Large Language Models (LLMs) are rapidly being embedded in consumer and service robots, enabling richer human–robot interaction, multimodal reasoning, and language-driven autonomy. However, the computational and lifecycle costs of training, inference, and continuous upgrade cycles raise urgent digital sustainability concerns: energy consumption, network dependency, privacy exposure, and hardware obsolescence. In this conceptual paper, we introduce the Sustainable Language-Driven Autonomy Framework (SLAF), a modular architecture and set of operational policies that align multimodal LLM reasoning with sustainability goals. SLAF decomposes the intelligence stack into Perception & Preprocessing, Local Cognition (Edge), High-level Reasoning (LLM), and Control & Execution layers, mediated by an Adapter responsible for compact semantic encoding, adaptive triggers, caching, and energy budgets. We propose quantitative primitives and trade-off models (e.g., energy-per-inference Einf, calls-per-mission Ncalls, mission energy Emission) and an evaluation protocol to make sustainability claims comparable and auditable. Finally, we map how SLAF addresses four research questions on zero-shot generalization, energy-efficient architectures, software-first lifespan extension, and cloud/on-device trade-offs. We conclude with a roadmap for empirical validation, lifecycle analysis, and user-centered studies to operationalize sustainable, language-enabled robotics.

Industrial Control Systems (ICS) are increasingly interconnected with enterprise IT and cloud services, yet their communications remain largely unprotected due to the limited adoption of Transport Layer Security (TLS) and other cryptographic standards. Legacy devices often lack the resources to support TLS, and operators face performance constraints and complex certificate management. To address this gap, we present P4ICS, a framework that provides confidentiality, integrity, and replay protection for industrial protocols by shifting security functions from endpoints into P4-programmable switches. P4ICS transparently parses and protects Modbus, DNP3, EtherNet/IP, and MQTT traffic, establishing switch-to-switch encrypted tunnels that secure untrusted network segments while preserving interoperability with legacy equipment. Our evaluation on an ad hoc physical testbed shows that P4ICS introduces only a modest overhead compared to plaintext communication, while consistently outperforming TLS, reducing delays by about 12% for Modbus and DNP3, 43% for EtherNet/IP, and 47% for MQTT. By leveraging in-network computing, P4ICS delivers a practical and deployable security layer for Industry 4.0 communications, narrowing the gap between available secure protocol profiles and their limited use in operational ICS.

Human-robot interaction (HRI) is an evolving field with a growing emphasis on enabling robots to understand and perform tasks based on natural language commands. Recently, Large Language Models (LLMs) have emerged as a promising tool for such tasks, offering the potential to enable zero-shot learning and flexible interaction without task-specific training. In this paper, we explore the use of LLMs for zero-shot navigation and exploration tasks in robotic systems, specifically evaluating their performance with the PR2 Clearpath and Khepera IV robots in a simulated environment. Our findings demonstrate promising results, particularly in the LLM’s ability to exhibit exploratory behavior and iterative reasoning when faced with ambiguous or incomplete visual input. These capabilities suggest a strong potential for LLMs in human-robot interaction. However, challenges were also identified, such as difficulties with target recognition, object misidentification, hallucination of information, and issues with movement execution, highlighting the need for improvements in these areas for real-world applications.

Abstract Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.

Monitoring electric vehicles' battery status and forecasting their state of health is still an open challenge. To determine how and why a battery degrades over time, we have extensively monitored a Nissan Leaf's battery pack for more than one year. Collecting more than 4.5 million samples via a custom monitoring connected device to investigate how different driving behaviors affect battery aging. In addition, the best driving behaviors based on the battery's optimal temperature are revealed, including speed, acceleration and brake pedal pressure, and horsepower.

Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the Multi-perspective Patient Representation Extractor (MPRE) for disease prediction. Specifically, we propose Frequency Transformation Module (FTM) to extract the trend and variation information of dynamic features in the time-frequency domain, which can enhance the feature representation. In the 2D Multi-Extraction Network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the First-Order Difference Attention Mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.

