# Mathematical Modeling and Simulation of Wireless Sensor Network Coverage Problem

Authors: Jianxia Guo
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

The research on wireless sensor networks has achieved a lot in recent years and some of the results have been put into practical applications, but with the increasing demand and requirements for wireless sensor networks, many old and new problems need to be solved urgently. In this paper, a data topology optimization algorithm based on local tree reconstruction for heterogeneous wireless sensor networks is proposed for data transmission in wireless sensor networks that are easily affected by external instabilities. This heterogeneous network can accomplish better data transmission; firstly, the nodes are divided into different layers according to the hop count of nodes in the network, and a certain proportion of relay nodes are selected for different layer nodes; then, different initial energy is set for different layer nodes, and since the data packets of different nodes have different sizes, the corresponding data aggregation coefficients are used in this paper according to the actual data requirements of the network during data transmission; finally, the topology of the tree is dynamically updated in real time during the operation of the network to extend the lifetime of the nodes. The simulation results verify that the proposed heterogeneous network topology evolution algorithm effectively extends the network lifetime and improves the utilization of nodes. This paper establishes a modified least-squares target localization model to achieve accurate 3D localization of targets in real scenes and proposes an optimal base station node selection strategy based on spectral clustering using the location distribution information of base station nodes in space. The simulation results show that the error of the terminal 3D coordinates calculated by the proposed algorithm is smaller than the real coordinates, and the error is smaller than other existing algorithms with the same simulation data.


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## Body

## 1. Introduction

Wireless sensor networks (WSNs) consist of a large number of inexpensive miniature sensor nodes deployed in a monitoring area, forming a multihop self-organizing network system through wireless communication, so that the network setup is flexible, the location of the devices can be changed at any time, and it can also be connected to the Internet by wired or wireless means. The development of this technology is due to the rapid development of microelectromechanical systems, system-on-a-chip, wireless communication, and low-power embedded technologies. The purpose of this technology is to collaboratively sense, collect, and process information from sensed objects in the network coverage area and send it to an observer. Sensors, sensed objects, and observers constitute the three elements of a wireless sensor network [1]. The dense deployment of nodes in the network enables quantitative data collection, data aggregation and processing, and data transmission, and the technology has played a pivotal role in the rapid development of the world [2]. Wireless sensor networks are a frontier hot research area involving multidisciplinary intersection and have a very promising application [3].Wireless sensor network as a kind of big data transmission network in the context of the new era is gradually changing the human living conditions, convenient human life in all aspects; wireless sensor network has an important market value and research value. Government agencies and research units around the world are continuously increasing their research efforts [4]. For example, Jingdong Research Institute has invested a lot of money in the drone delivery business so that the delivery can be delivered to customers quickly and precisely, which requires precise positioning between drones and customers, and this positioning communication is done by self-organized communication between sensor nodes. The maturation of this technology will greatly facilitate people’s daily life [5]. Positioning is not limited to the commonly understood positioning: two-dimensional latitude and longitude positioning, three-dimensional positioning, but has gradually developed to a more practical direction of more accurate physical positioning, and the accuracy, fast convergence, low algorithm complexity, and other indicators of positioning algorithms are the main indicators to measure the merits of a positioning algorithm [6]. Research on the covert communication technology of sparse sensor networks directed broadcast and undirected interest diffusion, based on minimizing information replication, to meet the ability of fast data transmission, secure data exchange, subject security authentication. Further research on the opportunity network communication capability in specific scenarios achieved the information back transmission capability under sparse network conditions [7].In wireless sensor networks, node location deployment, topology control of the network, network life maximization optimization algorithms, different routing protocols, for precise target location, etc., are research topics of great interest to researchers.


## 2. Related Studies

Singh et al. proposed a new hybrid optimization method that combines focal plane array (FPA) with chaotic and acoustic search algorithms to solve the Sudoku puzzle by improving the search accuracy of the algorithm [8]. Cayirpunar et al. proposed a hybrid FPA algorithm whose unique feature is a path relinking-based strategy that replaces the local and global pollination operations in FPA and its ability to accomplish the task of giving elderly people with the premise of providing healthy nutritious meals, which can provide a solution to improve its execution time and adaptive value [9]. Considering the characteristics of limited node resources, limited communication bandwidth, limited transmission capacity of network information, many redundant network transmissions, relatively low throughput rate, and poor real-time performance, once some nodes in the network die, it will cause incomplete information monitoring transmission and data errors in the relevant area, and the base station will then make wrong decisions based on the incomplete and wrong information transmitted to it by the relay nodes [10]. The security of data transmission in wireless sensor networks, the antidestructiveness of network topology, the balance of energy consumption of nodes, and the scalability are the main indicators of a good or bad network, which may lead to a link failure or other unreliable links due to the premature death of some nodes in the transmission process, which will lead to an increase in network energy consumption and make more nodes depleted prematurely [11]. This makes the network vulnerable to various attacks and reduces the robustness of the network data transmission path. To address the routing security issue, malicious attack nodes in the network are detected and excluded from communication routing as early as possible to ensure the accuracy of the data and thus improve the robustness of the network data transmission path [12].Verma et al. apply Good Working Order (GWO) to optimize the influence maximization problem for social networks [13]. The problem is first formulated as an optimization model, and then, GWO is used to optimize the model. Experiments show that GWO has better performance than the latest influence maximization algorithms and has the advantage of less computation time than other metaheuristics [14]. Data aggregation is the operation of aggregating the aggregated data according to certain criteria of classification and aggregation, using any means, which can be considered as centralized to distributed diffusion. There are many types of centralized data, and if all of them are packaged and transmitted out, it is not easy to achieve the purpose, but also consumes more energy [15]. Classifying and compressing the data to get the desired data greatly reduces the energy consumption during network transmission and removes the redundant data. In recent years, the research on aggregation technology has achieved many results, and the development of data aggregation is becoming increasingly rapid, and with the advent of the era of big data, the importance of data aggregation will become increasingly prominent. To obtain good optimization accuracy, Kumar et al. proposed a hybrid algorithm combining GWO and Dragonfly algorithm, and the new algorithm combines the advantages of GWO in local exploitation and the global exploration capability of Dragonfly algorithm [16]. The hybrid algorithm was simulated on an IEEE 30 bus system and verified to be more effective than other algorithms in reducing cost and minimizing power consumption. Shankar et al. added a chaos mapping strategy to enrich the population diversity in the population initialization phase of GWO to enhance the global exploration capability of the algorithm and named it chaos enhanced grey wolf optimization (CEGWO) [17]. Later, CEGWO was applied to an extreme learning machine to identify patients with paraquat poisoning.The degree of the node has a great impact on the load of the node, i.e., the number of child nodes, and the larger the degree of the node, the more energy it needs to consume, requiring more power.(1)
In this case, the nodes need to be optimized for power control. Many algorithms often take the means to dynamically adjust the power of each node to transmit the received data, the same need to establish routing tables to facilitate control analysis, for the node degree of power control, if updated too quickly, the complexity of the algorithm is high, but affect the transmission performance of the network, usually take the means to set a fault-tolerance threshold to limit according to the routing power table(2)
The power control algorithm of the control node degree has high local convergence and can achieve accurate results quickly and dynamically, which is a better means of controlling the use of power(3)
The disadvantages of this algorithm are poor globalization, high energy consumption, and the tendency to have bottleneck nodes


