A UAV-Enabled Time-Sensitive Data Collection Scheme for Grassland Monitoring Edge Networks

TL;DR

The paper addresses time-sensitive data collection in grassland monitoring edge networks (GMENs) using a single UAV with limited energy. It formulates a mixed-integer programming model for the time-sensitive data collection problem (TSDCMP) and proposes the cooperative heuristic CHTSC, which combines an iterated local search (ILS) with a modified dynamic programming (MDP) evaluator to capture temporal-spatial correlations among AP service requests. The approach jointly optimizes UAV trajectory, arrival times, and data-collection durations under energy constraints, showing superior performance over two strong baselines across 12 large-scale instances in simulations. This work advances UAV-enabled grassland monitoring by incorporating spatiotemporal data correlations and deadlines, enabling more reliable data collection with limited UAV energy and reducing data overflow at APs.

Abstract

Grassland monitoring is essential for the sustainable development of grassland resources. Traditional Internet of Things (IoT) devices generate critical ecological data, making data loss unacceptable, but the harsh environment complicates data collection. Unmanned Aerial Vehicle (UAV) and mobile edge computing (MEC) offer efficient data collection solutions, enhancing performance on resource-limited mobile devices. In this context, this paper is the first to investigate a UAV-enabled time-sensitive data collection problem (TSDCMP) within grassland monitoring edge networks (GMENs). Unlike many existing data collection scenarios, this problem has three key challenges. First, the total amount of data collected depends significantly on the data collection duration and arrival time of UAV at each access point (AP). Second, the volume of data at different APs varies among regions due to differences in monitoring objects and vegetation coverage. Third, the service requests time and locations from APs are often not adjacent topologically. To address these issues, We formulate the TSDCMP for UAV-enabled GMENs as a mixed-integer programming model in a single trip. This model considers constraints such as the limited energy of UAV, the coupled routing and time scheduling, and the state of APs and UAV arrival time. Subsequently, we propose a novel cooperative heuristic algorithm based on temporal-spatial correlations (CHTSC) that integrates a modified dynamic programming (MDP) into an iterated local search to solve the TSDCMP for UAV-enabled GMENs. This approach fully takes into account the temporal and spatial relationships between consecutive service requests from APs. Systematic simulation studies demonstrate that the mixed-integer programming model effectively represents the TSDCMP within UAV-enabled GMENs.

Figures (6)

• Figure 1: Three-tier network architecture for time-sensitive data collection by a UAV in GMENs.
• Figure 2: Example of real-time data collection by a UAV in GMENs: (a) The shortest distance Hamiltonian cycle. (b) The path of real-time data collection.
• Figure 3: Queueing model for real-time data collection scheme by a UAV.
• Figure 4: Example of the local search strategy based on temporal-spatial correlations for passer-by APs: (a) The shortest time Hamiltonian cycle. (b) The APs data collection based on temporal-spatial correlations.
• Figure 5: Comparison of the average objective function value of different algorithms by varying the number of APs from 15 to 40 across three different network types.

Theorems & Definitions (2)

• Theorem 1
• Proof 1