This study establishes a cost-efficient AI-based framework for inferring multi-depth soil moisture profiles, addressing a critical sensor deployment gap in Precision Agriculture. Using a multi-year dataset (2023-2025) from an apple orchard, we compared tree-based ensemble models (RF, ET, and XGBoost) across six depths (10-60 cm). These models were selected for their high interpretability and efficiency in resourceconstrained edge-computing environments. Results identify a dual-sensor setting (10 cm and 50 cm) as the optimal strategy to bracket the root zone. The RF model was the most robust ($R^{2}$up to 0.9740). Our analysis confirms that depth continuity is the dominant predictor, while meteorological variables provide vital compensatory signals for non-adjacent layers. This framework offers a scalable solution that reduces hardware burdens without sacrificing accuracy for irrigation management.

The rapid expansion of the Internet of Things (IoT) demands effective communication protocols that accommodate mobile and static end devices (EDs). LoRaWAN (Long Range Wide Area Network), a pioneering low-power wide-area network (LPWAN) technology, uses Adaptive Data Rate (ADR) approaches to optimize resource allocation, particularly for static EDs. However, traditional ADR approaches are ineffective in mobile contexts as they struggle to adapt to changing network conditions, resulting in significant packet loss and higher retransmission rates. Although innovative technologies, such as the Blind ADR (BADR), have been devised to improve the performance of mobile EDs, they still fall short of dealing with the unpredictable nature of mobile EDs. To address these challenges, this paper presents a novel HybridQ-ADR mechanism suitable for static and mobile EDs. This approach addresses the constraints of BADR and related methods in mobile scenarios. In particular, it provides a more efficient solution to reduce packet loss and collisions in dense and dynamic LoRa-based IoT networks. This is achieved by allocating Spreading Factors (SF) using signal orthogonality to minimize interference and provide reliable communication. Furthermore, the proposed HybridQ-ADR mechanism provides a new clustering technique based on estimated path loss. Specifically, it divides EDs into clusters and assigns different channels to each cluster, improving SF allocation and data transmission speeds. The proposed HybridQ-ADR mechanism includes a mobility-aware, Doppler-constrained SF allocation strategy, limiting each ED’s maximum SF based on its Doppler/mobility load to maintain reliable performance at high speeds. Performance evaluations using simulations and testbed implementations show that HybridQ-ADR improves latency, packet success rate, power consumption, and throughput for both static and mobile EDs.

With the rapid deployment of 5G technology, Cellular Vehicle-to-Everything (C-V2X) communication is becoming increasingly critical for enabling real-time data exchange in connected and autonomous vehicles. This demands ultra-low latency and high reliability, especially in high-mobility environments. In this study, we evaluate the performance of two prominent IoT application layer protocols—MQTT and ZeroMQ—for V2X communications over 5G networks. A comprehensive simulation environment was developed using OMNeT++ integrated with INET for networking, Veins for vehicle mobility, and Simu5G to emulate the 5G infrastructure. Custom modules for both protocols were implemented to assess their behavior under varying data loads. Performance metrics, particularly end-to-end latency, were analyzed across diverse traffic scenarios. Results demonstrate that ZeroMQ, due to its lightweight and brokerless architecture, consistently outperforms MQTT in terms of latency, making it a strong candidate for latency-sensitive V2X applications.

Evapotranspiration (ET0)—the sum of evaporation and plant transpiration—is a key variable for optimizing water use in precision agriculture. With increasing challenges due to climate change and water scarcity, accurate ET0forecasting is essential for designing efficient irrigation systems that enhance productivity while conserving resources. This study evaluates advanced time-series models for ET0forecasting—Nixtla TimeGPT-1, Long Short-Term Memory Networks (LSTM), and Kolmogorov–Arnold Networks (KAN)—using IoT data from Campo de Cartagena (Murcia, Spain). Results show that KAN achieves superior performance for multi-step forecasting (MSE: 0.045), while Nixtla Linear excels in one-step predictions (MSE: 0.009). These findings provide practical insights into model selection for adaptive irrigation strategies under diverse climatic conditions.

Monitoring avian species is fundamental to detect any negative impact from human activity. In our case study, we focus in particular on the La Mata lagoon in Torrevieja (Alicante, Spain), aiming at the monitoring of two gull species that often share the same environment: Larus Michahellis (Yellow-Legged Gull), and Ichthyaetus Audouinii (Audouin’s Gull). As of 2020, Ichtyaetus Audouinii has been included within the International Union for Conservation of Nature (IUCN) Red List, with the status of vulnerable as the global population has experienced a rapid decrease, which is expected to be approaching 40% between 2006–2030, and is projected to continue declining at a similar rate over the next three generations. Since conservation of these species requires informed, prompt and efficient decision-making, we propose constantly monitoring the ecosystem by deploying an AI-enabled IoT infrastructure to listen to bird sounds, and automatically identify bird species. To this end, we relied on the BirdNET artificial neural network for the acoustic analysis. However, using BirdNET models must be carefully planned to produce insightful data that can drive informed decisions. For this reason, we discuss our methodology and the logic behind tuning the most important parameters according to the proposed use case.

Modern cloud applications, particularly those resembling professional social networks, demand data management systems capable of handling heterogeneous, highly interconnected data. Traditional relational databases are often inadequate for such dynamic environments. This paper proposes a polyglot persistence architecture that combines document, graph, and key–value data stores to address diverse data storage and query requirements. Moreover, by integrating Large Language Models (LLMs) as an intelligent query and analytics interface, the system can interpret natural language requests, generate structured queries across multiple data stores, and provide personalized insights. We discuss the architectural rationale, outline the integration of LLMs with multi-database systems, and propose future research directions.

Named Data Networking (NDN) has been proposed as a next-generation Internet architecture to address the limitations of the traditional TCP/IP model. Caching is a vital component of NDN that significantly reduces network delay by enabling in-network content storage. However, conventional cache management methods often struggle to accurately predict content request patterns, or fail to do so efficiently within limited time constraints. This leads to low cache hit ratios and increased content retrieval latency. This paper introduces an innovative caching strategy, ECLAT-LRU Predictive Caching, which integrates the ECLAT association rule mining algorithm with the Least Recently Used (LRU) cache replacement policy. By leveraging vertical data processing, the proposed method improves pattern prediction accuracy and enhances cache utilization, particularly in larger cache environments. Simulation results demonstrate that ECLAT-LRU provides effective caching performance across varying cache sizes. Moreover, it is observed that employing the ECLAT algorithm in this framework reduces data retrieval time during pattern recognition when compared to traditional approaches such as Apriori, leading to faster and more scalable cache management. These findings confirm that ECLAT-LRU Predictive Caching efficiently reduces processing time and offers a robust solution for improving caching performance.

This paper presents a machine learning-based approach for network intrusion detection. It relies on a fast binary classifier to quickly distinguish between benign and malicious traffic, being the actual type of attack detected in an independent second stage. The model was trained and evaluated using a benchmark dataset that combines the well known CIC-IDS2017, CIC-IDS2018, and UNSW-NB15 datasets, following extensive preprocessing and exploratory data analysis. We compared multiple algorithms (Neural Networks, XGBoost, Random Forest, and Logistic Regression) based on standard metrics like precision, F1 score, ROC-AUC, and inference time, among others. Experimental results show that XGBoost consistently achieves the best balance between classification performance and deployment efficiency.

The rapid adoption of electric vehicles (EVs) demands advanced residential charging solutions, where user behavior varies widely due to factors like electricity tariffs and weather conditions. Such conditions make charging predictions particularly complex. This study addresses this challenge by predicting the time an EV remains connected to a residential charger using a dataset of 106,260 sessions. To this end, we propose the Connection Time Neural Model (CTNM), a deep neural network designed to model the complex dynamics of domestic charging, and we introduce the Weighted Error Metric (WEM), a novel metric that penalizes overestimations and under-estimations differently to reflect their real-world impacts on both grid management and user experience. Utilizing bidirectional charger data, we focus solely on connection time, bypassing energy prediction. CTNM is benchmarked against state-of-the-art methods (i.e., Random Forest, Dense Neural Network, XGBoost, and Support Vector Regression) using Mean Absolute Error, Root Mean Squared Error, and WEM as performance metrics. Results demonstrate CTNM’s superiority, reducing average error by 18%, and weighted error by 31% compared to Random Forest, thanks to its deep architecture and integration of contextual features, like variable tariffs and weather.

Autonomous driving technology has achieved remarkable advancements, offering substantial potential to revolutionize traffic safety and smart mobility. However, when faced with rare scenarios (weather, accident scenes, and lighting), autonomous driving systems can still only play a limited role due to insufficient learning in these rare situations. To address this challenge, we propose a novel approach that leverages low-rank adaptation (LoRA) and Mixture of Experts (MoEs) technologies to enhance the performance of pretrained autonomous driving models in handling rare situations. Specifically, we first use LoRA to fine tune the pretrained model of autonomous driving to focus on capturing knowledge related to rare scenarios and enhance the model’s ability to handle rare situations. Furthermore, we introduce MoEs and propose local, global, and hybrid adaptive solutions to overcome LoRA’s fixed intrinsic rank limitation. These approaches enable adaptive adjustment of LoRA’s rank, and improve the model’s performance from both local and global perspectives. Finally, we design detailed algorithms for different adaptation schemes. Extensive experiments demonstrate that our proposed solutions not only effectively improve the performance of the autonomous driving perception model in rare scenarios but also maintain lower inference latency compared to baseline methods.

In recent years, the rapid progress of Artificial Intelligence (AI) coupled with the rapid connectivity offered by the Internet of Things (IoT) has paved the way for a transformative framework that holds immense potential for the implementation of novel and groundbreaking ideas, and to provide innovative solutions to enhance the daily life of people, especially in healthcare domain. However, patient overcrowding remains as a glaring issue that plagues hospitals, especially during seasonal diseases such as influenza, leading to extended waiting periods for numerous patients, or dropping out of the queue by others. Therefore, FedRx, a new privacy-preserving Federated Distillation (FD) approach, is proposed in this paper. Such approach relies on the Internet of Vehicles (IoV), where each vehicle acts as a form of Mobile Edge Computing (MEC) to sense disease and generate digital medical prescriptions to avoid overcrowding in hospitals and pharmacies, while also reducing network burdens, improving sustainability, and enhancing the quality-of-life factors for people in smart cities.

Air pollution in metropolitan areas is one of their main problems, which has led to the implementation of Low Emission Zones (LEZ) in different cities worldwide. Estimating the benefits such LEZ can provide heavily depends on correctly predicting the resulting pollutant concentrations due to vehicle emissions. Yet, one of the main problems in traffic-related research is that the emission models of traffic simulators merely allow determining the mass emissions of vehicles, and not the concentrations of these pollutants as experienced by citizens. Although urban pollutant dispersion models have been developed to fill this gap, seamless integration with traffic simulators remains elusive. One of the solutions recently adopted is to create frameworks that integrate both concepts. In this work, we present a novel tool called SUMO2GRAL, that facilitates the creation of an integrated framework. In particular, it combines the SUMO traffic simulator with the GRAL pollutant dispersion model, in order to obtain reliable concentrations of urban air pollutants. Based on those results, researchers shall be able to help city administrations in their policymaking process. In addition, we demonstrate how this tool works, and compare it against a state-of-the-art solution. The validation results show that, even when an expert uses the state-of-the-art solution, our tool is still able to substantially improve the overall time spent in the process, making it about 130 times faster. This improvement helps in the process of focusing on creative tasks by offering the ability to test different scenarios in a much faster way.

LoRaWAN (Long Range Wide Area Network) is a communications protocol stack based on LoRa which provides long-range connectivity and low power consumption, making it a strong candidate for Internet of Things (IoT) device connectivity. However, unlike other technologies like WiFi or Bluetooth, LoRaWAN lacks an automatic mechanism to facilitate the connection of new devices to a network, which is undoubtedly a limitation for widespread device deployment. This paper presents AutoLoRaConfig, a rule-based system designed to automate the configuration and registration of devices in LoRaWAN networks, reducing the manual intervention required, and therefore minimizing potential human errors. The proposal aims to simplify the work required by users regarding the device configuration and registration process, enabling agile and efficient large-scale deployments. Results indicate that, for a set of 100 devices, automated deployment is 136.6 times faster than the manual procedure.

