The deployment of machine learning (ML) on edge devices and ultra-low-power microcontrollers (MCUs) has led to the emergence of two dominant paradigms: Edge ML and TinyML. Edge ML leverages moderately powerful devices—such as embedded systems and AI-capable computing platforms—for real-time inference. In contrast, TinyML pushes the boundaries by enabling ML capabilities on ultra-low-power microcontrollers operating at milliwatt-level power consumption. This paper presents a comprehensive comparative analysis of these two approaches, supported by experimental results from an object detection application (egg counting) implemented on both an MCU-based device (Arduino Nicla Vision) using TinyML and a single-board unit (Raspberry Pi) employing Edge ML. We evaluate key metrics, including power consumption, latency, and the trade-offs between INT8 and FLOAT32 model representations. Our findings show that TinyML achieves significantly lower energy consumption than Edge ML for comparable tasks, positioning it as a more suitable choice for battery-operated IoT applications.

This paper presents a novel communication and processing architecture for AIoT (Artificial Intelligence of Things) deployments that integrates Visible Light Communication (VLC) with LoRaWAN and on-device Machine Learning (TinyML). The proposed system addresses key challenges in rural and outdoor agricultural environments, such as heterogeneous sensor capabilities, limited connectivity, and the need for low-power operation. VLC is employed for high-throughput short-range data transmission between edge devices, while LoRaWAN enables long-range remote control and cloud connectivity. The architecture supports real-time analysis and classification of environmental and plant data, including image-based fruit monitoring, to predict water stress conditions. By combining efficient data transmission with local ML-based decision-making, the solution enhances irrigation management and contributes to resource optimization in PA settings. This work offers a scalable, low-cost framework adaptable to other Environmental Intelligence applications.

TinyML enables efficient on-device machine learning for resource-constrained edge devices, addressing privacy and computational challenges in real-world applications. This paper presents a TinyML-based Edge-Cloud system using the FOMO (Faster Objects, More Objects) model deployed on a constrained edge device to analyze visitor engagement in Smart Museums. Unlike conventional camera-based tracking, our approach ensures privacy-preserving analytics by processing visual data locally, extracting only anonymized metrics (e.g., dwell time) to allow for holding power (HP) calculations. We optimize FOMO via data augmentation (DA) and quantization, significantly improving performance; with the optimal configuration, we achieved 86.5% F1-score at 155ms inference latency (a 9.2× speedup over the baseline configuration) while maintaining 88% precision. Our results demonstrate that TinyML-enhanced edge deployment offers a robust, low-latency, and privacy-compliant solution for museum analytics, outperforming traditional cloud and wearable-based approaches.

Road surface quality is a key determinant of mobility safety, comfort, and sustainability in modern cities. Traditional pavement monitoring relies on costly and sparse inspection campaigns, which limit scalability. Recent advances in Edge Artificial Intelligence (Edge AI) and the widespread adoption of smart-phones open new opportunities for low-cost, distributed, and near real-time assessment of road infrastructure. In this work, we investigate the feasibility of classifying road surface conditions using mobile sensors available on consumer devices, including accelerometers, gyroscopes, and GPS modules. We collected data from bicycles and cars using a custom web application, applied a feature extraction and preprocessing pipeline, and evaluated multiple machine learning models. Our results demonstrate that accurate classification of surface types is achievable, with XGBoost and Random Forest models outperforming alternatives, particularly in car-based deployments. These findings highlight the potential of Edge AI to enable scalable, comfort-aware mobility services and support municipalities in proactive infrastructure management.

Intelligent Environments require perception systems capable of adapting to evolving tasks, heterogeneous sensing conditions, and the resource constraints of large-scale IoT deployments. This work shows an edge-centric pipeline in which a highcapacity model, specifically YOLO12n, operates at the gateway to automatically label visual data and guide the specialization of TinyML models deployed on low-power devices. Focusing on two representative mobility-related classes, cat (present in COCO) and scooter (absent from COCO), we analyze the behavior of YOLO models under zero-shot conditions, during finetuning, and when incrementally integrating new classes. Results show that YOLO12n consistently outperforms YOLO11n in zeroshot evaluation and reaches near-perfect detection accuracy (mAP@50 up to 0.995) after only a few epochs of fine-tuning, with inference times below 5 ms on GPU-equipped edge nodes. When adding the new scooter class, the model rapidly adapts, yet subsequent fine-tuning on cats reveals strong catastrophic forgetting. Rehearsal-based retraining effectively mitigates this degradation, even when using replay buffers as small as$\mathbf{1 0 - 2 0 o r i g i n a l}$dataset. These findings demonstrate that lightweight edge auto-labeling combined with efficient replay mechanisms enables sustainable, privacy-preserving, and continuously adaptive perception pipelines for next-generation Intelligent Environments and IoT monitoring infrastructures.

