Federated Learning (FL) enables devices to collaboratively train a shared model by exchanging parameters with a central server rather than raw data, thereby enhancing privacy, scalability, and efficiency. As modern devices are increasingly equipped with diverse sensors, supporting multiple tasks has become essential. Multi-model Federated Learning (MEFL) extends FL by allowing a single server to coordinate the training of multiple independent tasks, improving overall performance and resource utilization. In this work, we propose MultiFedAvg with Model-wise Data Heterogeneity Awareness (MultiFedAvg-MDH), the first approach to adapt multi-model orchestration in MEFL according to each model’s data heterogeneity. Our key insight is that, under highly non-IID conditions, prioritizing training intensity over frequency leads to more robust learning. Experimental results demonstrate that MultiFedAvg-MDH improves system performance by up to 16.49% while also reducing performance variability.

The integration of multiple distributed ledgers in Intelligent Transportation Systems (ITS) introduces challenges for scalable and interoperable authentication. Traditional schemes, which rely heavily on Public Key Infrastructure (PKI), face limitations related to certificate management and key escrow. To address these issues, we propose a federated authentication system based on certificateless public key cryptography (CL-PKC) to enable seamless cross-domain and cross-chain authentication without relying on traditional certificates. The proposed approach is designed to operate at the edge, where authentication is performed close to the user to reduce latency and support mobility. Leveraging the CL-PKC scheme, each user independently generates and manages their own cryptographic keys. Simulation results show reduced credential generation time, lower network usage, and improved latency under heavy and cross-domain conditions.

The design of effective mobility-aware systems requires a deep understanding of how different user groups navigate and interact within urban environments, enabling the development of services and infrastructures that adapt to dynamic movement patterns. Tourist mobility patterns, in particular, offer valuable insights into the spatial and social dynamics that influence system design decisions for smart cities, recommendation engines, and urban planning applications. This work presents a comprehensive approach to modeling urban mobility networks by examining tourist behavior through network analysis techniques. Using Rome as a case study, we leverage a large dataset of Foursquare check-ins collected over 18 months to construct a co-visitation graph where nodes represent points of interest and edges reflect shared visits by the same users. Through network analysis of Rome’s tourist mobility, our work identifies clear structural differences between visitor types: long-term visitors form cohesive, highly clustered mobility networks, while short-term visitors create more fragmented interaction patterns. Key points of interest (especially transportation hubs and cultural landmarks) serve as essential connectors shaping network flow, following preferential attachment dynamics. Mobility-aware systems play a crucial role in leveraging such insights to design adaptive, data-driven services that respond to varying mobility behaviors, ultimately enhancing urban efficiency and user experience.

The technological development of mobile devices introduced new challenges to the problem of context awareness. The efficient interpretation of context requires considering the adequate performance given resource limitations, such as battery and processing power. Additionally, to achieve real-time context awareness, it is necessary to enhance the quality of data gathering at mobile devices. In this work, we present an efficient sampling algorithm that leverages the sparsity property of specific raw context, such as the RSSI (Received Signal Strength Indication) measures. We show that the wavelet transform, which has different bases, is a well-known technique for this kind of sampling, and the selection of the Coiflet basis is more interesting than the classical use of the Haar basis.

The Internet of Drones (IoD) is expected to revolutionize the utilization of drones in urban environments. Drone delivery applications are expected to become commonplace in people's everyday lives. Such applications will establish a network where drones will have organized airspace access, with variations in network node density based on location and time. Within this environment, the IoD network may encounter communication gaps among drones. In scenarios like drone delivery, altering a drone's route solely for message delivery within the network is impractical. We aim to deploy an auxiliary drone network to support IoD applications, similar to using auxiliary networks in vehicular networks for message delivery. This study introduces the AuxIoD method, which utilizes a genetic algorithm to position auxiliary drones strategically, bridging communication gaps within the IoD network. Compared to other strategies, our approach facilitated the delivery of approximately 30% more messages in the evaluated scenario.

Location-based services (LBSs) play a vital role in many Internet applications. Privacy is a mandatory aspect of these tasks, including protecting the user's sensitive information from malicious entities. Location privacy-preserving mechanisms (LPPMs) were designed to ensure privacy for LBS users, and several strategies have emerged, such as cloaking mechanisms. Likewise, several attacks appeared to threaten the user's privacy, being based on ground-related aspects. Therefore, we must investigate new strategies to enhance privacy protection mechanisms. Unmanned Aerial Vehicles (UAVs) can provide assisted coverage to ground users in different tasks, including the support of LPPMs. However, the existing strategies rely on some unfeasible premises, and this collaboration needs to be adequately explored. Therefore, in this study, we propose Sky Cloaking, which promotes the opportunistic connection between ground users and UAVs in such a way the UAVs manage the user's query, creating a cloaking region and hampering the success of an attacker. Through a comprehensive evaluation, we demonstrated that SkyCloaking can ensure high levels of location privacy to LBS users with a slight impact on the communication channel, overcoming existing strategies. In the best scenarios, SkyCloaking protected more than 80% of the user trajectory, mitigating the exploitation of users' sensitive information.

The rise of mobile devices and growing concerns about model privacy have posed significant challenges in distributed artificial intelligence, especially due to the heterogeneity of devices, leading to model generalization and resource management issues. Federated Learning (FL), a method where machine learning models are trained collaboratively by sharing only local parameters with an aggregation server, faces challenges in model convergence, optimization, and communication overhead due to this heterogeneity. This paper introduces FedSCCS, an FL-based framework designed for such heterogeneous settings. FedSCCS clusters devices based on the similarity of their models, allowing for efficient model aggregation and improved resource utilization. Our evaluation, set against established benchmarks, shows that FedSCCS achieves superior accuracy compared to existing methods, indicating a promising direction for scalable and tailored FL solutions.

Federated Learning (FL) has emerged as a privacy-preserving powerful tool in decentralized Machine Learning (ML) environments. However, real-world scenarios often face bandwidth limitations that can be overwhelmed when all clients simultaneously perform training and communicate with the server in a federated system. Consequently, selection mechanisms are critical for identifying optimal subsets of clients to participate in the federation. Traditional selection methods, however, typically do not allow clients the autonomy to decide whether or not to contribute to the federation. Therefore, this paper proposes LetsFed, a client selection framework that respects client independence throughout the training process. The LetsFed framework differentiates between participating and non-participating clients, employing targeted selection mechanisms to address system challenges effectively. Empirical results demonstrate that LestFed can outperform, in dynamic client participation environments, literature solutions by up to 40%, reducing unnecessary data transmission by as much as 29%, while also enhancing the efficacy of the selection process.

There is a shortage of mobility datasets - real or synthetic - available in the literature, limiting the development of new research. This generates demand for newer large-scale datasets, considering time, space, and population size. This work introduces a novel, two-step approach to generating large-scale mobility data considering the surrounding context, such as traffic. We validate our approach using two real mobility datasets, resulting in an enriched, large-scale dataset with more than 1 million origin-destination trips, which we share with the community to enable new research opportunities.

Unmanned aerial vehicles (UAVs)–also known as drones or Unmanned Aircraft–have found diverse applications in various fields owing to their significant advantages, including fast mobility and rapid deployment. UAVs are crucial in aerial networks, providing increased coverage and on-demand connectivity as mobile nodes. In recent years, UAVs have made room to leverage the Internet of Things (IoT) to the sky, enhancing air-to-ground communication and pointing toward the next generation of UAV networks. As this expansion is still in its early stages, there are several aerial network terminologies, each with similarities and differences, depending on the deployment domain and the services they offer. However, studies have yet to discuss these different terminologies consistently. Key aspects have yet to be thoroughly explored, such as the anticipated requirements for deploying these networks and how they relate to the various terminologies. This work systematically analyzes the existing terminologies of UAV networks, considering their requirements and applications, shedding light on their intersections and differences. Furthermore, we present the demands for the next generation of UAV networks and discuss how they impact the design of UAV-related applications, aiding in the design of new protocols, tools, and technologies for both industry and academia. Lastly, we highlight the emerging trends and challenges associated with deploying and integrating these networks.

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.

Shortly, the airspace will have different types of aerial networks due to the continuous growth of demands for aerial applications. As these networks compete for airspace, it is necessary to manage them efficiently. Also, these networks are expected to communicate with each other and with terrestrial networks forming hybrid aerial-terrestrial networks (HATNs). Efficient protocols for data packet delivery in HATNs are crucial for these networks to work together. In addition, cooperation between networks can be essential for different emergencies. However, communication in HATNs is challenging because of the plurality of node types. This work presents an altitude-based routing protocol for Hybrid Aerial-terrestrial Networks (ARAT). ARAT is a geocast and store-carry-forward protocol with promising performance in sparse scenarios. Simulation results show that ARAT outperforms two protocols (a baseline protocol and the GeoUAV protocol) by 50% for the packet delivery ratio metric. Also, to the best of our knowledge, ARAT is the first protocol to consider a collaboration between Aerial Networks and Internet of Drones (IoD) with the traffic of drones constantly.

Location Privacy Protection Mechanisms (LPPMs) have been designed to enhance privacy in the Internet of Drones (IoD), however, they present suitable privacy levels only in specific network conditions. Also, they can lead to a lack of Quality of Service (QoS) if applied in unfavorable conditions. Thus, the dynamic assignment of the most suitable LPPM, given the IoD conditions, is a significant challenge. Reinforcement Learning (RL) represents a useful concept to handle this problem, given its exploratory characteristics and being able to enhance the knowledge about network dynamics. In this study, we propose IoDAPM, an RL-based approach for the Dynamic Assignment of Protection Mechanisms in IoD. Through simulations, we extensively trained the RL-based model, exploring the possible IoD network conditions. With this model, we carried out a comparative evaluation of existing LPPMs. The results highlighted that IoDAPM outperforms the compared mechanisms considering the QoS, providing enhanced performance regarding location privacy, energy efficiency, and flight delay.

In Unmanned Aerial Vehicles (UAVs), the real-time detection and identification of unauthorized UAVs is a significant challenge to be appropriately addressed. Currently, supervised-based learning models (e.g., Deep Neural Networks) can detect the presence of authorized UAVs with reasonable accuracy. Still, they can not handle properly the wide range of unknown signals in the airspace, mainly their categorization. Clustering techniques (e.g., DBSCAN) can be applied to identify and classify unfamiliar signals. However, the uncertainty regarding the nature of unknown sounds can lead to a large dimensional problem, hampering the performance of these techniques. Given these issues, we proposed DissIdent, a dissimilarity-based method for identifying unknown drones. Our approach takes advantage of the dissimilarity concept, in which a function of proximity maps extensive and multi-dimensional problems to a binary problem. DissIdent can identify patterns from different features through an intelligent workflow, mitigating the trade-off between the traceability and accuracy of massive multi-class problems. We carried out an extensive evaluation of DissIdent, comparing it with eight different approaches. The results pointed out DissIdent as a robust approach to detection and identification tasks, overcoming the compared methods. DissIdent addressed accuracy rates higher than 93% in all scenarios, presenting a concise detection and identification of unauthorized drones.

This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles’ data before uploading it to processing stations. We evaluate the proposed solution in a realistic simulation scenario derived from data traffic containing more than 700,000 individual car trips for 24 hours. We compare our approach with both a reservation-based algorithm and the optimal solution. Our results indicate an upload cost reduction of 30.92% using the centrality-based algorithm and improving the aggregation rate by up to 10.45% when considering the centralized scenario.

Vehicle trajectory data is paramount in many applications and research areas, such as vehicular networks and Intelligent Transportation Systems (ITS). However, data gathered from location acquisition devices generally contain positional errors that hinder its applicability, and therefore processing techniques are necessary to improve the quality of trajectory data. For instance, physical constraints of the road network that bounds the vehicles’ movement can be used to represent a trajectory better. Therefore, this paper proposes an efficient framework to reconstruct road-network constrained trajectories from GPS-based datasets. The framework employs novel processing algorithms and models to prepare even low sampled trajectories, which naturally present gaps, for real applications. Besides that, we present a novel real-world benchmark dataset to evaluate trajectory reconstruction and map-matching algorithms and perform extensive experimental evaluations using the new dataset and another one from the literature to compare the proposed framework to related work. The experimental results show that the proposed framework has a better time complexity and accuracy than the other methods in all evaluated scenarios.

In this work, we propose a solution for detecting botnet attacks on the Internet of Things (IoT) by identifying anomalies in the temporal dynamics of their devices. Given their limited computing capabilities, IoT devices are more vulnerable to attacks than conventional computers. In this scenario, botnets have a high degree of severity since they are used to trigging distributed denial-of-service attacks, which are amplified by a large number of IoT devices. Thus, solutions aiming to identify and mitigate the damage caused by botnets in IoT are urgent and essential. We evaluate the number of packets a device transmits, following a multiscale ordinal patterns transformation, and use Isolation Forest for anomaly detection. By investigating how devices evolve, we can distinguish between normal and anomalous behaviors. We apply the proposed solution to detect two major botnets for IoT: Mirai and Bashlite. We evaluated our model throughout two experimental setups. The first, using a single model for all devices, reaching 99.5% of accuracy and 99.6% of specificity, and the second, by tuning a model per device, reaching 100% of accuracy. These results show that, with the proper transformation, it is possible to use simple methods for detecting anomalies in IoT devices’ behaviors.

This work proposes a novel vehicle-assisted data delivery algorithm called VDDTP. VDDTP creates an extended trajectory model and uses predicted road-network constrained trajectories to calculate packet delivery probabilities. Next, it applies the predicted trajectories and some proposed heuristics in a data forwarding strategy to improve the vehicular network's global metrics (i.e., delivery ratio, communication overhead, and delivery delay). We perform extensive experiments using a real-world and large-scale trajectory dataset for evaluating vehicular network applications. The results demonstrate the algorithm's ability to improve the global metrics compared to related work.

The Internet of Drones (IoD) is a network paradigm that allows drones to perform several services, gathering and sharing location-based information, representing a piece of the next generation of the Internet of Things (IoT). Location privacy is a paramount requirement in IoD. A few Location Privacy Protection Mechanisms (LPPMs) are designed for IoD. Unfortunately, they are not energy-aware approaches, essential for IoD protocols since drones have power limitations. We present the MixRide, an energy-aware LPPM for IoD to overcome these issues. It provides location privacy through the aerial-grounded vehicle collaboration, where the drones take a ride with grounded vehicles, changing their pseudonyms while saving energy. A comparative experimental evaluation pointed out that MixRide can provide location privacy to the IoD at the same level as the state-of-the-art LPPM while improving the drone's power consumption. Our results also provide new insights on the trade-off between the delay caused by the ride and the drone's energy power consumption.

Recently applications that use a single drone are evolved into an Internet of drones (IoD) ecosystem. The IoD has a complex control as it needs to coordinate the drones in the airspace, which involves mobility and communication between them. It facilitates the IoD control if drones frequently report their positions to the control base, use well-defined airways to fly, and are managed through Software-Defined Internet of Drones (SDIoD). In the shared airspace, drones are expected to exchange messages with each other. In this work, we propose PSDIoD, a Position-based routing protocol for SDIoD, which has two versions. The first, called PSDIoD-SP (Shortest Path), considers the shortest path in the airways to select the routes. The second, called PSDIoD-EA (Energy-Aware), examines the radio power consumption estimated to choose the route. Due to nodes having a limited battery, power is a crucial factor in IoD. Both of which consider the knowledge of the position of drones to route packets. This work advances the state of the art by proposing efficient routing solutions that were developed in a suitable way for the IoD environment. The results show that PSDIoD versions are better in power consumption, packet delivery ratio, delay, and overhead when compared to a baseline protocol. Furthermore, PSDIoD-EA is approximately 9% more economical in terms of power consumption when compared to PSDIoD-SP.

In this paper, we focus on two fundamental aspects in the design of Bus-based Vehicular Networks: the building of bus mobility scenarios for validating solutions and the dissemination of messages on the network. We present a methodology for generating bus mobility scenarios based on official data and simulation tools. In this regard, we consider the city’s road map and additional traffic infrastructure elements. In addition, traffic demand for different days of the week is generated based on GTFS data provided by the city’s transport agency. We validate our scenarios with official data, and through a comparative analysis, we show the relevance of this work compared to existing alternatives in the literature. We present a novel routing protocol named BR3C, which aims to forward messages between bus lines for data dissemination. BR3C (Bus Routing protocol based on Community and Centrality Characteristics) considers social metrics extracted from the contacts between bus lines for decision-making. As a result, our protocol significantly reduces delivery latency while keeping delivery ratio values similar to state-of-the-art.

The recent advancement of drone technologies and communication protocols allows us to envision a robust and dynamic mobile vehicular network paradigm called the Internet of Drones (IoD). In this environment, drones will perform several location-based services (LBSs) for users, awakening the interest of malicious entities whose intention is to hamper the service. Hence, drones need high protection regarding their localization in LBSs. However, there is a lack of Location Privacy Protection Mechanisms (LPPMs) for an IoD scenario. Dummy-based LPPMs provide proper location privacy in traditional mobile networks, mainly for sparse configurations. The design of this mechanism for IoD can overcome this deficiency. This study proposes a novel dummy-based LPPM for the IoD, called TDG, that focuses on the IoD topology characteristics regardless of near drones being the first approach presented in this context. Through extensive experiments, we show that TDG can provide proper location privacy for drones in sparse configurations, reducing the use of the wireless communication channel. TDG can protect the real drone’s trajectory up to more than 90% of the time, leaking less than 25% of the drone’s real coordinates.

Internet of Drones (IoD) is an architecture that aims to enable different drones to share the same airspace. This architecture can help coordinate drone access to airspace in urban environments. Considering that the IoD is a dynamic network, it is possible to have scenarios in which drone traffic is sparse when, for instance, the network has isolated drones. In this case, the drones’ communication range does not reach any other drone. Thus, store-carry-forward protocols may be suitable for maintaining network communication. Moreover, different networks can collaborate to fill these communication gaps. In this study, we explore the collaboration between IoD and Bus Networks. Our analysis shows that maintaining a hybrid communication between drones and buses can fill the gaps in the communication between drones. The main goal of this work is to present the IoDSCF – a store-carry-forward routing protocol for joint Bus Networks and the Internet of Drones (IoD). IoDSCF takes advantage of both networks to extend the communication reachability. Our results reveal that IoDSCF presents better results in the number of delivered packets and end-to-end delay than a solution based only on communication between drones. This is a promising strategy for data communication, mainly in smart cities.

This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional classification algorithms is not possible. Thus, we claim that solutions for IoT scenarios should avoid using raw data directly, preferring their transformation to a new domain. In the ordinal patterns domain, it is possible to capture the temporal dynamics of raw data to distinguish them. However, to be applied to this challenging scenario, TSCLAS follows a strategy for selecting the best parameters for the ordinal patterns transformation based on maximizing the class separability of the time series dynamics. We show that our method is competitive compared to other classification algorithms from the literature. Furthermore, TSCLAS is scalable concerning the length of time series and robust to the presence of missing data gaps on them. By simulating missing data gaps as long as 50% of the data, our method could beat the accuracy of the compared classification algorithms. Besides, even when losing in accuracy, TSCLAS presents lower computation times for both training and testing phases.

An event can be defined as something that happens at a particular place and time. A traffic event is a specific event kind that occurs on roads and affects the users’ mobility. Traffic events can be helpful in a variety of Intelligent Transportation System (ITS) applications, such as routing planning and emergency notifications. The detection of traffic events is not a trivial task, given the particularities of the environment and data availability. In this work, we propose a workflow that guides an ITS application designer on modeling how to detect events of interest, given the application’s requirements and the available data characteristics. As part of the workflow, we propose a decision component that selects the most appropriate event extraction algorithm for a particular scenario. An instance of the proposed model using two social networks as data sources and four machine learning algorithms was implemented as a case study. The results reveal that it was possible to extract a significant part of the expected events, all of them with complete what, where, and when information.

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.