Many mobile devices are usually equipped with multiple interfaces, providing the opportunity of using multipath transport protocols such as Multipath TCP (MPTCP) to boost performance. The multipath congestion control algorithm (CCA) in MPTCP plays a vital role in achieving high performance and multipath fairness in mobile environments where the paths are often heterogeneous and dynamic. Such environments are very challenging for existing one-size-fits-all CCAs to achieve high performance while ensuring multipath fairness. In this paper, we present a novel framework, Disco, to dynamically select the most appropriate CCAs for MPTCP subflows at runtime according to the perceived network condition. Extensive experiments show that compared with existing multipath CCAs, the proposed solution can improve the average throughput by 19% – 25% and reduce the average queuing delay by up to 21 % while it barely does harm to multipath fairness.

In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city.

Type 2 diabetes is the most common chronic disease for the elderly people. This disease is difficult to be cured and causes continued medical expenses. The early and personalized risk assessment of type 2 diabetes is necessary. So far, various type 2 diabetes risk prediction methods have been proposed. However, these methods have three major issues: 1) not fully considering the importance of personal information and rating information of healthcare system, 2) not adopting the long-term temporal information, and 3) not comprehensively capturing the correlation between the diabetes risk factor categories. To address these issues, the personalized risk assessment framework for elderly people with type 2 diabetes is needed. However, it is very challenging due to two reasons, namely imbalanced label distribution and high-dimensional features. In this paper, we propose diabetes mellitus network framework (DMNet) for type 2 diabetes risk assessment of elderly people. Specifically, we propose tandem long short-term memory to extract the long-term temporal information of different diabetes risk categories. In addition, the tandem mechanism is used to capture the correlation between the diabetes risk factor categories. To balance the label distribution, we adopt the method of synthetic minority over-sampling technique with Tomek links. To form the better feature representations, we utilize entity embedding to solve the problem of high-dimensional features. To evaluate the performance of our proposed method, we conduct the experiments on a real-world dataset called Research on Early Life and Aging Trends and Effects. The experiment results show that DMNet outperforms the baseline methods in terms of six evaluation metrics (i.e., accuracy of 0.94, balanced accuracy of 0.94, precision of 0.95, F1-score of 0.95, recall of 0.95 and AUC of 0.94).

Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving.

Autonomous vehicles have received great attention in the last years, promising to impact a market worth billions. Nevertheless, the dream of fully autonomous cars has been delayed with current self-driving systems relying on complex processes coupled with supervised learning techniques. The deep reinforcement learning approach gives us newer possibilities to solve complex control tasks like the ones required by autonomous vehicles. It let the agent learn by interacting with the environment and from its mistakes. Unfortunately, RL is mainly applied in simulated environments, and transferring learning from simulations to the real world is a hard problem. In this paper, we use LIDAR data as input of a Deep Q-Network on a realistic 1/10 scale car prototype capable of performing training in real-time. The robot-driver learns how to run in race tracks by exploiting the experience gained through a mechanism of rewards that allow the agent to learn without human supervision. We provide a comparison of neural networks to find the best one for LIDAR data processing, two approaches to address the sim2real problem, and a detail of the performances of DQN in time-lap tasks for racing robots.

The idea of equipping smart devices with the capacity to form social connections with their peers is gaining popularity. Indeed, it is a feature with a lot of promise for fostering device collaboration, speeding up and improving service discovery, and evaluating the reliability of devices by utilizing appropriate features based on peer evaluations in social networks. While numerous architectural solutions have been offered along with various concepts for creating the devices’ social network, the question of how to build and sustain the devices’ social connections has been neglected. This paper proposes a possible implementation of a Social Virtual Object as a constituent entity of the Social Internet of Things (SIoT) based on the concept of virtualization. In the experimental evaluation, the performances in the management of the relationships that originate from the proximity among objects and that represent the greatest difficulties of realization are analyzed. The field trials show the effectiveness of the proposed system in creating relationships and highlight some problems that should be addressed in the formalization of the social relationship in the SIoT.

