Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• The Internet of Things (IoT) Off-Grid concept addresses environments lacking commercial power and internet connectivity.
• IoT devices in these areas generate data requiring local storage on servers.
• Efficient data retrieval from these isolated systems is a significant challenge.

Purpose of the Study:

• To conduct a systematic literature mapping (SLM) on algorithms for data searching and retrieval in IoT Off-Grid scenarios.
• To identify optimal communication strategies and pathways between local servers and drones.
• To determine suitable simulation tools for validating proposed solutions.

Main Methods:

• Systematic Literature Mapping (SLM) to identify relevant research.
• Analysis of algorithms for data search and path optimization.
• Review of communication protocols (primarily WiFi 802.11) for drone-server interaction.
• Evaluation of simulation platforms, with a focus on OMNeT++.

Main Results:

• Algorithms for determining the best drone path were identified, often based on the Traveling Salesman Problem (TSP).
• WiFi 802.11 emerged as a predominant communication technology for data transfer between drones and local servers.
• OMNeT++ was recognized as a prominent simulator for validating these off-grid IoT solutions.

Conclusions:

• The research provides a foundational understanding of algorithms and technologies for data management in IoT Off-Grid environments.
• The findings guide the development of efficient drone-based data collection systems for areas without conventional infrastructure.
• Further research can leverage identified algorithms and simulators to enhance the robustness and scalability of IoT Off-Grid networks.