Selection and Ranking of Fog Computing-Based IoT for Monitoring of Health Using the Analytic Network Approach

. Numerous raised areas are established in the ﬁeld of fog computing (FC), applied for various purposes, and are evaluated for running analytics on various devices including devices of internet of things and many others in a disseminated way. FC progresses the prototype of cloud computing to network edge leading various possibilities and services. FC improves processing, decision, and intervention to take place through devices of IoT and communicate essential details. The idea of FC in healthcare based on frameworks of IoTis exploited by determining dispersed delegate layer of comprehension between the cloud and sensor hubs. The clouds suggested systems improved to overcome several challenges in ubiquitous frameworks of medical services such as energy eﬃciency, portability, adaptableness, and quality issues by accommodating right to take care of deﬁnite weights of the distant medical services group and sensor networks. The proposed research work has considered the analytic network process (ANP) for selection and ranking of FC-based IoTfor health monitoring systems. The approach works in situation when complexity arises for health monitoring. Results of the study show the success of the research for facilitating healthcare.


Introduction
Fog computing is a structure positioned anywhere between the cloud and the data source where information is computed and stored and the applications are placed to operate the requirements for some specific purpose. FC, like edge computing, takes the assistance of cloud's and power nearby to where information is formed and functioned. Fog computing and edge computing are used by various people as both involve taking understanding and computation together for the formation of information. is is generally performed for reliability improvement but can also be performed for other reason such as adherence and protection. Abundant platforms are recognized in the field of FC which are applied for various purposes and are assessed for successive analytics on different devices comprising IoT devices and several others in a disseminated means. FC improves the prototype of cloud computing to the network edge, leading various potentials and services. FC improves processing, decision, and intervention to take place through devices of IoT and spread essential details.
FC plays an important role in healthcare, and with the help of this platform, various mechanisms are proposed. Patients suffering from chronic disease are monitored through fog computing [1]. e challenging task is the collection of context-sensitive data associated with patient health. e use of sensor-to-cloud is not only feasible, and the layer of fog computing can make a difference. By doing this, the efficiency of the entire system grows. e issues of security and deployment of fog computing layer are also analyzed. e large growth and developments in the IoT have offered excessive possibilities in the sector of healthcare. e fitness trackers, wireless technology, and body sensors have significant effect on health system reliability and living efficiency. e applications of wearable devices are increasing which measure physiological factors, enhance adherence to exercise in various populations from athletes to patients, and promote health. Fog-assisted computational efficient wearable sensor networks have been considered for monitoring health through IoT. e applications of wearable devices for monitoring respiratory frequency, hear rate, and movement cadence through physical activity are examined. e collected data through the sensor are uploaded to the connection of IoT system Ethernet module, and the official individual access is provided to monitor the health of athletics through Internet. e wearable devices and its applications demonstrate how the resources of computing cost can reduce while maintaining health requests to access information of health stored in cloud and fog distributed setting. e effectiveness, reliability, and user-friendliness are demonstrated through results of the experiments [2].
Health clinics for sports performance services and medicine of sports team are presented with the applications of technology for supporting athletes return to play in various areas of sports. For analyzing and monitoring health operative optimization of sports person, machine learning algorithms are presented. With the help of experimental results and discussions, the efficiency of the designed system is assessed [3]. Outside the environment of hospital, wireless body sensor nodes are used to monitor patient based on realtime IoT.
ese devices include bio-sensor for capturing signals from body of patient and transmitting the collected signals through wireless transmitters to a server in real time.
e study presented a real-time scheme of encoding performing approximation of wavelet coefficients for sparse encoding of bio-signals and iterative thresholding for reducing bandwidth consumption and reducing energy of the wireless body sensor node. e proposed system results presented energy improvement of system level 96% with an impact of 2% on signal quality [4]. e research has demonstrated a novel "Intelligent" skin monitoring device allowing patients of rural areas to remotely monitor diseases of skin. e approach includes cloud-based IoT and artificial intelligence for analyzing medical images and predictions of disease. e research also addresses the impact of the season. e proposed framework offers diagnosis and prevention for addressing the issues faced by people of remote areas with less facility of skincare [5]. e research work presented in this paper has considered and used the analytic network process approach for selection and ranking of FC based IoT for monitoring of health. e approach works in situation when complexity arises for health monitoring. e paper is organized as follows. Section 2 depicts the literature of the proposed research inline with FC-based IoT for healthcare. Section 3 represents the literature for evaluation and quality assurance of FC-based IoTfor monitoring of health. Selection and ranking of FC-based IoT for health monitoring are briefly given in Section 4. Section 5 shows the conclusion of the paper.

