## 2 Related Research  
Sensor nodes in WSNs are composed of three types: Sink node, data source sensor node for sensing information and relay sensor node for forwarding data. In the intelligent agent algorithm of IoT network layer, data is collected by intelligent agent. The intelligent mobile agent is made up of control node, address information and carrying data. It starts from the Sink node; then, the multiple source nodes to collect data are visited. Moreover, the data sensed by the data source sensor are compressed and fused by the intelligent agent. Finally, data are brought back to the Sink node. Compared with the traditional client-server computing model of WSNs, the efficiency of data collection and aggregation based on intelligent agent is higher [4]. In recent years, wireless sensor networks use the concept of mobile agent to reduce energy consumption and obtain effective data collection. Generally, in the data collection based on a mobile agent, it is an important and necessary step to find out the optimal travel planning of a mobile agent. However, with the expansion of network scale, single agent travel planning has two main disadvantages: task delay and large scale of mobile agent. Therefore, multi-agent travel planning overcomes the shortcomings of single agent travel planning [5]. The intelligent agent design of WSNs can be divided into four parts: general framework, route planning, middleware design and agent cooperation. The main algorithm of intelligent mobile agent is to plan the mobile route of agent. Because the Sink node already knows the location of all source nodes, the route planning work is completed in the Sink node. That is, the Sink node has a global view. The route planning of agent is centrally controlled by Sink node, which coincides with the idea of software-defined network. The disruptive potentials of the IoT entail multifaceted requirements and development issues (large scale deployments, heterogeneity, cyberphysicality, interoperability, distributed smartness, self-management, etc.). To adequately tackle them and to comprehensively support the development of the IoT ecosystem, the Agent-Based Computing (ABC) represents a proper and solid modeling, programming and simulation paradigm [6]. WSNs have many potential applications, such as battlefield surveillance, medical applications, field surveillance and disease prevention. WSNs need data transmission to be processed. However, due to the dynamic changes of wireless channel in WSN, providing performance communication becomes a major challenge. Because of energy limitation, the lowest energy consumption is required for the sensor nodes on the premise of ensuring reliability. The technology to solve this problem is cooperative communication, which is considered a scalable, energy-saving and fault-tolerant solution. Cooperative communication nodes cooperate with each other to form multiple forwarding paths. The performance of WSN, especially the reliability of the network, can be improved by cooperative communication through utilizing the smart and spatial distribution of wireless media. A multi-mobile agent travel planning algorithm (CL-MIP) is proposed in the literature [7]. It divides the problem of multi-mobile agent WSNs into four steps: (1) calculating the density center of the source node in the network; (2) taking the density center as the center of circle, with the predefined radius, the source nodes that are covered are grouped into one group; (3) computing the order in which each mobile agent accesses the source nodes in the group; (4) continuing to iterate until there is no unmarshalled node in the network, and sending multiple mobile agent packets in parallel. A system structure for the research of multi-mobile agent travel planning is provided by CL-MIP. In the travel planning of multiple mobile agents, the first thing that needs to be considered is the problem of source node grouping in the network.