# Security and Privacy Protection of Internet of Vehicles Consensus Algorithm Based on Wireless Sensors

Authors: Yao Zhang; Gaoqing Ji
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Due to its large network scale, open communication environment, unstable wireless network, and other characteristics, it is extremely vulnerable to attacks and causes security problems, resulting in the collapse of the Internet of Vehicles system. The application of the Internet of Vehicles is becoming more and more extensive, but there are still problems such as information security and privacy leakage in the Internet of Vehicles. Through the analysis of the security threats and privacy protection requirements faced by the Internet of Vehicles system, this paper mainly studies information security, vehicle identity privacy, and location privacy in the process of Internet of Vehicles wireless communication. Therefore, it is urgent to conduct research on the information security and privacy protection issues of the Internet of Vehicles. This paper discusses the research on the security and privacy protection of the consensus algorithm for the Internet of Vehicles based on wireless sensors, compares and analyzes the wireless sensor data privacy protection protocols based on sharding technology, Tongtai encryption technology, and perturbation technology, and selects an optimized Kalman consensus filter. The algorithm is applied to the node information exchange of the sensor network, and two filters (low pass and band pass) are used to unify the observations and covariance of the network. Estimation of the sensor network state with and without data packet loss, the effect of system estimation error under different packet loss rates, data privacy protection algorithm performance, vehicle network data communication volume, and confusion factors on algorithm efficiency and the node energy consumption was compared and analyzed. Based on the application of wireless sensors, the estimation error and inconsistency estimation error of the algorithm in this paper finally converge to about 0.5, and both can maintain good stability and have good robustness. In addition, the communication volume of the algorithm in this paper is about 30% of the SCPDA algorithm. The Kalman consensus filtering algorithm reduces the amount of confusing data sent, improves privacy protection, and also achieves lower communication overhead.


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## Body

## 1. Introduction

With the development of the Internet of Things, the concept of the Internet of Vehicles is rapidly popularized and applied. As a smart transportation network, the Internet of Vehicles faces many security threats. The Internet of Vehicles realizes the interaction between vehicles, roads, users, and Internet information. The various modules are organically combined and mutually infiltrated. They are extremely vulnerable to technical attacks and have potential security problems. In addition, there are issues such as the protection of users’ private information. However, the application of the Internet of Vehicles is becoming more and more extensive with the development of the times. Therefore, it is necessary to carry out research on the information security and privacy protection methods of the Internet of Vehicles. In recent years, with the rapid economic and social development, the development trajectory of all walks of life has become more and more complex, especially the urban transportation network, as an important part of urban infrastructure, and has become more and more critical in modern life. It can be said that the smoothness of the urban transportation system will have a direct impact on people’s daily production and life.The application of wireless sensor network continues to penetrate into every corner of people’s life and work. As people’s understanding of applications continues to deepen, many wireless sensor network applications expose the inadequate protection of user privacy data. Adding hidden information and disturbance data to the wireless sensor improves privacy protection and can realize the restoration and dynamic adjustment of hidden data. Combined application with consensus algorithm can achieve high privacy protection performance.The problems of infinite sensors and Internet of Vehicles applications are becoming more and more obvious, and there are more and more researches on them. Khan et al. reviewed the virtualization of wireless sensor networks. They provide a state-of-the-art comprehensive review and in-depth discussion of research issues. They will introduce the basic knowledge of WSN virtualization and stimulate its relevance through carefully selected scenarios. Existing work is to introduce in detail and critically evaluate the use of a set of requirements derived from scenarios. And the related research projects are reviewed. They also discussed some research problems and put forward some suggestions on how to solve these problems. However, the application scenarios of the wireless sensors they studied are not clear, and there are still various problems in practical applications [1]. Wang et al. proposed and introduced the concept, architecture, and application of parallel IoV (PIoV) by identifying the cyberphysical social characteristics of IoV in the Internet of Vehicles. The three main components of PIoV are introduced: artificial IoV for learning and describing physical IoV, computational experiments for evaluating and predicting the results and value of driving strategies, and parallel execution of prescribed physical IoV operations. PIoV makes it possible to realize safe, intelligent, effective, and efficient traffic management and control. The ultimate goal of PIoV is to make IoV have descriptive, predictive, and prescriptive intelligence based on par. However, their research has flaws in the systematic and complex traffic management [2]. Hu et al. proposed the universal Kalman consensus filter (UKCF). Based on the state transition matrix, each sensor can fuse the received information within a measurement period and track the object over time. Sensors with any sampling rate and working time can be incorporated into the collaborative tracking system. Optical observation is an effective method for tracking space objects (especially midorbit or high-orbit objects). First, the ground and space-based optical (SBO) tracking model was established. Then, a centralized fusion algorithm for asynchronous sensor networks is proposed. However, their experimental conclusions are not supported by the corresponding data [3]. Combining wireless sensor network technology with intelligent traffic control technology, using information fusion technology and Internet of Vehicles technology, more reliable and accurate detection parameters can be obtained, which is expected to manage and control traffic conditions in a more real-time, accurate, and efficient manner.This paper focuses on the security and privacy protection issues in the high-frequency interaction of Internet of Vehicles information under the cloud architecture, combined with the existing group signature authentication technology and distributed Internet of Vehicles authentication framework, and proposes a ring signcryption authentication based on group signature technology program. The innovations of this article are as follows: (1) the combination of theoretical analysis and empirical analysis not only theoretically analyzes the advantages and disadvantages of infinite sensors and the Internet of Vehicles but also conducts related experimental simulation tests to determine their safety and privacy protection method. (2) Infinite sensors with slicing technology and Kalman consistent filtering algorithm are selected to improve the information security and privacy protection performance of the Internet of Vehicles in many aspects and angles.


## 2. Consensus Algorithm Security and Privacy Protection Method for the Internet of Vehicles Based on Wireless Sensors

### 2.1. Wireless Sensor

#### 2.1.1. Overview of Wireless Sensors

As an emerging information technology, wireless sensor networks (WSN) have attracted great attention from military departments, industries, and academia in many countries around the world [4, 5]. The basic application of wireless sensor network is embodied in the cooperative perception and processing of the physical information of the monitored object through wireless transmission and information interaction, providing necessary information support for the real-time monitoring and rapid processing of events in the monitoring area [6, 7]. The RSU module completely enters the unmanned operation mode after initialization. Therefore, the attacker can easily access the device or tamper with the perception information, thereby destroying the collection and correctness of the source information, which will inevitably bring serious security threats to the Internet of Vehicles system.


#### 2.1.2. Privacy Attack Method of Wireless Sensor Network

All nodes in the wireless sensor network are similar, which is mainly reflected in the closeness of the energy, computing power, communication ability, and memory space between the nodes, and the attacker has an advantage in hardware facilities. Among the existing research results, mainly from the attack perspective, it is divided into two types: local perspective attack and global perspective attack [8, 9]. The global perspective attack realizes the communication interception of the entire network by capturing packets of the entire network. Local-view attacks often do not have hardware devices for global-view attacks and only rely on listening to a certain range of internal networks.Commonly used attack methods include the following four categories: monitoring, traffic analysis, data tampering, and replay attacks. Among them, monitoring and traffic analysis are nonactive attacks, while replay attacks and data tampering are active attacks [10, 11].


