Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?

With the current host-based Internet architecture, networking faces limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm mitigates such problems by releasing the need to have an end-to-end transport session established during the life time of the data transfer. Moreover, the ICN concept solves the mismatch between the Internet architecture and the way users would like to use it: currently a user needs to know the topological location of the hosts involved in the communication when he/she just wants to get the data, independently of its location. Most of the research efforts aim to come up with a stable ICN architecture in fixed networks, with few examples in ad-hoc and vehicular networks. However, the Internet is becoming more pervasive with powerful personal mobile devices that allow users to form dynamic networks in which content may be exchanged at all times and with low cost. Such pervasive wireless networks suffer with different levels of disruption given user mobility, physical obstacles, lack of cooperation, intermittent connectivity, among others. This paper discusses the combination of content knowledge (e.g., type and interested parties) and social awareness within opportunistic networking as to drive the deployment of ICN solutions in disruptive networking scenarios. With this goal in mind, we go over few examples of social-aware content-based opportunistic networking proposals that consider social awareness to allow content dissemination independently of the level of network disruption. To show how much content knowledge can improve social-based solutions, we illustrate by means of simulation some content-oblivious/oriented proposals in scenarios based on synthetic mobility patterns and real human traces.Comment: 7 pages, 6 figure

Social-aware Opportunistic Routing Protocol based on User's Interactions and Interests

Nowadays, routing proposals must deal with a panoply of heterogeneous devices, intermittent connectivity, and the users' constant need for communication, even in rather challenging networking scenarios. Thus, we propose a Social-aware Content-based Opportunistic Routing Protocol, SCORP, that considers the users' social interaction and their interests to improve data delivery in urban, dense scenarios. Through simulations, using synthetic mobility and human traces scenarios, we compare the performance of our solution against other two social-aware solutions, dLife and Bubble Rap, and the social-oblivious Spray and Wait, in order to show that the combination of social awareness and content knowledge can be beneficial when disseminating data in challenging networks

Connectivity Analysis in Vehicular Ad-hoc Network based on VDTN

In the last decade, user demand has been increasing exponentially based on modern communication systems. One of these new technologies is known as mobile ad-hoc networking (MANET). One part of MANET is called a vehicular ad-hoc network (VANET). It has different types such as vehicle-to-vehicle (V2V), vehicular delay-tolerant networks, and vehicle-to-infrastructure (V2I). To provide sufficient quality of communication service in the Vehicular Delay-Tolerant Network (VDTN), it is important to present a comprehensive survey that shows the challenges and limitations of VANET. In this paper, we focus on one type of VANET, which is known as VDTNs. To investigate realistic communication systems based on VANET, we considered intelligent transportation systems (ITSs) and the possibility of replacing the roadside unit with VDTN. Many factors can affect the message propagation delay. When road-side units (RSUs) are present, which leads to an increase in the message delivery efficiency since RSUs can collaborate with vehicles on the road to increase the throughput of the network, we propose new methods based on environment and vehicle traffic and present a comprehensive evaluation of the newly suggested VDTN routing method. Furthermore, challenges and prospects are presented to stimulate interest in the scientific community

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time