Detecting sedentary behavior has increasing attention due to its significant health implications. However, distinguishing these low-intensity activities in federated learning scenarios is notably more complex than general human activity recognition. This complexity comes from heterogeneous feature distributions that can arise even for the same labeled activity, e.g., running and playing soccer, which may exhibit different sensor patterns despite both representing high-intensity activities. This paper proposes a robust personalized federated learning approach for sedentary behavior classification under adversarial conditions. Our method leverages ordinal pattern descriptors to extract meaningful symbolic representations from wearable sensor time series, then applies a meta-learning framework with Siamese Neural Networks to rapidly adapt across clients. Next, a reputation mechanism further safeguards the global model by penalizing malicious updates. Experiments on multiple public datasets show that our method achieves high F1-scores compared to baselines, affirming its ability to maintain robust performance in privacy-sensitive and adversarial environments.

Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models without directly sharing sensitive data. However, despite the absence of explicit data sharing, significant security risks remain, such as gradient inversion attacks that reconstruct private data from local client updates. To mitigate these risks, Homomorphic Encryption (HE) has been widely explored, allowing computations on encrypted data without decryption. However, HE introduces substantial computational and communication overhead, limiting its practical feasibility. To address this challenge, this work presents SlidHE, an efficient technique that integrates packing and packet sparsification methods with various selection strategies to significantly reduce overhead while preserving privacy and maintaining model accuracy, even in heterogeneous (non-IID) data settings. Experimental results demonstrate that SlidHE outperforms existing methods by effectively balancing efficiency, security, and performance. The full implementation of SlidHE is available on GitHub1.1https://github.com/AllanMSouza/SlidHE

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 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.

This paper introduces a novel hierarchical federated learning model, Sedentary-SMELL, for classifying sedentary behavior using wearable device data. Our methodology involves transforming sensor data into Ordinal Patterns (OP) for efficient representation, training a federated autoencoder to capture standard features, and clustering users based on similar activity patterns. We employ meta-learning within clusters for enhanced pattern comparison and conclude with personalized model finetuning, adapting to individual user variations for accurate sedentary detection. Extensive testing on various datasets, including BaSA and Har UML 20, demonstrates the model’s superiority over traditional personalized methods, achieving remarkable F1scores of 0.9958 and 0.9124, respectively. Integrating a personalizing step further refines the model, tailoring it to individual user characteristics while retaining the core structure learned from meta-learning, surpassing the median performance of centralized models across all datasets.

Federated Learning (FL) is a distributed training paradigm wherein participants collaborate to build a global model while ensuring the privacy of the involved data, which remains stored on participant devices. However, proposals aiming to ensure such privacy also make it challenging to protect against potential attackers seeking to compromise the training outcome. In this context, we present Fast, Private, and Protected (FPP), a novel approach that aims to safeguard federated training while enabling secure aggregation to preserve data privacy. This is accomplished by evaluating rounds using participants’ assessments and enabling training recovery after an attack. FPP also employs a reputation-based mechanism to mitigate the participation of attackers. We created a dockerized environment to validate the performance of FPP compared to other approaches in the literature (FedAvg, Power-of-Choice, and aggregation via Trimmed Mean and Median). Our experiments demonstrate that FPP achieves a rapid convergence rate and can converge even in the presence of malicious participants performing model poisoning attacks.

Federated Learning (FL) allows distributed devices, known as clients, to train Machine Learning (ML) models collaboratively without sharing sensitive data. A characteristic of FL for mobile and IoT environments is system heterogeneity among clients, which can vary from low-end devices with constrained communication and computing resources to powerful devices with high-speed network access and dedicated GPUs. As the server must wait for all the clients to communicate their updates, slow clients (a.k.a. stragglers) will significantly increase the training time. To tackle this problem, we propose FedPulse, a Partial Training (PT) based mechanism to mitigate the effect of stragglers in FL. The idea is to reduce the training time by dynamically allocating smaller submodels to resource-constrained clients. Experimental results on famous classification datasets show that the proposed solution outperforms other submodel allocation mechanisms and reduces the training time by up to 58% with an accuracy loss of less than 1% when compared to FedAvg.

Federated Learning is a distributed approach in which multiple devices collaborate to train a shared global model. During its training, client devices must communicate their gradients to update the global model. This incurs significant communication costs (bandwidth utilization and number of messages exchanged), leading to many challenges (communication bottlenecks and scalability issues). Furthermore, the heterogeneous nature of clients’ datasets poses an extra training challenge. In this sense, we introduce FedCCSKD, a Federated Clustered Client Selection and Knowledge Distillation training algorithm, to decrease the overall communication costs. FedCCSKD is an innovative combination of: (i) client selection, and (ii) knowledge distillation approaches with three main objectives: (i) reducing the number of devices training at every round; (ii) increasing convergence speed; and (iii) mitigating the effect of clients’ heterogeneous data on the global model effectiveness. Our experimental evaluations on MNIST and MotionSense datasets demonstrate that FedCCSKD is highly efficient in training the global model until convergence. FedCCSKD reaches a higher accuracy score and faster convergence than state-of-the-art baseline models. Our results also show higher performance when analyzing the accuracy scores on the clients’ datasets.

The growing number of vehicles has led to increased emissions of polluting gases, necessitating accurate forecasting for effective mitigation strategies and sustainable urban development. Leveraging computational resources in vehicles, this study presents a framework, called EcoPredict, for predicting CO2emissions in collaborative vehicular network environments. The framework implements three forms of learning methods—centralized, federated, and split—using urban sensor networks for data collection. Experiments carried out in realistic vehicular mobility scenarios demonstrate the framework’s robustness and efficiency in providing real-time emission predictions. Each learning architecture has its own advantages and limitations regarding performance, training time, latency, communication overhead, and data privacy. Therefore, this work aims to assess their performance to analyze their effectiveness in urban environments.

Characterizing and monitoring patient activities through time series data is critical for identifying lifestyle patterns that may impact health outcomes. Sedentary behavior is a significant concern due to its association with various health risks. This study introduces a lightweight supervised classifier for healthcare applications based on ordinal pattern (OP) transformation to detect sedentary behavior in federated learning (FL) scenarios. Our hypothesis is grounded on the idea that sedentary behavior exhibits distinct dynamics compared to other activities, and information descriptors derived from the transformation of OPs effectively capture these differences. Next, we proceed with the FL training. We train a neural network (NN)-based encoder locally and send the local models to a server. The FL process updates the encoder weights based on the encoded representations of the clients’ data, enabling the model to learn from different participants. Finally, we personalize the model for the specific task of classifying sedentary behavior. Our approach utilizes a meta-learning framework, incorporating a Siamese NN to learn a similarity space. We fine-tune the model in this step by further training the last NN layer. This fine-tuning allows the model to adapt and specialize in accurately classifying sedentary behavior. We carry out a comprehensive analysis to support our hypothesis. We also extensively validated our proposal by comparing it with other methods over five different data sets. We obtain the best results using a smaller machine learning model compared with the best approaches in the literature. Specifically, our model has 78.73% times fewer parameters and consumes 48.67% times less energy than the best result in the literature.

