1. Introduction
Mobile communication technology, computer technology, and the Internet of Things are developing rapidly. Relevant researchers have proposed the Internet of Vessels, combining mobile self-organizing networks with inland river traffic scenarios [
1], that is, ships are connected through the network to achieve efficient interconnection between people and ships, ships and ships, cargo and shore with the purpose of refined shipping management, comprehensive industry services, and humanized travel experience. The Internet of Vessels supports instant communication between ships and channel infrastructure, ports, locks, etc., which can improve traffic efficiency and the speed of obtaining ship information as well as reduce traffic accidents. Inland shipping has a large capacity, small footprint, low energy consumption, and low pollution. It is one of the main transportation systems of bulk commodities in China, with irreplaceable cost advantages [
2]. Building an efficient inland riverboat network is the basic work of optimizing the inland river shipping system, which is of great significance for the development of inland river shipping, transportation, and environmental protection.
In recent years, researchers and research institutions both at home and abroad have studied the definition, structure, and operating mode of ship networking. Ship networking is defined as a network that connects ships to shore facilities through numeric entities [
3]. The authors in [
4] define the Internet of Vessels as a novel transportation ecosystem. It is suitable for maritime transportation by integrating emerging technologies related to the Internet of Things such as cloud computing, edge computing, and satellite communications. At present, the ship networking systems that have been built and put into operation include e-Navigation [
5] led by the International Maritime Organization, the Waterway-Information-Network [
6] led by the United States, the Ship-Area-Network [
7] led by South Korea, and the River Information Services (RIS) [
8] led by Europe, which have promoted inland shipping. China has also strengthened the construction of its inland-shipping information management system with great progress. At present, inland-shipping information management systems based on AIS, the BeiDou positioning system, RFID, and other technologies have been built in some waters such as the Yangtze River, the Pearl River, and the Beijing-Hangzhou Canal. Furthermore, certain social and economic benefits have been achieved.
At present, the networking methods of ship networking can be divided into three types: ship-based self-networking [
9], satellite networking [
10], and ground-base station networking [
11]. Ship-based self-networking and satellite networking are mainly used in marine scenarios and are less restricted because they do not require the support of shore-based base stations. Ground-base station networking is mainly used in coastal and inland river scenarios. On-board communication units and shore-based coordination units form network communication nodes, and shore-based coordination units can be used as a communication relay or directly provide calling services to ships. Ship-to-ship communication alone is not enough to maintain the stability of communication due to the mobility of ships and the need for real-time information acquisition. Therefore, the introduction of shore-based collaborative units to assist in network construction can enhance the robustness and robustness of the network.
Currently, the methods for ship–shore data transmission mainly include very high frequency (VHF) systems, the automatic identification system (AIS), mobile network communication, and satellite communication [
12]. Although the AIS system significantly enhances navigational safety, its narrow communication bandwidth and inability to conduct point-to-point communication are inherent in its technical principles. Shore-based mobile communication networks, relying on architectures such as cellular networks and wireless local area networks, facilitate maritime communication in nearshore and inland waterways, offering services to vessels within a certain range, and possessing advantages of security, stability, and high bandwidth [
13].
Regarding the coverage issue in inland waterway communication, Wang [
14] analyzed the equipment status of AIS ship stations in inland waterway navigation areas, identified characteristics of communication coverage blind spots for AIS shore-based systems, and proposed solutions such as deploying small AIS receiving stations to address this problem. To tackle the issue of 4G mobile network signal blind spots in inland waterways leading to potential signal interruptions for vessels at critical moments, the utilization of both the 4G mobile network and BeiDou short message communication methods was suggested [
15]. Furthermore, a 4G signal strength detection module and communication mode switching algorithm were designed to ensure continuous communication for vessels navigating inland waterways. In response to the intelligent navigation requirements for vessels in the Yangtze River waterway, Wang [
16] proposed a solution that integrated 4G and satellite communication technologies to achieve integrated ship–shore data fusion communication for direct navigation between rivers and seas. The SeaFi project [
17] employs WiFi technology to transfer large-scale data from ships to shore or vice versa, aiming to address data transmission challenges in ship–shore communication. By establishing an unmanned surface vehicle platform, integrating devices including WiFi communication and satellite-based global positioning system (GPS) signals, a network with a land-based control center was constructed. With the rapid growth of communication service demands, wireless communication networks are evolving toward heterogeneity, which aids in addressing complex communication scenarios, achieving resource sharing among different networks, managing interference, and enhancing the system capacity. However, resource allocation issues in heterogeneous networks have concurrently become critical technical challenges in resource management.