Environmental intelligence aims to improve the decision-making process for high social and environmental value ecosystems. To this end, data are collected using different sensors to allow monitoring of different variables of interest. Typically, these ecosystems cover a large geographical area, with spots of low or no connectivity, preventing their monitoring in real time. In this work, we propose AlLoRa (Advanced Layer LoRa), a modular, low-power, long-range communication protocol based on LoRa, that allows monitoring of remote natural areas. AlLoRa has been evaluated and tested in an operational oceanographic buoy that has been deployed to address the specific environmental crisis of the Mar Menor lagoon in southeastern Spain - a region spanning 135 Km2 currently undergoing severe eutrophication process. Our results reveal that AlLoRa offers good performance regarding transfer time, power consumption, and range. The throughput ranged from around 2 kbps with SF7 to approximately 300 bps with SF11; the power consumption per kilobyte transmitted varied from 395μWh to 428μWh depending on the specific device used. The Mesh mode test successfully maintained communication between nodes over 20.33 km. Further tests in various configurations under challenging conditions validated the mesh forwarding approach. Despite tripling the distance, the system maintained reliable data transfer, improving speeds from the original point-to-point setup.

As our cities become more complex and traffic demand grows, managing such traffic efficiently becomes challenging. Hence, solutions that allow building upon the current traffic light systems and that can be readily deployed are of global interest. In this work, we address the challenge of improving traffic light management at intersections. We propose an agent-based traffic light control system where an agent, one per intersection, dynamically regulates the light’s phase cycle depending on the current traffic conditions. To this end, we will rely on Deep Networks to adequately train agents to make good decisions. Simulation results in a realistic scenario using SUMO show that our proposed approach can significantly reduce waiting times, improving transit times by 44% compared to the standard fixed-timing method. Additionally, to assess the effectiveness and reliability of our control algorithm, we introduce new performance metrics.

Sensors are increasingly collecting data everywhere, changing how we relate to and manage the environment. These data answer scientific questions of researchers, but can also generate social, cultural, and political effects, reinforcing the need for data in near real time. In this work, a low-power, scalable, and sustainable communication solution based on LoRa is proposed to develop an oceanographic monitoring system composed of water quality monitoring buoys that can measure, among others, parameters such as water temperature, chlorophyll-a, turbidity, oxygen concentration, etc. An exhaustive evaluation is carried out with a real test bed in a 135 Km2coastal lagoon, demonstrating that our proposal offers good performance in terms of transfer time and latency, energy consumption, and range, showing good scalability with the number of buoys added.

Due to the increasing popularity of UAVs, and UAV applications, the need for accurate simulation tools is now greater than ever. Simulations allow for a fast, cheap, and most importantly, safe way to test new applications and protocols. However, due to their complexity, most of the existing UAV simulators only allow simulating either the UAV physics or their communication with a high accuracy. Yet, real applications require a tool that can simulate both. Hence, in this paper, we present a real time framework where our multi-UAV simulator ArduSim, and the popular network simulator OMNeT++, are combined to achieve an advanced co-simulation tool. We show that this co-simulation approach allows for a high accuracy in terms of both UAV physics, and communication between the UAVs, factors that have a clear impact on the performance of different protocols and applications that rely on UAV-to-UAV communications. Validation experiments show that the proposed co-simulation framework is able to perform adequately in real time under moderate workloads, even in standard desktop PCs.

The use of proprietary systems in Industry 4.0 often involves high economic costs. To address this issue, using low-cost devices with similar capabilities is becoming an increasingly popular alternative. However, these devices are prone to suffer the negative effects of electromagnetic interference (EMI) due to their placement in electrical panels alongside other electromechanical devices. To solve this problem, this article presents the autonomous Data Base Filter (aDBF). aDBF is an enhanced electromagnetic interference filtering mechanism capable of eliminating erroneous signals generated by EMI. aDBF has been specifically designed to autonomously (i.e., without the need for operator supervision or intervention) determine both the number of different product types elaborated in a production line, and the time instants when their manufacturing process starts and ends. In particular, aDBF goes through three stages: (i) pre-filtering, (ii) product change detection, and (iii) identification of valid signals. The results obtained after validating our proposal in three different manufacturing shifts demonstrate that the aDBF filtering mechanism works very accurately, as the maximum error introduced is of 0.93%.

In this work, we propose a system for video understanding through zero-shot reading comprehension using Socratic Models. Specifically, we create a language-based world-state history of events and objects present in a scene captured by an Unmanned Aerial Vehicle (UAV). To achieve this, video footage from RYZE Tello microdrones is transmitted to a ground computer for further processing. The semantically rich information offered by Large Language Models (LLMs) enables open-ended reasoning, such as event forecasting with minimal human intervention, in a cost-effective robotic system. BLIP-2 is employed to answer a given set of instructional prompts, creating a log-state of objects, humans, and hazards that can be searched. Simultaneously, it suggests probable actions in the scene and can assist the human controller with an estimated best command. The BLIP-2 instructional prompts are then combined with OpenAI's da-vinci-003/gpt-3.5-turbo to generate comprehensive video descriptions and summarize likely actions. The LLM-enhanced generated texts achieve a GUNNING Fog median grade level in the range of 7-12.

In this paper, we propose a novel approach for estimating the burned area of forest fires using the TabNet transformer-based architecture. Forest fires pose a significant threat to ecosystems, and accurate estimation of the affected area is essential for effective disaster management and resource allocation. We conducted a comprehensive analysis of various Machine Learning (ML) and Deep Learning (DL) methods, including Random Forest, Neural Networks, Neural Architecture Search (NAS), TabNet with Transformers, and Self-Supervised Learning with Autoencoders, to identify the most accurate and efficient model for area estimation. Our experiments employed a publicly available dataset, UCI Forest Fires, containing a combination of meteorological, geospatial, and categorical data. We implemented a thorough preprocessing pipeline that included handling categorical variables, standardization, and feature engineering. The results demonstrate that TabNet outperforms other methods, achieving state-of-the-art accuracy and generalization in predicting the target variable with a Mean Squared Error (MSE) of 2319 in training and 7781 in testing.

In this paper, we examine the efficacy of unsupervised learning approaches, particularly clustering and dimensionality reduction techniques, in practical applications such as image compression and geospatial data visualization. Initially, we scrutinize the applicability of the elbow rule in determining the optimal number of clusters within synthetic datasets of varying structures using the k-means algorithm. Subsequently, we evaluate the potency of density-based (DBSCAN) and hierarchical clustering algorithms in unearthing intrinsic patterns within these datasets. Shifting our focus towards practical applications, we leverage the k-means clustering for image compression, demonstrating a notable reduction in storage requirements without a significant compromise in visual quality. Our findings articulate that a strategic selection of cluster numbers can yield substantial compression rates. In the final segment of our investigation, we delve into the utility of dimensionality reduction techniques, specifically t-SNE and UMAP, in the realm of geospatial data visualization. Utilizing a dataset comprising distances between Spanish provinces, we gauge the proficiency of these techniques in preserving relative distances upon projection onto a two-dimensional plane. Our observations denote that both t-SNE and UMAP can generate precise visual representations of the original geographic layouţ with UMAP exhibiting exceptional performance in comparison with alternative methodologies. On a broader scale, our study underscores the versatility and practical utility of unsupervised learning techniques across an array of applications, ranging from image compression to geospatial data visualization. It emphasizes the significance of comprehending their foundational mechanisms and fine-tuning their hyperparameters to achieve optimal performance.

Long Range Wide Area Network (LoRaWAN) is a network protocol specifically designed to leverage the advantages of Long Range technology. LoRaWAN is employed to connect Internet of Things devices through a long-range network infrastructure, providing a secure and efficient communication layer that facilitates connectivity for a vast number of devices in a LoRa network. The aim of this study is to assess the real-life performance of LoRaWAN in different topographical environments. In particular, two scenarios (one flat and one mountainous) are compared, with an analysis of the success rate in data packet reception. The purpose is to examine how topography influences communication between LoRa devices and gateways, as well as to explore alternatives for overcoming challenges in rugged terrain environments. Therefore, in the mountainous environment, the study also investigates whether the use of drones could enhance communications. The findings of this study reveal that the topography of the terrain significantly impacts LoRa communications, resulting in a 58.63% decrease in the number of successfully received packets compared to a flat environment. Moreover, one-third of the nodes failed to establish communication with the ground gateway, and a partial improvement in connectivity was achieved by using gateways deployed on drones.

Climate change is one of the main problems that humanity is facing, and reducing air pollution is one of the actions that must be taken to fight it. Before we can reduce it, we must be able to accurately measure the current air pollution and identify the contamination sources. In this work, we propose the use of unmanned aerial vehicles (UAVs) to measure the air pollution and identify the highest contaminated areas (i.e. the pollution source). We developed an automatic multicopter guidance system that is capable of obtaining pollution data from an area in an efficient, autonomous, and precise way. Our guidance algorithms move a UAV based on the data that it measures at each location, in such a way that the UAV spends most of the time flying/measuring in areas where the pollution is higher. Hence, we are able to precisely map the contamination sources while reducing flight time by up to 85% compared to a full sweep of the scenario.

Vehicular networks have emerged in the past years as one of the most promising technologies in the context of Intelligent Transportation Systems. Several applications have recently been envisaged to improve the lives of citizens in different ways, particularly in the field of safety. In this work we focus on a different direction by leveraging on vehicular communication technologies and software-defined networking (SDN) to achieve a solution able to improve the travel time of emergency vehicles (EVs) when traveling in urban environments. In particular, we devise a solution where the route to be followed by EVs is shared with an SDN-enabled infrastructure, being then smartly rebroadcast using the roadside units (RSUs) that are near the region the EV will pass by in a near future. This way, standard vehicles that are ahead of the EV are notified about its coming, and clear the required lane to facilitate a faster and obstacle free path for the EV’s journey. Simulation experiments based on the SUMO traffic simulator for the city of Valencia, Spain, have shown that, if properly tuned, the proposed solution is able to reduce the travel time of EVs by nearly 15%, while maintaining the travel time of other vehicles mostly unaltered.

In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).

In the last decade, the popularity of UAVs has increased tremendously. Nowadays, many researchers are interested in UAV swarms. Coordinating a swarm of UAVs is a complicated task and many problems should be addressed before wide-spread adoption. In this work, we focus on the take-off for large-scale UAV swarms, with an extra focus on the assignment phase. The assignment phase is the first take-off stage whereby we decide which UAV on the ground goes to which place in the air. A good assignment algorithm, is quick, and at the same time reduce the total distance travelled as much as possible. We assess the performance of three different assignment algorithms: a heuristic, the original Kuhn-Munkres algorithm (KMA), and the KMA adapted for GPU use. Each algorithm was tested while varying the number of UAVs, as well as the type of flight formation. During the experiments, we measured the calculation time, total distance travelled, and number of flight paths crossing. In terms of total distance travelled, the KMA always outperforms the heuristic. However, the KMA takes longer (orders of magnitude) to calculate the assignment. Realistically, the KMA algorithm can only be used as long as the swarm does not contain more than 500 UAVs. From that point the GPU version of the KMA is faster. We can conclude that, in most cases, it is recommendable to use the KMA for the assignment as it will reduce the distance travelled to a minimum and, consequently, also reduce the number of flight paths crossing.

In this article, we analyze the use of non-intrusive monitoring sensors to detect dangerous situations in social events, a typical example being sexual harassment situations during parties. Non invasive sensors can be defined as devices that help measuring some specific parameter of interest remaining hidden or un-perceived by the user.We propose a solution based on a social application that can, in a non-intrusive way, detect sexual harassment situations and generate alerts automatically to the close by people so that detection of these situations can become quick and independent from the person being attacked. Our results are still preliminary but show that the project can certainly be developed into a working system that can help prevent control crimes from happening at public gatherings.

