Construction of an Innovative Development Model of Intelligent Media under the Coverage of a Wireless Sensor Network

A distributed network composed of sensor nodes with limited computing and communication capabilities is a wireless sensor network, which is a system that can autonomously and intelligently complete target tasks according to the surrounding environment. With its wide application in the fields of smart medical care, smart home, networks of vehicles, and smart media, its communication performance requirements are also increasing. According to the needs of different application scenarios, the first key problem to be solved in wireless sensor networks is what optimization coverage strategy to adopt, which will have a direct impact on the optimal allocation of very limited resources such as node energy, communication bandwidth, computing power, and other network services’ quality. Nodes adopt a probability-based joint perception model, and its algorithm is a node scheduling mechanism based on the connected dominating set to construct a tree. Each node sets the waiting time and becomes a candidate node priority according to the remaining energy and the distance from the parent node. In this paper, the issue of energy fairness is considered in heterogeneous wireless sensor networks. In order to prevent the occurrence of “energy holes,” this paper proposes a node deployment model similar to cellular networks. By deploying heterogeneous sensor nodes in this way, energy saving can be achieved. Based on this model, game theory is used to simulate the data packet transmission process between sensor nodes, an energy consumptionmechanism suitable for each stage of data transmission is proposed, and Nash equilibrium is finally obtained by rationally designing the measurement function. *e node deployment scheme proposed in this paper not only balances the energy consumption of the network but also prolongs the life cycle of the network. Aiming at the three-dimensional (3D) space environment, this paper studies a node deployment mechanism that guarantees connectivity and has a perceptual coverage degree of k with the help of the Reuleaux tetrahedron model. In this paper, a node deployment strategy is proposed and compared with the node deployment algorithm for 3D sensor network based on truncated octahedron.*e simulation experiment results show that the wireless sensor network with artificial intelligence can not only optimize the production process of intelligent media but also improve each link of the media industry chain and further catalyze the development of new formats in the media industry. *e experimental results also show that the algorithm can effectively meet the requirements of sensing coverage and connectivity coverage, the number of working nodes is small, the network life cycle is significantly prolonged, and the overall energy consumption of the network is reduced. *e study results of this paper provide a reference for follow-up research on the construction of innovative development model of intelligent media under the coverage of wireless sensor network.


Introduction
Information technology is leading the media into the era of intelligence. From the perspective of information dissemination channels, smart media has the characteristics of intelligence, interaction, openness, and novelty, including the software on the smart terminal and the content that is transmitted, as well as the artificial relationship between the two [1]. e information perception ability is especially important, and the integrity of the acquired information reflects the monitoring ability of the network. e positional relationship between nodes, transmission power, and hibernation of nodes can determine the communication between the wireless communication links between the nodes e use of wireless sensor networks in the media industry has become more and more common, and it has subtly affected the development trend of media industry and empowered the smart media [2]. e continuous development of wireless sensor networks with artificial intelligence technology has not only innovated the process of news production, but also improved all aspects of the media industry chain and catalyzed the development of new formats in the media industry. e deep integration of wireless sensor networks with artificial intelligence and media realizes the diversification of application scenarios [3]. Text recognition technology based on semantic analysis and deep learning is an innovative mode of media content production.
rough the simulation of human intelligence and cognitive behavior of writing robots, the creation of robots is realized and the writing ability of journalists is improved [4].
A wireless sensor network is multihop network formed in a self-organizing manner by a large number of small, inexpensive sensor nodes deployed in the monitoring area. It is coordinated by various sensor nodes to sense, collect, and process the information of the sensing objects in the covered area and transmit this information to sink node for processing by interested users [5]. Wireless sensor networks with artificial intelligence use virtual force algorithm to control the movement of sensor nodes, so that the nodes in the network are evenly distributed, thereby improving the coverage performance of the network. Since each directed sensor node may have many sensing directions, the sensing direction is different, and the coverage area of the sensor node is also different. e collection and transmission of smart media information also affect hardware design and energy-saving control. Service guarantee, information processing, and other aspects put forward new requirements [6]. According to the perception characteristics of smart media sensor nodes, the wireless sensor networks with artificial intelligence use the image difference degree calculation method to determine the degree of difference between the images collected by each smart media sensor node [7]. e image difference degree is smaller with lower computational complexity, and the smart media sensor nodes are divided into the same cluster, so that efficient data fusion processing can be performed at cluster head node [8].
Based on research results from previous scholars, this paper expounds the research status and significance of innovative development model construction of intelligent media; elaborates the development background, current status, and future challenges of wireless sensor networks; introduces the methods and principles of clustered two-hop model and coverage enhancement algorithm; constructs an innovative development model of smart media under the coverage of wireless sensor networks with artificial intelligence; analyzes smart media nodes and wireless control networks, discusses media system architecture and network routing protocol; proposes the media industry restructuring under the coverage of wireless sensor networks with artificial intelligence; performs automated content compilation and real-time content review; realizes algorithm recommendation and human-computer interaction; and finally carries out a simulation experiment and its result analysis. e detailed sections are arranged as follows: Section 2 introduces the methods and principles of clustered two-hop model and coverage enhancement algorithm; Section 3 analyzes innovation development model construction based on wireless sensor networks; Section 4 discusses intelligent media model implementation based on wireless sensor networks; Section 5 carries out a simulation experiment and its result analysis; Section 6 is the conclusion.

