# The Key Technology of Wireless Sensor Network and Its Application in the Internet of Things

Authors: Yao Zhang; Wanxiong Cai
Journal: Journal of Sensors
 (2022)

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## Abstract

At present, sensor networks and the Internet of Things have made rapid development and received more and more attention. Wireless sensor network technology is widely applied in many industries. As an important part of IoT sensor layer, it is an important infrastructure for IoT development. The purpose of this paper is to enhance the existing wireless sensor networks on the basis of existing technologies for better application in IoT. In this article, we introduce the structural features, key techniques, and some specific applications of wireless sensor networks in the IoT. Secondly, this paper studies and analyzes the principle of the cell membrane algorithm and its own characteristics. Aiming at the problem of energy constraint in wireless sensor network, an energy equilibrium clustering algorithm based on the cell membrane optimization algorithm is proposed in order to realize energy balance of nodes and distribution of network cluster heads. This algorithm divides the nodes by concentration and energy factors and divides them into globally balanced clusters by combining distance factors, which can well solve the problems of uneven distribution of cluster heads and unbalanced global power usage in sensor networks. On the basis of a lot of previous researches, this paper presents a QoS model, which is based on the routing protocol, clustering protocol, and data fusion scheme discussed in this paper. Through analysis and experiments, it is proved that this scheme improves energy consumption and flexibility by 78% compared with the previous one and achieves better network performance.


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## Body

## 1. Introduction

The so-called IoT, which is something connected to the Internet, founded in 2009 in China “experience China” center, and also, developing the IoT of the national IoT “twelfth five-year” development plan is also in the process of development; this move is to determine that IoT has an irreplaceable role in the new era, which is a significant position in the emerging technology sector. Wireless sensor network technology, as the backbone of the Internet of Things, needs continuous development to promote the whole IoT and even Huliang network and becomes a catalyst for global economic development.A wireless sensor network is a distributed sensor network with sensors at the tip that can sense and inspect the outside world. Sensors in WSN communicate wirelessly, so network settings are flexible, device locations can be changed at any time, and wired or wireless connections to the Internet are possible. A multihop self-organizing network formed by wireless communication.The Internet of Things is an emerging technology that connects various forms of wired and wireless networks with the Internet, thus connecting objects to each other and forming a huge network for monitoring, analysis, and control. Wireless sensor technology is used in many applications like battlefield surveillance, ambient and traffic detection, and industrial and agricultural production. Wireless sensor network is an essential component of the sensor layer of IoT, which is a deep extension of the original network and an important material basis for the growth of IoT. Applying the key technologies of wireless sensor networks in the Internet of Things will greatly improve the performance of related applications of the Internet of Things.An appropriate sensor node clustering algorithm can improve the efficiency of sensor networks in terms of power. Nevertheless, clusters require extra expenses, like the selection and assignment of cluster heads and the building of clusters. Leu et al. proposed a wireless sensor network area energy perception clustering method based on isolated nodes, which is called isolated node area energy perception clustering. To extend the lifespan of the network, the mean energy of the area and the proximity of the sensors to the confluence are employed to decide if individual nodes send data to the CH node or the confluence in the preceding round [1, 2]. Wang and Jiang presented a detailed comparison of the design and protocol architecture [3, 4]. To facilitate the monitoring of dissolved oxygen in large-scale aquaculture ponds, Ma et al. have realized distributed measurement, intelligent control, and centralized management [5]. IoT is ubiquitous in our daily life. It is used in our homes, hospitals, and deployed outdoors to control and report changes in the environment. It prevents fires, and it has many other beneficial functions. Yang et al.’s investigation consists of four parts. The first part will explore the most relevant limitations of IoT devices and their solutions. The second will introduce the classification of IoT attacks. The next section will focus on the mechanisms and architecture of authentication and access control. The final part will analyze security issues at different levels [6]. To sum up, most of the literature cited in this paper is about sensors, which is insufficient in the Internet of Things. Next, this paper will focus on the application of wireless sensor networks in the Internet of Things.Firstly, the thesis gives an overview of wireless sensor network. Then, the constraint relationship between the key technologies of wireless sensor network is analyzed. Then, the route management scheme based on clustering is described, and the detailed process of SCBRP algorithm and JAQ algorithm is given. Second is mainly based on the clustering of head inherit the protocol (ICBPWSN) which is described in detail, and the results of simulation experiment are given: first, it discusses the related data fusion technology and routing and topological structure, describes the data fusion (ALBDA) scheme based on the application layer, and finally gives the simulation and analysis results, and the fifth chapter is summary and outlook [7].


## 2. Proposed Method

### 2.1. Wireless Sensor Network

#### 2.1.1. Wireless Sensor Network

The wireless sensor network originated in the cold war period and was initially used in the military field to monitor the activities of the enemy. Wireless sensor network (WSN) technology is highly valued and widely used in many countries. It can be expected to play a greater part in industrial and farming production, urban planning as well as management, environmental monitoring, and battlefield monitoring in the future [8, 9]. As wireless sensor network has a very broad range of uses, especially in the military and smart home applications, it has become an international concern, involving a high degree of interdisciplinary and knowledge integration of the forefront of the hot research field; there are many technical issues to be further studied. Many developed countries have invested heavily in research on wireless sensor networks and are eager to apply them to a broad range of uses [10].


#### 2.1.2. Architecture of Wireless Sensor Network

Wireless sensor network is a kind of wireless ad hoc networks: it has several dozens to hundreds of even more a sensor node, through wireless communication links into a dynamic, more mobile peer-to-peer networks, usually including sensor node, the node, and the management node. The sensor node transmits the sensed data to the sink node in some way through wireless communication in the monitoring area and then finally reaches the user through other communication links. Through dynamic routing and mobile management technology, the network protocol stack of the whole network transmits information flow up to a certain level on unreliable and unstable channels. Figure1 shows the architecture of the wireless sensor [11, 12].Figure 1
The architecture of a wireless sensor.


#### 2.1.3. Wireless Sensor Network Nodes

Sensor node is a kind of miniature embedded device, which requires low price and low power consumption. These constraints will inevitably lead to the relatively weak processor capability and small memory capacity carried by it. Generally, the sensor node supplies energy through the battery pack. The architecture of the node is presented in Figure1. However, the energy of nodes, the constraints of various computing resources, the dynamic change of network topology, and the unstable connection status of links all make sensor networks more challenging, as shown in Figure 2.Figure 2
The components of sensor node.