Federated learning (FL) is a distributed approach that enables collaborative training of a shared machine learning (ML) model for a given task. FL requires bandwidth-demanding communication between devices and a central server, which is a cause of many issues such as communication bottlenecks and scaling in the network. Therefore, we introduce the CCS (Compressed Client Selection) algorithm aimed at decreasing the overall communication costs for fitting a model in the FL environment. CCS employs a biased client selection strategy that reduces the number of devices training the ML model and the number of rounds required to reach convergence. In addition, the compression method Count Sketch is implemented to reduce the overhead in client-to-server communication. A use case on the Human Activity Recognition dataset is performed to evaluate CCS and compare it with other state-of-the-art approaches. Experimental evaluations show that CCS efficiently reduces the overall communication overhead for fitting a model and its convergence in a FL environment. In particular, CCS reduces up to 90% the communication overhead compared to literature approaches while providing good convergence even in scenarios where the data are not-independently and identically distributed among client devices.

Edge Computing architecture provides computing capacity closer to users to fulfill the requirements of Future Internet services and applications, such as low latency. However, as users move at the edge and connect to different access points, service instances have to be migrated in order to keep service levels constant. Considering this issue, we introduce a graphbased algorithm to distribute data over networks considering time cost budgets. We then use this algorithm to develop a mobility-aware latency-constrained solution to position userspecific service instances, i.e., services that use application state and user session data, called Data Covers Framework (DCF). We compare DCF with other approaches for service provisioning at the edge that use fixed hosts or active service placement considering user mobility. Our experiments show that DCF achieves similar or better performance in terms of the percentage of packets delivered under latency requirements for different application classes while reducing the number of service migrations by 21% and the service interruption time by 41%.

Vehicular Edge Computing (VEC) has emerged to offer cloud computing services closer to vehicular users by combining vehicles and edge computing nodes into Vehicular Clouds (VCs). In this scenario, an intelligent task scheduler must decide which VC will run which tasks, considering contextual aspects like vehicular mobility and tasks’ requirements. This is important to minimize both processing time and monetary costs. However, such direct optimization can lead to unfairness in resource usage, easily leading to (as we will show) decreased performance. Towards this end, in this work, we propose FARID, a task scheduling mechanism that considers contextual aspects of its decision process and applies a probabilistic selection function on VCs to balance the processing load and increase the fairness in the use of vehicular resources. Compared to state-of-the-art solutions, FARID has a higher level of fairness and can schedule more tasks while minimizing monetary costs and system latency.

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

Virtual sensing models have been used to generate synthetic data and provide complementary information. However, with the increase in cases related to COVID-19 and the lockdown, a problematic factor is that virtual sensing models may produce different results than they should since the environment has become better with the decrease in traffic conditions and industrial production. Therefore, this article will evaluate virtual sensing models for the city of São Paulo, using pre-pandemic and pandemic data in a lockdown scenario. As a result, we analyzed that even with these behavioral changes in the city, the pre-pandemic model produced similar results to the lockdown period model.

Future Internet applications, such as the vehicular use cases, impose requirements that challenge current networking paradigms. Hence, other networking paradigms have been proposed, such as Software-Defined Networking (SDN) and Information-Centric Networking (ICN), aiming to change network management and operation fundamentals to meet those requirements. Leveraging these networking paradigms, in this paper we present the Mobility-aware Service-Centric Networking (MSCN), an ICN-inspired solution to mitigate mobility-related networking issues by focusing on service provisioning rather than content. Due to the requirement of installing specialized hardware, ICN-based solutions face adoption issues. Still, SDN features can be used to emulate ICN behaviour without the requirement for deployment of new infrastructure. Therefore, we present an SDN-based implementation of MSCN (SD-MSCN), which relies on already installed SDN-enabled switches and the OpenFlow protocol. We evaluate the performance of our proposal by comparing SD-MSCN with other SDN-enabled ICN and IP protocols. Simulations show that our proposal outperforms IP-based solutions in the presence of user mobility events. Nevertheless, it is possible to produce proactive IP-based solutions that achieve similar performance as our proposal. However, our proposal has a significantly better performance than IP-based solutions in environments with frequent service mobility events.

Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.

Due to the increased computational capacity of Connected and Autonomous Vehicles (CAVs) and worries about transferring private information, it is becoming more and more appealing to store data locally and move network computing to the edge. This trend also extends to Machine Learning (ML) where Federated learning (FL) has emerged as an attractive solution for preserving privacy. Today, to evaluate the implemented vehicular FL mechanisms for ML training, researchers often disregard the impact of CAV mobility, network topology dynamics, or communication patterns, all of which have a large impact on the final system performance. To address this, this work presents FLEXE, an Open Source extension to Veins that offers researchers a simulation environment to run FL experiments in realistic scenarios. FLEXE combines the popular Veins framework with the OpenCV library. Using the example of traffic sign recognition, we demonstrate how FLEXE can support investigations of FL techniques in a vehicular environment.

The demographic growth in cities has increased carbon dioxide (CO2) emissions, a significant problem that challenges societies worldwide. The CO2 emission raises the pollution levels causing health risks for the people and contributes to climate change. Moreover, the transportation sector is responsible for 20.6% of CO2 emissions. Thus, it is necessary to reduce vehicle fuel consumption to minimize CO2 emissions. This paper proposes a system based on artificial intelligence techniques: a fuzzy controller and a neural network to find the instantaneous speed, which reduces vehicle fuel consumption. The proposed system employs only vehicle and highway information, which means communication between vehicles is not required. The simulation scenario comprises a loaded truck traveling through a highway with slopes based on a data set. Results derived from simulation show that both techniques produce a lower fuel consumption than the standard Simulation of Urban MObility (SUMO) algorithm.

Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordE r tR affI c convoluTiOn R 1-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.

