No abstract

Bandwidth and power hungry applications are proliferating in mobile networks at a rapid pace. However, mobile devices have been suffering from a lack of sufficient battery capacity for the intensive/continuous use of these applications. In addition, the mobile ecosystem is currently heterogeneous and comprises a plethora of networks with different technologies such as LTE, Wi-Fi, and WiMaX. Hence, an issue must be addressed to ensure that quality of experience (QoE) is provided for the users in this scenario: an energy-efficient strategy that is designed to extend the battery lifetime of mobile devices. This paper proposes an architecture which provides an intelligent decision-making support system based on Fuzzy Logic for saving the energy of mobile devices within an integrated LTE and Wi-Fi network. The simulated experiments show the benefits of the solution this architecture can provide by using QoE metrics.

The flying ad hoc network (FANET) has emerged as an alternative access technology for regions that have no fixed infrastructure or are hard to reach. This new type of network is composed of devices called unmanned aerial vehicles (UAVs) that communicate with each other, but there is no specific routing protocol to FANET applications that allows efficient communication between these devices. This paper proposes a FANET adaptive routing protocol based on the fuzzy system. The validation of the FANET adaptive protocol was performed through simulation using Network Simulator version 2 (NS-2) and, it was assessed by quality of service (QoS) and quality of experience (QoE) metrics.

In the new context of Next Generation Networks, Aerial Ad Hoc Networks, also known as FANET (Flying Ad Hoc Network), are being used to monitor areas of difficult access. Owing to the dynamism and autoconfiguration of this type of network, a strategy is needed to position its devices (drones) to ensure it can achieve good performance. In light of this, this paper proposes a flight path planning model, which involves a metaheuristic optimization-based approach. The proposal relied on the artificial neural networks to optimize the positioning of the relay device, so that the throughput between the other devices could be increased; the benefits of the proposal were demonstrated through simulations.