In the last few years, new scenarios have emerged for drone applications including those of risk scenarios. In this case, it is essential to have communication among drones whenever they perform tasks that need some sort of coordination, management or synchronization, for instance. In this work, we propose IoDAGR – Internet of Drones Airway-based Geocast Routing, a geocast protocol to transmit messages, such as alerts, to a specific geographical region in a drone network. IoDAGR advances the state of the art of geocast protocols for drones by proposing an efficient solution to this scenario. Simulation results revealed that IoDAGR presents promising results when compared to a baseline protocol.

In recent years, we have witnessed the viability of applying cloud computing concepts to the domain of vehicular networks. A basic component of this infrastructure derived from the merge of cloud computing and vehicular networks is a Vehicular Micro Cloud (VMC), also known as vehicular cloudlets. A VMC is a cluster of connected vehicles that share computational resources. Despite being the focus of many studies in recent years, we still do not have a clear understanding of the characteristics of VMCs in large-scale urban scenarios. In this paper, we investigate some fundamental characteristics of stationary and mobile VMCs obtained from a realistic vehicular mobility trace. We characterize the dwell time and the inter-arrival time in stationary VMCs. Also, using statistical modeling, we identify theoretical distributions that best fit these metrics. For mobile VMCs, we reveal how they occur throughout the city along the day, discussing evolution and lifetime aspects.

The Internet of Drones (IoD) refers to a robust mobile network with well-defined airways where drones perform interoperable services, enhancing the deployment of Smart Cities. Automatic Drone Identification (ADI) is a protection mechanism to detect and avoid malicious drones, where different techniques have been used, such as acoustic signals. In this field, the sound generated by propellers and motors has particular characteristics, being a potential aspect to explore; however, this investigation is still missing. This study examines the use of rhythm-based descriptors as input features to ADI, based on the hypothesis that the acoustic signal generated by different drones has different rhythmic properties. Aiming to explore and validate our approach, we formulate an ADI methodology using rhythm-based features. We use a freely available drone audio dataset, comparing our results with a baseline study. As a result, our classification model improves 3.47% the baseline binary classification and 2.97% the multiclass classification, reaching accuracy rates of 0.9985 and 0.9591, respectively. Although the improvements are narrow, they point out that acoustic features have a great potential to enhance ADI mainly in dense drone-based environments, where drone identification is an essential task.

The Internet of Drones (IoD) is a novel network paradigm that presents a unique mobile network scenario with particular characteristics. Hence, privacy is a mandatory aspect to be assured, but there is a lack of studies regarding location privacy protection in IoD. Mix Zones is a location privacy protection mechanism well explored in terrestrial mobile networks, however, its investigation in IoD is still missing. In this work, we propose a novel Mix Zones-based approach, called MixDrones, that changes the airway of a drone besides its pseudonym. Hence, we advance the state of the art of location privacy protection mechanisms for IoD, in which MixDrones is the first approach proposed in this context. We carried out experiments through simulations comparing our approach with the traditional mechanism regarding anonymization coverage and resilience. We also evaluate the possibility of drone collision occurrence. The results pointed out that MixDrones provided a better location privacy protection than the traditional mechanism, anonymizing a large number of drones and being resilient through a trajectory-based de-anonymization attack, with less than 25% of trajectories being de-anonymized in all scenarios. Moreover, MixDrones mitigated the side effects of airway change, presenting low rates of airways change competition situations.

Cloudlet-based mobile cloud offloading is an emerging technology designed to augment mobile elements by migrating resource-hungry components to adjacent local resource pooling. However, the Cloudlet resources are usually limited in terms of the computing utility, storage and network bandwidth. In this scenario, the remote Cloud infrastructure can provide additional computing and storage utility during run-time; the heterogeneous offloading methods for different mobile applications and diverse offloading resources complicate the Cloudlet-based offloading and resource allocation process. As a result, a considerable amount of delay is caused by setting up the execution environment, communication overhead and waiting in the queue, which significantly downgrade the QoS and the usability of such systems. In this paper, we propose a novel hybrid offloading model to solve the heterogeneous resource-constraint offloading issues in the Cloudlet, concerning the offloading energy and execution efficiency. A queue-based offloading framework is developed to formulate and analyze the mixed migration-based and partition-based offloading behaviors in the Cloudlet. The execution and energy-aware heterogeneous offloading resource allocation problem is formalized, and a Particle Swarm Optimization heuristic solution is presented. A SARIMA-based load prediction model is designed in the Cloudlet to achieve fine-grain proactive resource allocation. Experimental results reveal that the proposed framework can effectively reduce the offloading energy cost and execution time, compared to currently existing solutions.

The increase in the vehicular fleet in the last decade has caused several problems in urban scenarios. Modeling VANETs as graphs enables us to apply graph theory algorithms, allowing a more complex and deeper analysis of reality. In this work, we identify three different approaches to model vehicular networks and analyze two real well-know large-scale traces from Rome and San Francisco city. We perform analysis on the network aspect and in the vehicular aspect as an individual node. This analysis was made to verify which one can achieve the highest reliability and accuracy.

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 proposes a new framework to reconstruct vehicle trajectories from GPS datasets. GPS-embedded vehicles generate massive vehicular trajectory data that are crucial for many applications, such as route recommendation, traffic analysis, urban planning, and Intelligent Transportation Systems (ITS). In order to achieve that, it is necessary to employ processing techniques such as noise filtering, segmentation, and map matching to prepare the data to be properly used by real applications. The proposed framework aims to reconstruct the trajectories completely, even from low sampled data, which naturally present gaps. Experimental results show the effectiveness and efficiency of the framework to reconstruct trajectories with different sampling rates and different characteristics.

In this paper, we address a fundamental problem in vehicular networks, which consists of sending messages from a source vehicle to a destination vehicle. This problem becomes even more complex in the absence of fixed infrastructure or any other controlling entity. Although there are some solutions in the literature to work around this problem, they can cause significant network overhead and generate an amount of redundant data. In this regard, we develop a routing protocol that considers individual vehicular mobility as a determining factor for routing decisions. Through simulations using realistic vehicular mobility trace, we have observed that our strategy considerably decreases network overhead and the number of hops between source and destination while maintaining similar values for delivery ratio and latency.

In addition to being one of the primary means of transport, with the advent of sensing and communication technologies, buses belonging to the public transport system have gained a new role in urban centers. They have been applied as a powerful vehicular network that covers an entire city, called BUS-VANET. For the design and validation of solutions for this type of network, the nodes' mobility information is essential. For instance, data from the buses' GPS trajectories can be used to understand the dynamics of encounters between them. This knowledge can be applied to design applications and services for different users, besides providing the necessary information to properly manage this important public transport solution. However, real-world trajectories have several imperfections. In particular, GPS trajectories are heterogeneous, asynchronous, and typically contain a low sample rate. These characteristics impose certain limitations on the use of this dataset in the design of solutions for a BUS-VANET. In this work, we propose a hybrid method of calibrating trajectories based on historical information of trajectories and a road network to overcome these problems. We showed that our method surpasses the state-of-the-art techniques in several perspectives through evaluation with realistic data.

Eco-driving techniques are methods that drivers can take in order to improve their vehicles’ fuel efficiency. One way of implementing such methods is to recommend changes in driving habits focusing on saving fuel. Recommendation systems in the context of efficient driving may take numerous data sources as input and serve multiple purposes. In this work, we develop a recommendation system that suggests changes in engine revolutions per minute (RPM) that reduce fuel consumption. We simulate the effects of this system using a vehicular sensor dataset that contains location, speed, RPM, and consumption. We also investigate the effects of including location data in the recommendations as a way to leverage local habits and behaviors. Both methods reduced fuel consumption in all recorded trips. Moreover, using only local data yielded a mean fuel reduction of 43%, whereas using the entire dataset reduced the fuel consumption in 56% on average. Upon analyzing the resulting changes, we noted that such a difference in fuel consumption is due to the local recommendations not having access to global optimal data points and accounting for local behavior that is affected by aspects such as terrain.

In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location-Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS' routes against Google Maps' routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Information (RI), and Area Tags (AT), to enrich T-MAPS' suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user's viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).

Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.

Tourism has become a global economic force; for this reason, offer better services for tourists is indispensable. We explore social media data to study how tourists move through time and space and the factors that influence their movements in four major cities worldwide. In addition, we propose a new approach, based on a topic model, that enables the automatic identification of mobility pattern themes, which, ultimately, leads to the better understanding of the profile of users. Our results have implications in several segments, enabling better services for tourists in different locations that can be culturally distinct.

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.

In this work, we propose Traffic Data EnrichmentSensor (TraDES), towards a low-cost traffic sensor for Intelligent Transportation System (ITS) based on heterogeneous data fusion. TraDES aims at fusing data from vehicular traces with road traffic data to enrich current spatiotemporal traffic data. In that direction, we propose a robust methodology to group spatially and temporally these different data sources, producing a vehicular trace with its respective traffic conditions, which is given as input to a learning-based model based on Artificial Neural Networks (ANN). Hence, TraDES is an enriched traffic sensor that is able to sense (detect) traffic conditions using a scalable and low-cost approach and to increase the spatiotemporal traffic data coverage.

Understanding the mobility of vehicles plays a fundamental role in the design of solutions for intelligent vehicular networks. Considering that different types of vehicles have different characteristics of mobility, we are interested in investigating how bus mobility impacts the formation of these networks. In this sense, we present clear understanding the bus mobility for vehicular networks. Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland. Particularly, our study reveals key features of a vehicular network obtained from a real bus mobility trace such as the network structure over the days and how the components of the networks are arranged in the space and time. Additionally, we investigate the potential of bus mobility for urban sensing. In summary, due to network fragmentation identified in our analysis, data dissemination mechanisms that use store-carry-and-forward and street-centric routing are more indicated for intelligent vehicular networks based on bus mobility. Moreover, we show how the use of buses as sensors can compose a powerful urban sensing infrastructure.

Understanding mobility is a fundamental task in the design of mobile networking solutions. The adoption of mobility traces is extremely relevant both to obtain a meaningful understanding of mobility and to create realistic simulation scenarios. However, those traces may have different features that lead to conclusions inconsistent with reality and, consequently, impact the performance of the proposed solutions. In this work, we propose a methodology to evaluate mobility traces considering their spatial and temporal aspects. Furthermore, we review real, publicly available, and widely adopted mobility traces and discuss the application them to vehicular networks. The results show that the use of mobility data for vehicular networks is extremely timely, but that they must undergo a process of quality improvement.

Currently, we have a huge amount of geo-referenced data (e.g., GPS records, cell phone data records, and geo-tagged social media) generated by humans and different sorts of vehicles. Such data provides fundamental observations about human activities and behaviors. The knowledge extraction from these data can offer unprecedented opportunities in different areas such as social and economic progress, urban planning, smart cities, and service provision. These are examples that can benefit from such analysis of this large data collection. In this tutorial, we will present and discuss what can be accomplished from the analysis of this mobility data considering different types of mobility data and the corresponding data analytics techniques.

The increase in traffic congestion in big cities causes negative impacts on several areas, such as health and the economy. One of the main alternatives to reduce traffic jams are Intelligent Transportation Systems (ITS) that use technologies such as inductive loops, Global Positioning System (GPS), and wireless communication. The vehicular networks enable the wireless communication in the vehicular environment. This communication may occur from Vehicle to Vehicle (V2V) or from Vehicle to Infrastructure (V2I). However, the installation and maintenance of the infrastructures necessary to cover urban areas may have high costs. Therefore, this work presents a new Distributed and low-overhead protocol for Traffic Congestion control (DisTraC) based on V2V communication. The protocol aims to reduce the average travel time of vehicles ensuring a low communication overhead. Simulations results shown that DisTraC outperforms other studies found in the literature in terms of communication overhead and is more efficient in reducing traffic congestion.

Vehicular mobility traces are datasets of vehicle location records in a region with high spatio-temporal precision. The access to this sensitive information can threaten the safety and privacy of drivers, given that the analysis of this data makes it possible to discover other contextual and latent information, such as daily home routes or workplace's address. To prevent this, many obfuscation and anonymization techniques have been proposed to mitigate the problem of user location privacy. In this work, we analyze an anonymization technique called mix-zone, which selects urban regions that promote the simultaneous anonymization of vehicles by changing their current pseudonym. We show how information about drivers' behavior in a city, such as their road preferences, can be used to reidentify their trajectories. To do this, we present a simple and efficient re-identification technique that uses only two georeferenced points as input data. We validate our technique with a real dataset of taxicabs, being able to re-identify up to 100% of the anonymized trajectories.

In this work, we apply information theory metrics to car trips logged by volunteers around the world and use quantifiers such as location entropy to reveal aspects of users' mobility, like the context in which trips happened. The dataset used in this work was collected from the enviroCar project and contains not only location logs but also sensor readings associated with each location. Information theory measurements can also reveal relationships between sensor measurements in order to reveal rare occurrences and reduce uncertainty. This work shows that it is possible to differentiate driving contexts and capture relationships among sensors using location entropy and mutual information, respectively. These contributions pave the way for developing new features that may ultimately improve traffic context classification results.

Vehicular sensing network consists of a promising remote sensing paradigm, in which a variety of new applications will be possible through the processing of data periodically collected by vehicles. However, the acquisition of a large amount of sensing data and the increasing demand for traffic cellular requires mechanisms to decongest the cellular network infrastructure. Thus, this work uses a centrality measure to propose an offloading scheme in which certain vehicles will collect data from their neighbors and transmit it in the cellular uplink. The presented article models the problem as a minimum d-hop dominating set and create a greedy algorithm to solve it. The evaluation considers a realistic dataset to evaluate the proposed approach. When compared with the non-offloading scheme, it shows a cost reduction of up to 82.09% in the best-case and 13.45% in the worst-case.

In this work, we present Dribble a learn-based timer scheme selector to manage topology changes caused by mobility in the Internet of Things (IoT) context. IoT has turned smart devices part of our everyday lives. They are in everywhere with many shapes, sizes, and capabilities. For IoT to become even more ubiquitous, it is necessary to overcome the challenges posed by mobility. One of them is the management of topology changes, especially at the network layer. Currently, routing protocols check the topology through an advertisement timer scheme. Such schemes face a basic trade-off between being fast to find topology problems and concurrently be energy and overhead control saver. Although there are timer schemes designed to mobile context, all devices are governed by the same one, which is a hard assumption since IoT is heterogeneous and naturally, devices have different behaviors. Thus, Dribble learns the devices' mobility pattern and then it assigns a custom-made timer scheme conveniently for each device. Our results show that personalized timer schemes present better performance than single traditional timer schemes such as Trickle Timer (TT) and Reverse Trickle Timer (RevTT).

Interpath contention is a phenomenon experienced in a MultiPath TCP (MPTCP) connection when its subflows dispute resources of shared bottlenecks in end-to-end paths. Although solutions have been proposed to improve MPTCP performance in different applications, the impact of interpath contention on the multipath performance is little understood. In this paper, we evaluated such phenomenon experimentally in disjoint paths-an ordinary multipath scenario where subflows dispute bottlenecks of paths physically disjointed in a connection. Under several path conditions determined from emulations of capacity, loss, and delay of bottlenecks, we analyzed the influence of MPTCP mechanisms such as packet scheduling, congestion control, and subflow management. Differently from other studies, we observed that the very first influence was caused by the current subflow manager, full-mesh, with dichotomous impact on the multipath performance when establishing several subflows per disjoint path. Experimental results showed that contention among subflows can lead to positive (goodput improvement) or negative (goodput degradation) impacts according to the bottleneck conditions. In certain conditions, simply establishing subflows in single-mesh, with at most one subflow per disjoint path, could avoid interpath contention while improving goodput significantly, by doubling the performance of full-mesh under different conservative congestion controls.

Recent advancements in underwater wireless sensor networks (UWSNs) are enabling day-one underwater monitoring applications. However, energy efficient and reliable UWSNs must be developed for achieving large scale and sustainable underwater monitoring and exploration applications. In this regard, the use of the underwater acoustic channel poses several daunting challenges. The acoustic channel is energy hungry and has low reliability, which shortens the UWSN lifetime and diminishes the performance of underwater monitoring applications. In the literature, both challenges have been addressed separately by means of duty-cycling and opportunistic routing, respectively. In this paper, we shed light on the symbiotic design of anypath routing and duty-cycling for sustainable UWSNs. We propose novel strobed preamble low power listening (LPL) and low power probing (LPP) methodologies for the design of asynchronous duty-cycling protocols, which symbiotically consider anypath routing for data delivery. Moreover, we develop an analytical framework for the performance evaluation of such a symbiotic design. Numerical results highlight potentials and drawbacks of this proposed approach in different classes of UWSN applications, and provide useful insights for the future symbiotic design of opportunistic routing and duty-cycling protocols for UWSNs.

Software Defined Vehicular Network (SDVN) is a new network architecture inspired by the well-known Vehicular Ad Hoc Network (VANET), applying the concepts of Software Defined Network (SDN). The SDVN proposes a complete data flow management by a module that controls the routing actions. However, it is necessary to verify that the security requirements are still satisfied. This paper presents Sentinel, a new defense mechanism to detect flooding attack by time series analysis of packet flow and mitigate the attack creating a flow tree to find out the source of spoofed packets. We divided the results between the detection rate of victim vehicles and the efficiency of mitigation method. The algorithm was able to mitigate the attack flow in different parameters. Sentinel reached an average mitigation rate of more than 78% in all densities scenarios.

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.

An increasing number of users have been adopting route recommendation systems, mostly motivated by the convenience that those systems bring to their traffic experiences. Usually, those systems observe the historical and current traffic conditions in order to evaluate and recommend the fastest routes. However, besides mobility aspects, more contextual information such as unplanned street events and neighborhood safety, are not taken into account in the recommendation process. With this in mind, we propose a platform to support context-aware route recommendation systems. The proposed platform aims to improve existing recommendation algorithms or enable the proposal of new ones. To assess it, we use datasets of routes suggested by Google Maps in the city of Curitiba, Brazil, official open data provided by the city and also data generated voluntarily by citizens in a participatory sensing fashion. Our results show the existence of an opportunity for route planners to provide personalized services to users, which is an important step towards the development of context- aware vehicular networks. Besides, these results illustrate how publicly available big data can be explored to improve context-aware route recommendations.

Vehicular Networks represent an emerging technology that enables the use of network services by drivers and vehicle passengers. However, the heterogeneity of vehicular networks, which can use various wireless technologies, represents a challenge for the development of communication protocols. The Software Defined Vehicular Networks (SDVN) paradigm allows flexible design of these new protocols. This work proposes a new geocast protocol for Software Defined Vehicular Networks. Simulation results reveal that the proposed protocol outperforms the Inter- Vehicular Geocast protocol and the flooding algorithm in terms of delivery rate and signaling overhead.

A social network is a valuable source of data that is useful to understand a wide range of events happening around the world, with each user being a potential contributor to accomplish this task. This work proposes a novel method to detect events in Twitter based on the calculation of entropy of the content of tweets in order to classify the most shared topic as an event or not. We observed that the entropy of the bigrams extracted from tweets are subject to a continuous phase transition when social media users start to react and interact with an event that is taking place. Hence, we propose a method to detect this phase transition, and consequently detect an event, and extract the keywords related to the corresponding event. We compared the performance of our method to other approaches of the literature and we observed that our method is the more regular among three metrics and reached the best overall performance. Furthermore, we present evidence that our method is very sensitive to correctly detect events that occur almost at same time.

Vehicular networks are seen as the key communication solution for intelligent transportation systems. An essential task for the development of solutions for vehicular networks is to understand aspects related to their communication topology along the time, mainly because it is directly impacted by vehicular mobility. In this sense, a natural question that arises is how can we model the communication topology in order to have a real representation of network connectivity? Particularly, this question becomes even more complex when we consider the dynamic behavior of mobility over time. In the literature, there are some efforts that aim to model the topology of a vehicular network to better understand its dynamics. However, we note that current approaches have limitations in the temporal perspective leading to the loss of important information. In this work, we show the strengths and weaknesses of current approaches in the characterization and analysis of vehicular network topology. In addition, we present how a model derived from the temporal network theory can be applied to capture the dynamics of a large-scale realistic vehicular mobility trace.