Assuring an efficient Quality of Service (QoS) for heterogeneous Internet of Things services is a challenge, mainly due to spectrum scarcity and bandwidth limitations on the radio access network. To solve these problems, efficient management of airtime per station (STA) is necessary. In this work, we propose a scheduler to perform dynamic network slicing in IEEE 802.11ah Networks. The proposed scheduler is based on virtualization technologies to assure QoS restrictions per slice and can be deployed in the Access Point (AP) or in a virtual machine connected to the AP. Network metrics are used to verify QoS violations per slice over time. Once a QoS restriction is detected, the scheduler performs a reallocation of resources re-configuring the Restricted Access Windows parameters that compose a slice, adjusting airtime per STA. To evaluate the proposed scheduler, we consider a dynamic policy. Simulation results in a smart city scenario show that the dynamic policy is able to scale the network and satisfies QoS slice requirements.

Future vehicular applications will rely on communication between vehicles and other devices in their vicinity. Technologies, such as LTE-V2X, are awaited to operate under the Cellular Vehicle-to-Everything (C-V2X) standard to make this communication possible. However, current LTE technology has to go through transformations to enhance its performance in vehicular communications. One possible enhancement for LTE is the usage of the latest handover schemes, such as RACH-less and Make-before-break (MBB), to create seamless mobility. In the current study, we propose a RACH-less MBB handover scheme using Software-Defined Networks (SDN). Our main contributions are: (i) unifying lower layer handover operations with controller network updating procedures; and (ii) creating a signaling protocol that allows base stations and controllers to exchange information needed for timing alignment of the UE without executing a RACH procedure. Simulation results show that our proposed handover scheme has a shorter execution time and reasonable signaling overhead when compared to baseline schemes from the literature.

The use of contextual data to suggest distinct types of routes helps to understand new aspects of a city that may change the perception of drivers about routes. The impact of these aspects may differ from driver to driver requiring a way to change the suggestion according to the driver's point of view. Therefore, this paper presents an approach that identifies distinct situations in multiple types of contextual data and proposes a personalized and context-aware vehicle rerouting service called PONCHE. It considers common characteristics found in every dataset of spatiotemporal data to overcome the necessity of processing specific aspects of distinct data types. Regarding personalized service, each driver's profile is reflected into contextual data type weights considered by the system, i.e., the intensity he/she wants to avoid a contextual region. With that, a driver's profile may ignore a determined contextual data type. Performance evaluation results show that PONCHE identifies the best routes according to the weights given by a driver. It also improves the quality of contextual information obtained according to traffic, crime, and vehicle crashes. This study takes into consideration contextual data from Austin and Chicago in the USA, enabling comparison with two distinct cities.

Contextual data characterize distinct regions of the city, allowing them to differentiate them according to security, entertainment, services, among others. Using contextual data to suggest routes helps to understand new aspects of a city that can change users’ perceptions of different routes. The impact of each type of contextual data may vary according to the user’s profile, which is not taken into account in most of the systems proposed by the literature. Besides, it is necessary to consider the behavior of contextual data, which changes according to the type of data. To tackle the problems mentioned above, we propose a route suggestion system with space-time risk, called GIN. The system consists of three modules, namely: identification of contextual windows, context mapping, and route personalization. Moreover, we propose a strategy to decrease the number of route requests to improve system scalability. The results show that the system adapts to sensitive changes in user’s profiles. We obtained promising by using the behavior of contextual data to avoid unnecessary requests. This strategy allowed a reduction of up to 50% of requests made to the system.

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a significant role in the safety of the vehicles and passengers. The extensive amount of sensors in each car will generate vast amounts of data that cannot be processed promptly by onboard units. Edge and fog computing are emerging solutions for remote data processing for autonomous vehicles, offering higher computing power, as well as the low latency required by autonomous driving. However, due to the highly distributed nature of fog and edge computing servers, CAV mobility may pose a challenge to keep services close to end-users and maintaining QoS. In this paper, we propose MOSAIC, service migration, and resource management algorithm for intra-tier and inter-tier communication in edge and fog computing. The proposed solution performs proactive migration of services based on mobility information, server resources, QoS, and network conditions. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low-latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions.

Intelligent Transport Systems rely extensively on the proper management of vehicular resources, as well as the underlying vehicular communication. Services and applications are made accessible through the communication of vehicles, which is expected to happen without any interruption; however, the high mobility of vehicles is detrimental towards their connectivity and communication stability. In this highly dynamic and heterogeneous vehicular scenario, we assume the existence of multiple communication interfaces and high movement speed of nodes. Thus, we propose the development of a flow mobility management policy. The policy is devised following a decentralized design, being flexible and capable of providing transparent flow management to users while maintaining continuous service access. We conducted experimental simulations for evaluating the proposed architecture, as well as making performance comparisons with three related flow management works. The results showed that the proposed policy reduces the delay of information exchange to approximately 50 ms, decreases handover time (0.5s), and maximizes the input of information around 95%.

In order to feed a Traffic Congestion Detection System (TCDS), road safety messages (beacons) are continuously exchanged on Vehicular Ad hoc Networks (VANETs) through the IEEE 802.11p control channel. In VANET, the number of beacons in the communication network increases as the number of vehicles on the roads increases, raising communication costs. For a TCDS, clustering algorithms have been used to detect source and level of the traffic congestion based on vehicular density, as well as group similar traffic data that may lead to a reduction in the amount of data on the network. However, these clustering approaches have been employed to work only in a static dataset. Therefore, we propose a Fog Computing Framework that employs an adapted DBSCAN to reduce the amount of data produced in an online traffic data stream environment. The aim is to offer a more suitable approach for reducing the online traffic data stream, which is sent from Fog to the Cloud without losing accuracy of information related to road congestion. The evaluation results have shown that there is a dependence relationship between the size of the DBSCAN's radius, the amount of reduced data, and the congestion level accuracy.

Efficient content distribution over vehicular ad hoc networks (VANETs) is a challenging task due to highly topology changes caused by vehicle mobility. In this context, Vehicle Named-Data Networks (VNDN) architecture improves the performance and reliability in delivering content by providing content-centric network communication and caching capabilities. However, the success of VNDN architecture depends on mitigating the broadcast storm problem during the cache discovery process, where the network performance impairment occurs due to the waste of resources generated. In this paper, we propose a receiver-based cache discovery protocol based on degree-centrality for VNDN, called CLYMENE. The protocol paves the way for efficient content distribution by minimizing the broadcast storm problem. Simulation results show that CLYMENE enhances the cache discovery by 80.59% while allowing a content delivery rate of 39.49% and reducing the number of transmissions in the cache discovery process at 70.65% compared to existing protocols.

