Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks

The cellular coverage using unmanned aerial vehicles (UAVs) is gaining attention from research community and telecom industry with the rapid deployment of Internet of things (IoTs). For collection/dissemination of data, IoTs-based cellular technologies are being provided [1]. For provision of services in wider coverage area, the cellular networks are augmented with UAVs. UAVs are the trustable solution for improving of the efficiency, enhance throughout, cost and boosting capacity [2]. These UAVs are temporarily deployed in the air to cover an area where the user demand is increased or in the case of disasters or to provide connectivity in areas where permanent infrastructure is not currently possible. This also includes areas where physical infrastructure is available, but the user density is very high (event, stadium, etc.). The developments of UAVs not only provide the solution but also provide the load balancing, and easily cover the maximum demanding area [3]. For this purpose, an efficient approach and mechanism is required.

Previously, Cell on Wheel (COW) is used as a temporary solution in which a vehicle was designed to carry a mobile microcellular base station [4]. This solution was developed in conjunction with the Telstra Next Generation deployment plan to extend the coverage area to cover any event or emergency [5]. There are few situations in which the cow could fail, for example in areas of natural disaster where the road infrastructure necessary for displacement is not available. The basic reason for UAVs as a favorable solution for a wide range of neighborhood applications is the ability of its free and independent movement, to any hard to reach areas [6, 7]. These UAVs are furnished with the base station (BS) hardware, and these act as a flying BS, creating an attractive alternative to predictable roof or pole attached base stations. In radio communication, the BS is a wireless communication station mounted at a fixed location and used to connect as part of wireless telephone scheme. The BS relays the conversation, message and data to base stations in other cells by the wireless, cable communication or through a cable network or through a combination of wireless and cables.

The placement of these UAV-based BS in a microcell network in order to get optimal coverage area where the user are not stationary is a challenging task [8, 9]. Providing best placement while keeping the number of UAVs optimal is hard to achieve. Multiple solutions for this problem are discussed in the literature, where majority have focused on provision of the energy efficient solutions for placement of UAVs. Among them, very less attention has been paid toward energy aware solutions and additionally; such solutions have only considered implicit utilization of energy to estimate the available energy [10]. However, the explicit usage of UAV energy has not been addressed yet, which also has severe effect on the UAV survivability.

The main contribution of this paper is twofold:

The rest of this paper is organized as follows. Section 2 presents the related work. The methodology describes in Sect. 3. Prototyping details are explained in Sect. 4. The experiment results are present in Sect. 5. Section 4 includes the conclusion and future directions of our research.

The UAV base stations (UAV-BS) enhance network coverage and area capacity by moving supply toward demand when required [11, 12]. However, deployment of such UAV-BS can face certain restrictions that need to be considered while designing a solution. The major concern is the lifetime of a UAV for which it could remain in operation. The most important factor affecting the UAVs lifetime is energy source. Following are some of the recent papers related to UAV-BS in cellular networks that focus on energy aware deployments. Thus, it is the duration for which the ground users are getting the services.

The prolonged lifetime in UAV-based network is normally achieved by designing energy efficient solutions [13‐16]. Wang et al. [17] suggested in their work set of rules for UAV base stations that predicted an energy-efficient placement so that should serve users by minimal transmit energy. Also, the most appropriate placement of the UAV-BS was presented in their work by decoupling the deployment problem in horizontal and vertical dimension determined the most desirable UAV position that minimizes the transmission power by performing the simulations for hotspot and non-hotspot scenarios.

Chen, et al. [14] describe a battery-operated version of the UAV power consumption model which is then applied to a situation of UAV flight. The UAV consumes less electricity while it travels at excessive horizontal speed at some stage in the task, because low speed is not really greatest for the UAV for the energy scenario. The cause is that the hovering power is stable ultimately after the flying time. Cabreira, et al. [18] write in the paper that in case of using UAV automobile dynamics, turning angle and optimal speed must be considered to minimize electricity intake. Their proposed algorithms mainly involved about energy intake considering the mission and electricity constraints of the UAVs. Thus, improving velocity in straight parts of the path leads to energy consumption minimization. In the paper, the path divided into forty-five small elements (straights and curves) for every straight a part of the coverage it became taken into consideration: the acceleration, the steady speed flight and the deceleration. By using the equal velocity of 8. zero m/s for long elements and the reduction of 45% in velocity before turns referred to as entrance speed of UAV by using a battery of value 46, 681 J.