The feasibility of vehicular networks is directly related to the understanding of mobility patterns, which is a necessary knowledge for the elaboration and application of novel algorithms and technologies for such networks. Thereupon, mobility traces have an important role in the exploration of mobility patterns. Since mobility traces represent the movement of entities in real-life situations, their comprehensive study is a great opportunity to characterize mobility behaviors, which can then be applied to improve the design of mobile networks. In this work, we perform a thorough characterization of a taxi dataset regarding spatial and temporal characteristics, to better understand how these factors affect traffic conditions. To this end, we define a new metric, called path optimality, that represents the rate of trips that follow the shortest path between two points. We use this metric to understand what are the conditions that affect the mobility pattern of drivers on a large city.

The link aggregation technique not only provides robustness to computer networks, but can also be a solution to the link saturation problem. This technique combines several physical interfaces to create a virtual link, adding up their existing bandwidth. In addition to increasing the throughput for data transmission, such a technique provides a quick and transparent recovery if a particular link becomes unavailable. Considering that the use of Software-Defined Networking (SDN) in business environments increases every day, this research presents a way to create link aggregations in such environments. This brings a better availability of services, among other benefits. To do so, an architecture that allows automatic, scalable and self-adaptive link aggregations was defined and implemented. As a way of evaluating such implementation, three algorithms were created using different premises: Hash Table, Traffic Analysis and Virtual Round-Robin. All implementations were tested in virtual and real environments. In both, the Open vSwitch open platform was used for packet switching and the Ryu controller was chosen to control the switches.

The research community has been investigating the potential of processing and wireless communication of vehicles in a transportation system. In this case, VANETs aim to exploit the communication and sensing capabilities of vehicles to feed data into applications and services. VANETs also contribute to the improvement of Advanced Driver Assistant Systems and Intelligent Transportation Systems, which aim to provide services to users such as safer and more comfortable trips. Many of these systems need to authenticate their users, but they do so in a way that an attacking driver can use them. This work explores the driver identification as an extra authentication factor to local services and vehicular networks. In this respect, a virtual sensor was developed to determine the driver's identity, with precision above 98% in our experiments, using embedded sensor data. This virtual sensor was also used to identify a suspected driver. Besides, based on the suspect's identification, we discuss the impacts of these drivers in the data dissemination in a vehicular network.

In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to improve traffic and transit comprehension. Initially, we showed the significant correlation between Twitter's feed and traditional traffic sensors. Then, we presented the Twitter MAPS (T-MAPS) a low-cost spatiotemporal model to improve the description of traffic conditions through tweets. T-MAPS enhance traditional traffic sensors by carrying the human lens into the transportation system. We conducted a case study by running T-MAPS and Google Maps route recommendation, in which, we showed T-MAPS viability, as an additional traffic descriptor. As a result, we noticed the median of route similarity reached 62%, and for a quarter of the evaluated trajectories, the similarity achieved between 75% and 100%. Also, we presented three route description services, based on natural language analyzes, Route Sentiment (RS), Route Information (RI), and Area' Tags (AT) aiming to enhance the route information.

Oceans are a great unknown. To change this worryingly reality, underwater wireless sensor networks (UWSNs) have been proposed for the automated and real-time data collection from ocean, including the life and events beneath them. Currently, the underwater acoustic channel is the most viable technology for long-range underwater wireless communication, but its use impairs the data collection in UWSNs. It presents strong signal absorption and is severely affected by human-made and natural noise in the aquatic environment. Therefore, data collection in UWSNs is unreliable. In the recent years, opportunistic routing has been proposed to improve UWSN communication's reliability and, consequently, data delivery. However, not always the proposed opportunistic routing protocols will perform well, as the neighborhood configuration of a node might not be dense enough or at a maximum distance that would favor data communication. In this paper, we proposed the power control-based opportunistic routing protocol, named PCR, for reliable and energy-efficient data delivery in UWSNs. The proposed PCR protocol selects the most suitable transmission power level at each underwater sensor node, aimed at improving the packet delivery probability at each hop. To avoid the selection of high power transmission and the uncontrolled inclusion of neighboring nodes in the next-hop candidate set, which would drastically increase the energy consumption, the PCR protocol considers the energy waste that will occur in each neighboring underwater sensor node. Numerical results showed that PCR improves the packet delivery probability and reduces the energy waste for data delivery by adjusting the proper transmission power and selecting the suitable candidate set, leading to energy conservation when compared with related proposals presented in the literature.

There are many mobility models in the literature with diverse formats and origins. Besides the existence of studies that analyze and characterize these models, there is a need for a framework that can compare them in an easy way. MOCHA (Mobility framework for CHaracteristics Analysis) is a tool that characterizes and makes possible the comparison of mobility models without any hard work. We implemented 9 social, spatial and temporal characteristics, which were extracted from various (real and synthetic) distinct mobility traces. MOCHA has a classifying module that attributes each characteristic the statistic distribution that better describes it. As a validation process, all the traces were compared using the T-SNE method for data visualization, resulting in the approximation of similar traces. One of the advantages of using MOCHA is its ease of use, being able to read diverse traces formats and converting them to its standard format, allowing that different types of traces, such as check-in, GPS, contacts, and so on, to be compared. The metrics used in the tool can become a standard for trace analysis and comparison in the literature, allowing a better vision of where one trace belongs related to others. MOCHA is available for download at https://github.com/wisemap-ufmg/MOCHA.

Underwater wireless sensor networks (UWSNs) have been proposed for autonomous monitoring of underwater environments. Despite the recent advances on underwater acoustic modems, unreliable data delivery and high energy cost are two critical research problems that remain in UWSNs. In this context, energy-efficient and reliable data collection protocols must be proposed for UWSNs. In this paper, we propose to combine power control and anypath routing in order to simultaneously address both problems in UWSNs. We propose an analytical that considers the characteristics of power control, multiple next-hop forward nodes of anypath routing, underwater acoustic channel and aquatic environment. The model devised in this work is a mathematical tool to enable researchers and practitioners to study important performance metrics of UWSN applications, when power control and anypath routing are jointly explored. Moreover, it is helpful to provide insights for the future design of joint power control and anypath routing protocols for UWSNs. Numerical results show that for a reduced number of next-hop candidate nodes, data delivery can still be improved by increasing the transmission power of the sender. Conversely, for a high number of next-hop candidates nodes, a high transmission power leads to energy waste.

Learning about people's perception that emerges from urban areas has been an interesting multidisciplinary research goal because it has a great potential to ease the hard task of understanding intrinsic characteristics of urban areas. To this end, we propose an approach that explores spatial and semantic aspects in free-text messages shared on location-based social networks (LBSNs) for uncovering and mapping the perception reflected regarding urban outdoor areas. Studying outdoor areas of Chicago, we show that LBSN data carry valuable information about places and could also be used to extract urban perception, helping to better understand urban areas from many aspects. We demonstrate, through a survey with volunteers, that our approach has the potential to correctly capture the opinion considered by the users regarding the reflected perception of those areas, indicating that it could be a feasible alternative for the task under study.

Physical sensors are an important part of control systems, especially vehicular control systems. Sensor readings help drivers to control their vehicles as well as their internal systems while keeping a vehicle stable and running. Currently, a modern luxury car carries hundreds of diverse and precise sensors and not all of them are visible to the driver. However, there are phenomena and aspects for which there are no physical sensors available. Virtual sensors combine readings from multiple sensors in order to develop their own output values based on conditions and models, and, eventually, substitute and monitor failing physical sensors, as well as sense complex variables. Designing a virtual sensor is usually a difficult process due to the complexity of the different processing stages it comprises. This work studies the process of creating and prototyping vehicular virtual sensors, describing the development stages and presenting examples of virtual sensors created with a framework developed to facilitate the design process.

The focus of this work is on the detection of incidents that have a direct impact on the traffic of vehicles in large cities, such as, accidents, road constructions-renovations and traffic jams using Online Social Networks(OSNs). The proposed model aims to find problems being reported, as well as information on the location of the event. The results obtained were significant in the task of categorizing the incident, reaching up to 94% accuracy and 98% of general hits in the task of determining usual traffic incidents, besides promising results in obtaining references to the points in the city where the incidents take place, with up to 58% recall.

Opportunistic routing (OR) has emerged as a promising paradigm to the design of routing protocols for underwater sensor networks (UWSNs). However, despite of its advantages, it introduces a critical problem that has been neglected until now: the immutable transmission priority level of the next-hop forwarding nodes. This characteristic can lead to an overuse of a unique node (or a few of them), quickly depleting its battery, creating network partitions, shortening the network lifetime and, consequently, degrading the application's performance. In this paper, we shed light on the need for mechanisms for rotating the forwarding priority level between candidate nodes. We propose a baseline new lightweight energy-aware opportunistic routing (EnOR) protocol, leading to a balanced energy consumption and prolonged UWSN network lifetime. EnOR rotates the transmission priority level of the forwarding candidate nodes by considering the remaining energy, link reliability and packet advancement of them. Simulation results reveal that EnOR effectively extends the network lifetime as compared with other underwater sensor network opportunistic routing protocols.

People-centric sensing is a research topic that aims to obtain and analyze urban data from crowdsourcing, such as participatory and opportunistic sensing. Data provided by these sources increase our knowledge about different aspects of our lives in urban scenarios, which can help us to understand and address issues that cities face. Thus, the sustainable people participation is crucial to the development of this sensing paradigm. In this direction, we focus on a central element for the deployment of people-centric sensing applications: guarantee sustainable participation of users. For this, we discuss the existing challenges at the main components of an architecture to support people-centric sensing. In order to enrich this discussion, we also evaluate the incentive mechanisms used by Foursquare, mechanisms that could be used, with proper adaptation, in several types of sensing systems. Among the results, we found evidence that a specific type of incentive (mayorship-based) could be very effective to increase users' engagement. Moreover, we present a set of policies to be incorporated into an existing or new people-centric sensing architecture to complement traditional incentive mechanisms.

The recent exponential growth of the Internet of Things (IoT) and its mobile devices asks for the advancement of mobile networks technology. In such direction, current and next generations of cellular and vehicular networks foresees adhoc communication. In such paradigm, as entities are constantly moving, connections between them are intermittent and of little, if none, reliability. Recent studies have explored entities connections and how to design efficient ad-hoc communication algorithms for them. In this paper, using real mobility data, we study how and to what extent the entities mobility affects the network (global) and the entities (local) connectivity. We propose and leverage existing mobility metrics to capture both the depth and the spread of entities trajectories. Furthermore, we show how global connectivity is related to the depth of the trajectories, while local connectivity is related to the spread of the trajectories. Finally, as we use both human and vehicular mobility data, we discuss how their mobility nature can affect their mobility characteristics and connectivity.

The development of actions to reduce fuel consumption and emissions and increase transportation systems' efficiency have become a huge challenge. Thus, a low-cost solution to improve fuel efficiency and reduce environmental damages is eco-driving, a group of behaviors focused on improving these aspects. Fuel consumption varies according to different factors: two different vehicles are expected to consume more or less fuel according to their engines' sizes or depending on the person who is driving them. In this work we present a gear virtual sensor for manual transmission cars, which adds information to understand drivers' habits, allowing to analyze individually each gear in relation to consumption. Our methodology developed gives the driver recommendations of the best gear considering speed and torque, reaching up to 29% averaged of efficiency in the fuel consumption and 21% averaged in CO2emissions reduction.

In D2D opportunistic networks, nodes need to cooperate acting as relays for transmitting messages to other nodes according to an opportunistic routing algorithm. To store these messages until they are propagated, each node uses a buffer with limited capacity. However, when multiple messages are forwarded in the network, the number of incoming messages may exceed the nodes' capacity, causing a buffer overflow. In this scenario, message dropping policies are very important to this problem, because when a message is dropped, there is a chance that other copies of this message still exist in the network. In this work, we propose a new buffer management algorithm for opportunistic routing in D2D networks named ST-Drop (Space-Time-Drop). We have evaluated our solution in three different types of opportunistic routing algorithms: epidemic-based, probabilistic, and social-aware. We have conducted simulations using two different publicly available data sources and considered different network traffic loads. Compared to other message drop policies, ST-Drop obtained the highest message delivery ratio in all considered scenarios and the lowest overhead when applied to the state-of-art social-aware and probabilistic routing algorithms, namely, Bubble Rap and Prophet.

In this work we propose, implement, and evaluate Group Regularity Model (GRM), a novel mobility model that accounts for the role of group meetings regularity in human mobility. We show that existing mobility models for humans do not capture the regularity of human group meetings present in real mobility traces. We characterize the statistical properties of such group meetings in real mobility traces and design GRM accordingly. We show that GRM maintains the typical pairwise contact properties of real traces, such as contact duration and inter-contact time distributions. In addition, GRM accounts for the role of group mobility, presenting group meetings regularity and social communities' structure. Finally, we evaluate state-of-art social-aware protocols for opportunistic routing and show that their performance in synthetic traces generated by GRM is similar to their performance in real-world traces.

Smart cities emerge in computer science as a topic to cover how the technology of information and communication can be used in the urban centers to monitor its dynamics and allow the improvement of services for the citizens. In these urban centers, different methodologies are used in order to collect data and provide them to applications. These data come from several heterogeneous sources, thus there is an effort to integrate and standardize them before their use. Also, a significant amount of this data has spatio-temporal annotations, which may be used to analyze the city dynamics, such as the mobility flow. Due to these characteristics of the data generated in urban centers, and also the possibilities brought by their use and analyses, this work presents a novel approach to collect, integrate and perform some analysis tasks in mobility data from smart cities. Thus, the SMAFramework can analyze mobility patterns based on a Multi-Aspect Graph (MAG) data structure. To show the potential of the framework, it is proposed a method to analyze the saptio-temporal correlation between data from two different data sources in the same city. Real data collected from social media and a taxi system of the city of New York are used to evaluate this method. The obtained results allowed to understand some of the applicabilities of the framework and also provided some insights on how to use the framework to resolve specific problems when analyzing mobility in urban environments.

Recently, information-centric networking has been proposed to VANETs scenarios for improving content delivery of infotainment applications. Using the ICN paradigm, content-oriented search and in-network caching have the potential to improve content delivery in spatial- and time-dependent applications for VANETs and smart transportation. However, uncontrolled Interest packet transmissions for content search will result in a waste of resources and diminish the performance of VANETs' applications. In this paper, we propose a lightweight protocol to tackle the Interest broadcast storm problem during a content search in information-centric VANETs. The proposed protocol considers the distance between a current forwarder and its neighboring vehicles to opportunistically control redundant Interest packet transmissions in vehicular named data networking. Simulation results show that the proposed protocol improves the content delivery rate by 60% while decreases the Interest packet transmissions by 40%, in the scenario of a low number of content producers in the network.

Simulation is the most frequently adopted approach for evaluating protocols and algorithms for Vehicular Ad hoc Networks (VANETs) and Delay-Tolerant Networks (DTNs). Usually, simulation tools use mobility traces to build the network topology based on the existing contacts between mobile nodes. However, quality of the traces, in terms of spatial and temporal granularity of each entry in the logfile, is a key factor that impacts the network topology directly. Therefore, the reliability of the results depends strongly on the accurate representation of the real network topology by the vehicular mobility model. We show that five widely adopted existing real vehicular mobility traces present gaps, leading to fallible outcomes. In this work, we propose a solution to fill those gaps, leading to more fine-grained traces, which lead to more trustworthy simulation results. We propose and evaluate a data-based solution using clustering algorithms to fill the gaps of real-world traces. In addition, we also present the evaluation results that compare the communication graph of the original and the calibrated traces using network metrics. The results reveal that the gaps do indeed induce network topologies differing from reality, decreasing the quality of the evaluation results. To contribute to the research community, we have made the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.

The popularity of personal devices has been creating a new location-based content era, where users produce, share and access content anytime and anywhere. Mobile devices in the same area can cooperate to each other by using a Device-to-Device (D2D) communication and maximize the usage of network resources and allow the creation of new applications and experiences. In this paper, we investigate the dynamics of physical proximity of peers in a long-term study. We used one year of collected data from social media for an analysis of spatiotemporal features of how users encounter each other and the opportunities for content offloading in cellular networks. The results provided insights about the regularity of encounters, the role of routine and other spatial characteristics.

The growing importance of smart devices calls for effective user authentication mechanisms. We argue that state-of-the-art authentication mechanisms are either vulnerable to known attacks or do not meet usability needs. To address this problem we designed NomadiKey, a user-to-device authentication mechanism based on nomadic keyboard keys. NomadiKey increases security level by placing keys at different screen coordinates each time NomadiKey is activated. Besides, NomadiKey preserves usability by maintaining the traditional relative position of keys. We compare NomadiKey with other user authentication mechanisms under different attacks using statistical models and simulation. We also evaluate NomadiKey's usability with 18 users. Our results show that NomadiKey increases security compared to widely-deployed PIN authentication with limited impact on authentication times.

Lately, there has been a growing interest in connecting opportunistic routing (OR) and low duty-cycling methodologies in underwater sensor network (UWSNs) applications. This connection improves the data collection reliability and prolongs the network lifetime. When sensor nodes operate in a duty-cycling manner, the properly sleep interval selection and its on-the-fly adjustment should be addressed. Both tasks are challenging when opportunistic routing protocols are used at the network layer, as they should consider the presence of the next-hop candidates set. In this paper, we propose a modeling framework to evaluate the effects of the sleep interval on the energy consumption of duty-cycled UWSNs, which employ opportunistic routing protocol at the network layer. We investigate the sleep interval control problem in the OR scenarios, formulating it as an optimization problem with the goal of extending the network lifetime. Our simulation results show that different fixed sleep interval duty-cycles do not impact on the average energy consumption whereas the sleep interval control can prolong the UWSN lifetime.

Vehicular network applications are emerging to bring many benefits to the users during their journey. However, the design and deployment of such applications require studies that provide insightful information about the network formed by vehicles. To this end, in the last years researchers have been characterizing real mobility traces collected from taxis equipped with GPS devices. However, these traces present temporal and spatial gaps, as it was demonstrated in other studies. In this work, we compare the real original traces with their calibrated version (i.e., with no gaps) to show that the existing gaps expressively affect key metrics such as contact duration, inter-contact time, and network capacity. As result, we contribute to the research community by presenting important analysis of real vehicular network topology and by showing the importance of calibrating the traces before evaluating vehicular network applications.

In recent years, there is a growing research interest in smart city applications based on opportunistic communications. The opportunistic networks in these scenarios are composed of disconnections, network partitions, high delay and strong influence of human mobility. To cope with these challenges, social-inspired approaches have been proposed considering the structure of networks and personal user features, however, limited studies have explored temporal variations of features and influence of exogenous variables, disregarding adaptive forwarding policies recommended for dynamic scenarios. In this paper, we address these challenges while investigating both, the temperature and the season calendar, as environmental features able to model the behavior of users' mobility and peer contacts. The results showed distinct social and spatiotemporal features characterized by thermal conditions able to affect the network performance. We also identified critical points of temperature able to provide early signals about the network changes. Finally, our results indicate that environmental data are crucial information towards the design of the next generation opportunistic mobile networks.

In the era of mobile computing, understanding human mobility patterns is crucial in order to better design protocols and applications. Many studies focus on different aspects of human mobility such as people's points of interests, routes, traffic, individual mobility patterns, among others. In this work, we propose to look at human mobility through a social perspective, i.e., analyze the impact of social groups in mobility patterns. We use the MIT Reality Mining proximity trace to detect, track and investigate group's evolution throughout time. Our results show that group meetings happen in a periodical fashion and present daily and weekly periodicity. We analyze how groups' dynamics change over day hours and find that group meetings lasting longer are those with less changes in members composition and with members having stronger social bonds with each other. Our findings can be used to propose meeting prediction algorithms, opportunistic routing and information diffusion protocols, taking advantage of those revealed properties.