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.

Monitoring invasive species is essential for protecting biodiversity in sensitive ecosystems. In the natural park of Torrevieja (Alicante, Spain), domestic cats threaten local bird populations, including endangered species. To address this issue, we developed a system leveraging deep-learning models, including YOLO (You Only Look Once), to detect cats in real-time automatically. Our solution involves deploying edge-AI cameras equipped with LoRa communication technology to efficiently transmit detection data to the cloud. This infrastructure enables continuous monitoring, accurate detection, and prompt invasive species reporting while optimizing power consumption and network bandwidth. In this paper, we present the development and evaluation of multiple deep-learning models, assess their prediction accuracy, and discuss the integration of LoRa technology to enhance data transmission in remote areas. Our findings demonstrate the feasibility and effectiveness of using edge-AI and IoT technologies for biodiversity conservation in protected natural environments.

Measuring ephemeral stream flows is essential for ecological and hydrological studies. However, their intermittent nature and remote locations pose challenges for conventional monitoring methods, which often consume excessive energy to capture rare events. We address this with BODOQUE (Bimodal Observational Device for Optimizing Quantification of Ephemeral streams), a dual-mode system that leverages Tiny Machine Learning (TinyML) on low-power microcontrollers. The system remains in an energy-saving sensing state and activates high-precision measurements only when water flow is detected. We present a model selection methodology that balances detection accuracy with inference cost, enabling reliable operation within hardware constraints. To enhance adaptability in diverse environments, we developed a specialized component that facilitates dataset expansion through new field samples. This supports ongoing retraining to maintain model performance under changing conditions. A comprehensive evaluation using real-world data demonstrates that our system can achieve up to 97% annual energy savings compared to traditional continuous monitoring approaches.

As context-aware location-based services (LBS) become increasingly important in many Internet of Things (IoT) verticals, such as logistics or industry 4.0, indoor localization is now an essential feature to be integrated in these solutions. For this purpose, fingerprinting-based solutions arise as a feasible solution, especially when integrating artificial intelligence on the edge, supported by computational and memory-restricted embedded devices, as it does not depend on a cloud-based deployment. In this work, we integrate this new paradigm, known as TinyML, and compare the implementation of a machine learning (ML) model when using only WiFi Received Signal Strength Indicator (RSSI) or WiFi Channel State Information (CSI) data. We tested two different scenarios, a single sample or time series, with different configurations of the trained neural network. Our results show that a CSI data ML model always outperforms an equivalent RSSI approach, with a massive difference in performance for the time-series case.

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.

Efficient long-range communication is critical for environmental monitoring, especially when dealing with large data transfers in remote areas. We present an AI-driven dynamic RF configuration mode, which combines the advanced capabilities of a novel AI*LoRa model with an enhanced RF configuration request mechanism. By leveraging TinyML, AI*LoRa dynamically adjusts key parameters of the physical layer, based on real-time environmental data, ensuring robust and energy-efficient communication. We conducted extensive real-world testing across distances ranging from a few meters to over 100 kilometers to collect the dataset necessary for training our model. The results demonstrate that our approach achieved over 90% accuracy in predicting optimal settings, leading to an average improvement of over 170% in communication efficiency. These findings underscore AI*LoRa's significant potential to enhance long-range IoT deployments.

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.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

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.

LoRaWAN has proven helpful in many application areas, such as meteorological monitoring in sparsely populated areas. LoRaWAN End Nodes send data to a LoRaWAN Gateway, conveying it to network and application servers using an IP connection. In remote areas, connectivity is often unavailable, but these areas can provide a wealth of data valid for disaster prevention, wild animal tracking, climate assessment, and modeling, among others. We present a LoRaWAN Range Extender, which extends the coverage of existing gateways without requiring any modification to the existing LoRaWAN infrastructure. This device could double the achievable range of a standard LoRaWAN link or enable nodes to be serviced behind obstacles that prevent direct communication with the Gateway. The Range Extender must be within the communication reach of an existing LoRaWAN Gateway while also being reachable by the End Nodes of interest. It can be located in places without cellular coverage, and since it is cheaper to operate and install, it can lower both capital and operational expenses.

