Energy Efficient Data Forwarding in Disconnected Networks Using Cooperative UAVs

Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs\u27 energy more efficiently

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

On Mobile Sensor Data Collection Using Data Mules

International audienceThe sensor data collection problem using datamules have been studied fairly extensively in the literature.However, in most of these studies, while the mule is mobile, allsensors are stationary. The objective of most of these studies isto minimize the time needed by the mule to collect data from allthe sensors and return to the data collection point, from whereit embarked on its data collection journey. The problem studiedin this paper has two major differences with the earlier studies.First, in this study we assume that both the mule as well asthe sensors are mobile. Second, we do not attempt to minimizethe data collection time. Instead we minimize the number ofmules that will be needed to collect data from all the sensors,subject to the constraint that the data collection process has to becompleted within some pre-specified time. We show that the muleminimization problem is NP-Complete and provide a solution byfirst transforming it to a generalized version of the minimumflow problem in a network and then solving it optimally usingInteger Linear Programming. Finally, we evaluate our algorithmsthrough extensive simulation and present the results