Traffic congestion causes drivers' frustration and costs billions of dollars annually in lost time and fuel consumption. In order to overcome such issues, this paper presents a mechanism for Intelligent Transport Systems named FOX (Fast Offset XPath), which aims to detect and manage traffic congestion in Vehicular Ad hoc Networks. FOX is implemented in a FOG computing environment, taking advantage of the aspects inherent to this platform, such as scalability, low latency, the importance of geographical location and network conditions. The focus is to reduce the time to process, reroute and notify vehicles. Simulation results show that the proposed mechanism can reduce the average trip time, CO2emissions and fuel consumption. In particular, the average trip time was decreased approximately in 32%, the average fuel consumption in 14% and the stop time in 59%.

Vehicular network applications may be benefited by the use of simultaneous network interfaces to maximize through-put and reducing latency. In order to take advantage of all radio interfaces of the vehicle and to provide a good quality of service for vehicular applications, we have developed an architecture that performs the management of the flow mobility based on some classes of application for vehicle network. Our goal is to minimize the time of handover between the rings of flows in order to meet the minimum requirements of vehicular applications, as well as to maximize the throughput. Simulations have been conducted to analyze the performance of the proposed architecture by comparing it to other previously devised architectures. As a result, the proposed architecture presented a low delivery time of messages, packets with lower loss and lower delay.

Congestion is a major problem in large cities. One of the main causes of congestion is the sudden increase of vehicle traffic during peak hours. Current solutions are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of these changes on near future traffic patterns. Hence, these approaches are unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another. With this issue in mind, we propose an intelligent traffic system called CHIMERA, which improves the overall spatial utilization of a road network and also reduces the average vehicle travel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is more efficient in forecasting congestion and is able to re-route vehicles appropriately, performing a proper load balance of vehicular traffic.

In underwater sensor networks, the design of energy efficient and reliable data collection protocols is a daunting challenge. In this context, topology control and opportunistic routing are promising techniques for improving reliability and conserve energy. However, due to the challenges of the underwater acoustic channel, the vast knowledge acquired and the solution proposed so far in the context of terrestrial wireless ad hoc sensor networks cannot be applied directly to underwater acoustic sensor networks. In this work, we shed light on network topology modeling from a routing viewpoint. We model the probabilistic multipath routing behavior driven by opportunistic routing protocols in underwater sensor networks. Afterward, we propose the PCen centrality metric to measure the importance of underwater sensor nodes to the data delivery task through opportunistic routing protocols. PCen is aimed to identify critical nodes that can be used to guide topology control solutions. Our simulation results consider different network densities and reveal the presence of a few number of nodes with high PCen centrality value that will have a high rate of carried traffic, being critical for the network performance.

Standard routing protocols for IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) are mainly designed for data collection applications and work by establishing a tree-based network topology, which enables packets to be sent upwards, from the leaves to the root, adapting to dynamics of low-power communication links. The routing tables in such unidirectional networks are very simple and small since each node just needs to maintain the address of its parent in the tree, providing the best-quality route at every moment. In this work, we propose Matrix, a platform-independent routing protocol that utilizes the existing tree structure of the network to enable reliable and efficient any-to-any data traffic. Matrix uses hierarchical IPv6 address assignment in order to optimize routing table size, while preserving bidirectional routing. Moreover, it uses a local broadcast mechanism to forward messages to the right subtree when persistent node or link failures occur. We implemented Matrix on TinyOS and evaluated its performance both analytically and through simulations on TOSSIM. Our results show that the proposed protocol is superior to available protocols for 6LoWPAN, when it comes to any-to-any data communication, in terms of reliability, message efficiency, and memory footprint.

The consumer electronics industry is witnessing a surge in Internet of Things (IoT) devices, ranging from mundane artifacts to complex biosensors connected across disparate networks. As the demand for IoT devices grows, the need for stronger authentication and access control mechanisms is greater than ever. Legacy authentication and access control mechanisms do not meet the growing needs of IoT. In particular, there is a dire need for a holistic authentication mechanism throughout the IoT device life-cycle, namely from the manufacturing to the retirement of the device. As a plausible solution, we present Authentication of Things (AoT), a suite of protocols that incorporate authentication and access control during the entire IoT device life span. Primarily, AoT relies on Identity- and Attribute-Based Cryptography to cryptographically enforce Attribute-Based Access Control (ABAC). Additionally, AoT facilitates secure (in terms of stronger authentication) wireless interoperability of new and guest devices in a seamless manner. To validate our solution, we have developed AoT for Android smartphones like the LG G4 and evaluated all the cryptographic primitives over more constrained devices like the Intel Edison and the Arduino Due. This included the implementation of an Attribute-Based Signature (ABS) scheme. Our results indicate AoT ranges from highly efficient on resource-rich devices to affordable on resource-constrained IoT-like devices. Typically, an ABS generation takes around 27 ms on the LG G4, 282 ms on the Intel Edison, and 1.5 s on the Arduino Due.

Predicting the traffic jam in urban areas is a challenge, specially when the goal is to perform short-term forecasting. We can find in the literature some advances in algorithms and techniques to handle this issue, but there is still room for innovative solutions. For example, new approaches considering different sources of information about city dynamics and urban social behavior. In fact, one of the goals of this paper is to show the benefits of using this type of data to improve short-term traffic prediction. This paper propose STRIP, a novel short-term traffic prediction model that combines logistic regressions with two urban data sources: historical data of traffic flow obtained from online maps, such as Bing Maps, and users' check-ins, shared on participatory sensor networks, which capture the routines of city inhabitants (here known as social sensors). Simulation results show that STRIP improves the accuracy of state of the art studies, specially when using data from social sensors as input.

Many Vehicular Ad-hoc Networks (VANETs) applications require that each vehicle knows precisely its current position in real time. The Global Positioning System (GPS) is technology most widely used to determine the positioning of vehicles in VANETs. However, the GPS has several drawbacks, one of them is the lack of accuracy of the measurement of impact is the most unacceptable disadvantage. In this work, we propose a roadside unit- based localization scheme to improve the accuracy level of the vehicles' position for VANETs. In this way, each one of roadside units fix the relative position error and informs all vehicles that are within of the coverage area. The proposed solution requires few roadside units, which represents a low-cost of deployment, and it was able to reduce GPS error in this critical area from 7.21 m to 0.74 m.

As an important component of Smart Cities, transportation system plays a critical role to address the sustainability and mobility of the society. One key concern is that the number of vehicles continuously increases faster than the available infrastructure, as well as the traffic congestion is a difficult issue to deal with. Several solutions to Intelligent Transportation Systems (ITS) have been proposed to identify congestion and re-route the vehicles afterwards. In this direction, this work introduces SPARTAN, a fully distributed ITS solution, which notifies drivers about congested areas through Vehicle-to-Vehicle communication and employs a real-time decision making mechanism used to reroute vehicles to avoid the congested areas. Simulation results show the effectiveness of SPARTAN in calculating new routes and disseminating them to vehicles that approaching a congestion area. As a consequence, SPARTAN reduces the travel time and the congestion time in urban scenarios when compared to existing approaches.

Underwater wireless sensor networks (UWSNs) have been showed as a promising technology to monitor and explore the oceans in lieu of traditional undersea wireline instruments. Nevertheless, the data gathering of UWSNs is still severely limited because of the acoustic channel communication characteristics. One way to improve the data collection in UWSNs is through the design of routing protocols considering the unique characteristics of the underwater acoustic communication and the highly dynamic network topology. In this paper, we propose the GEDAR routing protocol for UWSNs. GEDAR is an anycast, geographic and opportunistic routing protocol that routes data packets from sensor nodes to multiple sonobuoys (sinks) at the sea's surface. When the node is in a communication void region, GEDAR switches to the recovery mode procedure which is based on topology control through the depth adjustment of the void nodes, instead of the traditional approaches using control messages to discover and maintain routing paths along void regions. Simulation results show that GEDAR significantly improves the network performance when compared with the baseline solutions, even in hard and difficult mobile scenarios of very sparse and very dense networks and for high network traffic loads.

In this work, we study the impact of data aggregation in WSNs with mobile sink nodes that can move at higher speeds. In these cases, propagated queries cannot be answered by using the sink's position when the query was sent, since the sink node will probably be elsewhere. Therefore, conventional data aggregation schemes may not be appropriate due to this high speed. Thus, we propose and evaluate the performance of three new algorithms for data routing and aggregation in WSNs when the sink is moving at a high speed. These algorithms explore the sink movement to create a routing graph composed of the union paths that intersect the sink's trajectory. At each hop, the sink speed and current network delays are used as metric to guide in-network data aggregation. Our results clearly show significant energy savings and efficient data delivery achieved by the proposed algorithms in different scenarios.

The Medium Access Control layer in Wireless Sensor Networks handles the failures that occur when a node sends a frame to a dormant neighbor. Some protocols work in such a way that when a node shares its working schedule with its neighbors, each sender node schedules its transmission to the moment when the next node is awake. In others, the nodes do not share information and when one needs to send a frame, it first discovers the time when the next node will be awake. Both approaches are limited due to additional transmissions. This work proposes a hybrid policy that combines the existing ones in order to minimize their costs. Simulation results reveal that our solution often reduces the number of transmissions and energy consumption of the existing policies in the evaluated scenarios.

Some Inherent Vehicular Ad Hoc Networks (VANETs) characteristics, such as intermittent connectivity, highly dynamic topology, and hard delay constraints, make data communication a challenging task in these networks. Due to such peculiarities, in this work we study the impact of using vehicles predicted locations as a metric for data communication in VANETs. In our proposal, called LPRV (Localization Prediction-based Routing for VANETs), packet forwarding is performed by nodes with predicted future localization closer to the delivery destination, without the need for exchanging additional control message. The proposed algorithm also explores the knowledge of a digital map to limit the scope of message exchanges in the shortest path for vehicles between source and destination. We compared our proposal to both classic Flooding and SIFT (Simple Forwarding over Trajectory) algorithms. Our results clearly demonstrate the efficiency of the proposed solution in different scenarios, especially in terms of delivery rate, number of hops and delay, with a reduced number of message transmissions.

Traffic jams frustrate drivers and cost billions per year in time and fuel consumption. In order to avoid such problems, this paper presents an intelligent transportation system that collects real-time traffic information and is able to detect and manage traffic congestion based on this information. Simulation results show that the proposed protocol can reduce the average travel time, CO2 emission and fuel consumption. In particular, the average travel time was reduced in approximately 23%, the average fuel consumption in 9% and average CO2 emission in 10%.

Vehicular networks are seen as the basis for the development of intelligent transportation systems. Therefore, and in order to take advantage of the benefits from these transportation systems, the development of standards that meet the specific characteristics of vehicular networks was required. The "Wireless Access in Vehicular Environments" (WAVE) was developed in order to fulfill this need. This standard presents an architecture based on a division into multiple channels, which are set for certain types of applications, and it uses a switching mechanism for the selection of channels, since only one channel is active at a given time. However, in certain scenarios, this channel switching mechanism introduces an undesirable effect that allows different vehicles to transmit simultaneously, thus resulting in collisions. This work proposes a solution to this problem with the development of a mechanism based on the recalculation of the transmission delay, which will work along with other features, such as the broadcast suppression mechanism, in order to ensure greater performance in transmissions.

Overtaking vehicles is a risky task and could cause serious accidents, especially on two-lane highways. There are various efforts in order to make this a safer task. An alternative to this is the use of communication between vehicles and advanced techniques to decide the safest time for overtaking. Thus, in this paper we propose a driver assistance service that uses real-time information transmitted among vehicles and formal methods to calculate the trajectories and to assign the optimal behavior to overtake. For this, we used Probabilistic Model Checking (PMC), which explores all the possibilities of the system indicating the correct configuration to the vehicles involved. In the evaluated study case, the results showed that it is possible to find a secure configuration in a scenario with three vehicles and a collision's probability of 98%.

This work models the traffic congestion minimization as the multicommodity flow problem, which minimizes the amount of vehicles that travels in the same streets. Based on two constructive heuristics proposed in this study (Random Routes and Sorted Routes), we have developed and evaluated four new heuristics, being two local search and two metaheuristics of reactive GRASP. The results have shown that heuristics based on Random Routes have better performance, reducing the travel time in up to 56%, the CO2 emissions in up to 18% and fuel consumed in up to 18%, in average.

Most Vehicular Ad-hoc Network (VANET) applications require the delivery of a variety of content to vehicles. Furthermore, content in such applications is usually geo-localized (i.e., designated to a specific region of interest). To help with the delivery of geo-localized content in VANET applications, content replication strategies can be exploited to keep content available where the interested vehicles are expected to be. To this end, we propose a Geo-Localized Origin-Destination-based Content Replication (GO-DCR) solution that relies on the vehicles' origin and destination points to select the vehicles most likely appropriate to replicate content. We compare GO-DCR to an existing solution through simulation. The results reveal that GO-DCR increases the content availability while reducing delivery cost.

Vehicular ad-hoc network (VANET) is a special type of mobile networks that has received significant attention from the academia and the industry in recent years. VANET applications are emerging as new opportunities for the automobile industry to provide advanced services to connected vehicles and their users. Most of those services, like information, safety, and entertainment systems, require a variety of content to be delivered to vehicles. An useful concept in this case is the one of content replication, which defines that content should be placed close to its potential clients. However, existing replicating solutions, as originally proposed for the Internet, are not suitable to VANETs because of their particular characteristics, such as highly dynamic topology, existence of push-based applications, information-centric nature, location- and time-dependent content, and intermittent connections. Therefore, we propose and evaluate in this work new techniques to replicate content in VANETs that rely on the vehicles' origin-destination (O-D) points. The results reveal that our solutions were able to increase the content availability in two case studies, named city-wide and region-wide.

The problem of reliable data delivery at low energy cost arises as one of the most challenging research topics in underwater wireless sensor networks (UWSNs). Reliable data delivery is demanding because of the acoustic channel impairments and channel fading. Moreover, it is energy hungry given the high cost of acoustic communication. In wireless ad hoc & sensor networks, separately, opportunistic routing has been employed to improve data delivery whereas duty cycled operation mode has been adopted to achieve energy efficiency and prolonging network lifetime. In this paper, we investigate the benefits and drawbacks of collision between opportunistic routing paradigm and duty cycle techniques in UWSNs. We propose an analytical model to study and evaluate the performance of opportunistic routing protocols under duty cycled settings designed from three mainly paradigms: simple asynchronous, strobed preamble and receiver initiated; and different network densities and traffic loads. The results show that while duty cycle reduces the energy consumption, it affects negatively in the opportunistic routing performance, increasing the delay and the expected number of transmissions to deliver a packet. The simple duty cycled approach is shown to be suitable for applications that require long-lived network and can tolerate some degree of packet losses. Our results indicate that strobed preamble-based duty cycle is the most effective approach to be integrated with opportunistic routing, when high fidelity monitoring is required, even having not the best performance in terms of energy savings.

Simulation is the approach most adopted to evaluate Vehicular Ad hoc Network (VANET) and Delay-Tolerant Network (DTN) solutions. Furthermore, the results' reliability depends fundamentally on mobility models used to represent the real network topology with high fidelity. Usually, simulation tools use mobility traces to build the corresponding network topology based on existing contacts established between mobile nodes. However, the traces' quality, in terms of spatial and temporal granularity, is a key factor that affects directly the network topology and, consequently, the evaluation results. In this work, we show that highly adopted existing real vehicular mobility traces present gaps, and propose a solution to fill those gaps, leading to more fine-grained traces. We propose and evaluate a cluster-based solution using clustering algorithms to fill the gaps. We apply our solution to calibrate three existing, widely adopted taxi traces. The results reveal that indeed the gaps lead to network topologies that differ from reality, affecting directly the performance of the evaluation results. To contribute to the research community, the calibrated traces are publicly available to other researchers that can adopt them to improve their evaluation results.

This paper describes a formal probabilistic analysis of protocols and applications proposed in Vehicular Ad-Hoc Networks (VANET). Services using this technology have been studied and must be tested in a realistic way in order to work properly. Therefore, we have proposed a complete modeling structure which includes mobility, communication and signal propagation modules. We have used PRISM, a model checker for probabilistic systems, instead of traditional simulations. It determines exact probabilities and performance bounds,even if the model is non-deterministic. We present an analysis of a Vehicular Warning System involving three automobiles. The case study shows the influence of the initial positions, speed and timeout on communication, indicating that an interval of three seconds to broadcast packages is enough to guarantee 99% of reception's chance with a good message traffic. Furthermore, this work presents a practical example to future analysis of VANET.

Vehicular ad hoc networks (VANETs) are no longer a futuristic promise but rather an attainable technology. The majority of services envisioned for VANETs either require the provisioning of multimedia support or have this support as an extremely beneficial feature. However, the highly dynamic topology of VANETs poses a demanding challenge for the fulfillment of the stringent requirements for video streaming. In this paper, we provide a deep understanding of the issue of unicast video streaming over VANETs and propose a novel protocol, VIRTUS. In video streaming, many packets are transmitted consecutively in a short period of time. VIRTUS takes this into consideration and extend the duration of the decision of nodes to forward packets from a single transmission to a time window. Furthermore, VIRTUS calculates the suitability of a node to relay packets based on a balance between geographic advancement and link stability. We also propose an extension, that separates the process of relay node selection from the transmission of video content and adopts a density-aware mechanism that adapts its behavior according to local density. Consequently, VIRTUS makes use of the reactive aspect of receiver-based solutions while remaining scalable to increases in transmission rates and density. We report through extensive realistic experiments the benefits of using VIRTUS towards delivering video at a higher quality, in a timely fashion, with lower overhead and fewer collisions.

With the advancement of information systems, means of communications are becoming cheaper, faster, and more available. Today, millions of people carrying smartphones or tablets are able to communicate practically any time and anywhere they want. They can access their e-mails, comment on weblogs, watch and post videos and photos (as well as comment on them), and make phone calls or text messages almost ubiquitously. Given this scenario, in this article, we tackle a fundamental aspect of this new era of communication: How the time intervals between communication events behave for different technologies and means of communications. Are there universal patterns for the Inter-Event Time Distribution (IED)? How do inter-event times behave differently among particular technologies? To answer these questions, we analyzed eight different datasets from real and modern communication data and found four well-defined patterns seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is an extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We also show three potential applications of the SFP: as a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications, as a technique to detect anomalies, and as a building block for more specific models that aim to encompass the particularities seen in each of the analyzed systems.

The high quality video streaming in vehicular networks is an urgent topic to provide services on safety and infotainment on the road. To provide high quality video streaming, Forward Error Correction (FEC) is one of the most popular approaches to ensure the needed quality for video streaming by generating duplicated packets. However, several factors might cause problems to FEC in a VANET, like the limited resources of wireless networks, a highly dynamic topology and the large amount of data in video streaming. The duplicated packets might exceed the network capacity. Therefore, in this work, the retransmission mechanism is used to ensure the transmissions, rather than the FEC. The main problem of the retransmission mechanism is the delay. A multi-path solution based on a disjoint algorithm is proposed to reduce the interference and contention, leading to a higher transmission rate and an acceptable delay. In this solution, only I-frames are transmitted through the TCP protocol, and the inter-frames are transmitted through the UDP protocol. To improve the delay of TCP transmissions, a TCP-ETX algorithm is integrated to select the suitable path for TCP transmissions. Simulations are conducted and several results are presented by comparing them to other protocols. Based on the simulation results, the designed multi-path solution protocol provides a higher video quality with a reasonable delay than that of the other protocols.

Information dissemination applications (e.g. weather, news, advertisements, traffic, and so on) are among the most promising ones for vehicular networks. However, disseminating information to all vehicles in vehicular networks is not a trivial task because of their specific characteristics like high dynamic topology and different density values along time and space. The proposals found in literature focused on improved flooding schemes that, in realistic scenarios, may cause unnecessary overhead and redundant data. This work addresses the dissemination to all vehicles problem by assuming that some vehicles are more appropriate to be good disseminators than others. This hypothesis was validated through a large-scale urban mobility scenario and two heuristics were proposed to select the appropriate disseminators. Results showed that the proposed heuristics were able to achieve high coverage (more than 97% of vehicles) with low redundant data.