The climatic conditions of places with a high tourist vocation greatly affect the flow of visitors. In the coastal context, measurements of environmental values such as sea water temperature, wave period and height or direction of currents, are carried out using buoys of considerable size and at a great distance from the coast. This results in a lack of information to satisfy the preferences of tourists and guarantee adequate safety in certain sea conditions. In this paper, we show the implementation of a small buoy that can be positioned near the beaches and that, through a Social Internet of Things platform, can share information with tourists. The first results of a real installation are also shown and what challenges need to be addressed for an efficient use of such a system.

This paper revisits the opportunities of using WiFi offloading for Vehicle to Internet (V2I) communication, and how this has changed over the last decade. With the rollouts of provider-managed WiFi networks that are more structured and operate under authenticated regimes, WiFi offloading, or use of available (roadside) WiFi networks for V2I data communication, has different opportunities and challenges. To study the current landscape,we develop a system (X-Fi), which efficiently selects, associates to, authenticates with, and performs WiFi offloading for V2I communication with these networks, and a tool (X-Perf), which illustrates opportunities of WiFi offloading available today in these networks, with measurements and experiments across four metro areas across three continents over 22 months. Our results indicate the feasibility of achieving 1 GB/hour application goodput, an order of magnitude higher than the number provided by open WiFi networks in the past, which can take a significant load away from alternative communication paths for V2I systems. Moreover, we provide several implications on transport protocols and WiFi deployments to shed light on the use of such WiFi networks for V2I communication.

COVID-19 is a highly contagious virus. Blood test is one of effective methods for COVID-19 diagnosis. However, the issues of blood test are time-consuming and lack of medical staff. In this paper, four deep learning hybrid models are proposed to address these issues (i.e., CNN+GRU, CNN+Bi-RNN, CNN+Bi-LSTM, CNN+Bi-GRU). In addition, two best models, CNN and CNN+LSTM, from Turabieh et al. and Alakus et al., are implemented, respectively. Blood test data from Hospital Israelita Albert Einstein is used to train and test six models. The proposed best model, CNN+Bi-GRU, is accuracy of 0.9415, precision of 0.9417, recall of 0.9417, F1-score of 0.9417, AUC of 0.91, which outperforms the best models from Turabieh et al. and Alakus et al. Furthermore, the proposed model can help patients to get blood test results faster than traditional manual tests without errors caused by fatigue. The authors can envisage a wide deployment of proposed model in hospitals to alleviate the testing pressure from medical workers, especially in developing and underdeveloped countries.

Abstract Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

COVID-19 pandemic has led to serious economic and life losses. Face Masks serve as first infection barrier when used in public spaces. In this paper, we propose a new near-realtime method to automatically recognize face mask wearing that combines human posture recognition with convolutional neural network (CNN). We use the power of human posture recognition to perform background filtering and spatial reduction in the original images. The outcome is then used by a trained CNN model to identify if the subject is wearing a mask. We exploit Openpose to identify the skeleton of human body and locate the facial region thus spatially reducing the area to be processed by the CNN framework. We then adopt supervised learning approach to detect if a face mask is present. The CNN is trained using images, cropped to the supposed face mask covered region. This approach led to a substantial reduction in neural network complexity yet improving the recognition accuracy. The system has been evaluated in a multitude of scenarios using images taken in public places at different time of day and with different angles. Overall, our system achieves a recognition accuracy of 95.8% and 94.6% in daytime and nighttime respectively.

One of the main global challenges to cope with for a sustainable development is transport and mobility. They are undisputed key issues of modern life affecting people's well-being and quality of life, which are significantly impacting the environment. In this scenario, innovative self-driving car technologies and electric solutions, emerging sensing technologies, and interconnected infrastructure can lead to more efficient transport systems and innovative mobility services, keeping into account the urgent need to foster a sustainable development. This workshop aims to gather practitioners with different background and different perspective from both Academia and Industry to further the knowledge in the area of emerging smart technologies and infrastructures for Smart Mobility and Sustainability.