#### 2.1.3. Privacy Protection Technology of Wireless Sensor Network

In the existing wireless sensor networks, privacy protection technology is mainly divided into two aspects: data privacy protection technology and location information privacy protection technology. Location privacy protection technology is to prevent attackers from obtaining the target location through communication mode monitoring and analysis. Data privacy protection technology is to prevent attackers from eavesdropping on sensor nodes to obtain effective information through the link layer [12, 13]. At present, the technologies and solutions used for data privacy protection and location privacy protection are mainly divided into three categories, which are based on fragmentation technology, based on homomorphic encryption, and based on perturbation technology. The hardware facilities of the Internet of Vehicles are updated rapidly. The communication nodes in the Internet of Vehicles are high-speed moving vehicles, and it is very difficult to realize real-time control. Therefore, the identity authentication and privacy protection of the communication nodes in the Internet of Vehicles have become a major problem.(1) Data Privacy Protection Protocol Based on Fragmentation Technology. The main idea of data privacy protection protocol based on fragmentation technology is to achieve the purpose of privacy protection by slicing the original data and then reaching the target node through multipath transmission [14, 15]. The main protocols are SMART protocol. Its main idea is to hide the original data by slicing the original data. The realization process is mainly divided into three steps: slicing, mixing, and aggregation. However, its communication overhead is relatively large, and it is sensitive to data loss and does not have data integrity. ESPART protocol is an improved method of SMART protocol [16, 17].(2) Data Privacy Protection Protocol Based on Homomorphic Encryption. Homomorphic encryption is a complex cryptographic technique based on mathematics [18, 19]. The homomorphic encrypted data is processed to get an output, and this output is decrypted. The result is the same as the output result obtained by processing the unencrypted original data with the same method. The idea of homomorphic encryption originated from private homomorphism. Algebraic homomorphism and arithmetic homomorphism are subsets of private homomorphism. There are mainly AHE protocol and IPHCDA protocol [20, 21].(3) Data Privacy Protection Protocol Based on Disturbance Technology. The data privacy protection protocol based on perturbation technology mainly hides the real data by adding random numbers to the original data to achieve the purpose of data privacy protection [22, 23]. The main protocols are CPDA protocol. The advantage of this protocol is to achieve precise aggregation, but the disadvantage is that it is expensive to calculate and communicate. In the KIPDA protocol, the main idea of the protocol is to add some disguised data to the original data to achieve the purpose of hiding the original data, rather than encrypting the original data to make the original data indivisible [24, 25]. The process is mainly divided into four aspects: preallocation, reporting, aggregation, and base station processing. The advantage of KIPDA protocol is that it supports integrity verification and low computational overhead; the disadvantage is that it has high communication overhead and high space complexity.As shown in Figure1, a wireless sensor network consists of a large number of sensor nodes forming its own topological network in a self-organizing manner, and the collected object information is sent to the terminal or target of the network through intermediate nodes. Because of its inherent characteristics, such as large scale, self-organization, dynamics, reliability, and data centricity, wireless sensor networks face the danger of being monitored, traffic analysis, and data tampering. Therefore, data privacy protection is required. The Internet of Vehicles has strengthened the circulation of information and expanded data information, so the privacy protection of data needs to be strengthened [26, 27].Figure 1
Typical wireless sensor network structure diagram.


### 2.2. Basic Theory of Consistency

Consistency, as one of the tasks of distributed estimation in sensor networks, refers to the mutual exchange of state information between sensor nodes, so that the state estimation value of each sensor node to the target is finally the same. The consensus algorithm first started in statistics and management in the 1970s. Degroot first introduced the idea of consensus algorithm in 1974 when fusion of uncertain information from multiple sensors. Afterwards, Vicsek gave the most basic core idea of the consensus algorithm: individual exchange information with neighboring individuals and then the information between all individuals achieve global consistency. In recent years, the coordinated control of multiagent systems has received more and more research and attention. As the basis of coordinated control, consistency has been widely used in the research of flocking control, formation control, and aggregation problems. One thing is the information source, the other is the information object, and the new information received is obtained through the evaluation of the information object by the information source. If this evaluation is positive, that is, there is a compatible positive relationship between the information source and the information object, then people’s attitudes toward these two things are psychologically consistent [28, 29].Graph theory is a commonly used tool in the study of consistency problems. Define graphH as an ordered pair B,R, denoted as H=B,R. B is a nonempty set, which contains all the nodes in the sensor network. Generally, the set is defined as a set B=b1,b2,⋯,bn, and bi represents the i-th sensor node. R is the set of undirected continuations in set V, which is called edge set R=r1,r2,⋯,rn. If bi,bj∈R, it means that nodes i and j can communicate; in an undirected graph, for sensor networks, the edges between nodes have no direction. The adjacency matrix A=aij represents the closeness between any two sensor nodes, that is, the weight, which is a matrix used to describe the relationship between nodes and edges. Define aii=0,i=1,2,⋯,n; for i≠j, if bi,bj∈R, then aij=1, vice versa aij=0. The neighbor set of node i is defined as
(1)Mi=bj∈B:bi,bj∈R,Ki=Mi∪i.With the rapid development of Internet technology and modern communication technology, the Internet of Vehicles, as a new system of real-time interaction between virtual networks and traffic systems, emerges as the times require, coverage, fast communication [26, 30]. The Laplacian matrix L is used to describe another kind of topology, and the value is as follows:
(2)lij=∑jaij,i=j,−aij,i≠j.The degree of nodei is the matrix fi=∑j≠iaij.
(3)F=f10⋯00f2⋯0⋮⋮⋱⋮00⋯fn.SatisfyL=F−A.For a time-continuous network system, the dynamic equation of its nodes under the consensus protocol is(4)ẏio=uio=∑j∈Niyjo−yio.Considering the overall behavior of the system, according to Laplace’s definition, the above formula can be rewritten as follows:(5)ẏo=−Loyo.Among them,Lo is the Laplace matrix and yo=y1o,⋯,ynoT the state vector.If the topological structure of the system is fixed, we can see that the state information of each node will evolve to its neighbor nodes according to the consensus protocol, and finally, the state information of all nodes tends to be the same. The consensus algorithm is used to describe the rules of interaction between nodes. Assuming that there aren nodes in the system, at that time o⟶∞, yi−yj⟶0,∀i≠j the system is consistent. The consensus algorithms studied in this paper are all discrete consensus algorithms, and its general form is
(6)yil+1=yil+φ∑j∈Niaijlyjl−yil,i∈B.In the above formula,φ is the step size and satisfies
(7)0<φ<1ΔΔ=maxi∑i≠jaij.Rewrite the above formula into a matrix form as(8)yl+1=Qlyl,Ql=I−φLl.Among them,yl=yl1,⋯,ylnT is the state vector. All elements of Ql are nonnegative, and the sum of all rows is 1.A very important indicator to comment on the quality of the consensus algorithm is convergence speed. In graph theory, we often use the smallest nonzero eigenvalue of the Laplacian matrixLδ2L to represent it, where δ2L is used to characterize the convergence rate of consistency: if the larger, the better the algebraic connectivity of the network, the faster the convergence speed of the network.


## 2.1. Wireless Sensor

### 2.1.1. Overview of Wireless Sensors

As an emerging information technology, wireless sensor networks (WSN) have attracted great attention from military departments, industries, and academia in many countries around the world [4, 5]. The basic application of wireless sensor network is embodied in the cooperative perception and processing of the physical information of the monitored object through wireless transmission and information interaction, providing necessary information support for the real-time monitoring and rapid processing of events in the monitoring area [6, 7]. The RSU module completely enters the unmanned operation mode after initialization. Therefore, the attacker can easily access the device or tamper with the perception information, thereby destroying the collection and correctness of the source information, which will inevitably bring serious security threats to the Internet of Vehicles system.


### 2.1.2. Privacy Attack Method of Wireless Sensor Network

All nodes in the wireless sensor network are similar, which is mainly reflected in the closeness of the energy, computing power, communication ability, and memory space between the nodes, and the attacker has an advantage in hardware facilities. Among the existing research results, mainly from the attack perspective, it is divided into two types: local perspective attack and global perspective attack [8, 9]. The global perspective attack realizes the communication interception of the entire network by capturing packets of the entire network. Local-view attacks often do not have hardware devices for global-view attacks and only rely on listening to a certain range of internal networks.Commonly used attack methods include the following four categories: monitoring, traffic analysis, data tampering, and replay attacks. Among them, monitoring and traffic analysis are nonactive attacks, while replay attacks and data tampering are active attacks [10, 11].