Resource Allocation Strategy Based on the Clustered Two-Hop Model.
e bandwidth required by smart media information, especially video information, is higher than that required by ordinary scalar information, and the bandwidth required by high-end smart media sensor nodes is at least an order of magnitude higher than the bandwidth required by ordinary smart media information. e corresponding sensor node consumption is also much higher. In addition to collecting simple data from traditional wireless sensor networks, wireless sensor networks with artificial intelligence can also collect and process a variety of complex data information such as sound, image, and video. e collection and transmission of smart media information also affect hardware design and energy-saving control. Service guarantee, information processing, and other aspects put forward new requirements [9].
Suppose node i enters the presleep state after a random period of time a, and then it will send a presleep state message to its neighbor node j; at the same time, through the exchange of information with node i, the remaining energy of node i at this time is known, and sleep state A i is as follows: where a i is the remaining energy of node i at this time, b j is the remaining energy threshold of node i, Δa i is the initial energy value of node i, and Δa j is the power consumption during state transition of node j. From simple line-of-sight propagation to encountering various complex objects on the ground, while the mobile station itself is moving, this causes the frequency to shift at the receiving end. erefore, the signal will be affected by various forms of fading when passing through the wireless channel, and the power B i of the received signal can be expressed as follows: where b − i is the distance vector between the base station and the mobile station, and its absolute value is the distance between them.
is formula reflects the three effects of wireless channels on the signal; b + i is the random variable distribution with zero mean; n is the standard deviation of each signal component; m is the fading value of each signal component.
Adaptive fusion spanning tree can not only optimize the energy consumption of data transmission and data fusion at the same time, but also evaluate the gain and cost of data fusion in the process of data forwarding. e algorithm's criterion for minimizing network transmission and fusion energy consumption is to search for partner nodes (c i , c j ) in the network to minimize the calculation result: where C (c i , c j ) is the sum of the transmission and fusion energy consumption of the link (c i , c j ); A i is the unit fusion energy consumption of the link; B i is the unit transmission energy consumption of the link; and Δc i and Δc j are the amount of data collected by nodes c i and c j , respectively. For smart media sensor networks with limited node energy, high-bandwidth and low-energy technologies need to be studied and applied to wireless sensor networks. e data storage center and the gateway are interconnected and are mainly responsible for the storage of local data. e frequently used information can be directly obtained from the data storage center. ere is no need to obtain data from sensor nodes through multiple hops each time, which is important for saving network energy. e smart media information finally reaches the aggregation node through the gateway, and from the aggregation node to the external network, the aggregation node mainly implements the query and task distribution of the front-end software to the network.

