Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge.

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).

In this paper, we propose to use the FBLMS (Fast Block Least Mean Square) algorithm for Direct Path and Zero-Doppler Multipath Interferences (DPI and ZDMI) cancellation in DVB-T (Digital Video Broadcasting Terrestrial) Passive Bistatic Radars (PBRs). Diverse adaptive techniques are considered in passive radars' literature but most of them are computationally intensive when the DVB-T signal is used as an illuminator of opportunity, i.e., very high bandwidth. Using Fast Fourier Transform (FFT) for coefficient updating, the FBLMS algorithm shows a faster convergence rate, a shorter processing time and better CAF (Cross Ambiguity Function) results.

This paper investigates the problem of automatic target detection in a Pareto background under multiple target situations. The number of interfering targets is assumed to be unknown. We derive the Enhanced Variability Index Automatic Selection and Detection Constant False Alarm Rate (EVI-ASD-CFAR) Processor. This latter selects and matches dynamically the suitable detector among the Geometric Mean (GM)-CFAR, Greatest Of(GO)-CFAR and Trimmed Mean (TM)-CFAR. The unknown background level is then estimated and set to the corresponding threshold. The detection performances of the proposed processor are assessed via Monte Carlo simulations.