1. Introduction
The concept considered here as IoT Off-Grid,
Figure 1, represents an environment where sensors and controllers are installed in a place totally devoid of electricity and communication. Therefore, it is necessary to generate electricity to supply the site. This generation can be obtained with the use of photovoltaic equipment, wind energy, or another form of own generation of electric energy. A photovoltaic system is neededwith batteries sized for the load needed by the environment and an inverter to meet the energy demand in the IoT Off-Grid environment.
The information generated in this IoT Off-Grid environment will be collected periodically through a data mule (DM), in this case a drone, also identified in the literature as an unmanned aerial vehicle (UAV). The flight autonomy of the drone will be considered, along with selection of the best route, as well as the age of the information collected, considering the time of generation in the environment and the time of collection by the drone. A scenario is shown in
Figure 2.
1.1. SLM Research Objectives and Questions
The main objective of this study is to systematically map current research on data collection using a data mule in a geographically dispersed IoT. Considering that the data will be collected through a drone in one or more local networks with several sensors and a data concentrator of this local network, these local networks are isolated in environments devoid of internet and with local infrastructure to this environment. For the simulation of the proposed model, a network simulation software suitable for the environment will be used.
In this sense, this SLM also aims to identify, categorize, and synthesize important studies in this area.
As research questions (RQ), there are the following:
RQ1—What algorithm is used in routing for data collection?
RQ2—What technology is used for the reception of data by the drone?
RQ3—What network simulator software was used in the study?
1.2. Article Organization
In
Section 1 we provide, in addition to the introduction, the objectives and research questions of the SLM. In
Section 2, the planning for the execution of the SLM is presented. The search string and search sources with selection procedures are highlighted, as well as inclusion and exclusion criteria.
Section 3 describes the SLM execution process with the steps of inclusion, selection, and evaluation. Backward and forward snowballing procedures are also presented.
Section 4 presents the results obtained based on the specified requirements.
Section 5 describes the works selected in this study and
Section 6 presents the conclusions of this SLM.
4. Requirements Results
An overview presenting a word cloud, based on the content of all titles of all selected works, is presented in
Figure 4. Considering the search string encompasses IoT and data collection and UAV, these terms can be identified in the word cloud in a prominent way in relation to the others. Other words also stand out in the cloud, such as algorithm, networks, planning, and internet, among others with less intensity.
Among the 20 selected works, 9 are from conference papers and 11 are from journal articles. Regarding the date of publication,
Figure 5 presents the number of publications in each year in the period from 2017 to 2021 (until August).
The distribution of the authors of the works considering the country of their institutions is presented in
Table 4 and in
Figure 6.
Considering the institutions, the distribution of authors, according to their institutions, is shown in
Figure 7.
After reading and evaluating all the works selected in the previous phase, the results related to the different research questions of this SLM are presented.
RQ1—What algorithm is used in routing for data collection?
RQ2—What technology is used for the reception of data by the drone?
RQ3—What network simulator software was used in the study?
4.1. RQ1—Algorithm Used in Routing
The predominant algorithm in the evaluated works to define the shortest path to be taken by the drone during data collection is the traveling salesman problem (TSP) algorithm, a well-known method to determine the shortest path to reach different points with the least distance. For a total of 11 works that present routing algorithms, 5 deal with TSP. TSP can be implemented with a heuristic method for a finite number of points reached by one drone. Another algorithm that stands out in the studies is the one that deals with the path in an ant colony, known as ant colony algorithm, or TSP-ant colony optimization (ACO). The ANT algorithm is found in two studies. A list of the algorithms used in routing is presented in
Table 5. Regarding this RQ1, it is important to note that of the 20 selected works, 11 presented specific routing algorithms while the others presented prototypes with data collection without routing algorithms. Additionally, three survey/review papers on routing algorithms were selected and are included in the total of 20 final papers.
4.2. RQ2—Technology Employed to Receive Data
Regarding the technology used for data collection, the works that presented practical cases with prototypes always pointed out which technology was used for data collection. Some works with mathematical models and theoretical simulations did not deal with communication for data collection. A list of the technologies used to receive the data, referring to 11 works, is presented in
Table 6.
4.3. RQ3—Network Simulator Software
Of the 20 evaluated works, 6 presented simulations of the models with the presentation of the results found after simulation. A list of the different simulators found in the works is presented in
Table 7.
5. Work Description
In this systematic mapping of the literature, several works related to data collection using drones stand out. In Gagliarde et al. [
5], there is an agro-meteorological data collection system using a Bluetooth Low Energy (BLE) transceiver as communication. It presents a prototype with its configuration used for the experiment.
Wang et al. [
6] present a precision adjustable trajectory planning (PATP) scheme. The paper also considers the power consumption of wireless communication in the on-demand PATP (OD-PATP) scheme.
For Cao et al. [
7], the communication used the ZigBee 2.4 GHz protocol and the algorithm to determine the collection path based on an optimization algorithm such as the ant colony algorithm. He presented an experiment with collection at four centralizing points.
