# Design and Construction of Intelligent Corridor of Tourist Attractions Based on Sensor Network

Authors: Ling Hu
Journal: Journal of Sensors
 (2022)

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## Abstract

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development.


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## Body

## 1. Introduction

With the development of social technology, the term intelligence has gradually come into people’s view, and the development trend of intelligence has been relatively rapid, thus gradually replacing manual labor, facilitating our life, and even greatly improving work efficiency [1]. As the third information technology revolution, the Internet of Things (IoT) is developing rapidly and has been highly valued by governments, enterprises, and research institutes all over the world and has gained some research results. At the same time in the era of the Internet of Everything, IoT technology is particularly important in various fields of our life. At present, IoT technology has been commonly used in life, making our life more convenient and faster, especially in the field of forest and marine monitoring. Wireless sensor networks (WSNs) as the underlying key sensing technology of IoT technology, known as the sensing “peripheral nerve,” are the key to data collection and wireless transmission, which is of great significance and can make the multiple physical worlds, intelligent computing world, and complex human. It is the key to data collection and wireless transmission, which is of great significance to interconnect the multiple physical worlds, intelligent computing worlds, and complex human societies [2]. With the advent of the 5G era and the rapid development of smart terminals, 5G provides strong technical support to drive the innovation of IoT, and the development of the 5G standard meets the demand of IoT for network speed, bandwidth, and security. Under the vision of the Internet of Everything and the high popularity of IoT applications, there will be a large amount of IoT smart terminal data generated anytime and anywhere, and the IoT gateway data will be diversified, multiquantified, and complicated. This also brings how to achieve the agility, efficiency, and stability of data management for the IoT gateway data management problem.In the context of continuous high economic growth, China’s natural resources and land space development process is gradually accelerating, ecological and environmental problems are increasingly tightening, and ecological environmental protection is urgent. Based on this, China has issued a series of policy documents on the construction of ecological civilization and ecological environmental protection. With the construction of ecological civilization and the reform of the ecological civilization system, the development model of ecological priority has become mainstream, and China has taken important steps towards the development goal of “Beautiful China.” Tourism is an important industry in the world, and all countries are strengthening the role of tourism in economic development, hoping to enhance the competitiveness of their tourism industry in the global arena. The way people travel has changed a lot in recent years, and the choice of tourist destinations, the way they visit, and the evaluation criteria have all changed greatly [3]. The Internet of Things technology has been widely used in life, making our life more convenient and faster, especially in the field of forest and ocean monitoring. Travel has become an important part of people’s leisure life and an important way for contemporary people to enjoy their lives. The rapid spread of modern transportation such as self-driving, high-speed rail, has driven the rapid change in the way people travel. In choosing the way to travel, many people no longer need the travel agency to arrange the designated route and tour; often, they decide where to travel, what to travel, how to travel, where to live, what to eat, and what to buy. More than 85% of tourists choose self-guided tours, more than 60% choose self-driving tours, and many even want to experience self-driving outbound tours. The quality of a region’s tourism products and tourist reputation not only depends on the quality of service of tourist attractions, hotels, travel agencies, and other tourism industries, but the regional comprehensive environment is also an important influencing factor [4]. The original tourism development model often leads to the isolation of tourist areas and residential areas, so that the regional residents do not get the corresponding benefits in tourism development and discourage residents to maintain the regional tourism image. In addition to the concept of geographically all-territory tourism, it also includes the concept of tourism for all people in the whole field and region of tourism-related industries, creating a tourism landscape everywhere in the whole region, where everyone builds a tourism image and participates in tourism. Territorial tourism also emphasizes that the purpose of local tourism development is not only to serve tourists and improve economic income, but also to improve regional infrastructure, drive the development of related industries, improve service management through tourism development, and boost the local economy and improve the living standards of residents through the tourism industry.The beautiful natural ecological environment is the advantage of tourism development, and only environment-friendly ecotourism is the road to the healthy and sustainable development of tourism. However, the current ecotourism planning mostly follows the traditional tourism planning ideas, which do not effectively implement the concept of ecotourism and cannot meet the requirements of natural ecological protection [5]. This paper takes the edge effect theory in the field of ecology as the entry point, uses the edge effect theory to guide ecotourism planning, enriches the theoretical system of ecotourism planning, and provides direction for the development of the whole area of tourism. Territorial tourism advocates blurring the boundaries inside and outside scenic spots, serving both residents and foreign tourists, and protecting local natural ecology and human ecological environment, and the integration of scenic spots and town environment will be the mainstream form of future tourism development and construction [6]. Territorial tourism is to develop and build the region as a whole tourist destination and to achieve the integration of environmental quality inside and outside the attraction’s scenic spots, so that all the people build the regional tourism image, to create a regional overall tourism environment. Promoting the development of all-area tourism requires us to change the historical tourism pattern of scenic spots, hotels, and restaurants; optimize the overall landscape environment of the entire region; and improve the tourism-related infrastructure and public service system.