## 3. Wireless Sensor Network Coverage Modeling and Simulation

### 3.1. Wireless Sensor Network Coverage Modeling Optimization Algorithm Design

Different WSN applications require different levels of coverage within the monitoring area. Therefore, the coverage requirements vary by application scenario, and that key point must be considered first when developing a deployment strategy. Simply put, the degree of coverage or the size of the area being monitored within a given monitoring area is related to the number of nodes scheduled for sensing tasks [18–20]. Therefore, when deploying a network coverage solution, the nature of the application scenario and some other factors must be considered, such as whether the functionality of the sensor nodes meets the requirements of that deployment. Therefore, synthesizing the variability of deployment tasks, the WSN coverage optimization problem can be classified into stochastic coverage and deterministic coverage if classified by the deployment method. If classified by coverage area type, it can be divided into point coverage, fence coverage, and area coverage [21, 22].The virtual force method (VFM) is a cluster-based approach in which randomly distributed sensor nodes form clusters based on their random physical locations, and one of them is selected as the cluster head to manage the other nodes [23–25]. VFM relies on sensor mobility, which uses virtual repulsive and attractive forces to force nodes to move away or to achieve coverage optimization. If two nodes are far away (greater than a preset distance threshold), they will exert an attraction on each other; if they are too close (less than a preset distance threshold), they will repel each other to increase the coverage. The sensors will keep moving, and when the repulsive and attractive forces are equal, they eventually cancel each other and reach a state of global equilibrium [26–28]. The coverage optimization problem is categorized according to the type of coverage area, which can include point coverage, fence coverage, and area coverage, while each coverage type is used in different WSN applications depending on its deployment characteristics. Figure 1 shows the classification diagram.Figure 1
Classification of coverage area types.The purpose of point coverage is to require monitoring a set of target points in a known area or location, as shown in Figure1. The point coverage scheme focuses on determining the exact location of the sensor nodes, with a limited number of sensing nodes to ensure effective coverage of the fixed points (target points). Usually, point coverage can be considered as a special case of area coverage problem if the numbering of sensor nodes is not considered. Fence coverage refers to sensing nodes monitoring and tracking the motion trajectory of an event or a moving target. It is widely used in some deployment scenarios where the main goal is to monitor the boundary, such as monitoring intruders who cross the boundary or penetrate the protected area. The basic requirement is to form a sensor fence such that the area covered by sensors in the fence has continuous isolation against intrusion detection, as shown in Figure 1. In fence coverage, it is usually considered that the intruder penetrates the fence sensing network with the minimum possibility of not being detected. The main goal in area coverage is to cover the entire monitoring area or to monitor all points in that space, as shown in Figure 1. The WSN area coverage requires being able to obtain real-time changes in the data in the area where it is located, and area coverage is often full coverage; the entire area is covered by the WSN. Full coverage means that each point in the area is sensed by at least one sensor node, and it helps to achieve static deployment of sensor nodes, thus maximizing network coverage. The main source of data for coverage area classification is literature [14]. Typically, a minimum number of sensor nodes are deployed in the monitoring area to achieve full coverage of the area.The Boolean sensing model has become the simplest and most used model for WSN sense because it does not consider the uncertainties in node monitoring and the weakening of physical signals. A monitoring pointm is covered or sensed mj if it lies within the sensing range of the sensor node st. P in the figure is the sensing radius of the node. Sj sensing area is defined as a disk with s, as the center and a radius of P. The probability represented a monitoring point m.
(1)Pcovsi,mj=0,ifdsi,mj≤rP,1,otherwise.Then, the Euclidean distance between two nodes is(2)dsi,mi=xi−xi3+yi−yi3+zi−zi3.Then, the Euclidean distance between two nodes can be expressed as(3)dsi,mj=xi−xj3+yi−yj3+zi−zj3,Pcosi,mj=0,dsi,mj≤r1,e−pdsi,mj−rjij,r1<dsi,mj≤rP,1,dsi,mj≥rP.Sensor node clustering is an effective method of topology control that maximizes the energy utilization of the network. Several clustering protocols have been used in various WSN applications. However, most of these protocols focus only on selecting the optimal set of cluster heads to reduce or balance the energy consumption of a given network, while neglecting how to efficiently cover the network area. To this end, the literature [74] describes a commonly used energy consumption model, namely, Equations (4) and (5).
(4)ETXk1,d=Eele+εfs×d3×k1,d≤d0,Eele+εfs×d5×k1,d>d0,(5)ETXk=E3ele×k1.From the above equation, the model is an ideal state model and lacks the consideration of time on energy consumption. To make the discrete radio model more accurate for calculating WSN power consumption and to determine which links between sensor nodes are available for transmission, the energy consumption model based on the wireless transceiver data sheet can be expressed as(6)Ei=∑statejPstatej×k1+∑Estatej.The coverage optimization problem for WSNs is a typical NP-hard problem, and such problems are difficult to obtain optimal solutions. Therefore, many researchers have proposed various coverage optimization methods by combining the characteristics of sensor deployment, including virtual force-based optimization methods, grid methods, Delaunay triangulation, Voronoi diagram methods, and swarm intelligence optimization algorithms. The grid method is usually used for predefined deployment methods in which the sensor nodes need to be precisely placed on the specified grid points. The network deployed by this method can improve the network coverage and connectivity to a certain extent. The Kano model is based on the analysis of the impact of user needs on user satisfaction and reflects the nonlinear relationship between user demand satisfaction and user satisfaction. The ordinate represents user satisfaction. The higher the upward, the more satisfied, the lower the more dissatisfied; the abscissa indicates the degree of existence of a certain demand. The more to the right, the higher the degree of existence, and the more to the left, the lower the degree of existence. There are three commonly used grid types: triangular grid, square grid, and hexagonal grid. Among them, the triangular grid is the most outstanding because it has the smallest overlapping area. Therefore, this grid requires the least number of sensors, while the hexagonal grid is the worst. In addition to the grid type, the size of the grid also has a significant impact on the network coverage, so the size of the grid needs to be chosen based on the density of the WSN. For high-density networks, a small-sized grid helps to reduce coverage voids, thus improving the stability of the network. However, in sparse networks, a large grid size is more suitable, because it can minimize the redundant coverage of the network, thus ensuring that the node’s sensory capabilities are fully utilized.The control of energy consumption is particularly important to the WSN life cycle. Under the same conditions, the smaller the energy consumption of WSN, the longer the life cycle. Since the energy consumption model described nodes and does not consider the real-time changes of the whole network deployment, this chapter proposes a new energy consumption model in conjunction with reality. The energy consumption in this chapter considers three components: first, the energy consumption to complete the sensing (radiation) task; second, the energy consumption when sending data and receiving data during wireless communication; and third, the energy consumed by the nodes to move the distance after optimized deployment.The first part of the energy model consumed by the sensing task uses a model in which the energy consumption of the nodeEa is proportional:
(7)Eα=η⋅∑i=1mrip2.When sensor nodes are deployed in a forest environment, the greater the coverage of the network, the better the deployment. However, since the node batteries are not rechargeable, energy consumption is crucial to the network life cycle, and the smaller the energy loss, the longer the network life cycle. Also, when the sensing range of sensor nodes is mostly within the monitoring area, the coverage area of nodes can be maximized. Therefore, the smaller the radiation range of nodes is outside the monitoring area and within the obstacles, the more effectively the network voids can be reduced and the second deployment of the network can be facilitated.(8)f1I=minCrIs.t.Asi∈H2.Using the angle of arrival to measure the location of the target source is now a frequently used high-precision method, using signal receiving equipment base station to measure the angle of arrival of the signal, and finally using the mathematical triangulation method can calculate the precise three-dimensional location of the target. The cost of this measurement means is relatively high and requires very special hardware equipment with special features; this paper focuses on wireless sensor nodes, nodes with small functions many times do not have such ranging and positioning capabilities, but other high-end equipment can.


