Connect Everywhere: Wireless Connectivity in Protected Areas

TL;DR

This paper proposes a resource management strategy for wireless connectivity in protected areas that minimizes energy consumption by sharing infrastructure and offloading computation, ensuring QoS and utilizing green energy.

Contribution

It introduces a novel energy-efficient resource management solution combining infrastructure sharing and peer-to-peer offloading tailored for protected areas.

Findings

Achieves 52% energy savings with the proposed algorithm.

Simulation results show high accuracy in energy and traffic load prediction.

Energy savings decrease with higher QoS priorities and more active resources.

Abstract

Mobile connectivity has become more important, especially for visitors to parks and protected areas. However, governments' policies prohibit cable wiring in these areas in order to preserve the beauty and protect the historical interest of the landscape. Through observed practices from other countries, mobile network operators and other licensed service providers can cooperate with the administrators of such protected areas in order to provide mobile connectivity without disturbing the environment. However, the most pervasive problem is the high energy consumption of the wireless systems and it becomes expensive to power them using the electricity grid. One attractive solution is to make use of green energy to power the communication systems and then share the base station (BS) infrastructure that is co-located with the edge server, an entity responsible for computing the offloaded delay-sensitive workloads. To alleviate this problem, this paper offers a resource management solution that seeks to minimize the energy consumption per communication site through (i) BS infrastructure and resource sharing, and (ii) assisted (peer-to-peer) computation offloading for the energy-constrained communication sites, within a protected area. Using this resource management strategy guarantees a Quality of Service (QoS). The performance evaluation conducted through simulations validates our analysis as the prediction variations observed shows greater accuracy between the harvested energy and the traffic load. Towards energy savings, the proposed algorithm achieves a 52 % energy savings when compared with the 46 % obtained by our benchmark algorithm. The energy savings that can be achieved decreases as the QoS is prioritized, within each communication site, and when the number of active computing resources increases.