## 2. Related Works

Wireless sensor network has a good market due to the advantages of flexible network setting, high reliability, and self-organization, which is suitable for many applications. With the rapid development of wireless sensor network technology, some excellent algorithms have emerged, and the wireless sensor network technology is becoming more mature in terms of communication efficiency, stability, and reliability, and the application of wireless sensor networks is becoming more extensive [7]. There are many wireless sensor network technologies on the market, such as Wi-Fi and Bluetooth technology, suitable for smaller range applications; the transmission distance is often only a few dozen meters; when the range is too large, the transmission of information is poor, or even cannot be delivered, and the power consumption of these two is relatively large; the general use of batteries cannot meet the long-term power supply, usually must access to fixed power supply. Lora can maintain its excellent performance of low power consumption and low cost when it achieves longer distance communication, but accordingly, as the number of nodes it can accommodate increases, when the network size expands, various unrelated nodes tend to forward invalid messages, increasing the routing detour and improving power consumption. Although the communication distance of a single Zigbee is short, the number of nodes that can be grouped can be as many as 216, enabling outward expansion, while security and reliability are strong and can be automatically withdrawn from the network; according to tests, a dry battery can supply a node for more than ten months. Chang and Wang proposed a smart sensor system for air quality monitoring, which consists of three units: a smart sensor unit, a smartphone, and a server [8]. The smart sensor unit uses sensors to sample air quality data anytime and anywhere and sends the data to the smartphone via Bluetooth, which displays the data to the user and sends the data to the server for processing, completing functions such as visualization, and air quality monitoring and Web publishing. In terms of key technologies for intelligent sensing systems, Rodríguez-Molina et al. designed the RS-485 bus hardware circuit and software driver to achieve automatic resource allocation, dynamic adaptation to system structure changes, and automatic registration of sensor parameters, but the system lacks a serial port baud rate adaptive mechanism [9]. Due to the low power consumption and small size of the microsatellite, the serial port is still used as its interface, and the satellite serial port is modified to make it have an automatic registration function. Tsetsos et al. have made great achievements in researching smart sensors, and they have researched object-oriented modeling for smart sensors, plug-and-play strategies, and other related aspects and applied these researches to various applications such as smart sensing systems [10]. Reflecting the complexity of the spatial distribution of habitat landscape elements, this chapter selects the landscape shape index (LSI) and the patch shape index (SHAPE) to analyze the complexity of the habitat landscape shape.By the end of the 1840s, the economies of Western capitalist countries were developing rapidly because of the first industrial revolution, and as the environment improved dramatically, tourism began to grow as well. By the twentieth century, Western countries had reached high levels of economic development and urban environments, stimulating large numbers of residents of developing countries to enter the country for tourism. Foreign scholars have not explicitly proposed the concept of regional tourism, but many aspects of their tourism development concepts are like those of regional tourism. Ecotourism has made great achievements in terms of both breadth and depth of research [11]. The concept of the greenway has been defined from open space, ecological, environmental, and linear perspectives. The concept of the greenway is mainly derived from European boulevards, Olmsted’s parkways, and Howard Field City. At present, domestic scholars carry out less research on ecotourism corridors, and the relevant studies are mainly carried out on greenways, scenic paths, and heritage corridors, but the studies on greenways, scenic paths, and heritage corridors are mainly carried out for tourism development and cultural preservation, and the consideration of regional ecological background is relatively lacking [12]. Therefore, the current research on ecotourism corridors can hardly meet the practical requirements, and the traditional planning strategies can hardly cope with the new problems faced in the construction of ecotourism corridors, and the corresponding research contents and planning strategies need to be further discussed and improved. Xiao et al. concluded in their research on domestic ecotourism-related papers that the current research on ecotourism mainly focuses on the development of ecotourism areas in nature reserves, forest parks, and small geographical areas in various provinces and cities [13]. Katebi et al. pointed out that the current research objects of ecotourism are mainly small-scale protected areas such as national parks, coastal reserves, wetland parks, and large-scale spatial scales [14].Although the traditional IoT gateway technology has stabilized, the traditional IoT gateway system, data parsing simply uses a single data processing model, serial parsing of data packets, which is time-consuming and inefficient. In the face of today’s era, the massive amount of IoT diversified data, and the drawbacks of the traditional gateway data management concept will be seriously amplified [15]. The existing functions of the traditional gateway severely limit the multipurpose flexible settings of the gateway, multiscenario flexibility, and efficient real-time processing of multiple data. The gateway communication network model is single, which limits the network’s provisioning adaptation capability. From relevant studies at home and abroad, heritage conservation has experienced a historically important development from heritage expansion to heritage areas [16]. The current trend of heritage conservation has shifted from the conservation of single heritage elements to the conservation of mixed heritage combining natural elements and cultural elements; from the static conservation of heritage to dynamic and living conservation; and from the conservation of previously fragmented points of heritage to the conservation of regions. In the process of regional linear heritage conservation, both theoretical approaches, cultural routes, and heritage corridors emphasize the integrated approach to cultural landscape conservation [17]. The study of the spatial structure of traditional heritage areas is more subjective and based on the personal experience of the researcher often, which makes it difficult to conduct scientific and quantitative analysis. Traditional heritage area studies mainly include establishing conservation areas, classifying protection levels, restoring heritage landscapes, etc. These measures can achieve localized heritage conservation in the region in a relatively short period. However, due to the lack of evaluation and analysis of the structure of the heritage area system, it is difficult to produce effective integrated interaction among the localities, the conservation of heritage is disconnected from people’s lives, and the conservation and utilization of heritage plays a smaller role in the overall regional development and is not conducive to the enhancement of the overall value of the heritage area system. In contrast, microscale heritage conservation focuses on solving the problem of conservation of heritage monoliths. Considering the specific situation, the test equipment may occasionally move, so relying only on the device address of the terminal to achieve the management of the equipment often fails to achieve the desired effect. When the equipment is moved due to human or experimental needs, the device cannot be managed in real time.