Named Data Networking (NDN) is attracting increasing attention from researchers and companies due to its characteristics and its promised results as a better alternative to the current TCP/IP Internet. Among these features are the use of names instead of addresses, and the use of caches in the nodes. Both have proved to be an excellent addition to network functionality that allow receiving information from a nearby location while relieving the pressure on the main servers. Yet, caches are still limited compared to the huge amount of data consumed. Most research has focused on finding and caching the most relevant data, to retrieve it in the future from the nearest point. Most research agrees that the data that needs to be stored is the one that is constantly requested by many consumers, and this theory has been generally effective in most research works. However, high data consumption levels are not always considered important, especially in academic or corporate environment. This is particularly true whenever the consumption of data associated to the institution’s own servers is very low compared to the other data, such as entertainment videos and private messages from social networking sites. Hence, the data that is stored and delivered in a short time is not essential for these institutions. Also, the servers that are discharged from the pressure are not affiliated with these institutions either. The existence of these cases proved by our study on real consumption data belonging to the Amar Telidji University of Laghouat in Algeria, where we found through simulations that only 4% of the overall traffic is associated with data belonging to the university itself. In this paper, we propose a new placement strategy named NBCC (Naive Bayes Classifier for Caching). The NBCC is used to cache the imported data by classifying the received content using a Multinomial Naive Bayes classifier that can classify the received data using only their names. The strategy is shown to be effective and provides the best results compared to other state-of-art strategies.

Nowadays, human overpopulation is stressing our ecosystems in different ways, being agriculture a critical example as different predictions point towards food shortages in the near future. In such context, smart farming is becoming key to optimize natural resources so that different crops are grown efficiently, consuming as few resources as possible. In particular, greenhouses have shown to be an effective approach to producing a high volume of vegetables/fruits in a reduced space and within a short time span. Hence, optimizing greenhouse functioning results in less water and nutrient consumption, less energy use, faster growth, and better product quality. In this paper, we take a step in this direction by studying the best approach to forecast greenhouse temperature based on univariate time-series analysis. In particular, several widely used time-series libraries such as Prophet by Facebook, Greykite by LinkedIn and TPOT are studied to figure out which performs better for this particular scenario. Results show that the maximum prediction error ranges from 1.5 to 3 degrees Celsius, and, in general terms, Greykite is found to be the best performing library for this particular environment.

There are many models that attempt to predict vehicular speed in urban and interurban roads, the noise pollution caused by traffic in cities, or even the traffic flow based on historical data from cameras or from people's mobile phones. Such information can be useful for administration authorities, and for researchers attempting to improve the living conditions of citizens. In this context, the aim of the present study is to design a model capable of predicting the traffic flow in the city of Valencia, Spain, based on data collected by electromagnetic loops distributed throughout the city. With a good traffic prediction, it will be possible to foresee possible traffic jams, and also to trigger countermeasures to mitigate them. Therefore, two models based on two recurrent neural networks of Long Short-Term Memory (LSTM) type have been designed to predict the traffic flow in the different streets of Valencia at the different hours of the day. We also study the influence of the specific characteristics used on the accuracy of the model. The results of our experiments show that, despite the high heterogeneity in terms of per-street traffic behaviour, it is possible to reach useful prediction models with low errors.

In recent years, the adoption of unmanned aerial vehicles (UAVs) has widely spread to different sectors worldwide. Technological advances in this field have made it possible to coordinate the flight of these aircraft so as to conform a swarm. A UAV swarm is defined as a group of UAVs working collaboratively to carry out more complex missions or perform tasks more efficiently. Common applications of these swarms include rescue missions, precision agriculture, and border control, among others. However, there are still certain problems that prevent us from ensuring the success of their mission, especially as the number of drones in a swarm increases. In this paper, we specifically address the problem of a swarm take-off by optimizing the total time involved, while guaranteeing the safety of the UAVs during the take-off stage. To this end, we propose a new approach that combines a collision detection algorithm based on trajectory analysis with a batch generation mechanism that we use in order to determine the take-off sequence. Experiments show that our algorithm offers an efficient solution, managing to improve the performance of existing take-off techniques.

Climate change is one of the greatest challenges for modern societies. Its consequences, often associated with extreme events, have dramatic results worldwide. New synergies between different disciplines, including artificial intelligence (AI), Internet of Things (IoT), and edge computing can lead to radically new approaches for the real-time tracking of natural disasters that are also designed to reduce the environmental footprint. In this article, we propose an AI-based pipeline for processing natural disaster images taken from drones. The purpose of this pipeline is to reduce the number of images to be processed by the first responders of the natural disaster. It consists of three main stages: 1) a lightweight autoencoder based on deep learning; 2) a dimensionality reduction using the$t$-distributed stochastic neighbor embedding algorithm; and 3) a fuzzy clustering procedure. This pipeline is evaluated on several edge computing platforms with low-power accelerators to assess the design of intelligent autonomous drones to provide this service in real time. Our experimental evaluation focuses on flooding, showing that the amount of information to be processed is substantially reduced, whereas edge computing platforms with low-power graphics accelerators are placed as a compelling alternative for processing these heavy computational workloads, obtaining a performance loss of only$2.3\times $compared to its cloud counterpart version, running both the training and inference steps.

The use of UAVs has been growing steadily over the last years. Now that even the industry is adopting them for a wide range of activities, it can be said with certainty that UAVs will become an important asset for many enterprises. We foresee that, due to affordable prices, applications with groups of UAVs, also called swarms, will become mainstream. Swarms of UAVs can perform tasks faster and/or with more redundancy, and other tasks are only possible by collaborative work of UAVs. However, there are still many challenges to be solved before swarms of UAVs can be used safely. One of the challenges is the takeoff; i.e., takeoff should be safe (no collisions) and fast at the same time. An important part of the takeoff is the assignment task; i.e., determining which UAV goes where. In this work we will compare the effectiveness of three assignment algorithms, in terms of total distance travelled, number of flight paths crossing, and calculation time. We specially focus on irregular patterns. Our results show that the Kuhn-Munkres Algorithm (KMA) is preferable in almost all cases. It ensures that the total distance travelled by all UAVs is minimal, and most importantly it reduces the number of flight paths crossing each other (i.e. potential collisions). This is a very important metric because it allows for fast (semi) simultaneous takeoff procedures, which are not possible if the chances of collision are high.

The field of Unmanned Aerial Vehicles (UAVs) is gaining momentum thanks to the amazing capabilities of these flying devices. In particular, small aircrafts using vertical takeoff and landing (VTOL) are among the preferred solutions in the civilian sector thanks to their low cost, simplicity of operation, and the ability to carry powerful sensing devices. When combined to create a swarm, the potential of such UAVs is further extended by allowing to perform more complex missions efficiently. However, as the number of UAVs involved becomes higher, many issues arise that can result into mission failures. In this paper, we specifically address the swarm takeoff problem from an optimization perspective. We propose a new takeoff scheme based on the Munkres algorithm that solves the assignment problem in polynomial time. Our evaluation studies the taking off complexity of large swarms and analyze the computational and quality trade-off of our proposal. Experiments show that the Munkres algorithm offers optimal solution with a low computation overhead.

Vehicle-related pollution is a main issue for many large metropolitan areas, and city administrations seek effective methods to improve the life quality of citizens, especially in downtown areas, and near places like hospitals or schools. One of the methods recently adopted is closing down these areas to traffic. However, the impact of such approaches has not been thoroughly studied. In this paper we assess whether limiting traffic for environmental reasons is feasible and efficient. In particular, we analyze the impact of blocking a street/road to avoid pollution in that zone, showing how our proposed route assignment algorithm is able to deviate traffic to minimize circulation in those places, In addition, we determine how the overall vehicle emissions in that area vary due to the traffic restrictions enforced. Experimental results show that, even when closing a single street, pollution indexes increase by 3-4 %, which raises doubts regarding the applicability and effectiveness of such methods.

The goal of this paper is to evaluate the dissemination of information using traces of real vehicles from two cities: Rome and San Francisco. Concretely, we perform an analysis of the temporal and spatial characteristics of these traces. Then, using the Epidemic Protocol, we evaluate the diffusion of the information according to the size of the message. As expected, the experiments show that the size of the message has a great impact on the dissemination of these messages. Eventually, we propose an improvement to the Epidemic protocol, called Epidem icX2, which is based on the division of large messages to increase their delivery opportunity. The results show that EpidemicX2 increases the diffusion (number of vehicles receiving the message) reducing the delivery time and the incurred overhead when compared to the Epidemic protocol.

Unmanned Aerial vehicles (UAVs) have gained a lot of interest over the last years due to the many fields of potential application. Nowadays, researchers are becoming interested in groups of UAVs working together. The collaborations between UAVs open a wide field of opportunities, because they are typically able to do more sophisticated tasks than a single UAV. However, collaboration between multiple UAVs is still a complex task, and significant challenges need to be addressed before their mainstream adoption. For instance, the automatic reconfiguration of a swarm can be used to adapt the swarm to changing application demands to solve a task in a more efficient and effective manner. However, the chances of collision become high if reconfiguration is not carefully planned. In this work we propose an approach to allow changing the shape of a UAV formation during flight through a computational inexpensive method that is able to decrease collision chances significantly. During the experiments we tested different reconfiguration events that are prone to collisions. Results have shown that our approach maintains a safe distance (greater than 5 meters) between the UAVs, while keeping the time overhead limited to a few tenths of a second. Furthermore, scalability tests have proven that our approach can handle the reconfiguration of at least 25 UAVs simultaneously.

As we experience an unprecedented growth in the field of Unmanned Aerial Vehicles (UAVs), more and more applications keep arising due to the combination of low cost and flexibility provided by these flying devices, especially those of the multirrotor type. Within this field, solutions where several UAVs team-up to create a swarm are gaining momentum as they enable to perform more sophisticated tasks, or accelerate task execution compared to the single-UAV alternative. However, advanced solutions based on UAV swarms still lack significant advancements and validation in real environments to facilitate their adoption and deployment. In this paper we take a step ahead in this direction by proposing a solution that improves the resilience of swarm flights, focusing on handling the loss of the swarm leader, which is typically the most critical condition to be faced. Experiments using our UAV emulation tool (ArduSim) evidence the correctness of the protocol under adverse circumstances, and highlight that swarm members are able to seamlessly switch to an alternative leader when necessary, introducing a negligible delay in the process in most cases, while keeping this delay within a few seconds even in worst-case conditions.

The development and deployment of the so-called Industrial Internet of Things (IIoT) have significantly increased the control and monitoring capabilities of companies, and thus their potential productivity. In this paper, we propose the use of Raspberry Pi devices in industrial environments to mea-sure productivity parameters. Our proposal can economically and efficiently gather data related with the availability and productivity of industrial machinery. However, since low-cost devices are prone to suffer the negative effects of electromagnetic interferences, we additionally propose an alternative to prevent signal alterations caused by them. More specifically, we propose a filtering mechanism called Smart Coded Filter (SCF), which eliminates wrong signals caused by electromagnetic interferences, and, therefore, highly improves the accuracy when estimating the availability metric. Results obtained demonstrate that our low-cost device provided with the SCF completely ignores 100% of wrong availability data, while reducing up to 70% the number of records stored into the database.

In this paper we present a flexible protocol based on LoRa technology that allows for the transfer of “content” to large distances with very low energy. LoRaCTP provides all the necessary mechanisms to make LoRa reliable, by introducing a lightweight connection set-up and ideally allowing the sending of an as-long-as necessary data message. We designed this protocol as a communication support for edge based IoT solutions given its stability, low power usage and the possibility to cover long distances. We present the evaluation of the protocol with various sizes of data content and various distances to show its performance and reliability.

As we witness the unrelenting growth of the UAV sector, novel and more sophisticated applications keep emerging every year, with many more in the horizon. Among these, applications that require the adoption of UAV swarms are among the most complex, as deploying swarms requires the interaction and cooperation of all the UAVs involved, which can become quite challenging. In this work we specifically focus on the swarm takeoff procedure for UAVs of the Vertical Take-Off and Landing (VTOL) type, proposing a heuristic that achieves reduced computing overhead while introducing near-optimal assignments of UAV positions in the swarm formation selected. Such heuristic is complemented by an efficient and collision-free takeoff approach that relies on adequate ordering and inter-UAV communications to achieve a sequential phased takeoff. A large number of experiments using our own ArduSim emulation platform, which is totally compatible with real drone code, evidence the improvements achieved in terms of time overhead and safety when compared to both ideal and agnostic approaches.

Unmanned Aerial Vehicles (UAVs), popularly known as drones, are foreseen as mobile infrastructures that support communications when a fixed infrastructure is missing in vehicular networks. UAVs can act as message relays between ground vehicles or broadcast alerts in emergency situations. Simulation that involves UAVs, combined with ground vehicles, should support 3D transmission features as the drone is not positioned in a flat surface. Results from real test bed experiments showed that irregular terrains that form hills and mountains can act as obstacles that limit the communications range. In this paper, we propose a simulation framework that runs within the OMNeT++ simulator which exhibits results comparable to the ones obtained in the real test bed, when applied to different scenarios. In the simulation, a measurement of the communications quality between UAVs and cars that considers 3D real-world terrain features which will have an impact on signal attenuation shows that the level of realism has improved when compared to simulation experiments that only consider planar communications.