Autonomous cars have been in the making for over 15 years. Skepticism has taken the place of initial hype and enthusiasm. Current autonomous driving systems give no guarantee of 100% correctness and reliability, and users are not willing to take a chance on a car that is unable to cope with all the possible driving scenarios. Robotic drivers are expected to be perfect. Major players such as Tesla and Waymo rely on highly detailed maps and very large sensor data in a race to build the ultimate robotic driver to cope with all possible driving scenarios. This approach optimizes for safety but delays the dream of fully autonomous cars. In this paper we consider robot-drivers as teen-drivers eager to learn how to drive but prone to mistakes in the beginning. The question we are trying to investigate is "what if we allow autonomous cars to make mistakes like young human drives do?" In this paper, we explore reinforcement learning for small size autonomous vehicles fusing information from several sensors including a camera, color sensors, and sonar sensors. The robot-drivers have initially no information about the driving scenarios they learn with experience through a mechanism of rewards designed to quickly help our robot-teen to learn its driving skills.

Mobility and ambient conditions are key factors in urban environments, affecting well-being and quality of life. In this context, sensors, smart mobility, networks, connectivity can play a significant and strategic role, being exploited with the aim of improving data and information available to public administration and to each citizen. In this way, they can be supported in having more sustainable and aware behaviours and in getting useful information and services, improving their daily activities. In this paper, we present a prototype of smart bike eco-system, designed with the aim of collecting, aggregating and sharing data about air pollution and about the urban environment, which can be exploited in a smart mobility context thanks to sensor and vehicular networks.

Mobility has become a basic premise of network communications, thereby requiring a native integration into 5G networks. Despite numerous efforts to propose and standardize effective mobility-management models for IP, the result is a complex, poorly flexible set of mechanisms. The natural support for mobility offered by information centric networking (ICN) makes it a good candidate to define a radically new solution relieving limitations of the traditional approaches. If consumer mobility is supported in ICN by design, in virtue of its connectionless pull-based communication model, producer mobility is still an open challenge. In this paper, we look at two prominent ICN architectures, content centric networking (CCN) and named data networking (NDN) and we propose MAP-Me, an anchor-less solution to manage micro-mobility of content producers via a name-based CCN/NDN data plane, with support for latency-sensitive streaming applications. We analyze MAP-Me performance and provide guarantees of correctness, stability, and bounded stretch, which we verify on real ISP topologies. Finally, we set up a comprehensive simulation environment in NDNSim 2.1 for MAP-Me evaluation and comparison against the existing classes of solutions, including a realistic trace-driven car-mobility pattern under a 802.11n radio access. The results are encouraging and highlight the superiority of MAP-Me in terms of user performance and network cost metrics. All the code is available as open-source.

In this paper we conduct a performance evaluation of privacy protocols for Information Centric Networking (ICN). Our contribution is three-fold: Firstly, we define a simple but complete performance framework for comparing current and future solutions. Secondly, we conjecture and prove the existence of unsafe replicas, namely cached content that remains available to users whose access has been revoked. Thirdly, we propose a performant protocol that solves the problem of unsafe replicas without tampering with the caching functionality of ICN.

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.

The need for Internet access from moving vehicles has been steadily increasing in the past few years. Solutions that rely on cellular connectivity are becoming impractical to deploy due to technical and economic reasons. Car-Fi proposes an approach that leverages existing home Wi-Fi access points configured in dual-access mode, in order to offload all data traffic from the congested and expensive cellular infrastructure to whatever Wi-Fi network is available. Thanks to an improved scanning algorithm and numerous optimizations to the connection setup, Car-Fi makes downloading large amounts of data from a moving car feasible.

This paper proposes Navigo, a location based packet forwarding mechanism for vehicular Named Data Networking (NDN). Navigo takes a radically new approach to address the challenges of frequent connectivity disruptions and sudden network changes in a vehicle network. Instead of forwarding packets to a specific moving car, Navigo aims to fetch specific pieces of data from multiple potential carriers of the data. The design provides (1) a mechanism to bind NDN data names to the producers' geographic area(s); (2) an algorithm to guide Interests towards data producers using a specialized shortest path over the road topology; and (3) an adaptive discovery and selection mechanism that can identify the best data source across multiple geographic areas, as well as quickly react to changes in the V2X network.