Coverage Enhancement Algorithm in Wireless Multimedia
Sensor Networks. In the artificial intelligent wireless sensor network, the sensor node needs to process the acquired information, including compression coding and information fusion. Taking video information as an example, the energy required by the processing video information is often equivalent to the energy consumed by the transmission process. erefore, the energy consumption of video information acquisition and processing must be considered when studying artificial intelligent wireless sensor network life cycles. When planning the network life cycle, the video data needs to be compressed in order to effectively utilize the bandwidth resource node [10]. e volume of the three-dimensional monitoring area is d, the coverage rate in the three-dimensional monitoring area is e, the number of distributed sensor nodes is n, the sensing range of each sensor node is D, and assuming that the sensing areas of these sensor nodes do not overlap each other. and the relationship between the coverage rate e and the required number of sensor nodes n can be deduced as follows: ere are overlapping parts in the sensing area of nodes in the wireless sensor network coverage, so the coverage e achieved and the actual number of sensor nodes required should be greater than n.
Suppose that the area under the coverage of sensor node i is R i , the area under the coverage of node j is R j , and the two nodes use the same perception model and parameters. If S represents the area of the fan-shaped sensing area and T represents the area of the intersection of the two node coverage areas, the common coverage U ij of R i and R j can be expressed as follows: In order to test the intersecting characteristics of the coverage area of two sensing model nodes with adjustable fan-shaped direction, the distance S between the two sensor nodes and the angle T between the sensing directions are changed. e common coverage between the two nodes is calculated according to the formula rate to obtain the threedimensional relationship diagram between U ij and S and T.
In the coverage enhancement algorithm, the two random variables x 1 and x 2 , respectively, represent the data collected by the two discrete source nodes i 1 and i 2 . e probability distribution of x i is P(x i ), i � 1, 2. i 1 and i 2 send the collected data to the sink node. According to the traditional source coding method, the lower limit of the node sending rate f(x i ) is where d 1 x is a mean square error of the average video input, e 1 x is a coefficient associated with the coding efficiency, b 1 x is the power consumption factor of the link transmission data, c 1 x is the power consumption factor of the node that receives data, and E x i is a dual variable.
However, the power consumption of data compression is too large, and the energy of the node is used to transmit less, which in turn affects the transmission of data. us, when studying the network life cycle, it is necessary to jointly consider node energy, video quality, power allocation, and link bandwidth allocation factors. Sensor nodes near the aggregation node consume more energy and have a shorter life cycle; sensor nodes farther away from the aggregation node need to collect and encode video data, with relatively low energy consumption and long life cycle. When the quality requirements of intelligent media are high, sensor nodes need to increase the data transmission rate.

Smart Media Nodes and Wireless Control
Networks. e differences in image information collected by each smart media sensor node are related to its position in the monitoring area. Typically, the smart media sensor node adjacent to the ground is more likely to have a perceptual area, so adjacent smart media sensor nodes will collect a lot of repeated redundant image information, resulting in network communication in the process of data collection. As shown in Figure 1, the early stage of media integration was based on the integration of various media forms and terminals driven by technology. Media that were originally mutually independent with clear boundaries in the media industry could technically achieve the integration of communication channels. e artificial intelligence network wireless sensor network adopts the image difference calculation method with low computational complexity according to the perception characteristics of the intelligent media sensor nodes, compares the differences between the images collected by the media sensor nodes, and reduces the image differences [11]. e smart media sensor node is divided into the same cluster so that efficient data fusion processing can be performed at the cluster head node. e proportional fair scheduling algorithm assigns a priority to each media terminal within the wireless sensor network of the manual intelligence network x ij , and users with high priority will be prioritized. e calculation formula of priority F ij (x) is as follows: where x i(j+1) is the instantaneous data rate supported by the user, reflecting the channel quality of the current time; x i(j−1) is energy efficiency; x (i+1)j is a data transmission rate; and . is a energy-efficient data transmission rate e update rule G ij (x) is as follows: is assigned to each cluster in the number of sub-load modes of the first node. e main advantage of proportional fair scheduling algorithm is to comprehensively consider the service fairness between users and their channel conditions, and it is possible to achieve better compromise between fairness and system throughput.
When the node receives a group, if the group is discarded, the packet is allowed to enter the service queue waiting to be forwarded. If there is n nodes in the path, when the group reaches i, there is a relationship: e maximum value of h i is closely related to the maximum value of r i . r i is the moment of packet generation, k is a packet survival time, and r i is a scalar packet output rate.
Under the influence of the new media environment, traditional media is expanding, using its characteristics, deriving new visual forms, and coexisting with smart media forms. Media interconnection has become a global theme since the rise of electronic media. In recent years, the continuous updating and upgrading of wireless sensor networks with artificial intelligence technology have driven the rapid development of the media industry. Digital production methods, digital viewing environments, interactivity, and nonlinear narratives have gradually changed the traditional media industry, giving people a new audiovisual and psychological feeling; more imagination brings a comprehensive intelligent media innovation model. Intelligent media have penetrated into all aspects of digital existence. In the process of practical application, communication, aesthetics, art, photography, video, film, animation, graphic images, multimedia, digital audio, and computer software have derived a communication platform for digital visual media and artistic production.