## 3. Intelligent Sensing Network Design

The IoT network is gradually becoming huge and has developed from a single point-to-point data interaction to the current trend of point-to-point and face-to-face gradually realizing the interconnection of things and the combination of people. IoT network is moving towards high concurrency, and high reliability, to make objects conscious and sentient and capable of environment-aware information analysis and message interaction like people. In an IoT network, the gateway is the crucial point to build the local network of perception layer and to control the stable operation of many end node devices flexibly and efficiently, as the gate of information interaction between the network hardware layer and cloud server, and to control the direction of information flow [18]. Describing landscape patterns and changes is a widely used quantitative analysis method in ecological research. Although the indices of different landscape patterns are different, they are highly correlated. The architecture of IoT is mainly divided into three layers: perception layer, network layer, and application layer. The data between the three layers show a chain transfer process. The sensing layer senses and measures the environmental information, and the data is transmitted to the cloud server through the network layer. These sensors are like the five senses of a human being, capturing the environmental signals around them and analyzing and judging the captured signals. The network layer is equivalent to the neural system network in the human body, which transmits the sensory information to the cloud server. Wireless sensor networks (WSNs), as the underlying key sensing technology of IoT technology, are called the “peripheral nerve” of sensing and are the key to data collection and wireless transmission.Inspired by the biological habits of insects in nature, a new monarch butterfly optimization (MBO) algorithm was proposed in 2015 based on the migration behavior of monarch butterflies. The monarch butterfly optimization algorithm has strong parallel computing capability and is widely used in the fields of target recognition and classification, image processing, and function optimization. The latest monarch butterfly optimization algorithm is inspired by a new group intelligence algorithm to find food according to the life habits of butterflies, which is characterized by high convergence accuracy and has wide applications in function optimization, pattern recognition, and image processing [19]. However, there are some shortcomings of the MBO algorithm in the process of solving combinatorial optimization problems, mainly in the following aspects. Firstly, the migration operator of the MBO algorithm is mainly used to generate the next generation of individuals by arbitrarily selecting individuals in two populations, which is easy to lead to the loss of individuals; thus, the resulting solution is not a globally optimal solution but a local optimal solution and loses the emperor butterfly global optimal empirical value. Secondly, the MBO algorithm selects only the better individuals in the process of generating offspring, which will lead to discarding some relatively poor individuals; thus, its search process leads to a lack of population diversity and cannot find the global optimal solution, and the search speed is slow.
(1)xi,kt+1=dx−αxi,kt+1,where dx is the step size for finding the solution during the current iterative calculation of the individual butterfly and α denotes the weight coefficient, which corresponds to the value of α=1/t. For the algorithm to find the global optimal solution, the particle swarm optimization-monarch butterfly optimization (PSO-MBO) algorithm is applied to expand the population size, mainly optimizing the migration operator and the butterfly adjustment operator. In the process of generating new individuals, the algorithm can accept relatively poor solutions with a certain probability to avoid causing the loss of population diversity, speed up the convergence of the algorithm, and increase the diversity of the butterfly population in the subsequent search for new fitness values.IoT gateway data will be diversified, quantified, and complicated. This also brings the problem of how to realize the agility, efficiency, and insufficient stability of data management for IoT gateway data management. To demonstrate the superior performance of the particle swarm optimization imperial butterfly (PSO-MBO) and ELM methods proposed in this section, the methods are simulated and compared with the basic PSO algorithm, GWO algorithm, and MBO algorithm. The performance of their algorithms is first compared by function tests of single-peak and multipeak functions, followed by extensive experiments on the coverage performance of heterogeneous sensing nodes of HWSNs to verify the superior performance of the proposed algorithms. Firstly, the environmental area to be monitored is assumed to be100×100 m2 in the monitoring environment, where 50 sensor nodes are arbitrarily