The articles in this special issue address the technologies and applications supported by the Internet of vehicles (IoV). The new IoT is driving the evolution of conventional vehicle networks into the IoV. The difference of the vehicle concept in VANET and IoV makes these two scenarios essentially different in the device, communications, networking, and services aspects. In VANET, a vehicle is mainly considered as a node to disseminate messages among vehicles. In the IoV paradigm, each vehicle is considered as a smart object equipped with a powerful multisensor platform, communications technologies, computation units, and Internet protocol (IP)-based connectivity to the Internet and to other vehicles either directly or indirectly. In addition, a vehicle in IoV is envisioned as a multicommunication model, enabling the interactions between intravehicle components, vehicles and vehicles, vehicles and road, and vehicles and people.

This paper presents the first prototype of Multipath Ad-hoc Data Network (MADN) a clean slate protocol for ad-hoc wireless content distribution. Inspired by the recent advances on Information Centric Networking (ICN), MADN is namely a pull-based protocol that emphasizes on seamless route redundancy and multi-path data delivery. It is complementary to the former IP protocol and therefore does not implement primitives for machine-to-machine communication. In consideration of targeting specifically the wireless medium, it uses rateless encoded data packets to counterbalance packet loss and to benefit from multi-source data distribution. A major difference between MADN and previous works is the use of the BlooGo algorithm in place of the conventional routing protocols - e.g. random walks, landmarks or coordinate spaces. The main advantages of using BlooGo is that it delivers the packets in considerably less hops while it maintains alternative routes open as backup of the shortest ones. This paper serves two aims, firstly it proposes a new protocol for multipath content distribution in ad-hoc environments, secondly it presents its architecture and how this interfaces with the final applications. Ultimately, the implementation is intentionally kept modular in order to facilitate deploying and evaluation of new solutions and new configurations - e.g. different caching algorithms or different types of Erasure Codes - without major refactoring of the codebase.

Existing mobile devices have the capability to use multiple network technologies simultaneously to help increase performance; but they rarely, if at all, effectively use these technologies in parallel. We first present empirical data to help understand the mobile environment when three heterogeneous networks are available to the mobile device (i.e., a WiFi network, WiMax network, and an Iridium satellite network). We then propose a reliable, multi-path protocol called Multi-Path TCP with Network Coding (MPTCP/NC) that utilizes each of these networks in parallel. An analytical model is developed and a mean-field approximation is derived that gives an estimate of the protocol's achievable throughput. Finally, a comparison between MPTCP and MPTCP/NC is presented using both the empirical data and mean-field approximation. Our results show that network coding can provide users in mobile environments a higher quality of service by enabling the use of multiple network technologies and the capability to overcome packet losses due to lossy, wireless network connections.

The ever-increasing processing power that can today support large scale and detailed simulations increased the depth of the research carried out on protocols and apps developed for single hop and multi-hop Vehicular Ad Hoc Network (VANET) environments. It is now possible, for example, to verify the effectiveness of peer-to-peer one-hop file exchange protocols between vehicles, while taking into account the effects that buildings have on point-to-point transmissions at street intersections, or also verify how a high density of vehicles can impact the transfer of multimedia information through multiple hops between passengers involved into an online game. However, what has not been possible so far, for the obvious reason that no highly dense VANETs in reality exists, is to effectively test any type of application or communication protocol in a real setting, especially for the scenarios concerned with multi-hop communications. But this may change, with the introduction of a creative approach to VANET research: we will here describe how it is possible to experiment with applications and protocols in scenarios that are close to reality, by simply using a few real vehicle resources. As an example of how this can be done, we will provide preliminary results from a set of experiments on a vehicular highway accident warning system, results that would have not been observable in reality without the adoption of our creative methodology.

Streaming services have grown rapidly in the last few years and providers of video on-demand, such as Netflix or YouTube, are increasing the number of users even more quickly. The majority of these companies implement their services using huge Content Delivery Networks that are as much powerful as expensive, e.g. Amazon and Akamai. In this paper we propose a hybrid CDN/P2P solution that aims at reducing the infrastructural costs exploiting local caching and P2P while guaranteeing an optimal quality of service. The proposed architecture uses a classic CDN complemented by a geographically distributed layer where P2P can be activated exploiting network, content awareness and locality. The performance of the proposed solution is evaluated by means of a prototype implementation that has been deployed using the PlanetLab network and the Amazon AWS cloud services. Our findings show that the proposed approach provides adaptive, flexible, scalable and content centric service to the end users while significantly reducing the infrastructural costs.

