Resource allocation for data gathering in UAV-aided wireless sensor networks

Abstract

As the development of unmanned aerial vehicle (UAV) communication in the residential and commercial areas, different from the traditional wireless sensor networks (WSNs), in this paper we consider a two-layer wireless networks for data gathering, one is sensor nodes to UAVs layer, the other one is UAVs to the outside layer. The sensor nodes sense data and transmit to UAVs, and then UAVs send data to the outside. In this paper, we only focus on the first layer, and we suppose the capacity is sufficient for UAVs to transmit their gathered data in the second layer. The sensor nodes aim to maximize the total data transmitting rate. Because of the limited resources (i.e. limited bandwidth, limited energy of each node, and so on) in the system, thus how to allocate the limited resources to maximize the total data transmitting rate is challenging, and it is necessary to propose an efficient algorithm to allocate the resources. We formulate the problem that jointly considers bandwidth allocation and energy allocation to maximize the total transmitting rate while guarantees the rate in each time slot for each sensor node. Then an optimal algorithm based on dynamic programming named DPBA is proposed. Finally, we conduct the extensive simulation to compare our proposed algorithm and the benchmark algorithm (i.e. equal resource allocation algorithm, denoted by ERAA). Simulation results show that our proposed algorithm outperforms the benchmark algorithm.

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

“…Then, this data is sent from the drone to the central analysis station. The bandwidth allocation and energy allocation for sensing and transmitting data in the two steps have been considered and discussed [53]. Therefore, the combination of drones and WSNs can provide a solution for the utilization of energy resources.…”

UAV-Assisted Data Collection in Wireless Sensor Networks: A Comprehensive Survey

“…The problems addressed in Table 2 such as power expenditures, the optimized trajectory of UAVs is to tackle the problems such as path planning, increase network lifetime [81,83], as shown in Table 3. HHA [80] x SN-UAV [63] x UAV-WSN [81] x x Fat UAV-AS-MS [84] x URP [85] x x C-UAV-WSN [28] x x x rHEED [86] x x UADG [56] x DPBA [87] x x EEDGF [88] x PCDG [89] x x…”

Performance Analysis for Multi-Layer Unmanned Aerial Vehicle Networks

“…Although function ǫ j (s) is not in the closed form, it is easy to evaluate and analyze. 2 Furthermore, due to the above independency, we can also obtain the upper bound of the total density of MAN as λ b T = k∈K λ b k .…”

“…In the area which needs quick deployment of the base station (BS) due to disaster or events, UAVs are expected to act as a temporal BS as well [1]. Furthermore, the data collection from the devices under certain energy constraints can be done by using UAVs [2]. In addition, demands on the data acquisition using UAV in crowd surveillance have arisen [3].…”