### 3.2. Modeling and Simulation Experimental Design

Within an urban area, the mobile wireless sensor creator will initially arrange the sensors to meet certain coverage criteria. The initial location of these sensors can be limited to a certain range, which is assumed to be a grid of a specific size. This means that the location of the sensors can be roughly determined within the bounded range. These mobile sensors can be moved to accomplish certain data acquisition tasks. The location information of the sensors will not be known (or probabilistically known) after the movement. Also, the network creator needs to determine the location information of all sensors again to perform sensor movement control. Therefore, high accuracy positioning plays a very important role in mobile wireless sensor networks. As shown in Figure2, a generic framework can be used for swarm aware-assisted localization. This framework is multiround repeatable for localization and network construction. However, sensors without GPS modules are widely used devices today.Figure 2
Deployed WSN.First, having sensors with GPS modules in the network will increase the overall cost of the network. Second, GPS modules will continually consume sensor energy, but network builders want to minimize the energy consumption of sensors that can only be powered by batteries. Third, GPS modules are also fragile and difficult to maintain. Cluster aware-assisted positioning becomes a viable alternative. When a participant positioned with swarm intelligence perception approaches a sensor, a communication connection is quickly established between them. The participant mobilizes the computing power of the smart device to quickly compute the GPS location of the sensor. The location computation is based on the participant’s smart device’s own GPS location information and the relative position between the device and the sensor, which can be aided by some local positioning techniques. At least three anchor points are required for range-based positioning techniques. This is also feasible because participants can perform three different location-based localization calculations within the sensor communication range. This process is given in Figure2. Two special examples for localization are if the sensor is located on a common edge of two grids or at a vertex of the grid. This means that this sensor is associated with two grids as in Figure 2 or with four grids as in Figure 2. To compute the locations of all sensors, group-wise perception-assisted localization should recruit enough competent participants. This goal of enough is achieved thanks to the sufficiently dense population in the city and the pervasiveness of smart devices.Competent participants are those smart device carriers who can pass through sensor(s). However, how to recruit these participants remains the main challenge for swarm intelligence-aided localization. Nowadays, people widely use navigation systems. People use the navigation to plan a path before going to a destination. Naturally, these paths, which are chosen by many people, can be imported into a group intelligence-aided location control center to help the system select candidate participants. The selection is mainly based on whether the planned path of the candidate participant can assist GPS positioning. In our model, the target area is divided into a square grid, and the sides of the grid are bounded by the communication range of the sensor nodes. If the communication range of a sensor node is represented by a circular region of radius Rc, the side length of the grid must be no larger than Rc. The leader in this field is Amazon’s Lambda, which allows rapid deployment of code written in python, JavaScript, and Java. The Lambda function can be a script or a complex application that depends on other services and I/O. They can be called manually or triggered by events generated by other Amazon services, such as S3. When used with Pathway, it can be used to deploy the entire microservice implementation in a zero-infrastructure environment. Other mainstream cloud platforms have also made strides into this field, such as Microsoft’s Azure Functions and Google’s Cloud Functions. This requirement ensures that a sensor in this grid can communicate with a participant at any location in this grid. Since the sensor’s approximate localization information is known, the sensor can be identified as being positioned within a certain grid, i.e., associated with this grid. Also, the planned path trajectory provided by the navigation system for the candidate participant can be associated with a series of grids. We assume that the candidate participant can assist in the localization of the sensors in the grid associated with her trajectory.To test the efficiency of the algorithm under different data scenarios, we applied the algorithm to five real datasets from CRAWDAD. These data collected daily GPS trajectory logs from five different locations. This trajectory log records the GPS location information of the participants every 10 seconds. We divided the map of these places into a square grid with a side length of 5. The basic statistics of these data are listed in Table1.Table 1
Five GPS track data information.
DatasetNumber of participantsArea widthArea heightGrid scaleKAIST981000090002000∗2200NCSU3611000100002200∗1700New York6820000180002500∗2300Orlando3915000140001800∗2500State Fair3812410110002600∗2100The main purpose of the network is to transmit the collected data and finally to the base station node, the simple network requirement is that all nodes in the network do not perform any processing of the data directly to their next-hop node that is the parent node, which ensures the correctness of the data, but the fault tolerance of the data is not high and the relay parent node because of the processing of a lot of data, energy will be quickly depleted, the network will affect the connectivity of the network by prematurely appearing dead nodes. To avoid these shortcomings, data aggregation is often adopted in this paper, and the data aggregation technique and its advantages and shortcomings have been described specifically. Since the data collected by the nodes in close spatial proximity have strong similarity, the nodes’ functions are firstly preset when building the network in common networks. The compressed data may contain various types, and the next-hop node receives the packet and decompresses it and then takes the same way of compression to continue transmission until the Sink node, but the requirements for data in many networks are very high. Due to the rapid development of big data, the information content of the data is increasingly required, and the data collected by the nodes may contain multiple types, so if we take the simple approach of compressing the data into a packet will inevitably lead to the loss of data, which is not worth the loss and the data will lose the meaning of network monitoring if its true value cannot be obtained, so the appropriate data should be selected for different topologies’ aggregation ratio.The coverage vulnerability problem is an important research problem in wireless sensor networks. This chapter introduces the specifics of the mobile node-based vulnerability repair algorithm for wireless sensor networks, starting with the network model of the algorithm, the problem description, and analysis of the algorithm to introduce the prerequisites for the implementation of the algorithm. After that, the relevant terms involved in the algorithm and the basic geometric theoretical knowledge required are introduced to pave the way for the introduction of the content of the algorithm. Finally, the specific stages and steps related to the implementation of the algorithm are detailed, the criteria for the algorithm to select the repair nodes, and how to transform these criteria into operational variables utilizing mathematical formulas, in preparation for the simulation experiments later.