Efficient protocols for data packet delivery in mobile underwater sensor networks (UWSNs) are crucial to the effective use of this new powerful technology for monitoring lakes, rivers, seas, and oceans. However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. In this work, we present a feasible solution for improving the data packet delivery ratio in mobile UWSN. The GEographic and opportunistic routing with Depth Adjustment-based topology control for communication Recovery (GEDAR) over void regions uses the greedy opportunistic forwarding to route packets and to move void nodes to new depths to adjust the topology. Simulation results shown that GEDAR outperforms the baseline solutions in terms of packet delivery ratio, latency and energy per message.

Intelligent Transport Systems (ITS) can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a proposal for collaborative identification and minimization of vehicular congestion. CARTIM uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, the dissemination of consolidated information may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally (in vehicles), CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (e.g., vehicle density etc). The proposed technique also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related works) and minimize it (based on a heuristic).

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.

A Vehicular Ad Hoc Network (VANET) is a subclass of Mobile Ad Hoc Networks, which provides wireless communication among vehicles as well as between vehicles and roadside units. Providing safety, traffic efficiency and user comfort for drivers and passengers are promising goals of these networks. To reach these goals many envisaged applications for VANETs will rely on data dissemination, turning this task into an essential service. However, due to some inherent characteristics of VANETs, such as intermittent connectivity, highly dynamic topology, and diverse network densities, data dissemination is a challenging activity in this kind of network. Although data dissemination has been extensively studied in the literature, existing solutions do not effectively address the broadcast storm and network partition problems when considered together. To deal with both problems, we propose a network partition-aware geographical data dissemination protocol, which eliminates the broadcast storm and maximizes the data dissemination capability across network partitions with short delays and low overhead. The proposed algorithm is able to autonomically adjust message transmissions in the network. Simulation results show that our proposed approach provides good efficiency with respect to the coverage of event processing, delay and overhead.

Data dissemination protocols suffer a critical drop in performance due to the topology changes and dynamic behavior of vehicular scenarios, which leads to increases in overhead rate, delivery delay and packet losses. Aiming at develop a protocol easily adaptable to dynamic vehicular scenarios, we propose TODD - an adaptive Traffic-Oriented Data Dissemination protocol. It leverages real-time traffic information to dynamically choose the best relay vehicles. This process is done by analyzing a metric calculated for each candidate vehicle, which may give preference to specific vehicle characteristics such as speed and distance to destination, based on the current traffic information. Besides that, a centralized version of TODD (CTODD) is proposed in order to deal with the lack of real-time traffic information stored in the vehicles. Simulation results show that both protocols can deliver up to 55% more packets with up to 85% less overhead than other important protocols from literature.

Local maximum problem, where the node fails in determine the next-hop neighbor to continue forwarding the packet towards the destination, severely degrades the performance of geographic routing protocols. This degradation is more substantial in underwater sensor networks, that intrinsically have harsh environments and high energy consumption due to the underwater acoustic communication characteristics. In this work, we focus on the performance of the most commonly three methodologies used in design of local maximum recovery procedures of geographic routing protocols for underwater sensor networks: power control, bypassing void regions, and mobility controlled. Taking into consideration the underwater acoustic communication characteristics, we further develop a network energy consumption model for representative solutions of local maximum recovery procedure designed from these methodologies and then we evaluate their performance in terms of the improvements on routing task and the energy consumption. Simulation results show that all three methodologies improve the routing even in hard scenarios of lower network density whereas particularities of each methodology impacts on the network energy consumption.

Energy efficient and reliable communication are two very important and conflicting requirements in the design of large-scale, self-organizing wireless sensor networks (WSNs). By reducing the transmission power level of the nodes, energy conservation is achieved whereas the communication reliability is degraded. We propose a novel opportunistic routing protocol to reduce the energy consumption while keep the communication reliability in acceptable levels. Transmission power Control-based Opportunistic Routing (TCOR) saves energy by reducing the transmission power of the nodes while maintains the communication reliability by employing the opportunistic forwarding paradigm, leveraging the broadcast nature of wireless transmission medium. We propose an expected energy cost function for next-hop forwarder set selection, which considers the multiple available transmission power levels and the impact of each one on the next-hop packet reception probability.

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.

Motivated by studies of wireless ad hoc networks in obstructed environments, in this work we focus on the problem of establishing connectivity at an intersection of two roads, or streets, surrounded by obstacles. An instance of the problem is defined by four street segments meeting at an intersection point, a street width, a node density at each street segment and a transmission range of the communication nodes. The problem that we focus on consists in computing the probability that there is a communication path connecting all four intersecting street segments. We propose a simplified model that approximates the connectivity properties in this setting and compute a lower bound for the connectivity probability. We compare our result to a Euclidean Line-of-Sight (LoS) model and show that our model provides an accurate approximation, while greatly simplifying the analytical treatment.

We here investigate the potential of participatory sensor networks derived from location sharing systems, such as Foursquare, to understand the human dynamics of cities. We propose the City Image visualization technique, which builds a transition graph mapping people's movements between location categories, and demonstrate its use to identify similarities and differences of human dynamics across cities by clustering cities according to their citizens' routines. We also analyze centrality metrics of the transition graphs built for different cities, considering transitions between specific venues. We show that these metrics complement the City Image technique, contributing to a deeper understanding of city dynamics.

The use of topological features, more specifically, the importance of an element related to its structural position, is a subject widely studied in the literature. For instance, the theory of complex networks provides centrality measures that have been applied to a large variety of fields (e.g., social sciences and biology). In this work, we propose a new topological measure, the Sink Betweenness (SBet), which stems from the theory of complex networks but is adapted to Wireless Sensor Networks (WSNs) to capture relevant information for this kind of network. We also provide a distributed algorithm to calculate it, and show its applicability to two different scenarios. The first one is focused on data fusion applications for event-driven WSNs, where we devise a tree-based data collection algorithm that takes advantage of node centrality to improve the data fusion efficiency. The second scenario is focused on energy balancing problems, more specifically in a problem called energy hole , where nodes closer to the sink are more likely to relay a larger number of packets than those that are further. This phenomenon is strongly related to the topology induced by the deployment of nodes along the sensor field, and it can be effectively captured by the SBet metric. Thus, we devise a data collection algorithm that is able to distribute the relay task more evenly. Simulation results show that the SBet metric can be satisfactorily used in both scenarios. We compare the proposed approach with some of the most efficient available data fusion algorithms, and show that the proposed algorithm generates consistently good-quality data collection infrastructures which require significantly smaller overhead. The use of SBet allows to alleviate the energy-hole effects by evenly balancing the relay load, and thus increasing the network lifetime. These two applications illustrate how the topology awareness can be used to improve different network functions in a WSN.

Participatory sensing systems (PSSs) have the potential to become fundamental tools to support the study, in large scale, of urban social behavior and city dynamics. To that end, this work characterizes the photo sharing system Instagram, considered one of the currently most popular PSS on the Internet. Based on a dataset of approximately 2.3 million shared photos, we characterize user's behavior in the system showing that there are several advantages and opportunities for large scale sensing, such as a global coverage at low cost, but also challenges, such as a very unequal photo sharing frequency, both spatially and temporally. We also observe that the temporal photo sharing pattern is a good indicator about cultural behaviors, and also says a lot about certain classes of places. Moreover, we present an application to identify regions of interest in a city based on data obtained from Instagram, which illustrates the promising potential of PSSs for the study of city dynamics.

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.

An important issue in the design of a wireless sensor network (WSN) is to devise techniques to make efficient use of its energy, and thus, extend its lifetime. When two or more WSNs are deployed in the same place and their sensors cooperate with the other networks, they may improve their operability, by extending its lifetime by trading routing favors or increasing the data entropy by a common data aggregation. Despite being obvious and simple, this idea brings with it many implications that hinder cooperation between the networks. Whereas a WSN has a rational and selfish character, it will only cooperate with another WSN if this provides services that justify the cooperation. The goal of this work is to present the Virtual Cooperation Bond (VCB) protocol, that is a distributed protocol that makes different WSNs to cooperate, enabling cooperation if, and only if, all the different WSNs benefit with the cooperation. In the simulation results, we consider WSNs with different configurations and we show that the proposed protocol enables cooperation solely when the cooperation is beneficial to both networks, and in this case, it saves their energies and extends their lifetimes.

Vehicular Ad hoc Networks (VANETs) have attracted the attention of the research community recently as they have opened up a myriad of on the road applications and increased their potential by providing accident-free and intelligent transport systems. The envisaged applications, as well as some inherent VANET characteristics make data dissemination an essential service and a challenging task in these networks. The existing solutions for data dissemination do not effectively address broadcast storm and network partition problems when considered together. To tackle these problems, we propose a novel GEographical Data Dissemination of Alert Information and Aware of Network Partition (GEDDAI-NP), which eliminates the broadcast storm and maximizes data dissemination capabilities across network partitions with short delays and low overhead. The simulation results show that the data dissemination performed by GEDDAI-NP provides better efficiency than other algorithms, outperforming them in different scenarios in all the evaluations carried out.

Transmission Control Protocol (TCP) suffers degradation in wireless networks because, among other factors, low quality links tend to interrup a TCP connection. Most of the routing protocols use the number of hops as path metric without considering the link condition. Expected Transmission Count (ETX) calculates the estimated number of transmissions to successfully transmit a packet, but it fails to calculate the correct number of transmissions for a TCP segment. In addition, the overhead of ETX is very high because it needs to probe all destinations to calculate the link loss probability. In this paper, we propose a novel cross layer path metric, TCP-ETX, which calculates the accurate number of transmissions for TCP segments and minimizes the protocol overhead. To evaluate our proposal, we conduct simulations and compare our solution to existing protocols, which shows that our protocol can offer a significant improvement on the TCP performance.

Underwater sensor networks have recently been proposed as a way to observe and explore the lakes, rives, seas, and oceans. However, due to characteristics of the acoustic medium, efficient protocols for delivering data must exist. In this work, we propose a novel geographic routing protocol with network topology control for underwater sensor networks, that adjusts the depth of the nodes in order to organize the network topology for improving the network connectivity and forward data where the greedy geographic routing fail. The proposed protocol is the first geographic routing protocol for underwater sensor networks that considers the sensor node vertical movement ability to move it for topology control purpose. The simulation results show that, with the topology control, the fraction of disconnected nodes and nodes located into communication void regions, are drastically reduced and consequently the delivered data rate is improved. It achieves more than 90% of data delivered even in hard and difficult scenarios of very sparse or very dense networks.

Wireless Sensor Networks (WSNs) are comprised mainly of resource-constrained sensor nodes that can be used to monitor areas of interest. In sensor networks, replacement of node batteries is usually infeasible and power consumption must be minimal to increase node lifetime. A large fraction of power consumption is controlled by the MAC layer, which orchestrates access to the wireless medium trading-off power consumption and throughput. Unfortunately, this orchestration requires exchange of additional data and itself ends up consuming energy. In this work we design a novel approach that does not require extra data to be exchanged. More precisely, we present ID-MAC: an identity-based MAC protocol for WSNs. In ID-MAC, sensor nodes compute when they should wake up in order to transmit and receive frames from their neighbors without exchanging coordination messages. Our evaluation shows that, by eliminating coordination messages, ID-MAC saves energy and prolongs the network's operating lifespan.

In this work, we propose a new Greedy Forward algorithm to perform routing and data aggregation in WSNs. Differently from current Greedy Forward algorithms, our approach takes advantage of a sink node capable of long-range communication and uses the RSSI (Received Signal Strength Indicator) of exchanged packets to aggregate and forward data. Based on this, we propose the GRDA (Greedy Routing and Data Aggregation) algorithm with two different variations: GRDA Selection and GRDA Election. In the GRDA Selection, neighbors exchange packets with RSSI information and the next hop of the packet is then selected from a routing table at each step. In the GRDA Election, the next hop is dynamically elected at each step and no extra packets are required for the routing task. Our approach also takes advantage of RSSI values to create a time metric to guide in-network data aggregation. Our results clearly show significant energy savings and efficient data delivery achieved by the proposed algorithms in different scenarios with all the benefits of a Greedy Forward algorithm.

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.

Cities are not identical and evolve over time, and sensing in large scale can be used to capture these differences. Research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in restricted scenarios (e.g., factory). However, sensing large scale areas, such as big cities, brings many challenges and incurs high costs related to system building and management. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices, and building what has been named participatory sensing systems. This work analyzes an emerging type of network derived from this type of system, the Participatory Sensor Network (PSN), where nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on four datasets of participatory sensing systems (27 million of records), we show that this type of network brings many challenges related to structural problems, e.g. instant coverage very limited, and also because of big data issues, which may restrict the use of this emerging type of network. However, it presents also, as shown here, many advantages and open opportunities, mainly related to large scale study of cities dynamics.

Under-Water Wireless Sensor Networks (UWSNs) are a particular class of Wireless Sensor Networks (WSNs) in which sensors are located, as the name suggests, underwater. Applications of UWSNs range from oceanographic data collection to disaster prevention. UWSNs are vulnerable to attacks and because of their idiosyncrasies, security solutions for ground WSNs might not be applicable underwater. As a result, there is a need for mechanisms exclusively tailored to underwater environments. In this work we address the problem of authentication in UWSNs. We evaluate energy costs for different digital signature schemes for end-to-end authentication and discuss the tradeoffs involved in a number of scenarios. Our results show that schemes that perform well in ground WSN do not necessarily do well in UWSNs; and shed light on characteristics of a digital signature scheme that make them particularly suited to underwater networks.

Localization and synchronization are fundamental services in Wireless Sensor Networks (WSNs), since it is often required to know the position and the global time of sensor nodes to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes' deployment. Since manual configuration of sensor nodes is an impractical activity, it is necessary to rely on specialized algorithms to solve the localization and synchronization problems. With this in mind, in this work we propose a joint solution for the 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with a GPS flies over the sensor field area broadcasting its geographical position. Therefore, sensor nodes are able to estimate their own geographical position and global time without the need of equipping them with a GPS device. By means of simulations, we show that our proposed joint solution leads to smaller time-synchronization and localization errors when compared to existing solutions.

In this work, we analyze an alternative model for obstructed wireless networks. The model is based on a grid structure of one-dimensional street segments and two-dimensional street intersections. This structure provides a realistic representation of a variety of network scenarios with obstacles and, at the same time, allows a simple enough analysis, which is partly based on percolation theory and partly based on geometric properties. We propose three different ways of modeling the geometric part of the network and derive analytical bounds for the connectivity probability and the critical transmission range for connectivity in the network. Finally, we present extensive simulations that demonstrate that our analytical results provide good approximations, especially for high density scenarios.

Underwater sensor networks have recently been proposed as a way to observe and to explore the lakes, rivers, seas, and oceans. A challenging issue in these networks is the communication, mainly due to the impairments of the acoustic transmission. Thus, efficient mechanisms to improve the data delivery must be proposed. In this work we present a novel anycast greedy geographic forwarding protocol and two topology control mechanisms. The proposed geo-routing protocol considers the anycast network architecture in the data forwarding process. The proposed centralized topology control (CTC) and distributed topology control (DTC) mechanisms organize the network via depth adjustment of some nodes. The simulation results show that with these mechanisms, the data packet delivery ratio achieves more than 90% even in hard and difficult scenarios of very sparse or very dense networks, the end-to-end delay and energy consumption per delivered packet is reduced.

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 Ad hoc NETworks (VANETs) have been widely used to provide data exchange services in Intelligent Transport Systems (ITS), which should facilitate user's routines. At the same time that user's routines are improved, they can also be used as an important source of data for helping the decision-making process of VANET services. These routines can be acquired from vehicle's trajectories through the use of Global Positioning Systems (GPS). In this context, the combination of location-based data and data forwarding methods brings interesting challenges since these topics are not directly related. Therefore, the key idea of this work is to provide a geocast routing mechanism in VANETs based on daily movements of users in their vehicles. The main contribution is related to the delivery rate, which increases in partitioned and sparsely connected networks. The results show the efficiency of our new method to perform opportunistic routing based on location data, which discovers the best routes to forward packets through the network.

Vehicular Ad-Hoc Network (VANET) applications are context-aware since they need environment and local contexts to operate properly and help their users on many aspects, like indicating the best route to take or warning about accidents and traffic congestion situations. These applications need a context provider system responsible for collecting, analyzing, reasoning and making contexts available to them. In this work we propose a smart context provisioning middleware, called ConProVA, that provides context information to applications, is able to infer logical context and deals with conflicts of interest. We evaluate through simulation an instance of ConProVA considering an accident detection and avoidance application. The results show that it is possible to improve the users' satisfaction by reducing total travel time and carbon dioxide emission when accidents occur.

Evaluating large scale vehicular networks solutions is a great challenge once real experiments are not feasible yet and there are no large scale testbeds available. Thus, the most used evaluation technique is simulation, which requires a realistic mobility model to provide accurate results. However, building large scale realistic vehicular mobility models is a hard task and many important characteristics have been neglected, like the origin-destination (O-D) macroscopic one. In this work it is proposed a trace-based O-D macroscopic model inferred by the adoption of statistical techniques in order to characterize a validated traffic model. This work contributes to the research community by providing a more realistic O-D macroscopic model, which can be considered a complement for many traffic generators available in literature.

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.

How often do individuals perform a given communication activity in the Web, such as posting comments on blogs or news? Could we have a generative model to create communication events with realistic inter-event time distributions (IEDs)? Which properties should we strive to match? Current literature has seemingly contradictory results for IED: some studies claim good fits with power laws; others with non-homogeneous Poisson processes. Given these two approaches, we ask: which is the correct one? Can we reconcile them all? We show here that, surprisingly, both approaches are correct, being corner cases of the proposed Self-Feeding Process (SFP). We show that the SFP (a) exhibits a unifying power, which generates power law tails (including the so-called "top-concavity" that real data exhibits), as well as short-term Poisson behavior; (b) avoids the "i.i.d. fallacy", which none of the prevailing models have studied before; and (c) is extremely parsimonious, requiring usually only one, and in general, at most two parameters. Experiments conducted on eight large, diverse real datasets (e.g., Youtube and blog comments, e-mails, SMSs, etc) reveal that the SFP mimics their properties very well.

Large scale dense Wireless Sensor Networks (WSNs) will be increasingly deployed in different classes of applications for accurate monitoring. Due to the high density of nodes in these networks, it is likely that redundant data will be detected by nearby nodes when sensing an event. Since energy conservation is a key issue in WSNs, data fusion and aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. In this work, we propose a novel Data Routing for In-Network Aggregation, called DRINA, that has some key aspects such as a reduced number of messages for setting up a routing tree, maximized number of overlapping routes, high aggregation rate, and reliable data aggregation and transmission. The proposed DRINA algorithm was extensively compared to two other known solutions: the Information Fusion-based Role Assignment (InFRA) and Shortest Path Tree (SPT) algorithms. Our results indicate clearly that the routing tree built by DRINA provides the best aggregation quality when compared to these other algorithms. The obtained results show that our proposed solution outperforms these solutions in different scenarios and in different key aspects required by WSNs.

Wireless sensor networks are designed to be deployed in the physical environment to monitor a wide variety of real-world phenomena. These networks are composed of various small sensor nodes deposited in a given area. In these networks, the battery replacement is unfeasible and thus the energy is a critical resource that must be considered by all protocols. In order to reduce the energy consumption, protocols for MAC layer use techniques that turn off the radio of sensor nodes. However, in many protocols, all sensor nodes operate on the same static schedule in which they wake up periodically at the same fixed interval of time. This scheme might not be appropriate in dynamic scenarios in which the traffic varies. In these situations, it is desirable to adjust the operation mode of sensor nodes according with the network traffic. In this work, we present the Sensor-MAC with Dynamic Duty Cycle (SMAC-DDC), a new MAC protocol that adjusts the duty cycle of sensor nodes according with the network traffic. Through a cross layer interaction, SMAC-DDC uses the information of the network layer to adjust the value of the duty cycle of sensor nodes. Simulations results show that the proposed protocol reduces the latency and the energy consumption of a network, keeping the delivery ratio.

