Wireless networks are subject to high levels of congestion. This problem affect most prominently ad-hoc wireless networks, given their multi-hop characteristic. Some works attempted to tackle this problem (i.e., redundant packet aggregation, in-network caching, packet flow control, medium access control), but these are inefficient on wireless shared medium. Therefore, we propose dynamic Node Suppression for Congestion Control (NSCC), a congestion control strategy for mobile named-data networks based on dynamic node suppression. By choosing forwarding nodes, NSCC can reduce redundant packet transmissions. NSCC results indicate that the solution complements the named-data packet delivery of NDN networks, being effective both in mitigating congestion and in optimizing the delivery of NDN data packets.

In wireless networks, the Named Data Networking (NDN) architecture maximizes contents' availability throughout multiple network paths based on multicast-based communication combined with stateful forwarding and caching in intermediate nodes. Despite its benefits, the downside of the architecture resides in the packet flooding not efficiently prevented by current forwarding strategies and mainly associated with the native flooding of the architecture or malicious packets from Interest Flooding Attack (IFA). This work introduces iFLAT, a multicriteria-based forwarding strategy forinterestFLooding mitigATion on named data wireless networking. It follows a cross-layer approach that considers: (i) the Received Signal Strength (RSS) to adjust itself to the current status of the wireless network links, (ii) the network traffic (meInfo) to detect flooding issues and traffic anomalies, and (iii) a fake Interest Blacklist to identify IFA-related flooding. In doing so, the strategy achieves better efficiency on Interest flooding mitigation, addressing both native and IFA types of flooding. We evaluated the proposed strategy by reproducing a Flying Ad hoc Network (FANET) composed of Unmanned Aerial Vehicles (UAVs) featured by the NDN stack deployed in all nodes. Simulation results show that iFLAT can effectively detect and mitigate flooding, regardless of its origin or nature, achieving greater packet forwarding efficiency than competing strategies. In broadcast storm and IFA scenarios, it achieved 25.75% and 37.37% of traffic reduction, whereas Interest satisfaction rates of 16.36% and 51.78% higher, respectively.

Named Data Networking (NDN) is a new network architecture based on the content-based model that has fundamental differences to the legacy path-based model. The first is focused on the content itself, and the latter focuses on the location of data. Both models provide different benefits to the end-user and the network, but they often cannot inter-operate due to their significant design differences. Therefore, we propose a new network architecture and protocol, both named NDN-Fab, to allow the inter-operation between path-based and content-based network architectures. NDN-Fab is based on the Fabric model and allows the edge network to operate with content-centric architectures, such as the NDN, while the core network can operate with a high-performance path-based communication model. The Core network uses the NDN-Fab protocol that can be embedded in the programmable data plane hardware of the core network's switches, allowing for the sending of multicast packets and link-layer packet steering. Our results show that NDN-Fab can be more efficient in packet forwarding than IPv4, achieving up to 150 times lower average delay, 100 times lower network dispersion, and it managed to process 21% more packets.

Mobility, social interactions, and other human characteristics shall support Future Mobile Networks in routine prediction and resource management. This work investigates human-aware metrics supporting services or protocols leveraging opportunistic communication. These metrics represent different types of knowledge extracted from people routine present in their movements. Because of the strong routine component of human mobility, such metrics capture different but recurrent behaviors on wireless encounters between mobile users. We report the experience through a case study with a real-world dataset along with results from trace and metrics analysis. The results show heterogeneity in metric coefficients and contact occurrence and duration in different periods of the day, highlighting the need for characterising traces before their use.

Over the years, network monitoring tools have evolved to keep pace with the advances in networking technologies and paradigms, such as virtualization and softwarization, and more recently, data plane programmability. However, there have been few advances regarding user interfaces and interactions in network management systems. In this paper, an investigation based on the human-computer interaction perspective is presented and improvements on the usability aspects of network monitoring tools are proposed. First, we conducted a survey to capture general information from network administrator professionals. On the basis of these initial data, we identified and specified two personas to represent the groups of users. Thereafter, a study focused on observing users' interaction and experience with a well-known network monitoring tool was conducted. Finally, our qualitative analysis revealed several findings that were used to develop recommendations to aid developers in the (re)design of these tools to enhance end-user interaction.

Some of current Information-centric cellular networks strongly rely on clustering techniques to group users with similar preferences or behavior and assign them to a common base station. As a result, the most useful contents are kept near to users, thereby alleviating backhaul bottlenecks. Despite the performance gains, such strategies frequently adopt the same replacement policy, irrespectively the users' profile. This paper presents a simulation-based study that evaluates the performance of cache replacement policies through clusters formed by users according to their music listening habits. It shows the benefits of using caching strategies according to users' pattern of behavior on downloading songs. To accomplish such goals, we carried out a data mining based analysis of traces obtained from a music streaming service to identify user groups that share similar listening habits. The resulting clusters supported the experimental study carried out to evaluate the performance of three usual replacement policies.

Network densification by small cell networks has become the main alternative for mobile network operators to deal with an ever-increasing traffic growth. In this context, Named Data Networking (NDN), an evolution of Content-Centric Networking (CCN), emerges as an alternative to improve data offloading by the promotion of in-network caching. In this paper, we propose a user clustering scheme that takes advantage of the affinity among users with respect to frequency of content requisition and common interest for content for a more efficient edge caching. The proposed strategy is evaluated in a varied set of scenarios, including different cache sizes, communication models, or concentration levels of content popularity. Simulation results show that the proposed strategy increases both cache hit ratio and data offloading in Content-Centric Cellular Networks.

Advanced applications have caused end-users to show a new interest in accessing network measurement and status data across multiple domains, in order to detect occasional network problems. Diversity of network technologies, technical solutions and network managers add to the complexity of providing such information, which, when available, includes technical details that are meaningful only to specialists. However, context awareness can ease the recommendation process underlying a monitoring infrastructure. This research investigates a novel approach for traffic monitoring in view of the context information used to provide network performance recommendations in the most productive way.