Media System Architecture and Network Routing Protocol.
In the case where the position, energy, processing speed, and removability of the node in the artificial intelligent wireless sensor network is universally limited, the scheduling scheme, coverage performance, and network information acquisition capabilities are enhanced. e system of different media is used to provide strong motivation with art innovation and realize the combination of visual main sensory in the visual media and human bidirectional communication interaction. e traditional data transmission between the multiweighted nodes of the wireless network and the cellular network can ensure the quality of wireless communication between nodes, so that the data can be smoothly transmitted to the sink node; the manual intelligent wireless sensor network is used to monitor the field of monitoring [12]. In short, smart media is the sum of information clients and servers that can perceive users and bring them a better experience. Figure 2 shows the media system architecture and network routing protocol for the smart media innovation development model and media industry restructuring based on wireless sensor networks with artificial intelligence.
Assume that the source node s sends n packets to the target node i. e data packets in the ideal channel are not discarded due to noise interference and link error, followed by packet schedule, and the segment j can receive all n packets. Accordingly, the throughput of the single-channel H ij is as follows: In this formula, |k| is the average length of the packet, s |e| ij is the length of the path, |E| is clock beam, s |k| ij is the wave wavelength, |D| is the path loss coefficient, and s |d| ij is the distance between the sender and the receiver.
In a realistic environment, due to the interference and target of environmental factors, the perceived model is not a complete cover enhancement algorithm, but a perceptual model based on probability: where I ij is the probability of any point i perceived by the sensor node j, t − ij is a geometric distance between node i and point j, t + ij is a physical parameter associated with the sensor between node i and point j, k ij is the position of the sensor node, and r ij is the location of the perceptual point. If the node does not select the second time slot and there is no other node to send a message on the second time slot, the node reduces the number of imaginary competition nodes and further increases the probability of selecting the third time slot to send data. According to the secondary push, the node selects the probability of transmitting data in the i-th slog: In this formula, l is the distribution parameter (0 < l < 1). If other nodes send messages during the process of selecting the time slot, the nodes must restart the competition process. Figure 3 shows the relationship between frequencies and time of example media a, b, and c in the smart media innovation development model and media industry Node A

Allocation field
Description field Control field Node B Node C Node D  Mathematical Problems in Engineering 5 restructuring.
e smart media innovation development model under the coverage of the wireless sensor networks with artificial intelligence is based on the user as the core and realizes the distribution of diversified content service products to diversified media terminals. e operation of the media industry requires that the power relations between various levels and departments within the media no longer exist and are replaced by a super hub that can realize resource sharing and ensure multiplatform and multichannel collaborative work. Driven by the new framework, acquisition, editing, technical, and operating personnel will assemble several small project teams to develop a more flexible organizational form oriented toward products and projects [13]. Program providers can concentrate resources to develop the best professional programs through program transactions, allowing the radio station to make personalized arrangements while ensuring program quality. Using big data and cloud computing technology can not only master massive information data resources, but also integrate these resources to build a specialized, large-scale, and modern content database, and provide high-quality information services.

Automated Content Writing and Real-Time Content
Review. ere are many advantages in providing programs for various radio stations by a national broadcasting network. e core of the reconstruction is the flattening of the organization, the construction of various operation units around the product, and the implementation of the product manager-led system. In addition to receiving some content from record companies or other cooperative organizations, most of the content needs to be self-made, which invisibly increases the operating cost of the station and also causes a waste of human and financial resources (Figure 4). In addition to producing some professional and accompanying programs, the model can also use the feature of broadcast without image transmission to develop radio singer contest programs and radio debate programs, so that some viewers can find a place to display their talents in the broadcast. In addition, the more popular quiz programs with prizes can also be transplanted to the radio [14].
Taking the number of hops from the source node to the destination node as m, the time required for the node to send data as t m , and the source node sending n data packets as an example, the energy consumed by single-channel and multichannel single-path networks to send data packets is k n , and the signal transmission power model for the sender and receiver to send or receive data is where J is the receiving power of the receiver, r m is the transmitting power of the sender, t n is the antenna gain of the sender, and k m is the antenna gain of the receiver. Coverage is the ratio of the total area under the coverage of all nodes in the network to the total area of the target area. In the past, the same coverage rate only measured the intersection between the coverage areas of two nodes. e concepts and application scenarios of the two should be distinguished and understood. erefore, the coverage rate L ij can be expressed by the following formula: where u ij and w ij are the coverage area of the node and the entire target area, respectively; v is the function used to calculate the area; z ij is the subarea that the sensor node can cover when the sensing direction vector is u ij ; and W is the unit vector of direction of gravity. A typical radio transmits bursts directly and no longer has the traditional intermediate frequency and radio frequency circuits. At this time, the radio signal can be regarded as a baseband signal or a radio frequency signal. Using ultrawideband radio as a transmission method is of special significance for reducing the size of sensor nodes and reducing energy consumption and is especially suitable for the design requirements of small sensor nodes. Because the energy consumed for transmitting data, receiving data, forwarding data, and listening to the channel is different, each node plays a different role in the cluster, so the status of the nodes in the cluster changes at any time [15]. In addition, since the number of communication links that each node needs to establish cannot be predicted in advance, it is also difficult to dynamically adjust, which makes the bandwidth utilization rate of the entire network low. Each node has to support multiple communication frequencies, which places high requirements on the node hardware. When the node enters the back-off state, it starts a back-off timer, and it ends the back-off state when the back-off time is reached in time.