Clock synchronization is particularly challenging in resource constrained networks. This paper presents an accurate, coherent and bandwidth efficient synchronization scheme called TurboSync. Unlike traditional solutions that synchronize pairs of nodes, TurboSync is able to synchronize entire node clusters. TurboSync relies on principal component analysis with missing data. Packets are broadcasted on the medium and their capture times at each node side are used to compute the clock conversion parameters. To have a complete and usable set of capture times for each broadcast, we propose to fill out the missing packet timestamps at the transmitters' side using an inference mechanism. TurboSync synchronizes all the clocks in the cluster at a time which leads to coherent clock conversion between nodes. Our performance results show better accuracy compared to the RBS protocol.

Advances in portable technologies and emergence of new applications stimulate interest in urban vehicular communications for commercial, military, and homeland defense applications. Simulation is an essential tool to study the behavior and evaluate the performance of protocols and applications in large-scale urban vehicular ad hoc networks (VANET). In this paper, we propose CORNER, a low computational cost yet accurate urban propagation model for mobile networks. CORNER estimates the presence of buildings and obstacles along the signal path using information extrapolated from urban digital maps. A reverse geocoding algorithm is used to classify the propagation situation of any two nodes that need to communicate starting from their geographical coordinates. We classify the relative position of the sender and the receiver as in line of sight (LOS) or nonline of sight (NLOS). Based on this classification, we apply different formulas to compute the path loss (PL) metric. CORNER has been validated through extensive on-the-road experiments, the results show high accuracy in predicting the network connectivity. In addition, on-the-road experiments suggest the need to refine the fading model to differentiate between LOS, and NLOS situations. Finally, we show the impact of CORNER on simulation results for widely used applications.

Social system networks with high data rates and limited storage will discard data if the system cannot connect and upload the data to a central server. We address the challenge of limited storage capacity in mobile health systems during network partitions with a heuristic that achieves efficiency in storage capacity by modifying the granularity of the medical data during long intercontact periods. Patterns in the connectivity, reception rate, distance, and location are extracted from the social system network and leveraged in the global algorithm and online heuristic. In the global algorithm, the stochastic nature of the data is modeled with maximum likelihood estimation based on the distribution of the reception rates. In the online heuristic, the correlation between system position and the reception rate is combined with patterns in human mobility to estimate the intracontact and intercontact time. The online heuristic performs well with a low data loss of 2.1%-6.1%.

Road congestion results in a huge waste of time and productivity for millions of people. A possible way to deal with this problem is to have transportation authorities distribute traffic information to drivers, which, in turn, can decide (or be aided by a navigator) to route around congested areas. Such traffic information can be gathered by relying on static sensors placed at specific road locations (e.g., induction loops and video cameras) or by having single vehicles report their location, speed, and travel time. While the former approach has been widely exploited, the latter has come about only more recently; consequently, its potential is less understood. For this reason, in this paper, we study a realistic test case that allows the evaluation of the effectiveness of such a solution. As part of this process, (a) we designed a system that allows vehicles to crowd-source traffic information in an ad hoc manner, allowing them to dynamically reroute based on individually collected traffic information; (b) we implemented a realistic network-mobility simulator that allowed us to evaluate such a model; and (c) we performed a case study that evaluates whether such a decentralized system can help drivers to minimize trip times, which is the main focus of this paper. This study is based on traffic survey data from Portland, OR, and our results indicate that such navigation systems can indeed greatly improve traffic flow. Finally, to test the feasibility of our approach, we implemented our system and ran some real experiments at UCLA's C-Vet test bed.

