Despite their widespread adoption, cellular networks face growing vulnerabilities due to their inherent complexity and the integration of advanced technologies.One of the major threats in this landscape is Voice over IP (VoIP) to GSM gateways, known as SIMBox devices.These devices use multiple SIM cards to route VoIP traffic through cellular networks, enabling international bypass fraud with losses of up to $3.11 billion annually.Beyond financial impact, SIMBox activity degrades network performance, threatens national security, and facilitates eavesdropping on communications.Existing detection methods for SIMBox activity are hindered by evolving fraud techniques and implementation complexities, limiting their practical adoption in operator networks.This paper addresses the limitations of current detection methods by introducing SigN , a novel approach to identifying SIMBox activity at the cellular edge.The proposed method focuses on detecting remote SIM card association, a technique used by SIMBox appliances to mimic human mobility patterns.The method detects latency anomalies between SIMBox and standard devices by analyzing cellular signaling during network attachment.Extensive indoor and outdoor experiments demonstrate that SIMBox devices generate significantly higher attachment latencies, particularly during the authentication phase, where latency is up to 23 times greater than that of standard devices.We attribute part of this overhead to immutable factors such as LTE authentication standards and Internet-based communication protocols.Therefore, our approach offers a robust, scalable, and practical solution to mitigate SIMBox activity risks at the network edge.

Mobility is a fundamental aspect of human life, and mobility data offers valuable insights into user behavior. Yet, this data also exposes users to privacy risks given pattern unicity in their trajectories, i.e., the singularity in the displacements made by users. Existing strategies to quantify such user exposure either focus only on the sequences of places visited by each user, as the widely used uniqueness measure, or are tied to specific attack models. We here introduce MoBES, a novel, scalable, customizable and highly interpretable measure of user exposure in mobility data. MoBES leverages multiple existing metrics to build a multi-dimensional space, which in turn is used to capture each user's mobility signature behavior. MoBES quantifies user exposure based on how distinct a user's signature is from her neighbors in the defined metric space. As such, MoBES is designed to be a fundamental expression of user behavior, and not tied to any specific attack model. We evaluate MoBES on a real mobility dataset, showing that it effectively captures user exposure within the behavioral metric space. We also compare MoBES with the uniqueness measure, showing that MoBES is able to uncover users who, even though visiting the same places as others in the crowd, are still at risk of exposure due to the unicity of their mobility behavior.

Understanding mobile-user behavior requires joint modeling of mobility and traffic, as data consumption is shaped by where, when, and how users travel. Despite this clear intuition, most studies still treat the two in isolation, missing the intricate dependencies between them at the individual level. This paper propose a novel approach that explicitly captures the interplay between traffic and mobility behaviors using fine-grained mobile datasets. Using week-long eXtended Data Records (XDRs), we identify 13 interpretable features and pinpoint the mobility traits that truly drive traffic variation. These insights support a privacy-preserving user abstraction that represents each timeline as a sequence of discrete mobility–traffic states, capturing temporal dynamics and heterogeneity while generalizing across regions. We then introduce a probabilistic likelihood model that scores any mobility–traffic pairing, enabling cross-modality prediction and statistically sound fusion of fragmented logs. Experiments on four provincial datasets covering 1.3 million Chilean users show that the model reliably separates plausible from implausible behavior and generalizes from dense urban cores to mixed rural–urban contexts. The framework is descriptive, generative, and transferable, paving the way for anomaly detection, personalized QoE adaptation, and realistic network simulation.

International bypass fraud, also known as SIMBox fraud, involves diverting international cellular voice traffic from regulated routes and rerouting it as local calls in the destination country. It has significantly affected cellular networks worldwide, generating $3.11 Billion of losses annually and threats to national security. Yet, SIMBox fraud remains an ongoing challenge, eluding operators detection due to the continual refinement of fraudulent behavior that is often overlooked in the design and validation of detection methods.