#### 2.1.4. Characteristics of Wireless Sensor Network

(1) Limited Resources. Extreme finite availability of resources for sensor nodes is shown in Table 1.Table 1
Limited representation of sensor node resources.
Limited aspectsThe specific performanceEnergy Co., Ltd.Nonrecyclable, button battery powered and nonreplaceable, energy must be used efficientInduction abilityA single node has only partial inductive capability, such as acceleration, electromagnetic field, sound, light intensity, and some other characteristics, and must make full use of inductive dataAbility to calculateThe memory is generally less than 100 KB, and the main frequency is less than 1000 MHZ, but there are also 128 K, such as Atmega128The ability to communicateThe coverage range of node radio waves is less than 100 meters, and the communication bandwidth of dozens of kbps will also have uncertain changes(2) Large-Scale Networks. Large scale is reflected in two aspects: the sensor network covers a larger area, and the sensor nodes are deployed in a larger density. In this way, the sensor network can obtain more real and comprehensive information. Collecting information from a large number of nodes can increase the information precision and decrease the accuracy requirements for individual node sensing data [13]. The existence of redundant nodes in large-scale multinode distributed deployment can make wireless sensor networks more fault tolerant.(3) Self-Organizing Dynamic Adaptive Network. Because of the wireless sensor network (WSN) is in most cases were randomly deployed (such as by aircraft or from animals carry) to the object to be observed environment, this requires that the sensor network nodes have the capability of self-organization as well as the ability to collaborate on network information transmission [14].In addition, existing roofing structures of wireless sensor systems may fail as a result of external interference (environmental elements and vandalism) or power depletion. The capacity of a wireless connection can change depending on the environment or its own conditions. Sensor nodes, observed objects, and observers may change their positions. Sensor nodes, observed objects, and observers may change their positions; and dynamic human intervention to the control center (e.g., militarily preventing certain sensor nodes from falling into enemy forces). In order to effectively use energy to prolong the life of nodes, the nodes can be added or withdrawn dynamically. All these require wireless sensor networks to have dynamic adaptive capability [15].(4) Bad Environment. Wireless sensor networks typically work in the wild or at least unattended and often work without interruption but can also be subject to malicious human interference, information leakage, or the transmission of bug messages (similar to Byzantine errors, the implication is that trying to achieve consistency by means of message passing on an unreliable channel with message loss is impossible).(5) Data-Centric Networks. General networks, network devices, and other resources are located by the unique port address in the network in different ways. Sensor networks are event and mission networks, and it is meaningless to talk about a single node without sensor networks. The uniqueness of the node number in a wireless sensor network is determined by the designing of the specific network traffic agreement. The upper application of wireless sensor network only tells the network whether there is an event happening in the area it is concerned about monitoring, or the network actively notifies the upper application of an event, and the user will not give a query task to a node. In this way, the wireless sensor network is a data-centric network because of the way to query or communicate with data [16].The wireless sensor network is in a harsh environment, and its storage and energy are strictly limited. Therefore, when designing a WSN security solution, storage overhead must be considered. In Yeh, the gateway node needs to save the public keys of all users. In this solution, the smart card only needs to save the GWN identification data [17]. GWN only needs to save its own private key and public key, and each sensor node only needs to save the public key, which greatly reduces the storage space. The LED light block cipher was proposed at the CHES 2011 conference. The LED algorithm has a block length of 64 bits, supports a key length of 64/128 bits, and has 32 encryption rounds. At the same time, the encryption algorithm used in this scheme is an LED lightweight encryption algorithm, which does not require the use of a private key, and is simple and efficient. Sorting out and summarizing the memory space required by several lightweight block encryption algorithms under different plaintext lengths, the outcomes are presented in Table 2. The Advanced Encryption Standard in Cryptography, also known as Rijndael encryption, is a block encryption standard adopted by the US federal government.Table 2
Comparison of memory space required by each algorithm.
Encryption algorithm631262525041008AES10009.2510234.310120.3410254.3110234.63TWINE2661.382354.012415.652365.213651.13LED2569.342145.212136.362145.353425.12


### 2.2. Application of Wireless Sensor Network Technology in the Internet of Things

#### 2.2.1. Application in the Military Field

Wireless sensor networks have many features that are useful in the military, such as rapid deployment, free organization, high concealability, and high fault tolerance, which make them popular in the battlefield and can work great in extremely harsh environments. Aimed at this one big advantage, there will be a huge amount of sensor nodes, through the mode of transportation, including aircraft artillery, around the goal of all kinds of parameters, such as temperature humidity, terrain, voice, and coordinates, such as information acquisition, and then through the secret transmission channel, the information back to the processing, in this way, the real-time monitoring of the enemy’s movements and battlefield assessment, and implementation of simulation way work out a plan for the best, make the combat forces and its effectiveness, and reduce the unnecessary loss [18].


#### 2.2.2. Application in Industrial Field

Wireless sensor network is very popular in the industrial field; it eliminates the artificial instability, making it possible to work every day, in the field of industrial safety, traffic control, security systems, logistics management, and so on. The application in the field of industrial safety is the most. In after dealing with the explosion of sensor node and the optimized technology, it can be put on people have hurt environment, real-time monitoring of the safety of staff, and process line for dangerous working environment environmental parameters in time and can let a person even remote control instead of people to work; therefore, wireless sensor network technology can optimize industrial process, reduce the probability of safety accidents in industrial production, and can ensure the quality of industrial production under the premise of improving the quality of the products.


#### 2.2.3. Application in the Field of Medical Care

China has begun the aging of the population; in the medical care, problems are increasingly prominent, and real-time attention to the disease of patients has become a major problem to be solved. However, wireless sensor network monitoring system plays a significant role in this respect. Doctors can install tiny and precise sensors on patients to detect and collect real-time physiological information of patients, so as to understand the development of patients’ conditions at any time. On the other hand, real-time monitoring also provides more reference materials for medical institutions. Through the analysis and processing of various parameters, the root causes of diseases can be found, so as to develop effective drugs and save more people [19].


#### 2.2.4. Application in the Field of Smart Home

In the last few years, the Internet of Things is developing into a smart home. Intelligent home means highly intelligent home automation system, the application of a lot of high-tech system products and equipment, and artificial intelligence, improves the safety and comfort of the home environment, and in this premise achieves energy conservation and environmental protection. The principle is to use a large number of wireless sensor network nodes to make it self-organizing interconnection, so as to achieve the interconnection and control of home devices; this is the smart home [20].


### 2.3. Ant Colony Algorithm and Its Application in Wireless Sensor Network Routing

Ant colony algorithm has the characteristics of distributed computing, positive feedback of information, and heuristic search and is essentially a heuristic global optimization algorithm in evolutionary algorithms. To illustrate the ant colony method better, we use the figure below to illustrate it in detail. As shown in Figure3, assuming that the ant starts from point A of the ant nest and the food source is at point D, the ant walks at the same speed. The length of ACD path is 20 units of distance, while the length of ABD path is 10 units of distance. As the ant departs from site A, it does not have prior knowledge of which route is optimal (no pheromone), so the probabilities of ABD and ACD are the same. After a distance of 10 units, the ants on the ABD route have managed to find food, and the ants on the ACD route have arrived at point C just now. After 20 units of distance, the ants of ABD path have successfully returned to point A of the ant nest, and the ants of ACD path have reached point D and found food. After 40-unit distance, the ants of ACD return from food source D to ant nest A, and they have a concentration of 2 units of pheromone, while the concentration of the pheromones in the ABD pathway changes to 4 units, that is, the pheromone concentration of ABD path: the pheromone concentration of ACDpath=2:1. Then, when the subsequent ant colony selects the ABD and ACD paths, two ants will be sent to the ABD path, and one ant will be sent to the ACD path. After 40-unit distance, the concentration of the ABD path pheromone is equal to 3 : 1. If this is repeated, the ratio will increase from 4 : l.5 : 1.6: l, that is, positive feedback will be formed, and finally, the route ABD will be chosen by every ant in the colony [21, 22].Figure 3
Ant colony algorithm path diagram.


#### 2.3.1. Implementation of Ant Colony Algorithm

The connecting route pheromone among cityi and city j at time t was set as tijt, and the pheromone concentration on each city path was the same at the initial moment, set as tij0=t0.When the antk determines the next city to arrive based on the pheromone intensity of intercity connectivity routes,
(1)Pijkt=tijtα⋅ηijtβ∑tiutα⋅ηiutβ,u∈Nki,0,otherwise.After all ants finish a period, the information of pheromones on each city route is updated according to Equation (2):
(2)tijt+1=1−ptijt+Δtij,0<p<1.Among them,(3)Δtij=∑k=1nΔtijk.According to different pheromone update strategies, the basic ant colony algorithm has different models:(4)Δtijk=QLk,ij∈lk,0,otherwise,(5)Δtijk=Qdij,ij∈lk,0,otherwise,(6)Δtijk=Q,ij∈lk,0,otherwise.In the above three models,Q is a constant, representing the total amount of pheromone released by the ant once in a cycle, Lk is the path length of ant k between city i and city j, Lk is the path taken by the ant, and dij is the side length.The simulation experiment is programmed and tested using MATLAB2010b; the algorithm uses various parameters to be derived from experience and trial calculations, and the initial value settings are shown in Table3.Table 3
Values of each parameter of the algorithm.
ParameterParameterAnt colony population size49a2b3Pheromone initial volatilization coefficient0.4Pheromone enhancement factor99Maximum iteration coefficient98


### 2.4. Relationship between Key Technologies of Wireless Sensor Networks

The critical wireless sensor network technologies include network topology control, network protocols, data fusion, data management, QoS assurance, embedded operating system, time synchronization, and location information. Key technical issues, such as low-power short-range wireless telecommunication technique 1171 and security, have some challenging relationships.