Algorithm Recommendation and Human-Computer
Interaction. In addition to collecting simple data from traditional wireless sensor networks, wireless sensor networks with artificial intelligence can also collect and process a variety of complex data information such as sound, image, and video. Since each directed sensor node may have many sensing directions, the sensing direction is different, and the coverage area of the sensor node is also different. Network communication has changed the relationship between the communicator and the communication tool, and the audience and the communicator are on an equal footing. Rating measurement completes the transition from sampling to full sampling and can provide real-time, dynamic, and efficient data analysis for media organizations, which provides a natural foundation for the innovation of the production and broadcasting system. To solve this problem, the algorithm first uses virtual force to adjust the position of the node globally and then uses the particle swarm optimization algorithm to locally adjust the sensing direction of the node to reduce the overlap area between nodes, at the same time reducing the blind area between nodes and improving the overall area coverage ( Figure 5). As the nodes change, the comparison chart of coverage ratios of random deployment, optimization based on virtualized algorithms, and optimization of particle swarm optimization changes [16]. After adjusting the position of the node through the action of the virtual force, the coverage rate was significantly improved.
When forwarding data, the more the layers that are forwarded per unit time, the longer the transmission distance between the sender and the receiver, and the higher the forwarding speed per unit time. e main energy consumption of the sensor is for receiving data, and the energy consumption M i of the node receiving a data packet is as follows: where Q(i) is the energy consumption of sending or receiving one bit of data, O i and P i are the energy consumption of the wireless antenna amplifierwhen the reference distance  is less than the distance K between the forwarding end and the receiving end.
Each sensor node updates the original position (i, j) with the new position (x, y) according to the virtual force, where d ij is the maximum moving distance of the sensor node, e ij is the virtual force acting on the sensor node, d x and d y are the components of the x-axis and y-axis of the virtual force, and e x and e y are the virtual force thresholds of the x-axis and yaxis. When the virtual force on the sensor node is less than this value, it is considered that it does not need to move: where d ij (x) is the local optimal angle experienced by the j-th dimension of the particle i, d ij (y) is the perception direction of the j-th particle in the i-th generation population, e ij (x) is the global optimal angle, and e ij (y) is the number of loop iterations. From the perspective of the development of the media industry, the rapid development of productivity gave birth to the market economy. e needs of market economy development gave rise to traditional mass media. Mass media promoted the prosperity of the market economy. e innovative development model of smart media is developed by relying on traditional mass media. Before the advent of the Internet, mass communication was a point-to-face, one-way communication activity, that is, an organized and large-scale directional activity directed at a broad audience led by the communicator [17]. Whoever controls the media can control the information and public opinion. Network communication has changed the relationship between the communicator and the communication tool, and the audience and the communicator are on an equal footing. Due to the lack of gatekeepers, anonymity, interactivity of the audience, equal rights of the Internet, and generalization of news information itself, the emergence of many communication characteristics makes the values that emphasize the unified purpose of text production and news concepts in the online news environment.