Another paper [15] proposed a scheme which presents the UAV offloading (air-offloading and ground-offloading) approaches. In the air-offloading approach, a UAV can offload its computing responsibilities to nearby UAVs that have to be had computing and electricity sources. The floor-offloading technique enables the project offloading carrier to an edge cloud server that is related to floor stations. This hybrid offloading scheme comprises three major modules to lengthen the life of UAVs by way of saving resources through task offloading process. Furthermore, this scheme efficiently reduces the project blocking probability and the cease-to-quit latency of managing a computing mission.

Fotouhi, et al. [16] provided option to improve communique power performance is to develop optimum transmission schedule of UAVs, mainly while UAVs are flying in a predetermined trajectory fixed-wing UAVs can movement over the air, which makes them substantially more energy effective and capable of carry heavy payload. Small commercial UAVs typically have a flight time of 20–30 min, while some big UAVs can last for hours. Researchers in this study focused on two types of energy saving reducing communication energy minimize the transmission power energy efficiency is to develop most desirable transmission time table of UAVs, especially while UAVs are flying in a predetermined trajectory. To reduce mechanical cost an energy intake model is needed. However, changing the height might reduce the performance of UAVs.

However, in these solutions the focus is on a less energy usage in terms of computational power and does not focus on transmission requirement or estimation that for how long the UAV remains present in the air. There are some solutions that optimize the energy consumption by minimizing the transmission power [19, 20]. The decision regarding the use of energy consumption has been implemented both as centralized as well as distributed approach. In [21], the author has proposed an approach in which the UAV report the statistics to the base station and the BS proposes a scheduling model that minimizes the UAV energy consumption. The base station in its scheduling informs the UAV about the time slot and power information. A similar centralized entity-based solution has also been proposed that is dependent upon cluster heads [22]. These cluster heads (Terrestrial nodes) are in direct communication with the UAV and ground users are connected with the cluster heads. But these types of solutions have their own limitations.

However, these solutions are also dependent on the estimated energy that accordingly adjusts the transmission power, this way; the UAV coverage is also affected. A third side of energy, the mechanical energy, has also been discussed by some researchers [23]. These solutions reduce mechanical energy of UAVs and are dependent upon multirotor, fixed-wing and hybrid fixed/rotary UAVs. Similarly, in [24], the authors isolated the two power consumptions into static and dynamic, the static the fixed consumption of the UAV and dynamic is dependent upon the load either low traffic load or high traffic load. In another study [25], the authors suggested a dynamic planning that is also dependent upon traffic intensity. They proposed to have different planning for day and nights, as in night the active users are less as compared to day timings and accordingly they plan the placement of UAV.

In recent past, multiple studies have been made in the domain of UAV placement as it could provide a better alternative for temporary cell construction in the uncomplimentary cell sites. The parameters that influence the placement of UAV are key factors that can severely affect the network performance. The lifetime of a UAV is greatly dependent upon the energy available. From the literature, six types of energy consumption parameters have been extracted. Energy spent on (a) horizontal movement, (b) vertical movement, (c) hovering, (d) processing, (e) backbone connectivity/communication and (f) providing front haul/ access to users. However, all these six parameters have not been given full consideration for energy aware solutions, which provide the estimated time of UAV for which it can remain in the air. This estimation greatly helps in UAVs placement/replacement planning.

Authors in [17] proposed an energy-efficient placement algorithm by decoupling the drone deployment in horizontal and vertical locations, by using minimum required transmit power. They perform simulation on hotspot and non-hotspot scenarios, and the numerical results show the linear association between the minimum horizontal location and optimal altitude. They first find the optimal horizontal position of the Drone base station (DBS). The average path loss against the altitudes for various horizontal distances between the edge users and the drone, there lies a point of minimum value of average path loss of the edge users and the relevant drone altitude. Furthermore, by decreasing the horizontal distance of the edge users from the DBS, the minimum average path loss of the edge and the altitude decrease. Once DBS obtains the minimum horizontal distance, corresponding optimal drone altitude is calculated. The results show the power saving in the urban, suburban and dense urban environments optimal altitude.