The automotive industry has been continuously investing in the modernization of the vehicles by the addition of more sensors and computational power. With this evolution, Intelligent Transportation Systems (ITS) make up a services framework that seeks to mitigate problems in the road sector. Many ITS services are facilitated by creating vehicular clouds (VCs) by using the communication capabilities of other vehicles to provide cloud services closer to vehicular applications. However, often the computational resources present in the vehicles are underutilized. For this reason, we propose in this work a mechanism that efficiently allocates computational tasks to be performed in VCs. Simulation results on a realistic mobility trace show that, with our mechanism, tasks are more allocated, the reward from allocating these tasks was higher, resource waste was minimized, and less CPU is used in the allocation processing. Also, the proposed mechanism is statistically close to a globally optimal solution.

One of the main challenges in urban development faced by large cities is related to traffic jam. Despite increasing efforts to maximize the vehicle flow in large cities, to provide greater accuracy to estimate the traffic jam and to maximize the flow of vehicles in the transport infrastructure, without increasing the overhead of information on the control-related network, still consist in issues to be investigated. Therefore, using artificial intelligence method, we propose a solution of inter-vehicle communication for estimating the congestion level to maximize the vehicle traffic flow in the transport system, called TRAFFIC. For this, we modeled an ensemble of classifiers to estimate the congestion level using TRAFFIC. Hence, the ensemble classification is used as an input to the proposed dissemination mechanism, through which information is propagated between the vehicles. By comparing TRAFFIC with other studies in the literature, our solution has advanced the state of the art with new contributions as follows: (i) increase in the success rate for estimating the traffic congestion level; (ii) reduction in travel time, fuel consumption and CO2 emission of the vehicle; and (iii) high coverage rate with higher propagation of the message, maintaining a low packet transmission rate.

The development of new smart objects for the sensing and actuation of a given place or environment led both the academia and industry to research and propose new protocols and intelligent systems to support such objects. One of the systems that has been gaining prominence is the smart residential environments. In this context, homes are equipped with smart objects to manage the living resources. However, managing such objects in residential environments requires data contextualization, i.e. collecting data from heterogeneous devices and actuate on the environment through context information generated from such data. To solve this problem, we propose an intelligent decision system based on the fog computing paradigm, which provides an efficient management of residential applications. The proposed solution is evaluated both in simulated and real environments. When compared with other studies from the literature in a simulated environment, the proposed solution shows a higher success rate with a lower delay in the decision-making process, higher efficiency in information dissemination with a lower overhead in the communication infrastructure, and increased robustness in processing with a lower power consumption. These results are also observed when considering a real environment evaluation.

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.

Traffic congestion in large cities became more intense considering the last years. Basically, this growth is attributed to the wide use of a single mode of transport due to the lack of alternatives capable of efficiently supplying urban traffic demand. In this sense, in economic terms, it is estimated that billions of dollars are wasted every year due to the extra expenses with fuels and maintenance caused by traffic. In order to minimize the economic and environmental damages caused by congestion, this work presents FIRE-NRD: A fully-distributed Traffic Management System for Next Road Decision. In the proposed solution, vehicles, while moving, are able to analyze and share a study of the traffic flow and thus provide sufficient knowledge in order to resolve "the next road decision", i.e., the next road choice to decrease its travel time. FIRE-NRD is based only on local data about traffic information and totally collaborative, where neighbor vehicles share their knowledge. FIRE-NRD is compared to literature solutions and present better results considering network and traffic metrics, such as number of messages and average travel time.

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.

Virtual Machines (VMs) offer great flexibility, reusability, and portability to manage applications in physical resources of a Cloud data center or a mobile cloud. These VMs may suffer a migration from one datacenter to another or from one cloudlet to another, due to the characteristics of vehicular cloud, such as high mobility and the need to meet the demands of a particular service. In this way, establishing a VM migration policy in a vehicular cloud becomes a challeng e, since it has to deal with the unique challenges of vehicular networks, as well as to meet the demands of users' services without virtual machine migration, which impacts on the performance of such service. In this paper, we propose a decision policy based on multiple attributes to migrate VM in a vehicular cloud scenario. In this way, the proposed policy allows to decide more quickly if a VM migration should be carried out and to which cloudlet this machine should be migrated. Simulation results showed that the proposed policy reduced in 2% the amount of VM migration in the network, decreased in 3% the blockages of the migration requests, as well as a reduction in the inference time of approximately 5 ms.

Recently, Network Slicing in Radio Access Technologies (RATs) has been proposed as an approach to divide the wireless network infrastructure into isolated logical slices which are defined following their requirements and features in a service-driven way. To the best of our knowledge, the application of Network Slicing has not been explored in IEEE 802.11ah Networks. In this work, we propose a Virtual Network Slicing Broker (VNSB), a Virtual Network Function (VNF) instantiated inside a Software Defined Network (SDN) controller which communicates with an IEEE 802.11ah network Access Point (AP) via a southbound Application Program Interface (API). Based on information obtained by the northbound API, the slicing broker gets the information contained in the slicing templates, which describes the services features and respective Quality of Service (QoS) restrictions. With these data, the slicing broker makes logical slices in the context of the Restricted Access Windows (RAW) which are periodically sent by the AP to the stations (STA) via the Raw Parameter Set (RPS). Since there is no hardware with the IEEE 802.11ah standard available on the market, we validate the proposed solution through extensive simulations in a typical smart city scenario. Results showed the broker capacity to build and manage logical slices per service, respecting the QoS restrictions of each offered service. Moreover, the proposed slicing broker makes use of available resources of neighbor slices reducing delay, packet loss, and efficiently maximizing throughput per slice.

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.

The growth in many countries of the population in need of healthcare and with reduced mobility in many countries shows the demand for the development of assistive technologies to cater for this public, especially when they require home treatment after being discharged from the hospital. To this end, interactive applications on mobile devices are often integrated into intelligent environments. Such environments usually have limited resources, which are not capable of processing great volumes of data and can expend much energy due to devices being in communication to a cloud. Some approaches have tried to minimize these problems by using fog microdatacenter networks to provide high computational capabilities. However, full outsourcing of the data analysis to a microfog can generate a reduced level of accuracy and adaptability. In this work, we propose a healthcare system that uses data offloading to increase performance in an IoT-based microfog, providing resources and improving health monitoring. The main challenge of the proposed system is to provide high data processing with low latency in an environment with limited resources. Therefore, the main contribution of this work is to design an offloading algorithm to ensure resource provision in a microfog and synchronize the complexity of data processing through a healthcare environment architecture. We validated and evaluated the system using two interactive applications of individualized monitoring: (1) recognition of people using images and (2) fall detection using the combination of sensors (accelerometer and gyroscope) on a smartwatch and smartphone. Our system improves by 54% and 15% on the processing time of the user recognition and Fall Decision applications, respectively. In addition, it showed promising results, notably (a) high accuracy in identifying individuals, as well as detecting their mobility; and (b) efficiency when implemented in devices with scarce resources.