### 2.1.3. Privacy Protection Technology of Wireless Sensor Network

In the existing wireless sensor networks, privacy protection technology is mainly divided into two aspects: data privacy protection technology and location information privacy protection technology. Location privacy protection technology is to prevent attackers from obtaining the target location through communication mode monitoring and analysis. Data privacy protection technology is to prevent attackers from eavesdropping on sensor nodes to obtain effective information through the link layer [12, 13]. At present, the technologies and solutions used for data privacy protection and location privacy protection are mainly divided into three categories, which are based on fragmentation technology, based on homomorphic encryption, and based on perturbation technology. The hardware facilities of the Internet of Vehicles are updated rapidly. The communication nodes in the Internet of Vehicles are high-speed moving vehicles, and it is very difficult to realize real-time control. Therefore, the identity authentication and privacy protection of the communication nodes in the Internet of Vehicles have become a major problem.(1) Data Privacy Protection Protocol Based on Fragmentation Technology. The main idea of data privacy protection protocol based on fragmentation technology is to achieve the purpose of privacy protection by slicing the original data and then reaching the target node through multipath transmission [14, 15]. The main protocols are SMART protocol. Its main idea is to hide the original data by slicing the original data. The realization process is mainly divided into three steps: slicing, mixing, and aggregation. However, its communication overhead is relatively large, and it is sensitive to data loss and does not have data integrity. ESPART protocol is an improved method of SMART protocol [16, 17].(2) Data Privacy Protection Protocol Based on Homomorphic Encryption. Homomorphic encryption is a complex cryptographic technique based on mathematics [18, 19]. The homomorphic encrypted data is processed to get an output, and this output is decrypted. The result is the same as the output result obtained by processing the unencrypted original data with the same method. The idea of homomorphic encryption originated from private homomorphism. Algebraic homomorphism and arithmetic homomorphism are subsets of private homomorphism. There are mainly AHE protocol and IPHCDA protocol [20, 21].(3) Data Privacy Protection Protocol Based on Disturbance Technology. The data privacy protection protocol based on perturbation technology mainly hides the real data by adding random numbers to the original data to achieve the purpose of data privacy protection [22, 23]. The main protocols are CPDA protocol. The advantage of this protocol is to achieve precise aggregation, but the disadvantage is that it is expensive to calculate and communicate. In the KIPDA protocol, the main idea of the protocol is to add some disguised data to the original data to achieve the purpose of hiding the original data, rather than encrypting the original data to make the original data indivisible [24, 25]. The process is mainly divided into four aspects: preallocation, reporting, aggregation, and base station processing. The advantage of KIPDA protocol is that it supports integrity verification and low computational overhead; the disadvantage is that it has high communication overhead and high space complexity.As shown in Figure1, a wireless sensor network consists of a large number of sensor nodes forming its own topological network in a self-organizing manner, and the collected object information is sent to the terminal or target of the network through intermediate nodes. Because of its inherent characteristics, such as large scale, self-organization, dynamics, reliability, and data centricity, wireless sensor networks face the danger of being monitored, traffic analysis, and data tampering. Therefore, data privacy protection is required. The Internet of Vehicles has strengthened the circulation of information and expanded data information, so the privacy protection of data needs to be strengthened [26, 27].Figure 1
Typical wireless sensor network structure diagram.


## 2.1.1. Overview of Wireless Sensors

As an emerging information technology, wireless sensor networks (WSN) have attracted great attention from military departments, industries, and academia in many countries around the world [4, 5]. The basic application of wireless sensor network is embodied in the cooperative perception and processing of the physical information of the monitored object through wireless transmission and information interaction, providing necessary information support for the real-time monitoring and rapid processing of events in the monitoring area [6, 7]. The RSU module completely enters the unmanned operation mode after initialization. Therefore, the attacker can easily access the device or tamper with the perception information, thereby destroying the collection and correctness of the source information, which will inevitably bring serious security threats to the Internet of Vehicles system.


## 2.1.2. Privacy Attack Method of Wireless Sensor Network

All nodes in the wireless sensor network are similar, which is mainly reflected in the closeness of the energy, computing power, communication ability, and memory space between the nodes, and the attacker has an advantage in hardware facilities. Among the existing research results, mainly from the attack perspective, it is divided into two types: local perspective attack and global perspective attack [8, 9]. The global perspective attack realizes the communication interception of the entire network by capturing packets of the entire network. Local-view attacks often do not have hardware devices for global-view attacks and only rely on listening to a certain range of internal networks.Commonly used attack methods include the following four categories: monitoring, traffic analysis, data tampering, and replay attacks. Among them, monitoring and traffic analysis are nonactive attacks, while replay attacks and data tampering are active attacks [10, 11].


## 2.1.3. Privacy Protection Technology of Wireless Sensor Network

In the existing wireless sensor networks, privacy protection technology is mainly divided into two aspects: data privacy protection technology and location information privacy protection technology. Location privacy protection technology is to prevent attackers from obtaining the target location through communication mode monitoring and analysis. Data privacy protection technology is to prevent attackers from eavesdropping on sensor nodes to obtain effective information through the link layer [12, 13]. At present, the technologies and solutions used for data privacy protection and location privacy protection are mainly divided into three categories, which are based on fragmentation technology, based on homomorphic encryption, and based on perturbation technology. The hardware facilities of the Internet of Vehicles are updated rapidly. The communication nodes in the Internet of Vehicles are high-speed moving vehicles, and it is very difficult to realize real-time control. Therefore, the identity authentication and privacy protection of the communication nodes in the Internet of Vehicles have become a major problem.(1) Data Privacy Protection Protocol Based on Fragmentation Technology. The main idea of data privacy protection protocol based on fragmentation technology is to achieve the purpose of privacy protection by slicing the original data and then reaching the target node through multipath transmission [14, 15]. The main protocols are SMART protocol. Its main idea is to hide the original data by slicing the original data. The realization process is mainly divided into three steps: slicing, mixing, and aggregation. However, its communication overhead is relatively large, and it is sensitive to data loss and does not have data integrity. ESPART protocol is an improved method of SMART protocol [16, 17].(2) Data Privacy Protection Protocol Based on Homomorphic Encryption. Homomorphic encryption is a complex cryptographic technique based on mathematics [18, 19]. The homomorphic encrypted data is processed to get an output, and this output is decrypted. The result is the same as the output result obtained by processing the unencrypted original data with the same method. The idea of homomorphic encryption originated from private homomorphism. Algebraic homomorphism and arithmetic homomorphism are subsets of private homomorphism. There are mainly AHE protocol and IPHCDA protocol [20, 21].(3) Data Privacy Protection Protocol Based on Disturbance Technology. The data privacy protection protocol based on perturbation technology mainly hides the real data by adding random numbers to the original data to achieve the purpose of data privacy protection [22, 23]. The main protocols are CPDA protocol. The advantage of this protocol is to achieve precise aggregation, but the disadvantage is that it is expensive to calculate and communicate. In the KIPDA protocol, the main idea of the protocol is to add some disguised data to the original data to achieve the purpose of hiding the original data, rather than encrypting the original data to make the original data indivisible [24, 25]. The process is mainly divided into four aspects: preallocation, reporting, aggregation, and base station processing. The advantage of KIPDA protocol is that it supports integrity verification and low computational overhead; the disadvantage is that it has high communication overhead and high space complexity.As shown in Figure1, a wireless sensor network consists of a large number of sensor nodes forming its own topological network in a self-organizing manner, and the collected object information is sent to the terminal or target of the network through intermediate nodes. Because of its inherent characteristics, such as large scale, self-organization, dynamics, reliability, and data centricity, wireless sensor networks face the danger of being monitored, traffic analysis, and data tampering. Therefore, data privacy protection is required. The Internet of Vehicles has strengthened the circulation of information and expanded data information, so the privacy protection of data needs to be strengthened [26, 27].Figure 1
Typical wireless sensor network structure diagram.


