT architectures with analytical models [8, 10, 12, 13]. Other papers have focused on fog computing with emerging technologies. Sodhro et al. have proposed a novel 5G-enabled transmission power control (TPC) algorithm for QoS optimization. Sodhro et al. have also promoted some years later an AI-based cost-effective and efficient healthcare application with fog computing. However, none of these works studied have issues with availability. In [8], the authors propose a performance evaluation with queuing network to assess Internet of Robotic Things systems associated with edge, fog, and cloud computing paradigms. Gonçalves et al. [12] developed stochastic Petri net models for a surveillance monitoring system aiming to obtain the highest dependability. In [13], the authors proposed a queueing network model for the performance evaluation of an Internet of Healthcare Things infrastructure associated with a three-layer cloud–fog–edge computing continuum. Santos et al. [10] propose a queuing network-based message exchange architecture to evaluate the performance of a smart building infrastructure associated with edge and fog computing. Other studies (also related to IoT with analytical models) have focused on availability, but without including an edge computing layer or observing availability just at a single system point [7, 9, 11]. The authors in [9] proposed a performability SRN model of an edge/fog-based medical information system for the performability quantification of medical data transactions in hospitals. In, the authors adopt analytical models to assess the performance of smart hospital systems availability without having to invest in real equipment beforehand. Brito et al. [11] propose stochastic Petri net models to evaluate both performance and availability of mobile edge computing environments. In [7], the authors use an interdisciplinary approach combining stochastic models with optimization algorithms to analyze how failures impact e-health monitoring system availability. This letter proposes to evaluate a classic smart building architecture composed of sensors and a fog computing layer. Unlike previous works, this study brings two continuous time Markov chain (CTMC) models that evaluate the availability metric by applying a redundancy strategy in the fog layer. Furthermore, a sensitivity analysis showed the impact of parameter variation under the system availability. All the performed evaluations were executed using the advanced modelling tool Mercury. Evaluated architecture The evaluated scenario (shown in Figure 1) is composed of three essential parts that are also observed in previous studies [7, 9, 10], including: (i) a sensors computing layer, located at the building rooms, for continuous integration of temperature, motion and fire sensors; (ii) a communication network that encompasses all wireless telecommunications equipment, such as routers and repeaters distributed throughout the building. (iii) a fog computing layer for storage and monitoring to be used by the building administrator. In this work, we consider only one computing node characterized as a physical server called fog server node. To compute the availability of such a server, we consider the power source, hardware, OS, Docker, and containers. Fig. 1Open in figure viewerPowerPoint Evaluated architecture, divided into three macro components Base availability CTMC model The CTMC shown in Figure 2 considers the three macro-components of the architecture presented above. A CTMC is necessary due to the interdependency between the subsystem components, and also means that a closed-form equation of steady-state availability can be obtained, which is useful for computing desired measures. Each state considers the three individual states always in the same order. The first indicator is facing the sensors (s). The second indicator represents the communication network (n). The third indicator represents fog computing (f). All three components can have up (U) or down (D) states. Considering two states for each component, we have included all possible states in the base model: # s t a t e s = 2 3 $\#states = 2^{3}$ . All three components must be up (UUU) for the system to be available. The other states (greyed out) indicate system unavailability. If any one of the three components fails, the system fails. The probabilistic failure (λ) and repair (μ) rates of each arc are presented in Table 1. A reliability building block (RBD) model was adopted to calculate the failure and repair rates of the