deployed, including heterogeneous nodes with different sensing radii in the range [5, 20], which are some random numbers, and the initial energy of the nodes is 1 J, and the energy of the heterogeneous nodes is 5 J. The simulation software is MATLAB R2017a.The impact of general data fusion methods on network performance is the increase in data transmission delay because performing data fusion causes cluster head nodes to perform some data computation and consume some time. So, the general method of data fusion is used for those applications where the application scenario does not require high data transmission latency. In this paper, we consider the transmitted data in the process of transmission from the source node to the destination node to overcome the blocking of the path, link failure, node energy consumption, data transmission rate, and many other problems to ensure that the transmission latency is as low as possible. Sink successfully receives the source node which is the heterogeneous sensing node. The total number of packets isNr, and the parameter Ttrans is the average transmission delay of the smart sensing network. A comparison of the transmission delay curves of the WSNs network for the four algorithms is shown in Figure 1.Figure 1
Test results of convergence curve of the function.In the intelligent switch control system based on wireless sensor network, considering some real-time problems encountered in the process of equipment management, the locking of electric equipment can be realized by the address of the control terminal [20]. Considering the specific situation, the test equipment may occasionally exist mobile, so that the management of equipment relying only on the device address of the terminal often does not achieve the desired effect, when the equipment encounters artificial or experimental. When the equipment is moved by human or experimental needs, it cannot manage the equipment in real-time. To solve the above problems, we introduce a positioning algorithm for the subnodes to achieve accurate positioning of the nodes, so that when the devices move, they can still be accurately positioned to achieve accurate management of the devices. The connection with the smart sensor is completed through the UART serial interface; the embedded system completes the basic sensor management functions and parses, processes, and encapsulates the data sent by the smart sensor and sends the packaged data information to the IoT cloud platform through the network interface.
(2)At=Cskipd,i+1−Ap.DV-HOP algorithm uses the hop count between nodes for positioning, multiplying the average hop distance of the network with the minimum hop count to derive the estimated distance between two nodes. DV-HOP algorithm is to achieve the purpose of continuously broadcasting messages in the network by selecting a reference node; the node that receives the message will receive the message, record the hop count, and forward it, and then the hop count will be added one more if more than one reference node. The size of the hop count is compared, and the smallest hop count is stored at the same time. Since the distance of the reference node (anchor node) is known, the average hop distance can be calculated based on the minimum hop count and the known position information of the reference node, and the average hop distance is broadcasted.(3)Lengthi,j=ℓi,j×Hopsi×sizej.The algorithm is simple and easy to implement because it only sends and receives messages and does not require additional hardware devices, but it requires high network connectivity, and increasing the number of reference nodes can improve network connectivity, while the cost is also increased, which is more suitable for relatively dense networks with high network connectivity.The smart sensor contains an internal analog signal to the digital signal circuit, which can be processed by its internal MCU to obtain a digital signal; a storage unit to store sensor parameters (static and dynamic parameters) and other data, etc.; and an embedded MCU microprocessor with programming functions to process data and interact with the IoT gateway [21]. The functional design of each module of the smart sensor is shown in Figure 2. The IoT gateway is an important part of the smart sensor system and is the medium between the smart sensor and the IoT cloud platform to handle the communication between the sensor and the remote user. The connection to the smart sensor is completed through the UART serial interface; the embedded system completes the basic sensor management functions and parses, processes, and encapsulates the data sent by the smart sensor and sends the encapsulated data information to the IoT cloud platform through the network interface. The network connectivity is required to be high. Increasing the number of reference nodes can improve the network connectivity, and the cost is also increased. It is more suitable for relatively dense networks with high network connectivity.Figure 2
Intelligent sensing network functional module diagram.