## 2.2. Basic Theory of Consistency

Consistency, as one of the tasks of distributed estimation in sensor networks, refers to the mutual exchange of state information between sensor nodes, so that the state estimation value of each sensor node to the target is finally the same. The consensus algorithm first started in statistics and management in the 1970s. Degroot first introduced the idea of consensus algorithm in 1974 when fusion of uncertain information from multiple sensors. Afterwards, Vicsek gave the most basic core idea of the consensus algorithm: individual exchange information with neighboring individuals and then the information between all individuals achieve global consistency. In recent years, the coordinated control of multiagent systems has received more and more research and attention. As the basis of coordinated control, consistency has been widely used in the research of flocking control, formation control, and aggregation problems. One thing is the information source, the other is the information object, and the new information received is obtained through the evaluation of the information object by the information source. If this evaluation is positive, that is, there is a compatible positive relationship between the information source and the information object, then people’s attitudes toward these two things are psychologically consistent [28, 29].Graph theory is a commonly used tool in the study of consistency problems. Define graphH as an ordered pair B,R, denoted as H=B,R. B is a nonempty set, which contains all the nodes in the sensor network. Generally, the set is defined as a set B=b1,b2,⋯,bn, and bi represents the i-th sensor node. R is the set of undirected continuations in set V, which is called edge set R=r1,r2,⋯,rn. If bi,bj∈R, it means that nodes i and j can communicate; in an undirected graph, for sensor networks, the edges between nodes have no direction. The adjacency matrix A=aij represents the closeness between any two sensor nodes, that is, the weight, which is a matrix used to describe the relationship between nodes and edges. Define aii=0,i=1,2,⋯,n; for i≠j, if bi,bj∈R, then aij=1, vice versa aij=0. The neighbor set of node i is defined as
(1)Mi=bj∈B:bi,bj∈R,Ki=Mi∪i.With the rapid development of Internet technology and modern communication technology, the Internet of Vehicles, as a new system of real-time interaction between virtual networks and traffic systems, emerges as the times require, coverage, fast communication [26, 30]. The Laplacian matrix L is used to describe another kind of topology, and the value is as follows:
(2)lij=∑jaij,i=j,−aij,i≠j.The degree of nodei is the matrix fi=∑j≠iaij.
(3)F=f10⋯00f2⋯0⋮⋮⋱⋮00⋯fn.SatisfyL=F−A.For a time-continuous network system, the dynamic equation of its nodes under the consensus protocol is(4)ẏio=uio=∑j∈Niyjo−yio.Considering the overall behavior of the system, according to Laplace’s definition, the above formula can be rewritten as follows:(5)ẏo=−Loyo.Among them,Lo is the Laplace matrix and yo=y1o,⋯,ynoT the state vector.If the topological structure of the system is fixed, we can see that the state information of each node will evolve to its neighbor nodes according to the consensus protocol, and finally, the state information of all nodes tends to be the same. The consensus algorithm is used to describe the rules of interaction between nodes. Assuming that there aren nodes in the system, at that time o⟶∞, yi−yj⟶0,∀i≠j the system is consistent. The consensus algorithms studied in this paper are all discrete consensus algorithms, and its general form is
(6)yil+1=yil+φ∑j∈Niaijlyjl−yil,i∈B.In the above formula,φ is the step size and satisfies
(7)0<φ<1ΔΔ=maxi∑i≠jaij.Rewrite the above formula into a matrix form as(8)yl+1=Qlyl,Ql=I−φLl.Among them,yl=yl1,⋯,ylnT is the state vector. All elements of Ql are nonnegative, and the sum of all rows is 1.A very important indicator to comment on the quality of the consensus algorithm is convergence speed. In graph theory, we often use the smallest nonzero eigenvalue of the Laplacian matrixLδ2L to represent it, where δ2L is used to characterize the convergence rate of consistency: if the larger, the better the algebraic connectivity of the network, the faster the convergence speed of the network.


## 3. Consensus Algorithm Security and Privacy Protection Model for the Internet of Vehicles Based on Wireless Sensors

The security design goals of the consensus algorithm for the Internet of Vehicles based on wireless sensors are as follows: (1) privacy. The sensitive data of each node is only allowed to be known by themselves or other authorized nodes, and any unauthorized node cannot obtain other information in any way. The data fusion privacy protection mechanism must be able to prevent eavesdropping attacks and collusion attacks that may be caused by the compromise of multiple nodes. When the sensor network is maliciously attacked, the node has the ability to hide its own data from other nodes. Malicious attackers can steal private data by eavesdropping. A good performance data fusion privacy protection algorithm must have a good resistance to such attacks. (2) Effectiveness. The initial goal of data fusion is to reduce the amount of information in transmission, thereby saving resources and power consumption. However, in the data fusion privacy protection model, in order to achieve the ability of privacy protection, sensor networks need to add additional computational overhead. A good performance data fusion privacy protection model should minimize the additional costs incurred for privacy protection. (3) Data confidentiality. Ensure that the node’s private data is not disclosed to any unauthorized nodes. The difference with privacy is that data confidentiality is mainly aimed at the security of sensitive data transmission process. It prevents adversaries from stealing information on the communication link but allows both parties for the purpose of communication to know the plaintext information of the data; that is, the information is authorized. The nodes of both parties to the communication are public. For example, ifA sends information to B, as long as the information is not intercepted by a third party, the confidentiality requirements can be met, while privacy protection requires that all nodes including node B cannot be read A’s private information content. In WSNs, data confidentiality can be achieved through symmetric encryption. (4) Data integrity, In WSNs, the base station node submits the results of data fusion to the monitoring center, and the user makes judgments about the environment or makes decisions on important events through the results, so in the process of data transmission and fusion processing, it should ensure that the data is not tampered by malicious attackers. Protecting data from being intentionally or unintentionally modified is a basic requirement for data integrity [31, 32].


### 3.1. Kalman Consensus Filtering Algorithm

Olfati-Saber et al. used a consensus algorithm to design a consensus filter and used this filter in the neighbor node information exchange of the sensor network. The variance is unified.The algorithm updates the estimated value of each node’s state. First, initialize(9)Q0i=Q0,y¯10i=y0.Then, receive new data from neighbor nodes, and estimate the consistency fusion a priori:(10)Ki=Mi∪i,(11)ukj=JjTTj−1zkj,∀j∈Ki,yki=∑j∈Jiukj,(12)Ukj=JjTTj−1Jj,∀j∈Ki,Dki=∑j∈JiUkj.The Kalman consensus state is estimated as(13)Cki=Qki−1+Dki−1,(14)y^kk=y¯kk−1i+Ckiyki−Dkiy¯kk−1i+φCki∑j∈Niy¯kk−1j−y¯kk−1i.Kalman consensus state prediction:(15)Qk+1j=AMkiAT+H,(16)y¯k+1ki=Ay^kki.Figure2 shows the network form of the consensus algorithm.Figure 2
Consensus algorithm network.In the algorithm, a priori estimated value, target observation value, and target observation covariance information need to be exchanged. Equation (15) is mainly used to process the observations and covariance accordingly, and equation (16) combines the Kalman filter algorithm with the consensus algorithm, which is the core of the algorithm, which is the prior estimation of neighbor nodes. The value is merged, and the estimated value of the node itself is also updated. The combination of these two algorithms can achieve better estimation results and at the same time reduce the estimation error of the sensor network. This is very beneficial to the scalability of the sensor network. When the number of sensors in the network increase or decrease, we do not need to rely on a specified sensor to transmit information. You can directly select the sensor closest to the control center. Then, you can proceed estimated data transmission.


### 3.2. Optimized Consistent Kalman Filter Algorithm

For a target system, it is assumed that there aren sensor nodes. Then, the dynamic equation of the target system and the observation equation of the sensor node are
(17)yl+1=Byl+ϖl,rli=Giyki+bki.For sensor networks, the consensus filtering algorithm estimates the target state. The state estimation is achieved through information exchange with neighboring nodes. Therefore, the estimation accuracy of the target state of the node is not only determined by the estimation performance of the node itself but also by the neighboring node. State estimation performance is related. For consistency filtering, we need to find a better consistency gain to make the estimation performance of the network better.The formula of the distributed filtering algorithm based on the consensus algorithm is(18)y^lli=yll−1i+Kl−1iRli−Giyll−1i+Vli∑j∈Niy^ll−1j−y^ll−1i,y^l+1lj=By^lli+Ili∑j∈Niy^llj−y^lli.In formula (18), Vki and Iki are the consistency gain of the estimation term and the consistency gain of the prediction term, respectively. The consistency gain term is used twice in the article, so the data transmission amount and calculation time between nodes are increased. Therefore, in order to save the amount of data transmission and calculation time between nodes, this chapter improves on the original algorithm and proposes a new state estimation algorithm:
(19)y^l+1l+1i=By^lli+LkiRli−Giy^lli−Vli∑j∈NiBy^lli−By^llj.Formula (19) proposed in this chapter ignores the prediction item consistency gain Ili and saves the process of prediction consistency fusion and saves calculation time. In order to determine the value of the estimation consistency gain term Vli, the algorithm is guaranteed to converge, and the network estimation error is small. Define the estimated error of sensor i as
(20)eli=y^lli−yl,and then,
(21)el+1i=B−GliHieli−Vli∑j∈NiBy^lli−By^llj+Klivli−ϖk.Define the mean square estimation error as(22)φl=1m∑i=1mReli2.Then, the estimated error cosquare at timek+1 is
(23)Hl+1i=ReliTGliTGlieli−VlieliTGliT∑i−Vli∑iTGlieli+Vli2∑iT∑i+vliTHliTHlivli+ϖlTϖl.This step assumes that the system estimation error covariance converges. Let(24)a=∑iT∑i>0,b=rliTGliT∑i,c=rliTGliTGlirli+vliTHliTHlivli+ϖlTϖl.And becausea>0, it means that the covariance of the estimated error function of the system Kl+1i and the consistency gain of sensor node iVli∗ is a parabolic relationship, so the consistency gain of the system has an optimal solution Vli∗, so that the estimated error covariance of sensor i at k+1 the variance is the smallest. Make
(25)∂Hl+1i∂Vli=0.Use the derivation method to obtain the optimal consistency gain of sensori.
(26)Vli∗=rliTGliT∑i∑iT∑i,because
(27)φl=1n∑i=1nHli.Therefore, the mean square estimation error of the filtering algorithm is also the smallest. When the consistency gain of the sensor network nodei is optimal, the optimized consistency filtering algorithm is
(28)y^l+1l+1i=By^lli+GliWli−Giy^lli−Vli∗∑j∈NiBy^lli−By^llj.


