In total, 14 most common terms that describe services in fog computing e-health applications were chosen and used to get a view of the first perspective. Table 1 summarises the results on these terms appearing in a representative set of articles.

From the data in the table it becomes evident that there are only six significant service-related terms (distinguished from the rest by a horizontal line) in the literature on fog computing e-health applications: monitoring, framework, architecture, data security, alert generation and prediction. “Monitoring” is the term most frequently used in articles, with a number of uses up to three times as low as any other term considered. All significant terms use maximum numbers (marked with the grey background) that vary from 16 to 46 times and appeared in the last year.

The main trends of the use of significant terms are shown in Fig. 3. From the pictorial representation in the figure it is clear that all six significant terms are trending, i.e. their use during the years 2017–2022 is increasing. It is seen that five terms during the years 2020 and 2021 had a decrease, though their use in 2022 peaked.

Software is an important part of the fog computing of e-health that is responsible for network simulation and verification, application front-end, back-end operation, and the operating environment. In total, 21 most common terms describing software in fog computing e-health applications were chosen to gain a view of the second perspective. Table 2 summarises the results on these terms appearing in a representative set of articles.

From the data in the table it becomes apparent that there are only six significant software-related terms (outlined by a horizontal line) in the literature on fog computing e-health applications: programming languages, cloud, simulators, frameworks, machine learning and operating systems. “Programming languages” is the term most frequently used in articles, with a number of uses up to three times as low as any other term considered. All significant terms use maximum numbers (marked with the grey background) that vary from 14 to 43 times and appear during the last four years under consideration.

The main trends of significant term use are shown in Fig. 4 using a line fitting to-data technique. From the figure it is evident that three of the six significant terms are trending, i.e. their use during the years 2017–2022 is increasing, which are: “programming languages”, “cloud” and “simulators”. The increase in the usage of terms “machine learning” and “framework” was minimal. Only the trend of the use of the term “operating system” (whose usage in 2019 peaked) is decreasing. It is seen that four terms during the years 2020 and 2021 had a decrease though their use in 2022 peaked.

The fog environment commonly consists of few end devices that are heterogeneous, making the system more diverse. When developing fog applications, the computing platform, its devices, and network heterogeneity should be considered. Such systems consist of edge, fog, and cloud layer devices, IoMT, general-use sensors, and sometimes batteries, if devices are mobile. We will separately analyse sensor node and fog node hardware.

Sensor node hardware. In total, 11 most common terms describing sensor node hardware in fog computing e-health applications were chosen and used. The upper part of Table 3 summarises the results on these terms appearing in a representative set of articles.

The table data grounds that in the literature on fog computing e-health applications, there are five significant sensor node hardware-related terms (distinguished from the rest by a horizontal line): sensors, smartphones, microcontroller units (MCU), network modules and specialised sensor nodes. The term “sensor” is the most frequently used term in articles, with a number of uses up to six times lower than any other term considered. All significant terms use maximum numbers (marked with the grey background) that vary from 7 to 71 times and appear from 2019 to 2022.

The main trends of significant term use are shown in Fig. 5(a) using a line fitting to-data technique. From the figure it is clear that one of the five significant terms – “sensor” is trending. The increase in the usage of the terms “smartphone” and “MCU” was minimal (which peaked in the last three years) and the decrease in the usage of the term “sensor node” was also minimal (which peaked in 2020). Only the trend of usage of the term “network module” (which peaked in 2019) was decreasing.

Fog node hardware. In total, 9 most common terms describing fog node hardware in fog computing e-health applications were chosen and used. The lower part of Table 3 summarises the results on these terms appearing in a representative set of articles. There are six significant fog node hardware-related terms (outlined by a horizontal line in the table) in the literature on fog computing e-health applications: single board computers (SBC), specialised fog nodes, more powerful personal computers (PC), microcontroller units (MCU), smartphones and network modules. “SBC” is the most commonly used term in articles, with a number of uses up to five times as low as any other term considered. All significant terms use maximum numbers (marked with the grey background in the table) which vary from 4 to 16 times and appear from 2018 to 2022.