## 4. Traditional Corridor Construction and Tourism System Planning and Design

Heritage corridors are linear. The heritage theme is linear, an attribute that distinguishes it from independent cultural relics, historic and cultural cities, historic lots, scenic areas, etc. Also because of its linear nature, the conservation of sites requires an enhanced integrated management approach and holistic planning strategies rather than a traditional, single-site conservation approach. The corridors are of various scales and can be lines of ancient historical events, Great Wall sites, industrial canal sites or railway lines, or site corridors formed by a series of site sites in series. The spatial level of the heritage corridor is analyzed, and its composition includes four major elements: a green ecological corridor, sites, walking trails, and an interpretation system. The spatial relationship between the nodes formed by the ecological corridor, sites, and walking trails is shown in Figure3.Figure 3
Schematic diagram of the spatial structure of the heritage corridor.The quantitative analysis of habitat landscape patterns using landscape pattern indices can provide sufficient feedback on information not easily interpreted directly in landscape patterns, elucidate the basic patterns of the spatial distribution of elements in the landscape, and describe landscape patterns and changes, which is a widely used quantitative analysis method in ecological research. Different landscape pattern indices differ, but they are highly correlated. There are various landscape pattern indices, which can be divided into patch-level index, patch type-level index, and landscape-level index [22]. Since the patch-level index only describes the characteristics of individual patches, it cannot describe the structural characteristics of a certain landscape type patch and the landscape pattern, so this paper chooses the patch type-level index and landscape-level index for analysis. Tourism has become an important part of people’s leisure life and an important way for contemporary people to enjoy life.FRAGSTATS is a software for calculating landscape pattern indices, developed by Oregon State University, USA. It only needs a reasonable basis for defining and measuring the landscape to describe the characteristics of patches through the calculation of indices, and the software can calculate multiple landscape pattern indices at the same time, which is more functional. In practice, the raster classification map processed by ArcGIS is input, then the relevant processing parameters are set, and finally, the corresponding index type is selected to obtain the result.(4)IIC=∑i,j=1naij/nlij−2AL,where aij is the area of patch i and patch j, respectively, n is the total number of patches in the habitat landscape, nlij is the number of connections between patch i and patch j, and AL is the area of the whole landscape. The value of IIC ranges from 0,1, where the smaller the IIC value is, the smaller the connectivity between habitat patches, and vice versa. The original tourism development model often leads to the isolation of the tourist area and the residential area, so that the residents in the area do not get the corresponding benefits in the tourism development, and the enthusiasm of the residents to maintain the regional tourism image is discouraged.The shape characteristics of habitat landscape are an important part of the structure of habitat landscape, and the shape characteristics of different habitat landscapes have an important influence on the ecological processes such as species migration and reproduction, reflecting the complexity of the spatial distribution of habitat landscape elements. In this chapter, the landscape shape index (LSI) and the patch shape index (SHAPE) are selected to analyze the complexity of habitat landscape shape. The landscape shape index (LSI) is the simplest and most direct index to describe the complexity of the shape of a landscape type, and the larger the LSI value, the more complex the shape of the landscape type. The LSI values of all habitats in the inactive areas of the study area, except for the agricultural landscapes (H3), were larger than the LSI values of the habitats in the active areas, as shown in Figure4.Figure 4
Landscape shape complexity index.Figure4 shows that the shape of the habitat landscape in the black muntjac inactive zone is more complex and fragmented than in the active zone. Among all habitat landscapes in the black muntjac active area, inactive area, and the whole study area, the landscape types with the largest LSI values are all other forest landscapes (H2), which further proves that other nonforest landscapes bring cutting effects on the whole forest landscape, especially other forest landscapes, and aggravate the fragmentation of forest landscapes in the study area. The landscape types with the smallest LSI values are all watersheds (H6), indicating that watersheds in the study area have the most regular shape and the least complexity. The smallest landscape type with the lowest LSI value is water (H6), indicating that the shape of water in the study area is the most regular and the least complex, and the shape of water is relatively regular due to the construction of some reservoirs in the study area.