Mobility patterns are inherently linked to human nature (e.g., individual variability, temporal dynamics, behavioral factors, curiosity, social interaction), making mobility prediction a multifaceted and challenging problem that requires sophisticated models and comprehensive data. Machine learning (ML) models excel at predicting the location a person will be at the next time interval, but they often raise privacy concerns. To address these privacy issues while maintaining the benefits of ML models, Federated Learning (FL) offers a distributed framework that enables collaborative training of human mobility prediction models without requiring the sharing of highly sensitive location data. However, in the domain of FL for individual mobility prediction, prior work lacks a thorough understanding of the many factors that may impact the performance of FL-based prediction models. In this work, we provide a comprehensive study of the impact of various aspects related to human behavior, data characteristics, ML algorithmic solutions, and FL architectural structuring. We quantify the impact of such factors on effectiveness (accuracy) and efficiency (execution time, memory, and energy usage) of the prediction, revealing that, ignoring these factors lead to misleading result interpretation, and acknowledging them empowers both effectiveness and efficiency results.

Detecting transportation modes’ usability in spatiotemporal urban trajectories can provide valuable insights into the mobility preferences of urban populations, helping epidemic prevention and urban quality-of-life improvement. With this goal, we introduce POPAyI, a strategy that bases its design on the Ordinal Pattern (OP) transformation applied to mobility-related time series. POPAyI can quantify time-series dynamics with a low-complex cost, muscling time series’ characteristics without the need for high computational and methodological complexities as the current Machine Learning (ML) and Deep Learning (DL) literature. POPAyI uses polar representation and captures amplitude information in time series, bringing the multivariate capability to the standard 1D OP transformation. Our experiments show that POPAyI: (i) perfectly adapts to multi-dimensional mobility time series and natural non-linear mobility behavior. (ii) presents consistent detection results in any considered number of transportation mode’s classes with efficiency in terms of storage and computation complexity, using fewer features than ML approaches and computational resources than DL methods, e.g., reaching 10000 fewer parameters than a lightweight DL approach while increasing by 3% the F1-score.

WiFi-based crowdsensing is a major source of data in a variety of domains such as human-mobility, pollution-level estimation, and, opportunistic networks. MAC randomisation is a backbone for preserving user-privacy in WiFi, as devices change their identifiers (MAC addresses). MAC association frameworks in the literature are able to associate randomized MAC addresses with a device. Such frameworks facilitate the continuation and validity of works based on device-based identifiers. In this paper, we first question and verify the reliability of these frameworks with respect to the datasets (scenarios) used for their validation. Indeed, we observe a substantial discrepancy between the performances obtained by these frameworks when confronting them with different contextual environments. We identify that the device heterogeneity in the input scenario is privacy-preserving. Henceforth, we propose a novel metric: randomization complexity, capable of successfully catching the degree of randomization in evaluated datasets. Existing and new frameworks can thus be benchmarked using this metric to ensure their reliability for any datasets with similar or lower randomization complexities. Finally, we open discussions on the potential impact of the benchmarks in the domain of MAC randomization.

Domain-wide recognized by their high value in human activity and network monitoring studies, cellular network traffic (i.e., Charging Data Records, named CDRs), however, present accessibility and usability issues, restricting their exploitation and research reproducibility. This paper tackles such challenges by modeling CDRs that fulfill real-world data attributes. Our designed framework, named Zen leverages LSTM to realistically model network users’ traffic behavior through a 4-stage generative pipeline. Results show that Zen’s models accurately capture individual and global distributions of a fully anonymized real-world traffic CDRs dataset. Finally, we validate Zen CDRs ability of reproducing daily cellular behaviors of the urban population and its usefulness in practical networking applications such as Radio Access Network’s power savings, and anomaly detection as compared to real-world CDRs.

In this paper, we propose the first framework that introduces the concept of the user’s bounded trajectory. We propose to leverage the signal strength of users’ public WiFi probe requests collected from measurements of multiple deployed WiFi sniffers. First, we investigate and characterize errors in RSSI-based radial-distance (between the user and each sniffer) estimation. Then, we approximate such radial distances leverage and deduce bounds associated with a user’s position. Finally, we infer a user’s bounded trajectory using the spatiotemporal bounds of users’ locations over time. We guarantee the bounds to enclose a user in space and time, with 95% confidence and a 10% margin of error. Using real-world and large-scale synthetic datasets under heterogeneous contexts and wireless conditions, we infer trajectories with bounds’ width of less than 10m in 70% of cases with users’ inclusiveness close to 100%.

The coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user quality of experience (QoE). However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user QoE and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE under the constraints of UE capabilities and the availability of network resources. In order to make the problem manageable, we assume that the regular user consumes video content keeping some time interval between them, and this user moves slowly inside the coverage of a base station. We evaluate our proposal using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and cache hit ratio by at least 14% in comparison with the other method.

WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs’ limited coverage, offloading performance is heavily impacted by the users’ mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter’s mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models ($\approx $37% acc. for the best predictor). We show that mobility regularity properties improve predictive performance up to$\approx $35%. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets.

Bluetooth devices naturally emit many public signals. It opens new paths for passive mobility analysis, and allows building larger datasets direly needed by the research on mobile systems, but also raises new practical challenges. One of them is the correlation between the public packets and the emitters. The Bluetooth standard forces devices to change the identifier they embed within the public packets regularly, but we show in this paper it does not prevent packet correlation. The number of devices changing their MAC address at any given time is expectedly small; we only need to find a property differentiating a handful device at a time for MAC association. In this paper, we propose such a property and demonstrate the efficiency of the association strategy on Bluetooth Low Energy.

A major limiting factor for prediction algorithms is the forecast of new or never before-visited locations. Conventional personal models utterly relying on personal location data perform poorly when it comes to discoveries of new regions. The reason is explained by the prediction relying only on previously visited/seen (or known) locations. As a side effect, locations that were never visited before (or explorations) by a user cause disturbance to known location's prediction. Besides, such explorations cannot be accurately predicted. We claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. As recently demonstrated [3, 15], there exist regularities in returning and exploring visits. Moreover, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we infer an individual's next type of transition: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. We conduct extensive experiments on three real-world GPS mobility traces. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities. To the best of our knowledge, we are the first to propose a framework solely based on personal location data to tackle the prediction of visits to new places.

With growing privacy concerns over the last decade, two of the most notable wireless technologies – i.e., BLE and WiFi – are being more and more investigated in terms of privacy vulnerabilities. In this paper, we explore this problem, prospect the related consequences, and alert the need for privacy-preserving public packets. We identify key flaws in the current design of public packets like beacons and probe requests. We discuss them as the cause of privacy issues that require the community’s attention. We address the flaws in detail and propose solutions that facilitate the devices to protect user privacy. We also give recommendations based on the findings to the standard.

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.

Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.

This paper investigates the precise identification of physical phenomena in the Internet of Things (IoT) context, which is one of the main challenges when dealing with the massive scale of IoT data. For this, we use information theory quantifiers in the characterization and classification of physical phenomena to minimize the effects of the lack of proper descriptions and the high heterogeneity of IoT sensors. Thus, by understanding the dynamics behind physical phenomena, we perform the classification of sensor data based on their expected behavior, not their data points. By using a simple classification algorithm, we show that the behavioral dynamics of some physical phenomena are more affected by different geographical regions than others. This gives a classification accuracy of 75% when all phenomena are considered and of 93% when considering only the invariant ones, with a worst case of false positives of 12%. This result indicates the high potential of our technique to correctly identify physical phenomena from sensor data, a fundamental issue for several applications, even in an unreliable IoT environment.

Human mobility has been studied from different perspectives. One approach addresses predictability, deriving theoretical limits on the accuracy that any prediction model can achieve in a given dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in the dataset, filtering out factors that depend on the specificities of the prediction method adopted. In this paper, we revisit the state-of-the-art method for estimating the predictability of a person's mobility, which, despite being widely adopted, suffers from low interpretability and disregards external factors that have been suggested to improve predictability estimation, notably the use of contextual information (e.g., weather, day of the week, and time of the day). We also conduct a thorough analysis of how this widely used method works, by looking into two different measures (one proposed by us) which are easier to understand and, as shown, capture reasonably well the effects of the original technique. Additionally, we investigate strategies to incorporate different types of contextual information into predictability estimates, and show that the benefits vary depending on the underlying prediction task. Finally, we propose and evaluate alternative estimates of predictability which, while being much easier to interpret, provide comparable results to the state-of-the-art.

The 5G networks enable new touristic services with challenging communication requirements, such as augmented reality (AR) applications, and allow the visitors to enjoy a touristic experience that involves both the physical and virtual space. Here, we propose a novel multi- user travel itinerary planning framework based on an optimal problem formulation that considers both individual trip itinerary (e.g., tourist's preferences, time or cost) and touristic service constraints (e.g., nearby edge cloud resources and application requirements). The main idea is to maximize the itinerary score of individual visitors, while also optimizing the resource allocation at the edge. We consider two services, video streaming and AR, and evaluate our framework using data from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution adapted to this scenario.

Employing mobile devices to perform data analytics is a typical fog computing application that utilizes the intelligence at the edge of networks. Such an application relies on the knowledge of the mobility of mobile devices and their users, e.g., to deploy computation tasks efficiently at the edge. This paper surveys the literature on the mobility-related utilization of operator-collected CDR (charging data records) - the most sig- nificant proxy of large-scale human mobility studies. We provide an innovative introductory guide to the CDR data preliminary. It reveals original issues regarding CDR-based mobility feature computation and applications at the edge. Our survey plays an important role in utilizing mobile devices in terms of both human mobility investigation and fog computing.

In this paper, we aim at answering the question "at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?". Our quest for a response unveils (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. We conduct analyses using fine-grained GPS trajectories of 119 users worldwide. Our findings have potential applications in ubiquitous computing and mobile service design, in terms of energy efficiency, location-based service operations, active probing of subscribers' positions in mobile networks and trajectory data compression.

In this paper, we leverage two large-scale real-world datasets to provide the first results on the limits of predictability of cellular data traffic demands generated by individual users over time and space. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of mobile traffic consumption patterns in isolation. Our results show that it is theoretically possible to anticipate the individual demand with a typical accuracy of 85% and reveal that this percentage is consistent across all user types. Then, we analyze the joint predictability of the traffic demands and mobility patterns. We find that the two dimensions are correlated, which improves the predictability upper bound to 90% on average.

This paper presents a mobility-aware channel allocation strategy for clustered ad hoc network. Our main novelty is to consider the mobility associated with the number of times that the channel with larger spectral distance is allocated to guide the channel allocation process, while quickly responding to changes in the network topology. In our performance evaluation, we use a realistic mobility model based on user behavior and we consider the evaluation metrics of throughput, packet delivery rate, end-to-end delay, overhead, and spectral distance. Obtained results show that our strategy presents lower overhead when compared with TABU algorithm, higher throughput, and lower end-to-end delay when compared with RANDOM and LD algorithms.

Opportunistic communication, off-loading, and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security, and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends' devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends' devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones.

This article presents a user behavior aware channel allocation scheme for mobile ad hoc networks. The proposed scheme uses a distributed approach to dynamically allocate the IEEE 802.11b/g spectrum. Our main contribution is to consider the user behavior, represented by the mobility, traffic, and node degree to guide the channel allocation process. In addition, we use a mobility model based on the social context found in people sharing common interests. The results show that our strategy presents throughput 15,43% and 17,74% higher when compared with RANDOM and LD algorithms, respectively.

Understanding mobile data traffic demands is crucial to the evaluation of strategies addressing the problem of high bandwidth usage and scalability of network resources, brought by the pervasive era. In this paper, we conduct the first detailed measurement-driven modeling of smartphone subscribers' mobile traffic usage in a metropolitan scenario. We use a large-scale dataset collected inside the core of a major 3G network of Mexico's capital. We first analyse individual subscribers routine behavior and observe identical usage patterns on different days. This motivates us to choose one day for studying the subscribers' usage pattern (i.e., “when” and “how much” traffic is generated) in detail. We then classify the subscribers in four distinct profiles according to their usage pattern. We finally model the usage pattern of these four subscriber profiles according to two different journey periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently imitates different subscriber profiles in two journey periods, when compared to the original dataset.

Everyday, vehicles transit in a city and along their trajectories, they encounter other vehicles. The frequency of these encounters is influenced by many factors, such as: vehicle speed, destinations, traffic conditions, and the period of the day. However, these factors are justified by the public roads limits and the driver's behavior. The people present daily routines and similar behaviors that have a great impact in the daily traffic evolution. In this work, we present a numerical analysis of real and realistic data sets that describe the mobility of a set of vehicles. Social metrics are computed, and the results obtained are compared to random graphs in the direction to verify if vehicular network presents a social behavior. Finally, we discuss new social perspectives in vehicular networks.

People have routines and their mobility patterns vary during the day, which have a direct impact on vehicular mobility. Therefore, proto- cols and applications designed for Vehicular Ad Hoc Networks need to adapt to these routines in order to provide better services. With this issue in mind, in this work, we propose a data dissemination solution for these networks that considers the daily road traffic variation of large cities and the relationship among vehicles. The focus of our approach is to select the best vehicles to rebroadcast data messages according to social metrics, in particular, the clustering coefficient and the node degree. Moreover, our solution is designed in such a way that it is completely independent of the perceived road traffic density. Simulation results show that, when compared to related protocols, our proposal provides better delivery guarantees, reduces the network overhead and possesses an acceptable delay.

This paper tackles the WiFi hotspot deployment problem in a metropolitan area by leveraging mobile users' context and content, i.e., their trajectories, scenario interactions, and traffic demands. The careful deployment of hotspots in such areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption. Our proposed strategy considers the restrictions imposed by transportation modes to people trajectories and the space-time interaction between people and urban locations, key points for an efficient network planning. Using a real-life metropolitan trace, we show our routine-based strategy guarantees higher offload ratio than the current approach in the literature while using a realistic traffic model.

This paper tackles the WiFi hotspot deployment problem in a metropolitan area by leveraging mobile users' context, i.e., their trajectories and scenario interaction. The careful deployment of hotspots in such areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption. Our proposed strategy considers the restrictions imposed by transportation modes to people trajectories and the space-time interaction between people and urban locations, key points for an efficient network planning. Using a real-life metropolitan trace, we show our strategy guarantees high coverage time with a small set of deployed hotspots.

In this paper, we propose a two-layered parking lot management system for charging scheduling of electric vehicles (EVs) considering a realistic vehicular mobility pattern. EVs are categorized in two groups based on their mobility patterns: Regular EVs and Irregular EVs. We use the data from an vehicular mobility trace collected from the Canton of Zurich for the regular EVs and a probabilistic pattern built on top of this Zurich trace aiming at modeling the behavior of irregular EVs. To the extend of our knowledge, this is the first EV charging scheduling study in the literature that utilizes a big-scale realistic vehicular mobility trace. The performance of the proposed system is compared with well-known routine unaware scheduling mechanisms (e.g., First Come First Serve) with regard to maximizing the parking lot revenue. Our results show that, our proposed system outperforms well-known routine unaware scheduling mechanisms and it is evident that real environments would benefit from using such parking lot management systems in Smart Cities.

Broadcast data dissemination is a fundamental building block for many applications in Vehicular Ad hoc Networks. In the literature, there are solutions to deal with data dissemination in urban environments, but they solely focus on either intermittently connected topologies or well-connected topologies. However, depending on the time of day or the geographical location in a city, the network topologies can change dramatically. Hence, protocols proposed to operate under these networks should be able to adapt themselves to the traffic condition at hand. To tackle this problem, we propose U-HyDi, a broadcast data dissemination protocol suited for urban scenarios with zero infrastructure support. By using solely one-hop neighbor information, U-HyDi can seamless operate under intermittently connected networks by applying store-carry-forward techniques to deliver messages even when there is no end-to-end path. Moreover, under well-connected networks, U-HyDi employs a combination of sender-based and receiver-based broadcast suppression techniques to avoid excessive contention at the link layer. Simulation results show that U-HyDi has a low overhead and a low delivery delay. Furthermore, U-HyDi is able to deliver messages to almost all vehicles in a given region of interest.

Envisioned applications for VANETs will rely extensively on the exchange of broadcast messages to deliver data to vehicles located in a region of interest. Many data dissemination protocols have been proposed in the literature to suppress this need. Surprisingly, most of them were designed to operate exclusively under dense or sparse networks. However, it is reasonable to assume that diverse traffic conditions will coexist in realistic scenarios. Therefore, data dissemination protocols for VANETs should be designed to perceive the traffic condition at hand and adapt accordingly. With this in mind, in this paper we propose HyDiAck, a data dissemination protocol for urban VANETs that relies exclusively on local one-hop neighbor information to deliver messages under dense and sparse networks. In dense scenarios, HyDiAck selects vehicles inside a forwarding zone to rebroadcast messages to further vehicles. Moreover, the protocol employs implicit acknowledgements to guarantee robustness in message delivery under sparse scenarios. When compared to two related protocols - UV-CAST and slotted-1-persistence - simulation results for both Manhattan grid and real city street scenarios show that HyDiAck decreases both the latency to disseminate messages and the network overhead, and also guarantees message delivery to all vehicles in the region of interest.

We focus on dissemination of content for delay tolerant applications, (i.e. content sharing, advertisement propagation, etc.) where users are geographically clustered into communities. We propose a novel architecture that addresses the issues of lack of trust, delivery latency, loss of user control, and privacy-aware distributed mobile social networking by combining the advantages of decentralized storage and opportunistic communications. The content is to be replicated on friends' devices who are likely to consume the content. The fundamental challenge is to minimize the number of replicas whilst ensuring high and timely availability. We propose a greedy heuristic algorithm for computationally hard content replication problem to replicate content in well-selected users, to maximize the content dissemination with limited number of replication. Using both real world and synthetic traces, we show the viability of the proposed scheme.

Video dissemination to a group of vehicles is one of the many fundamental services envisioned for Vehicular Ad hoc Networks. For this purpose, in this paper we describe VoV, a video dissemination protocol that operates under extreme traffic conditions. Contrary to most existing approaches that focus exclusively on always-connected networks and tackle the broadcast storm problem inherent to them, VoV is designed to operate under any kind of traffic condition. We propose a new geographic-based broadcast suppression mechanism that gives higher priority to broadcast to vehicles inside especial forwarding zones. Furthermore, vehicles store and carry received messages in a local buffer in order to forward them to vehicles that were not covered by the first dissemination process, probably as a result of collisions or intermittent disconnections. Finally, VoV employs a rate control mechanism that sets the pace at which messages must be transmitted in an attempt to avoid channel overloading and to overcome the synchronization effects introduced by the channel hopping mechanism employed by IEEE 802.11p. When compared to two well-known solutions -- UV-CAST and AID -- we show that our proposal is more efficient in terms of message delivery, delay and overhead.

In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to network applications that rely on human routines, such as, e.g., opportunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from different kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing.

Vehicular Mobility is strongly influenced by the speed limits and direction of the public roads. At the same time, the driver's behavior produces great influences in vehicular mobility. People tend to go to the same places, at the same day period, through the same trajectories, which le ad them to the appearance of driver's daily routines. These routines lead us to the study of mobility in VANETs under a social perspective and to investigate how effective is to explore social interactions in this kind of network. The work herein proposed presents the characterization and evaluation of a realistic vehicular trace found in literature. Our aim is to study the vehicles' mobility in accordance to social behaviors. With our analysis is possible to verify the existence of regularity and common interests among the drivers in vehicular networks. Finally, we discuss how the social metrics may be used to improve the performance of protocols and services in Vehicular Networks.

Network discovery is a fundamental task in different scenarios of IEEE 802.15.4-based wireless personal area networks. Scenario examples are body sensor networks requiring health- and wellness-related patient monitoring or situations requiring opportunistic message propagation. In this paper, we investigate optimized discovery of IEEE 802.15.4 static and mobile networks operating in multiple frequency bands and with different beacon intervals. We present a linear programming model that allows finding two optimized strategies, named OPT and SWOPT, to deal with the asynchronous and multichannel discovery problem. We also propose a simplified discovery solution, named SUBOPT, featuring a low-complexity algorithm requiring less memory usage. A cross validation between analytical, simulation, and experimental evaluation methods is performed. Finally, a more detailed simulation-based evaluation is presented, when considering varying sets of parameters (i.e., number of channels, network density, beacon intervals, etc.) and using static and mobile scenarios. The performance studies confirm improvements achieved by our solutions in terms of first, average, and last discovery time as well as discovery ratio, when compared to IEEE 802.15.4 standard approach and the SWEEP approach known from the literature.

This paper presents a new routing protocol for Delay Tolerant Networks (DTNs), based on a distributed swarm intelligence approach. The protocol is called Cultural Greedy Ant (CGrAnt), as it uses a Cultural Algorithm (CA) and a greedy version of the Ant Colony Optimization (ACO) metaheuristic. The term greedy implies the use of a deterministic transition rule to exploit previously found good paths or explore new paths by selecting, from among a set of candidates, the most promising message forwarders. CGrAnt chooses each next node toward the message destination based on pheromone concentration (i.e., global information) whenever it is available. However, as the pheromone is not always available due to connectivity partitions, local information (i.e., heuristic function) captured from DTN nodes also supports a routing decision. Specific metrics and information gathered from the evolution are stored in Situational, Domain, and Historical Knowledge. The knowledge composes the CA's belief space, which is used to guide and improve the search. CGrAnt is compared with two DTN routing protocols (Epidemic and PROPHET) in an activity-based scenario. The results show that CGrAnt achieves a higher delivery ratio and lower byte redundancy than Epidemic and PROPHET.

We consider the problem of neighbor discovery in wireless networks with nodes operating in multiple frequency bands and with asymmetric beacon intervals. This is a challenging task when considering such heterogenous operation conditions and when performed without any external assistance. We present linear programming (LP) optimization and two strategies, named OPT and SWOPT, allowing nodes performing fast, asynchronous, and passive discovery. Our optimization is slotted based and determines a listening schedule describing when to listen, for how long, and on which channel. We compare our strategies with the passive discovery of the IEEE 802.15.4 standard. The results confirm that our optimization improves the performance in terms of first, average, and last discovery time.

Here, we first investigate data collection delegation strategies based on the relative importance of nodes in their social interactions. Second, by considering a prediction strategy that estimates the likelihood of two nodes meeting each other, we investigate how the delegation strategies perform on predicted traces. We evaluate the delegation strategies both in terms of coverage, and size of the delegation using real mobility data sets.

We propose a flexible proactive data dissemination approach for data gathering in self-organized Wireless Sensor Networks (WSN). Our protocol Supple, effectively distributes and stores monitored data in WSNs such that it can be later sent to or retrieved by a sink. Supple empowers sensors with the ability to make on the fly forwarding and data storing decisions and relies on flexible and self-organizing selection criteria, which can follow any predefined distribution law. Using formal analysis and simulation, we show that Supple is effective in selecting storing nodes that respect the predefined distribution criterion with low overhead and limited network knowledge.

