In the last years, the Unmanned Aerial Vehicles (UAVs), also known as drones, are becoming always more attractive due to their capacity of a rapid deployment and their wide range of application in many real world scenarios. Among the various fields of application, recently, the use of drones in the precision agriculture is becoming much relevant for the researchers community. The studies related to agriculture concern different aspects such as livestock monitoring, crops and water levels. The drones are able to perform these tasks thank to a series of different sensors and actuators equipped on board. Cameras on board allow, through opportune algorithms, the gathering of detailed information about plants health. If a health problem is detected then the the drone can intervene precisely on the specific problem. The contribution of this work is a communication protocols analysis applied to the problem of controlling a fleet of drones against parasites attacks to the crops. Moreover, the study of the different approaches aims to measure their performance and costs. In particular, the various approaches face also the issues of exploring the area in the shortest time possible avoiding that the same area is explored from more drones, discovering the parasites and preventing their proliferation spraying the right quantity of pesticide. The drones, being equipped with limited quantities of both fuel and pesticide, can ask for help to other drones to complete the elimination of the parasites. To face these last issues some recruitment protocols have been tested, focusing on bio-inspired one.
In the last few years, the rising interests in the field of UAV (Unmanned Aerial Vehicle) created new opportunities to develop applications and services in several domains. Some of the most important are the smart farming and precision agriculture domains. Thanks to the feasibility and the versatility of these systems it is possible to accomplish several jobs with the same device. In this application context, we propose a new system composed of a master smart station, some slave satellite stations and UAV fleet. It is developed on the basis of the M2M communication paradigm where devices cooperate between them in an autonomous way to perform assigned tasks. In particular, we designed a smart station which is responsible for managing fleet by assigning tasks and scheduling activities. Moreover, it is also responsible for managing satellite stations for assisting the fleet during its operations. In particular, these stations stock energy during their idle state. We propose an all-in-one hardware solution based on energy harvesting approaches all implemented in the smart stations. Moreover, they supply energy in an on-demand way to the UAV devices which are asking for recharge during their missions. Finally, we designed a customized protocol in an M2M environment based on the MQTT framework to ensure communication between devices and to coordinate operations.
Granting an high level of Security in Wireless Networks is one of the most important required task provided by IEEE802.1x standards for modern wireless network environments such as Mobile Ad Hoc Networks (MANET). In this field, the use of directional antennas could help to reduce negative effects brought by the majority of the network security attacks such as eavesdropping and DOS (Denial of Service). Therefore, classic directional antennas are often not sufficient to mitigate these kinds of issues due that they are poor in terms of hardware architecture. One of the most significant issues in directional MAC (Medium Access Control) communications is represented by deafness problem; however, in this paper, we present a novel approach exploiting deafness issue and beamforming that has the goal to mitigate main security threats, through a node insulation mechanism that allows to improve the overall network security level in directional MANET.
In this paper, the attention is focused on the design of a decentralized Intelligent Transportation System (ITS) architecture in an Internet of Vehicle (IoV) environment. Introducing several independent Fog nodes, it is possible to take advantages of their computational properties for decreasing latency of the Centralized ITS Systems. Thus, a micro area controlled system is achieved. In this way, it will be possible to quickly and easily redesign ITS strategies. In this work, data coming from IoV layers are locally analyzed for checking anomalies by adopting mining algorithm. If something is going wrong, fog nodes locally face off the anomaly by taking a decision on incoming vehicle flow rates by choosing to limit or to completely block incoming traffic. In this work, we propose a cooperative strategy among decentralized ITS nodes for increasing safety and comfort of the drivers along their journeys. When local ITS policies fail, we propose a cooperative mechanism to find a macro-area optimized strategy. ITS node which detects issue will assume the role of master. Instead, neighbor fog nodes are involved by assuming the role of slave. Master node will decide policies and the slave node will apply these policies in the pertinent area. When issue is solved, the partnership is declared finished by the master. Each node returns master of their area. Moreover, this architecture is also based on a customized protocol for the IoV layer where Vehicle-to-Everythings (V2X) communications are optimized for assuring a good throughput and reducing interference.
In the last years the physical security in transportation systems is becoming a critical issue due to the high number of accidents and emergency situations. With the increasing availability of technological applications in vehicular environments researchers aimed at minimizing the probability of road accidents. In this paper, we propose a new platform able to discover dangerous driving behaviors. We based our application on the on-board diagnosis standard, able to provide all the needed information directly from the electronic vehicle control unit . We integrated the received data with a fuzzy logic approach, obtaining a description of the driver behavior. The overall system can take several initiatives (alarms, rpm corrections, etc.), in order to notify the driver bad behavior. The performance of the proposed scheme has been validated through a deep campaign of driving simulations.