#### 2.4.1. Topology Control and Other Key Technologies

Topology management in wireless cell networks is used to eliminate unnecessary communication links between nodes by power management and selection of backbone web nodes and finally form a network topology structure of efficient data forwarding under the premise of satisfying the coverage and connectivity of the network. Its relationship to other key technologies is shown in Figure4.Figure 4
Their location between topology and other key technologies.Due to the relatively large size of wireless sensor network (WSN), the node number is numerous, and adopting the method of plane management will become very difficult with the increase of network scale and at the same time make data in order from the sensor nodes for data management and integration. MAC protocol and time synchronization protocol are needed in the formation of hierarchical topology control. The MAC protocol determines when a node is allowed to send packets and generally controls all access to the physical layer. Conversely, when topology control is formed, a good topology will also improve the efficiency of MAC protocol and time synchronization protocol. In other words, they have a two-way relationship [23, 24].The same is true for target location. With hierarchical structure, when a node detects the occurrence of an event, it will immediately obtain the bit yellow of the event or the location of the node, so that cluster cooperation can obtain the location information to locate the event. Topology structure also has a great impact on routing protocol. After the formation of hierarchical structure, the node only needs to know the routing information of the neighbor node, that is, it needs to know that the neighbor node can relay to the cluster-head node, and the cluster-head node needs to maintain the routing information of the base station and all its member nodes. Hierarchical structure refers to a structure of automated test code. The feature of this structure is that the complex test code is divided into three levels of one-way dependencies, and the test logic in the test code built with the hierarchical structure becomes clear and easy to understand and maintain [25].After the formation of the hierarchical topology structure, in order to save energy, the induction data in each cluster can wait for the induction data of the downstream node and then forward to the upstream node after fusion, while the cluster head periodically waits for the induction data of all members in the cluster to arrive and send back to the base station through fusion. For the data management requests required by the application layer, the corresponding logic layer semantics can be mapped to the corresponding physical entity semantics, and then, the requested cluster makes the final response to the request through the cluster head.


## 2.1. Wireless Sensor Network

### 2.1.1. Wireless Sensor Network

The wireless sensor network originated in the cold war period and was initially used in the military field to monitor the activities of the enemy. Wireless sensor network (WSN) technology is highly valued and widely used in many countries. It can be expected to play a greater part in industrial and farming production, urban planning as well as management, environmental monitoring, and battlefield monitoring in the future [8, 9]. As wireless sensor network has a very broad range of uses, especially in the military and smart home applications, it has become an international concern, involving a high degree of interdisciplinary and knowledge integration of the forefront of the hot research field; there are many technical issues to be further studied. Many developed countries have invested heavily in research on wireless sensor networks and are eager to apply them to a broad range of uses [10].


### 2.1.2. Architecture of Wireless Sensor Network

Wireless sensor network is a kind of wireless ad hoc networks: it has several dozens to hundreds of even more a sensor node, through wireless communication links into a dynamic, more mobile peer-to-peer networks, usually including sensor node, the node, and the management node. The sensor node transmits the sensed data to the sink node in some way through wireless communication in the monitoring area and then finally reaches the user through other communication links. Through dynamic routing and mobile management technology, the network protocol stack of the whole network transmits information flow up to a certain level on unreliable and unstable channels. Figure1 shows the architecture of the wireless sensor [11, 12].Figure 1
The architecture of a wireless sensor.


### 2.1.3. Wireless Sensor Network Nodes

Sensor node is a kind of miniature embedded device, which requires low price and low power consumption. These constraints will inevitably lead to the relatively weak processor capability and small memory capacity carried by it. Generally, the sensor node supplies energy through the battery pack. The architecture of the node is presented in Figure1. However, the energy of nodes, the constraints of various computing resources, the dynamic change of network topology, and the unstable connection status of links all make sensor networks more challenging, as shown in Figure 2.Figure 2
The components of sensor node.


### 2.1.4. Characteristics of Wireless Sensor Network

(1) Limited Resources. Extreme finite availability of resources for sensor nodes is shown in Table 1.Table 1
Limited representation of sensor node resources.
Limited aspectsThe specific performanceEnergy Co., Ltd.Nonrecyclable, button battery powered and nonreplaceable, energy must be used efficientInduction abilityA single node has only partial inductive capability, such as acceleration, electromagnetic field, sound, light intensity, and some other characteristics, and must make full use of inductive dataAbility to calculateThe memory is generally less than 100 KB, and the main frequency is less than 1000 MHZ, but there are also 128 K, such as Atmega128The ability to communicateThe coverage range of node radio waves is less than 100 meters, and the communication bandwidth of dozens of kbps will also have uncertain changes(2) Large-Scale Networks. Large scale is reflected in two aspects: the sensor network covers a larger area, and the sensor nodes are deployed in a larger density. In this way, the sensor network can obtain more real and comprehensive information. Collecting information from a large number of nodes can increase the information precision and decrease the accuracy requirements for individual node sensing data [13]. The existence of redundant nodes in large-scale multinode distributed deployment can make wireless sensor networks more fault tolerant.(3) Self-Organizing Dynamic Adaptive Network. Because of the wireless sensor network (WSN) is in most cases were randomly deployed (such as by aircraft or from animals carry) to the object to be observed environment, this requires that the sensor network nodes have the capability of self-organization as well as the ability to collaborate on network information transmission [14].In addition, existing roofing structures of wireless sensor systems may fail as a result of external interference (environmental elements and vandalism) or power depletion. The capacity of a wireless connection can change depending on the environment or its own conditions. Sensor nodes, observed objects, and observers may change their positions. Sensor nodes, observed objects, and observers may change their positions; and dynamic human intervention to the control center (e.g., militarily preventing certain sensor nodes from falling into enemy forces). In order to effectively use energy to prolong the life of nodes, the nodes can be added or withdrawn dynamically. All these require wireless sensor networks to have dynamic adaptive capability [15].(4) Bad Environment. Wireless sensor networks typically work in the wild or at least unattended and often work without interruption but can also be subject to malicious human interference, information leakage, or the transmission of bug messages (similar to Byzantine errors, the implication is that trying to achieve consistency by means of message passing on an unreliable channel with message loss is impossible).(5) Data-Centric Networks. General networks, network devices, and other resources are located by the unique port address in the network in different ways. Sensor networks are event and mission networks, and it is meaningless to talk about a single node without sensor networks. The uniqueness of the node number in a wireless sensor network is determined by the designing of the specific network traffic agreement. The upper application of wireless sensor network only tells the network whether there is an event happening in the area it is concerned about monitoring, or the network actively notifies the upper application of an event, and the user will not give a query task to a node. In this way, the wireless sensor network is a data-centric network because of the way to query or communicate with data [16].The wireless sensor network is in a harsh environment, and its storage and energy are strictly limited. Therefore, when designing a WSN security solution, storage overhead must be considered. In Yeh, the gateway node needs to save the public keys of all users. In this solution, the smart card only needs to save the GWN identification data [17]. GWN only needs to save its own private key and public key, and each sensor node only needs to save the public key, which greatly reduces the storage space. The LED light block cipher was proposed at the CHES 2011 conference. The LED algorithm has a block length of 64 bits, supports a key length of 64/128 bits, and has 32 encryption rounds. At the same time, the encryption algorithm used in this scheme is an LED lightweight encryption algorithm, which does not require the use of a private key, and is simple and efficient. Sorting out and summarizing the memory space required by several lightweight block encryption algorithms under different plaintext lengths, the outcomes are presented in Table 2. The Advanced Encryption Standard in Cryptography, also known as Rijndael encryption, is a block encryption standard adopted by the US federal government.Table 2
Comparison of memory space required by each algorithm.
Encryption algorithm631262525041008AES10009.2510234.310120.3410254.3110234.63TWINE2661.382354.012415.652365.213651.13LED2569.342145.212136.362145.353425.12


## 2.1.1. Wireless Sensor Network

The wireless sensor network originated in the cold war period and was initially used in the military field to monitor the activities of the enemy. Wireless sensor network (WSN) technology is highly valued and widely used in many countries. It can be expected to play a greater part in industrial and farming production, urban planning as well as management, environmental monitoring, and battlefield monitoring in the future [8, 9]. As wireless sensor network has a very broad range of uses, especially in the military and smart home applications, it has become an international concern, involving a high degree of interdisciplinary and knowledge integration of the forefront of the hot research field; there are many technical issues to be further studied. Many developed countries have invested heavily in research on wireless sensor networks and are eager to apply them to a broad range of uses [10].