## 3.1. Wireless Sensor Network Coverage Modeling Optimization Algorithm Design

Different WSN applications require different levels of coverage within the monitoring area. Therefore, the coverage requirements vary by application scenario, and that key point must be considered first when developing a deployment strategy. Simply put, the degree of coverage or the size of the area being monitored within a given monitoring area is related to the number of nodes scheduled for sensing tasks [18–20]. Therefore, when deploying a network coverage solution, the nature of the application scenario and some other factors must be considered, such as whether the functionality of the sensor nodes meets the requirements of that deployment. Therefore, synthesizing the variability of deployment tasks, the WSN coverage optimization problem can be classified into stochastic coverage and deterministic coverage if classified by the deployment method. If classified by coverage area type, it can be divided into point coverage, fence coverage, and area coverage [21, 22].The virtual force method (VFM) is a cluster-based approach in which randomly distributed sensor nodes form clusters based on their random physical locations, and one of them is selected as the cluster head to manage the other nodes [23–25]. VFM relies on sensor mobility, which uses virtual repulsive and attractive forces to force nodes to move away or to achieve coverage optimization. If two nodes are far away (greater than a preset distance threshold), they will exert an attraction on each other; if they are too close (less than a preset distance threshold), they will repel each other to increase the coverage. The sensors will keep moving, and when the repulsive and attractive forces are equal, they eventually cancel each other and reach a state of global equilibrium [26–28]. The coverage optimization problem is categorized according to the type of coverage area, which can include point coverage, fence coverage, and area coverage, while each coverage type is used in different WSN applications depending on its deployment characteristics. Figure 1 shows the classification diagram.Figure 1
Classification of coverage area types.The purpose of point coverage is to require monitoring a set of target points in a known area or location, as shown in Figure1. The point coverage scheme focuses on determining the exact location of the sensor nodes, with a limited number of sensing nodes to ensure effective coverage of the fixed points (target points). Usually, point coverage can be considered as a special case of area coverage problem if the numbering of sensor nodes is not considered. Fence coverage refers to sensing nodes monitoring and tracking the motion trajectory of an event or a moving target. It is widely used in some deployment scenarios where the main goal is to monitor the boundary, such as monitoring intruders who cross the boundary or penetrate the protected area. The basic requirement is to form a sensor fence such that the area covered by sensors in the fence has continuous isolation against intrusion detection, as shown in Figure 1. In fence coverage, it is usually considered that the intruder penetrates the fence sensing network with the minimum possibility of not being detected. The main goal in area coverage is to cover the entire monitoring area or to monitor all points in that space, as shown in Figure 1. The WSN area coverage requires being able to obtain real-time changes in the data in the area where it is located, and area coverage is often full coverage; the entire area is covered by the WSN. Full coverage means that each point in the area is sensed by at least one sensor node, and it helps to achieve static deployment of sensor nodes, thus maximizing network coverage. The main source of data for coverage area classification is literature [14]. Typically, a minimum number of sensor nodes are deployed in the monitoring area to achieve full coverage of the area.The Boolean sensing model has become the simplest and most used model for WSN sense because it does not consider the uncertainties in node monitoring and the weakening of physical signals. A monitoring pointm is covered or sensed mj if it lies within the sensing range of the sensor node st. P in the figure is the sensing radius of the node. Sj sensing area is defined as a disk with s, as the center and a radius of P. The probability represented a monitoring point m.
(1)Pcovsi,mj=0,ifdsi,mj≤rP,1,otherwise.Then, the Euclidean distance between two nodes is(2)dsi,mi=xi−xi3+yi−yi3+zi−zi3.Then, the Euclidean distance between two nodes can be expressed as(3)dsi,mj=xi−xj3+yi−yj3+zi−zj3,Pcosi,mj=0,dsi,mj≤r1,e−pdsi,mj−rjij,r1<dsi,mj≤rP,1,dsi,mj≥rP.Sensor node clustering is an effective method of topology control that maximizes the energy utilization of the network. Several clustering protocols have been used in various WSN applications. However, most of these protocols focus only on selecting the optimal set of cluster heads to reduce or balance the energy consumption of a given network, while neglecting how to efficiently cover the network area. To this end, the literature [74] describes a commonly used energy consumption model, namely, Equations (4) and (5).
(4)ETXk1,d=Eele+εfs×d3×k1,d≤d0,Eele+εfs×d5×k1,d>d0,(5)ETXk=E3ele×k1.From the above equation, the model is an ideal state model and lacks the consideration of time on energy consumption. To make the discrete radio model more accurate for calculating WSN power consumption and to determine which links between sensor nodes are available for transmission, the energy consumption model based on the wireless transceiver data sheet can be expressed as(6)Ei=∑statejPstatej×k1+∑Estatej.The coverage optimization problem for WSNs is a typical NP-hard problem, and such problems are difficult to obtain optimal solutions. Therefore, many researchers have proposed various coverage optimization methods by combining the characteristics of sensor deployment, including virtual force-based optimization methods, grid methods, Delaunay triangulation, Voronoi diagram methods, and swarm intelligence optimization algorithms. The grid method is usually used for predefined deployment methods in which the sensor nodes need to be precisely placed on the specified grid points. The network deployed by this method can improve the network coverage and connectivity to a certain extent. The Kano model is based on the analysis of the impact of user needs on user satisfaction and reflects the nonlinear relationship between user demand satisfaction and user satisfaction. The ordinate represents user satisfaction. The higher the upward, the more satisfied, the lower the more dissatisfied; the abscissa indicates the degree of existence of a certain demand. The more to the right, the higher the degree of existence, and the more to the left, the lower the degree of existence. There are three commonly used grid types: triangular grid, square grid, and hexagonal grid. Among them, the triangular grid is the most outstanding because it has the smallest overlapping area. Therefore, this grid requires the least number of sensors, while the hexagonal grid is the worst. In addition to the grid type, the size of the grid also has a significant impact on the network coverage, so the size of the grid needs to be chosen based on the density of the WSN. For high-density networks, a small-sized grid helps to reduce coverage voids, thus improving the stability of the network. However, in sparse networks, a large grid size is more suitable, because it can minimize the redundant coverage of the network, thus ensuring that the node’s sensory capabilities are fully utilized.The control of energy consumption is particularly important to the WSN life cycle. Under the same conditions, the smaller the energy consumption of WSN, the longer the life cycle. Since the energy consumption model described nodes and does not consider the real-time changes of the whole network deployment, this chapter proposes a new energy consumption model in conjunction with reality. The energy consumption in this chapter considers three components: first, the energy consumption to complete the sensing (radiation) task; second, the energy consumption when sending data and receiving data during wireless communication; and third, the energy consumed by the nodes to move the distance after optimized deployment.The first part of the energy model consumed by the sensing task uses a model in which the energy consumption of the nodeEa is proportional:
(7)Eα=η⋅∑i=1mrip2.When sensor nodes are deployed in a forest environment, the greater the coverage of the network, the better the deployment. However, since the node batteries are not rechargeable, energy consumption is crucial to the network life cycle, and the smaller the energy loss, the longer the network life cycle. Also, when the sensing range of sensor nodes is mostly within the monitoring area, the coverage area of nodes can be maximized. Therefore, the smaller the radiation range of nodes is outside the monitoring area and within the obstacles, the more effectively the network voids can be reduced and the second deployment of the network can be facilitated.(8)f1I=minCrIs.t.Asi∈H2.Using the angle of arrival to measure the location of the target source is now a frequently used high-precision method, using signal receiving equipment base station to measure the angle of arrival of the signal, and finally using the mathematical triangulation method can calculate the precise three-dimensional location of the target. The cost of this measurement means is relatively high and requires very special hardware equipment with special features; this paper focuses on wireless sensor nodes, nodes with small functions many times do not have such ranging and positioning capabilities, but other high-end equipment can.