Video streaming capabilities over Vehicular Ad Hoc Networks (VANETs) are crucial to the development of interesting and valuable services. However, VANETs are a challenging environment to this kind of communication due to the dispersion and movement of vehicles. In this work, we present a feasible solution to this problem. The VIdeo Reactive Tracking-based UnicaSt protocol (VIRTUS) is a receiving-based solution that uses vehicles' current and future location for a selection policy of relaying nodes. It fulfills video streaming requirements without incurring into an excessive number of transmissions. Besides that, it outperforms other baseline solutions.

The growth of mobile computing has increased the demand for wireless communication, causing a higher demand for the wireless medium as well as spectrum pollution. Smart radios, also called cognitive radios, monitor the network to identify the best available channel, in order to avoid interference. This paper proposes a framework for the development and testing of protocols for wireless sensor networks that employ cognitive radios (CRSN). We also developed two spectrum decision protocols for CRSN, which provide distributed mechanisms to select the best wireless channel based on the application's QoS requirements. Simulations of low, medium and high noise scenarios have shown that the protocols improve the delivery rate by up to 69%, while keeping the delay and energy consumption unaltered.

In this work we propose a framework based on the small world features to design Heterogeneous Sensor Network (HSN) topologies with QoS. The framework creates three different topologies and each topology has its goals related to the latency and energy consumption during data communication. We also propose a routing algorithm that uses the created topologies in order to provide QoS in HSN. Simulation results showed that the three topologies can provide different levels of QoS that can be used in many sensor network applications. We also compare our framework to the literature topology to provide QoS in HSN and our framework presents better tradeoff between energy consumption and latency during data communication.

Vehicular Networks routing protocols must deal with several issues such as high mobility, high speed and, consequently, high disconnection rate among the network nodes. In this paper, we propose VCARP, a geocast routing protocol for Vehicular Networks that takes into account context informations of the network (such as nodes location and destination) to make routing decisions. It consists of a shared cache mechanism used to avoid packets loss due to full caches, and a flow-based routing to reduce network overhead caused by unnecessary packet retransmissions. Simulations show that the proposed protocol can increase packet delivery rate by 40% and decrease overhead by up to 89%, compared to another geocast protocol of the literature. In addition, we have studied the influence of cache size and neighbors discovery period duration on the packet delivery rate.

Event detection is a topic widely discussed in the wireless sensor network (WSN) community. The goal of this process is to identify when the collected data represents an event occurrence. The lack of uniformity when approaching this problem, such as the modeling of the data collected by sensors, is an obstacle to the comparison among the different proposals. In this work, we present an extension of the Diffuse framework for scenarios regarding event detection, while considering settings with inaccurate and fault-susceptible measurements made by non-ideal sensing devices in a noisy environment. Based on that framework, we compare a new proposed algorithm with a method based on the literature, obtaining hit rates of event detection above 72% in settings with 30% of sensor faults.

Transmitting video content over Vehicular Ad Hoc Networks (VANETs) faces a great number of challenges caused by strict QoS (Quality of Service) requirements and highly dynamic network topology. In order to tackle these challenges, multipath forwarding schemes can be regarded as potential solutions. However, route coupling will severely impair the performance of multipath schemes. In this work, we present a LocatIon-Aware multIpaTH videO streamiNg (LIAITHON) scheme to address video streaming over urban VANETs. LIAITHON uses location information to discover two relatively short paths with minimum route coupling effect. The performance results have shown it outperforms the underlying single path solution as well as the node-disjoint multipath solution.

Connectivity properties of wireless networks in open space are typically modeled using geometric random graphs and have been analyzed in depth in different studies. Such scenarios, however, do not often represent situations encountered in practice, like urban environments or indoor spaces, which are deeply affected by obstacles. In this work, we present a model for obstructed wireless ad hoc networks consisting of a set of n nodes, deployed at random in a lattice square of size g×g, with a common transmission range r. For positioning the nodes in the field, all segments are considered as one-dimensional, but for communication purposes, we add a parameter µ to model the segments' width. Our model can be used to study the structure of obstructed networks analytically, as well as to simulate and evaluate a variety of node deployment strategies and the resulting network topologies. We derive analytical forms for the probability of existing crossing links between parallel and perpendicular segments sharing an intersection, toward a first topological characterization of our model. Moreover, we compute a lower bound for the probability of connectivity at intersections between segments, and apply percolation theory to derivate the Critical Transmission Range for connectivity in the overall network, i.e., the minimum transmission range that generates communication graphs that are connected with high probability.

Information and Communications Technology is increasingly becoming a part of our daily activities. Nowadays, we are able to sense a broad range of entities in the world: from physical entities that comprise the Internet of Things (IoT) to social entities that include people in social networks. We can expect to have a ubiquitous sensing infrastructure in different environments, with varying needs and complexities. While these sensors/entities are mostly static, smart phones are taking center stage as the most widely adopted and ubiquitous computing device. Smart phones have enormous potential for the study of human social networks and human behavior "in vivo," in a natural context outside laboratories. Besides their computing power, smart phones are currently available with an increasing rich set of embedded sensors, such as GPS, accelerometer, microphone, camera, gyroscope and digital compass. Sensing vast areas becomes more feasible when people carrying their portable devices collect data and collaborate among themselves. Systems that enable sensed data in this way are named participatory sensing systems (PSSs). In those networks, the shared data is not limited to sensor readings passively generated by the device, but also includes proactive user observations. A challenge is how to obtain meaningful information from this large amount of data at different scales along the time. Some of the traditional techniques that have been used to process those distinct data sources are information fusion, data mining and machine learning. Notice that the consumers of such information can be, in one extreme, a large set of cooperating or non-cooperating entities, and, in the other, an individual or a "thing", for instance.

Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available, consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a Cloud-Offloaded GPS (CO-GPS) solution that allows a sensing device to aggressively duty-cycle its GPS receiver and log just enough raw GPS signal for post-processing. Leveraging publicly available information such as GNSS satellite ephemeris and an Earth elevation database, a cloud service can derive good quality GPS locations from a few milliseconds of raw data. Using our design of a portable sensing device platform called CLEO, we evaluate the accuracy and efficiency of the solution. Compared to more than 30 seconds of heavy signal processing on standalone GPS receivers, we can achieve three orders of magnitude lower energy consumption per location tagging.

The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

Data aggregation is one of the main methods to conserve energy in wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and, consequently, reducing communication costs. Most data aggregation protocols are generally based on a static routing scheme. Although those protocols can save energy by eliminating data redundancy, in dynamic scenarios, they can incur in high overhead to reconstruct the routing tree. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSNs. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which can adapt to different scenarios without incurring the overhead of the other methods. DST maximizes the number of overlapping routes and selects routes with the highest aggregation rate. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate efficiency and quality of the routing tree. Simulation results show that the routing tree built by DST provides the best efficiency compared with other algorithms outperforming them for different scenarios in all evaluations performed.

A typical Wireless Sensor Network (WSN) assumes a homogeneous set of nodes in terms of capabilities. However, this kind of network suffer from poor fundamental limits of latency during the data communication. Another model of WSN assumes a heterogeneous set of nodes with different capabilities (especially in terms of communication range and energy reserves) called Heterogeneous Sensor Networks (HSNs). In this work, we propose a tree-based approach based on small world concepts to design HSNs. The proposed model introduces an adjustable search space in order to find H-sensors to create shortcuts between them. The endpoints of these shortcuts are nodes with more powerful communication range and energy reserves to support the long communication range. The model was designed in such a way that the created shortcuts among the powerful nodes create a connected topology among them. As a consequence of this, it is not necessary that the nodes with lower capabilities forward messages as a bridge to interconnect the shortcuts. Simulation results showed that with just a few powerful nodes, a wireless sensor network can be tuned into a HSN with small world features. Also, when the shortcut addition creates a connected topology among the H-sensors, the data communication latency is significantly reduced.

Dense sensor networks are typically deployed for fine-grain monitoring in a wide range of applications. Due to this high density of nodes, it is very likely that both spatially correlated information and redundant data be detected by several nearby nodes, which can be exploited to save energy by these nodes that are sensing an event. In this work, we propose an Energy-Aware Spatial Correlation mechanism (ESC) based on correlation regions to perform efficient data collection. The sensed information of a region is forwarded to the sink by only one node inside that region. Also, the area of a correlation region can be changed dynamically by the sink node to achieve the required accuracy of the sensed information. Simulation results show that using ESC, an event can be sensed with 97% of accuracy, and 75% of the nodes' residual energy can be saved within the phenomena area when compared with the classical approach for data collection.

One of the main applications in Wireless Sensor Networks (WSNs) is the accurate monitoring of extensive areas. However, due to the high density of nodes, redundant and correlated data are usually sensed by nearby nodes over time. Since saving energy is a key aspect of these networks, it is important to take advantage of these correlations to decrease communication and data exchange. However, current proposals usually results in high delays and outdated data arriving at the sink node. In this work, we go further and fully exploit both spatial and temporal correlations to perform near real-time data collection in WSNs. Also, in our proposal, called Efficient Data Collection Aware of Space-Time Correlation (EAST), obtained results clearly indicate that an event can be sensed with a high accuracy of more than 99.7% while still saving the residual energy of the nodes in more than 14 times when compared to the accurate data collection strategy.

How often humans communicate with each other? What are the mechanisms that explain how human actions are distributed over time? Here we answer these questions by studying the time interval between calls and SMS messages in an anonymized, large mobile network, with 3.1 million users, over 200 million phone calls and 300 million SMS messages, spanning 70 GigaBytes. Our first contribution is the Truncated Autocatalytic Process (TAP) model, that explains the time between communication events (ie., times between phone-initiations) for a single individual. The novelty is that the model is ‘autocatalytic’, in the sense that the parameters of the model change, depending on the latest inter-event time: long periods of inactivity in the past result in long periods of inactivity in the future, and vice-versa. We show that the TAP model mimics the inter-event times of the users of our dataset extremely well, despite its parsimony and simplicity. Our second contribution is the TAP-classifier, a classification method based on the inter-event times and in addition to other features. We showed that the inferred sleep intervals and the reciprocity between outgoing and incoming calls are good features to classify users. Finally, analyze the network effects of each class of users and we found surprising results. Moreover, all of our methods are fast, and scale linearly with the number of customers.

Historically, energy management in computer science has been predominantly treated as an activity of hardware optimization. A great deal of the effort in this area is concentrated on component activation, deactivation, and resource scheduling in order to provide a reduction of total power consumption. This work focuses on the study of power consumption from the developer's point of view, using a reliable power measurement framework to validate the literature's premise that programming options, such as multiplication operations, are high consumers of power energy. Besides some elementary operations and authors' suggestions about alternatives for power consumption reduction on the programming stage, we also compare and evaluate two well-known and widely applied algorithms for large number multiplication: Karatsuba and Toom-Cook. The obtained results provide guidelines to the developer in the programming phase to choose, in some cases, the best technique to reduce power consumption, speed up the software, and establish a maximum power limit for the completed software.

Underwater Sensor Networks (UWSNs) and Aeronautical Ad Hoc Networks (AANETs) are Mobile Ad Hoc Networks (MANETs) that need to consider the 3D nature of the network. UWSN presents the opportunity for many applications such as coast surveillance, and 4D monitoring (space and time) for ocean and biology studies. In AANET, aircrafts can communicate among each other and enhance situation awareness. In this work we study the fundamental properties of 3D MANETs: Link Probability, Node Degree and Network Coverage. This work was motivated by fundamental problems currently faced in the deployment of underwater sensor networks and aeronautical ad hoc networks. Link Probability is important in MAC protocols development. Determining network coverage is important for sensing applications, i.e., how to guarantee sensing coverage. Node degree is useful in scheduling duty cycles. Since energy is a limited resource, utilizing duty cycles is a major technique to improve network lifetime. We present analytical results for link probability, node degree and network coverage for 3D MANETs assuming random uniform distribution. These results may be applied to a variety of scenarios, platforms and applications. In addition, we describe applications that would benefit from our results.

In this work, we propose a small world model to design Heterogeneous Sensor Networks (HSNs). The proposed model takes into account the communication pattern of this network to create shortcuts directed to the monitoring node, decreasing data communication latency. The endpoints of these shortcuts are nodes with more powerful communication range and energy reserves to support the long communication range. However, this kind of communication causes a high interference in the wireless channel. For this, the proposed model uses the multi-interface and multi-channel capability of the MAC layer to reduce the number of collisions during data communication. Simulation results showed that with just a few powerful nodes, a wireless sensor network can be tuned into a HSN with small world features. Also, when the shortcuts are directed to the monitoring node and assigned to a different wireless channel, the number of collisions and the data communication latency are reduced.

Sink nodes are responsible for aggregating data gathered by a Wireless Sensor Network (WSN) and transmitting them to a monitoring facility. Usually, these are static nodes that serve as gateways to infrastructured networks. In some applications, these sink nodes move around the monitoring area, usually in lower speeds (e.g., robots, dirigibles). In this work, we go further and study WSN scenarios in which sink nodes can move at higher speeds, such as an Unmanned Aerial Vehicle (UAV) or even an airplane. In these cases, propagated queries cannot be answered by using the sink's position when the query was sent, since the sink will be elsewhere due to its high speed. Thus, we propose and evaluate the performance of three algorithms for data query in WSNs when the sink is moving at a high speed. Our results show clearly the need for new algorithms for these scenarios as well as the good performance of our proposed algorithms.

Ubiquitous systems are generally embedded into the environment and provide their services all the time and everywhere. In order to increase transparency and personalization, ubiquitous applications are normally context-aware, i.e., they use information about entities of interest to adapt their services. Since they are connected to everyday elements, such systems are frequently shared by two or more users, who may provide conflicting contextual data. Therefore, these systems can reach an inconsistent state, in which they are unable to decide how to perform their intended adaptations. This work proposes a novel methodology that detects and solves conflicts of interest for ubiquitous context-aware applications with different characteristics. Besides, the developed approach considers the trade-off between users' satisfaction and resources consumption in order to select and apply a conflict resolution algorithm. Results obtained through simulations showed that the proposed solution is flexible, dynamic, and able to provide users' satisfaction as well as to save system resources.

Context-awareness allows ubiquitous systems to adapt their services according to the current users' characteristics and the surrounding environment. Most of the context-aware applications found in literature only consider external contexts like location and temperature. However, internal contexts, like feelings and physiological needs, could also be relevant to adaptation decisions. Nevertheless, these contexts are hard to be obtained because, usually, they can not be gathered by physical sensors. In this work it is proposed a system that infers feelings and physiological needs based on the semantic processing of sentences posted in Twitter by the users of ubiquitous context-aware applications. The preliminary qualitative results show that the inference system is accurate when the sentences are well-formed and do not present typos.

In wireless sensor networks, there are some network functions and applications that need to know the local time of a node in order to timestamp a sensed data. In general, it may be unfeasible to have a synchronization hardware in each sensor. In this work, we propose a heterogeneous wireless sensor network model based on small world concepts to improve synchronization algorithms for wireless sensor networks. This improvement has two goals: (i) reduce the synchronization error for large wireless sensor networks, and (ii) increase the resilience of synchronization algorithms in the presence of node failures. Therefore, we modified and evaluated the Flooding Time Synchronization Protocol in the proposed model. Simulation results show that the use of small world based models in wireless sensor networks can significantly reduce the synchronization error and also increase the resilience in the synchronization problem.

In wireless sensor networks, data aggregation is critical to network lifetime. It implies that data will be aggregated while flowing from multiple sources to a specific node named sink. The construction of routing trees aware of the data aggregation has a considerable cost and solutions in the literature are not efficient for scenarios where the events are of short duration. This paper presents the Dynamic Data-Aggregation Aware Routing Protocol (DDAARP) for wireless sensor networks. This novel protocol builds dynamic routes, which improve the cost and quality of final routing tree. It also reduces the number of messages necessary to set up a routing tree, maximize the number of overlapping routes, selects routes with the highest aggregation rate, and performs reliable data aggregation transmission. DDAARP was compared with two existing solutions reported in the literature regarding communication costs, delivery efficiency and tree quality created. routing tree built by DDAARP has the best quality of aggregation compared with other algorithms. Results show that DDAARP outperforms these solutions for different scenarios in all evaluations performed. Furthermore, it also shows that the proposed algorithm is a good solution for scenarios with short-term events and for events of long duration.

Ubiquitous systems are generally embedded into the environment and provide their services all the time and everywhere. In order to increase transparency and personalization, ubiquitous applications are normally context-aware, i.e., they use information about entities of interest to adapt their services. Since they are connected to everyday elements, such systems are frequently shared by two or more users, who may provide conflicting contextual data. Therefore, these systems can reach an inconsistent state, in which they are unable to decide how to perform their intended adaptations. This work proposes a novel methodology that can detect and solve conflicts of interest for ubiquitous context-aware applications with different characteristics. Besides, the developed approach considers the trade-off between quality of services and resources consumption in order to select and apply a conflict resolution algorithm. Results obtained through simulations showed that the proposed solution can provide reasonable users' satisfaction levels, as well as to save system resources.

Data aggregation plays an important role in energy constrained wireless sensor networks (WSN). Redundant data can be aggregated at intermediate nodes of a WSN reducing the number of messages exchanged and consequently reducing communication costs. In this work we consider the problem of constructing a dynamic and scalable structure for data aggregation in WSN. Although there are many proposed solutions to data aggregation in WSN, most of them build the data aggregation structure based on the order in which events occur. This kind of structure leads to low quality routing trees and does not address the load balancing problem, since the same tree is used throughout the network life. To tackle these challenges we propose a novel routing protocol called Dynamic and Scalable Tree (DST), which reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, and selects routes with the highest aggregation rate. The routing tree created by DST does not depend on the order of events and is not held fixed along the occurrence of events. DST was extensively compared with two solutions reported in the literature regarding communication costs, aggregation rate and quality of the routing tree. Results show that the routing tree built by DST provides the best aggregation quality compared with other algorithms outperforming them for different scenarios in all evaluations performed.

An important aspect in the design of pervasive/ubiquitous applications is to evaluate them before their deployment in real scenarios. A possible strategy to assess them is to perform simulations that provide useful insights and results to the application designer. However, the simulation of pervasive/ubiquitous applications and environments has proven to be a considerable challenge for several reasons. For instance, many of the technologies (e.g., hardware and software) required to assess them either do not yet exist or are not momentarily available or accessible in terms of cost and readiness, not to mention other important requirements such as monitoring, communication management and profile/context state tracking of entities in the system. In order to make this process simpler and feasible, we defined a Web assessment tool that provides a simulation approach for ubiquitous environments by using Web technologies such as Asynchronous JavaScript and XML, Web application frameworks and a mapping/location Web service. Using these technologies, we implemented a pervasive service: a tourist guide service. With this tool, we were able to evaluate the proposed service quantitatively by studying the interactions between the users/entities involved, monitor their profile/context states, and track their movements across a predefined map area.

The context-aware computing is a research field that defines systems capable of adapting their behavior according to any relevant information about entities (e.g.,people, places and objects) of interest. The ubiquitous computing is closely related to the use of contexts, since it aims to provide personalized, transparent and on-demand services. Ubiquitous systems are frequently shared among multiple users, once they are designed to be embedded into everyday objects and environments such as houses, cars and offices. In scenarios where more than one user shares the same ubiquitous context-aware application, conflicts may occur during adaptation actions due to individual profiles divergences and/or environment resources incompatibility. In such situations it is interesting to use computer supported collaborative work techniques in order to detect and solve those conflicts, considering what is better for the group but also being fair enough with each individual demand, whenever possible. This work presents the important concepts on the collective ubiquitous context-aware applications field. Furthermore, it proposes a new methodology for conflicts detection and resolution that considers the trade-off between quality of services and resources consumption.