## 3.1. Kalman Consensus Filtering Algorithm

Olfati-Saber et al. used a consensus algorithm to design a consensus filter and used this filter in the neighbor node information exchange of the sensor network. The variance is unified.The algorithm updates the estimated value of each node’s state. First, initialize(9)Q0i=Q0,y¯10i=y0.Then, receive new data from neighbor nodes, and estimate the consistency fusion a priori:(10)Ki=Mi∪i,(11)ukj=JjTTj−1zkj,∀j∈Ki,yki=∑j∈Jiukj,(12)Ukj=JjTTj−1Jj,∀j∈Ki,Dki=∑j∈JiUkj.The Kalman consensus state is estimated as(13)Cki=Qki−1+Dki−1,(14)y^kk=y¯kk−1i+Ckiyki−Dkiy¯kk−1i+φCki∑j∈Niy¯kk−1j−y¯kk−1i.Kalman consensus state prediction:(15)Qk+1j=AMkiAT+H,(16)y¯k+1ki=Ay^kki.Figure2 shows the network form of the consensus algorithm.Figure 2
Consensus algorithm network.In the algorithm, a priori estimated value, target observation value, and target observation covariance information need to be exchanged. Equation (15) is mainly used to process the observations and covariance accordingly, and equation (16) combines the Kalman filter algorithm with the consensus algorithm, which is the core of the algorithm, which is the prior estimation of neighbor nodes. The value is merged, and the estimated value of the node itself is also updated. The combination of these two algorithms can achieve better estimation results and at the same time reduce the estimation error of the sensor network. This is very beneficial to the scalability of the sensor network. When the number of sensors in the network increase or decrease, we do not need to rely on a specified sensor to transmit information. You can directly select the sensor closest to the control center. Then, you can proceed estimated data transmission.


## 3.2. Optimized Consistent Kalman Filter Algorithm

For a target system, it is assumed that there aren sensor nodes. Then, the dynamic equation of the target system and the observation equation of the sensor node are
(17)yl+1=Byl+ϖl,rli=Giyki+bki.For sensor networks, the consensus filtering algorithm estimates the target state. The state estimation is achieved through information exchange with neighboring nodes. Therefore, the estimation accuracy of the target state of the node is not only determined by the estimation performance of the node itself but also by the neighboring node. State estimation performance is related. For consistency filtering, we need to find a better consistency gain to make the estimation performance of the network better.The formula of the distributed filtering algorithm based on the consensus algorithm is(18)y^lli=yll−1i+Kl−1iRli−Giyll−1i+Vli∑j∈Niy^ll−1j−y^ll−1i,y^l+1lj=By^lli+Ili∑j∈Niy^llj−y^lli.In formula (18), Vki and Iki are the consistency gain of the estimation term and the consistency gain of the prediction term, respectively. The consistency gain term is used twice in the article, so the data transmission amount and calculation time between nodes are increased. Therefore, in order to save the amount of data transmission and calculation time between nodes, this chapter improves on the original algorithm and proposes a new state estimation algorithm:
(19)y^l+1l+1i=By^lli+LkiRli−Giy^lli−Vli∑j∈NiBy^lli−By^llj.Formula (19) proposed in this chapter ignores the prediction item consistency gain Ili and saves the process of prediction consistency fusion and saves calculation time. In order to determine the value of the estimation consistency gain term Vli, the algorithm is guaranteed to converge, and the network estimation error is small. Define the estimated error of sensor i as
(20)eli=y^lli−yl,and then,
(21)el+1i=B−GliHieli−Vli∑j∈NiBy^lli−By^llj+Klivli−ϖk.Define the mean square estimation error as(22)φl=1m∑i=1mReli2.Then, the estimated error cosquare at timek+1 is
(23)Hl+1i=ReliTGliTGlieli−VlieliTGliT∑i−Vli∑iTGlieli+Vli2∑iT∑i+vliTHliTHlivli+ϖlTϖl.This step assumes that the system estimation error covariance converges. Let(24)a=∑iT∑i>0,b=rliTGliT∑i,c=rliTGliTGlirli+vliTHliTHlivli+ϖlTϖl.And becausea>0, it means that the covariance of the estimated error function of the system Kl+1i and the consistency gain of sensor node iVli∗ is a parabolic relationship, so the consistency gain of the system has an optimal solution Vli∗, so that the estimated error covariance of sensor i at k+1 the variance is the smallest. Make
(25)∂Hl+1i∂Vli=0.Use the derivation method to obtain the optimal consistency gain of sensori.
(26)Vli∗=rliTGliT∑i∑iT∑i,because
(27)φl=1n∑i=1nHli.Therefore, the mean square estimation error of the filtering algorithm is also the smallest. When the consistency gain of the sensor network nodei is optimal, the optimized consistency filtering algorithm is
(28)y^l+1l+1i=By^lli+GliWli−Giy^lli−Vli∗∑j∈NiBy^lli−By^llj.


## 4. Security and Privacy Protection of Internet of Vehicles Consensus Algorithm Based on Wireless Sensors

### 4.1. Consistent Filtering Algorithm with Packet Loss

In the target tracking process, data packet loss is inevitable. There has been a lot of research on network packet loss. In practical applications, network data transmission often has a series of problems such as network delay, sensor failure, and network congestion. Packet loss is more common, and there are two main types of network packet loss: first, the packet loss when the network node and its neighbor nodes exchange information, that is, communication packet loss; second, the monitoring data of the target state by the node in the network is lost. That is, no data is monitored, and a schematic diagram of the state estimation of the sensor network with packet loss is shown in Figure3. A network state estimation method for packet loss rate includes the following steps: a streaming media server initializes parameters of a filter, receives a reception report from a mobile terminal, and extracts packet loss rate information therefrom; when the packet loss rate is greater than a set packet loss rate threshold, consider that the network has a sudden change in bandwidth, use filter 2, and judge the current state of the network accordingly.Figure 3
Schematic diagram of state estimation of a sensor network with packet loss.