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.

Effective environmental monitoring is crucial for managing global environmental challenges and providing the necessary data for Environmental Intelligence (EI). This discipline involves the integration of data from various sources to gain a comprehensive understanding of specific regions or processes. In this paper, we introduce BODOQUE, a hardware-software infrastructure to monitor water flows in ephemeral streams where the water rarely flows with great force. BODOQUE uses a low-power TinyML-based camera to detect the presence of water, activating a more complex system to measure flow only when the water flows, thereby optimizing energy consumption. This device is being deployed in the Segura basin, Murcia, Spain. This region is grappling with severe environmental issues that affect the Mar Menor, a unique saltwater lagoon. This paper focuses on the power-saving capabilities of BODOQUE, comparing the energy consumption of different edge devices running the code that measures water flow in the streams. Our goal is to determine the optimal hardware setup for the system based on our experiments, which involve performance and energy consumption tests. The results provide valuable information for future environmental monitoring systems, considering the best balance among the device's cost, performance, and energy consumption.

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.

This paper focuses on the use of machine learning (ML) algorithms for greenhouses temperature forecasting. We use a new approach called TinyML, an emerging technique with high potential for a low-power environments. Our focus in this work was to understand which are the limits of this approach and how much complexity can the ML models have with the used devices. Our results reveal that a TinyML-based device such as an Arduino Nano 33 BLE Sense can execute easily low computational constrained ML models while consuming very low power compared to a high-end device. In particular, a simple multilayer perceptron (MLP) is targeted, offering accurate predictions in terms of RMSE, MAE and R2, which is remarkable considering that the models under study are extremely light and simple.

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.

MQTT is becoming a clear standard in the IoT arena. Its flexibility in interconnecting various devices by decoupling timing and locations from the interaction among clients is extremely interesting in current deployments. The centralized approach of MQTT brings anyway drawbacks that are currently being tackled by researchers and practitioners. In this paper we provide a preliminary evaluation of the process of embedding an MQTT broker using the 5G Network Virtual Function approach. Through a real testbed, we measure how an MQTT broker can scale at the edge by regulating the incoming traffic using a load balancing approach.

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.

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.

Message queuing telemetry transport (MQTT), one of the most popular application layer protocols for the Internet of Things, works according to a publish/subscribe paradigm where clients connect to a centralized broker. Sometimes (e.g., in high scalability and low-latency applications), it is required to depart from such a centralized approach and move to a distributed one, where multiple MQTT brokers cooperate together. Many MQTT brokers (both open source or commercially available) allow to create such a distributed environment: however, it is challenging to select the right solution due to the many available choices. This article proposes, therefore benchmarking framework for distributed MQTT brokers (BORDER), a framework for creating and evaluating distributed architectures of MQTT brokers with realistic and customizable network topologies. Based on isolated Docker containers and emulated network components, the framework provides quantitative metrics about the overall system performance, such as End-to-End latency as well as network and physical resources consumed. We use BORDER to compare five of the most popular MQTT brokers that allow the creation of distributed architectures and we release it as an open-source project to allow for reproducible researches.

An Analysis of many IoT deployments showed that most of them can address the sustainable development goals (SDGs) and the UN’s 2030 agenda. Interestingly, most of these projects concentrate on five SDGs: 1) industry, innovation, infrastructure; 2) smart cities and communities; 3) affordable and clean energy; 4) good health and well-being; and 5) responsible production and consumption. Examples include a remote water-monitoring solution that ensures clean water in regions with an indigenous population and smart lighting initiatives in Chinese cities that halve total power output.

Over the last few years we have witnessed an exponential growth in the adoption of LoRaWAN as LPWAN technology for IoT. While LoRaWAN offers many advantages, one of its limitations is the paltry data rate. Most IoT applications don't require a high throughput but there are some that would benefit from a higher data rate. In this paper, we present TurboLoRa, a system that combines the strengths of LoRaWAN while providing a higher data rate by synchronizing the transmission of multiple LoRaWAN devices. Our proposal allows to combine cheap devices making it a frugal solution to this kind of problems. We present some preliminary results obtained using a real prototype of TurboLoRa.