Recently, many cities are facing mobility and safety issues, commonly related to traffic congestion and the high number of city-wide criminal incidents. Several Intelligent Transportation Systems (ITS) were proposed to overcome mobility issues. Meanwhile, some safety-based systems were proposed to guide pedestrians and drivers toward safest paths. However, most of these systems address only a single issue. Hence, in order to avoid traffic congestion, an ITS may guide vehicles toward risky areas, while a safety-based system may guide them toward congested ones, focusing only on the safety of drivers and passengers. This paper introduces FnS (Faster and Safer), a hybrid ITS which employs accurate knowledge about traffic conditions and unsafety levels on roads for improving the safety of drivers and passengers at the same time it deals with traffic congestion. Simulation results under a realistic scenario have shown that FnS outperformed state-of-the-art approaches that deal with mobility or safety issues.

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.

The constant sharing of information among vehicles is of vital importance to provide different types of service in Intelligent Transportation Systems (ITS). Typically, ITS apply the sharing benefit to carrying out tasks such as extracting knowledge of vehicle traffic conditions and its distribution. The ITS that use this approach are able to perform the knowledge distribution, however, they lack of mechanisms to select the most appropriate vehicles to do so. It is common, in these systems, such tasks are performed by all vehicles. Consequently, it could easily cause a network overhead because of the highly redundant knowledge about the traffic that is being transmitted. With this in mind, we propose a system for information management and knowledge distribution named TRUSTed. The proposed system applies the egocentric betweenness measure to select the most relevant vehicle to carry out such tasks. Simulation results have shown that TRUSTed outperforms other systems found in the literature in several requirements.

With the growth of large urban centers, some problems arise and solutions must be sought to contain them. In this context, traffic congestion is one such problem, where road infrastructure does not follow the high growth in the number of vehicles. Traffic Management Systems (TMS) arise to mitigate traffic-related problems, with automatic detection of slow roadways and vehicle rerouting to avoid such routes. Such applications are supported by the Vehicular Ad hoc NETworks (VANETs), where traffic information are disseminated between the vehicles or central server, and thus the better decisions about traffic management can be made. However, the data dissemination in VANETs is a challenging task, due to the short-range communication and high node mobility. Thus, this paper introduces a protocol for Data Dissemination based on Metrics of Complex Networks, called CRONOS. It provides data dissemination of traffic data with low overhead and high coverage. Simulation results show that CRONOS reduced the number of transmissions by 95%, the congestion time by 48.95%, and travel time by 18.11% for a TMS application.

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.

Due to the emergence of new services offered by intelligent transport systems and vehicular clouds, control of communication between vehicles and the Internet becomes indispensable, in which such control needs to deal with the demand for services provided by such systems, without overloading network technologies. Thus, in this paper, we propose the development of a flexible architecture capable of meeting not only the network demands, but also the quality of service parameters of each class of service that are offered by intelligent transport systems or vehicular clouds through the Internet. Thus, we seek to maximize the flow of information without compromising network performance. For the evaluation of the proposed architecture as well as to make a comparison with three related works in the literature we use the simulator NS3. The results showed that the proposed mechanism reduces packet loss, maximizes the input information and decreases handover time.

Recently, many cities are facing challenging mobility and safety issues. The former is commonly related to traffic congestion, as a consequence of uncontrolled population growth and accelerated urbanization. The latter regards to elevated number of city-wide criminal incidents. Several Intelligent Transportation Systems (ITS) were proposed to overcome mobility issues; meanwhile, some safety- based systems were proposed to guide pedestrians and drivers toward safest paths. However, most of these systems tackle only one of the issues. Hence, an ITS can guide vehicles toward risky areas, in order to avoid traffic congestion, while a safety-based system can guide them toward congested roads, focusing on the safety of drivers and passengers. This paper introduces itsSAFE (Intelligent Transportation Systems for improving SAfety and traFfic Efficiency), an ITS which employs accurate knowledge about traffic conditions and unsafety levels on roads for improving the safety of drivers and passengers at the same time it deals with traffic congestion. Simulation results under a realistic scenario have shown that itsSAFE outperformed state-of-the-art approaches that deal with mobility or safety issues, by effectively dealing with traffic efficiency and safety.

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.

Vehicular Ad Hoc Networks (VANETs) allow users, services, and vehicles to share information and will change our life experience with new autonomous driving applications. Multimedia will be one of the core services in VANETs and are becoming a reality in smart environments, ranging from safety and security traffic warnings to live entertainment and advertisement videos. However, VANETs have a dynamic network topology with short contact time, which leads to communication flaws and delays, increasing packet loss, and decreasing the Quality of Experience (QoE) of transmitted videos. To cope with this, neighbor vehicles moving on the same direction and wishing to cooperate should form a platoon, where platoon members act as a relay node to forward video packets in autonomous VANETs. In this article, we introduce a game theory approach for platoon‐based driving (GT4P) for video dissemination services in urban and highway VANET scenarios. GT4P encourages the cooperation between neighbor vehicles by offering reward (e.g., money or coupon) for vehicles participating in the platoon. In this sense, GT4P establishes a platoon by taking into account vehicle direction, speed, distance, link quality, and travel path, which reduces the impact of vehicle mobility on the video transmission. Simulation results confirm the efficiency of GT4P for ensuring video transmissions with high QoE support compared to existing platoon‐based driving protocols.

Situational Awareness (SAW) is a widespread concept in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. A poor SAW can induce humans to failures in the decision-making process, leading to losses of lives and property damage. Data fusion processes present opportunities to enrich the knowledge about situations by integrating heterogeneous and synergistic data from different sources and transforming them into more meaningful subsidies for decision-making. However, a problem arises when information is subject to problems concerning its quality, especially when humans are the main sources of data (HUMINT). Motivated by the informational demand from the emergency management domain and by the limitations and challenges of the state of the art, this work proposes and describes a new information fusion model, called Quantify (Quality-aware Human-Driven Information Fusion Model), whose main contribution is the exhaustive use of the quality information management throughout the fusion process to parameterize and to guide the work of humans and systems. To validate the model, an emergency situation assessment system prototype was developed, called ESAS (Emergency Situation Assessment Systems). Then, experts from the Sao Paulo State Police (PMESP) tested the prototypes and the system was evaluated using SART (Situation Awareness Rating Technique), which showed higher rates of SAW using the Quantify model, compared to the model from the state-of-the-art, especially in questions relating to the components of resource supply and situational understanding.