fog server node. The RBD model has included five blocks in series: power source (PS), hardware (HW), operating system (OS), Docker (DC), and containers (CT). The fog server ( A F o g $A_{Fog}$ ) availability is calculated as: A F o g = A P S × A H W × A O S × A D C × A C T $ A_{Fog} = A_{PS} \times A_{HW} \times A_{OS} \times A_{DC} \times A_{CT}$ . The parameters for such RBD are presented in Table 2. For RBD, the configuration parameters are set as the mean time to fail and repair (MTTF and MTTR). All parameters are represented in the hours unit. All input parameters of the models were collected from recent literature. Table 1. Base CTMC input parameters Sensors Network Fog server node Parameters λ 0.0000033333 0.0000120163 0.0017699115 μ 1 0.0833333333 1.4285714286 Table 2. RBD input parameters to calculate the fog fail and repair rates Container Docker Operating system Hardware Power source Parameters MTTF 1258 2516 2880 8760 8757 MTTR 0.25 0.25 1 1.6 4.8 Fig. 2Open in figure viewerPowerPoint Base model encompassing the three macro-components: sensors (s), network (n) and fog server node (f) Considering that failure and repair times are exponentially distributed, the availability of a component i may be expressed by A i = λ i / ( λ i + μ i ) $A_{i} = \lambda _{i}/(\lambda _{i} + \mu _{i})$ . The unavailability is expressed as U A = 1 − A S y s $UA = 1 - A_{Sys}$ . The number of nines of availability can be obtained by log 10 ( UA ) $\log _{10}(\text{UA})$ . The downtime during one year is calculated as: D T Y = U A × 8760 $DTY = UA \times 8760$ h. The availability model can be evaluated by closed-form (see Equation 1), which was obtained from the CTMC (Figure 2). A closed-form solution solves a given problem in terms of functions and mathematical operations from a given generally accepted set. The Mathematica software generated the closed formula, and it outputs the result of the system stationary availability obtained by the model: A s y s = μ f × μ n × μ s λ s × μ f × μ n + λ n × μ f + λ f + μ f × μ n × μ s \begin{equation} Asys=\frac{\mu f \times \mu n \times \mu s}{\lambda s\times \mu f\times \mu n+{\left(\lambda n\times \mu f+{\left(\lambda f+\mu f \right)} \times \mu n\right)} \times \mu s} \end{equation} (1) Sensitivity analysis For sensitivity analysis of this work, the percentage difference technique was employed. This procedure estimates the percentage difference by shifting a minimal value input parameter to its maximum value. The whole range of potential values for each parameter must be used to determine the sensitivity of the parameters. Equation (2) shows how the sensitivity index is calculated by the percentage difference method. The expression max{Y(θ)} and min{Y(θ)} are, respectively, the maximum and minimum values of output, measured when varying the θ parameter over a range of its n possible values of interest. If Y (θ) is known to vary monotonically, only the extreme values of θ (i.e., θ1 and θ n $\theta _n$ ) can be used to calculate max{Y(θ)}, min{Y(θ)} and as a result S θ $S_\theta$ {Y}: S θ { Y } = max { Y ( θ ) } − min { Y ( θ ) } max { Y ( θ ) } \begin{equation} {S_\theta }\lbrace Y\rbrace = \frac{{\max \lbrace Y(\theta )\rbrace }- {\min \lbrace Y(\theta )\rbrace }}{{\max \lbrace Y(\theta )\rbrace }} \end{equation} (2) Table 3 presents the sensitivity indexes of all six parameters in the base model. The results indicate that the fog server node has the highest sensitivity indexes, encompassing failure and repair rates. The fog is, therefore, the most critical component, which means that fog has the greatest impact on the steady-state availability of the system. Table 3. Sensitivity analysis result. The parameters are ranked by level of importance Parameter Sensitivity index λ f $\lambda _f$ 6.974363806684393E-4 μ f $\mu _f$ 3.5076072843124244E-4 λ n $\lambda _n$ 8.809638172650184E-5 μ n $\mu _n$ 5.598262474786998E-5 λ s $\lambda _s$ 1.4371989156752085E-5 μ s $\mu _s$ 9.414550662762359E-6 Figure 3 presents the visual impact of each system parameter variation. Each parameter was varied by 25% and 50% to upside and downside from the baseline point (red line). Although the graphs seem to be very similar, the fog server node is unique that varies three in the number of nines from minimum and maximum values. Fig. 3Open in figure viewerPowerPoint Steady-state availability of the system varying each parameter at a time Extended availability CTMC model A new model has been proposed based on the sensitivity analysis results. Figure 4 