### 4.2. Comparison of System Estimation Errors with Different Packet Loss Rates

In order to know that there is no node communication loss in the network and only nodes monitor data packet loss, whether the algorithm with packet loss compensation proposed in this chapter will make the network estimation accuracy higher and the consistency between nodes better. Figure4 shows that there is no communication loss between nodes in the system, and only the node monitors the data packet loss and the packet loss rate m=0.4, comparison of consensus estimation errors.Figure 4
Comparison of system estimation errors with different packet loss rates.Figure5 shows that the estimation error of the algorithm in this paper finally converges to about 0.5, and the estimation error can maintain the stability very well. Therefore, the estimation error of the algorithm is smaller than that of the random extraction consensus algorithm. After the introduction of the packet loss compensation protocol, the increase the estimation accuracy of the algorithm is increased. In addition, the inconsistent estimation error of the algorithm in this chapter finally converges to about 0.5 after 100 iterations, while the random sampling consensus algorithm only converges to about 1 after 100 iterations, so the algorithm in this chapter is significantly better than the inconsistency of the random sampling consensus algorithm. Because the random sampling consensus algorithm will make the consistency gain value too large when seeking the optimal consistency gain, the difference between the estimated values between nodes will become larger, and the inconsistency error will also increase.Figure 5
Comparison of estimation errors and nonuniform estimation errors of two algorithms when two kinds of packet loss exist at the same time.When there are both communication packet loss and internode monitoring data packet loss in the sensor network, the communication packet loss rate between nodes is set ton=0.2 and the node monitoring data packet loss rate m=0.4. In the above figure, when there are two kinds of packet loss in the network at the same time, the consistent Kalman filter algorithm proposed in this paper has a smaller estimation error. Because the random sampling consensus algorithm monitors data packet loss for nodes, when there is node communication packet loss in the network, the estimation error of the algorithm may not converge. In the figure below, when there are two types of packet loss in the network, the algorithm proposed in this chapter enables the network nodes to maintain a good consistency in the target estimation, and the robustness is better. For the random sampling consensus algorithm, the nodes in the network can no longer maintain the consistency of the target state estimation.Table1 shows that the system monitoring data packet loss is m=1−p=0.1, and the communication packet loss between nodes is n=0.2. When 0.6, there is no packet loss compensation algorithm between nodes, and there is a packet loss compensation algorithm between nodes. It can be seen that when there is packet loss compensation, the system’s estimation error and nonuniformity error are smaller. It shows that when there are two kinds of packet loss in the network at the same time, the algorithm can resist the impact of node loss on the network, the consistency between nodes is better, and the algorithm has better robustness.Table 1
Comparison of the sum of estimation errors of the two algorithms.
Sum of estimation errorsEstimation errorInconsistent estimation errorn=0.6, has packet loss compensation algorithm50.3823.46n=0.6, no packet loss compensation algorithm80.2933.49n=0.2, has packet loss compensation algorithm20.3710.28n=0.2, no packet loss compensation algorithm40.2530.26


### 4.3. Performance Comparison of Privacy Protection Algorithms

The performance comparison results of the algorithms are shown in Table2. The data fusion scheme based on the hop-by-hop encryption mechanism requires frequent encryption and decryption operations at the intermediate nodes, which increases the computational cost and time delay. At the same time, in order to deal with internal attacks, it is necessary to use data disturbance technology; the fusion scheme of end-to-end encryption mechanism is implemented by homomorphic encryption algorithm, which can effectively deal with internal and external attacks. The intermediate node directly integrates the ciphertext, saving encryption and decryption. In time overhead, the time delay is small, but the fusion operation supported by homomorphic encryption is limited; the nonencrypted privacy protection strategy does not require a key distribution process, and all sensor nodes do not need to perform encryption and decryption operations, reducing communication and computing overhead. But its ability to protect privacy depends on the technical implementation of privacy protection.Table 2
Performance comparison of data fusion privacy protection algorithms.
Strategy classificationRepresentative agreementAdopting technologyThe main advantageMain disadvantageHop-by-hop encryption mechanismSMARTSegmentation and reorganizationAccurate fusion results and better privacy protectionOnly supports SUM; high communication overheadCPDAPerturbation technologyAccurate fusion resultsOnly supports SUM; communication and calculation overhead are largeEnd-to-end encryption mechanismAHEHomomorphic encryption function AHSStrong ability to cope with collusion attacksOnly supports SUMCDAHomomorphic encryption function DFLow communication and calculation costOnly supports SUM; weak ability to resist collusion attacksNonencrypted mechanismKIPDADisguised dataSupport nonlinear fusionWeak privacy protectionGP2SDisturbance histogramSupport multiple types of aggregation to deal with collusion attackApproximate clusteringTable3 compares the performance of some representative algorithms for data aggregation. In the data aggregation operation, according to the difference between the network model and the security goal, the main technology and optimization strategy adopted by the data aggregation algorithm will also be different, resulting in the support of aggregation type, security, energy consumption, accuracy and integrity verification, etc. There is a big difference.Table 3
Performance comparison of privacy protection data clustering algorithms.
Representative agreementCPDAKIPDASMARTAHETaskData aggregationTechnology usedHop-by-hop encryptionHop-by-hop encryption, data confusionHop-by-hop encryption, segmentation, and reorganization technologyEnd-to-end encryption, homomorphic encryptionThe main advantageAccurate aggregation resultsSupport nonlinear aggregationAccurate aggregation resultsEnsure the integrity of the aggregation resultsMain disadvantageThe communication volume is large, the calculation cost is high, and only the sum operation is supportedThe accuracy of aggregated data is low, and the integrity of aggregated data cannot be guaranteedCannot guarantee the integrity of the aggregated data, only supports the sum operationOnly supports the sum operation


### 4.4. Data Traffic Analysis of the Consensus Algorithm of the Internet of Vehicles

Energy consumption is an important indicator to measure the effectiveness of an algorithm. The TAG algorithm fuses the sensitive data of all sensor nodes in WSNs and does not provide privacy protection functions. SMART and PPND algorithms use the TAG algorithm to build a data fusion tree based on the addition of a privacy protection mechanism, which will inevitably increase the algorithm. The energy consumption is mainly concentrated on the two parts of calculation and data transmission and reception. At the same time, several algorithms are similar in calculation. Each sensor node performs the same decryption, fusion processing, and encryption operations. Therefore, the computational overhead does not affect the comparison result of the algorithm energy consumption. The comparison can indirectly measure the characteristics of the six algorithms in terms of energy consumption.Figure6 shows the comparison results of the data traffic of the six algorithms at different epoch duration values. The experimental results show that the query cycle has little effect on the communication overhead of this algorithm. When m is equal to k, the consistent Kalman algorithm proposed in this paper is more energy-efficient and efficient than other algorithms. It can be clearly derived from the theoretical formula. As reflected in the figure, the communication volume of the Kalman consensus algorithm is that of the SCPDA algorithm. It ranges from 31% to 37%. For WSN, a low energy consumption algorithm can prolong its service life. By reducing the amount of confusing data sent, the consistent Kalman algorithm achieves lower communication overhead under the premise of limited impact on privacy protection capabilities.Figure 6
Algorithmic data traffic comparison.Figure7 shows the fusion accuracy of the six algorithms under different Epoch Duration. In the figure, PPND-1 is the fusion accuracy of the nonfailed node calculated according to the formula, and PPND-2 is the accuracy represented by the sum of the actual fusion result calculated according to the formula/the actual data measured by the node. It can be seen that when epoch duration≥2 s, the fusion accuracy of the consistent Kalman filter algorithm is above 0.93, which is similar to the TAG algorithm and better than SMART (J=2).Figure 7
Comparison of data fusion accuracy of algorithms.


### 4.5. Comparison of the Safety Performance of Wireless Sensors

It can be found from Figure8 that in the case of data obfuscation and similar privacy, the privacy protection of consistent Kalman is slightly higher than that of CESPT algorithm. This is because the latter reduces the number of data pieces sent in the network, and it does not guarantee that each the number of out degrees of the node guarantees the sum of the in-out degrees of the node. Therefore, the privacy protection is reduced to a certain extent. However, we can also see from the figure that if the value of q can be controlled in a sufficiently small range, the privacy protection of several algorithms can be regarded as similar. This provides conditions for the subsequent comparison of data traffic under the condition of ensuring the same privacy protection.Figure 8
Comparison of the security performance of CESPT, SCPDA, and this algorithm.Figure9 shows the analysis of the accuracy of the clustering algorithm. We compare SCPDA, CESPT, and the consistent Kalman algorithm and get a comparative analysis of accuracy based on different query cycles (0-50 s). When the query cycle is small, the accuracy of the three algorithms is not much different, but when the query cycle becomes large, the accuracy of the algorithm in this paper is more than 90%, which is 60% higher than the accuracy of the SCPDA algorithm, which shows that the algorithm in this paper is in progress. Infinite sensors have high accuracy in the work process of the Internet of Vehicles.Figure 9
Accuracy analysis of aggregation algorithm.