Urban mobility becomes one of the most challenging issues in large urban centers, since traffic congestion is a daily problem. In order to address this issue, a number of researchers, from both academia and industry, have studied several Traffic Management Systems (TMS) approaches to improve urban mobility. However, the existing approaches do not consider an essential factor: the population information. Within this context, this work proposes a new approach, called APOLO, that employs historical knowledge of mobility patterns of the drivers to obtain a global view of the road network. APOLO is different from others research approaches that need constant information exchange among the vehicles and the central server in order to obtain a global view of road traffic condition. These existing approaches can lead to network overload and have a high data processing cost in real-time. Results show that APOLO improves vehicles' mobility compared to well-known approaches, which indicates that APOLO could be a potential alternative for providing TMS services with valuable mobility knowledge.

Vehicular Ad-hoc NETworks (VANETs) promise a wide scope of multimedia services ranging from security and traffic announcements to entertainment and advertising videos. However, VANETs have a dynamic network topology with short contact time, decreasing the Quality of Experience (QoE) of transmitted videos due to frequent disconnections in the communication between neighbours vehicle. Those disconnections cause communication flaws and delays, increasing the packet loss during video transmissions. To cope with this, neighbor vehicles moving on the same direction and wishing to cooperate could form a platoon to disseminate live videos. In this paper, we introduce a platoon protocol based on game theory for video dissemination with QoE support, called P2V. The proposed protocol provides cooperation between neighbor vehicles to establish a platoon by taking into account vehicles direction, speed, and distance, where P2V provides a reward (money or coupon) for vehicles participating in the platoon. Simulation results confirm the efficiency of the P2V protocol to ensure video transmission with high QoE support compared to BLR and XLinGO 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.

Urban mobility has become one of the most challenging issue in urban centers. As a consequence, traffic congestion has become a daily problem. Several Advanced Traffic Management Systems (ATMS) have been proposed to improve overall traffic efficiency. However, these systems inefficiently exchange traffic information, which can lead to network overload. In order to overcome the mobility problem and improve the efficiency in dealing with vehicle traffic, this paper introduces a fully-distributed advanced traffic management system based on opportunistic content sharing, named PANDORA. Simulation results indicate that PANDORA outperforms the assessed solutions in various scenarios, considering different key requirements of ATMS.

An increase of vehicles in a city without an efficient infrastructure of traffic management can cause damages not only financial but also environmental and social. In order to support urban traffic system to relieve the traffic congestion and the damage caused by congestion of vehicles, in this paper, we propose a mechanism for Intelligent Transport Systems named FOg RoutE VEhiculaR (FOREVER), in order to assist the traffic management in Vehicular Networks (VANET). For achieving this, FOREVER will detect and recommend an alternative route for the vehicles to avoid previous congestion. FOREVER is based on FOG computing paradigm that aims of to compute and modify the route of the vehicle to avoid the formation of congestion. Thus, the results show that FOREVER had a reduction about 7.9% of the CO2emissions, 8.3% the stop time and 7.6% of the trip time.

Efficient and reliable data dissemination is crucial to most of the vehicular network applications. However, the inherent vehicular network characteristics make data dissemination a challenging task. Typically, the existing algorithms generate too many redundant packets that saturate the network, thus causing frequent channel contention and packet collisions. To tackle these problems we propose an Efficient Encounter-based Event Dissemination Protocol. By exploiting the distance and the encountering probability of the vehicles to the event, the proposed protocol is capable of performing an efficient data dissemination in both urban and highway scenarios. An extensive set of simulation experiments was conducted to evaluate the performance of the proposed algorithms. Results showed that the proposed protocol outperforms the selected approaches in terms of retransmission packets and delivery ratio.

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.

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.

Ego-network concept has been systematically studied, since this kind of network employs only locally available information to analyze its structure. Degree, closeness, and betweenness are widely studied centrality measures. Among the three measures presented, betweenness centrality in ego-networks is the most used in several fields such as Wireless Mesh Networks, Wireless Sensor Networks, and Delay Tolerant Networks. However, surprisingly, that measure has not been largely investigated in Vehicular ad hoc Networks (VANETs). In this paper, we contribute to filling this gap by designing and implementing the egocentric betweenness measure in VANETs, besides we compare it to the sociocentric betweenness measure.

Vehicular Ad-hoc NETworks (VANETs) are composed of moving vehicles with the ability to process, store, and communicate via wireless medium. VANETs promise a wide scope of services, such as, safety and security, traffic efficiency, and others. For instance, a VANET application can detect, control and reduce traffic congestion based on data that describes traffic patterns. However, disseminating data in VANET is a challenging task, due to its particular characteristics, i.e., heterogeneous density, short-range communication, and node mobility. Since, existing protocols for data dissemination do not effectively address the high overhead, in this paper, we proposed a Data Dissemination protocol Based on Centrality (DDBC) for urban scenarios. The simulation results show that DDBC protocol offers good efficiency in terms of delays and overhead, while achieve network coverage around 90%.

Understanding traffic conditions, in an urban environment, by means monitoring or/and predicting is not an easy task. In Intelligent Transportation Systems, the reliable vehicle route prediction has meaningful application value. Vehicle route prediction can increase the variety of VANETs applications such as predicting traffic situation ahead, optimal route recommendation, driver assistant, and automatic vehicle behaviors. Due to the challenge imposed by the vehicle route prediction and its wide application, researchers in both industry and academia have focused their efforts on this area. In order to explore this area, this paper describes an approach to predict the vehicle's future path in a realistic urban scenario. For that, it employs a hidden Markov model along with the outcome of the Viterbi algorithm to make a probabilistic prediction. The parameters modeling is estimated based on information extracted from the travel route dataset and computed in an offline manner. In the online phase, the routes prediction is carried out. Our approach has high accuracy rate according to the numerical simulations results. Moreover, we believe that the VANETs applications previously mentioned can take advantage of our approach.

Typically, the electric power companies employ a group of power meter readers to collect data on the customers energy consumption. This task is usually carried out manually, which can lead to high cost and errors, causing financial losses. Some approaches have tried to minimize these problems, using strategies such as discovering the minimal route or relying on vehicles to perform the readings. However, errors in the manual readings can occur and vehicles suffer from congestion and high fuel and maintenance costs. In this work, we go further and propose an architecture to the Automatic Meter Reading (AMR) system using Unmanned Aerial Vehicles (UAV). The main challenge of the solution is to design a robust and lightweight protocol that is capable of dealing with wireless communication collisions. Therefore, the main contribution of this work is the design of a new protocol to ensure wireless communication from UAV to the power meters. We validated and evaluated the architecture in an urban scenario, with results showing a decrease of time and distance when compared to other approaches. We also evaluated the system proposed with Linear Flight Plan, the Ant Colony Optimization and Guided Local Search metaheuristic. Our mechanism attains an improvement of 98% in reducing the message collisions and reducing the energy consumption of the power meters.