## 2.1.2. Architecture of Wireless Sensor Network

Wireless sensor network is a kind of wireless ad hoc networks: it has several dozens to hundreds of even more a sensor node, through wireless communication links into a dynamic, more mobile peer-to-peer networks, usually including sensor node, the node, and the management node. The sensor node transmits the sensed data to the sink node in some way through wireless communication in the monitoring area and then finally reaches the user through other communication links. Through dynamic routing and mobile management technology, the network protocol stack of the whole network transmits information flow up to a certain level on unreliable and unstable channels. Figure1 shows the architecture of the wireless sensor [11, 12].Figure 1
The architecture of a wireless sensor.


## 2.1.3. Wireless Sensor Network Nodes

Sensor node is a kind of miniature embedded device, which requires low price and low power consumption. These constraints will inevitably lead to the relatively weak processor capability and small memory capacity carried by it. Generally, the sensor node supplies energy through the battery pack. The architecture of the node is presented in Figure1. However, the energy of nodes, the constraints of various computing resources, the dynamic change of network topology, and the unstable connection status of links all make sensor networks more challenging, as shown in Figure 2.Figure 2
The components of sensor node.


## 2.1.4. Characteristics of Wireless Sensor Network

(1) Limited Resources. Extreme finite availability of resources for sensor nodes is shown in Table 1.Table 1
Limited representation of sensor node resources.
Limited aspectsThe specific performanceEnergy Co., Ltd.Nonrecyclable, button battery powered and nonreplaceable, energy must be used efficientInduction abilityA single node has only partial inductive capability, such as acceleration, electromagnetic field, sound, light intensity, and some other characteristics, and must make full use of inductive dataAbility to calculateThe memory is generally less than 100 KB, and the main frequency is less than 1000 MHZ, but there are also 128 K, such as Atmega128The ability to communicateThe coverage range of node radio waves is less than 100 meters, and the communication bandwidth of dozens of kbps will also have uncertain changes(2) Large-Scale Networks. Large scale is reflected in two aspects: the sensor network covers a larger area, and the sensor nodes are deployed in a larger density. In this way, the sensor network can obtain more real and comprehensive information. Collecting information from a large number of nodes can increase the information precision and decrease the accuracy requirements for individual node sensing data [13]. The existence of redundant nodes in large-scale multinode distributed deployment can make wireless sensor networks more fault tolerant.(3) Self-Organizing Dynamic Adaptive Network. Because of the wireless sensor network (WSN) is in most cases were randomly deployed (such as by aircraft or from animals carry) to the object to be observed environment, this requires that the sensor network nodes have the capability of self-organization as well as the ability to collaborate on network information transmission [14].In addition, existing roofing structures of wireless sensor systems may fail as a result of external interference (environmental elements and vandalism) or power depletion. The capacity of a wireless connection can change depending on the environment or its own conditions. Sensor nodes, observed objects, and observers may change their positions. Sensor nodes, observed objects, and observers may change their positions; and dynamic human intervention to the control center (e.g., militarily preventing certain sensor nodes from falling into enemy forces). In order to effectively use energy to prolong the life of nodes, the nodes can be added or withdrawn dynamically. All these require wireless sensor networks to have dynamic adaptive capability [15].(4) Bad Environment. Wireless sensor networks typically work in the wild or at least unattended and often work without interruption but can also be subject to malicious human interference, information leakage, or the transmission of bug messages (similar to Byzantine errors, the implication is that trying to achieve consistency by means of message passing on an unreliable channel with message loss is impossible).(5) Data-Centric Networks. General networks, network devices, and other resources are located by the unique port address in the network in different ways. Sensor networks are event and mission networks, and it is meaningless to talk about a single node without sensor networks. The uniqueness of the node number in a wireless sensor network is determined by the designing of the specific network traffic agreement. The upper application of wireless sensor network only tells the network whether there is an event happening in the area it is concerned about monitoring, or the network actively notifies the upper application of an event, and the user will not give a query task to a node. In this way, the wireless sensor network is a data-centric network because of the way to query or communicate with data [16].The wireless sensor network is in a harsh environment, and its storage and energy are strictly limited. Therefore, when designing a WSN security solution, storage overhead must be considered. In Yeh, the gateway node needs to save the public keys of all users. In this solution, the smart card only needs to save the GWN identification data [17]. GWN only needs to save its own private key and public key, and each sensor node only needs to save the public key, which greatly reduces the storage space. The LED light block cipher was proposed at the CHES 2011 conference. The LED algorithm has a block length of 64 bits, supports a key length of 64/128 bits, and has 32 encryption rounds. At the same time, the encryption algorithm used in this scheme is an LED lightweight encryption algorithm, which does not require the use of a private key, and is simple and efficient. Sorting out and summarizing the memory space required by several lightweight block encryption algorithms under different plaintext lengths, the outcomes are presented in Table 2. The Advanced Encryption Standard in Cryptography, also known as Rijndael encryption, is a block encryption standard adopted by the US federal government.Table 2
Comparison of memory space required by each algorithm.
Encryption algorithm631262525041008AES10009.2510234.310120.3410254.3110234.63TWINE2661.382354.012415.652365.213651.13LED2569.342145.212136.362145.353425.12


## 2.2. Application of Wireless Sensor Network Technology in the Internet of Things

### 2.2.1. Application in the Military Field

Wireless sensor networks have many features that are useful in the military, such as rapid deployment, free organization, high concealability, and high fault tolerance, which make them popular in the battlefield and can work great in extremely harsh environments. Aimed at this one big advantage, there will be a huge amount of sensor nodes, through the mode of transportation, including aircraft artillery, around the goal of all kinds of parameters, such as temperature humidity, terrain, voice, and coordinates, such as information acquisition, and then through the secret transmission channel, the information back to the processing, in this way, the real-time monitoring of the enemy’s movements and battlefield assessment, and implementation of simulation way work out a plan for the best, make the combat forces and its effectiveness, and reduce the unnecessary loss [18].


### 2.2.2. Application in Industrial Field

Wireless sensor network is very popular in the industrial field; it eliminates the artificial instability, making it possible to work every day, in the field of industrial safety, traffic control, security systems, logistics management, and so on. The application in the field of industrial safety is the most. In after dealing with the explosion of sensor node and the optimized technology, it can be put on people have hurt environment, real-time monitoring of the safety of staff, and process line for dangerous working environment environmental parameters in time and can let a person even remote control instead of people to work; therefore, wireless sensor network technology can optimize industrial process, reduce the probability of safety accidents in industrial production, and can ensure the quality of industrial production under the premise of improving the quality of the products.


### 2.2.3. Application in the Field of Medical Care

China has begun the aging of the population; in the medical care, problems are increasingly prominent, and real-time attention to the disease of patients has become a major problem to be solved. However, wireless sensor network monitoring system plays a significant role in this respect. Doctors can install tiny and precise sensors on patients to detect and collect real-time physiological information of patients, so as to understand the development of patients’ conditions at any time. On the other hand, real-time monitoring also provides more reference materials for medical institutions. Through the analysis and processing of various parameters, the root causes of diseases can be found, so as to develop effective drugs and save more people [19].