In dense HetNets, spectrum resource allocation is one of the most significant challenges. This issue involves partitioning a segment of spectrum resources into multiple smallest resource units such as subcarriers or physical resource blocks and then allocating these resources to users in the system to meet their rate requirements while minimizing the interference as much as possible. Palanisamy [
18] proposed a strategy to reduce interference including power control and channel allocation. However, further research is needed on how to reduce the time complexity while ensuring performance. Fan [
19] proposed a distributed iterative algorithm that maximizes the overall efficiency of all base stations/access points while guaranteeing terminal quality of service, which is applicable to various heterogeneous network scenarios. In heterogeneous networks, users can choose different levels of base stations for access compared to traditional single networks. The selection of access points is an important issue; for example, in 4G mobile communication networks, the signal reception strength is typically defined as the criterion for user access to base stations. Kazmi [
20] studied the access selection problem of users at different hierarchical base stations in heterogeneous networks and proposed an access strategy, but this strategy overlooks resource allocation issues and considers limited factors. Therefore, to achieve optimal overall system performance, both spectrum resource allocation and user access issues should be considered together.
Regarding the allocation of communication resources in maritime traffic systems, Long et al. [
21] proposed a ship wireless communication resource allocation method based on the ant colony algorithm to maximize the communication capacity as the objective function and used the ant colony algorithm to find its optimal solution. Jiang [
22], based on the two indicators of throughput and fairness, established a slot resource allocation optimization model to achieve precise slot resource allocation in ship wireless communication networks. Yao proposed a channel allocation method for maritime communication systems based on throughput and load balancing. Yang [
23] optimized a water communication network resource allocation strategy with the objectives of minimizing energy consumption and delay, solved it through convex optimization, and the simulation results showed that the scheme could achieve a good balance between energy consumption and delay. Wang [
24] introduced new communication technology into maritime ship communication systems and proposed a channel resource allocation scheme for system interference control to increase the number of devices in the network.
A reasonable deployment of shore-based collaborative units can improve the network performance of ship networking and reduce construction costs. At present, more studies have focused on the deployment of mid-road side units instead of the deployment strategy of shore-based collaborative units in the Internet of Things. The deployment strategy of roadside units in highway scenarios has been studied to establish optimization goals for communication energy loss and network connectivity [
25]. The deployment strategy of roadside units has also discussed to improve the positioning accuracy of vehicles [
26]. A deployment strategy was proposed to minimize the number of roadside units and ensure the quality of traffic service [
27]. The needs of ship–shore communication services and the Internet of Vehicles are quite different due to the special navigation methods of inland river ships. Therefore, the rational deployment of shore-based collaborative units is of great significance to the needs of the intelligent navigation business of inland ships.
3. Deployment Strategy of Shore-Based Collaborative Units
3.1. Problem Description
For a channel with a length of S, shore-based collaborative units include a mobile communication base station and a WiFi base station. A two-layer heterogeneous communication network is carried out in the area to ensure signal coverage and meet the communication needs of inland riverboat networking. The deployment strategy is analyzed from the type of the base station and the configuration of the base station.
The type of the base station is defined from the communication technology used by the base station and the operating frequency band such as a 4G base station with an operating frequency band of 1.9 GHz and a WiFi5 base station with an operating frequency band of 5 GHz, if there are a total of M types of base stations to choose from including types of mobile communication base stations and types of WiFi base stations ().
The configuration of base stations should consider the total bandwidth, transmission power, tower/pole height (the antenna hanging height is consistent with the tower/pole height), and antenna type. For each type of base station, there are , , , and options for its configuration factors. Therefore, each type of base station has configurations.
The selection matrix for available base-station types and configurations is
where
is a variable of 0–1, indicating the selection of the communication base station;
= 1 indicates that the first type of configuration is selected;
= 0 indicates no selection. Due to the need to build a two-layer heterogeneous network based on mobile communication and WiFi,
When the first configuration of the
mth type is selected,
is the transmission power of the base station and
is the bandwidth. According to the signal-coverage link budget and the corresponding propagation model [
16], the coverage radius
r of the base station can be obtained.
