The Internet of Things (IoT) is a new heterogeneous system integrated by the various end users (sensors and terminals) with different technologies. However, the limiting factor is bandwidth in the IoT due to the exploding end users and the network bandwidth requirements. A novel IoT model, which integrates the power-line carrier (PLC) and the wireless network (WN), is proposed to solve the bandwidth problem from the architecture, especially in the areas lacking network facilities. In addition, we exploit an effective virtual layer (EVL) which allows the different end users to access the system model seamlessly. Then, the attractor selection algorithm based on Markov chain (MASA) is employed to select an optimal path among the PLC or WN. The simulation results demonstrate that the proposed system model has the smaller average queuing delay than other algorithms and makes the model more stable and robust.
With the increasing requirements of the network services on account of the popularity of the smart end users (sensors and terminals), users expect the network environment in which they can readily access the internet at any time and any place. Especially in the Internet of Things (IoT), the smart end users are expected to reach 212 billion entities deployed globally at the end of 2020 [
In the traditional heterogeneous networks, the researchers mainly apply ultradense networks (UDN) to increase the network capacity [
In the traditional networks, there are three main types of selection path algorithms: the path selection based on multiple attribute decision making (MADM), the path selection based on fuzzy logic, and the path selection based on game theory. The path selection based on MADM includes compensatory algorithm and noncompensatory algorithm. The noncompensatory algorithm is used to find the candidate network which meets the minimum requirement of the terminal, while the compensatory algorithm chooses the optimal network by considering multiple attributes. The most MADM algorithms are the compensatory algorithm in the heterogeneous network. Wu et al. [
In this paper, we propose the heterogeneous PLC and WN model for the IoT, which is the extended work based on our previous work [
The rest of this paper is organized as follows. The system model is surveyed in Section
In this section, we propose the system model of the HIoT. We define a virtual layer for the protocol conversion. Then, the queuing model, which is the foundation for the mathematical calculations, is provided. Finally, we present the control model which ensures the normal operations of the system model.
Many access technologies, including cellular, WiFi, WiMAX, PLC, Long-Term Evolution (LTE), and General Packet Radio Service (GPRS) coexist in IoT. The protocols and the formats of the packets (the frames) are different from those of the different access technologies [
Process of the protocol conversion.
As shown in Figure
While the data (e.g., files and packets) arrive in the node (sensor) to find another data currently being queued or in transmission, the data stand in the queue. The queuing model of the HIoT is shown in Figure
Queuing model of the HIoT.
We assume that every node has the access control mechanism to coordinate the transmission process. The access control mechanism consists of four parts: the feedback module, the storage module, the algorithm module, and the scheduling module. The feedback module collects the path information (congestion, disconnection, etc.) between the two nodes which communicate with each other. The information is transmitted to the algorithm module based on MASA for selection of the optimal path. The algorithm module is the core in the access control mechanism, and the algorithm module progress is shown in Figure
Algorithm module process.
According to the queuing theory, the mean queue size is one of the important indicators to judge the system performance so that it is widely used in the network research studies [
For the convenience of research, the two-path Markov chain is applied to the queuing process between the PLC and WiFi which models the simple HIoT. The two-path Markov chain for the HIoT model is illustrated in Figure
Two-path Markov chain for the HIoT queuing.
Variables and definition.
Variable | Definition/description |
---|---|
|
The arrival rate of the PLC or the WiFi |
|
The service rate of the PLC or the WiFi |
|
The probability of finding |
|
The probability of finding |
|
The probability of the system being at the PLC state only |
|
The probability of the system being at the WiFi state only |
|
The rate of leaving the PLC state |
|
The rate of leaving the WiFi state |
According to the transfer characteristic of the Markov chain, the average rate at which a point (state) is entered equals to the average rate at which a transition from the point occurs. Thus, the equilibrium state in Figure
The partial generating functions of the model are defined as
Then, (
According to (
From (
Since
Summation of (
Finally, from (
We define auxiliary quantities
Then, after some development,
The quantities
In the previous work [
Based on the above assumptions, the multipath Markov chain is shown in Figure
Multipath Markov chain for the HIoT queuing.