Vehicular networks are on the fast track to become a reality either through a car manufacturer that introduces a communication device in the car electronics or through an aftermarket vendor such a GPS navigator or a in-vehicle entertainment system. This paper introduces VERGILIUS a nouvelle urban mobility and propagation toolbox designed to streamline the mobility trace generation and path loss computation in vehicular network studies. The aim of VERGILIUS is to enable a whole new level of simulation through the introduction of Urban Maps, finely tunable motion patterns, and detailed trace analysis.

Simulation is essential to evaluate the performance of large scale vehicular networks. It is logistically challenging (and prohibitively expensive) to run tests with more than a few dozens experimental vehicles. Given the critical role of simulation in the evaluation of VANET protocols in large scale scenarios, it is important to guarantee realism of the models. This paper focuses on the accuracy of urban propagation models and their impact on vehicular protocol results. In a city-based vehicular network we compare the predominant two ray model and a recently proposed Corner model. We identify a number of factors that undermine the validity of the Two Ray model, for example, the presence of buildings causing propagation disruption and the heavy weight border effects that incorrectly compensate for the presence of hidden terminals in the networks. The paper analyzes a small scale urban vehicular scenario which unveils the issues to be considered in large scale vehicular simulations.

Multicast streaming is gaining increasing importance in wireless ad hoc networks, in part because ad hoc scenarios often include team activities and the requirement for distribution of audio, video and situation awareness to the members. At the network level, techniques for routing the multimedia streams are quite mature. Much more challenging is the allocation of resources, the fair sharing among streams and the control of congestion.

C-VeT's backbone is based on the UCLA's IEEE 802.11 campus-wide wireless infrastructure and is complemented by a wireless mesh provided by MobiMesh. The nodes participating in the mesh are installed in strategic campus location and perform packet routing as well as serve as access points for the mobile nodes. The mesh core network is deployed using IEEE 802.11a interfaces at 5.9 GHz and directional antennas while the access service is offered using IEEE 802.11g interfaces in the 2.4 GHz band. The network management and monitoring infrastructure has been deployed using a wide area wireless network technology in the ISM band of 900 MHz red links. The 900 MHz digital radio, provides enough bandwidth to perform realtime monitoring of the vehicular network and maintenance of the network nodes. The 900 MHz wireless infrastructure has been laid down to guarantee an independent channel to be used for network management and monitoring operations without interferences with on going experiments.

Several diseases and medical conditions require constant monitoring of physiological signals and vital signs on daily bases, such as diabetics, hypertension and etc. In order to make these patients capable of living their daily life it is necessary to provide a platform and infrastructure that allows the constant collection of physiological data even when the patient is not inside of the coverage area. The data must be rapidly "transported" to care givers or to the designated medical enterprise. The problem is particularly severe in case of emergencies (e.g. natural disasters or hostile attacks) when the communications infrastructure (e.g. cellular telephony, WiFi public access, etc) has failed or is totally congested. In this paper we present an evaluation of of the vehicular ad-hoc networks (VANET) as an alternate method of collecting patient pre-recorded physiological data and at the same time reconfiguring patient medical wearable body vests to select the data specifically requested by the physicians. Another important use of vehicular collection of medical data from body vests is prompted by the need to correlate pedestrian reaction to vehicular traffic hazards such as chemical and noise pollution and traffic congestion. The vehicles collect noise, chemical and traffic samples and can directly correlate with the "stress level" of volunteers.

Abstract — As the technology available on cars is increasing, a wide range of applications, from safety to entertainment, are becoming factually accessible to passengers. Many of these applications involves a one-to-many transmission model where a single car broadcasts a message that has to be forwarded, even with multiple hops, in a very short time to all the other cars located within a range of few kilometers from the source. Since the high mobility and density of a car network scenario, specific solutions need to be devised to choreograph a fast-delivery multihop broadcast. To this aim, we developed a practical and efficient technique that allows cars to estimate their communication range with the help of a very limited message exchange and exploit this information to reduce the number of transmissions, as well as the hops to be traversed, and hence the time, required by a broadcasted message to reach all the cars following the sender within a certain distance.