In [26], the researcher develops the photovoltaic power management system (PPMS) which manages power from photovoltaic modules and a battery pack for multirotor UAV power. They use the state of charge concepts grounded on extended Kalman filter (EKF) and complementary filter (CF) for estimation of flight time. It also calculates the possible flight time during hovering flight mode and hint of the remaining energy of the battery pack by using the slope of the state of charge graph. During takeoff, hovering and landing flight modes patterns are estimated in these three modes of UAVs and the mean value is calculated. There were three main power connectors connected to the photovoltaic modules, battery pack and UAV. Voltage and the current of all three main power connectors were monitored. During low sunlight, battery pack mostly deliveries the required drone power. During daytime, photovoltaic modules deliver the power required from the drone. They keep track of the voltage and current data of the photovoltaic modules, battery pack and UAV, progress time and battery pack temperature. According to the results, estimated flight time increased up to 54.14 min at 11:00 a.m. and decreased down to 6.70 min at 18:00 p.m. The results also indicated that if there were no clouds covering the sun, the UAV could fly for about an hour at around noon which was much higher than the flight time of the traditional multirotor UAV. The future work is to improve the current version of the PPMS.

The UAV altitude affects the energy from two perspectives: (a) the energy required by motors to increase the UAV altitude and (b) with higher altitude the transmission power also increases in order to cover the required area on ground. In this research, takeoff, landing and moving vertical or horizontal while changing the altitude and increasing the coverage radius have not considered. But because we are working on 80% usage of UAV so its rational to ignore such parameters in order to focus the transmission impact on airtime and limiting the research scope. Figure 6 shows the impact of UAV altitude on radius, and the radius tends to increase when the UAV increases its altitude, thus also affecting scattered users and the density that needs to be covered.

Increasing the altitude of a UAV also effects on its transmission power, and now the UAV requires more powerful transmission in order to cover the increased radius. The UAV are reliant on a limited size on chip battery. More power full transmission depletes the battery abruptly thus directly dropping the available amperes. Figure 7 shows the same in comparison with the increasing altitude. The amp draw increases in linear fashion as the altitude is increased in start, but later on especially at altitude above 500 m, the transmission power has a noticeable effect on the ampere draw of the battery and it results in less flight of a UAV. Thus, disrupting the service availability and suspension of the UAV operation.

Figure 8 shows the airtime estimation with and without considering the transmission impact in comparison with the altitude. It is witnessed that as the altitude is increased, the required transmission power also increases proportionally and it directly affects the estimated airtime of a UAV. It is worth to note that initially, the slope of airtime estimation decreases slightly but later on the decline on fast pace.

The difference discussed in Fig. 8 (with and without the transmission impact) is also calculated separately in order to access the scale of unavailability of UAV service as depicted in Fig. 9. This difference is substance depending upon the UAV application as the case may be of critical nature.

The UAVs deployment is largely dependent upon the available energy, and it is a key factor in UAV planning. The under- or overestimation of UAV air timing leads to wastage of resources or inefficiency of mission critical projects. Multiple factors influence the estimation of air timing but the majority of the literature concentrates on only flying time. In this research, the other factors are also accounted for that improved the estimation. In this research, the energy consumption is bifurcated into two usage scenarios (1) Implicit and (2) Explicit. The implicit usage was given its due weight in the previous solution; therefore, this research focused on explicit factors that have discussed in the literature but have not been given concentration in estimating the airtime and left as negligible factor. However, simulation has been performed and it is witnessed that the explicit factors of transmission power have severe effect on airtime estimation which is a factor not to be ignored in mission critical operation. In future, we will enhance the proposed idea and test with more parameters such as data centric fuzzy approach [37] and drone networking for marine applications [38].