Unmanned Aerial Vehicles (UAVs) can act as re-lays in areas with limited infrastructure to support car-to-car communications. Prior studies on UAV-to-car communications showed that the irregularity of the terrains has a significant impact on link quality. Thus, in this paper, we propose a positioning technique that relies on Particle Swarm optimization (PSO) to optimize the positioning of a UAV in the vehicular environment by considering the irregularities of the terrains that might hinder Line-of-Sight (LOS) conditions. The proposed technique takes into account the path loss caused by the terrains. Simulation results show that the optimization algorithm allows us to determine the best position for the deployed UAVs throughout time by considering the movement of the cars, and also accounting for adjustments in terms of flight altitude. In particular, the latter is adjusted by considering the position of the cars on the ground and the profile of surrounding terrains to determine potential communications blockages, while respecting international regulations regarding flight altitude restrictions.

Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPS-based landing, that typically deviates from the intended target by 1 to 3 meters.

Opportunistic Networking (OppNet) is a consolidated networking paradigm based on intermittent connectivity between wireless mobile devices. In OppNet, mobile nodes store, carry and forward messages by taking advantage of wireless ad-hoc communication opportunities. A common approach for the diffusion of information in OppNet is the epidemic protocol, which carries out a fast information diffusion at the expense of increasing the usage of local buffers on mobile nodes. Nevertheless, due to this high local buffer usage and the usually reduced hardware capabilities of Internet of Things (IoT) devices, OppNet utilisation for IoT applications can be severely reduced. Therefore, a way to reduce the local buffer usage is to set a message expiration timer that forces the removal of old messages in the local buffers. Nevertheless, since dropping messages may reduce the speed of message diffusion, we propose to conveniently adjust this expiration timer, using a model to obtain the optimal expiration times that achieve performances similar to those ideal approaches where no expiration is considered, with a significant reduction of local buffer usage.

Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion. To address this problem it is necessary to implement an efficient solution to control traffic that generates benefits for citizens such as reducing vehicle journey times and, consequently, environmental pollution as well. By properly analyzing traffic demand it becomes possible to predict future traffic conditions, and use this information for the optimization of routes. Such an approach becomes especially effective if applied in the context of automated vehicles, which have a more predictable behavior, thus being capable of mitigating the effects of traffic congestion by improving the traffic flow of the city in a centralized manner. This paper performs an experimental study of traffic congestion in an area characterized by intense traffic in the city of Valencia, Spain. By comparing the traffic flow in a typical day with our proposed improved solution, we show that significant benefits are achieved. In particular, we have created an interface to connect an existing urban traffic simulator (SUMO), and a network simulator (OMNeT++), with a modified route server. The latter continually updates present and future traffic conditions to properly balance traffic throughout the city. Experimental results show that our proposed Traffic Prediction Equation, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

Unmanned Aerial Vehicles (UAVs) are being widely used as a valuable tool in critical situations, especially when the access to the target area is difficult or hazardous. In these situations, a swarm of UAVs can provide additional benefits by covering larger areas, thereby speeding up the fulfillment of a mission, or by carrying heavier loads that a single UAV would not support. In this paper we present MUSCOP, a protocol that allows a set of UAVs to properly synchronize their flight in the fulfillment of planned missions. The results obtained show that MUSCOP achieves a high degree of swarm cohesion even in the presence of channel losses, introducing minimum synchronization delays and low position offset errors compared to an ideal scenario.

Message diffusion in Vehicular Networks takes place upon the establishment of ephemeral contacts among vehicles. This approach has one severe limitation: since the amount of information transmitted in a contact is limited by the transmission speed and the contact duration, large messages are not likely to be exchanged, and thus their diffusion is severely limited. In this paper, we study the impact that different social aspects have on the spreading of messages, showing that splitting a large message into smaller parts can improve its diffusion. Based on this idea, we propose an extension of the epidemic protocol called Xpread. We have developed an analytical model based on Population Processes to evaluate the impact and the efficiency of the partition scheme, showing that a fixed size partition is the best option, while also providing a simple expression to obtain the optimal size. Finally, we show that the diffusion of the messages is improved up to two times with a slight reduction in the delivery time and overhead. These results were also confirmed with simulation experiments.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

The performance of Opportunistic Networks relies mainly on users mobility. It is in fact mobility that creates the opportunities for contacts and therefore for data forwarding. The evaluation of these networks is usually based on either synthetic mobility models or real mobility traces generally characterized by a fixed number of users. In this paper, we focus on a mostly unexplored area characterized by crowded scenarios with people renewal, i.e., with users that can either enter or leave the evaluated scenario. By using a pedestrian mobility simulator we define realistic people mobility traces that allow the evaluation of different degrees of users densities and renewal rates. Using this methodology, we studied the performance of various existing routing protocols in scenarios with high degree of people renewal and by varying the messages sizes. The experiments confirm that the renewal rate has an important impact on the performance of the protocols, which becomes particularly evident when the message size is large. Overall, we observe that controlled flooding algorithm such as Spray & Wait can obtain good packet delivery results with lower overhead than with respect to probability based protocols, avoiding also the implementation complexity of the latters.

The number of Unmanned Aerial Vehicles (UAVs) flying around is increasing year after year due to their popularity. This new scenario considerably increases the risk of collision among them, which makes the development of protocols to avoid this issue an emerging topic. This work focuses on the development of a collision avoidance protocol between multi-copters performing planned missions. Our proposal is based on the periodic submission of state information, and estimations concerning future UAV positions, allowing collision risks to be detected early. Collision avoidance is achieved by forcing UAVs to stop when they are flying critically near each other, and establishing a priority to determine which UAV shall go through the critical area first; this decision is based on a per-UAV unique identifier. We prove that our solution is robust enough to avoid collisions in all the situations tested; in addition, the overhead introduced by our protocol in terms of additional UAV flight time is low.

Vehicular networks are gradually emerging due to the expected benefits in terms of enhanced safety and infotainment services. However, outside main metropolitan areas, little infrastructure currently deployed, which may hinder these services. To mitigate this problem, Unmanned Aerial Vehicles (UAVs) are envisioned as mobile infrastructure elements, supporting communications when fixed infrastructure is missing. This way, in emergency situations, UAVs can offer services to vehicles including broadcasting alerts or acting as message relays between ground vehicles. Our work attempts to be a first step in this direction by presenting experimental measurement results regarding communications quality between cars and UAVs. In particular, we varied the altitude of the drone and its antenna orientation, and the car's antenna location to assess their impact on performance. Based on the experimental results achieved, we find that UAVs communicating in the 5 GHz band using IEEE 802.11 technology are able to deliver data to moving cars within a range of more than three kilometers, achieving more than 0.5 of packet delivery ratio up to 2.5 kilometers under the optimal configuration settings.

Ensuring the required trustiness among communicating peers is an important task in Vehicular Adhoc Networks (VANETs), especially for safety-related applications where the margin of error is extremely undesired. Most the safety applications are a kind of decision aided system, and final decision is always taken by humans. Thus, in addition to securing inter-vehicle communication, the human factor must be also considered. With the appearance of 5G technology it became possible to connect VANET to any other network including Online Social Networks (OSNs). In this paper, we took advantage of this possibility to connect VANET and OSN, for the purpose of estimating the drivers honesty based on their OSN profiles. Afterward, we combined both inter-vehicle and OSN-based trust to compute the overall trust about the different vehicles and their drivers. Conducted simulation show that our proposal offers more than 5% detection ratio than the classical inter-vehicle solution. Furthermore, it also reduced the detection error ratio by about 3% with a reduced standard deviation for both detection and error ratios.

The diffusion of information using Opportunistic Networks relies mainly on users mobility. In fact, mobility creates the opportunities for contacts and therefore for data forwarding. The evaluation of this process in crowded spaces with people renewal, i.e. with users that can either enter or leave the evaluated scenario, is still basically unexplored, despite being one of the most suitable areas of application for these networks. In this paper, we focus on evaluating the dissemination of information in a subway station. We obtained real data from a local subway station through observation. These data were used in a pedestrian mobility simulator to generate people mobility traces to be used in an Opportunistic Network simulator. The analysis of the temporal and spatial characteristics of these traces reflects the realism of the generated scenario. In the experiments, we compare the direct delivery and epidemic diffusion protocols. As expected, epidemic diffusion compared to direct delivery increases drastically the number of users that receive the message. Furthermore, we propose an improvement of this protocol, called EpidemicX2 that is particularly effective for large messages.

In recent years, vehicular networks have experienced a fast evolution, and vehicles reaching the market are starting to be equipped with wireless communication devices. One of the main applications being developed for these environments is content delivery, which can help at, e.g., warning when an accident occurs. In this paper we compare two different content broadcasting strategies offering efficient content diffusion in vehicular environments. Specifically, we rely on Raptor codes to increase robustness in the presence of losses. Experimental results based on a vehicular testbed we deployed show that the overall approach is effective on both Access Point-based and RoadSide Unit (RSU) based content diffusion strategies. In fact, we demonstrate that the procedure required to connect to the access point introduces a non-negligible delay, which causes the results to be substantially improved when using RSUs (ad-hoc mode).

Currently we can witness how unmanned aerial vehicles (UAVs) have suddenly become an active research area, and are enjoying widespread acceptance, while also starting to play a relevant role in many areas of our society. Nonetheless, despite significant progress was made in recent years, many more improvements are expected in a near future. In this paper we address the issue of inter-UAV communications by proposing a methodology for performance evaluation based on a tool able to handle operational issues related to tests, along with a set of scripts that automate the statistical analysis of results, producing different types of charts. By deploying actual UAVs in a controlled testbed environment, we performed several experiments and produced output results that evidence the validity and applicability of our approach. Overall, the proposed solution allows accelerating the study of how different wireless technologies perform in such environments by significantly simplifying and automating most of the associated tasks.

Opportunistic networks are a form of mobile ad-hoc networks that exploit users mobility to provide data sharing. Individual nodes store, carry and forward messages using direct communication between devices; no fixed infrastructure of communication is required. The final goal is to reach the totality of the participating users. The efficiency of message diffusion depends on the users' mobility, the transfer time, and the device buffer management. User's mobility determines the number of contacts and their duration while the transfer time depends on the size of the interchanged messages and the channel throughput. The device buffer management is a critical factor since, due to its limited size, the chosen policy for forwarding messages when a connection is available and for message dropping when the buffer is full can impact greatly on the diffusion of the messages. In this paper we focus on this issue, evaluating the impact of different buffer management approaches in two different scenarios: pedestrian and vehicular. The results show that the best buffer management is the combination of smallest message forwarding and largest message dropping. We also show that a TTL (Time to Live) of 24 hours allows the best diffusion because it corresponds to the daily movement pattern of the nodes.

In the IoT world, establishing a strong mobile network architecture will be critical for organizations to bring together people, processes, data and things. Among the various available protocols and standards to network IoT entities, the Message Queue Telemetric Transport (MQTT) is already a reference solution. It provides a publish/subscribe messaging transport specifically designed to be used in devices with limited resources over constrained networks. MQTT's main limitation is its low resilience with respect to device mobility, so that the connections could suffer frequent and long lasting disruptions or high bit error rates that severely degrade normal communications. In this work we propose an architecture to increase the robustness of MQTT by integrating a Disruption Tolerant Network (DTN) approach. The architecture has been evaluated through several experiments using real devices to validate its feasibility, and to derive some guidelines for its use.

This article presents an Android application for safety driving based on OsmAnd, an open source platform offering offline maps and navigation. Our application extends OsmAnd to achieved smart navigation features by creating a network of vehicles. These features allow informing regular vehicles about incoming emergency vehicles, which include ambulances, police cars and fire brigades. The goal is to alert the driver in a timely manner so that he can take navigation decisions based on the information received. For proper operation, the application relies on message dissemination in the scope of a vehicular ad-hoc network, a goal that is achieved with the support of our proposed GRCBox hardware. By running on top of GRCBox, our application is provided with V2V communication abilities despite running on off-the-shelf Android terminals. Based on performance results obtained in restrictive urban canyon scenarios, we find that the application is able to reach, in the worst case, vehicles located near the intersection from streets located up to 80 meters away from the sender. By setting the message sending rate to 10 Hz, we find that the inter-packet arrival time is between 100 ms to 900 ms, meaning that notifications will reach other vehicles in less than 1 second.