Simulation Experiment Design.
A new round of information technology innovation accelerates the integration and communication between traditional media and new media groups and promotes the formation of a parallel network world and digital world in physical space. Video content consumption and advertising resources are multiplied. For traditional media to survive in the era of big data, grasping the user's experience of subtle needs will be the key to its success or failure, and it must accumulate in terms of core technology and data resources. It provides a guarantee for real-time and energy efficiency under a flat network structure, without additional network control and without overhead for power control and geographic location positioning. In the past, the production and broadcasting system was a management system in which broadcasting organizations gathered the power of the entire society to transfer the production of some non-news programs to production organizations under the premise of ensuring that they had the right to spread. On the basis of adjusting the position, the particle swarm optimization algorithm is used to locally adjust the perception direction of the node, and it can be seen that the coverage of the network has increased a lot. e future media should be smart media, which needs to be combined with technologies such as big data and copyright cloud, so that it has human-like perception and can provide multifaceted, multilevel, personalized, and niche information services. e transmission delay between nodes is usually affected by the degree of local competition, the degree of congestion, and the quality of the channel. It uses location and delay information; the sender estimates the speed of the data packet transmission to the neighbor node and forwards it with the smallest end-to-end delay. Nodes need to periodically exchange control information and introduce control overhead. Real-time demand is expressed as an end-toend deadline, that is, the maximum transmission time of a data packet from the sender to the receiver. In a multihop network, the end-to-end deadline is not the only criterion for determining the urgency of data packets [18]. e time between the sender and the receiver to start transmitting the data packet until the response message received is the roundtrip delay between the two ends, and the receiver will add the time it takes to process the data packet to the response message when sending the response message ( Figure 6). e simulation results show that the new mechanism provides better delivery rate and throughput performance, effectively reduces node energy consumption, and extends the network life cycle.

Result
Analysis. Smart media will be highly automated, and the operator will manage load balancing, power switching, and other massive media operations. Distributed decision-making and control algorithms will make effective media management possible. New technologies can change the existing communication modes, such as content   production technology, video sharing technology, and terminal presentation technology. ere is also a technology gap theory for the media market. In complex real-time systems, decisions must be made quickly. It is necessary to develop automatic decision-making and control algorithms for the media and to restructure the media industry [19,20]. In addition, adopting a hierarchical structure and allowing some data to be processed by low-level programs will help data management. In the era of digital broadcasting, the function of broadcasting will no longer be limited to sound transmission. e use of digital broadcasting technology to disseminate images, data, and even video may become the development direction of future broadcasting. Relationship between delay time and packet loss rate with different coverage values in smart content writing and content review is shown in Figure 7. e impact of the economic environment on the media industry is also reflected in the scale and speed of development. Specific to smart media, the three correspond to the media's audience size, viewing time, and level of loyalty and adherence to the media. Technologies have always been the foundation and catalyst of media development and have continuously promoted the replacement and transformation of media [20,21]. However, the clustering algorithm calculates the method of improving the intensive area of the knowledge, the computational complexity is high, and the wireless smart media sensor network is not suitable for practical applications. e current media is centrally controlled by the artificial intelligence network and wireless sensor network. In smart media, the roles of providers and users will be redefined, users can also sell electricity to the media, and the media operator is responsible for management. Behind the progress of communication technology is the evolutionary process of survival of the fittest, and the media competition is in a pattern where the winners or giants are often the first to master new technologies. In today's multimedia environment, smart media audiences are more likely to switch to alternative media driven by new media consumption concepts and personalized needs.

Conclusions
is paper constructs an innovative development model of smart media under the coverage of wireless sensor networks with artificial intelligence, analyzes smart media nodes and wireless control networks, discusses media system architecture and network routing protocol, proposes the media industry restructuring under the coverage of wireless sensor networks with artificial intelligence, performs automated content compilation and real-time content review, realizes algorithm recommendation and human-computer interaction, and finally carries out a simulation experiment and its result analysis. e smart media innovation development model under the coverage of the wireless sensor networks with artificial intelligence is based on the user as the core and realizes the distribution of diversified content service products to diversified media terminals. Wireless sensor networks with artificial intelligence use virtual force algorithm to control the movement of sensor nodes, so that the nodes in the network are evenly distributed, thereby improving the coverage performance of the network. In wireless sensor networks with artificial intelligence, sensor nodes need to process the collected information, including compression coding and information fusion. e results show that the wireless sensor networks with artificial intelligence not only can innovate the production process of smart media, but also can improve all aspects of media industry chain and further catalyze new format development in media industry. e deep integration of wireless sensor networks with artificial intelligence and media has realized the diversification of application scenarios. Coverage enhancement algorithm based on semantic analysis and deep learning is an innovative development model for media content production; it simulates human intelligence and cognitive behavior through advanced algorithms, achieves good human-computer interaction, and promotes media creation capabilities. e study results of this paper provide a reference for the follow-up research on the smart media innovation development model and media industry restructuring under the coverage of wireless sensor networks with artificial intelligence.

Data Availability
e data used to support the findings of this study are available from the author upon request.

Conflicts of Interest
e author declares no conflicts of interest.