In Yang et al. [
8], research on the main problems in IoT data collection using drones is presented. The paper considers the possible ways of grouping sensors into clusters. Regarding the planning of drone paths, they cite several algorithms: initially as the traveling salesman problem in the fast path planning with rules (FPPWR) algorithm, an alternative formulating the movement between sensors as a Markov chain. The use of Q-learning neural network, A-star algorithm, and genetic algorithms are also considered in the work.
A scheme of waking the sensor only when it is going to transmit is presented in Trotta et al. [
9], and they propose a framework called BEE-DRONES for large-scale wireless sensor networks. It considers the optimal trajectory together with the lifetime of the sensors. The problem is transformed into a multiple-variable optimization with a centralized or distributed heuristic solution over multiple graphs. It uses OMNeT++ as a simulator [
25].
In Goudarzi et al. [
10], the BL-TSP algorithm is pointed out to define the search path between the data collection points. Besides the classic traveling sales problem (TSP), it considers Bezier curves so that it smooths the paths.
Zhang and Li’s work [
11] does not consider routing, but considers the collection of a remote point with sensor activation via LoRa and data transmission using IEEE 802.11 ac. A use case is presented with equipment based on specific hardware, assembled to meet the project.
In Min et al. [
12], a dynamic collector node scheme is proposed, where the determination of the collector node takes into account the drone speed and latency in data collection without predetermined information. A three-tier protocol is presented in Quin et al. [
13]. An application layer over a modified implementation of ContikiMAC and over IEEE802.15.4 2.4 GHz. This protocol is called UIWP (UAV integrated WSN protocol) and the experiment considered the effects of speed, altitude, and approach angle of the node with the data to be collected. They considered a drone and a node at work.
In the work of Safia et al. [
14], a distributed algorithm considering energy efficiency is proposed, called HCS (Hilbert-order collection strategy). The mobile collector establishes the path based on the Hilbert values of the node locations. Potter et al. [
15] present the use case for collecting data in monitoring an environment with temperature, pH, and conductivity sensors. They detail the construction of the collector node and the hardware embedded in the drone for communication and data collection.
In Liang et al. [
16], a data collection scheme using a protocol based on DTN (delay tolerant network) is proposed. To determine the path, an algorithm using a Hilbert curve is proposed. As an emulation tool, CORE (Common Open Research Emulator) was used.
The LoRa communication is presented in Behjati et al. [
17] as a facilitator for the use of drones in monitoring large-scale livestock on rural farms. For the optimization of the drone path, a genetic algorithm is proposed, with an optimization using ant colony algorithm called EPSO (enhanced particle swarm optimization). It uses the LoRaWAN technology with multichannel as communication. The paper presents details of the implementation of the practical case applied to a rural farm.
A review of different techniques in Aggarwal and Kumar [
18] is found to plan drone routing. Planning techniques are classified into three categories: representative techniques, cooperative techniques, and non-cooperative techniques. Representative techniques are (a) probabilistic scripts; (b) random trees of fast exploration; (c) Voronoi diagram; (d) A-star algorithm. Cooperative techniques are presented: mathematical models; bio-inspired models; machine learning models; multi-objective optimization models.
In Lima et al. [
19], a work on data collection carried out in the forest is presented. Using 802.11 communication, the study evaluates the performance of this communication in collecting photographic images of animals collected by cameras installed in the forest. The work presented by Abdelhamid [
20] considers data collection in an environment with damaged or inoperative infrastructure due to emergency situations. It considers, as determining the path for the drone, a generalization of the traveling salesman problem. It considers three planning schemes: (a) coverage first; (b) priority first; (c) balanced.
An algorithm based on the simple division of the area to be visited is proposed by Medani et al. [
21] and the results are measured based on the NS3 simulator [
26].
In the work of Xu and Che [
22], a brief review of algorithms to solve the traveling salesman problem is presented. In Liu et al. [
23], the routing algorithm considers the age of the collected information as a priority. Both a solution based on dynamic programming and a genetic algorithm are presented. Another work that considers the age of information is presented in Changizi and Emadi [
24], not for data collection but for updating data at points with IoT. It considers a prioritization in the selection of points, based on the knapsack problem combined with the traveling salesman problem.
The work of Wang et al. [
27] explores vehicular crowdsourcing (VC) by a group of unmanned vehicles (UVs) such as drones and unmanned ground vehicles to collect these data from points of interest (PoIs). They explicitly consider to navigate a group of UVs in a three-dimensional (3D) disaster work zone to maximize the amount of collected data, geographical fairness, and energy efficiency while minimizing data dropout due to limited transmission rate.
A study of the mobile access point deployment in workflow-based mobile sensor networks is provided by Jin et al. [
28]. They categorize mobile users (MUs) workflows according to a priori knowledge of MUs’ staying durations at mission locations into complete and incomplete information workflows. They formulate the cost-minimizing mobile access point deployment problem in both categories into multiple (mixed) integer optimization problems, satisfying MUs’ QoS constraints.
A novel drone-based collaborative sparse-sensing framework droneSense is proposed by Zhao et al. [
29]. droneSense selects a minimum number of points of interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement.