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Drone Based Sensor Network Scenario for the Efficient Pedestrian’s EEG Signal Transmission

The various technologies related to the monitoring human health in real-time for the emergency situations are developing these days. Mostly the human pulse is used for measuring as the vital signs so far, but the EEG became a major research trend now. However, there are some problems measuring and sending EEG signals of all the people walking down the street to the dedicated server. Especially, there are some restrictions for collecting and sending EEG signals in 2-dimensional space in real-time. Therefore, I suggests an efficient network model using 3-dimensional space of drones to avoid the restrictions. The models are designed, simulated, and evaluated with the Opnet simulator.


Ⅰ. Introduction
The drone industry is a major trend in our information technology society. Many companies are competing for the various applications using drones such as the drone mailing services, geographical photos and so on. Moreover, the government authorities as law enforcement agencies, there are many applications using drones such as analyzing road traffic congestion through with it [1].
Some corporations marketing and manufacturing drones for civilian purposes, and the industries that support these manufacturers, have identified the enormous economic potential which may be derived from the sale and maintenance of drones [2]. They use head up display to compete with opponents in the drone competition with the head up display device. There is a research on the head up display to control future vehicular system [3].
And also monitoring vehicular or drones related issues are studied these days. There is a study related on the scientometric analysis through paper analysis of each organization and author to decide research direction for autonomous driving vehicles [4].
Some recent studies related on the drones show a variety aspects from military aircraft to civilian aircraft. The studies of these unmanned aircraft are the kinetic analysis required complex processes. The studies of these unmanned aircraft is the kinetic analysis requires complex processes, because these support by the aerodynamic forces on the unmanned aircraft study [5,6].
Mostly the drones use GPS for the positioning function. And there are many sensors available for the specific operations. Some studies require as a complete drones, variety of sensors to be employed for their location, sensor data pre-processing and processing, sensor fusion, map building, motion planning, motion control, etc. [7].
Therefore, in this paper, some EEG signal transmission network scenarios are designed and simulated for the network performance. Then analyze the result to suggest an efficient EEG transmission sensor network. In section II, the EEG signal transmission via drones will be suggested. In section III, the specification of the various EEG network using drones scenarios will be presented and explained. Then, in section IV, the simulation result of the scenarios is compared and analyzed for the efficient scenario. Finally, the conclusion is made in section V.

EEG Signal Propagation using Drones
To transfer the EEG signals to the server using drones have to consider many things. For instance, to avoid the collisions they need to have infrared sensors. However, for the performance of the network consists of many drones collect and propagates the EEG signals to the server has to be efficient. I believe this is the first paper to present transmitting EEG signals to the server via drones.
There are many people on the city street and it can be divided into many sections to manage effectively.
To obtain the better performance, the efficient network scenario is required. For instance, the numbers of the pedestrians in certain area and the numbers of the server to collect the data in a section will affect the performance.

Ⅲ. Various EEG Signal Drone Network Scenario
Mainly, the two types of network scenario are suggested and simulated for this paper: One network scenario is no routers to propagate the end devices as pedestrians. Another words, the end device directly sends the EEG signals to the coordinators in the center of the network.
The other network scenario is some mobile routers propagates the EEG signals from the end devices.
This could not help the performance of the network much since the routers are few and they have the mobility. So that they don't have much chance to encounters with the end devices as adjacent nodes. This is the major purpose of the experiment to evaluate the potential of the mobile routers.
Additionally, the network with few fixed routers network is designed and compared with the two network scenarios at the end. Even though we can expect that this will performs the best of all, It is necessary to figure it out the effectiveness of the scenario. And how many mobile routers are needed to cope up with the fixed router network structure. Basically, the EEG signal network using drones are designed with sensor nodes. The network consists of end zigbee devices, zigbee routers, and zigbee coordinators.

Fig. 2 The private many network scenario
The no routers network is named as a 'Private many' for the simulation shown in Fig. 2. There are 30 zigbee end devices which are considered as pedestrians.  Finally, the additional 'FixRout many' network scenario looks just like as the Fig. 3 except for the no boundaries of the routers. It will be compared and discussed with the first two scenarios at the end of the simulation.
The specification of the device is summarized in the Table 1. The ACK mechanism, the CSMA-CA parameters, and the parameters of physical layer is specified for the simulation.
The packet reception-power threshold is related to the network perimeters of the zigbee end devices to transmit the data.

IV. Simulation Result and Analysis
Mainly, the first and the second scenario discussed in the previous chapter will be analyzed and discussed with various network parameters. The additional fixed router scenario will be compared and analyzed at the end of this chapter.
The network performance parameters chosen for the simulation result are the global throughput and the data received rate in the zigbee coordinator.    The fixed router scenario is designed and simulated to evaluate. Then I have obtained the throughput comparison graph shown as Fig. 7. The result showed enormous gaps between fixed router scenario and others than I have expected. So I have added 4 times of mobile routers in the network to compensate their gap.

Fig. 8 Throughput Comparison with Many Routers Scenario
The middle graph line of Fig. 8 shows the result of the expanded routers scenario. But the throughput doesn't seemed have progressed.
Therefore, I have concluded that adding the numbers of mobile routers did not affects much to the performance of the network.

V. Conclusion
In the future, most people walking down the street might need to be monitored their EEG signals for their health or medical emergencies.
There are some problems measuring and sending their EEG signals to the dedicated server in For the further study, the suggested scenario in this paper will be applied to the patients in the hospital network.