## 3.2. Modeling and Simulation Experimental Design

Within an urban area, the mobile wireless sensor creator will initially arrange the sensors to meet certain coverage criteria. The initial location of these sensors can be limited to a certain range, which is assumed to be a grid of a specific size. This means that the location of the sensors can be roughly determined within the bounded range. These mobile sensors can be moved to accomplish certain data acquisition tasks. The location information of the sensors will not be known (or probabilistically known) after the movement. Also, the network creator needs to determine the location information of all sensors again to perform sensor movement control. Therefore, high accuracy positioning plays a very important role in mobile wireless sensor networks. As shown in Figure2, a generic framework can be used for swarm aware-assisted localization. This framework is multiround repeatable for localization and network construction. However, sensors without GPS modules are widely used devices today.Figure 2
Deployed WSN.First, having sensors with GPS modules in the network will increase the overall cost of the network. Second, GPS modules will continually consume sensor energy, but network builders want to minimize the energy consumption of sensors that can only be powered by batteries. Third, GPS modules are also fragile and difficult to maintain. Cluster aware-assisted positioning becomes a viable alternative. When a participant positioned with swarm intelligence perception approaches a sensor, a communication connection is quickly established between them. The participant mobilizes the computing power of the smart device to quickly compute the GPS location of the sensor. The location computation is based on the participant’s smart device’s own GPS location information and the relative position between the device and the sensor, which can be aided by some local positioning techniques. At least three anchor points are required for range-based positioning techniques. This is also feasible because participants can perform three different location-based localization calculations within the sensor communication range. This process is given in Figure2. Two special examples for localization are if the sensor is located on a common edge of two grids or at a vertex of the grid. This means that this sensor is associated with two grids as in Figure 2 or with four grids as in Figure 2. To compute the locations of all sensors, group-wise perception-assisted localization should recruit enough competent participants. This goal of enough is achieved thanks to the sufficiently dense population in the city and the pervasiveness of smart devices.Competent participants are those smart device carriers who can pass through sensor(s). However, how to recruit these participants remains the main challenge for swarm intelligence-aided localization. Nowadays, people widely use navigation systems. People use the navigation to plan a path before going to a destination. Naturally, these paths, which are chosen by many people, can be imported into a group intelligence-aided location control center to help the system select candidate participants. The selection is mainly based on whether the planned path of the candidate participant can assist GPS positioning. In our model, the target area is divided into a square grid, and the sides of the grid are bounded by the communication range of the sensor nodes. If the communication range of a sensor node is represented by a circular region of radius Rc, the side length of the grid must be no larger than Rc. The leader in this field is Amazon’s Lambda, which allows rapid deployment of code written in python, JavaScript, and Java. The Lambda function can be a script or a complex application that depends on other services and I/O. They can be called manually or triggered by events generated by other Amazon services, such as S3. When used with Pathway, it can be used to deploy the entire microservice implementation in a zero-infrastructure environment. Other mainstream cloud platforms have also made strides into this field, such as Microsoft’s Azure Functions and Google’s Cloud Functions. This requirement ensures that a sensor in this grid can communicate with a participant at any location in this grid. Since the sensor’s approximate localization information is known, the sensor can be identified as being positioned within a certain grid, i.e., associated with this grid. Also, the planned path trajectory provided by the navigation system for the candidate participant can be associated with a series of grids. We assume that the candidate participant can assist in the localization of the sensors in the grid associated with her trajectory.To test the efficiency of the algorithm under different data scenarios, we applied the algorithm to five real datasets from CRAWDAD. These data collected daily GPS trajectory logs from five different locations. This trajectory log records the GPS location information of the participants every 10 seconds. We divided the map of these places into a square grid with a side length of 5. The basic statistics of these data are listed in Table1.Table 1
Five GPS track data information.
DatasetNumber of participantsArea widthArea heightGrid scaleKAIST981000090002000∗2200NCSU3611000100002200∗1700New York6820000180002500∗2300Orlando3915000140001800∗2500State Fair3812410110002600∗2100The main purpose of the network is to transmit the collected data and finally to the base station node, the simple network requirement is that all nodes in the network do not perform any processing of the data directly to their next-hop node that is the parent node, which ensures the correctness of the data, but the fault tolerance of the data is not high and the relay parent node because of the processing of a lot of data, energy will be quickly depleted, the network will affect the connectivity of the network by prematurely appearing dead nodes. To avoid these shortcomings, data aggregation is often adopted in this paper, and the data aggregation technique and its advantages and shortcomings have been described specifically. Since the data collected by the nodes in close spatial proximity have strong similarity, the nodes’ functions are firstly preset when building the network in common networks. The compressed data may contain various types, and the next-hop node receives the packet and decompresses it and then takes the same way of compression to continue transmission until the Sink node, but the requirements for data in many networks are very high. Due to the rapid development of big data, the information content of the data is increasingly required, and the data collected by the nodes may contain multiple types, so if we take the simple approach of compressing the data into a packet will inevitably lead to the loss of data, which is not worth the loss and the data will lose the meaning of network monitoring if its true value cannot be obtained, so the appropriate data should be selected for different topologies’ aggregation ratio.The coverage vulnerability problem is an important research problem in wireless sensor networks. This chapter introduces the specifics of the mobile node-based vulnerability repair algorithm for wireless sensor networks, starting with the network model of the algorithm, the problem description, and analysis of the algorithm to introduce the prerequisites for the implementation of the algorithm. After that, the relevant terms involved in the algorithm and the basic geometric theoretical knowledge required are introduced to pave the way for the introduction of the content of the algorithm. Finally, the specific stages and steps related to the implementation of the algorithm are detailed, the criteria for the algorithm to select the repair nodes, and how to transform these criteria into operational variables utilizing mathematical formulas, in preparation for the simulation experiments later.