Related Work
Various approaches are presented for monitoring healthrelated activities. Various generic monitoring systems are existing. Dhingra et al. [6] have offered research on the analysis integrated fog and distributed computing arrangement acquainted with limitations of latency, realtime analytics, and network congestion of basic cloud services for monitoring of traffic. e planned approach of cloud and fog framework is linked with Twitter for sending alerts concerning congestion of traffic. e system of IoTand fog-based healthcare for identification and regulation of Chikungunya virus (CHV) is introduced. Fuzzy-C mean clustering is used to evaluate the infected users and to provide customers from the fog layer with thoughtful and alert concerns quickly. In addition, social network analysis is used on cloud workers to speak to the CHV outbreak. With the help of the social network analysis graph, the outbreak role index is measured which signifies the possibility of any user to spread or receive the infection. It produces alerts of warning to healthcare agencies and government for controlling CHV outbreak in risk proven regions. e advantages of the research concerning cloud and fog services for obtaining efficient bandwidth with good quality of service are shown by the experimental results [7].
To find, shape, and screen customers affected with mosquito-borne diseases (MBDs), a new system of IoT sensors, fog computing, and cloud computing is planned to classify, differentiate, and monitor the users with MBD infection. e research aims to stop the outbreak of MBDs at the initial stage. e system is proposed, the similarity factor is measured for differentiating MBDs, and J48 was considered for classification of category for each user infection. e preventive procedures are formed in a split second and sent to the client from the fog layer if any anomaly should occur. Radio frequency identification is considered for identification of the closeness of customers. Temporal network analysis uses proximity information to monitor and talk to the present state of the flare-up of the MBDs. In order to find MBDs, the examining assessments of the system resulted low error rate and high accuracy of 94% [8]. A comprehensive set of topology control (TC) approach for managing and constructing a large-scale smart city network of IoT is presented [9]. e problem of TC is approached in two phases consisting of construction and maintenance phase. In the first phase, a cost effective network of IoT including fog gateways is built while in the second phase the resource utilization is optimized. For realizing the objectives, efficient algorithms are proposed and wide-ranging simulation is done based on experimental and real IoT data sets. e effectiveness of the approach is demonstrated and compared with the available algorithms. e efficient development of innovation in medical care makes it emphatically possible for clinicians to screen and go to patients with constant illness to provide patients with adequate treatment in remote areas.
A scientific examination of robotics telesurgery established on 5G, artificial intelligence, and tactile internet is presented. e paper has described the issues and challenges faced in telesurgery. A fog-based supporting interactive model is demonstrated for expediting and reducing the process latency [10]. e influence of fog computing paradigm in healthcare is studied highlighting the key assistances concerning latency, power consumption, and network usage. With foundation of these parameters, the 2 Complexity fog-based supporting health monitoring system is planned and its evaluation performance is carried out. e experimental results of the approach demonstrated the enhancement possibilities for minimizing data traffic with security enhancement on health information and providing optimized insights of patient health [11].

Evaluating and Quality Assurance of FC-Based IoT for Observing Health
In the literature so far, various approaches and mechanisms are available to monitor healthcare activities. Verma et al. [12] have presented a new IoT-fog cloud supporting physical system to diagnose and stage classification of ulcerative colitis (UC) through Naïve Bayes classifier and deep neural network. Real-time alert generation is the key point of the study from fog layer in the situation of emergency needed to the user with UC. Haghi Kashani et al. [13] analyzed that the quality of service confirming approaches comes under three categories including service management, application management, and communication management for the years during 2013 till October 2018. e planned study has demonstrated the tools, merits, demerits, evaluation types, and factors of quality of service. Lastly, the challenges and issues based on the reviewed studies have been suggested which need further research for the approaches in fog computing. A business process model and notation extension for enabling the IoT aware business process modeling are proposed at first. e approach considers the heterogeneous IoT and non IoT resources, quality of service constraints, resource capacities, and so forth. Secondly, the approach has considered a novel IoT-fog cloud-based architecture. irdly, the research has modeled the proposed BPMN 2.0 extension, smart autistic child, and coronavirus disease 2019 monitoring system. e effectiveness of the proposed system is illustrated with the help of extensive experiments [14]. Figure 1 graphically decipts the IoT-based health monitoring system [4]. e figure consists of three tiers and the details. is figure mainly discusses the IoTbased health monitoring system. Figure 2 represents the components involved and architecture [15]. e figure contains IoT sensors, GPS module, WiFi module, collected health data, wireless local gateway, wireless remote gateway, and controller.
Karatas and Korpeoglu [16] have considered fog computing and geographically distributed cloud-based architecture of IoT and planned an approach to place IoT data into the components such as fog and cloud data centers. Data are stored in various types, and each of the data type is required by several applications. e research has modeled the problem of data placement and proposed the effective and efficient algorithm for placement of data produced and disbursed through location effectively accessed by applications which require the type of data. For evaluating the proposed research, extensive experiments are done and the effectiveness of the research is shown.
Various searching processes were done in order to obtain associated materials.
e Springer library was searched for the query defined for the search process. e materials were graphically shown for understanding. Figure 3 graphically represents the details of the papers searched in the given library. e disciplines of the papers were also studied which are shown in Figure 4.
Alemneh et al. [17] have proposed a two-way subjective logic-based trust management enabling a service requester for verifying a service provider is offering a secure and reliable service and the service provider can check the trustworthiness of the service requester. Aslam et al. [18] have presented a solution of an automated fog node audit certification approach which certifies the secure fog layer by the proposed fog layer promising mechanism. Figure 5 shows the details of the papers searched in the IEEE library. e figure shows that more papers are published as conference papers. Figure 6 is to represent the locations of the conferences where the papers are published. Figure 7 depicts the topics of publications. e details of publication titles and publications are shown in Figure 8.
Bandopadhaya et al. [19] have planned an integrated solution of healthcare monitoring for the soldiers implemented in adversative surroundings circumstances through the IoT with distributed computing. Each health parameters of individual requires to be monitored on a real-time basis and succeeding examination of the dataset for commencing suitable healthcare support on time. Bharathi et al. [20] have presented an energy efficient particle swarm optimizationbased clustering approach for efficient selection of cluster head amongst different devices of IoT. e devices of IoT for healthcare sensing data are categorized into a form of cluster, and a cluster head will be elected by the use of the approach. e data are forwarded by the cluster head to the cloud server. Fog devices are used for transmitting data of the IoT devices to the cloud server. e ANN algorithm is used as the classification model for diagnosis of the healthcare data for identification of the severity of disease. Debauche et al. [21] presented the health monitoring system based on a fog IoT cloud through environmental signal and physiological permitting to generate relative information for the daily activities. e proposed system monitors behavioral changes and health state of elderly people. Recovery and monitoring rehabilitation processes of patients are provided by the proposed system. e system consists of local gateway for storing data locally, wireless sensor network, and Lambda cloud architecture for processing data.
e ScienceDirect library was considered as search library for achieving associated materials. Figure 9 shows the details of the types of articles along with number of articles.
In this library, the year wise search process was also done to show the increase/decrease in research in the area. Figure 10 depicts the year wise details of paper. e figure shows that there is increase in research work in the area. e publication titles were searched in the same library, and it is shown in Figure 11. e subject areas along with publications were found in the same library. Figure 12 graphically represents the subject areas with publications. Complexity e Wiley online library was considered as search library for identification of relative materials. Figure 13 depicts the papers type with publications. Figure 14 shows the subjects along with the publications. Figure 15 is the presentation of papers published in the given library.  Figure 1: IoT-based health monitoring system.