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 recent years, the number of vehicles has increased faster than the available infrastructure. Consequently, traffic congestion has become a daily problem affecting several aspects of modern society, including regional economic development. In this way, Traffic Management System (TMS) have been proposed to improve the traffic efficient and minimize traffic congestion problems. These systems rely on gather traffic-related data in a central entity to identify congestion and suggest alternative routes. However such approach adds load in communication channel depending on the traffic density. In this way, this paper introduces FASTER, a fully-distributed TMS to improve the overall vehicle traffic efficiency that does not overloads the communication channel, providing a suitable distributed solution. Simulation results indicate that our FASTER outperforms the assessed solutions in different scenarios and in different key requirements of TMS.

Several Intelligent Transportation Systems (ITS) rely on localization to enable services ranging from comfort to safety applications. Following this same idea, the use of Dedicated Short Range Communications (DSRC) devices based on the IEEE 802.11p standard, and the Vehicular Ad Hoc Networks (VANETs) paradigm, have resulted in the deployment of several protocols, services and applications that need different localization accuracy levels. The most common solution for localization is based on Global Navigation Satellite Systems (GNSS). GNSSs have problems of unavailability in dense urban areas, tunnels and multilevel roads. Moreover, GNSS have errors in the range of 5-15 meters. These unavailability and error problems are not acceptable for several VANET and ITS safety applications. In this work, we investigate and characterize the GNSS problems of error and unavailability based on datasets of real GNSS devices installed in vehicles and develop a Geodesic Dead Reckoning solution to overcome the GNSS unavailability issue.

Wireless sensor networks consist of hundreds or thousands of nodes with limited energy resources. Due to the high density of nodes in this kind of network, redundant data will be detected by nearby nodes. Since the network lifetime is a key issue in wireless sensor networks, in-network data aggregation can be exploited in order to reduce the number of messages exchanged and consequently reduce the energy consumption. Although there are many data aggregation solutions in wireless sensor networks, most of them 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 Continuous Enhancement Routing Solution named as CER, an approach for computing increasingly better routing trees. CER was extensively compared to three other known solutions: the Shortest Path Tree (SPT), Data Aggregation Aware Routing Protocol (DAARP) and Dynamic Data Aggregation Aware Routing Protocol (DDAARP). The obtained results show that CER outperforms these solutions in all evaluations performed.

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.

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.

Cities with a large number of people are currently facing urban mobility problems, especially the problem of traffic congestions. This not only has an adverse effect on the economy of the city, but also impairs the quality of life of its citizens. One measure that can be adopted to mitigate these problems is the use of systems that help identify, reduce, and/or avoid these traffic jams, such as intelligent transport systems. In this context, we propose an intelligent traffic information system called UCONDES, which is based on inter-vehicle communications and can be applied to detect and reduce congestion in urban centers. Simulation results shows that, when compared to original vehicular mobility trace, our solution reduces the average trip time, and the overall CO2 emission and fuel consumption. More specifically, the average travel time for drivers was reduced by approximately 26%, resulting in a reduction of fuel consumption by 23% and the CO2 emission by 25%.

In this work, we study the design of a Wireless Sensor Network based on the Small world models. By modeling a sensor network with small world features, it is possible to decrease the average path length to interconnect the sink and sensor nodes. The goal of this work is to analysis the Newman & Watts and Kleinberg small world models in wireless sensor networks. The simulation results showed that both models are able to create a sensor network with small world features, however, the Newman & Watts model has better results regarding the path length, clustering coefficient and data communication latency. On the other hand, the Kleinberg model reduces more the energy consumption during data communication.

This work proposes an intelligent decision system for a residential infrastructure based on wireless sensors and actuator networks, called ResiDI. ResiDI is equipped with battery-powered nodes to ensure that they are deployable anywhere in the house without the need for wiring, drilling or any pre-existing infrastructure. The key intelligence of ResiDI is distributed in the decider nodes, which are able to make decisions locally without the need to send traffic from the sensor nodes to the sink. The network intelligence core is based on a neural network that seeks to improve the accuracy of the decision-making, together with a temporal correlation mechanism that is targeted at reducing the energy consumption. When compared with an approach adopted in the literature, the results show that ResiDI is efficient in different scenarios in all evaluations performed.

Vehicular Ad hoc Networks (VANETs) are an emerging technology that allows vehicles to form self-organized networks without the requirement of permanent infrastructure. VANETs have opened up a myriad of on the road applications and increased their potential by providing intelligent transport systems. The envisaged applications, as well as some inherent VANET characteristics (i.e., highly dynamic topology, diverse network densities, and intermittent connectivity) make data dissemination an essential service and a challenging task in these networks. Many data dissemination protocols have been proposed in the literature. However, most of these protocols were designed to operate exclusively under dense or sparse networks. In addition, the existing solutions for data dissemination do not effectively address broadcast storm and network partition problems simultaneously. To tackle these problems, we propose a suitable multi-hop broadcast protocol named as TURBO that relies exclusively on local one-hop neighbor information to deliver messages under dense and sparse networks. We compared our protocol with other protocols from literature - AID, DBRS, GEDDAI, and simple Flooding. Simulation results show that TURBO performs data dissemination with better efficiency than other protocols, outperforming them in different scenarios in all the evaluations carried out.

In this research, we propose a protocol to create different logical topologies that consider the same physical network topology for the data routing problem in Heterogeneous Sensor Networks (HSNs). The HSN in question has two types of sensor nodes, called L-Sensors (sensor with Low hardware capabilities) and H-Sensors (sensors with High hardware capabilities). The proposed protocol creates different topologies, each designed to meet different application requirements, and use a fraction of the links between H-sensors. In addition, each topology has a tradeoff between latency and energy consumption during data communication. The simulation results show that our routing algorithm based on different topologies reduces energy consumption compared to a literature protocol.

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.

Vehicular Ad-Hoc Networks (VANETs) are a specific type of moving networks in which the nodes are vehicles with processing, storage and wireless communication capacity. VANETs face a number of challenges in terms of data dissemination due to the volatile density of vehicles and frequent changes in the network topology induced by the high mobility of the vehicles and of short-range communications. The envisaged applications, as well as some inherent characteristics of the VANETs render the data dissemination an essential service and a challenging task in these networks. Many data dissemination protocols have been proposed in the literature, nevertheless, most of such protocols do not deal simultaneously with the problems of broadcast storm and network partition. To face such problems, we propose a new data dissemination protocol in vehicular networks named DRIFT, which operates in highway environments. The DRIFT eliminates the broadcast storm problem and maximizes the data dissemination in partitioned networks with little delay and low overhead. When compared with four known solutions, we show that our proposal for data dissemination executes it with higher efficiency than other protocols, exceeding them in different scenarios in all the undertaken evaluations.