Data communication represents the greatest energy cost of wireless sensor networks. Both MAC and network layers of the protocol stack are responsible for data communication in these networks. This work proposes the Receiver- based Medium Access Control (Rb-MAC) protocol to be used in a combined solution with a receiver-based routing scheme. The main advantage of combining receiver-based MAC and routing protocols is that this solution does not use neighbor tables and, consequently, it saves energy when compared with MAC and routing protocols that use these tables. Simulation results reveal that receiver-based data communication has a great data delivery ratio, and reduces energy consumption, number of transmissions, and collisions when compared with a sender-based data communication. Moreover, we use a model checking technique to perform the formal verification of Rb-MAC.

In this work, we propose an on-line model to design heterogeneous sensor network topologies with small world features. The proposed model takes into account the data communication flow in this kind of network to create network shortcuts toward the sink node in a way that the communication between the sink and the sensor nodes is optimized. The end-points of these shortcuts are nodes with more powerful hardware, leading to a heterogeneous sensor network. We evaluate the on-line model and show that it presents the same small world features observed in the theoretical model. When the shortcuts are created toward the sink node, with a few number of powerful sensors, the network presents better small world features and interesting tradeoffs between energy and latency in the data communication when compared with the original model proposed in the literature.

The context-aware computing is a research field that defines systems capable of adapting their behavior according to any relevant information about entities (e.g., people, places and objects) of interest. Ubiquitous systems are closely related to the use of contexts and are frequently shared among multiple users, once they are designed to be embedded into everyday environments such as houses, cars, and offices. In scenarios where more than one user shares the same ubiquitous context-aware application, conflicts may occur during adaptation actions due to individual profile divergences and/or environment resource incompatibility. In such situations it is interesting to use some mechanism to detect and solve those conflicts, considering what is better for the group but also being fair enough with each individual demand, whenever possible. This work presents the important concepts in the collective ubiquitous context-aware applications field through a disaster emergency attendance application. Furthermore, the proposal of a methodology for conflict detection and resolution is described. The obtained simulation results showed how this methodology can maintain the application working properly while solving the collective conflicts, managing resource consumption and providing an interesting user satisfaction average.

Reprogramming is an important and challenging problem in wireless sensor networks because it is often necessary to in-network sensor processing. Thus, over-the-air programming is a fundamental service that relies upon reliable broadcast for efficient distribution. In this work we use small world features to improve the over-the-air programming. The small world based protocol takes into account the communication workflow of sensor networks to create shortcuts toward the sink, thus improving the reprogramming process. The endpoints of these shortcuts are nodes with more powerful hardware, resulting in a heterogeneous wireless sensor network. We then evaluate the behavior of the small world based protocol regarding the number of transmitted messages, energy consumption and time to reconfigure the network.

This paper presents the Data-Aggregation Aware Routing Protocol, DAARP, for wireless sensor networks. This novel protocol reduces the number of messages necessary to set up a routing tree, maximizes the number of overlapping routes, selects routes with the highest aggregation rate, and performs reliable data aggregation transmission. DAARP was compared to three existing solutions reported in the literature regarding communication costs, delivery efficiency, aggregation rate and aggregated data delivery rate. The results show that DAARP outperforms these solutions for different scenarios in all evaluations performed.

This work focuses on increasing the network coverage for stream traffic in disaster scenarios, which is often critical for preventing damages to property and, most importantly, loss of life. The technique described here, based on the so-called virtual access points (VAPs), and increases the network coverage in a non intrusive and transparent way. It creates a distributed and cooperative cache among the mobile nodes in the affected area. When using the VAP technique, nodes cooperatively work as virtual access points rebroadcasting messages they have in their own cache helping nodes that didn't have access to those pieces of message. The main advantages include that the proposed technique does not rely on any specific characteristic of the network, is transparent, and achieves highly improves the efficiency of stream traffic dissemination resulting in much lower message loss. Our results show that VAPs greatly enhance network coverage. In fact, for some of the scenarios presented, it allows the system to remain operational under conditions it would otherwise be inadequate for stream traffic.

The establishment of a localization system is an important task in wireless sensor networks. Due to the geographical correlation between sensed data, location information is commonly used to name the gathered data and address nodes and regions in data dissemination protocols. In general, to estimate its location, a node needs the position information of at least three reference points (neighbors that know their positions). In this work, we propose a different scheme in which only two reference points are required in order to estimate a position. To choose between the two possible solutions of an estimate, we use the known direction of the recursion. This approach leads to a recursive localization system that works with low-density networks (increasing by 40 percent the number of nodes with estimates in some cases), reduces the position error by almost 30 percent, requires 37 percent less processor resources to estimate a position, uses fewer beacon nodes, and also indicates the node position error based on its distance to the recursion origin. No GPS-enabled node is required, since the recursion origin can be used as a relative coordinate system. The algorithm's evaluation is performed by comparing it with a similar localization system; also, experiments are made to evaluate the impact of both systems in geographic algorithms.

In this paper we consider the problem of dissemination of stream based traffic using access points as roadside equipment. We consider how areas outside direct coverage can still be reached by means of other vehicles acting as virtual access points (VAPs). The developed technique, allows transparently extending the reach of roadside access points to uncovered road areas. VAPs are designed to avoid interference, operating in regions bounded outside any AP. The simulations show the presented mechanism to exhibit gain, in terms of received messages, of 30% and perhaps more significantly, that streaming data delivery becomes feasible.

In this work, we propose a new Greedy Forward algorithm to be used in Wireless Sensor Networks (WSNs) routing protocols. Differently from current Greedy Forward algorithms, which depend on the position information of nodes, our approach uses only the Received Signal Strength Indicator (RSSI) of exchanged packets. Since nodes' position are usually computed based on three or more distance estimations (commonly by means of RSSI measurements) and the positions of beacon nodes (which have inaccuracies by themselves), by using only RSSI measurements, we should achieve better results. Based on this observation, we propose the RSSR (Received Signal Strength Routing) algorithm with two variants: RSSR Election and RSSR Selection. In the RSSR Election, the next hop is dynamically elected at each step and no packets are required for the routing task. In the RSSR Selection, neighbors exchange packets with RSSI information and the next hop of the packet is then selected from a routing table at each step. Our results indicate clearly that the proposed algorithms have all the benefits of a Greedy Forward algorithm but with better performance and without requiring positions information.

In wireless sensor networks (WSNs), detected events need to be localized in both time and space. As a result, sensor nodes need to have precisely synchronized clocks as well as to be localized in a common spatial reference system. While synchronization and localization algorithms have been proposed to solve these problems independently, in this paper we propose to combine both synchronization and localization into a single problem that we refer to as the time-space localization problem. We then propose the enlightness algorithm (enhanced and lightweight time and space localization), a new and enhanced time-space localization algorithm for wireless sensor networks. Our proposed algorithm, based on a previously proposed time-space localization algorithm called Lightness, uses mathematical ranking and weighting techniques to enhance the time computation process. We present an extensive set of experiments to evaluate the performance of our new algorithm. Our results indicate clearly that the proposed scheme can scale to thousands of nodes (over 2048) while reducing the synchronization error by half when compared to the original lightness algorithm. In comparison to the FTSP algorithm (flooding time synchronization protocol), a well known synchronization algorithm, our proposed enlightness algorithm is able to synchronize the clocks of the sensor nodes with a four times greater precision in small networks and about a 10 times greater precision in large networks.

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.

Mobile Ad Hoc Networks present several new challenges, mainly due to mobility. Publish/Subscribe is a communication paradigm that fits this network model well since the entities involved in the communication process do not need to be online at the same time or know each otherpsilas addresses. In order to develop properly a Pub/Sub based Architecture for MANETs, it is essential that designers understand how mobility impacts the performance of any solution. This work presents an analysis of the influence of mobility on a previous solution and extends it based on a different perspective on mobility in MANETs. We show that in using this new approach we have managed to achieve better match rates while sending substantially fewer messages.

Data communication may have a strong impact on the design of a wireless sensor network (WSN) since the energy cost related to the data transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other "traditional" data networks such as the Internet. In a WSN, there is a special node called sink that is either the origin or the destination of a message whereas, in the other networks, data communication happens between arbitrary communicating entities. In this scenario, the latency and the energy consumption during communication between the sink node and the other nodes can be optimized using Heterogeneous Sensor Networks (HSNs) created by applying the small world concepts. In order to generate a network with small world features, one should add a small number of long-range links, called shortcuts, whose endpoints are equipped with more powerful hardware, mainly the energy resources. In this work, we propose and evaluate two small world models that can be used in the design of HSNs. In particular, the directed angulation toward the sink node model exhibits the most interesting tradeoff between energy consumption and latency and the sink node as source/destination model presents the best results related to energy consumption and latency in the nodes near to the sink.

An important issue in the design of a wireless sensor network (WSN) is to devise techniques to make efficient use of its energy, and thus, extend its lifetime. When two WSNs are deployed at the same place and their sensors cooperate with the other networks forwarding their packets, the distance of the transmissions decreases and, therefore, the power consumption as well. The goal of this work is to examine the extent to which different WSNs can cooperate and save their energy. Simulation results reveal that different densities and data collecting rates among WSNs, the routing algorithm and the path loss exponent have major impact in the establishment of cooperation.

The increasing use of wireless communications in mobile devices starts a new level of resource management. Users with mobile devices accessing wireless hot spots are a commonplace, and, thus, their management is becoming more important. Furthermore, some aspects of mobile devices are not yet well explored. For example, when the signal energy level at the wireless interface achieves a threshold value, the device increases automatically the transmission power. If the signal fading occurs due to an interference at the wireless channel, this increase in the transmission power will generate more interference. To avoid this problem, another type of management is needed.

A typical problem in wireless sensor networks is how to collect and send historical information from all sensor nodes to the base station. As the available energy of these nodes is a critical resource, it is typically not feasible to transmit a full-resolution data feed from each sensor node to the base station. Therefore, it is important to apply techniques of data reduction so that fewer bits can be transmitted. In this work, we propose an algorithm for differentiated codification such that the sensor nodes send only the differences of their readings to a common data base. Simulation results reveal that the algorithm performs well in applications where the sensor nodes collect similar readings.

In a wireless sensor network, data communication may have a strong impact on its design since the energy cost related to the transmission is typically much higher than the energy cost to perform data processing. Typically, data communication in a WSN tends to be different from other ldquotraditionalrdquo data networks such as the Internet. In a WSN, there is a special node called sink node that is either the origin or the destination of a message whereas in the other networks data communication happens between arbitrary communicating entities. In this scenario, the theory of complex networks is being employed in the design of wireless sensor networks, which have certain non-trivial topological features. One of the most well-known examples of complex networks is small-world network. In this work, we will use the small-world concept as a modeling technique to build efficient data dissemination in a wireless sensor network. We propose and evaluate two small-world models that can be used in the design of a WSN. In particular the Sink Node as Source/Destination model (SSD) exhibits the most interesting tradeoff between energy and latency allowing the design of strict applications that demand a small latency and energy consumption.

Wireless sensor networks (WSNs) are commonly treated as a distributed database system that is accessed by means of a query language. However, the computation of such queries are usually performed by information fusion techniques. Information fusion has been used by applications to detect/classify events, track targets, and filter noisy measurements. In this tutorial, we discuss some of the information fusion techniques that are currently used in WSNs.

Localization in time and space can be defined as the problem of solving both synchronization and positioning problems at the same time. This is a key problem for wireless sensor networks that need to determine the timing and location information of detected phenomena, especially for tracking applications. In this paper, we discuss the relationship between these two problems and propose the Mobilis (Mobile Beacon for Localization and Synchronization) algorithm, a new time-space localization algorithm for wireless sensor networks. The main aspect of the Mobilis algorithm is the use of a mobile beacon for both localization and synchronization. A mobile beacon is a node that is aware of its time and position (e.g. equipped with a GPS receiver) and that has the ability to move around the sensor field. In our algorithm, the synchronization component uses the packets required by the positioning component to improve its performance. Similarly, the positioning component benefits from the communication required by the synchronization component to decrease errors. We also present an extensive set of experiments and simulations to evaluate the performance of our algorithm.

A wireless sensor network (WSN) is energy constrained, and the extension of its lifetime is one of the most important issues in its design. Usually, a WSN collects a large amount of data from the environment. In contrast to the conventional remote sensing - based on satellites that collect large images, sound files, or specific scientific data - sensor networks tend to generate a large amount of sequential small and tuple- oriented data from several nodes, which constitutes data streams. In this work, we propose and evaluate two algorithms based on data stream, which use sampling and sketch techniques, to reduce data traffic in a WSN and, consequently, decrease the delay and energy consumption. Specifically, the sampling solution, provides a sample of only log n items to represent the original data of n elements. Despite of the reduction, the sampling solution keeps a good data quality. Simulation results reveal the efficiency of the proposed methods by extending the network lifetime and reducing the delay without loosing data representativeness. Such a technique can be very useful to design energy-efficient and time-constrained sensor networks if the application is not so dependent on the data precision or the network operates in an exception situation (e.g., there are few resources remaining or there is an urgent situation).

Monitoring applications define an important class of wireless sensor networks (WSNs). In these applications, the network perceives the environment and searches for event occurrences (phenomena) by sensing different physical properties, such as temperature, humidity, pressure, ambient light, and movement. In such cases, location in time and space of both phenomenon and nodes are usually required for tracking and correlation purposes. In the former case, nodes' clocks are synchronized, while in the latter, nodes' physical location (e.g., latitude, longitude) are discovered. The need for such information in WSNs and the proximity and similarity of both problems suggest they can be addressed as a single problem, which we call the localization in time-space. In this paper, we discuss the relationship between these problems and also propose the Synapse (synchronization and positioning for sensor networks), a time-space localization system for sensor networks. Simulation results show that the proposed algorithm is able to find the nodes' location while synchronizing their clocks with a precision of a few microseconds. A key advantage of Synapse is that it reduces the communication cost compared to the situation wherein localization and synchronization are separately solved.

This paper describes a technique to apply formal methods to verify protocols for mobile ad hoc networks. In contrast to other related proposals, our solution does not attempt to model any particular network configuration. Instead, our solution focuses on the possible implications caused by network configurations to the behavior of a routing protocol for MANETs. Following this strategy we were able to find design errors in some well established protocols. The proposed technique uses formal verification, more specifically model checking, to detect, in a simple way, problems such as routing loops, delivery message failures and errors in the protocol state machine.

This work presents a sampling data stream algorithm for wireless sensor networks (WSNs). The proposed algorithm is based on sampling techniques applied to data histograms created from original data streams acquired by sensor nodes. As a result, the algorithm provides a sample of only log n items to represent the original data of n elements. We show that by using our algorithm, we can save energy and reduce delay in WSN applications in different scenarios while keeping a good data quality.

A fundamental goal of a wireless sensor network (WSN) is to collect and deliver data to external applications. Due to the strong constraints of these networks, routing algorithms usually consider application-specific characteristics and, consequently, there is no self-contained algorithm appropriate for every case. In particular, many WSN applications are event- driven. In such scenarios, the behavior of the network may vary a lot, which favors different algorithms at different instants. In these cases, hybrid adaptive solutions are more suitable by allowing the adoption of a better routing strategy in response to the variation of the network conditions. In this work, we evolve this approach by considering the temporal characteristic of event occurrence and detection through an estimation model. The proposed solution, called multi, predicts the need of routing infrastructure creation and maintenance for the adaptation between a reactive and a proactive strategy. To show the advantages of the proposed approach, an instance of multi is evaluated through simulations. Comparisons with its independent reactive and proactive components and other proposed solutions show improvements on energy consumption.

Localization systems have been identified as a key issue to the development and operation of the Wireless Sensor Networks (WSN). A DV-Hop localization system works by transforming the distance to all beacon nodes from hops to units of length measurement (e.g., meters, feet) using the average size of a hop as a correction factor. Despite its advantages, a DV-Hop algorithm has some disadvantages, such as its large communication cost that limits its scalability, and its mapping from hops to distance units that introduces errors that are propagated to the computation of a node location. This last issue has been solved by some recent works, but the scalability problem still is an open problem that limits this technique to small or medium sized networks. In this work, we propose a novel approach that uses Voronoi diagrams in order to scale a DV-Hop localization algorithm while mantaining or even reducing its localization error. Two types of localization can result from the proposed algorithm: the physical location of the node (e.g., latitude, longitude), or a region limited by the node's Voronoi cell. The algorithm evaluation is performed by comparison with similar algorithms. We show how the proposed algorithm can scale in different aspects such as communication and processing costs when increasing the number of nodes and beacons.

This work presents the design of real-time applications for wireless sensor networks (WSNs) by using an algorithm based on data stream to process the sensor data. The proposed algorithm is based on sampling techniques applied to data histograms created from original data streams acquired by sensor nodes. As a result, the algorithm provides a sample of log n items to represent the original data stream of n elements. In this work, we show how to use the data reduction algorithm in real-time WSN design.

In this work, we propose an integrated solution to jointly solve the localization and data routing problems in sensor networks. In our solution, the localization system divides the network into Voronoi cells to reduce communication costs, and as a result, the system allows two ways for specifying a node location: the nodes ' physical location or the Voronoi cell it belongs to. This localization system yields a natural hierarchy based on Voronoi cells that is used by the integrated routing algorithm we propose. The proposed geographic routing algorithm may use either the nodes ' coordinates or the Voronoi cells to forward data packets. A key advantage of this routing algorithm is that no additional communication is required for route discovery, leading to a cost-free and very scalable routing algorithm. Such a dual solution, called Cellular (Cell Unified Localization And Routing) algorithm, to solve the localization and routing problems, represents a new trend of algorithms for sensor networks, where multiple problems are jointly solved to provide cheaper and integrated solutions.

This work presents and evaluates an end-to-end wireless network QoS architecture, named DiffMobil. DiffMobil is composed by DiffServ over the IP core network and by the following modules over the circuit switched network: QoS negotiation, congestion control (composed by diagnosis and admission control) and flow control. DiffMobil [9, 11] can be implemented over any kind of wireless network: GPRS, UMTS, IMS/NGN. The concepts proposed by each module, do not depend on the network technology. As ns-2 (Network Simulator) [14] was used, experiments to evaluate the DiffMobil guarantees were conducted with simulations of service requirements by GPRS users. GPRS was the technology chosen because we have implemented a complete GPRS network over ns-2. Performance metrics of a GPRS environment simulated through ns-2 show that DiffMobil offers end-to-end QoS.

There are many approaches that can be applied to the management of wireless sensor networks (WSN) and nowadays the research in this field is only beginning. A management approach determines how the management monitor and control functions are performed by the network and network elements. Since a wireless sensor network presents severe resource constraints, a certain management approach must be chosen properly in order to optimize resource usage and at the same time satisfy the application requirements. In this paper we try to answer the following question: what is the tradeoff between to migrate management services or to adopt traditional approaches for WSN management? We have concluded from simulated and experimental results that the choice of the best approach to be adopted must consider the size of the network, the configuration of the nodes, the application goals and the management complexity, in terms of managed objects. We have also noticed that, despite the service migration approach being theoretically interesting for WSN management, for the sensor networks and sensor nodes hardware current technological situation, the approach would be applied with some negative impact on the performance of the network.

Localization in time and space can be defined as the problem of solving both synchronization and positioning problems at the same time. This is a key problem for wireless sensor networks that need to determine timing and location information of detected phenomena, especially in tracking applications. In this paper, we discuss the relationship between these two problems and propose the Lightness algorithm (Lightweight Time and Space localization), a new time-space localization algorithm for wireless sensor networks. Our proposed algorithm not only takes advantage of the additional hardware resources required by a positioning system to improve the performance and scalability of the synchronization component, but also benefits from the additional communication needed by the synchronization component to decrease positioning errors. We also present an extensive set of experiments to evaluate the performance of our algorithm. Our results indicate clearly that the proposed scheme scales to thousands of nodes (over 2048) while keeping a synchronization error of a few microseconds (nearly 10µs) with an O(n) communication cost. The additional packets needed to compute clocks' drift can also be used to decrease the positioning errors to almost one third of the initial positioning.

