Forwarding strategies for opportunistic data gathering in mobile IoT

• Autor(a) principal: Oliveira, Rui Filipe Marques de
• Data de Publicação: 2018
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/25985

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work focuses on two main topics to enhance the forwarding decision: i) forwarding strategies that make use of location-aware and social-based to perform neighborhood selection, ii) and packet selection mechanisms to provide Quality of Service (QoS). The neighborhood selection is performed through multiple metrics, resulting in three forwarding strategies: (1) Gateway Location Awareness (GLA), a location-aware ranking classification making use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns; (2) Aging Social-Aware Ranking (ASAR) that exploits the social behaviours of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles; (3) and to merge both location and social aforementioned algorithms, a hybrid approach emerges, thus generating a more intelligent mechanism. Allied to the forwarding criteria, two packet selection mechanisms are proposed to address distinct network functionalities, namely: Distributed Packet Selection, that focuses primarily on data type prioritization and secondly, on packet network lifetime; and Equalized Packet Selection, which uses network metrics to calculate a storage packet ranking. To do so, the packet number of hops, the packet type and packet network lifetime are used. In order to perform the evaluation of the proposed mechanisms, both real and emulation experiments were performed. For each forwarding strategy, it is evaluated the influence of several parameters in the network's performance, as well as comparatively evaluate the strategies in different scenarios. Experiment results, obtained with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network, are used to evaluate the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. Later, these strategies' viability is also validated in a real scenario. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of moving networks as a smart city network infrastructure. To evaluate the proposed packet selection mechanisms, a First In First Out packet selection technique is used as ground rule, thus contrasting with the more objective driven proposed techniques. The results show that the proposed mechanisms are capable of provide distinct network functionalities, from prioritizing a packet type to enhancing the network's performance.