Financially sustainable solution is the need to migrate legacy IPv4 network infrastructure into latest networking paradigms viz. Software-defined networking (SDN) and the Internet protocol version 6 (IPv6) addressing. For Internet service provider (ISP) networks, IP routers are either to be upgraded or replaced with new device to transform the network into Software-defined IPv6 (SoDIP6) networks. In this paper, we classify the network routers require its hardware/firmware either to be upgraded or replaced using adaptive neuro fuzzy inference system (ANFIS), a well-known intelligent approach used in classification, prediction, and estimation. Device specification details with end-of-life and end-of-support information are stored in the knowledge base (KB) system. Device performance parameters, for example average CPU usage, throughput, and memory capacity are being extracted in real time using simple network management protocol (SNMP) and mapped with information obtained from the KB for input to ANFIS. The experimental results show that proposed approach is more accurate and optimal that assists service providers for smooth transitioning to SoDIP6 networks with optimum cost of migration.

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.

The growth of the Internet worldwide has been fuelled by the development of the “National Research and Education Networks” (NRENs), i.e., networks of academic and educational institutions. In Africa the establishment of NRENs is more recent. In this paper we analyse the readiness of African NRENs to be part of “The Things Network” (TTN), a network of IoT gateways that has fostered the growth of IoT in Europe by adopting a community network model. We analyse RTT and packet loss toward the nearest TTN network server, in African countries where RIPE Atlas (RIPE - “Réseaux IP Européens”, French for “European IP Networks”) probes are hosted both in academic and commercial networks. Our conclusion is that NRENs and commercial ISPs are on an equal foot in hosting TTN gateways in most countries we considered.

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.

MQTT, one of the most popular protocols for the IoT, works according to a publish/subscribe pattern in which multiple clients connect to a single broker, generally hosted in the cloud. However, such a centralised approach does not scale well considering the massive numbers of IoT devices forecasted in the next future, thus calling for distributed solutions in which multiple brokers cooperate together. Indeed, distributed brokers can be moved from traditional cloud-based infrastructure to the edge of the network (as it is envisioned by the upcoming MEC technology of 5G cellular networks), with clear improvements in terms of latency, for example. This paper proposes MQTT-ST, a protocol able to create such a distributed architecture of brokers, organised through a spanning tree. The protocol uses in-band signalling (i.e., reuses MQTT primitives for the control messages) and allows for full message replication among brokers, as well as robustness against failures. We tested MQTT-ST in different experimental scenarios and we released it as open-source project to allow for reproducible research.

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.

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.

Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.

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.

Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a “social” one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.

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.

In this work, we demonstrate that Vehicular Social Networks (VSNs) require specific data dissemination techniques. To this aim, we consider the Barabási model and we study the impact of its use for data dissemination in VSNs. Moreover, the use of a probabilistic data dissemination protocol based on social features [1] proves that the time-evolving dynamics of the vehicular social network graph presents a small-world structure, where nodes tend to connect through clusters whose average distance is low on average. This result highlights how VSNs are a specific class of vehicular ad hoc networks with peculiar features that distinguish them from classical online social networks.

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.

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.

This paper describes the deployment of the Mobile Opportunistic Disconnection-Tolerant Networks and Systems platform in heterogeneous real-world scenarios. Our aim is to prototype and test fully-distributed multi-layer complex DTN forwarding and processing algorithms in different real-time dynamic topologies with varying mobility, network resources and data workload (publish-subscribe) patterns. In this work, we describe two different real-world contexts of mobile heterogeneous sensing and query in disconnection-prone environments: 1) networked drones and vehicles for agricultural monitoring applications and 2) vehicles and drone communications for smart city applications in urban environments. We discuss the performance characteristics of different mobile disconnection prone communication protocols and dynamic architectures across a range of criteria.

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.