### 2.2.4. Application in the Field of Smart Home

In the last few years, the Internet of Things is developing into a smart home. Intelligent home means highly intelligent home automation system, the application of a lot of high-tech system products and equipment, and artificial intelligence, improves the safety and comfort of the home environment, and in this premise achieves energy conservation and environmental protection. The principle is to use a large number of wireless sensor network nodes to make it self-organizing interconnection, so as to achieve the interconnection and control of home devices; this is the smart home [20].


## 2.2.1. Application in the Military Field

Wireless sensor networks have many features that are useful in the military, such as rapid deployment, free organization, high concealability, and high fault tolerance, which make them popular in the battlefield and can work great in extremely harsh environments. Aimed at this one big advantage, there will be a huge amount of sensor nodes, through the mode of transportation, including aircraft artillery, around the goal of all kinds of parameters, such as temperature humidity, terrain, voice, and coordinates, such as information acquisition, and then through the secret transmission channel, the information back to the processing, in this way, the real-time monitoring of the enemy’s movements and battlefield assessment, and implementation of simulation way work out a plan for the best, make the combat forces and its effectiveness, and reduce the unnecessary loss [18].


## 2.2.2. Application in Industrial Field

Wireless sensor network is very popular in the industrial field; it eliminates the artificial instability, making it possible to work every day, in the field of industrial safety, traffic control, security systems, logistics management, and so on. The application in the field of industrial safety is the most. In after dealing with the explosion of sensor node and the optimized technology, it can be put on people have hurt environment, real-time monitoring of the safety of staff, and process line for dangerous working environment environmental parameters in time and can let a person even remote control instead of people to work; therefore, wireless sensor network technology can optimize industrial process, reduce the probability of safety accidents in industrial production, and can ensure the quality of industrial production under the premise of improving the quality of the products.


## 2.2.3. Application in the Field of Medical Care

China has begun the aging of the population; in the medical care, problems are increasingly prominent, and real-time attention to the disease of patients has become a major problem to be solved. However, wireless sensor network monitoring system plays a significant role in this respect. Doctors can install tiny and precise sensors on patients to detect and collect real-time physiological information of patients, so as to understand the development of patients’ conditions at any time. On the other hand, real-time monitoring also provides more reference materials for medical institutions. Through the analysis and processing of various parameters, the root causes of diseases can be found, so as to develop effective drugs and save more people [19].


## 2.2.4. Application in the Field of Smart Home

In the last few years, the Internet of Things is developing into a smart home. Intelligent home means highly intelligent home automation system, the application of a lot of high-tech system products and equipment, and artificial intelligence, improves the safety and comfort of the home environment, and in this premise achieves energy conservation and environmental protection. The principle is to use a large number of wireless sensor network nodes to make it self-organizing interconnection, so as to achieve the interconnection and control of home devices; this is the smart home [20].


## 2.3. Ant Colony Algorithm and Its Application in Wireless Sensor Network Routing

Ant colony algorithm has the characteristics of distributed computing, positive feedback of information, and heuristic search and is essentially a heuristic global optimization algorithm in evolutionary algorithms. To illustrate the ant colony method better, we use the figure below to illustrate it in detail. As shown in Figure3, assuming that the ant starts from point A of the ant nest and the food source is at point D, the ant walks at the same speed. The length of ACD path is 20 units of distance, while the length of ABD path is 10 units of distance. As the ant departs from site A, it does not have prior knowledge of which route is optimal (no pheromone), so the probabilities of ABD and ACD are the same. After a distance of 10 units, the ants on the ABD route have managed to find food, and the ants on the ACD route have arrived at point C just now. After 20 units of distance, the ants of ABD path have successfully returned to point A of the ant nest, and the ants of ACD path have reached point D and found food. After 40-unit distance, the ants of ACD return from food source D to ant nest A, and they have a concentration of 2 units of pheromone, while the concentration of the pheromones in the ABD pathway changes to 4 units, that is, the pheromone concentration of ABD path: the pheromone concentration of ACDpath=2:1. Then, when the subsequent ant colony selects the ABD and ACD paths, two ants will be sent to the ABD path, and one ant will be sent to the ACD path. After 40-unit distance, the concentration of the ABD path pheromone is equal to 3 : 1. If this is repeated, the ratio will increase from 4 : l.5 : 1.6: l, that is, positive feedback will be formed, and finally, the route ABD will be chosen by every ant in the colony [21, 22].Figure 3
Ant colony algorithm path diagram.


### 2.3.1. Implementation of Ant Colony Algorithm

The connecting route pheromone among cityi and city j at time t was set as tijt, and the pheromone concentration on each city path was the same at the initial moment, set as tij0=t0.When the antk determines the next city to arrive based on the pheromone intensity of intercity connectivity routes,
(1)Pijkt=tijtα⋅ηijtβ∑tiutα⋅ηiutβ,u∈Nki,0,otherwise.After all ants finish a period, the information of pheromones on each city route is updated according to Equation (2):
(2)tijt+1=1−ptijt+Δtij,0<p<1.Among them,(3)Δtij=∑k=1nΔtijk.According to different pheromone update strategies, the basic ant colony algorithm has different models:(4)Δtijk=QLk,ij∈lk,0,otherwise,(5)Δtijk=Qdij,ij∈lk,0,otherwise,(6)Δtijk=Q,ij∈lk,0,otherwise.In the above three models,Q is a constant, representing the total amount of pheromone released by the ant once in a cycle, Lk is the path length of ant k between city i and city j, Lk is the path taken by the ant, and dij is the side length.The simulation experiment is programmed and tested using MATLAB2010b; the algorithm uses various parameters to be derived from experience and trial calculations, and the initial value settings are shown in Table3.Table 3
Values of each parameter of the algorithm.
ParameterParameterAnt colony population size49a2b3Pheromone initial volatilization coefficient0.4Pheromone enhancement factor99Maximum iteration coefficient98


## 2.3.1. Implementation of Ant Colony Algorithm

The connecting route pheromone among cityi and city j at time t was set as tijt, and the pheromone concentration on each city path was the same at the initial moment, set as tij0=t0.When the antk determines the next city to arrive based on the pheromone intensity of intercity connectivity routes,
(1)Pijkt=tijtα⋅ηijtβ∑tiutα⋅ηiutβ,u∈Nki,0,otherwise.After all ants finish a period, the information of pheromones on each city route is updated according to Equation (2):
(2)tijt+1=1−ptijt+Δtij,0<p<1.Among them,(3)Δtij=∑k=1nΔtijk.According to different pheromone update strategies, the basic ant colony algorithm has different models:(4)Δtijk=QLk,ij∈lk,0,otherwise,(5)Δtijk=Qdij,ij∈lk,0,otherwise,(6)Δtijk=Q,ij∈lk,0,otherwise.In the above three models,Q is a constant, representing the total amount of pheromone released by the ant once in a cycle, Lk is the path length of ant k between city i and city j, Lk is the path taken by the ant, and dij is the side length.The simulation experiment is programmed and tested using MATLAB2010b; the algorithm uses various parameters to be derived from experience and trial calculations, and the initial value settings are shown in Table3.Table 3
Values of each parameter of the algorithm.
ParameterParameterAnt colony population size49a2b3Pheromone initial volatilization coefficient0.4Pheromone enhancement factor99Maximum iteration coefficient98


## 2.4. Relationship between Key Technologies of Wireless Sensor Networks

The critical wireless sensor network technologies include network topology control, network protocols, data fusion, data management, QoS assurance, embedded operating system, time synchronization, and location information. Key technical issues, such as low-power short-range wireless telecommunication technique 1171 and security, have some challenging relationships.