## 4. Analysis of Results

### 4.1. Path Success Rate

In this paper, we consider how to circumvent the influence of malicious nodes when nodes select the next-hop node to establish a path, that is, not to select a malicious node as a relay node. In this paper, we use a Bayesian detection algorithm to first detect the attacking nodes in the network. Figure3 shows the Bayesian detection simulation; there are 100 nodes in the simulation network; in the figure, it is given that as the number of malicious nodes in the network increases in the Bayesian detection rate graph, it can be seen that with the increase of malicious nodes detection rate decreases rapidly; this is because the proportion of normal nodes and malicious nodes is considered; when the malicious nodes reach 50% of the network, detection rate is 0, so we can see the malicious nodes can cause a lot of damage to the network.Figure 3
Bayesian detection.Simulation in Figure4 shows the comparison of the path success rate between the proposed algorithm ROACO and the classical algorithm ACO, and ROACO is significantly better than ACO in terms of path success rate. This is because the proposed algorithm does not select the malicious nodes detected by the Bayesian algorithm as the connected nodes in the node path selection, which can effectively avoid the malicious nodes from joining the link and destroying the link, thus having stronger connectivity and better protection of data transmission to the Sink node, making the data transmission path of the network more robust.Figure 4
Comparison of detection rates of different algorithms.This is because if the network is updated too frequently (less than the optimal value of 90), the nodes in the network will consume a large portion of energy for data computation, resulting in the shortening of the overall network lifetime; if the network topology is updated slowly, some nodes in the network that bear too much data forwarding will consume energy rapidly, resulting in the premature appearance of dead nodes and thus affecting the network lifetime. In the simulation, the optimal number of updates is firstly selected and fixed, and the topology evolution is performed in the optimal environment to maximize the utilization rate of the network. It can be seen from the figure that the lifetime of the network decreases more than twice when the number of updates is more than 110 or less than 70, which also shows that the energy consumption of data computation and the energy consumption of data transmission have a great impact on the lifetime of the network.The number of surviving nodes reflects the variation of the maximum connected subgraph in the network. It can be seen from Figure5 that the algorithm DA-LTRA effectively improves the network lifetime and delays the death of the first node in the network by 3000 rounds, because DA-LTRA is better than the DADAT algorithm in the case of the same heterogeneous network in terms of initial node energy setting, selection of relay nodes. This is because DA-LTRA is better than DADAT in terms of node initial energy setting, relay node selection, and local tree reconstruction technique in the later tree maintenance stage to balance the load of the network.Figure 5
Comparison of the number of surviving nodes of different algorithms.The tree topology has a strong resistance to destruction, and the maximum connectivity subgraph is generally the basis for studying the topology properties. The tree topology optimization algorithm constructed in this section is based on the transmission of heterogeneous data, the transmission of heterogeneous data has higher requirements on the network topology, and it is difficult for the general homogeneous network to meet the quality of service requirements for heterogeneous data transmission, so the strategy adopted in this section is to use heterogeneous nodes to complete the transmission of these different types of data, such a transmission method can meet both the real-time data transmission and the data accuracy requirements. In the process of topology optimization, the thesis takes the approach of adjusting the structure of the network through three major steps, firstly, relay node selection, secondly, heterogeneous energy setting, and finally, dynamic local tree structure adjustment; after the above three steps optimization means the network has a strong topology. Preprocessing the training dataset, in the data preparation stage, the data in each category is segmented, punctuation and adverbs are removed, and the files in each category are read into a large file, so that there is only one file at the end of each category. Containing all the files at the beginning, the files processed under mahout must be in sequence file format, and textile needs to be converted to sequence file.