In the new generation of networks, not only computers are connected but also objects like cars, streets, buildings, or just, everything. In general, these things communicate by means of internet using gateways. Simulating these systems can help to validate specific algorithms and concepts. However, it cannot give any accurate information about reality when some problems arise such as delays, disconbectivity, attacks, insecurity, and especially in the case of large networks. Also, the real implementation of such networks is very complex, time consuming and can be impossible when the nodes are situated in different cities or countries. In this demo we propose a new platform called CupCarbon-Lab based on the existing simulator CupCarbon, where the codes used in simulation can be directly injected in real connected embedded cards like Raspberry Pi cards. This platform can automatically generate from the software a real IoT network even it is already deployed, which can be reconfigured without the need to go through each node. It also helps to test the feasibility and the scalability of an algorithm in real conditions.

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.

Intelligent inter-vehicle communication is a key research field in the context of vehicular networks that applies in real-life applications (e.g., management of accidents, intelligent fuel consumption, smart traffic jams, etc.). Considering different roles of nodes based on their “social aptitude” to relay information could provide a social component in the vehicular structure that can be useful in getting a clear prediction of the topological evolution in time and space proving to be very effective in managing intelligent data forwarding. In this work we characterize a vehicular network as a graph using the link layer connectivity level and we classify nodes on the basis of specific attributes characterizing their “social aptitude” to forward data. Two forwarding approaches are presented, based on different socialites that allow to (i) select the most social node (i.e., a social hub) or (ii) choose among various social nodes.

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.

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.

Poor Internet performance currently undermines the efficiency of hyper-responsive mobile apps such as augmented reality clients and online games, which require low-latency access to real-time backend services. While edge-assisted execution, i.e. moving entire services to the edge of an access network, helps eliminate part of the communication overhead involved, this does not scale to the number of users that share an edge infrastructure. This is due to a mismatch between the scarce availability of resources in access networks and the aggregate demand for computational power from client applications. Instead, this paper proposes a hybrid edge-assisted deployment model in which only part of a service executes on LTE edge servers. We provide insights about the conditions that must hold for such a model to be effective by investigating in simulation different deployment and application scenarios. In particular, we show that using LTE edge servers with modest capabilities, performance can improve significantly as long as at most 50% of client requests are processed at the edge. Moreover, we argue that edge servers should be installed at the core of a mobile network, rather than the mobile base station: the difference in performance is negligible, whereas the latter choice entails high deployment costs. Finally, we verify that, for the proposed model, the impact of user mobility on TCP performance is low.

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.

LTE/4G is the next generation of cellular networks which specifically aims to improve the network performance for data traffic and is currently being rolled out by many network operators. We present results from an extensive LTE measurement campaign in Dublin, Ireland using a custom performance measurement tool. Performance data was measured for two local operators at a variety of locations within the city (including cell edge locations, indoors, outdoors, etc.) as well as for mobile users on public transport within the city, too. Using this data we derive a model of the characteristics of link layer RTT and bandwidth vs link signal strength. This model is suited to use for performance evaluation of applications and services, and since it is based on real measurements it allows realistic evaluation of performance.

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).

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.

To improve the performance of Massively Multiplayer Online Games (MMOGs) in mobile networks, we explore the potential benefits of an edge-assisted deployment model: part of the MMOG backend service executes closer to the end user at the edge of the LTE network. We investigate the impact on game late

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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.

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.

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.

The increasing penetration of smartphones, i.e., smart devices with multiple sensing and communication interfaces, creates the possibility to build novel types of networks. Wireless adaptors are, after the terminal screen, the strongest source of power consumption. It is therefore common for a node to occasionally turn off the networking device to save energy. The effect of the presence of nodes in the off-state must therefore be thoroughly evaluated. We propose an analytical model based on evolving graphs. Our proposal provides an exhaustive evaluation of an ideal routing protocol whose aim is to determine the worst case conditions when adopting a device switching-off strategy to save energy. The complexity of our algorithm is demonstrated to be O(nT2), n being the number of nodes, and T being the width of the time interval studied. We also show, through experimental results, that routing protocols for SWN must deal with 2 issues: a low percentage of delivered packets and with long paths.

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 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 paper, we address the design of spontaneous networks using a peer to peer (P2P) and Bluetooth technology. A spontaneous network is a small infrastructureless network formed when a group of people come together to participate in some collaborative activity. In this work, we develop a base library and application work ground for easy spontaneous networks development. Based on it, we present an experimental application that provides spontaneous networks with context and transparent services to interchange resources between peers. We describe the overall network architecture and present details of the implementation steps taken to create our P2P and Bluetooth based application. Finally we run some experiments in a small testbed to evaluate the performance and system behaviour. We present our findings in term of the duration of the inquiry procedure and throughput performance with respect to distance and node speed.