A gossip protocol randomly decides the set of nodes that will forward a packet it received. Gossiping was proposed to be used in dynamic topology networks such as Wireless Sensor Networks (WSNs). This work proposes Gossiping using the Energy Map (GEM), a new gossiping-based protocol to perform energy-aware broadcasting in WSNs. The key idea is to change the random selection of neighbors in a way that the selection process uses the energy map. In our protocol, the routing flow is directed to the nodes with the greatest energy reserves, balancing the network energy and preserving nodes localized inside low energy regions to perform sensing tasks.

Quality of communication in wireless systems is heavily dependent on several factors including interference on the radio signal and mobility of the communicating devices. However, it is very difficult to assess the transmitted signal and predict when a bad quality signal is being caused by interference or mobility. This work presents a new method for monitoring and analyzing wireless communications on two types of networks: IEEE 802.11 and Bluetooth. This technique uses a multi-scale control chart approach known as MCEWMA - Moving Centerline Exponential Weighted Moving Average - associated with multi-resolution analysis. The implementation of this control chart allows the description of the transmitted signal on each wireless channel with low cost and, more important, it can be used on on-line applications. To prove the efficiency of this method, a simple application was developed and tested and the results proved the efficiency of the proposed method.

Abstract In this paper, we formulate and solve the problem of how to dynamically distribute network resources in third‐generation mobile telecommunication systems, aiming to minimize the equipment maintenance and service provision costs. A mobility simulator was implemented in order to represent a hypothetical city, which was populated with different groups of users, whose mobility behavior and demand for different kinds of services was generated in order to simulate a typical 24‐h day behavior. Given the generated demand at a certain period of time, a server allocation is made in order to attend it. This is done by modeling the system as an integer‐programming problem and by, afterwards, running an optimization algorithm on it. The output from the optimization process was used to reconfigure the system by activating the selected servers and deactivating the others. The problem turned out to be NP‐hard, so a heuristic method was necessary in order to solve it. We chose the Lagrangean relaxation technique to do the task, and obtained good results in terms of the solution proximity to the optimum. The performed experiments demonstrated the model's sensitivity to different kinds of parameters, such as user mobility, demand distribution over time and space, time of day, type of area where events occur, and the relation between fixed and variable costs of the system. A significant system cost reduction was also achieved. The proposed approach allows a personalized and demand driven resource distribution. Copyright © 2006 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) are characterized by having scarce resources. The usual way of designing network functions is to consider them isolatedly, a strategy which may not guarantee the correct and efficient operation of WSNs. For this reason, in this paper we propose an integrated design of network functions. We take two important WSN functions - density control and routing - as an example and present two approaches to integrate them. In particular, we present two solutions, named RDC-sync and RDC-integrated, which integrate a geographical density control algorithm with tree routing. The simulations experiments performed prove that the integrated design improves the network performance, especially when density control and routing are fully integrated

Wireless sensor networks are ad hoc networks comprised mainly of small sensor nodes with limited resources and one or more base stations, which are much more powerful laptopclass nodes that connect the sensor nodes to the rest of the world. The advent of this tecnology over the last decade enables largescale deployment of such sensors. On the other hand, this poses the challenging of how the great amount of information generated by these sensor networks will be handled at the base station. In this paper, we propose a network processor architecture tailored specifically to handling information at sensor network base stations. Our approach optimize information processing by implementing tasks in hardware. We show that the novel architecture is one order of magnitude faster than an architecture based on traditional RISC processor.

In resource-constrained wireless sensor networks (WSNs), communication protocols need to be energyefficient. In this context, information-fusion techniques have been used to reduce packet transmissions, and role assignment algorithms have been proposed to define which nodes should perform information fusion. However, most of the role assignment algorithms for WSNs are proactive in the sense that roles are assigned even when no event is being detected. In this work, we propose a reactive algorithm that assigns roles only when an event is detected, therefore saving energy during the network inactivity. In addition, to save energy during event notification, our algorithm searches for the shortest paths that maximize data aggregation.

Clustered sensor networks have been shown to increase system throughput, decrease system delay, and save energy. While those with rotating cluster heads, such as LEACH, have also advantages in terms of security, the dynamic nature of their communication makes most existing security solutions inadequate for them. In this paper, we show how random key predistribution, widely studied in the context of flat networks, can be used to secure communication in hierarchical (cluster-based) protocols such as LEACH. To our knowledge, it is the first work that investigates random key predistribution as applied to hierarchical WSNs

The goal of a wireless sensor network (WSN) management solution is to promote network resources productivity and quality of services. This paper presents a comparative study of client/server (CS) and mobile agent (MA) approaches used in distributed self-management solutions for a hierarchical heterogeneous WSN, in which the managers are embedded into the cluster head nodes. These approaches were simulated and evaluated according to energy consumption. Considering energy consumption with transmission, the results reveal that the MA approach is more scalable than the CS one, tending to be more useful when the number of network elements and managed objects increases. On the other hand, the CS approach presents more interesting results regarding energy consumption with processing. Besides, the simulations performed also show that the MA size is an important parameter that impacts directly on the results

Low-power devices require energy-aware design and dynamic power management techniques to reduce the power consumption. Communication intensive devices make the decision of shutting down idle components very difficult, specially in wireless communication. This paper presents a new system-level dynamic power management technique to reduce the power consumption of communication intensive sensor nodes in wireless sensor networks. It analyzes the application-level information to determine the need for communication and to selectively shutdown hardware components, specially the radio. The hybrid automata framework is used to dynamically change duty cycles, making the best trade-off between energy conservation and quality of service, according to the application requirements. Simulation results reveal that application-level information is crucial to reduce power consumption and to increase the sensor node lifetime.

The establishment of a localization system is an important task in wireless sensor networks. Due to the geographical correlation of the sensed data, location information is commonly used to name the gathered data and address nodes and regions in data dissemination protocols. In general, to estimate its location, a node needs the position information of, at least, three reference points (neighbors that know their positions). In this work, we propose a different scheme in which only two reference points are required to estimate a position. To choose between the two possible solutions of an estimate, we use the known direction of the recursion. This approach leads to a recursive localization system that works with low density networks (increasing in 40% the number of nodes with estimates in some cases), reduces the position error in almost 30%, requires 37% less processor resources to estimate a position, uses less beacon nodes, and also indicates the node position error based on its distance to the recursion origin. No GPS-enabled node is required, as the recursion origin can be used as the relative coordinate system.

Wireless sensor networks (WSNs) are ad hoc networks comprised mainly of small sensor nodes with limited resources, and can be used to monitor areas of interest. In this paper, we propose a solution for securing heterogeneous hierarchical WSNs with an arbitrary number of levels. Our solution relies exclusively on symmetric key schemes, is highly distributed, and takes into account node interaction patterns that are specific to clustered WSNs.

Wireless sensor networks are ad hoc networks with severe resource constraints. These constraints preclude the use of traditional ad hoc protocols, and demand optimizations that incur in solutions specific to a class of applications. This article presents PROC, a protocol designed for continuous data dissemination networks, that interacts with the application to establish routes, allowing the application to reconfigure PROC on runtime. A performance evaluation in topologies varying from 50 to 200 nodes showed that PROC increases network lifetime around 7% to 12%, and has higher throughput than EAD and TinyOS Beaconing. Furthermore, PROC presents a softer performance degradation when the number of nodes in the network increases.

Medium access control (MAC) protocols manage energy consumption on the network element during communication, which is the most energy-consuming event on Wireless Sensor Networks (WSNs). One method to mitigate energy consumption is to adjust transmission power. This paper presents two approaches to adjust transmission power in WSNs. The first approach employs dynamic adjustments by exchange of information among nodes, and the second one calculates the ideal transmission power according to signal attenuation in the link. The proposed algorithms were implemented and evaluated with experiments, comparing their results with B-MAC, the standard MAC protocol in the Mica Motes 2 platform. Results show that transmission power control is an effective method to decrease energy consumption, and incurs in a negligible loss in packet delivery rates. For node distances of 5m, the proposed transmission power control techniques decrease energy consumption by 27% over B-MAC.

Both mobile ad hoc networks (MANETs) and peer-to-peer (P2P) networks are decentralized and self-organizing networks with dynamic topology and are responsible for routing queries in a distributed environment. Because MANETs are composed of resource-constrained devices susceptible to faults, whereas P2P networks are fault-tolerant, P2P networks are the ideal data sharing system for MANETs. We have conducted an evaluation of two approaches for P2P content discovery running over a MANET. The first, based on unstructured P2P networks, relies on controlled flooding, while the second, based on structured P2P networks, uses distributed indexing to optimize searches. We use simulations to evaluate the effect of network size, mobility, channel error rates, network workload, and application dynamics in the performance of P2P protocols over MANETs. Results show that unstructured protocols are the most resilient, although at higher energy and delay costs. Structured protocols, conversely, consume less energy and are more appropriate for MANETs where topology is mostly static.

Mobile networks are becoming increasingly popular in enterprise environments as a means for distributing information to a large community of highly dynamic users. In comparison to traditional wired networks, mobile networks are distinguished by a potentially much higher variability in users demand due to user mobility. Most previous content distribution techniques assume a static user demand distribution and, thus, may not perform well in mobile networks. This paper proposes and analyzes a mobile dynamic content distribution network model, which takes demand variations into account to decide whether to replicate a content and whether to remove previously created replicas in order to minimize total network traffic. We develop two solutions to our model: an offline optimal solution, which provides an ideal lower bound on the total traffic, and a practical heuristic online algorithm, which uses demand forecasting to make replication decisions. We provide a thorough evaluation of our solutions, comparing them against ACDN, the only previous dynamic content placement algorithm targeting bandwidth minimization that we are aware of. Our results show that our online algorithm significantly outperforms ACDN, reducing total network traffic by up to 85% in a number of experiments covering a large system design space.

In this paper, a new data dissemination algorithm for wireless sensor networks is presented. The key idea of the proposed solution is to combine concepts presented in trajectory-based forwarding with the information provided by the energy map of the network to determine routes in a dynamic fashion, according to the energy level of the sensor nodes. This is an important feature of an autonomic system, which must have the capacity of adapting its behavior according to its available resources. Simulation results revealed that the energy spent with the data dissemination activity can be concentrated on nodes with high-energy reserves, whereas low-energy nodes can use their energy only to perform sensing activity or to receive information addressed to them. In this manner, partitions of the network due to nodes that ran out of energy can be significantly delayed and the network lifetime extended.

Summary form only given. This work provides a solution to identify malicious nodes in wireless sensor networks through detection of malicious message transmissions in a network. A message transmission is considered suspicious if its signal strength is incompatible with its originator's geographical position. We provide protocols for detecting suspicious transmissions - and the consequent identification of malicious nodes - and for disseminating this information in the network. We evaluate the detection rate and the efficiency of our solution along a number of parameters.

A fundamental issue in the design of a wireless sensor network is to devise mechanisms to make efficient use of its energy, and thus, extend its lifetime. Due to the importance of energy conservation, it is highly desirable to define the amount of energy each protocol can spend to perform its goal. Using this idea, we can associate a finite energy budget with each network activity, and ask this activity to achieve its best performance using only its budget. This should be considered a new paradigm to design algorithms for networks that are battery powered, specially for wireless sensor networks. In this paper, we present this new paradigm, and show how it can be used to construct the energy map of a wireless sensor network. Our goal is to construct the best energy map using only a defined amount of energy. Results show that we can approach these performance limits using the proposed finite energy budget model.

Wireless Sensor Networks (WSNs) have emerged as a new monitoring and control solution for a variety of applications. Although the behavior of a WSN is characterized by the type of its application, a common element exist: faults are a normal fact, not isolated events as in traditional networks. Thus, in order to guarantee the network quality of service it is essential for the WSN to be able to detect failures and perform something akin to healing, recovering from events that might cause some of its parts to malfunction. In this work we propose and evaluate a failure detection scheme using a management architecture for WSNs, called MANNA. We take a deep look at its fault management capabilities supposing the existence of an event-driven WSN. This is a challenging and attractive kind of WSN and we show how the use of automatic management services defined by MANNA can provide self-configuration, self-diagnostic, and self-healing (some of the self-managing capabilities). We also show that the management solution promote the resources productivity without incurring a high cost to the network.

The management of huge, heterogeneous and highly distributed networks is a very complex task due to their low degree of integration. Such a network is that of Telemar, the largest telecommunications company of Brazil. The plant is composed of a large variety of switching equipments and transmission systems, differing not only by their vendors but also by their technology, age and way of monitoring and control. In order to solve that management problem, a distributed management platform called SIS (system for integration of supervision) was developed. This platform is fully operational and implemented in a real telecommunications plant. SIS has been evolving since the mid 1990s, when it was initially deployed. The platform was conceived to allow the management of new systems and showed to be a key factor for the company as its business focus also evolves (e.g., the management of data communication networks of other companies such as a bank, an airline company, and a large retailer). We discuss the development and use of the SIS platform, its design and main modules.

A wireless sensor network aims to collect data and, sometimes, control an environment. This kind of network is composed of hundreds to thousands of devices that have the capability of sensing, processing and wireless communicating, called sensor nodes. The sensor nodes are projected with small dimensions (cm/sup 3/ or mm/sup 3/) and this size limitation ends up restraining the node resources, like energy, processor and transceiver capacity. The task of building and deploying management systems in environments where there will be tens of thousand of network elements with particular features and organization is very complex. This task becomes worse due to the physical restrictions of these unattended sensor nodes. In this paper we have implemented and evaluated some automatic services of configuration and performance management, proposed by a WSN management architecture called MANNA. This architecture is based on the paradigm of self-management, which contains the automatic functions and services of management using a minimum of human interference. This work aims to evaluate different WSN configurations considering an application of continuous data sensing and dissemination, and the effects of the management solution proposed for this network. The built application does temperature and carbon monoxide concentration level monitoring, in an urban area. The results show the cost-benefit relations of the different organizations and demonstrates that management can promote the productivity of the resources and control the quality of the provided services.

In this paper we present a new tool called Bean-Watcher. This tool allows the semi-automatic generation of monitoring and management applications for wireless networks such as WLANs and WPANs. The architecture of the tool is based on a component model flexible enough to allow the creation of new components and the optimization of the components currently provided. BeanWatcher was designed to offer a development environment suitable for both expert and beginner users allowing them to choose the programming language that better fits the application requirements.

Power consumption became a crucial problem in the development of mobile devices, especially those that are communication intensive. In these devices, it is imperative to reduce the power consumption devoted to maintaining a communication link during data transmission/reception. The application of dynamic power management methodologies has contributed to the reduction of power consumption in general purpose computer systems. However, to further reduce power consumption in communication intensive real-time embedded devices, we have to consider the state of the computation and external events in addition to power management policies. In this paper we propose a model of an Extended Power State Machine (EPSM), where we adapt a Power State Machine to include the state of an embedded program in the power state machine formulation. This EPSM model is used to adapt the Quality of Service (QoS) in communication intensive devices to ensure low power consumption. In such development, a middleware layer fits in the system's architecture, being responsible for intercepting the data communication and implementing the EPSM. Also, a software tool was developed, allowing the Middleware Code to be generated based on the State Machine. A case study demonstrates the application of the proposed model to a real situation.

A wireless sensor network is a special kind of ad-hoc network with distributed sensing and processing capability that can be used in a wide range of applications, such as environmental monitoring, industrial applications and precision agriculture. Despite their potential applications, such networks have particular features imposed by resource restrictions, such as low computational power, reduced bandwidth and specially limited power source. In case of a network with a high density of sensor nodes, some problems may arise such as the intersection of sensing area, redundant data, communication interference, and energy waste. A management application is necessary to make the most of network resources. On the other hand, a high-density network can introduce a fault-tolerant mechanism, increase precision, and provide multi-resolution data. The network density control depends on the application. In this paper, we propose a method to set up which nodes should be turned off or on. The management may take the sensor node out of service temporally. Our design uses a Voronoi diagram, which decomposes the space into regions around each node. That schema could be used in management architecture for a wireless sensor network.

Mobile ad hoc networks (MANETs) and peer-to-peer (P2P) applications are emerging technologies based on the same paradigm: the peer-to-peer paradigm. Motivated, respectively, by the necessity of executing applications in environments with no previous infra-structure and the demand for applications that share, in a satisfying manner, files through the Internet, MANETs and P2P applications have brought onto themselves some interest from the community. As a characteristic of the distributed model which they follow, such technologies face a difficult task of routing requests in a decentralized environment. In this paper, we conducted a detailed study of a Gnutella-like application running over a mobile ad-hoc network where three different protocols were considered. The results show that each of the protocols analyzed performed well in some scenarios for some metrics yet had drawbacks in others.

In this paper we report our experience of introducing Web interfaces into a real distributed management platform that provides a consistent and uniform way of accessing services such as fault management, performance management, business management, call center, and service management. The use of Web interfaces that allow to access these services was greatly felt by the organization and improved enormously the quality of service provided to the clients.

The design of a wireless local area network (WLAN) has two major issues: determining the best placement of base stations (BS) and assigning the frequency channels for those stations. The correct BS placement minimizes installation costs. Adequate channel assignment reduces signal interference and improve network throughput. This paper reports a real experience where we applied the concepts of two classical outdoor problems namely the optimal base placement problem and the fixed channel assignment problem to build a WLAN in an indoor environment.

A mobile ad hoc network (MANET) is comprised of mobile hosts that can communicate with each other using wireless links. In this paper we present a novel routing algorithm called GPSAL (GPS/ant-like routing algorithm) which is based on the GPS (Global Positioning System) and mobile software agents modeled on ants for routing in ad hoc networks. We compare our algorithm to the location-aided routing (LAR) (Ko and Vaidya 1998) algorithm for MANET which is also based on GPS. Simulation results show that our algorithm has less overhead than LAR.

Mobile computing will continue to widen as the world of computing increasingly becomes the world of access to network services and resources. We study the problem of given a network comprised of users (in our case mobile) and a set of servers that provide a service, we are interested in finding an association between users and servers that minimizes the cost to access the servers in the network. We present the mathematical formulation of this problem and a user mobility model that is used to perform some experiments. This is a initial step towards studying this problem that will be very important in a mobile setting.

In protocol conformance testing, there is an important class of errors, namely coordination loss that cannot be anticipated because some source of error is external to both the tester and the implementation under test, (IUT). Furthermore, it is not possible to simulate their occurrence exhaustively in the test environment. Therefore they are very difficult to catch in the testing phase. In this paper we propose use self-stabilization as a design principle of design for testability (DFT) to overcome this testing problem. This will improve the reliability of protocol implementations derived from self-stabilizing protocol specifications. We present a novel algorithm and the corresponding design principles to design self-stabilizing protocols, and give an example of a self-stabilizing protocol.

We propose a superimposition algorithm than can be employed by a substrate to change its behavior according to current conditions or properties of the mobile environment. The algorithm has some interesting features such as it is completely distributed, works on-line, does not generate extra messages, and is based on unstable predicates. The algorithm proposed can be used by multiple local controllers that use information only from the subset of the participating mobile units. This is certainly an advantage over a centralized approach that is likely to be more expensive than distributed decisions since there are delays in the communication and the cost of sending extra messages.

An important question in the current TCP/IP stack is how to extend the Internet technology and its protocols to support mobile computing. The existing protocols were not designed with that goal in mind. The simple network management protocol (SNMP), the most widely-used protocol for network management on TCP/IP networks, is no exception. In this paper we propose using a superimposition architecture to improve the performance of the SNMP protocol in a mobile computing environment. Network-awareness is very important to mobile applications which sense rapid changes in network quality. The focus of our work has been to make mobile applications achieve better performance using a superimposition algorithm to adapt to resource variations.

Communication software is an implementation of the communication protocols involved in a distributed system. In this paper we examine the important factors related to protocol design that affect testing and testability of communication software.>