Traditional WiFi positioning relies on a predefined radio map, which is labor-intensive and time-consuming for professionals. Recently, crowdsourcing has emerged as a promising solution for ...facilitating WiFi positioning. To crowdsense a radio map, traces collected from normal users are merged to recover the original walking paths. In this work, we design a robust iterative trace merging algorithm called WiFi-RITA based on WiFi access points as signal-marks. The algorithm formulates the trace merging problem as an optimization problem in which each trace is translated and rotated to minimize the limitation of distances among traces defined by WiFi access points. WiFi-RITA is further enhanced by removing outliers. WiFi-RITA is robust to the rotation errors of traces and efficient for a large number of short traces. According to the crowdsensed radio map, a sensor fusion approach based on particle filter by fusing inertial sensors and a multivariate Gaussian fingerprinting is proposed to enhance the accuracy of crowdsourcing indoor positioning. The experiment results in two large-scale environments demonstrate that WiFi-RITA positioning with zero-effort calibration achieves high positioning accuracy, which outperforms Pedestrian Dead Reckoning (PDR) and fingerprinting with K Nearest Neighbor.
Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular ad-hoc networks ...(VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.
In this work, we propose MobiVNDN, a distributed framework for Vehicular Named-Data networking (VNDN) communications. MobiVNDN focuses on mitigating the degradation of communication performance ...caused by mobility and wireless communications in VNDN. MobiVNDN simultaneously addresses the effects of several problems including broadcast storms, message redundancy, network partitions, reverse path partitioning and content source mobility. Simulation results show that MobiVNDN is robust and efficient and that it outperforms other solutions from the literature. MobiVNDN also performs well when sharing the wireless communication medium with multiple applications.
The shift from host-centric to information-centric networking (ICN) promises seamless communication in mobile networks. However, most existing works either consider well-connected networks with high ...node density or introduce modifications to ICN message processing for delay-tolerant networking (DTN). In this work, we present agent-based content retrieval, which provides information-centric DTN support as an application module without modifications to ICN message processing. This enables flexible interoperability in changing environments. If no content source can be found via wireless multi-hop routing, requesters may exploit the mobility of neighbor nodes (called agents) by delegating content retrieval to them. Agents that receive a delegation and move closer to content sources can retrieve data and return it back to requesters. We show that agent-based content retrieval may be even more efficient in scenarios where multi-hop communication is possible. Furthermore, we show that broadcast communication may not be necessarily the best option since dynamic unicast requests have little overhead and can better exploit short contact times between nodes (no broadcast delays required for duplicate suppression).
The advent of Online Social Networks (OSNs) has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. Considerable research has focused on the ...social influence phenomenon and its impact on OSNs. Social influence plays a crucial role in shaping people behavior and affecting human decisions in various domains.
In this paper, we study the impact of social influence on offline dynamics to study human real-life behavior. We introduce Social Influence Deep Learning (SIDL), a framework that combines deep learning with network science for modeling social influence and predicting human behavior on real-world activities, such as attending an event or visiting a location. We propose different approaches at varying degree of network connectivity with the objective of facing two typical challenges of deep learning: interpretability and scalability.
We validate and evaluate our approaches using data from Plancast, an Event-Based Social Network, and Foursquare, a Location-Based Social Network. Finally, we explore the usage of different deep learning architectures, and we discuss the correlation between social influence and users privacy presenting results and some notes of caution about the risks of sharing sensitive data.
Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that ...deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious
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ultihop,
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ultipath, and
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ultichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol,
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mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low.
Federated Learning (FL) has rapidly become a crucial paradigm for training Machine Learning (ML) models when datasets are spread across several devices without compromising the privacy of the data ...owners. In vehicular networks, FL can be used to train driving models and object detection and classification over sensitive datasets to continuously improve user experience and driving safety. However, the majority of FL implementations cannot efficiently filter malicious vehicular users and low-quality contributions. This article proposes Distributed OT-based Federated Learning (DOTFL), an aggregation mechanism based on the clustering of the received trained Neural Networks Neural Network (NN) at the vehicular devices and on outlier detection. The proposed mechanism can detect malicious contributions by comparing them to previously received contributions and following a clustering approach. Furthermore, the convergence time of the FL process is improved by distributing trained NN weights directly through vehicle-to-vehicle links. Experimental analysis shows an improvement of up to 22% in terms of accuracy compared to state-of-the-art FL approaches. This is achieved by using clustering models and removing outliers, enabling a significantly lower presence of malicious contributions in aggregated models.
Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable ...dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processor) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.
Traffic management systems (TMS) are the key for dealing with mobility issues. Moreover, 5G and vehicular networking are expected to play an important role in supporting TMSs for providing a smarter, ...safer and faster transportation. In this way, several infrastructure-based TMSs have been proposed to improve vehicular traffic mobility. However, in massively connected and multi-service smart city scenarios, infrastructure-based systems can experience low delivery ratios and high latency due to packet congestion in backhaul links on ultra-dense cells with high data traffic demand. In this sense, we propose I am not interested in it (IAN3I), an interest-based approach for reducing network contention and even avoid infrastructure dependence in TMS. IAN3I enables a fully-distributed traffic management and an opportunistic content sharing approach in which vehicles are responsible for storing and delivering traffic information only to vehicles interested in it. Simulation results under a realistic scenario have shown that, when compared to state-of-the-art approaches, IAN3I decreases the number of transmitted messages, packet collisions and latency in up to 95 % , 98 % and 55 % respectively while dealing with traffic efficiency properly, not affecting traffic management performance at all.
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