We present an experimental context-aware application that provides context depended information to the visitors of a museum. The application combines the productivity of the Java platform with the universal connectivity of Bluetooth wireless technology. We present our first experiences using BIuetooth and Java technology in ubiquitous . We describe the overall architecture and detail the implementation steps taken to create our BIuetooth and Java based context-aware application. Finally we run some initial experiments in a small test-bed to evaluate the proposal performance and behaviour.

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.

This paper presents an analysis of the behavior of mobile ad hoc networks when group mobility is involved. We propose four different group mobility models and present a mobility pattern generator, called grcmob that we designed to be used with the ns-2 simulator. Using 2k factorial analysis we determine the most representative factors for protocol performance. We then evaluate the performance of a dynamic source routing (DSR) based MANET, using both TCP and UDP data traffic. The results are compared with the classical random waypoint mobility model. It is shown that the number of groups parameter is more important than the number of nodes one and that the impact of the area size is almost negligible. We make also evident that the mix of inter- and intra-group communication has the strongest impact on the performance. Finally, it is evidenced that the presence of groups forces the network topology to be sparser and therefore the probability of network partitions and node disconnections grows.

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.

An experiment relative to the use of the Bluetooth wireless technology to provide network support for context-aware applications is presented. We describe an approach to provide network interconnection using a combination of wireless and wired network technologies. We also describe the steps taken to create a Bluetooth based context-aware application. We finally evaluate using a small test-bed and simulation the overall performance of this technology when adopted in this specific area.

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.

Many protocols used in mobile ad hoc networks rely on the broadcasting capability, especially when performing a route discovery process. However, an efficient broadcasting protocol should be devised to reduce the unnecessary redundant rebroadcasting at some nodes (redundancy) as well as to increase the coverage area as much as possible (reachability). A few approaches have been developed in the literature. We propose a bounding algorithm, which is known to be an efficient candidate to accommodate the two goals, that is to increase reachability while limiting redundancy.

Untethered nodes in mobile ad hoc networks strongly depend on the efficient use of their batteries. In this paper, we propose a new metric, the drain rate, to forecast the lifetime of nodes according to current traffic conditions. This metric is combined with the value of the remaining battery capacity to determine which nodes can be part of an active route. We describe new route selection mechanisms for MANET routing protocols, which we call the minimum drain rate (MDR) and the conditional minimum drain rate (CMDR). MDR extends nodal battery life and the duration of paths, while CMDR also minimizes the total transmission energy consumed per packet. Using the ns-2 simulator and the dynamic source routing (DSR) protocol, we compare MDR and CMDR against prior proposals for energy-aware routing and show that using the drain rate for energy-aware route selection offers superior performance results. Methods keywords are system design and simulations.

Mobile ad hoc networks' (MANETs) inherent power limitation makes power-awareness a critical requirement for MANET protocols. We propose a new routing metric, the drain rate, which predicts the lifetime of a node as a function of current traffic conditions. We describe the minimum drain rate (MDR) mechanism which uses a combination of the drain rate with remaining battery capacity to establish routes. MDR can be employed by any existing MANET routing protocol to achieve a dual goal: extend both nodal battery life and connection lifetime. Using the ns-2 simulator and the dynamic source routing (DSR) protocol, we compared MDR to the minimum total transmission power routing (MTPR) scheme and the min-max battery cost routing (MM-BCR) scheme and proved that MDR is the best approach to achieve the dual goal.

We propose a simple algorithm that allows mobile nodes to create clusters autonomously to minimize power consumption. We then provide a routing protocol to integrate clusters into a mixed wired/wireless network. We have kept our proposal as simple as possible while also providing it with sufficient robustness and auto-recoverability to enable simple and fast node reconfiguration. We analyze the overall protocol architecture and we show that, in general, the CERA (cluster-based energy-saving routing algorithm) implementation allows up to 25% energy saving while keeping the overhead extremely low.