### 4.6. Influence of the Confusion Factor on the Efficiency of the Algorithm

SUM clustering simulation experiments can show thatm will have a certain impact on accuracy, but the effect is not obvious, and the change of ED has a greater impact on accuracy; especially at the stage when ED is small, its effect is particularly obvious. This is because when the ED is small, the probability of data conflicts will increase significantly. And the accuracy of the algorithm in this paper is significantly better than SCPDA. As the cycle continues to increase, the accuracy tends to stabilize.The CKF in the figure is the abbreviation of the consistent Kalman filter. Figure10 discusses the confusion factor m. The parameter m mainly affects the accuracy, security, and data communication of the algorithm. From the above research, it can be obtained that the accuracy of the algorithm is mainly affected by ED; it is not sensitive to the value of m. From theoretical analysis, it can be seen that as the value of m continues to increase within the specified range, the privacy leakage rate gradually stabilizes, and the communication volume continues to increase. There is an optimal value m to make the algorithm better in terms of communication volume and data privacy compromise. The analysis shows that the maximum value of m is the maximum initial degree of the aggregation tree node. When the maximum value of m is 10, the EFs of different m are shown in Figure 10: H represents the number of network nodes, and the lower the EF, the higher the efficiency of the algorithm. The simulation experiment can conclude that with the increase of m, the efficiency of the algorithm first increases and then decreases and finally stabilizes, and the best value of m is distributed among 3, 4, and 5. A large number of test experiments show that as the WSN topology changes, the optimal value of m will also change.Figure 10
The effect of aliasing factor m on the efficiency of Kalman consensus filtering algorithm.


### 4.7. Comparison of Node Energy Consumption in Wireless Sensor Networks

From the comparative analysis of the experimental results in Table4, in the process of data information query, the energy consumption of the ordinary node of the algorithm in this paper is 1/2 than that of the EPRN algorithm; at the same time, the energy consumption of the storage node is nearly 1/3 less than that of the EPRN, so it is obvious. It can be seen that the energy consumption of the algorithm in this paper is much lower than that of the traditional EPRN algorithm.Table 4
Average energy consumption of wireless sensor networks on common nodes and storage nodes.
NodeAverage energy consumption of ordinary nodesAverage energy consumption of storage nodesEPRNAlgorithmEPRNAlgorithm000.030.20.2100.040.083.54.2200.050.104.195.26300.060.145.117.73400.080.166.6810.28500.090.197.7212.21600.110.248.6414.36700.140.2710.0115.02800.160.2812.3416.11900.150.3113.2818.241000.180.3614.2219.21In Figure11, we compare the DTDCFE algorithm, the DOCF algorithm, and the algorithm in this paper. From the figure, we can see the superiority of the algorithm in this paper, and it can also be observed that the average mean square deviation MSDs can converge to a steady-state value. The state value increases with the increase of DT.Figure 11
Comparison of different estimation algorithm performance metric MSD.


## 4.1. Consistent Filtering Algorithm with Packet Loss

In the target tracking process, data packet loss is inevitable. There has been a lot of research on network packet loss. In practical applications, network data transmission often has a series of problems such as network delay, sensor failure, and network congestion. Packet loss is more common, and there are two main types of network packet loss: first, the packet loss when the network node and its neighbor nodes exchange information, that is, communication packet loss; second, the monitoring data of the target state by the node in the network is lost. That is, no data is monitored, and a schematic diagram of the state estimation of the sensor network with packet loss is shown in Figure3. A network state estimation method for packet loss rate includes the following steps: a streaming media server initializes parameters of a filter, receives a reception report from a mobile terminal, and extracts packet loss rate information therefrom; when the packet loss rate is greater than a set packet loss rate threshold, consider that the network has a sudden change in bandwidth, use filter 2, and judge the current state of the network accordingly.Figure 3
Schematic diagram of state estimation of a sensor network with packet loss.


## 4.2. Comparison of System Estimation Errors with Different Packet Loss Rates

In order to know that there is no node communication loss in the network and only nodes monitor data packet loss, whether the algorithm with packet loss compensation proposed in this chapter will make the network estimation accuracy higher and the consistency between nodes better. Figure4 shows that there is no communication loss between nodes in the system, and only the node monitors the data packet loss and the packet loss rate m=0.4, comparison of consensus estimation errors.Figure 4
Comparison of system estimation errors with different packet loss rates.Figure5 shows that the estimation error of the algorithm in this paper finally converges to about 0.5, and the estimation error can maintain the stability very well. Therefore, the estimation error of the algorithm is smaller than that of the random extraction consensus algorithm. After the introduction of the packet loss compensation protocol, the increase the estimation accuracy of the algorithm is increased. In addition, the inconsistent estimation error of the algorithm in this chapter finally converges to about 0.5 after 100 iterations, while the random sampling consensus algorithm only converges to about 1 after 100 iterations, so the algorithm in this chapter is significantly better than the inconsistency of the random sampling consensus algorithm. Because the random sampling consensus algorithm will make the consistency gain value too large when seeking the optimal consistency gain, the difference between the estimated values between nodes will become larger, and the inconsistency error will also increase.Figure 5
Comparison of estimation errors and nonuniform estimation errors of two algorithms when two kinds of packet loss exist at the same time.When there are both communication packet loss and internode monitoring data packet loss in the sensor network, the communication packet loss rate between nodes is set ton=0.2 and the node monitoring data packet loss rate m=0.4. In the above figure, when there are two kinds of packet loss in the network at the same time, the consistent Kalman filter algorithm proposed in this paper has a smaller estimation error. Because the random sampling consensus algorithm monitors data packet loss for nodes, when there is node communication packet loss in the network, the estimation error of the algorithm may not converge. In the figure below, when there are two types of packet loss in the network, the algorithm proposed in this chapter enables the network nodes to maintain a good consistency in the target estimation, and the robustness is better. For the random sampling consensus algorithm, the nodes in the network can no longer maintain the consistency of the target state estimation.Table1 shows that the system monitoring data packet loss is m=1−p=0.1, and the communication packet loss between nodes is n=0.2. When 0.6, there is no packet loss compensation algorithm between nodes, and there is a packet loss compensation algorithm between nodes. It can be seen that when there is packet loss compensation, the system’s estimation error and nonuniformity error are smaller. It shows that when there are two kinds of packet loss in the network at the same time, the algorithm can resist the impact of node loss on the network, the consistency between nodes is better, and the algorithm has better robustness.Table 1
Comparison of the sum of estimation errors of the two algorithms.
Sum of estimation errorsEstimation errorInconsistent estimation errorn=0.6, has packet loss compensation algorithm50.3823.46n=0.6, no packet loss compensation algorithm80.2933.49n=0.2, has packet loss compensation algorithm20.3710.28n=0.2, no packet loss compensation algorithm40.2530.26


## 4.3. Performance Comparison of Privacy Protection Algorithms

The performance comparison results of the algorithms are shown in Table2. The data fusion scheme based on the hop-by-hop encryption mechanism requires frequent encryption and decryption operations at the intermediate nodes, which increases the computational cost and time delay. At the same time, in order to deal with internal attacks, it is necessary to use data disturbance technology; the fusion scheme of end-to-end encryption mechanism is implemented by homomorphic encryption algorithm, which can effectively deal with internal and external attacks. The intermediate node directly integrates the ciphertext, saving encryption and decryption. In time overhead, the time delay is small, but the fusion operation supported by homomorphic encryption is limited; the nonencrypted privacy protection strategy does not require a key distribution process, and all sensor nodes do not need to perform encryption and decryption operations, reducing communication and computing overhead. But its ability to protect privacy depends on the technical implementation of privacy protection.Table 2
Performance comparison of data fusion privacy protection algorithms.
Strategy classificationRepresentative agreementAdopting technologyThe main advantageMain disadvantageHop-by-hop encryption mechanismSMARTSegmentation and reorganizationAccurate fusion results and better privacy protectionOnly supports SUM; high communication overheadCPDAPerturbation technologyAccurate fusion resultsOnly supports SUM; communication and calculation overhead are largeEnd-to-end encryption mechanismAHEHomomorphic encryption function AHSStrong ability to cope with collusion attacksOnly supports SUMCDAHomomorphic encryption function DFLow communication and calculation costOnly supports SUM; weak ability to resist collusion attacksNonencrypted mechanismKIPDADisguised dataSupport nonlinear fusionWeak privacy protectionGP2SDisturbance histogramSupport multiple types of aggregation to deal with collusion attackApproximate clusteringTable3 compares the performance of some representative algorithms for data aggregation. In the data aggregation operation, according to the difference between the network model and the security goal, the main technology and optimization strategy adopted by the data aggregation algorithm will also be different, resulting in the support of aggregation type, security, energy consumption, accuracy and integrity verification, etc. There is a big difference.Table 3
Performance comparison of privacy protection data clustering algorithms.
Representative agreementCPDAKIPDASMARTAHETaskData aggregationTechnology usedHop-by-hop encryptionHop-by-hop encryption, data confusionHop-by-hop encryption, segmentation, and reorganization technologyEnd-to-end encryption, homomorphic encryptionThe main advantageAccurate aggregation resultsSupport nonlinear aggregationAccurate aggregation resultsEnsure the integrity of the aggregation resultsMain disadvantageThe communication volume is large, the calculation cost is high, and only the sum operation is supportedThe accuracy of aggregated data is low, and the integrity of aggregated data cannot be guaranteedCannot guarantee the integrity of the aggregated data, only supports the sum operationOnly supports the sum operation