## 5. Intelligent Sensing Network Performance Testing

The software functional layer of the platform is tested, and the hardware subfunctional module circuits are tested. First asynchronously test the software and hardware implementations, after the test results are up to standard. Then synchronously test the compatibility between the software and physical hardware layers of the platform and jointly test the overall performance of the software embedded hardware. Improve regional infrastructure through tourism development, drive the development of related industries, improve service management, etc., through the tourism industry to comprehensively stimulate the local economy and improve the living standards of residents. The purpose of this link is to test whether the whole platform meets the intended functional requirements as well as the requirements of the actual production environment. Platform testing requires building a test environment that simulates the real production environment and conducting performance testing and functional testing.During the negotiation between a smart sensor and an IoT gateway, eitheri or i^ sends a proposal to the other party that assigns a value dai∈Dai to each attribute ai in the attribute set. For the proposal sent by i to o⟶, i^needs to determine the efficiency of the proposal. To quantify the efficiency of a proposal, both smart sensors and IoT gateways need a utility value to measure the efficiency of each ai value dai, and the utility function uai can calculate this utility value. And different attributes ai have different importance for smart sensors and IoT gateways. We can assign a weight to the attributes of smart sensors and IoT gateways according to their importance. For example, Wi=wai1,⋯,wain, where the value ai indicates the importance of the attribute dai for i and ∑i=1nwaii=0.The average network energy consumption in data fusion methods reflects the data fusion efficiency, which is the basis of optimization theory and is an important index for evaluating the performance of HWSNs. In the network energy consumption at the initial time, the network energy consumption of the four algorithms is consistent, and with the number of polling, the average residual energy of the network gradually decreases. Network energy consumption is an important factor reflecting the efficiency and reliability of WSNs data fusion; with the increase in the number of polling, the average energy consumption of the four algorithms of the WSNs change curve is shown in Figure5.Figure 5
Comparison results of average energy consumption of 4 networks.In Figure5, at the beginning of data fusion, the energy consumption of all four fusion algorithms is large, but as the simulation proceeds, the average energy consumption of all four algorithms gradually decreases, which is mainly because the energy consumption of heterogeneous nodes is large at the beginning and node death occurs, and the number of heterogeneous nodes gradually decreases at a later stage, and the average energy consumption also decreases [23]. From the comparison of the four algorithms, it can be concluded that the classical SEP cluster routing protocol has the largest network energy consumption, and the BAT-ELM-based data fusion strategy proposed in this chapter has the smallest energy consumption. As the number of simulation polling increases, the network energy consumption of the SEP data fusion-based approach decreases by nearly 93%, the BP neural network decreases by about 75%, and the ELM data fusion approach decreases by about 66.7%, and overall, the proposed data fusion approach has the least network energy consumption.In the Internet of Things network, the gateway is a very important point. It builds a local area network at the perception layer and can flexibly and efficiently manage many terminal node devices to run stably. To address the problems of large data communication, high network energy consumption, and low fusion rate in the data fusion process of HWSNs, this chapter proposes a data fusion method for HWSNs based on the bat algorithm optimized limit learning machine according to the time-space characteristics of heterogeneous sensing nodes. Considering the bat algorithm with fewer parameter settings and a simple model, the number of nodes in the implicit layer, network weights, and threshold parameters of the data fusion model can be obtained quickly. Simulation experiments show that the BAT-ELM-based data fusion algorithm for WSNs can effectively improve the data fusion efficiency, reduce the data transmission delay, and extend the network lifetime.