### 2.4.1. Topology Control and Other Key Technologies

Topology management in wireless cell networks is used to eliminate unnecessary communication links between nodes by power management and selection of backbone web nodes and finally form a network topology structure of efficient data forwarding under the premise of satisfying the coverage and connectivity of the network. Its relationship to other key technologies is shown in Figure4.Figure 4
Their location between topology and other key technologies.Due to the relatively large size of wireless sensor network (WSN), the node number is numerous, and adopting the method of plane management will become very difficult with the increase of network scale and at the same time make data in order from the sensor nodes for data management and integration. MAC protocol and time synchronization protocol are needed in the formation of hierarchical topology control. The MAC protocol determines when a node is allowed to send packets and generally controls all access to the physical layer. Conversely, when topology control is formed, a good topology will also improve the efficiency of MAC protocol and time synchronization protocol. In other words, they have a two-way relationship [23, 24].The same is true for target location. With hierarchical structure, when a node detects the occurrence of an event, it will immediately obtain the bit yellow of the event or the location of the node, so that cluster cooperation can obtain the location information to locate the event. Topology structure also has a great impact on routing protocol. After the formation of hierarchical structure, the node only needs to know the routing information of the neighbor node, that is, it needs to know that the neighbor node can relay to the cluster-head node, and the cluster-head node needs to maintain the routing information of the base station and all its member nodes. Hierarchical structure refers to a structure of automated test code. The feature of this structure is that the complex test code is divided into three levels of one-way dependencies, and the test logic in the test code built with the hierarchical structure becomes clear and easy to understand and maintain [25].After the formation of the hierarchical topology structure, in order to save energy, the induction data in each cluster can wait for the induction data of the downstream node and then forward to the upstream node after fusion, while the cluster head periodically waits for the induction data of all members in the cluster to arrive and send back to the base station through fusion. For the data management requests required by the application layer, the corresponding logic layer semantics can be mapped to the corresponding physical entity semantics, and then, the requested cluster makes the final response to the request through the cluster head.


## 2.4.1. Topology Control and Other Key Technologies

Topology management in wireless cell networks is used to eliminate unnecessary communication links between nodes by power management and selection of backbone web nodes and finally form a network topology structure of efficient data forwarding under the premise of satisfying the coverage and connectivity of the network. Its relationship to other key technologies is shown in Figure4.Figure 4
Their location between topology and other key technologies.Due to the relatively large size of wireless sensor network (WSN), the node number is numerous, and adopting the method of plane management will become very difficult with the increase of network scale and at the same time make data in order from the sensor nodes for data management and integration. MAC protocol and time synchronization protocol are needed in the formation of hierarchical topology control. The MAC protocol determines when a node is allowed to send packets and generally controls all access to the physical layer. Conversely, when topology control is formed, a good topology will also improve the efficiency of MAC protocol and time synchronization protocol. In other words, they have a two-way relationship [23, 24].The same is true for target location. With hierarchical structure, when a node detects the occurrence of an event, it will immediately obtain the bit yellow of the event or the location of the node, so that cluster cooperation can obtain the location information to locate the event. Topology structure also has a great impact on routing protocol. After the formation of hierarchical structure, the node only needs to know the routing information of the neighbor node, that is, it needs to know that the neighbor node can relay to the cluster-head node, and the cluster-head node needs to maintain the routing information of the base station and all its member nodes. Hierarchical structure refers to a structure of automated test code. The feature of this structure is that the complex test code is divided into three levels of one-way dependencies, and the test logic in the test code built with the hierarchical structure becomes clear and easy to understand and maintain [25].After the formation of the hierarchical topology structure, in order to save energy, the induction data in each cluster can wait for the induction data of the downstream node and then forward to the upstream node after fusion, while the cluster head periodically waits for the induction data of all members in the cluster to arrive and send back to the base station through fusion. For the data management requests required by the application layer, the corresponding logic layer semantics can be mapped to the corresponding physical entity semantics, and then, the requested cluster makes the final response to the request through the cluster head.


## 3. Experiments

### 3.1. Experimental Environment

In the experimental scenario, more than 100 sensor nodes are randomly deployed in a grid of 100″100. The energy of each node is set to the same initial value, and the communication radius of the nodes is 50. In other words, only two nodes with a distance of less than 50 are considered as adjacent nodes. The formula for calculating energy consumption was tested with the previous data. Channel of the data transfer rate of 2.5 Kbps, the length of each packet is 40 bytes, and every node looms induction data at a time.In order to demonstrate the performance of ALBDA data fusion scheme through experimental comparison using the above experimental environment settings, we chose two other data fusion schemes. No fusion scheme (NoAgg): each node does not know the existence of other nodes, and the data is not sent back to the base station through fusion; PerfectAgg: the node knows the optimal fusion node without additional communication and sends it to the base station after fusion. We recorded the event response latency, energy consumption, packet latency, packet loss rate, and the number of packets received by running each experimental scheme separately.


### 3.2. Experimental Parameters

Each node in the wireless sensor network is abstracted into nodes in the algorithm. If there aren nodes, the total number of nodes is n. The maximum iteration times of each selected cluster head is Gmax. The radius coefficient used to calculate the node concentration is r. The critical value is pa for the current optimal node to stop searching. The shrinkage of the search radius is pb.n nodes were randomly deployed in the wireless sensor network, and the concentration con of the surviving nodes within the radius of each node was calculated according to the radius coefficient r. For example, the concentration con of node I is defined as the sum of the distance from all valid nodes contained in the radius to node I and the percentage of the total number of nodes n times the radius, namely, formula (7):
(7)con=r∗Yn∗∑X−X12+Y−Y12,where Y is the total number of nodes within the radius coefficient r. It can be seen from this that the higher the concentration of the node, the greater the number of nodes around the node radius, or the smaller the sum of the distances from all nodes to this node if the number is the same. E is the ratio of the energy of each node to the mean power of the web. The factor value (p) of each node is determined by the concentration con of the node and the energy ratio of the node, e, that is, p=a∗con+b∗e, where a+b=1 and both a and b are greater than 0. Carrier factors are introduced to regulate the trajectory of node motion. The carrier factor of each node is defined as the ratio of the inverse of the distance between all effective nodes and this node and the total number of nodes n within the 2-search radius (2r), as shown in formula (8):
(8)g=1n∑1X−X12+Y−Y12.Among them,g is the average distance parameter, which means that the distance between nodes within 2 search radius and this node is small and vice versa. The routing algorithm was simulated in the simulation environment of MATLAB7.0, and the convergence of the algorithm was analyzed. The relevant test data are displayed in Table 4.Table 4
Experimental parameter setting.
ParameterValueNetwork coverage area140*90 m2Number of sensor nodes100Initial energy of node8 JPacket size465 bytesMetadata size18 bytesEnergy consumption per unit of data1 nJ/bitMaximum distance of communication between nodes30 m


### 3.3. Initialization of Experimental Nodes

Inn nodes, the nodes are sorted according to the size of factor value (p), and the nodes are divided according to the following proportion, as shown in Table 5.Table 5
The partition way of nodes.
The node typeAllocation proportionLow concentration node group (L)Sort the firstX times n nodesHigh concentration nonfat-soluble node group (HF)Sort the firstX times n to 2X times n nodesHigh fat-soluble node group (H)All the remaining nodesAccording to the above table, three node groups are divided, whereX represents the distribution ratio. The optimal solution can only emerge through multiple iterations, so the different allocation ratio will not have a great impact on the number of cluster heads and the optimal solution in each round. However, if X is too low or too high, it will lead to a large proportion gap between the three kinds of node groups and frequent jitter between the three kinds of node groups, which will extend the iteration times of the optimal solution and the iteration times of the three kinds of node groups and increase the calculation amount. In combination, the proportion of X is 15-20% in the experiment.We use the standard variance formula to evaluate the resource load. According to the resource load, calculate the total time for task scheduling and execution, as shown in Table6 below.Table 6
Task allocation time comparison.
Algorithm nameTask allocation execution timeACA algorithm79.8Min–Min arithmetic89.4FOA_ACA algorithm75.3


## 3.1. Experimental Environment

In the experimental scenario, more than 100 sensor nodes are randomly deployed in a grid of 100″100. The energy of each node is set to the same initial value, and the communication radius of the nodes is 50. In other words, only two nodes with a distance of less than 50 are considered as adjacent nodes. The formula for calculating energy consumption was tested with the previous data. Channel of the data transfer rate of 2.5 Kbps, the length of each packet is 40 bytes, and every node looms induction data at a time.In order to demonstrate the performance of ALBDA data fusion scheme through experimental comparison using the above experimental environment settings, we chose two other data fusion schemes. No fusion scheme (NoAgg): each node does not know the existence of other nodes, and the data is not sent back to the base station through fusion; PerfectAgg: the node knows the optimal fusion node without additional communication and sends it to the base station after fusion. We recorded the event response latency, energy consumption, packet latency, packet loss rate, and the number of packets received by running each experimental scheme separately.