Due to their deployment flexibility, Unmanned Aerial Vehicles have been found suitable for many application areas, one of them being air pollution monitoring. In fact, deploying a fleet of Unmanned Aerial Vehicles (UAVs) and using them to take environmental samples is an approach that has the potential to become one of the key enabling technologies to enforce pollution control in industrial or rural areas. In this paper, we propose to use an algorithm called Pollution-driven UAV Control (PdUC) that is based on a chemotaxis metaheuristic and a Particle Swarm Optimization (PSO) scheme that only uses local information. Our approach will be used by a monitoring Unmanned Aerial Vehicle to swiftly cover an area and map the distribution of its aerial pollution. We show that, when using PdUC, an implicit priority is applied in the construction of pollution maps, by focusing on areas where the pollutants' concentration is higher. In this way, accurate maps can be constructed in a faster manner when compared to other strategies. We compare PdUC against various standard mobility models through simulation, showing that our protocol achieves better performances, by finding the most polluted areas with more accuracy, within the time bounds defined by the UAV flight time.

As the use of Unmanned Aerial Vehicles (UAVs) increases, protocols to avoid collisions between them, and to achieve collaboration through swarm-based configurations, are receiving more attention by the research community. In this work we study the performance of communication links between drones in the 2.4 GHz wireless band, where a high interference from the radio control unit is expected. We performed a large set of experimental tests, and the results demonstrate that the use of the WiFi 2.4 GHz band for any application is not compatible with overwhelming majority of remote controls working in the same frequency band. Moreover, the distance between drones, the data packet size, and the engines speed are also factors affecting the communications link quality.

Intersection management is one of the challenges of vehicular communications in urban environments. Maximizing message delivery at intersections requires gaining awareness about when the vehicle is at the center of intersection, as buildings tend to obstruct wireless signals especially in the 5 GHz band. This paper addresses the issue of communications on different types of intersections by characterizing each intersection type through both real experiments and analytic modeling. The study has found that antenna location, distance to the intersection and line of sight conditions are all important factors for vehicular communications at intersections. Furthermore, this communication success also depends on the intersections characteristics, as different degrees of obstruction cause different delivery probabilities of sending messages.

Dishonesty detection in mobile and wireless networks is a task that typically relies on watchdog techniques. However, these medium overhearing-based techniques are prone to cause a high energy consumption. In an attempt to address this problem, several proposals adopted trust management as an alternative solution that is able to overcome the shortcomings of cryptography-based solutions when facing inside attacks. Unfortunately, these trust-based solutions remain mostly unable to reduce energy consumption. In this paper we propose a distributed time division-based monitoring strategy to achieve the required security levels while optimizing the energy consumption. Our proposal accounts for both trust and link duration among honest peers to fairly divide the monitoring period, and takes advantage of the periodically exchanged hello messages to make this solution fully distributed. Simulations results evidence the energy efficiency achieved by our proposal, especially for high density scenarios (>120 nodes) where the consumption becomes stable and does not increase with the number of nodes, while ensuring high detection ratios of malicious nodes (>85%).

In the past decade smartphones have experienced unprecedented adoption, being nowadays considered ubiquitous. In parallel, their complexity has also increased significantly since current devices are endowed with different types of sensors, thereby having multiple sensing capabilities. By combining the data gathered by different smartphones it is possible to achieve the monitoring of any region of interest with a high spatial and temporal granularity, thereby embracing the crowdsensing paradigm. In this paper we move in this direction by tuning smartphones so that they become accurate noise-sensing units. In particular, our focus is on the processes of sound capture and sound processing, determining the impact of different noise calculation approaches on the noise estimation accuracy; the latter is obtained through comparison against a professional noise sensing device. Through real experiments we find that, with a proper tuning, it is possible to achieve noise measurements in the entire dynamic range of typical smartphones (35 to 95 dB SPL) with an accuracy comparable to that of professional devices.

In this paper, we propose to use smartphones as an environmental sensor to measure noise pollution. We focused our study on determining the precise context for the capture of environmental noise through smartphones, and performed an analysis of the impact that the sensing task collection will have on energy consumption. To this purpose, we define different contexts to determine whether adequate environment sampling conditions are met, and we then apply classification algorithms to generate the most accurate decisions trees automatically. An analysis of resource consumption requirements associated with the different trees obtained shows that, despite their high accuracy, the resource consumption levels were prohibitive for this kind of applications. Thus, we propose an alternative decision tree that maintains the accuracy levels of automatically generated trees while significantly reducing the resource consumption introduced by the latter. Experimental results show that our proposed decision tree can reduce the energetic impact of our target application by about 60% when compared to the optimum theoretical tree generated through automatic classification procedures.

In-vehicle connectivity has experienced a big expansion in recent years. Car manufacturers have mainly proposed OBU-based solutions, but these solutions do not take full advantage of the opportunities of inter-vehicle peer-to-peer communications. In this paper we introduce GRCBox, a novel architecture that allows OEM user-devices to directly communicate when located in neighboring vehicles. In this paper we also describe EYES, an application we developed to illustrate the type of novel applications that can be implemented on top of the GRCBox. EYES is an ITS overtaking-assistance system that provides the driver with real-time video fed from the vehicle located in front. Finally, we evaluated the GRCbox and the EYES application and showed that, for device-to-device communication, the performance of the GRCBox architecture is comparable to an infrastructure network, introducing a negligible impact.

This paper presents the friendship and selfishness forwarding (FSF) algorithm for Delay Tolerant Networks. This novel solution is based on two social characteristics of nodes: friendship and selfishness. When a contact opportunity arises, FSF analyzes two aspects to make message forwarding decisions: first, FSF assesses the friendship strength among the pair of nodes, then it determines the individual selfishness of the relay node. Unlike other algorithms proposed in the DTN literature, we use a machine learning algorithm to quantify the friendship strength among pairs of nodes in the network. The individual selfishness of the relay node is determined by using a model based on the current level of its resources. The primary goal is to take into account the case where, despite a strong friendship with the message destination, the relay node does not accept processing the message to save its resources. By using trace-driven simulations we show that the FSF algorithm achieves better results in terms of delivery rate, average cost and efficiency.

Safety applications developed for vehicular environments require every vehicle to periodically broadcast its status information (beacon) to all other vehicles, thereby avoiding the risk of car accidents in the road. Due to the high requirements on timing and reliability posed by traffic safety applications, the current IEEE 802.11p standard, which uses a random access Medium Access Control (MAC) protocol, faces difficulties to support timely and reliable data dissemination in vehicular environments where no acknowledgement or RTS/CTS (Request-to-Send/Clear-to-Send) mechanisms are adopted. In this paper, we propose the Dynamic Token-Based MAC (DTB-MAC) protocol. It implements a token passing approach on top of a random access MAC protocol to prevent channel contention as much as possible, thereby improving the reliability of safety message transmissions. Our proposed protocol selects one of the neighbouring nodes as the next transmitter; this selection accounts for the need to avoid beacon lifetime expiration. Therefore, it automatically offers retransmission opportunities to allow vehicles to successfully transmit their beacons before the next beacon is generated whenever time and bandwidth are available. Based on simulation experiments, we show that the DTB-MAC protocol can achieve better performance than IEEE 802.11p in terms of channel utilization and beacon delivery ratio for urban scenarios.

Cooperative intelligent transportation systems (CITS), mainly represented by Vehicular Ad Hoc Networks (VANETs), were developed to enhance safety on roads before being generalized to support other comfort and efficiency applications. Most VANET applications, including safety ones, are based on multi-hop communications. Hence, a certain trustworthiness should exist among vehicles to ensure a reliable and trusted communication excluding dishonest peers from all network operations. In this paper we propose an hierarchical trust establishment solution able to cope with VANET applications and their requirements. Our solution is based on a three-level architecture, which enables it to adapt to the communication scenario and the required security level. Simulation results conducted done NS2 tool evidence that our solution is able to reach almost optimal attacker detection ratios (more than 90%) even in the presence of a significant number of attackers in the network (25%) while reducing the wrong relay decision ratios.

Air pollution monitoring has become an essential requirement for cities worldwide. Currently, the most extended way to monitor air pollution is via fixed monitoring stations, which are expensive and hard to install. To solve this problem, we have developed EcoSensor, a solution to monitor air pollution through mobile sensors. It is deployed with off-the-shelf hardware such as Waspmote (based on the Arduino platform), low-end sensors, and Raspberry Pi devices.

Abstract Trust establishment over vehicular networks can enhance the security against probable insider attackers. Regrettably, existing solutions assume that the attackers have always a dishonest behavior that remains stable over time. This assumption may be misleading, as the attacker can behave intelligently to avoid being detected. In this paper, we propose a novel solution that combines trust establishment and a risk estimation concerning behavior changes. Our proposal, called risk‐aware trust‐based architecture, evaluates the trust among vehicles for independent time periods, while the risk estimation computes the behavior variation between smaller, consecutive time periods in order to prevent risks like an intelligent attacker attempting to bypass the security measures deployed. In addition, our proposal works over a collaborative multi‐hop broadcast communication technique for both vehicle‐to‐vehicle and vehicle‐to‐roadside unit messages in order to ensure an efficient dissemination of both safety and infotainment messages. Simulation results evidence the high efficiency of risk‐aware trust‐based architecture at enhancing the detection ratios by more than 7% compared with existing solutions, such as T‐CLAIDS and AECFV, even in the presence of high ratios of attackers, while offering short end‐to‐end delays and low packet loss ratios. Copyright © 2016 John Wiley & Sons, Ltd.

In the next few years, important developments are expected in the Intelligent Transportation Systems (ITS) area. One of the key issues enabling future solutions is achieving an effective integration between mobile apps and vehicles. Such integration can be efficiently achieved on all existing vehicles by relying on the On Board Diagnostic (OBD-II) interface. This allows obtaining critical information such as speed, fuel consumption, gas emissions and system failures. In this paper we propose a vehicle emulation platform, called VEWE, that allows developing and testing OBD-II aware applications. The advantages of this approach include: avoiding the need for a real vehicle, allowing to easily generate realistic vehicle parameter patterns, and supporting emulated GPS functionality. We evaluate our platform by conducting a performance analysis in terms of OBD-II response times and channel capacity when relying on a Bluetooth adapter. We compare our results with respect to those obtained in real vehicles, and demonstrate that our VEWE platform behaves similarly to realistic on board devices, thereby providing a complete and reliable platform for smartphone application development.

Most applications developed for vehicular environments rely on broadcasting as the main mechanism to disseminate their messages. However, in IEEE 802.11p, which is the most widely accepted Medium Access Control (MAC) protocol for vehicular communications, all transmissions remain unacknowledged if broadcasting is used. Furthermore, safety message transmission requires a strict delay limit and a high reliability, which is an issue for random access MAC protocols like IEEE 802.11p. Therefore, transmission reliability becomes the most important issue for broadcast-based services in vehicular environments. In this paper, we propose a hybrid MAC protocol, referred as Dynamic Token-Based MAC Protocol (DTB-MAC). DTB-MAC uses both a token passing mechanism and a random access MAC protocol to prevent channel contention as much as possible, and to improve the reliability of safety message transmissions. Our proposed protocol tries to select the best neighbouring node as the next transmitter, and when it is not possible, or when it causes a high overhead, the random access MAC protocol is used instead. Based on simulation experiments, we show that the DTB-MAC protocol can achieve better performance compared with IEEE 802.11p in terms of channel utilization and beacon delivery ratio.

The duration of batteries is a key factor that must be considered in the design and deployment of applications and services on mobile devices. The users of this technology are not aware of the energetic impact of the applications executing in their devices, especially if these applications present an adaptive behavior. The goal of this paper is to study the power consumption associated to an opportunistic communication application. This will be achieved by analyzing the different operation stages of an application running on mobile and vehicular devices, using basic statistical analysis calculations on data collected by a logging application. Furthermore, this will allow analysing the impact of this kind of applications regarding battery performance, providing useful information to users and developers. The experiments were executed in an opportunistic network environment, using a floating content approach as a mechanism for information distribution in a geographically delimited zone.