The main trends of significant terms use are shown in Fig. 5(b) using a line fitting to-data technique. From the figure it is evident that two of the six significant terms are trending, the use of terms “fog node” and “PC” during the years 2017–2022 is increasing. The decrease of the usage of terms “SBC”, “MCU” and “smartphone” was minimal, while the usage of “network module” decreased significantly.

Network resources in the three-tier fog architecture can exist near edge devices in the dedicated fog equipment or within edge devices. This complexity requires the development of middleware that can manage the common pool of device resources, allocate them to application workloads, and work with various network connectivity and data processing technologies.

In total, 14 most common terms describing information technologies in fog computing e-health applications were chosen and used to get a view of the fourth perspective. Table 4 summarises the results on these terms that appear in a representative set of articles.

The table data grounds that there are only six significant information technologies-related terms (distinguished from the rest by a horizontal line) in the literature on fog computing e-health applications: networks, machine learning, neural networks, protocols, computational intelligence and data encryption. “Network” is the term most frequently used in articles, with a number of up to four times as low as any other term considered. All significant terms use maximum numbers (marked with the grey background) that vary from 19 to 81 times and appear from 2018 to 2022.

The main trends of significant term use are shown in Fig. 6 using a line fitting to-data technique. From the pictorial representation in the figure it is clear that three of the six significant terms are trending, the use of the terms “machine learning”, “neural network” and “computational intelligence” during the years 2017–2022 is increasing. The increase in the usage of the terms “data encryption”, “network” and “protocol” was minimal (whose use in 2022, 2019 and 2018 peaked accordingly).

With the rapidly increasing number of mobile and handheld devices, current computing architectures require computation over anywhere, anytime, and anything connectivity. As people and the computing nodes they use may be mobile, mobility is a barrier to computing in the fog computing environment because some of the data can also be stored at fixed locations. Therefore, the researchers are working on developing solutions for handling mobility, load balancing, task and services migration.

Mobility is one of the newest and least researched aspects of e-health fog computing. Two main groups can be distinguished: mobility of the device or of the service. The types of mobility devices present in the literature include wearable IoT devices or vehicle IoT nodes that can establish a connection with fog nodes distributed in the area.

In total, five most common terms describing mobility with autonomy in fog computing e-health applications were chosen and used to get a view of the fifth perspective. Table 5 summarises the results on these terms that appear in a representative set of articles.

There are two significant terms related to mobility and autonomy in the literature on fog computing e-health applications: wearable IoT devices and vehicle IoT nodes. “Wearable IoT device” is the term most frequently used in articles, with a number of up to three times lower than any other term considered. All significant terms use maximum numbers (on the grey background) that vary from 3 to 8 times and appear from 2020 to 2022.

The main trends of significant term use are shown in Fig. 7. From the figure it is clear that one term is trending, even though the use of the term “wearable IoT device” drops in 2021. On the contrary, the increase in the use of the term “vehicle IoT node” is minimal and decreases in years 2019 and 2022, respectively.

Computational intelligence (CI) uses a combination of five main paradigms (Engelbrecht, 2007): fuzzy logic, artificial neural networks, evolutionary computing, swarm intelligence, and artificial immune systems. In addition to these paradigms, probabilistic methods are frequently used together with CI techniques, which help deal with uncertainty or imprecision. Due to the popularity and large nomenclature of artificial neural networks, we made the decision to analyse this subset separately.

Table 6 presents a list of CI techniques that were applied during our period of investigation of fog computing in e-health. The data in the table are sorted in decreasing order of technique use times, and the statistically significant terms are separated by line. The data point to six significant techniques from 17 in total. Each significant application of the technique was explored in 6 to 20 articles. Moreover, it becomes clear that fuzzy logic based techniques – FS, FKNN, and FCM cover nearly 45%, the sum of all significant applications.