Wireless local gateway
Collect health data Wireless remote gateway

Selection and Ranking of FC-Based IoT for Monitoring of Health
Aazam et al. [22] presented a taxonomy of current offloading approaches adopted for the fields such cloud computing, FC, and the IoT. e study has discussed the technologies of middleware enabling offloading in a cloud-IoT case and the significant factors for offloading in a specific scenario. Research prospects associated with offloading in edge and 6 Complexity cloud computing are also presented. Abdelmoneem et al. [23] have proposed an interoperable cloud-fog-based architecture of IoT for healthcare. Diversity of the medical cases and mobility of the patient are supported by the proposed architecture. It demonstrates its architecture, user context, and environmental context. Individual module features are elaborated, and the association between the modules is discussed. Results of the simulations are adequate for cost, latency, and miss ratio. e process of this study was carried out step by step in this section. Table 1 shows the details of the components involved in fog computing IoT (FC-IoT). e details of the pairwise comparison of Table 1 are shown in Table 2. e same representation is given for another architecture of the given components. e details are given in Table 3. e pairwise comparison of Table 3 is shown in Table 4. Table 5 shows the architecture for component 1. e pairwise comparison process of Table 5 is given in Table 6. e same process of values is given for architecture 2 and is shown in Table 7.
e pairwise comparison process of Table 7 is shown in Table 8. e same process was done for rest of the architectures. After completing the process of pairwise comparisons, all the calculated values are brought into a single matrix called weighted matrix. Table 9 represents the weighted matrix.
For taking the decision regarding the components and its architectures, the weighted matrix was then converted into the limit matrix. Table 10 shows the limit matrix. Figure 16 graphically represents the ranking and selection of FC-based IoT for monitoring of health. e figure shows that FC-IoT 3 got the highest ranking for consideration.

Conclusion
FC is an architecture situated anywhere amongst the cloud and data source where information is computed and stored and the applications are placed to operate the requirements for some specific purpose. Fog computing and edge computing are used by various individuals as both involve understanding and computation together to where the formation of information is done. Numerous platforms and frameworks are considered and proven in the field of FC, applied for various purposes, and are assessed for running analytics on innumerable devices comprising IoT devices and many others in a disseminated way. FC enhances processing, decision, and intervention to take place by devices of the IoTand distributes essential details. e clouds suggested systems improved to overcome several challenges in ubiquitous medical services frameworks, such as manageability, energy efficiency, adaptability, and quality issues through accommodating right to take care of certain weights of the sensor network and a distant medical services group. e research work presented in this paper has used the analytic network process for selection and ranking of FCbased IoT for monitoring of health systems. e experimental results have shown that the proposed research work is efficient in the selection and ranking of fog computing-based IoT for monitoring of health systems. e approach works well in situation when complexity arises for health monitoring.

Data Availability
No data were used to support this study.

Conflicts of Interest
e authors declare that they have no conflicts of interest.  10 Complexity