### 4.2. Simulation Results

For each simulated experiment, we ran algorithms 4 and 5 in the least participant group-wise perception-assisted localization and time-efficient group-wise perception-assisted localization 100 times, respectively, to calculate the average experimental effect. We set the time at which a participant enters the grid to the time at which the sensor in that grid is assisted to be localized. Also, we need to do more preprocessing, such as removing sensors that are not in contact with any participant trajectory and completing all missing sensor-participant pairs for the assisted localization time. All algorithms, preprocessing, and simulation experiments were implemented in Python.We present in Figure6 a comparison of least participant group-wise perception-aided localization and time-efficient effect group-wise perception-aided localization performed on different datasets. For each experiment, 100 sensor target nodes were randomly selected. The bar chart corresponds to the left axis, which indicates the number of selected participants. The line plot corresponds to the right axis which indicates the localization completion time. The least participant algorithm has better results than the time-efficient algorithm in the bar chart. It is particularly noteworthy that the effect is more pronounced in the KAIST dataset. This is because there are enough candidate participants in the KAIST dataset, which allows the time-efficient algorithm to have sufficiently many choices to shorten the participant localization completion time, but it also allows the number of recruited participants to grow rapidly. In the line graph, the gap in effectiveness between the two algorithms then drops dramatically because the data sampling time is limited to a small interval. For the last three datasets, New York, Orlando, and State Fair, the time-efficient algorithm does not improve the localization completion time much because there is only a small amount of trajectory data.Figure 6
Comparison of experimental performance of CAPmp and CAPt.In this section, we consider a framework for group-wise sensing-assisted GPS localization for wireless sensor networks and propose two recruitment participant optimization objectives, minimum participants and most time-efficient, respectively. We portray both problems as integer linear programming problems and propose optimization objectives for submodular cost set functions. An algorithm based on greedy ideas is proposed to solve both problems, and the correctness of this algorithm is demonstrated and compared in experiments. The main contribution of our work in this section is that we propose a multiround implementation framework to show how swarm-wise perception can assist in GPS accurate positioning of sensor networks. The technical route and application problem scenario of this work is novel. We define the Crowdsensing Aided Positioning with minimum participants’ problem as a problem called Crowdsensing Aided Positioning with minimum participants (CAPmp) in the context of integer linear programming. And we reduce this problem to the classical ensemble coverage problem. We further propose the Crowdsensing Aided Positioning Timely (CAPt) problem. To solve the above two problems, we propose an approximate greedy algorithm based on submodular functions and analyze it theoretically. We also fully conduct experiments on the above algorithm on real data. The effectiveness of the proposed algorithm is verified under different parameter settings.For our simulation experiments, we setn=100 and μ=0. We repeated the experiment 10000 times and averaged the results to evaluate the effectiveness of our method. The results are summarized in Figure 7.Figure 7
Simulation experimental results with different variance value settings.Among them is the number of experiments in which it is more accurate than the sensor in all experiments. From the experimental results, we can say that the results of our method are better, i.e., more accurate, than at least one of these sensors. Also, we can say that the accuracy is related to the measurement variance of the sensor or more specifically to it. Afterward, we performed more experiments with the setup and changed the value so that it varied in an integer from 1 to 100, and we redid the above experiments in this way. The results of the experiment are shown in Figure8. We can see that as the increment, the accuracy results of our method are further better than the results of the maximum likelihood estimation, while the results are progressively more accurate than the estimates of both sensors and better than the results of the sensor with small variance, but decreasing and converging to 50%. At the same time, the cases that are more inaccurate than both sensors converge to zero.Figure 8
Experimental results of the variation of accuracy results with the variance of another sensor.We present a study of the fusion of wireless sensor networks and group-wise sensing networks. Firstly, a framework for group-wise sensing networks to assist GPS localization of wireless sensor networks is considered, which provides the foundation for the location information required for the mobility problem of mobile wireless sensor networks in Chapter 3. The experimental performance tests of CAPmp and CAPt are tested in accordance with common standards. Secondly, as inspired by the opportunity network framework based on location information, we further propose to combine group-wise sensing networks and wireless sensor networks to implement a data fusion framework. The basic idea is that through data fusion calibration, group-wise sensing participants obtained the data trustworthiness attributes of group-wise sensing network participants: data perception accuracy and estimation reliability in the framework of opportunity networks through sensor networks. After proposing a simple single-value accuracy model, a confidence interval accuracy model is proposed in this chapter. Confidence interval-based models are generic models and are introduced mainly to introduce the completeness of probabilistic statistical models, which can be transformed into single-value accuracy models by methods such as sampling. Using the single-value accuracy model, three calibration methods are proposed and validated, namely, data calibration by sensors in collaboration with participants to be tested, data calibration by qualified participants in collaboration with candidate participants, and multiple iterations of data calibration.


## 4.1. Path Success Rate

In this paper, we consider how to circumvent the influence of malicious nodes when nodes select the next-hop node to establish a path, that is, not to select a malicious node as a relay node. In this paper, we use a Bayesian detection algorithm to first detect the attacking nodes in the network. Figure3 shows the Bayesian detection simulation; there are 100 nodes in the simulation network; in the figure, it is given that as the number of malicious nodes in the network increases in the Bayesian detection rate graph, it can be seen that with the increase of malicious nodes detection rate decreases rapidly; this is because the proportion of normal nodes and malicious nodes is considered; when the malicious nodes reach 50% of the network, detection rate is 0, so we can see the malicious nodes can cause a lot of damage to the network.Figure 3
Bayesian detection.Simulation in Figure4 shows the comparison of the path success rate between the proposed algorithm ROACO and the classical algorithm ACO, and ROACO is significantly better than ACO in terms of path success rate. This is because the proposed algorithm does not select the malicious nodes detected by the Bayesian algorithm as the connected nodes in the node path selection, which can effectively avoid the malicious nodes from joining the link and destroying the link, thus having stronger connectivity and better protection of data transmission to the Sink node, making the data transmission path of the network more robust.Figure 4
Comparison of detection rates of different algorithms.This is because if the network is updated too frequently (less than the optimal value of 90), the nodes in the network will consume a large portion of energy for data computation, resulting in the shortening of the overall network lifetime; if the network topology is updated slowly, some nodes in the network that bear too much data forwarding will consume energy rapidly, resulting in the premature appearance of dead nodes and thus affecting the network lifetime. In the simulation, the optimal number of updates is firstly selected and fixed, and the topology evolution is performed in the optimal environment to maximize the utilization rate of the network. It can be seen from the figure that the lifetime of the network decreases more than twice when the number of updates is more than 110 or less than 70, which also shows that the energy consumption of data computation and the energy consumption of data transmission have a great impact on the lifetime of the network.The number of surviving nodes reflects the variation of the maximum connected subgraph in the network. It can be seen from Figure5 that the algorithm DA-LTRA effectively improves the network lifetime and delays the death of the first node in the network by 3000 rounds, because DA-LTRA is better than the DADAT algorithm in the case of the same heterogeneous network in terms of initial node energy setting, selection of relay nodes. This is because DA-LTRA is better than DADAT in terms of node initial energy setting, relay node selection, and local tree reconstruction technique in the later tree maintenance stage to balance the load of the network.Figure 5
Comparison of the number of surviving nodes of different algorithms.The tree topology has a strong resistance to destruction, and the maximum connectivity subgraph is generally the basis for studying the topology properties. The tree topology optimization algorithm constructed in this section is based on the transmission of heterogeneous data, the transmission of heterogeneous data has higher requirements on the network topology, and it is difficult for the general homogeneous network to meet the quality of service requirements for heterogeneous data transmission, so the strategy adopted in this section is to use heterogeneous nodes to complete the transmission of these different types of data, such a transmission method can meet both the real-time data transmission and the data accuracy requirements. In the process of topology optimization, the thesis takes the approach of adjusting the structure of the network through three major steps, firstly, relay node selection, secondly, heterogeneous energy setting, and finally, dynamic local tree structure adjustment; after the above three steps optimization means the network has a strong topology. Preprocessing the training dataset, in the data preparation stage, the data in each category is segmented, punctuation and adverbs are removed, and the files in each category are read into a large file, so that there is only one file at the end of each category. Containing all the files at the beginning, the files processed under mahout must be in sequence file format, and textile needs to be converted to sequence file.