The benefits of using CI techniques for fog computing in e-health applications are revealed in Table 7. Here data from articles about comparative research of CI techniques are summarised – listing achieved accuracy, e-health application, investigated and the best techniques (marked with the grey background). According to Section 2.6, all results are sorted in decreasing order of accuracy. It appears that all the eight best CI techniques – BBM, FHMM, BBN, FCM, FKNN, F-AMLF, W-NB, and HMM, achieve accuracies of 91.41% to 97.53%. Apparently, only BBN, FCM, FKNN and W-NB are significant terms (see Table 6 text on the grey background and above the line). However, the best techniques BBM, FHMM, F-AMLF and HMM require more attention from researchers.

Machine learning (ML) is a subfield of AI. With ML algorithms, a model is built based on sample data, known as training data, to make predictions or decisions. A subset of ML is also related to computational statistics, which focuses on making predictions using computers, but not all ML is statistical learning. The ML field is also impacted by mathematical optimisation theory that delivers methods and applications to the domain.

Table 8 presents a list of ML techniques that were applied in the 2017–2022 period for fog computing in e-health. According to Section 2.6, the data in the table are sorted in decreasing order of technique use times, and the statistically significant terms are separated by line. The data points to five significant techniques out of 33 in total. The applications of each significant technique – SVM, DT, NN, ML, and RF – were explored in 14 to 42 articles. SVM is the most commonly used technique and covers 30% of all significant applications. Apparently, the term “machine learning” that is too general in our context scores fourth.

The benefits of using ML techniques for fog computing in e-health applications are shown in Table 9. Here data from articles about ML techniques comparative research is summarised by listing achieved accuracy, e-health application, investigated and the best techniques (shown on the grey background). The data are sorted in decreasing accuracy order.

The table data clarify that all of the best 11 ML techniques – SVM, KNN, self-trained SVSL, RF, EN, HEHCRC, RC, J48 DT, MW, REPT, and AdaBoost – achieve accuracies from 79.10% to 99.84%. Apparently, only SVM, KNN, RF, J48 DT, and REPT are significant terms (see the text in Table 8 on the grey background), i.e. fairly addressed in the analyses. Thus, self-trained SVSL, EN, HEHCRC, RC, MW, and AdaBoost require more attention from researchers working in fog computing.

Artificial neural networks (ANN) or neural networks (NN), are computing systems inspired by the biological neurons of brains. An ANN is based on an artificial neuron that receives and processes signals and can signal other neurons connected to it. Typically, they are aggregated into layers, the number of which determines the depth of the NN.

Similarly to previous sections, Table 10 presents a list of ANN techniques that were applied during our period of investigation of fog computing in e-health, while Table 11 shows the benefits of using ANN techniques for fog computing in e-health applications.

The data in Table 10 point to seven significant techniques (separated by line) – ANN, CNN, LSTM, RNN, DNN, MLP, and Deep Learning – from 20 in total. Each significant application of the technique was explored in 8 to 34 articles. Distinctively, here the term “Artificial neural network” scores first and its usage is 27% of all significant applications. Apparently, the term “deep learning” that is too general in our context scores seventh.

Data from articles on comparative research on ANN techniques are summarised in Table 11. Here, as previously, achieved accuracy of e-health application, investigated, and the best techniques (marked with the grey background) are listed. According to Section 2.6, all results are sorted in decreasing order of accuracy.

It appears that all the best 20 ANN techniques achieve accuracies from 84.88% to 99.86%. Apparently, only CNN, DNN, ANN-BP, ANN, DHNN, t-CNN, t-ANN, MLSTM, ACNN, RNN, ANN-CBR, Bidirectional LSTM, and Stacked LSTM are significant terms (see Table 10 text on the grey background), i.e. fairly addressed in the analysed publications. Thus, more attention from researchers working in fog computing requires such techniques as MLNN, GSO-CCNN, Auto-IF, ANFIS-GA, FS-ANFIS, CCO-CSVM and ANFIS.