Citation: Rashidibajgan, S.;
Hupperich, T. Improving the
Performance of Opportunistic
Networks in Real-World Applications
Using Machine Learning Techniques.
J. Sens. Actuator Netw. 2022, 11, 61.
https://doi.org/10.3390/
jsan11040061
Academic Editor: Chengwen Luo
Received: 6 August 2022
Accepted: 20 September 2022
Published: 26 September 2022
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Journal of
Actuator Networks
Sensor and
Article
Improving the Performance of Opportunistic Networks in
Real-World Applications Using Machine Learning Techniques
Samaneh Rashidibajgan * and Thomas Hupperich
Department of Information Systems, University of Münster, 48149 Münster, Germany
* Correspondence: samaneh.rashidibajgan@wi.uni-muenster.de
Abstract:
In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables
carried by individuals, can communicate and save-carry-forward their messages. The message
transmission is often in the short range supported by communication protocols, such as Bluetooth,
Bluetooth Low Energy, and Zigbee. These devices carried by individuals along with a city’s taxis and
buses represent network nodes. The mobility, buffer size, message interval, number of nodes, and
number of messages copied in such a network influence the network’s performance. Extending these
factors can improve the delivery of the messages and, consequently, network performance; however,
due to the limited network resources, it increases the cost and appends the network overhead. The
network delivers the maximized performance when supported by the optimal factors. In this paper,
we measured, predicted, and analyzed the impact of these factors on network performance using
the Opportunistic Network Environment simulator and machine learning techniques. We calculated
the optimal factors depending on the network features. We have used three datasets, each with
features and characteristics reflecting different network structures. We collected the real-time GPS
coordinates of 500 taxis in San Francisco, 320 taxis in Rome, and 196 public transportation buses in
Münster, Germany, within 48 h. We also compared the network performance without selfish nodes
and with 5%, 10%, 20%, and 50% selfish nodes. We suggested the optimized configuration under
real-world conditions when resources are limited. In addition, we compared the performance of
Epidemic, Prophet, and PPHB++ routing algorithms fed with the optimized factors. The results show
how to consider the best settings for the network according to the needs and how self-sustaining
nodes will affect network performance.
Keywords: opportunistic networks; selfish nodes; buffer size; nodes movement; nodes density
1. Introduction
The Internet of Things (IoT) is an emerging paradigm concerned with bringing the
connectivity of real-world objects and things [
1
]. Such a situation opens up opportuni-
ties for a large number of various devices or things, such as wearable devices, laptops,
portable devices, and vehicles, to impart, communicate, and interact with one another.
Some applications include, yet are not restricted to, smart healthcare [
2
], smart cities [
3
],
smart environmental monitoring systems [
4
], and Smart Business [
5
]. In such sophisti-
cated scenarios, there is the possibility of finding heterogeneous static and mobile devices
(e.g., smartphones carried by individuals) equipped with different radios enabling data
transmission that might interact. The communication might occur only during specific
contact opportunities (i.e., depending on the communication protocol and range of cov-
erage) between heterogeneous and possibly disconnected static networks [
1
]. In a smart
city scenario, due to the high mobility and flexibility, the mobile sinks (e.g., cars, taxis,
and buses) might be utilized to collect the data from the static nodes (e.g., traffic sensors,
environmental monitoring stations) or disseminate control information.
Hence, such data might be relayed by any node and forwarded through other nodes
(e.g., via smartphones) even in the absence of a predefined end-to-end path between
J. Sens. Actuator Netw. 2022, 11, 61. https://doi.org/10.3390/jsan11040061 https://www.mdpi.com/journal/jsan
