Minimization of the Worst Case Average Energy Consumption in UAV-Assisted IoT Networks

Abstract

The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of Unmanned Aerial Vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving lowpower IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-Signal-toaverage-Interference-plus-Noise Ratio ( SINR) Quality of Service (QoS) constraints. We minimize the worst-case average energy consumption of the latter, thus, targeting the fairest allocation of the energy resources. The problem is non-convex and highly non-linear; therefore, we re-formulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst-case average energy consumption. Furthermore, we show that the target SINR is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

“…Indeed, every technique and technology conceived for performance improvements in terms of coverage, throughput (THP), dependability, and other KPIs, can be in principle leveraged for EC reduction as well, hence, EE. That is the case of massive MIMO [133], [134], UAV-based connectivity [135]- [137], satellite-assisted communications [138], [139], cooperation and diversity mechanisms [140]- [142]. Nevertheless, there are some technologies/techniques that are natively conceived for energy-limited/efficient operation, and these constitute the scope of this section.…”

“…However, this may not always be the most effective way to balance performance and EC in all scenarios, unless the EER metrics already include some guarantee for QoS support and/or maximum EC. If not, minimizing EC while maintaining QoS requirements, e.g., [135], [137], [140], [210], [335], or maximizing performance given energy constraints, e.g., [131], [202], [205], [290], [336], [337], may be more important.…”

Resilient Localization and Coverage in the Internet of Things

“…This method of sensor deployment offers greater control over resource constraints. Energy consumption optimization is typically the primary metric in many deterministic deployment strategies [2][3][4]. A common example of this type is grid-based deployment, which includes hexagonal, square, and equilateral triangular patterns.…”