## 1 Introduction  
One of the most common animal habits that efficiently yields undeniable outcomes in nature is cooperative work. For example, a swarm of birds has a V-shaped flight formation to reduce the induced drag and to increase their range [1]; ants maintain a separation distance to search for food optimally; and herbivores collectively migrate to defend against any predators in wild areas. Encouraged by these biological tactics of a group of multiple creatures, multi-agent systems (MASs) have attracted intense attention because of the vast number of applications in many aspects. They are moderately useful to carry out a range of different assignments, such as irrigating large agricultural areas, monitoring in the military, investigating and locating lost objects in inaccessible zones, and maintaining undersea oil pipelines [2; 3; 4]. In recent years, the formation control problem of MASs, whose purpose is to steer multiple objects in a network to achieve and maintain their predefined geometric pattern in their states, has increasingly fascinated the scientific community. Because of many versatile and helpful applications in real life, there have been several fruitful results in studies focusing on unmanned aerial vehicles (UAVs), ground mobile robots, autonomous underwater vehicles, and multisensor networks [5; 6; 7; 8]. Based on the types of sensed and controlled variables of the probed systems in MASs, an excellent survey paper with the topics of the formation problem categorized as position-based, displacement-based, and distance-based control is [9] (and see the references therein). The above-mentioned studies preferred to employ the first approach rather than the others. Multiple mechanical systems are mostly offered as a distributed control structure due to the limited computation and communication resources. Because each individual system in multi-agent systems possesses many sensors such as real-time kinematic-global positioning system (RTK-GPS), inertial measurement unit (IMU), WiFi, telemetry, and radio receiver, the whole of the investigated system can also be considered as a multi-sensor network system. In practice, each agent in a multi-sensor network commonly has limited energy resources and often broadcasts its own sampled data to task manager nodes through a communication network [10; 11; 12; 13]. Additionally, there is a restricted capability of data transmission in the network system bandwidth. It is clear that using common estimators in the majority of experimental devices, e.g., the Kalman filter and the extended Kalman filter, which require a large amount of system state information, would be unpractical. Moreover, approaches based on consensus and formation algorithms are also unpractical because they require a massive capability of communication among agents and a large amount of energy expenditures in the multi-sensor network. To alleviate the waste of energy consumption reasonably, efficiently making use of these resources plays an important role in cyber-physical system design. By applying most of the conventional control techniques to the distributed algorithm, it is assumed that each agent state feedback controller periodically executes and does not depend on whether system states are updated; its neighborhood information is completely exchanged. This implementation method may cause unnecessary work in hardware devices while there are many other required assignments, leading to overloads and network congestion. Due to some drawbacks of these existing schemes, many scientists in the cybernetics community have tended to research event-triggered control system design. Recent years have witnessed a growth of results regarding event-triggered control for a single-agent system [14; 15; 16] before this concept was originally extended to propose a control structure for MASs [17]. The core idea of the event-triggered control is that controllers or actuators are solely updated when some specific events occur rather than after the elapse of a fixed amount of time [16].