Then, we derive the expression of the average queuing delay in the HIoT system. Let the generating functions for each path be
Summing (
After introducing the substitution, (
After performing some algebra, we have
Then, we put (
By applying Cramer’s rule to (
The polynomial
Summation of (
Replacing (
The average number of files in the system is
Using Little’s law
The original attractor selection algorithm (OASA), which has two attractors corresponding to the two proteins, can adaptively change the external form of the cell according to the external environment. The OASA was applied in the network research studies in [
For the sake of simplicity, we denote
According to the selection attractor of the biology, the selection process can be described as follows: The cell adapts to the environment, which means that
In the HIoT, all transmission paths between the two nodes can be considered as the solution set. The value of activity
Variation of the selection probabilities over the parameter
Figure
Multipath selection process.
In this section, we provide the activity expression in the HIoT. Based on the adaptive attractor selection of the biology, the cell expression can be regarded as the path state and the attractor can be regarded as the path in the network selection. The high value of
Therefore, we map the average queuing delay into
Relationship between
Figure
Different shapes of
Different shapes of
In this section, we validate the average queuing delay against the simulation results firstly. Then, we contrast the proposed path selection algorithm with the existing path selection algorithms. The proposed algorithm named “MASA” is the EASA based on the multipath Markov chain. Unless otherwise stated, the file/flow sizes are the exponential distribution, and the file generation of users is a Poisson process and FCFS as the service policy.
We first validate the average queuing delay of the two-path Markov chain against the simulations. The network topology of the two-path selection is shown in Figure
Network topology of the two-path selection.
Variables and value.
Variable | Value |
---|---|
|
|
|
|
|
0.5 |
|
0.5 |
Figure
Average queuing delay of the different arrival rates.
Next, we consider the scenarios with the multiple access technologies (PLC, WiFi, LTE, cellular, and 3G) corresponding to the multipath model. The scenarios for different paths are shown in Table
Variables and definition.
Number of paths | Type of paths | Rate (Mbps) |
---|---|---|
|
PLC | 1.5 |
WiFi | 2 | |
Cellular | 2 | |
|
||
|
PLC | 1.5 |
WiFi | 2 | |
Cellular | 2 | |
LTE | 10 [ | |
|
||
|
PLC | 1.5 |
WiFi1 | 2 | |
Cellular | 2 | |
LTE | 10 | |
WiFi2 | 10 |
Figure
Different number paths against the average queuing delay.
Network topology of multipath selection.
In [
Average queuing delay against the number of nodes.
The average queuing delay comparisons between the MASA, AODV, GA, SAW, and ISRO_PG with the different number of nodes are illustrated in Figure
Secondly, we compare the transmission failure ratios of these algorithms with the different nodes number. Figure
Delivery ratio against the number of nodes.
Finally, we simulate the adaptability of these algorithms while the path is busy or the nodes are unable to work. Figure
Delivery ratio against the failures.
In this paper, the adaptive HIoT model was proposed to solve the problems of the bandwidth and the spectrum shortage in WN. Firstly, we propose a novel HIoT which integrates the PLC and WN. There is a virtual layer in the HIoT, which provides unified interface to realize the uniform communication protocol. Then, we apply the MASA which is an algorithm-based Markov chain to allocate the network resources adaptively. The accuracy of evaluation results is analyzed by the mathematical calculations and simulations. Then, the validity and stability of the system model are verified in the different scenarios by the simulations. Finally, the investigations of delivery ratio and average queuing delay based on different nodes numbers, compared with the AODV, GA, SAW, and ISRO_PG, are discussed in the HIoT. The simulation results show that the algorithm in [
The simulation for multipath selection data used to support the findings of this study have been deposited in the Mendeley repository (
This work has been partially published in 2018 IEEE International Conference on Energy Internet (ICEI) [
The authors declare that they have no conflicts of interest.
This work was supported by the National Science and Technology Major Project under Project 2018ZX03001016, the National Natural Science Foundation of China (61671073), the Project of Young Core Teacher in Higher Education of Anhui (gxyq2019070), and the Scientific Research Foundation of the Higher Education Institutions of Anhui Province, China (KJ2019A0630).