Message oriented middleware (MOM) refers to the software infrastructure supporting sending and receiving messages between distributed systems. AMQP and MQTT are the two most relevant protocols in this context. They are extensively used for exchanging messages since they provide an abstraction of the different participating system entities, alleviating their coordination and simplifying the communication programming details. These protocols, however, have not been thoroughly tested in the context of mobile or dynamic networks like vehicular networks. In this paper we present an experimental evaluation of both protocols in such scenarios, characterizing their behavior in terms of message loss, latency, jitter and saturation boundary values. Based on the results obtained, we provide criteria of applicability of these protocols, and we assess their performance and viability. This evaluation is of interest for the upcoming applications of MOM, especially to systems related to the Internet of Things.

Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle's Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles.

Message Oriented Middleware (MOM) refers to the software infrastructure that support ubiquitous information delivery among software and hardware systems. Two of the most relevant protocols in this context are AMQP and MQTT. Lately, they have been extensively used to exchange messages conserving network bandwidth, device memory and batteries. These protocols provide an abstraction of the communication programming details of the different participating system entities, alleviating their coordination and collaboration. However, these protocols have not been thoroughly tested focused on studying the impact of node mobility. In this paper we present an experimental evaluation of both protocols quantifying the effect of the node mobility in terms of message loss, latency, jitter and saturation boundary values. Based on the results obtained, we provide criteria of applicability of these protocols. This evaluation is of interest for the upcoming applications that can be supported by MOM, and in especial for communication in Machine to Machine (M2M) and Internet of Things (IoT).

Obtaining detailed pollution maps for urban environments is an effort that is gathering much interest by allowing to better regulate traffic and protect citizens from hazardous conditions. However, the scarcity of pollution sensors prevents obtaining the desired degree of detail, requiring alternative solutions to be deployed. In this paper we explore the concept of mobile pollution sensing by studying the feasibility of equipping buses with ozone measurement hardware to estimate ozone patterns for the city of Compiegne. Overall, we achieve accurate estimations, with error values typically ranging from 2% to 10%. Compared to solutions based on deploying static sensors on the different bus stops available, we find that the proposed mobile sensing approach is able to provide a degree of accuracy comparable to deploying tens of static sensors, substantially reducing costs and management.

ITS solutions suffer from the slow pace of adoption by manufacturers despite the interest shown by both consumers and industry. Our goal is to develop ITS applications using already available technologies to make them affordable, quick to deploy, and easy to adopt. In this paper we introduce an ITS system for overtaking assistance that provides drivers with a real-time video feed from the vehicle located just in front. This provides a better view of the road ahead, and of any vehicles travelling in the opposite direction, being especially useful when the front view of the driver is blocked by large vehicles. We evaluated our application using H.264 and MJPEG video encoding formats, and determined the most effective codec choice for our case. Experimental results allow us to be optimistic about the effectiveness and applicability of smartphones in providing overtaking assistance based on video streaming in vehicular networks.

Nowadays, the impact of global warming is causing societies to become more aware and responsive to environmental problems. As a result, pollution sensing is gaining more relevance. In order to have a strict control over air quality, the use of mobile sensors is becoming a promising alternative to traditional air quality stations. Mobile sensors allow to easily perform measurements in many different places, thereby offering substantial improvements in terms of the spatial granularity of the data gathered. Pollution monitoring near large industrial areas or in rural areas where transportation facilities are poor or inexistent can complicate the mobile sensing approach. To address this problem, in this paper we propose endowing Unmanned Aerial Vehicles (UAVs) with pollution sensors, allowing them to become autonomous air monitoring stations. The proposed solution has the potential to quickly cover a target region at a low cost, and providing great flexibility.

Platooning is both a challenging and rewarding application. Challenging since strict timing and reliability requirements are imposed by the distributed control system required to operate the platoon. Rewarding since considerable fuel reductions are possible. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However, TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if retransmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability compared to pure 802.11p.

Delivering data through the most reliable and trusted path is essential for any kind of network. Moreover, in highly mobile and dynamic networks such as VANETs, the problem is more complex since every node requires, at least, a previous knowledge about its own neighborhood to select the most adequate path. In addition, the open communication medium causes other problems that any routing protocol must manage, without forgetting the VANETs' sensitivity to delay. In this paper we propose a trust-based routing protocol for vehicular urban environments called TROUVE. The proposed protocol aims at finding the shortest and most trusted path to destination taking into account the real traffic information and the distribution of dishonest nodes in the network. In this study, simulation results show the effectiveness of our protocol in terms of data delivery and end-to-end delay. We also show that, even in the presence of a high number of misbehaving nodes, our protocol offers equally good results.

Mobile ad-hoc networks (MANETs) assume that mobile nodes voluntary cooperate in order to work properly. This cooperation is a cost-intensive activity and some nodes can refuse to cooperate, leading to a selfish node behaviour. Thus, the overall network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. However, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone can lead to poor performance when detecting selfish nodes, in term of precision and speed. This is specially important on networks with sporadic contacts, such as delay tolerant networks (DTNs), where sometimes watchdogs lack of enough time or information to detect the selfish nodes. Thus, we propose collaborative contact-based watchdog (CoCoWa) as a collaborative approach based on the diffusion of local selfish nodes awareness when a contact occurs, so that information about selfish nodes is quickly propagated. As shown in the paper, this collaborative approach reduces the time and increases the precision when detecting selfish nodes.

Research works regarding vehicular communications usually obviate assessing the proposals in scenarios including adverse vehicle densities, despite such scenarios are quite common in real urban environments. In this paper, we study the effect of these hostile conditions on the performance of different schemes providing warning message dissemination. We then propose the Junction Store and Forward (JSF) and the Nearest Junction Located (NJL) schemes, which were specially designed to be used in very low and very high density scenarios, respectively. Simulation results using real maps demonstrate how our proposed schemes are able to outperform existing warning message dissemination schemes in urban environments under adverse vehicle density conditions.

Video transmission over VANETs is an extremely difficult task not only due to the high bandwidth requirements, but also due to typical VANET characteristics such as signal attenuation, packet losses, high relative speeds and fast topology changes. In future scenarios, vehicles will provide other vehicles with information about accidents or congestion on the road, and in these cases offering visual information can be a really valuable resource for both drivers and traffic authorities. Hence, achieving an efficient transmission is critical to maximize the user-perceived quality. In this paper we evaluate solutions that combine different flooding techniques, and different video codecs to assess the effectiveness of long-distance real-time video streaming. In particular, we will compare the most effective video coding standard available (H.264) with the upcoming H.265 codec in terms of both frame loss and PSNR.

New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help emergency services better estimate the required resources. This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag). Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models that can predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy emergency services after an accident takes place.

A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times.

The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classification of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months.

Efficient schemes for warning message dissemination in vehicular ad hoc networks (VANETs) use context information collected by vehicles about their neighbor nodes to guide the dissemination process. These schemes maximize their performance when all the vehicles advertise correct information about their positions, and hence position errors may drastically reduce the performance of the dissemination process. We present a proactive Cooperative Neighbor Position and Verification (CNPV) protocol that detects nodes advertising false locations so as to mitigate the impact of adversarial users. We combine our mechanism with two warning dissemination schemes for VANETs, and demonstrate how these algorithms can benefit from the use of our security scheme in the presence of malicious nodes trying to exploit the inherent vulnerabilities of each algorithm.

Intelligent Transportation Systems (ITS) require collecting and distributing as much relevant information as possible to provide their services. Such information could also offer new possibilities to various service providers in the wider Smart City context. The distribution of this intelligence is carried out through various vehicular networking strategies, the most flexible of all being Delay Tolerant Networking (DTN). DTN protocols can cope with the problems derived from high mobility and the possibility of high node sparsity. Nevertheless, achieving a fair comparison of DTN solutions in an urban environment is a hard task. In this paper we present a generic DTN model that we use to compare various representative DTN solutions in a metropolitan scenario. We highlight the weak and strong points of each evaluated proposal by also taking into consideration different sending strategies adopted to improve the performance of DTN protocols.

A representative characterisation of inter-contact times between nodes is essential for the performance evaluation of mobile networks. The most common characterization of inter-contact times is based on the study of the aggregate distribution of contacts between individual pairs of nodes. The problem with this aggregate distribution is that it is not always representative of the individual pair distributions, especially in the short term and when the number of nodes in the network is high. Thus, deriving results from this characterisation, can lead to inaccurate performance evaluations results.

In Vehicular ad hoc Networks (VANETs), efficient dissemination of messages is a key factor to speed up the development of useful services and applications. In this paper, we propose a novel algorithm that automatically chooses the best dissemination scheme trying to fit the warning message delivery policy to the current characteristics of each specific vehicular scenario. Our mechanism uses as input parameters the vehicular density and the topological characteristics of the environment where the vehicles are located, in order to decide which dissemination scheme to use. Simulation results demonstrate the feasibility of our approach, which is able to support more efficient warning message dissemination in vehicular environments.

Driving security and comfort can be improved by applying Intelligent Transportation Systems (ITS) proposals. The low adoption rate of new ITS hardware and software products is slowing down the market introduction of these solutions. In this paper we present a driving safety application for smartphones based on a warning dissemination protocol called eMDR. The use of smartphones minimizes the hardware cost and eliminates most of the adoption barriers; users will no longer have to install new dedicated devices in their vehicles. Instead, they will simply have to install an application in their smartphone. Our application is integrated with a Navigation System which provides access to road maps, current location, and route information. We analyzed the behavior of the wireless channel and the GPS location service under different conditions to assess the feasibility of our proposal. Results showed that, in C2C communications, smartphones are able to provide a reasonable degree of connectivity, and that the degree of precision achieved is enough for certain types of driving safety applications.

Vehicle density is one of the main metrics used for assessing the road traffic conditions. In this paper, we present a solution to estimate the density of vehicles that has been specially designed for Vehicular Networks. Our proposal allows Intelligent Transportation Systems to continuously estimate the vehicular density by accounting for the number of beacons received per Road Side Unit, as well as the roadmap topology. Simulation results indicate that our approach accurately estimates the vehicular density, and therefore automatic traffic controlling systems may use it to predict traffic jams and introduce countermeasures.

The Delay Tolerant Networks (DTN) approach is considered the best strategy to address the specific issues of the VANETs, namely high mobility, variable node density or frequent radio obstacles. Several protocols have been proposed for DTNs, being the epidemic routing (and variations of it) the most representative protocol. Nevertheless, the availability of navigation systems, thanks to which each vehicle is aware of its location within a map, introduces the possibility for a new routing approach, known as Geographic Routing. In this paper we analytically evaluate the performance of our previously presented Map-based Sensor-data Delivery Protocol (MSDP). We introduce an analytical model that takes into account the effect of constrained buffers. The results show that adopting the Map-based Sensor-data Delivery Protocol (MSDP) routing mechanism allows achieving a reasonable delivery time with an insignificant overhead compared with epidemic routing.

In recent years, new applications, architectures, and technologies have been proposed for vehicular ad hoc networks (VANETs). Regarding traffic safety applications for VANETs, warning messages have to be quickly and smartly disseminated in order to reduce the required dissemination time and to increase the number of vehicles receiving the traffic warning information. In the past, several approaches have been proposed to improve the alert dissemination process in multihop wireless networks, but none of them were tested in real urban scenarios, adapting its behavior to the propagation features of the scenario. In this paper, we present the Profile-driven Adaptive Warning Dissemination Scheme (PAWDS) designed to improve the warning message dissemination process. With respect to previous proposals, our proposed scheme uses a mapping technique based on adapting the dissemination strategy according to both the characteristics of the street area where the vehicles are moving and the density of vehicles in the target scenario. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in scenarios based on real city maps.

Due to the increasing popularity of mobile devices and the growing development of wireless networks, nowadays automobiles are able to communicate among them and with the infrastructure using different wireless technologies, thus improving not only communications but also safety on the roads. In order to improve communications by maintaining the Quality of Service (QoS) required by applications (e.g. throughput, latency) while the car is moving, switching from one base station to another, Vertical Handover techniques are the most adequate solution. In this work we present a Vertical Handover Decision Algorithm powered by the IEEE 802.21 protocol which takes advantage of the current devices deployed in the vehicle's on-board unit by considering geolocation, car navigation and realistic propagation model of different underlying networks such as Wi-Fi, WiMAX, and UMTS. Results demonstrate that QoS can be guaranteed when location and networking parameters (such as packet delay and bandwidth offered) are jointly considered.