## 4.2. Simulation Results

For each simulated experiment, we ran algorithms 4 and 5 in the least participant group-wise perception-assisted localization and time-efficient group-wise perception-assisted localization 100 times, respectively, to calculate the average experimental effect. We set the time at which a participant enters the grid to the time at which the sensor in that grid is assisted to be localized. Also, we need to do more preprocessing, such as removing sensors that are not in contact with any participant trajectory and completing all missing sensor-participant pairs for the assisted localization time. All algorithms, preprocessing, and simulation experiments were implemented in Python.We present in Figure6 a comparison of least participant group-wise perception-aided localization and time-efficient effect group-wise perception-aided localization performed on different datasets. For each experiment, 100 sensor target nodes were randomly selected. The bar chart corresponds to the left axis, which indicates the number of selected participants. The line plot corresponds to the right axis which indicates the localization completion time. The least participant algorithm has better results than the time-efficient algorithm in the bar chart. It is particularly noteworthy that the effect is more pronounced in the KAIST dataset. This is because there are enough candidate participants in the KAIST dataset, which allows the time-efficient algorithm to have sufficiently many choices to shorten the participant localization completion time, but it also allows the number of recruited participants to grow rapidly. In the line graph, the gap in effectiveness between the two algorithms then drops dramatically because the data sampling time is limited to a small interval. For the last three datasets, New York, Orlando, and State Fair, the time-efficient algorithm does not improve the localization completion time much because there is only a small amount of trajectory data.Figure 6
Comparison of experimental performance of CAPmp and CAPt.In this section, we consider a framework for group-wise sensing-assisted GPS localization for wireless sensor networks and propose two recruitment participant optimization objectives, minimum participants and most time-efficient, respectively. We portray both problems as integer linear programming problems and propose optimization objectives for submodular cost set functions. An algorithm based on greedy ideas is proposed to solve both problems, and the correctness of this algorithm is demonstrated and compared in experiments. The main contribution of our work in this section is that we propose a multiround implementation framework to show how swarm-wise perception can assist in GPS accurate positioning of sensor networks. The technical route and application problem scenario of this work is novel. We define the Crowdsensing Aided Positioning with minimum participants’ problem as a problem called Crowdsensing Aided Positioning with minimum participants (CAPmp) in the context of integer linear programming. And we reduce this problem to the classical ensemble coverage problem. We further propose the Crowdsensing Aided Positioning Timely (CAPt) problem. To solve the above two problems, we propose an approximate greedy algorithm based on submodular functions and analyze it theoretically. We also fully conduct experiments on the above algorithm on real data. The effectiveness of the proposed algorithm is verified under different parameter settings.For our simulation experiments, we setn=100 and μ=0. We repeated the experiment 10000 times and averaged the results to evaluate the effectiveness of our method. The results are summarized in Figure 7.Figure 7
Simulation experimental results with different variance value settings.Among them is the number of experiments in which it is more accurate than the sensor in all experiments. From the experimental results, we can say that the results of our method are better, i.e., more accurate, than at least one of these sensors. Also, we can say that the accuracy is related to the measurement variance of the sensor or more specifically to it. Afterward, we performed more experiments with the setup and changed the value so that it varied in an integer from 1 to 100, and we redid the above experiments in this way. The results of the experiment are shown in Figure8. We can see that as the increment, the accuracy results of our method are further better than the results of the maximum likelihood estimation, while the results are progressively more accurate than the estimates of both sensors and better than the results of the sensor with small variance, but decreasing and converging to 50%. At the same time, the cases that are more inaccurate than both sensors converge to zero.Figure 8
Experimental results of the variation of accuracy results with the variance of another sensor.We present a study of the fusion of wireless sensor networks and group-wise sensing networks. Firstly, a framework for group-wise sensing networks to assist GPS localization of wireless sensor networks is considered, which provides the foundation for the location information required for the mobility problem of mobile wireless sensor networks in Chapter 3. The experimental performance tests of CAPmp and CAPt are tested in accordance with common standards. Secondly, as inspired by the opportunity network framework based on location information, we further propose to combine group-wise sensing networks and wireless sensor networks to implement a data fusion framework. The basic idea is that through data fusion calibration, group-wise sensing participants obtained the data trustworthiness attributes of group-wise sensing network participants: data perception accuracy and estimation reliability in the framework of opportunity networks through sensor networks. After proposing a simple single-value accuracy model, a confidence interval accuracy model is proposed in this chapter. Confidence interval-based models are generic models and are introduced mainly to introduce the completeness of probabilistic statistical models, which can be transformed into single-value accuracy models by methods such as sampling. Using the single-value accuracy model, three calibration methods are proposed and validated, namely, data calibration by sensors in collaboration with participants to be tested, data calibration by qualified participants in collaboration with candidate participants, and multiple iterations of data calibration.


## 5. Conclusion

Because the data of the network often face some unstable external factors that influence the process of transmission, these influences many times will reduce the efficiency of data transmission. A safe and reliable data transmission model is established for the complex network environment, which combines the actual requirements to ensure safe and reliable data transmission. The model considers energy, node distance, data redundancy, and link security in the node path selection method and presents an intelligent, secure, efficient, and robust data transmission path robust optimization algorithm based on ant colony algorithm. The algorithm is a fast converging global optimization algorithm with stronger robustness. The heterogeneous network topology optimization algorithm proposed in this paper can maximize the heterogeneous data transmission requirements. In Chapter 4, the nodes in the wireless sensor network are heterogeneous, and these nodes have different functions and different initial energies. The implementation of the algorithm is specifically divided into three steps: selecting relay nodes in different layers according to the proportion, setting different initial energies for the nodes in different layers, and local tree reconstruction, and finally, the heterogeneous network topology is established through the adjustment of the three steps. Under the same simulation conditions and simulation parameters, the simulation results of the algorithm proposed in this paper have higher performance and can effectively reduce the energy consumption of nodes in the network.



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*Source: 5537004-2021-03-19.xml*