The maximum allowable spacing
between the network sites of the same layer can be obtained according to the length of the overlapping coverage band, the signal coverage radius, the width of the channel, and the vertical distance from the base station to the edge of the channel (
Figure 3).
represents the length of the overlapping coverage belt;
represents the distance between the base station and the edge of the channel;
represents the width of the channel;
represents the signal coverage radius of the base station. The following relationship exists between the above parameters.
Therefore, the corresponding number of deployed base stations is
where ⌈⌉ means that the value in the symbol is rounded up to an integer.
3.2. Deployment Principles and Planning Objectives
Base-station planning considers the data transmission rate of the ship and shore, the power consumption of the base station, and the deployment cost. The data transmission rate of a base station refers to the number of bits transmitted by the base station per unit time. The maximum data transmission rate of a single base station is related to the bandwidth of the base station and its spectral efficiency. Spectral efficiency is an index used in wireless communication to evaluate the efficiency of radio-channel resource utilization and is mainly related to the modulation and coding method of base stations (i.e., the types of base stations).
is the spectral efficiency of type-m base stations. The maximum data transmission rate of a single base station can be calculated by spectral efficiency × bandwidth, namely
. Therefore, the maximum data transmission rate that the system can provide is
The planning of the data transmission rate of the base station is generally related to communication service requirements in the region. According to the calculation of the communication requirements in
Section 2.2, the maximum throughput
in the system can be obtained. Therefore, the objective function is
.
The power consumption of a base station refers to the energy consumption required by the base station to maintain normal operation, which increases with increased transmission power. Therefore, the transmission power of the base station can reflect the power consumption of the system. The objective function of the total system power consumption can be equivalently defined by
The costs of station deployment account for most of the investment in wireless network construction. Therefore, the costs of station deployment should be reduced as much as possible under the premise of ensuring network quality. Assuming that the construction costs of the same type of station are the same, the construction cost of the communication base station is related to the type, configuration, and number of base stations. When the
configuration of the
mth type is selected,
is the construction cost of a single site of the base station. The objective function of the total system cost can be defined by
An optimization model is established, and its objective function and constraints are
where
represents the budget cost of the system communication planning;
represents the maximum power allowed by the system;
indicates that the transmission rate supported by the system is greater than the total throughput required for ship-shore communication services;
indicates that the total transmission power of the base station in the system does not exceed the maximum allowable power;
3 shows that the total cost of base station planning does not exceed the budget value;
4 shows the edge rate of the base station not lower than the specified minimum value.
3.3. Model Solving Based on the Genetic Algorithm
In light of the multi-objective optimization problem proposed in the previous section, the Pareto optimal solution set was obtained by the multi-objective genetic algorithm. Single optimal solutions were screened to obtain the final planning plan of the base station. The steps are as follows.
(1) Genetic coding. The solution space of the above-mentioned base station planning should be a -order matrix, which corresponds to the configuration selection of each type of base station. Turn the solution space into binary strings, and the value in each solution space string represents a certain type of base station. Select its type of base-station configurations, and 0 means that this type of base station is not selected. Then, binary strings undergo binary coding.
(2) Target evaluation. Individual binary encoding is decoded to obtain a subset of corresponding base-station selections. Then, calculate the indicators corresponding to the subset. If the constraints are not met, the negative correlation factor of the individual is set to reduce the probability that the individual passes on to the next generation.
(3) Congestion calculation. Part of the solution is resolved in the area with the dense solution set to ensure the diversity of the population (Equation (15)).
where
and
represent the value of the
objective functions of the
and
points, respectively.
(4) Population update. The previous solution is used as the optimal solution set for the iteration after individuals are sorted by crowding. If the number of iterations is reached, the optimal solution set is output. Otherwise, the latter solution will be cross-mutated to produce a new dominant solution.
(5) Crossovers and mutations. Random crossovers are performed for the latter
solution. Then, variation is performed with the single-point machine (
Figure 4).
(6) Single optimal solution screening. The Pareto solution set should be decoded to convert into corresponding base-station planning. Then, screen the single optimal solutions, and determine the final planning of base stations according to Equation (16).
where
J is the number of Pareto optimal solution sets;
is the maximum value of objective function
in the Pareto optimal solution sets;
is the minimum value of objective function
;
is the minimum value of objective function
.