## 3.2. Experimental Parameters

Each node in the wireless sensor network is abstracted into nodes in the algorithm. If there aren nodes, the total number of nodes is n. The maximum iteration times of each selected cluster head is Gmax. The radius coefficient used to calculate the node concentration is r. The critical value is pa for the current optimal node to stop searching. The shrinkage of the search radius is pb.n nodes were randomly deployed in the wireless sensor network, and the concentration con of the surviving nodes within the radius of each node was calculated according to the radius coefficient r. For example, the concentration con of node I is defined as the sum of the distance from all valid nodes contained in the radius to node I and the percentage of the total number of nodes n times the radius, namely, formula (7):
(7)con=r∗Yn∗∑X−X12+Y−Y12,where Y is the total number of nodes within the radius coefficient r. It can be seen from this that the higher the concentration of the node, the greater the number of nodes around the node radius, or the smaller the sum of the distances from all nodes to this node if the number is the same. E is the ratio of the energy of each node to the mean power of the web. The factor value (p) of each node is determined by the concentration con of the node and the energy ratio of the node, e, that is, p=a∗con+b∗e, where a+b=1 and both a and b are greater than 0. Carrier factors are introduced to regulate the trajectory of node motion. The carrier factor of each node is defined as the ratio of the inverse of the distance between all effective nodes and this node and the total number of nodes n within the 2-search radius (2r), as shown in formula (8):
(8)g=1n∑1X−X12+Y−Y12.Among them,g is the average distance parameter, which means that the distance between nodes within 2 search radius and this node is small and vice versa. The routing algorithm was simulated in the simulation environment of MATLAB7.0, and the convergence of the algorithm was analyzed. The relevant test data are displayed in Table 4.Table 4
Experimental parameter setting.
ParameterValueNetwork coverage area140*90 m2Number of sensor nodes100Initial energy of node8 JPacket size465 bytesMetadata size18 bytesEnergy consumption per unit of data1 nJ/bitMaximum distance of communication between nodes30 m


## 3.3. Initialization of Experimental Nodes

Inn nodes, the nodes are sorted according to the size of factor value (p), and the nodes are divided according to the following proportion, as shown in Table 5.Table 5
The partition way of nodes.
The node typeAllocation proportionLow concentration node group (L)Sort the firstX times n nodesHigh concentration nonfat-soluble node group (HF)Sort the firstX times n to 2X times n nodesHigh fat-soluble node group (H)All the remaining nodesAccording to the above table, three node groups are divided, whereX represents the distribution ratio. The optimal solution can only emerge through multiple iterations, so the different allocation ratio will not have a great impact on the number of cluster heads and the optimal solution in each round. However, if X is too low or too high, it will lead to a large proportion gap between the three kinds of node groups and frequent jitter between the three kinds of node groups, which will extend the iteration times of the optimal solution and the iteration times of the three kinds of node groups and increase the calculation amount. In combination, the proportion of X is 15-20% in the experiment.We use the standard variance formula to evaluate the resource load. According to the resource load, calculate the total time for task scheduling and execution, as shown in Table6 below.Table 6
Task allocation time comparison.
Algorithm nameTask allocation execution timeACA algorithm79.8Min–Min arithmetic89.4FOA_ACA algorithm75.3


## 4. Discussion

### 4.1. Performance Analysis

#### 4.1.1. Routing Overhead

The calculation is displayed in Figure5. Improved ACO algorithm is given to ants before and after the ant packets are small, so the routing control information to increase the network load is small, meanwhile with the increase of transmission speed, while the routing control data increases but essentially unchanged routing control information, so the improved ACO algorithm of routing control information costs will be reduced.Figure 5
Routing information overhead.


#### 4.1.2. Total Energy Analysis

The lower the overall energy usage of the network, the lower the cabling expense of the network, and therefore, the longer the life-cycle of the system. The overall power consumed by the sensor is used as a measure, and the calculation is evaluated against the flood and basic ACO methods. The outcome is presented in Figure5.As can be seen from Figure6, the energy consumption of this algorithm is higher than that of the flood model and the ACO algorithm. However, due to the flooding, all nodes in the model are involved in the benefit and the detection data of the flooding, and with the extension of time, energy consumption presents sharp steep increase: MAC OS used the network time delay information update paths, and optimizing the path requires more ants work together. The improvement of ACO algorithm can prolong the life cycle of the path by taking into account the energy balance characteristics of ant dynamic optimization, avoid cyclic ant proliferation, and save the power consumed by the network.Figure 6
Comparison of energy consumption.


### 4.2. Multicore Wavelet Vector Machine Algorithm Analysis

Statistical learning theory is a small-sample learning theory proposed by Vapnik et al., focusing on the study of statistical laws and learning method properties in the case of small samples. Based on the characteristics of known samples, it finds the interdependent relationship between data through learning methods, so as to predict the future data or judge its properties.Suppose the practice model set of sizen is xi,yii=1n, xi⊂Rn, where xi represents the sample input data, and if it belongs to the first category, then yi=1. If you are in the second category, then yi is equal to minus 1.If there is a classification hyperplane,(9)wx+b=0.If samples can be separated without error, that is, samples of the same category are on the same side of the classification hyperplane, the sample set is said to be linearly separable, namely, to meet(10)wxi+b≥1,yi=1,wxi+b≤−1,yi=−1,where W is the weight vector and b is the bias.The distance from the pointxi of the sample to the categorical hyperplane is defined as follows:
(11)Δi=yiwxi+b=wxi+b,(12)δ=minδi,i=1,2,⋯,n.


#### 4.2.1. Comparison of the Results of Three Diagnostic Models

Figure7 shows a comparison of the diagnostic results for MKWRVM, RVM, and SVM. It can be seen that the number of error nodes diagnosed by the three diagnostic models is 4, 16, and 30, respectively, and the accuracy rate is 98.18%, 92.73%, and 86.36%, respectively. The results show that wavelet multicore correlation vector is better than RVM and SVM in diagnosis of wireless sensor nodes.Figure 7
Comparison of results of MKWRVM, RVM, and SVM of three diagnostic models of wireless sensor.


#### 4.2.2. Comparison of Node Consumption Capacity

In the nonfusion scheme, the sensor data of the nodes are not fused and sent directly to the base station, so the transmission of a large amount of redundant data will consume too much energy. More importantly, due to the different distance between the nodes and the base station, some nodes will consume too much energy, so the energy consumption of the nodes is uneven. We can also see from the energy consumption of the node in Figure8 that in addition to the energy consumed by selecting the cluster head, the energy consumption of ALBDA is close to the ideal fusion scheme, far less than that of the nonfusion scheme. In moreover, the power consumption of each node in ALBDA is relatively uniform.Figure 8
The comparison for node energy spent.


## 4.1. Performance Analysis

### 4.1.1. Routing Overhead

The calculation is displayed in Figure5. Improved ACO algorithm is given to ants before and after the ant packets are small, so the routing control information to increase the network load is small, meanwhile with the increase of transmission speed, while the routing control data increases but essentially unchanged routing control information, so the improved ACO algorithm of routing control information costs will be reduced.Figure 5
Routing information overhead.


### 4.1.2. Total Energy Analysis

The lower the overall energy usage of the network, the lower the cabling expense of the network, and therefore, the longer the life-cycle of the system. The overall power consumed by the sensor is used as a measure, and the calculation is evaluated against the flood and basic ACO methods. The outcome is presented in Figure5.As can be seen from Figure6, the energy consumption of this algorithm is higher than that of the flood model and the ACO algorithm. However, due to the flooding, all nodes in the model are involved in the benefit and the detection data of the flooding, and with the extension of time, energy consumption presents sharp steep increase: MAC OS used the network time delay information update paths, and optimizing the path requires more ants work together. The improvement of ACO algorithm can prolong the life cycle of the path by taking into account the energy balance characteristics of ant dynamic optimization, avoid cyclic ant proliferation, and save the power consumed by the network.Figure 6
Comparison of energy consumption.