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.

Several data dissemination protocols in VANETs have been proposed in the literature, however, most of these protocols do not efficiently address broadcast storm and network partition problems simultaneously. This work attempts to fill this gap by proposing a suitable broadcast protocol in vehicular networks, designated GTO, that addresses both the broadcast storm and network partition problems in highway environment. In GTO protocol, all vehicles send beacon packets notifying whether there is or not the information, besides we employ the technique of zone of preference to rebroadcast messages to further vehicles. We compared the proposed protocol with the other five protocols - ATENA, DV-CAST, SRD, AID and Flooding - The results of evaluation show that our proposed protocol was more efficient to disseminate the information, within area of interest, than other five protocols in different traffic conditions.

Vehicular Ad hoc Network (VANET) is a promising communication technology suitable for vehicular mobile networks. Represent networks of singular features, wherein the data dissemination is fundamental. The literature is plentiful in protocols, usually specific to address individual issues in well-defined scenarios. This work efforts are concentrated, mainly, to examine operating settings in protocols like AID, DBRS, and ADDHV for disseminating messages. A benchmarking explores strategies that address challenges such as network partitioning and the broadcast storm problem, which undertake the dissemination. The results of a set of metrics obtained in different vehicular traffic schemes complete the discussion held. Considerations for answers in coverage, delay, rate of delivery, broadcast, and packet loss support this initiative and motivate the development of an adaptive solution to fluctuations in carrier density.

Typically, electric power companies employs a group of employees known as meter readers to collect data of energy consumption of customers. To overcome the challenges and limitations of the literature approaches, we propose an automatic meter reading system based on Unmanned Aerial Vehicle and Wireless Sensors Networks. In our approach, each electric meter has one sensor node device with wireless communication capability and the Unmanned Aerial Vehicle flies the field in a predefined way to collect data from the wireless sensor nodes without having to visit each electrical meter. Simulation results show that our approach reduces the distance to perform the readings compared to literature solutions. We also present a use case analysis considering real parameters for meter readings.

Vehicular Ad-Hoc Networks (VANETs) are a specific type of moving networks in which the nodes are vehicles with processing, storage and wireless communication capacity. VANETs face a number of challenges in terms of data dissemination due to the volatile density of vehicles and frequent changes in the network topology induced by the high mobility of the vehicles and of short-range communications. The envisaged applications, as well as some inherent characteristics of the VANETs render the data dissemination an essential service and a challenging task in these networks. Many data dissemination protocols have been proposed in the literature, nevertheless, most of such protocols do not deal simultaneously with the problems of broadcast storm and synchronization problem. To face such problems, we propose a new data dissemination protocol in vehicular networks named ADD, which operates in highly dynamic highway environments. The ADD consists two mechanisms, broadcast suppression and delay desynchronization. ADD uses a preference zone to eliminate the broadcast storm problem and the delay desynchronization to eliminate the synchronization problem caused by 802.11p protocol. When compared with three known solutions SRD, Flooding and AID we show that our proposal for data dissemination executes it with higher efficiency than other protocols, exceeding them in different scenarios in all the undertaken evaluations.

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.

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.

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.

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.

A wireless sensor network (WSN) is designed to perform event detection, data collection, and reporting such data to a monitoring station. In many cases, it is necessary to know the location of sensor nodes to relate the detection of the event at a specific location. However, the geographical location of the sensor nodes in most applications can only be set after their deposition in the area of interest. Therefore, for the sensor nodes to know their location, it is necessary to use specific algorithms to solve the problem of discovering the geographical position of sensor nodes. This work addresses the problem of 3D localization in WSNs using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with GPS and it flies over the monitoring area broadcasting its geographical position. Thus, the sensor nodes are able to estimate their geographical position without being equipped with GPS receiver. Simulation results show that using an UAV leads to a smaller error in the calculation of geographic location when compared to solutions presented in the literature.

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.

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.

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.

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.

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.

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.

Wireless actors and sensor networks (WASNs) can be used as powerful and accurate tools for real time monitoring of physical environments subjected to emergency situations. This class of applications poses strict requirements of latency, fault tolerance and delivery reliability. Quality of service (QoS) mechanisms can help to meet those requirements; however, QoS solutions for WASNs still pose challenges compared to QoS solutions for traditional networks. This paper presents QBRP, a novel QoS based routing protocol, which is unique in at least two aspects: it meets, simultaneously, the application requirements for low latency, high delivery reliability, uniform energy consumption and fault tolerance; it takes advantage of the interactions among sensors, actors and sink to provide a better QoS solution for WASNs. Performance evaluation of QBRP shows that it outperforms other QoS mechanisms that also prioritize multipath routing for end-to-end delay differentiation in nearly 50%. QBRP has shown to be a potential solution for the monitoring of physical environments subjected to emergency situations, minimizing risks to lives and patrimony.

Wireless Sensor and Actor Networks (WSANs) are increasingly being used as powerful and accurate tools for supervision and control of physical environments subject to emergency situations. In a fire condition, for instance, the sensor and actor network status can change dramatically. This paper presents a novel routing protocol that is aware of these changes and adjusts the routes accordingly, aiming to keep the Quality of Service required by the application. Route selection for different types of packets is performed considering QoS parameters such as latency, delivery rate and packet loss rate. Our protocol is also energy-aware and fault tolerant. Performance evaluation results show that our protocol outperforms other existing QoS-aware protocols, providing selective traffic with lower end-to-end latency, packet loss, and control packet overhead, besides network energy balancing and fault tolerance.

Wireless actors and sensor networks (WASNs) can provide a more accurate real time monitoring tool for the emergency preparedness class of application. Quality of service can be an important mechanism to guarantee that the strict requirements for that class of application are met. This paper presents QARP, a QoS-aware routing protocol with service differentiation for WASNs. The publish/subscribe paradigm is used to promote the interaction among the sensor nodes, actor nodes and the sink. The QARP provides low latency and reliable delivery in the presence of failures (with fast subscription of new interests and uniform energy consumption). It uses the less expensive energy path for low priority packets that do not require low latency. A queuing model is used that supports lower transfer rate for lower priority packet delivery in jam conditions. Simulation results show that the protocol is efficient regarding QoS metrics. QARP can be a potential solution for the monitoring of context aware physical environments subject to emergency situations