Mobile ad-hoc Networks (MANETs) and Delay Tolerant Networks (DTN) rely on network cooperation schemes to work properly. Nevertheless, if nodes have a selfish behaviour and are unwilling to cooperate, the overall network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. Nevertheless, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce a wrong behaviour.

In a near future, vehicles will be provided with a variety of new sensors capable of gathering information from their surroundings. These vehicles will also be capable of sharing the harvested information via Vehicular Ad hoc NETworks (VANETs) with nearby vehicles, or with the emergency services in case of an accident. Hence, distributed applications based on VANETs will need to agree on a `common understanding' of context for interoperability, and therefore, it is necessary to create a standard structure which enables data interoperability among all the different entities involved in transportation systems. In this paper, we focus on traffic safety; specifically, we present a Car Accident lightweight Ontology for VANETs (CAOVA). The instances of our ontology are filled with: (i) the information collected when an accident occurs, and (ii) the data available in the General Estimates System (GES) accidents database. We assess the reliability of our proposal in two different ways: one via realistic crash tests, and the other one using a network simulation framework.

Powered by digital transmission advances in recent years, wireless sensor networks (WSNs) are beginning to experience a boom in different areas. Of special interest are those applications offering real-time tracking and monitoring of objects in motion, both indoors and outdoors. A WSN can have a large number of nodes, each with multiple neighbor nodes. These nodes work together to create a self-configuring mesh that can efficiently achieve a common objective. In this paper, we focus on the accuracy of intruder tracking and monitoring, when relying on low-cost binary detection sensing mechanisms. To overcome the limitations imposed by this kind of sensors, we propose an intruder tracking algorithm to estimate the intruder location. In our study, we adopted the IEEE 802.15.4 standard for radio communications, and the MRLG (Mobile-sink Routing for Large Grids) protocol to route data to a mobile sink. Experimental results based on a grid sensor deployment show that the tracking error, measured as the mean euclidean distance between the estimated and the real intruder locations, is typically maintained below 10 meters, validating the applicability of the proposed solution.

Abstract Security is an important topic in the context of mobile ad hoc networks (MANETs). The particular features of these networks, such as the use of open air as the transmission medium or computational constraints, make them vulnerable. One of the mechanisms to protect the communication is to provide anonymity, that aims at hiding the participants' identities, as well as the message contents and any kind of information about the transaction. For this purpose, some additional security mechanisms are required, usually based on cryptographic techniques. In this paper, we review the most relevant anonymity studies in the literature, starting with an analysis of proposals for wired networks, and then moving on to MANET environments. We also present a taxonomy to differentiate proposals according to the degree of anonymity offered. Copyright © 2010 John Wiley & Sons, Ltd.

Delivering contents over wireless networks can be a challenging task due to the intrinsic limitations of these environments. In this paper we propose RCDP, a solution which adopts an application layer FEC scheme based on Raptor codes to avoid retransmissions, optimizing content delivery for the unicast case. The proposed solution relies on end-to-end bandwidth estimations to perform rate control, achieving high throughput levels in lossy wireless environments. We have designed and implemented RCDP for the GNU/Linux platform to validate our approach under different channel conditions, varying packet loss ratio, end-to-end delay, and channel capacity. Experimental results show that the proposed solution is very efficient when facing high loss ratios and end-to-end delays, allowing for an efficient usage of channel resources in wireless scenarios.

In recent years, new applications, architectures and technologies have been proposed for Vehicular ad hoc networks (VANETs). Regarding traffic safety applications for VANETs, warning messages have to be quickly disseminated in order to reduce the required dissemination time and to increase the number of vehicles receiving the traffic warning information. In the past, several approaches have been proposed to improve the alert dissemination process in multi-hop wireless networks, but none of them is adapted to the propagation features of the scenario. In this paper, we present an adaptive algorithm designed to improve the warning message dissemination process. With respect to previous proposals, our proposed scheme uses a mapping technique based on adapting the dissemination strategy according to the characteristics of the street area where the vehicles are moving. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in simulated scenarios based on real city maps.

Wireless Sensor Networks (WSNs) have proliferated significantly in recent years. Nowadays they are used in many fields, such as military, environmental and industrial. Reliability and low latency are desirable characteristics of many WSN applications. In particular, time-critical WSN applications must be able to act according to the observed changes in the environment as quickly as possible, assuring that the information collected by the sensor nodes is correct. In these applications the response time is a critical factor. In this paper, we focus on WSN monitoring applications for both indoor and outdoor environments. We propose a near real-time monitoring system based on binary detection sensor that offers delay bounded tracking of events, such as gas and fire. The performance of gas and fire tracking applications is evaluated using the IEEE 802.15.4 technology and a routing scheme for WSNs that relies on sink announcements for route discovery. The proposed routing protocol is tuned to introduce the lowest possible end-to-end delay to data packet delivery, by reducing control traffic to a minimum. To evaluate the performance, we develop both gas and fire propagation models for a framework that allows simulating emergency events, thus allowing us to determine the degree of accuracy achieved in the monitoring process.

The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android- based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the applica- tion is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road.

In recent years, new architectures and technologies have been proposed for Vehicular ad hoc networks (VANETs).However, the experiments to validate these proposals tend to overlook the most important and representative factors. Moreover, the scenarios simulated tend to be very simplistic (highways or Manhattan-based layouts), which could seriously affect the validity of the obtained results. In this paper, we present a statistical analysis based on the 2kfactorial methodology to determine the most representative factors affecting traffic safety applications under real roadmaps. Our purpose is to determine which are the key factors affecting Warning Message Dissemination (WMD) in order to concentrate on such parameters, thus reducing the amount of simulation time required. Simulation results show that the key factors affecting warning messages delivery are the density of vehicles, and the roadmap used. Based on this statistical analysis, we consider that VANET researchers must evaluate the benefits of their proposals using different vehicle densities and city scenarios.

In traffic safety applications for Vehicular Ad hoc Networks (VANETs), warning messages have to be disseminated whenever a dangerous situation occurs to alert nearby vehicles. Using inefficient broadcast schemes may lead to ineffective dissemination of warning messages causing broadcast storm problems. In the past, several approaches have been proposed to reduce the so called broadcast storm in multi-hop wireless networks, but none of them is adapted to the features of the propagation scenario. In this paper, we present the Profile-driven Adaptive Warning Dissemination Scheme (PAWDS) to improve the warning message dissemination process. With respect to previous proposals, our PAWDS scheme uses an adaptive technique based on tuning the operation of the dissemination scheme according to the characteristics of the street area where the vehicles are moving. Our algorithm reported a noticeable improvement in the performance of alert dissemination processes in simulated scenarios based on real city maps.

Wireless technologies have been widely deployed in the last decade making users demand for continuous connectivity. Users not only demand being attached to a network, but possibly to the one with the highest performance, or at least to a network able to fulfill their requirements. To choose the best network among different candidates, the IEEE 802.21 standard has been developed. In this paper we aim at demonstrating the viability of performing Vertical Handover (VHO) processes based on the IEEE 802.21 protocol. To do so, we have evaluated a VHO strategy which considers network availability and maximum data rate in order to choose the best network candidate among the Wi-Fi, the WiMAX, and the UMTS. Moreover, throughout a set of experiments, we have also evaluated the maximum performance of the available networks.

Mobile computing and vehicular communications are becoming a very important paradigm for wireless communications, mainly because of their ability to adapt to different mobile applications. In this paper, we propose a new scheme for reducing the interference level during mobile transmissions in the VehiculAr inter-NETworking (VANET) environment, taking the advantage of the multi-channel nature of IEEE802.11p standard. In order to relieve the effects of the co-channel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical Signal-to-Interference Ratio (SIR) evaluation. The attention is focused on the routing level of VANET and we propose an interference aware routing scheme for multi-radio vehicular networks, wherein each node is equipped with a multi-channel radio interface. A new metric is also proposed, based on the maximization of the average SIR level of the connection between source and destination. Our solution has been integrated with the AODV routing protocol to design an enhanced Signal-to-Interference-Ratio-AODV (SIR-AODV). NS-2 has been used for implementing and testing the proposed idea, and significant performance enhancements were obtained, in terms of throughput, packet delivery and, obviously, interference.

Mobile Ad Hoc Networks (MANETs) have been an important research topic for the last years, playing a crucial role within the fast growing sector of mobile communications. At the same time, video applications over mobile devices are becoming widely used by nowadays mobile clients, where the quality in the transmission of such contents will determine the success of these applications in the future. Therefore, it is mandatory to find the best strategies to guarantee a good Quality of Service (QoS) to the end-user. In this work we present a set of novel strategies to improve the performance of video transmission over MANETs. These new strategies are based on distributed admission control protocols which has proved to be helpful at achieving an efficient video transmission system. Experimental results show that, when adopting the new strategies to determine the optimal number of layers to transmit, we can achieve better results compared to other existent approaches in terms of idle time periods, fairness and delay.

Vehicular mobile communications are attracting much attention in wireless communications field. In this paper a new routing protocol for the reduction of interference level during mobile transmissions in VANET environment is proposed, considering the availability of different channels in the DSRC spectrum. The attention is mainly focused on the routing level of VANET and we propose an interference aware routing scheme for multi-radio vehicular networks based on a new metric, for the maximization of the average SIR level of the connection between source and destination. The proposed idea has been integrated with the AODV routing protocol to design an enhanced Dynamic-Frequency-Interference-Aware-AODV (DFIA-AODV). The proposed idea has been tested and significant performance enhancements were obtained.

Nowadays, due to the ubiquity of wireless technologies, users demand continuous connectivity guaranteeing the Quality of Service (QoS) required for their communications. To fulfill those requirements, seamless Vertical Handover (VHO) is performed in order to maintain the connectivity among different wireless technologies while the user equipment moves across different coverage areas. In this work, we present a set of experiments to evaluate the vertical handover performance when relying on the IEEE 802.21 standard in scenarios where Wi-Fi, WiMAX and UMTS technologies are available. Experimental results show that a technology-aware vertical handover mechanism is able to achieve an adequate performance when traffic congestion is low.

Different applications for Wireless Sensor Networks (WSN), such as intruder detection and pursuit scenarios, require the support for mobility. In this paper we propose the novel Mobile-drain Routing for Large Grids (MRLG) algorithm, which is intended to support drain mobility in WSNs in an efficient manner. MRLG allows reducing the routing load by relying on local route recovery processes, which provides significant efficiency in scenarios with a large number of sensors. Preliminary experimental results show that, when compared to typical proactive routing strategies based on drain announcements, the MRLG algorithm allows to significantly boost performance in terms of packet delivery ratio, end-toend delay, and routing overhead.

Abstract Wireless communication technologies have now greatly impact our daily lives. From indoor wireless LANs to outdoor cellular mobile networks, wireless technologies have benefited billions of users around the globe. The era of vehicular ad hoc networks (VANETs) is now evolving, gaining attention and momentum. Researchers and developers have built VANET simulation software to allow the study and evaluation of various media access, routing, and emergency warning protocols. VANET simulation is fundamentally different from MANETs (mobile ad hoc networks) simulation because in VANETs, vehicular environment imposes new issues and requirements, such as constrained road topology, multi‐path fading and roadside obstacles, traffic flow models, trip models, varying vehicular speed and mobility, traffic lights, traffic congestion, drivers' behavior, etc. Currently, there are VANET mobility generators, network simulators, and VANET simulators. This paper presents a comprehensive study and comparisons of the various publicly available VANET simulation software and their components. In particular, we contrast their software characteristics, graphical user interface (GUI), popularity, ease of use, input requirements, output visualization capability, accuracy of simulation, etc. Finally, while each of the studied simulators provides a good simulation environment for VANETs, refinements and further contributions are needed before they can be widely used by the research community. Copyright © 2009 John Wiley & Sons, Ltd.

Wireless sensor networks designed for critical tasks must be able to offer near real-time feedback about emergency events, allowing to supervise these events with a reasonable degree of accuracy within strict delay bounds. Achieving such high responsiveness in a distributed environment requires several improvements to currently available WSN protocols and technologies at different layers; such enhancements typically rely on simulation, at least in a preliminary phase. To accurately evaluate the performance of WSNs in near real-time event tracking we developed an event generation framework compatible with the ns- 2 simulator that includes tools to model intruder detection events, as well as fire and gas propagation scenarios. In this paper we describe the analytical models developed, and then we present the potential capabilities of the proposed framework, along with some visual examples of different types of events which confirm its correct behavior.