## 4.1.1. Routing Overhead

The calculation is displayed in Figure5. Improved ACO algorithm is given to ants before and after the ant packets are small, so the routing control information to increase the network load is small, meanwhile with the increase of transmission speed, while the routing control data increases but essentially unchanged routing control information, so the improved ACO algorithm of routing control information costs will be reduced.Figure 5
Routing information overhead.


## 4.1.2. Total Energy Analysis

The lower the overall energy usage of the network, the lower the cabling expense of the network, and therefore, the longer the life-cycle of the system. The overall power consumed by the sensor is used as a measure, and the calculation is evaluated against the flood and basic ACO methods. The outcome is presented in Figure5.As can be seen from Figure6, the energy consumption of this algorithm is higher than that of the flood model and the ACO algorithm. However, due to the flooding, all nodes in the model are involved in the benefit and the detection data of the flooding, and with the extension of time, energy consumption presents sharp steep increase: MAC OS used the network time delay information update paths, and optimizing the path requires more ants work together. The improvement of ACO algorithm can prolong the life cycle of the path by taking into account the energy balance characteristics of ant dynamic optimization, avoid cyclic ant proliferation, and save the power consumed by the network.Figure 6
Comparison of energy consumption.


## 4.2. Multicore Wavelet Vector Machine Algorithm Analysis

Statistical learning theory is a small-sample learning theory proposed by Vapnik et al., focusing on the study of statistical laws and learning method properties in the case of small samples. Based on the characteristics of known samples, it finds the interdependent relationship between data through learning methods, so as to predict the future data or judge its properties.Suppose the practice model set of sizen is xi,yii=1n, xi⊂Rn, where xi represents the sample input data, and if it belongs to the first category, then yi=1. If you are in the second category, then yi is equal to minus 1.If there is a classification hyperplane,(9)wx+b=0.If samples can be separated without error, that is, samples of the same category are on the same side of the classification hyperplane, the sample set is said to be linearly separable, namely, to meet(10)wxi+b≥1,yi=1,wxi+b≤−1,yi=−1,where W is the weight vector and b is the bias.The distance from the pointxi of the sample to the categorical hyperplane is defined as follows:
(11)Δi=yiwxi+b=wxi+b,(12)δ=minδi,i=1,2,⋯,n.


### 4.2.1. Comparison of the Results of Three Diagnostic Models

Figure7 shows a comparison of the diagnostic results for MKWRVM, RVM, and SVM. It can be seen that the number of error nodes diagnosed by the three diagnostic models is 4, 16, and 30, respectively, and the accuracy rate is 98.18%, 92.73%, and 86.36%, respectively. The results show that wavelet multicore correlation vector is better than RVM and SVM in diagnosis of wireless sensor nodes.Figure 7
Comparison of results of MKWRVM, RVM, and SVM of three diagnostic models of wireless sensor.


### 4.2.2. Comparison of Node Consumption Capacity

In the nonfusion scheme, the sensor data of the nodes are not fused and sent directly to the base station, so the transmission of a large amount of redundant data will consume too much energy. More importantly, due to the different distance between the nodes and the base station, some nodes will consume too much energy, so the energy consumption of the nodes is uneven. We can also see from the energy consumption of the node in Figure8 that in addition to the energy consumed by selecting the cluster head, the energy consumption of ALBDA is close to the ideal fusion scheme, far less than that of the nonfusion scheme. In moreover, the power consumption of each node in ALBDA is relatively uniform.Figure 8
The comparison for node energy spent.


## 4.2.1. Comparison of the Results of Three Diagnostic Models

Figure7 shows a comparison of the diagnostic results for MKWRVM, RVM, and SVM. It can be seen that the number of error nodes diagnosed by the three diagnostic models is 4, 16, and 30, respectively, and the accuracy rate is 98.18%, 92.73%, and 86.36%, respectively. The results show that wavelet multicore correlation vector is better than RVM and SVM in diagnosis of wireless sensor nodes.Figure 7
Comparison of results of MKWRVM, RVM, and SVM of three diagnostic models of wireless sensor.


## 4.2.2. Comparison of Node Consumption Capacity

In the nonfusion scheme, the sensor data of the nodes are not fused and sent directly to the base station, so the transmission of a large amount of redundant data will consume too much energy. More importantly, due to the different distance between the nodes and the base station, some nodes will consume too much energy, so the energy consumption of the nodes is uneven. We can also see from the energy consumption of the node in Figure8 that in addition to the energy consumed by selecting the cluster head, the energy consumption of ALBDA is close to the ideal fusion scheme, far less than that of the nonfusion scheme. In moreover, the power consumption of each node in ALBDA is relatively uniform.Figure 8
The comparison for node energy spent.


## 5. Conclusions

Wireless sensor network as a new special distributed system can be obtained from its environment, several characteristics, so as to realize the remote sensing, fundamentally changed the process of human to obtain information, process information, and in this sense, the wireless sensor network (WSN) is extended to its tentacles in the natural environment. Wireless sensor networks tend to focus more on the data itself and less on other information, somewhat like grids. From its birth, it is destined to have huge application prospects, not only in the current fields of military, industrial, environmental, and scientific research, but also in our lives. This puts forward more challenges to our research. As a key technology for wireless sensor nets, the research will face more formidable challenges.This article first detailed analysis of the key technology of wireless sensor network’s (WSN’s) several aspects of the relationship between interdependence and mutual influence between them; thus, we are thinking about the key technology of any time; on the one hand, it cannot be isolated; we must consider other relevant aspects at the same time and provide the theoretical basis for our discussion later. The proposed SCBRP routing protocol provides the routing basis for the following clustering protocols, data fusion schemes and QoS schemes. Based on the features of wireless sensor networks, hierarchical network organization can be adopted for specific applications, and a clustering protocol ICBPWSN based on cluster head inheritance is proposed, which can quickly and energy-saving make the network form a hierarchical structure and avoid the random cluster head selection conflict in LEACH. Due to data fusion to the induction of network data processing, remove redundant, save energy, and increase the accuracy of the data, but due to limited resource extreme in the wireless sensor network (WSN), abnormal operating conditions may, with the time sensitivity of the handling of data cannot be too complicated, so in view of the existing research results, we suggest a fusion scheme based on a simple fusion function ALBDA; experiments show that it has obvious energy saving and the advantages of low bandwidth consumption and fast response data. With the progress of various related technologies of wireless sensor network, it is far from enough to only provide the classical wireless sensor network with the best possible service. This is the same with QoS in effect as in traditional networks, but it is a very difficult challenge to achieve this goal under the harsh conditions of wireless sensor networks. After the previous research in this paper, according to the previous research results, we proposed an energy-saving service scheme to provide QoS assurance, and we analyzed its advantages of energy saving and better adaptability.Wireless sensor has attracted great attention from the whole academic circle. However, due to the extremely restricted working environment of wireless sensor network, the research progress largely depends on the progress of electronic technology, communication technology, microelectromechanical technology, and computing technology. Under the current technical conditions, resource constraints make the research of key technologies very difficult, and the research of one aspect must also consider other relevant aspects. This paper only considers four aspects. If we can combine all aspects of key technologies, then we can more reasonably meet the demand, which is also the next step of research. For the clustering protocol, it is necessary to consider the optimization of the number of clusters. The flexibility of data acquisition and processing in data fusion needs further study. At present, QoS guarantees have the largest research space and the most formidable challenges, but it is also most affected by resource constraints. In addition, another important research topic for wireless sensor networks is network security, and more research is needed in the next step, especially the consideration of security policies and security mechanisms when considering other key technologies.



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*Source: 1817781-2022-05-09.xml*