We consider a simulation model of a warehouse with three types of wireless communication links: device-to-device (D2D) links, drone-assisted and infrastructure-based links (via the base station). In all study cases three types of mobile objects are considered: employees (humans), wireless access points in the form of flying robots (drones) and stationary devices (machines). As a result of the conducted research we propose a software tool which provides the graphical representation of mobility of the objects inside the warehouse with given parameters. It allows to assess the network coverage area and allows to study the Internet of Things (IoT) connectivity issues. The developed algorithm and its software implementation allowed to study the impact of diferent mobility models on the system-level performance of the considered data transmission system in terms of its network connectivity, defined as the ratio of connected devices. Our evaluation results demonstrate that the use of Reference Point mobility model under the same parameters setting of the system ensures the increase of coverage up to 10%. Therefore the additional area under the RP model is in average less than that under the RW model. Analysis of the obtained graphical results allows to estimate the impact of the number of flying wireless access points in the active state to the coverage area of the considered heterogeneous wireless communication system.
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In: K. E. Samouylov, L. A. Sevastianov, D. S. Kulyabov (eds.): Selected Papers of the VIII Conference “Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems”, Moscow, Russia, 20-Apr-2018, published at http://ceur-ws.org
Unmanned objects’ control and industrial robotization are currently the most popular
and economically interesting projects in the field of high technology for business.
According to analysts, the market of IoT by 2020 will increase up to 1.9 trillion USD. The
diagram on the Figure 1 shows that according to Ericsson‘s data by 2021 the number of
devices connected to the network will be about 28 billion and more than 15 billion will
be machine-to-machine (M2M) devices and consumer electronics [
The development of IoT makes it possible to increase the stability for the society and
the industry. The implementation of these technologies allows to reduce the resource
costs and enhance the eficiency of warehouses. The up-to-date hardware allows to
implement this technology in virtually all areas of interest starting from the control
of production, transport and logistics up to automatic security systems. The main
methods of the data transfer in such networks are the Wi-Fi and Bluetooth (at short
distances) technologies, and WAN (wide area networks) in primarily cellular networks
that can provide communication at long distances. The use of such technologies creates
a number of requirements for the data transmission network simultaneously with new
opportunities. The main task is to ensure a stable (reliable [
The goals of this study are the development and software implementation of simulation
modeling [
2.
As an object of industrial automation for simulation we consider an indoor area
(warehouse) of [100,100] meters, where human users and networked M2M devices are
allowed to move freely within the considered location according to their specific mobility
patterns. There are 100 static machines, 30 mobile warehouse employees and 5 drones
within the considered indoor area. Drones provide communication between the humans
and the machines, but in the same time the machines can be connected directly with
humans and with each other via the D2D interaction [
Humans are moving according to the Random Walk (RW) model at low speeds and
in a random direction. We consider two mobility models of the drones: the RW model
and the Reference Point (RP) model [
3.
To implement the above task, a program was developed in Python. The solution algorithm is presented in the Figure 3. To define the initial conditions the user is given the opportunity to enter the model time value (number of loop iterations), the step (the distance to which the object can move), the radius of the drones’ coverage, the coverage radius of D2D.
As a result of the program, the total number of objects with access to drones is calculated, as well as the number of people and the number of machines that have access to diferent types of communication at each step. To find the covered area, the total area of the warehouse is divided into equal squares, for each of which is defined its center, then the number of centers of squares included in the coverage area of the drones is counted. The resulting value, multiplied by the squares area, will constitute the area of the general coverage by the drones and the BS.
To assess the quality and eficiency of the model, are calculated parameters such as the total coverage of the base station and drones and the number of devices to which one or another type of communication is available. Also, as a result of the program, a schematic image of the state of the system is generated at each iteration. Figure 4 shows an example of the state of the model. Blue points are cars, reds are drones, green ones are people. Green circles are the active drone’ coverage areas, black circle — BS coverage area.
The developed software tool also allows to build graphs that show the ratio of diferent types of communication by the number of objects using them in percentage terms (Figure 5), number of drones in action (Figure 6), coverage area percentage (Figure 7). a) RW b) RP
As a result of the work of the developed software tool, there were obtained graphs, which make it possible to estimate the square of the coverage area of connection of the networked objects under consideration. Based on the data showed in Figure 5, we can conclude that for the case when the radius of D2D-connection coverage area equals 20m and the radius of drone-assisted coverage area equals 30m throughout the model time, the percentage of objects to which the device-to-device connection is available and the percentage of objects which are served over the infrastructure-based connections, is approximately the same and fluctuates around 70%. At the same time, the percentage of devices that are available through drones can fall below 20%. The comparison of ifgures 4–7 allows to estimate the dependence of the wireless system coverage area on the number of drones in the active state.
According to the data represented at Figure 6 and Figure 7, the total square of the summary coverage area of drones and BS fluctuates from 72.5%, with one working drone, to 87.5% with 5 working drones (data calculated for the radius of the drones coverage equal to 30m).
Graphs represented in Figure 7 allow to compare the additional area at diferent models of movement of drones. The use of RP model with the same parameters of the system ensures the increase of coverage to 10%. Therefore the additional area using the Reference Point mobility model is on average less than Random Walk. The developed software also allows to visualize the state of the system at any time instant, estimate the surplus coverage area of the wireless connection, and calculate the average performance for each type of connection.
The publication has been prepared with the support of the “RUDN University Program 5-100” and funded by RFBR according to the research project No. 17-07-00142. 5.