Vehicular networking is a new field that is expected to be widely adopted in the near future. One of the key applications inherent to this novel communications paradigm is content delivery to on-board users. In this paper we focus specifically on broadcast-based content delivery. We propose a content delivery scheme that is optimized for performance in order to improve the maximum amount of data than can be delivered, while also reducing delivery time to a minimum. With this goal our study combines both analytical and simulation results to determine the optimal packet size for content delivery so as to achieve the maximum throughput possible at different distances, and considering both static and mobile receivers. Experimental results show that our optimizations provide efficient delivery of multimedia contents for distances up to 200 meters when relying on IEEE 802.11p based broadcasting.

The deployment of mobile ad-hoc networks involves several configuration steps, which complicate research efforts and hinder user interest. This problem prompts for new approaches offering full autoconfiguration of terminals at the different network layers involved. In this paper we propose a novel solution for the autoconfiguration of IEEE 802.11 based MANETs that relies on SSID parameter embedding. Our solution allows users to join an existing MANET without resorting to any additional technology, and even in the presence of encrypted communications. Experimental testbed results using a real implementation of the proposed solution show that it is quite effective, allowing all the required configuration steps to take place in under 32 ms without involving any extra traffic overhead on the channel.

Research in Vehicular Ad hoc Networks (VANETs) has found in simulation the most useful method to test new algorithms and techniques. This is mainly due to the high cost of deploying such systems in real scenarios. However, when determining the factors that should be taken into account in these simulations, some features such as using real topologies, radio signal absorption due to obstacles and channel access are rarely included, and therefore, results obtained are far from being realistic. In this paper, we present a new Radio Propagation Model (RPM), called Real Attenuation and Visibility (RAV), proposed to simulate more realistically both attenuation of wireless signals (signal power loss) and the radio visibility scheme (presence of obstacles interfering with the signal path). We evaluated this model and compared it against existing RPMs using real scenarios. Simulation results confirmed that our proposed RAV scheme can better reflect realistic scenarios.

Emerging multimedia applications over mobile devices are becoming very popular, especially over infrastructure wireless networks such as cellular and WLANs. However, providing this kind of services over infrastructure-less networks like ad hoc networks presents many additional problems. One of these problems is how to share resources fairly among the users involved. In this article we propose a QoS framework supporting scalable video streaming in mobile ad hoc networks based on distributed admission control and video traffic awareness. Our framework promotes fairness between video flows in terms of resource consumption. It also guarantees a significant reduction of the idle times experienced by users during periods of network saturation, thus increasing the video playout time in reception for all users. Using the IEEE 802.11e MAC technology as our basis for traffic differentiation, our framework, called DACME-SV (Distributed Admission Control for MANET's - Scalable Video), relies on a periodic probing process to measure the available bandwidth and the end-to-end delay on the path. DACME-SV adopts a cross-layer approach to determine the optimum number of video layers to transmit at any given time, thus avoiding network congestion and guaranteeing an acceptable video quality at the destination. Experimental results show that idle time periods are substantially decreased, while exhibiting a good overall performance in terms of throughput and delay.

Concept maps have been around for quite some time, and their principles are deeply rooted on well-known learning theories. When used in the evaluation process as a tool to assess learning they have obvious benefits by allowing students to externalize their own mental trees of assimilated concepts seamlessly. However, the assessment of concept maps includes a strong degree of subjectiveness, which should be mitigated. In this paper we propose partitioning the concept map evaluation process according to the steps followed for creating them, along with objective metrics to assign a score to each of these steps. We also propose a formula that combines the partial scores to obtain the final score. Afterward we validate the proposed methodology, showing that the score variability associated with the evaluator is reduced by up to 23%.

Mobile ad hoc networks have a highly dynamic topology due to terminal mobility. Ad hoc routing protocols can cope with this mobility as they search for an alternative route when a currently-in-use path breaks. Despite of their ability to recover from path failures, the time elapsed until the route is reestablished deteriorates network performance. MANET routing protocols can detect link breakages faster when Link Layer Feedback (LLF) mechanisms are used. In this paper we evaluate the advantages and drawbacks of using feedback mechanisms. Our simulation results show that these mechanisms are appropriate for low mobility conditions. In contrast, feedback is not adequate for scenarios where terminals move faster due to frequent collisions mistaken as link breakages.

In this paper, we present a performance evaluation study analyzing the behavior of a generic warning message dissemination (WMD) mechanism in a 802.11p based VANET. In our WMD method, warning-mode vehicles notify nearby vehicles in order to improve traffic safety and to control traffic congestion. Our evaluation uses 2k factorial methodology to determine the most representative factors that affect WMD performance. We performed simulations to evaluate the impact of different characterizing factors. Performance metrics evaluated are: (a) the time required to propagate the warning messages, (b) the number of blind nodes (i.e., nodes that do not receive these packets), and (c) the number of packets received per node. Simulation results show that the propagation delay is lower when node density increases, and that the percentage of blind nodes highly depends on this factor too. Factors that affect the number of packets received include downtown size, the probability of being in downtown, and the number of nodes. Lastly, we discovered that the size of packets sent does not significantly impact WMD performance.

The IEEE 802.11e technology is receiving much interest due to the enhancements offered to wireless local area networks in terms of QoS. Other application fields for this technology are wireless ad-hoc networks, wireless mesh networks and vehicular ad-hoc networks. In the literature, most of the research works available focusing on the IEEE 802.11e technology offer simulation results alone, being hard to find empirical results of real implementations. Additionally, we consider that the IEEE 802.11e implementation on simulation platforms has not been thoroughly validated using real-life results. In this work we analyze the performance of the IEEE 802.11e technology in a real multi-hop ad-hoc network testbed, comparing the results obtained with those of the ns-2 simulation platform. Experimental results show a significant consistency in terms of overall trends, although remarkable differences can be appreciated in terms of both delay and throughput results. In general, we find that simulation-based results are always optimistic compared to real testbed performance.

Eavesdropping is an important threat in the context of mobile ad-hoc networks due to the use of open air as the transmission medium. As a consequence, some works trying to prevent this threat have been proposed. Some of these proposals focus on the use of anonymous routing protocols. In this paper we analyze two of the most popular: ANODR and MASK. We evaluate their performance through simulation in terms of throughput and routing overhead in order to measure the cost of providing anonymity. Simulation results show that these anonymous routing protocols reduce performance to inefficient levels.

In this paper we present BlueFriend, a novel application for mobile devices that takes advantage of Bluetooth functionalities to create mobile social networks. Our application runs on PDAs and smart phones equipped with a Bluetooth adapter. BlueFriend periodically scans the environment in search of

Deploying and testing vehicular ad hoc networks (VANETs) involves high cost and intensive labor. Hence simulation is a useful alternative prior to actual implementation. Most works found in the literature employ very simplistic radio propagation models (RPMs), ignoring the dramatic effects presented by buildings on radio signals. In this paper, we present three different RPMs that increase the level of realism, thereby allowing us to obtain more accurate and meaningful results. These models are: (a) the distance attenuation model (DAM), (b) the building model (BM), and (c) the building and distance attenuation model (BDAM). We evaluated these different models and compared them with the two-ray ground model implemented in ns-2. We then carried out further study to evaluate the impact of varying some important parameters such as vehicle density and building size on VANET warning message dissemination. Simulation results confirmed that our proposed BDAM significantly affects the percentage of blind vehicles present and the number of received warning messages, and that our models can better reflect realistic scenarios.

Providing quality-of-service (QoS) in wirelessadhocnetworks is an intrinsically complex task due to node mobility, distributed channel access, and fading radio signal effects. This goal can be successfully accomplished only through the cooperation of the different protocol layers involved. In this paper we propose a novel QoS architecture that is able to support applications with the bandwidth, delay, and jitter requirements in MANET environments. The proposed architecture is modular, allowing the plugging in of different protocols, which offers great flexibility. Despite its modularity, we propose optimizations based on interactions between the media access control (MAC), routing, and admission control layers which offer important performance improvements. We validate our proposal in scenarios where different network loads, node mobility degrees, and routing algorithms are tested in order to quantify the benefits offered by our QoS proposal. In particular, we have also used real H.264/AVC video traces to simulate video sources in order to measure the quality in terms of peak signal to noise ratio of the received video, so that the benefits of applying our QoS scheme to video sources can be assessed in terms of user satisfaction (from the applications perspective).

Wireless mesh networks (WMNs) require specialized management software to reduce the setup and configuration time. Although wireless routers can be configured independently, some parameters, like the ESSID or the channel, are common to all the network elements and a change in one of these parameters would cause partitioning problems if it does not take place on all routers simultaneously. In this paper we present MAYA, an efficient and secure tool specifically designed for wireless mesh management and configuration. MAYA augments the basic functionality offered by the firmware on routers to cover the global requirements of a WMN. In our analysis we detail the system architecture, its main components and the implementation details. We also evaluate the latency associated to the different management tasks under variable traffic conditions.

Evaluating and deploying all sorts of protocols and applications designed for wireless ad hoc networks in a real environment is an important and urgent task. Traditionally, all the proposals made rely solely on simulator results. However, as different research groups develop different solutions to solve problems related to these new networks, it becomes more and more important to migrate the proposed solutions to a real environment. In this work we present Castadiva, a test-bed architecture that allows validating software solutions for ad hoc networks using low-cost, of the-shelf devices and open source software. Through a friendly user interface, Castadiva offers the possibility to define and test different scenarios and traffic patterns, adding the possibility to export them to the NS-2 format for result comparison. A first evaluation of the tool reveals Castadiva as an efficient tool that offers real-life accuracy, while avoiding having a high number of mobile users available for test-bed experiments.

Multipath routing protocols have proved to be able to enhance the performance of MANET in terms of reliability, load balancing, multimedia streaming, security, etc. However, deploying a QoS framework on top of such routing protocols is a complex task, requiring an appropriate QoS strategy to be developed and deployed. In this paper we propose such a strategy, validating it through simulation. The results achieved show that the proposed QoS framework can perfectly coexist with multipath routing protocols, achieving significant improvements on the overall network performance, especially from the point of view of demanding applications.

QoS support in MANETs is a hard and challenging task due to the intrinsic complexities of these networks. In this work we present a solution called DACME to support real-time services in MANETs based on distributed admission control. Using the IEEE 802.1 Ie MAC technology as our basis for traffic differentiation, we develop a technique based on probes to assess available bandwidth in an end-to-end path, as well as the end-to-end delay and jitter. Results show that our technique is quite promising due to the degree of accuracy achieved estimating the different network parameters, while maintaining acceptable levels of traffic overhead and admission delay. Also, since no demands are imposed on intermediate stations, it can be easily deployed.

In this work, we propose a QoS architecture for MANETs based on a probe-based distributed admission control mechanism and the IEEE 802.11e technology. Our aim is to improve peer-to-peer communication in wireless mobile ad hoc networks by supporting real-time multimedia streaming. This technology can adapt to applications with bandwidth, delay and jitter constraints, and yet we keep to a minimum the requirements imposed on intermediate stations. Simulation results show that we successfully achieve our goal of supporting QoS-constrained applications in MANETs with a low overhead, confirming the adequateness of using probe-based admission control in these environments.

Mobile ad-hoc networks (MANETs) present quite large packet loss bursts due to mobility. In this work we propose two models based on hidden Markov models for estimating packet arrivals and packet loss patterns in MANETs. These models help the evaluation and tuning of multimedia streaming applications in terms of simulation time and required resources. In particular, we show how these models can be applied in the design of error concealment algorithms to increase the video coding resilience. The obtained results show that we get comparable results without the need for several long simulation runs. Finally, we also propose a set of new metrics for packet loss patterns analysis that can be of interest for the evaluation of audio/video streaming applications.

This paper focuses on the implementation of heterogeneous mobile ad hoc networks (MANET). A heterogeneous MANET is a wireless network setting without a fixed infrastructure where participating devices may be of different kind (e.g. desktop, laptop, or palmtop computers), may not share the same operating system, and may not share the same wireless technology. We describe the design and implementation of a prototype for the OLSR routing protocol, targeting multiple operating systems, devices, and radio technologies. We choose a proactive routing protocol to obtain optimal routes in a dense network with slow mobility patterns and to ease the portability task to the heterogeneous environment. We also optimized the OLSR protocol, making it more reactive to topology changes.