Some current and future pervasive data driven applications must operate in “extreme” environments where end-to-end connectivity cannot be guaranteed at all times. In fact, it is likely that in these environments partitions are, rather than exceptions, part of the normal network operation. In this paper, we introduce Cover, a suite of adaptive strategies to control the trajectory of “infrastructure” nodes, which are deployed to bridge network partitions and thus play a critical role in data delivery. In particular, we focus on applications where end (or target) nodes are mobile and their mobility is unknown. Our goal is then to deploy and manage infrastructure nodes so that application-level requirements such as reliable data delivery and latency are met while still limiting deployment cost and balancing the load among infrastructure nodes. Cover achieves these goals using a localized and adaptive approach to infrastructure management based on the observed mobility of target nodes. To this end, Cover takes advantage of contact opportunities between infrastructure nodes to exchange information about their covered zones, and thus, help monitor targets in a more efficient fashion. Through extensive simulations, we show how Cover's adaptive features yield a fair distribution of targets per infrastructure node based only on limited network knowledge.

This paper studies the unfairness issues of network coding in multi hop wireless networks. Most of the work on network coding focuses on the obtained throughput gain. They show that mixing lineally the packets at the intermediate nodes is capacity-achieving. However, network coding schemes designed only to maximize the throughput could be unfairly biased. The reason is that by mixing different flows, packets destined to one destination in order to be decoded need to wait for the reception of the whole mixed set of encoded packets that may be totally independent in terms of final destination. This may lead to highly unfair delay for small block data. To mitigate this unfairness, relay nodes may mix only packets going to the same destination. We call this strategy FairMix. Although FairMix may limit the maximum attainable throughput, it aims to make distinct for decoding delay of each destination corresponding to the size of the data block. In order to investigate this trade off, we compare the FairMix performance with a naive network coding which mixes packets destined to different destinations. The simulation under lossy wireless links, limited memory and bandwidth resources, and different block sizes shows that FairMix is effective in improving fairness among destinations in comparison to naive network coding.

This note define self-structuring in a large-scale networked system as the ability of the participating entities to collaboratively impose a geometric structure to the network. This refers to assigning virtual coordinates to participating entities and to dividing the entities in several partitions, in such a way that each entity knows to which partition it belongs.

As the size of mobile self-organizing networks increases, the efficiency of location services must increase as well so that addressing/routing scalability does not become an issue. In this paper, we propose a novel architecture, called Twins, tailored for self-organizing networks. Twins architecture involves addressing and locating nodes in large networks, forwarding packets between them, and managing in the presence of mobility/topology changes. Twins defines a logical multidimensional space for addressing and forwarding, while location service and management operations make use of a one-dimensional space. To improve scalability and performance, forwarding is hop-by-hop with greedy next-hop choice and the location service uses a rendezvous paradigm to distribute information among nodes. In this paper, we describe the Twins architecture and present a performance evaluation to assess scalability, fairness in the overhead distribution among nodes, and routing robustness.

The need for efficient location mechanisms is an important issue in scalable self-organizing networks. Existing solutions are inherently dependent on the spatial distribution of nodes in the topology. This leads to limitations that go against the principles of self-organization. In this paper, we propose Twins, an easily-managed location service for self-organizing networks. Twins defines a logical multidimensional space that is a strict mathematical representation of the network geographic space. This representation is obtained through Hilbert space-filling curves. The geographic space is used for addressing and routing, while localization is based on the curve. Control messages are routed based on the logical structure while data packets are routed in a hop-by-hop basis with greedy next-hop choice. In this paper, we evaluate the Twins management operations in terms of fairness of space sharing and logical/geographic distances between nodes and their location servers. Our results show that Twins assures a fair distribution of control overhead and scales well with the number of nodes.

This paper proposes the Tribe protocol, an indirect routing strategy for wireless self-organizing networks. The protocol is intended to be applied in environments with large number of users, where mobility is taken into account, and the correct operation of the system does not require the support of a fixed (wired or wireless) infrastructure. In Tribe, nodes build a network infrastructure which describes the node's relative location according to the current node's neighborhood. Furthermore, routing is unique and completely independent of any global connectivity ensured by a network-level routing protocol. The architecture is generic, self-organizing, and independent of IP-like addressing limitations.