## 6. Traditional Corridor and Tourism System Simulation Application

The MaxEnt model is a species distribution model based on the principles of ecological position and maximum entropy, which can predict the potential habitat distribution of species in the study area with only two types of data: species “occurrence points” and environmental characteristics variables. In the model construction process, the distribution point data and environmental variables of black muntjac were imported into the MaxEnt model, 25% of the distribution point data were randomly selected for model detection, the remaining 75% were used for model construction, and other parameters were set to default values, and the results were output in Logistic format. Model testing is an essential part of the model application. If the model prediction results are not tested, the accuracy and credibility of the results cannot be determined. The MaxEnt model used in this chapter has its testing function, which can test the simulation accuracy by the size of the area enclosed by the ROC curve (subject work characteristic curve) and the horizontal coordinate (AUC) generated by the model, and use Jackknife (knife cut method) to detect the importance of the influence of environmental variables on the distribution of black suede. As shown in Figure6, the AUC values of the training and validation sets were 0.959 and 0.943, respectively, indicating that the MaxEnt model achieved “excellent” prediction results and could well predict the habitat suitability distribution of black muntjac in the study area.Figure 6
ROC curve validation of habitat distribution prediction results.Model Builder is a graphical modeling tool for building geoprocessing workflows and scripts provided by the Model Builder ArcGIS, thereby simplifying the design and implementation of complex geoprocessing models. The Model Builder in ArcGIS can be created by expanding the toolbox in ArcCatalog, right-clicking My Toolbox, selecting New Toolbox, generating a new toolbox, and selecting New Model under Toolbox. Or directly on the main menu, select Model Builder under Geoprocessing. By running the model that has been built, the raster map is obtained as shown in Figure7. The obtained solution is not a global optimal solution but a local optimal solution, and the global optimal experience value of the monarch butterfly is lost.Figure 7
Model output result graph.If the IIC and PC values of suitable habitat critical area patches are greater than those of suitable habitat critical area patches before the corridor construction, it indicates that the corridor is effective. If the IIC and PC values of suitable habitat critical area patches after the corridor construction are higher than those of suitable habitat critical area patches before the corridor construction, the corridor construction is effective, but not effective. Before the construction of the corridor, the IIC and PC values between the patches of suitable habitat for black muntjac were around 0.015 and 0.017, respectively, and the landscape connectivity was low. Through the construction of the habitat corridor, the IIC and PC values between patches increased to 0.032 and 0.050, respectively, and their connectivity improved significantly. Therefore, the construction of the habitat corridor can effectively improve the connectivity between habitat patches, which has a positive effect on promoting the migration and dispersal of the black muntjac population in the key areas of suitable habitats.


## 7. Conclusion

In this paper, we use smart sensors to build a smart sensing network to construct a traditional corridor and explore the planning and application of the tourism system. A mathematical model for coverage optimization is established in a two-dimensional planar region, and a limit learning machine is applied to optimize the three weight coefficients of network coverage, network efficiency, and energy balance coefficient. The process of calculating the coverage ratio is considered with the chaotic initialization method to increase the number of populations. The population update is performed by optimizing the butterfly migration ratio and adjustment ratio through the particle swarm algorithm so that the monarch butterfly optimization algorithm can easily jump out of the local optimum and converge rapidly to find the global optimum solution. The simulation results show that the algorithm can significantly improve the network coverage during the deployment of WSNs nodes. An intelligent sensing system based on a UART serial port consisting of smart sensors, IoT gateways, and an IoT cloud platform is designed based on IoT. The functions of each part in the intelligent sensing system are analyzed and suitable hardware modules are selected. The problems of manual configuration of smart sensor parameters and UART serial communication parameters in the UART serial-based intelligent sensing system are analyzed, and research on the adaptive UART serial parameters and automatic registration of smart sensor parameters is proposed. The results of the green corridor suitability analysis and evaluation are used as the basic data for the construction of green corridors, and the framework direction of the overall construction of green corridors is proposed from four perspectives: construction principles, optimal area selection, construction plan comparison, and overall construction, and the construction of traditional corridors is simulated. Although this study has achieved certain results, however, there are still many questions about the knowledge involved in cultural heritage corridors and ecological restoration that deserve further exploration. The scientificity of the weight allocation of the indicator system, which is based on the characteristics of the edge zone and subjectively determined by using hierarchical analysis, can be further discussed. We hope to give criticism and correction for the shortcomings of this paper.



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*Source: 8366122-2022-09-12.xml*