shows the Markov chain for the system with redundancy applied to the fog server node component. The hot-standby strategy was applied in this model. The redundant fog server is ready to assume the processing immediately after the main server has failed. There is now a primary node (third position) and a redundant node (fourth position). The notation is similar to the previous model. Fig. 4Open in figure viewerPowerPoint Extended model applying redundancy under the fog node The ideal condition for this system is denoted by the UUUU state, in which all components are active under normal conditions. However, considering redundancy, we assume that the system will be operational even if one of the two fog server nodes is down. Thus, starting from a fully active state (UUUU), it can change for four states (DUUU, UDUU, UUDU, and UUUD), but the system remains up in the states UUDU and UUUD. As a result, the total availability equals the sum of the UUUU, UUDU, and UUUD states. States that might reflect the failure of a subsequent second or third component have been omitted for clarity and because they are less likely to occur. By omitting subsequent failures, we did not include the states: DDUU. UDDD, DUDD, DDUD, DDDU, UUUU, and DDDD. Such action does not impact the availability results based on previous simulations. Some states have only two output states. For example, the state UDDU can change only for UUDU or UDUU. The state UUDD can only change for UUUD or UUDU. Equation 3 presents the availability closed-form of the extended model. Due to the parameters, λ f $\lambda _f$ (main fog server) and λ f 1 $\lambda _{f1}$ (redundant fog server) have equal values, in the equation are represented by the parameter λ f $\lambda _f$ . The same applies to the fog server repair parameter. Equations (2) and (3) were generated from a reduced version of the base and extended models. They allow rapid verification of the system stationary availability in hypothetical scenarios coming from the components failure and recovery parameters variation without the need to elaborate and calculate the steady-state of the CTMC: A s y s = μ f × λ f + μ f × μ n × μ s α + β × λ s × μ n + γ × μ s + μ f 2 × λ s × μ n + γ × μ s \begin{eqnarray} Asys= \frac{\mu {}f \times {\left(\lambda f + \mu {}f \right)}\times \mu {}n \times \mu {}s}{ \alpha + \beta \times {\left(\:\lambda s\times \mu {}n+{\left(\gamma \right)}\times \mu {}s\right)}+\:\mu {}f^{2} \times {\left(\lambda s \times \mu {}n\:+{\left(\gamma \right)} \times \mu {}s \right)} }\hskip-7pt\nonumber\\ \end{eqnarray} (3)where α = λ f 2 × μ n × μ s $\alpha = \lambda f^{2} \times \mu {}n \times \mu {}s$ β = λ f × μ f $\beta =\lambda f \times \mu {}f$ γ = λ n + μ n $\gamma =\lambda n\:+\:\mu {}n$ . Comparing the models Table 4 presents the comparison of the two models in terms of availability, number of nines, unavailability, and downtime. The inclusion of the redundant fog server node significantly improved the system availability. The base model resulted in 97.890%, whereas the extended one resulted in 99.970% (a difference of two nines). The downtime of the base model is 184.75 hours, but only 2.6 in the extended model. Therefore, the inclusion of the fog server node is important to avoid unavailability over the year. Table 4. Comparison between the base and extended models Availability Unavailability Downtime #9 Base model 0.97890 0.02109 184.75496 1.67590 Extended model 0.99970 0.00029 2.60443 3.52679 Conclusion This letter proposed hierarchical RBD and CTMC models aiming to represent and evaluate a smart building monitoring system. Two CTMC have represented three macro components: sensors, network, and fog server node. A parametric sensitivity analysis was performed under a base architecture. We have extended the base model by including a redundant fog server node using the hot-standby strategy. The redundant fog server increased the availability in the range of one nine and decreased the downtime by about 182 h. This work has two main limitations: (i) encapsulation of parameters in just three macro-components; (ii) omission of two sequential failure states in the extended CTMC. We intend to apply other redundancy strategies and study the relationship between monetary cost and availability in future work. Acknowledgement This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Nos. 2021R1A2C2094943, 2020R1A6A1A03046811). Conflict of interest The authors have declared no conflict of interest. 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