## 4.4. Data Traffic Analysis of the Consensus Algorithm of the Internet of Vehicles

Energy consumption is an important indicator to measure the effectiveness of an algorithm. The TAG algorithm fuses the sensitive data of all sensor nodes in WSNs and does not provide privacy protection functions. SMART and PPND algorithms use the TAG algorithm to build a data fusion tree based on the addition of a privacy protection mechanism, which will inevitably increase the algorithm. The energy consumption is mainly concentrated on the two parts of calculation and data transmission and reception. At the same time, several algorithms are similar in calculation. Each sensor node performs the same decryption, fusion processing, and encryption operations. Therefore, the computational overhead does not affect the comparison result of the algorithm energy consumption. The comparison can indirectly measure the characteristics of the six algorithms in terms of energy consumption.Figure6 shows the comparison results of the data traffic of the six algorithms at different epoch duration values. The experimental results show that the query cycle has little effect on the communication overhead of this algorithm. When m is equal to k, the consistent Kalman algorithm proposed in this paper is more energy-efficient and efficient than other algorithms. It can be clearly derived from the theoretical formula. As reflected in the figure, the communication volume of the Kalman consensus algorithm is that of the SCPDA algorithm. It ranges from 31% to 37%. For WSN, a low energy consumption algorithm can prolong its service life. By reducing the amount of confusing data sent, the consistent Kalman algorithm achieves lower communication overhead under the premise of limited impact on privacy protection capabilities.Figure 6
Algorithmic data traffic comparison.Figure7 shows the fusion accuracy of the six algorithms under different Epoch Duration. In the figure, PPND-1 is the fusion accuracy of the nonfailed node calculated according to the formula, and PPND-2 is the accuracy represented by the sum of the actual fusion result calculated according to the formula/the actual data measured by the node. It can be seen that when epoch duration≥2 s, the fusion accuracy of the consistent Kalman filter algorithm is above 0.93, which is similar to the TAG algorithm and better than SMART (J=2).Figure 7
Comparison of data fusion accuracy of algorithms.


## 4.5. Comparison of the Safety Performance of Wireless Sensors

It can be found from Figure8 that in the case of data obfuscation and similar privacy, the privacy protection of consistent Kalman is slightly higher than that of CESPT algorithm. This is because the latter reduces the number of data pieces sent in the network, and it does not guarantee that each the number of out degrees of the node guarantees the sum of the in-out degrees of the node. Therefore, the privacy protection is reduced to a certain extent. However, we can also see from the figure that if the value of q can be controlled in a sufficiently small range, the privacy protection of several algorithms can be regarded as similar. This provides conditions for the subsequent comparison of data traffic under the condition of ensuring the same privacy protection.Figure 8
Comparison of the security performance of CESPT, SCPDA, and this algorithm.Figure9 shows the analysis of the accuracy of the clustering algorithm. We compare SCPDA, CESPT, and the consistent Kalman algorithm and get a comparative analysis of accuracy based on different query cycles (0-50 s). When the query cycle is small, the accuracy of the three algorithms is not much different, but when the query cycle becomes large, the accuracy of the algorithm in this paper is more than 90%, which is 60% higher than the accuracy of the SCPDA algorithm, which shows that the algorithm in this paper is in progress. Infinite sensors have high accuracy in the work process of the Internet of Vehicles.Figure 9
Accuracy analysis of aggregation algorithm.


## 4.6. Influence of the Confusion Factor on the Efficiency of the Algorithm

SUM clustering simulation experiments can show thatm will have a certain impact on accuracy, but the effect is not obvious, and the change of ED has a greater impact on accuracy; especially at the stage when ED is small, its effect is particularly obvious. This is because when the ED is small, the probability of data conflicts will increase significantly. And the accuracy of the algorithm in this paper is significantly better than SCPDA. As the cycle continues to increase, the accuracy tends to stabilize.The CKF in the figure is the abbreviation of the consistent Kalman filter. Figure10 discusses the confusion factor m. The parameter m mainly affects the accuracy, security, and data communication of the algorithm. From the above research, it can be obtained that the accuracy of the algorithm is mainly affected by ED; it is not sensitive to the value of m. From theoretical analysis, it can be seen that as the value of m continues to increase within the specified range, the privacy leakage rate gradually stabilizes, and the communication volume continues to increase. There is an optimal value m to make the algorithm better in terms of communication volume and data privacy compromise. The analysis shows that the maximum value of m is the maximum initial degree of the aggregation tree node. When the maximum value of m is 10, the EFs of different m are shown in Figure 10: H represents the number of network nodes, and the lower the EF, the higher the efficiency of the algorithm. The simulation experiment can conclude that with the increase of m, the efficiency of the algorithm first increases and then decreases and finally stabilizes, and the best value of m is distributed among 3, 4, and 5. A large number of test experiments show that as the WSN topology changes, the optimal value of m will also change.Figure 10
The effect of aliasing factor m on the efficiency of Kalman consensus filtering algorithm.


## 4.7. Comparison of Node Energy Consumption in Wireless Sensor Networks

From the comparative analysis of the experimental results in Table4, in the process of data information query, the energy consumption of the ordinary node of the algorithm in this paper is 1/2 than that of the EPRN algorithm; at the same time, the energy consumption of the storage node is nearly 1/3 less than that of the EPRN, so it is obvious. It can be seen that the energy consumption of the algorithm in this paper is much lower than that of the traditional EPRN algorithm.Table 4
Average energy consumption of wireless sensor networks on common nodes and storage nodes.
NodeAverage energy consumption of ordinary nodesAverage energy consumption of storage nodesEPRNAlgorithmEPRNAlgorithm000.030.20.2100.040.083.54.2200.050.104.195.26300.060.145.117.73400.080.166.6810.28500.090.197.7212.21600.110.248.6414.36700.140.2710.0115.02800.160.2812.3416.11900.150.3113.2818.241000.180.3614.2219.21In Figure11, we compare the DTDCFE algorithm, the DOCF algorithm, and the algorithm in this paper. From the figure, we can see the superiority of the algorithm in this paper, and it can also be observed that the average mean square deviation MSDs can converge to a steady-state value. The state value increases with the increase of DT.Figure 11
Comparison of different estimation algorithm performance metric MSD.


## 5. Conclusion

This paper mainly studies the security and privacy protection of the Internet of Vehicles consensus algorithm based on wireless sensors and conducts research on the information security and privacy protection of the Internet of Vehicles using wireless sensors and the Kalman consensus algorithm of slicing technology. Compared with other algorithms, the Kalman consensus algorithm proposed in this paper has better privacy protection performance and can maintain good stability and robustness in various situations. It is very important for the security and privacy protection of the Internet of Vehicles. The innovation of this article is to use a combination of theoretical research and empirical research to better consider users and managers from the perspective of practical applications. The shortcoming of this article is that it does not test more confusion factors when testing performance, which has certain limitations in the application of this algorithm. What should be considered in future work is that the information security of the Internet of Vehicles should not only focus on data security but also consider the security of hardware equipment, especially the hardware facilities that are closely related to data such as front-end equipment, transmission links, security protocols, and cloud management platforms.



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*Source: 6197638-2022-05-30.xml*