We propose a protocol architecture that integrates heterogeneous short-range wireless network technologies in a bounded environment. A common data transmission adaptation tier on top of a generic network interface tier provides total independence from the lower tier technology characteristics. This architecture handles station mobility, data interchange, and relaying frames among different devices using the dynamic cluster-based source routing (DcSR) protocol - a slightly modified version of the DSR protocol - and the intra-cluster data-dissemination protocol (Icdp). We present simulation results showing relevant energy savings in various scenarios without an important decrease of the performance of the network.

Energy saving is an extremely important design criteria for mobile ad hoc networks. We present the intra-cluster data-dissemination protocol (Icdp). The Icdp is used to group nodes around a special one, called the cluster leader (CL). The CL centralizes the power management mechanism and acts as a proxy for data transfer between the cluster and the rest of the nodes in the region. When a cluster node enables the power management it goes into "sleep" mode, to minimize power consumption. The CL buffers data frames for power saving stations and delivers them on the stations' request. We periodically distribute the cluster leader role among all the nodes inside the cluster, to not overload a single node. We evaluate the performance benefits of this proposal by simulating the use of our protocol to distribute real-time video in a bounded region.

The design of energy-efficient routing algorithms is a fundamental problem in a mobile ad-hoc network (MANET) where battery energy is a limited resource. We evaluate a power-conserving algorithm, based on the RTS/CTS dialogue of the IEEE 802.11 standard which dynamically switches off the radio network interface card of nodes when they are neither transmitting nor receiving a packet. We evaluate the algorithm through simulations over four well-known routing algorithms: dynamic source routing (DSR), ad-hoc on demand distance vector (AODV), temporally-ordered routing algorithm (TORA), and destination-sequenced distance-vector routing (DSDV). The obtained results indicate that for all the evaluated routing protocols, the power savings are similar and range between 25 per cent and 60 per cent of the total energy.

The design of efficient routing protocols is a fundamental problem in a Mobile Ad-Hoc Network (MANET). Many different protocols have been proposed in the literature, each one based on different characteristics and properties. Some of these protocols have been studied and their performances have been evaluated in detail, focusing on aspects such as routing overhead latency and route length. We concentrate on the energy consumption issues of routing protocols. We present a performance comparison of the DSR, AODV, TORA and DSDV routing protocols with respect to energy consumption, evaluating how the different approaches and algorithms affect the energy usage in mobile devices.

The load generated by new types of communications services related to multimedia and video transmission is becoming one of the major sources of traffic in WAN networks. Modeling this type of load is a prerequisite for any performance study. We approach the load-characterization problem from a global point of view by analyzing a set of 20 video streams. We developed resource-, subject-, and scene-oriented characterizations of coded video streams. We have also implemented multidimensional data-analysis techniques and applied a "scene working set" approach, as well as static and dynamic video traffic models. We show that the behavior of a video sequence can be predicted with high accuracy by applying the scene working set technique. Applications of this technique for predicting bandwidth demands and allocating buffers in an ATM switch are also described in this study.

This paper presents a simulation analysis of the impact of mobility on TCP/IP augmented with features to support host mobility in wide area networks. Our results show that the existing version of TCP can yield low throughput in highly mobile environments due to the fact that TCP cannot discriminate packets dropped due to hand-offs with those dropped due to congestion in one or more network resources. As a result, TCP invokes a congestion recovery process when packets are lost during internetwork hand-offs of the mobile host. We investigate a proposal in which the transport layer explicitly receives information from the network layer of any ongoing mobility. We show that by effectively capitalizing this information, TCP can appropriately extend the slow-start phase in the recovery process and achieve higher throughput. Based on the simulation analysis we also show the robustness of this scheme in the presence of both host mobility and network congestion.>

This paper presents a simulation analysis of the impact of mobility on data transport protocols. We consider the TCP/IP protocol augmented with features to support host mobility in wide area networks. Our results show that the existing version of TCP can yield low throughput in highly mobile environments due to the fact that TCP cannot discriminate packets dropped due to hand-offs with those dropped due to congestion in one or more network resources. We investigate a proposal in which the transport layer explicitly receives information from the network layer of any ongoing mobility. We show that by effectively capitalizing this information, TCP can appropriately extend the slow-start phase in the recovery process and achieve higher throughput. Based on the simulation analysis we also show the robustness of this scheme in the presence of both host mobility and network congestion.>