Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

Abstract

To cite this version:Mohamed-Ayoub Messous, Hichem Sedjelmaci, Sidi-Mohammed Senouci. Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system. Pervasive and Mobile Computing, Elsevier, 2017, <10.1016/j.pmcj.2017 Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system In this paper, we design and implement a novel generic mobility model, named Alphabased, for a fleet of small interconnected UAVs (Unmanned Aerial Vehicles) that collaborate to explore a geographic area (battle field, research and rescue missions, surveillance applications, etc.). In fact, due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the UAV's attributes. Hence, we propose to use a combination of energy level, coverage-area and network connectivity for mobility decision-making, in contrast to the literature where only network connectivity and area coverage are investigated. On the one hand, these two metrics are very important, especially for applications where achieving the best area-coverage and maintaining network connectivity represent an essential requirement. On the other hand, energy is another equally noteworthy constraint that should be taken into account. In fact, being a crucial resource for all mobile devices and especially for UAVs, the energy becomes vital to ensure the network lifetime and mission success. As far as we know, Alphabased mobility model is the first to ever consider a combination of these three metrics within the same decision-making criterion. A distributed scheme is adopted, where each UAV determines locally its future movement based on the information it receives from its neighbors. Moreover, a novel fuzzy inference system is implemented in order to compute the values of a followship weighting parameter, named Alpha. This latter is used to choose the most suitable neighboring UAV to follow. To validate the proposed mobility model, rigorous testing has been accomplished, through simulation work. Compared to Randombased and Forces-based mobility models, the Alpha-based mobility model achieves good coverage rate while maintaining connectivity.

A novel unmanned aerial vehicle‐sink enabled mobility model for military operations in sparse flying ad‐hoc network

“…Various mobility models have been developed to collect data from disconnected WSNs using mobile sinks or message passing (MF). 31,[40][41][42][43][44][45][46][47][48][49][50][51][52][53] Thus, so far, MF has proven to be very useful in sparse WSNs.…”

“…In the EACM model, the connectivity among UAVs is maintained using virtual forces, the coverage is maintained using the scan path, and energy is considered by exchanging cluster heads. Therefore, the EACM mobility model increases the lifetime of the network.Apart from coverage, and connectivity, the alpha‐based model (ABM) 43 also considers energy as an important factor while deciding the next movement of UAVs. Each node updates its neighbor table after every “t” seconds using the local information gathered from neighbors.…”

“…Apart from coverage, and connectivity, the alpha‐based model (ABM) 43 also considers energy as an important factor while deciding the next movement of UAVs. Each node updates its neighbor table after every “t” seconds using the local information gathered from neighbors.…”

A Game Theory Based Efficient Computation Offloading in an UAV Network

“…Recent technological advances have led to the emergence of smaller significantly cheaper UAVs which made them easier to acquire, maintain and handle, thus significantly increasing their usage in civilian applications. Indeed, UAVs proved to be useful in application like rescue missions, target detection, remote sensing, surveillance, service delivery, pollution detection and farming [1][2][3][4][5][6][7][8][9]. Drones can carry out exploration missions to replace human presence in areas which are inaccessible or hazardous.…”

Edge Computing for Visual Navigation and Mapping in a UAV Network

“…Unmanned aerial vehicles (UAVs), also commonly named drones, have proliferated extremely fast and continue to have far-reaching effects on today's society, transforming our lives and the way we do business. While big UAV platforms have been initially introduced for strategic and defense applications, relatively small and very flexible new UAV platforms are proving to be very useful for a wide variety of applications such as disaster damage assessment, law enforcement surveillance, and earth science data collection [1][2][3][4]. In order to accomplish their missions, UAVs are required to (i) collect data about their environment, (ii) compute and process the collected data for decision making, and (iii) relay specific information to their appropriate users [5][6].…”