Successes and failures during rescue operations after hurricane Katrina and the Twin Towers attack demonstrated the importance of supporting first responders with adequate means to perform their operations in an effective and safe way. From a networking point of view, one of the main challenges is that of providing first responders with multimedia information about the emergency as soon as possible, even from a remote location. To this aim, we designed an inter-vehicular communication system able to quickly discover and transmit real time multimedia information from around a crisis area to approaching first responders' vehicles. As vehicular communications are highly variable in nature, we endowed our system with a transmission range estimator that is put to good use to reduce the number of hops that a video triggering message sent by a vehicle will experience to reach its destination. Experimental results demonstrate the efficacy of our scheme in reducing the message delivery time and the traffic generated.

Abstract — Ensuring fairness among players engaged in online games is a challenging task. Yet, it is a fundamental requirement that can make the difference between having customers that persist or desist in using this kind of application. Answering to this demand, we present here an event delivery mechanism among mirrored game servers able to effectively uplift the fairness degree during game sessions through the heterogenesis of ends in targeting interactivity. We also provide extensive results that sustain our claim.

Fast-FMS is a new network protocol designed to support fast and effective multimedia delivery in 3G networks. In particular, Fast-FMS provides a reliable session between two mobile customers that want to transmit multimedia and data while on a voice conversation. The protocol leverages the multi-RAB feature of 2.5+ cellular networks to provide a new almost-real-time class of services that fill the gap between the costly circuit-switched video-call service and the basic services based on MMS. © 2005 IEEE.

Many distributed hash table topologies, such as Pastry, allow flexible choosing of a peer's neighbors while maintaining routing consistency. Traditionally, such flexibility has been used to only optimize the overlay only for latency. In this paper, we create a set of objective functions that allow a peer to select neighbors for its routing table which minimize ping time, maximize bandwidth, or attempt to do both. In conjunction with a novel algorithm for quickly finding peers that maximize a given objective function without settling to a local maximum in the identifier space, we show through simulation that routing tables optimized in a greedy fashion by each node can have significant impact on end-to-end latency and capacity, such as reducing end-to-end delay by over 50 percent.

Grido is an architecture that targets a network operator intending to provide enhanced services to its customers. This is achieved by setting up a "backbone" overlay network. A backbone overlay is a set of Internet hosts dedicated to providing overlay services. A network operator can view Grido as a sandbox for rapid prototyping and market adoption assessment of novel services. In the past, overlay networks have been designed to mitigate deployment issues of functionalities such as multicast and QoS at the network layer. Grido provides a WS-agreement based negotiation interface complying with the current Global Grid Forum (GGF) standards. We propose to use a novel virtual coordinates-assisted overlay construction and maintenance protocol. We demonstrate using simulations, that Grido incurs a low latency overhead while maintaining sparse connectivity on the backbone overlay. Grido also incurs low overhead for virtual coordinates estimation and chooses the closest 5% overlay node to any IP address, 95% of the time

The maturing distributed file sharing technology implemented by Napster has first enabled the dissemination of musical content in digital forms, permitting to costumers an ubiquitous reach to stored music files from around the world. In the post-Napster era, the Apple iTunes online music service has hit a record share of 16.7% in the MP3 player market. This is only the most prominent example of the success of digital music distribution based on packet network technologies. However, to the best of our knowledge, the most noteworthy aspect of the success of digital music distribution is that little about this music delivery technology is really new. To deeply change the trend of this technology business, we claim that wireless technologies must come on the scene. In particular, the digital music delivery model may take benefit by the integration of the wired Internet with a plethora of several, alternative wireless technologies, such as, for example, WiFi, WPAN and 3G. In this challenging context, we have developed a wireless Internet application designed to support the distribution of digital music to handheld devices. The main novelty of our software application amounts to its ability in providing a seamless music delivery service even in the presence of horizontal and vertical handoffs. We have taken measurements from real-world experiments that show the efficacy of the system we have developed.

.4brtroct- Nest generation nehvorlis with large bandwidth and long drlay pose a major challenge to TCP performance, especially during the startup period. In this paper we evaluate the performance of TCP RenaiNcwrcno. Vegns and Hoe's modification in large bandwidth delay nrhvork. We propose n modified Slow-start mechanism, rnllcd Adaptive Start (Astart), to improve the startup performance in such networks. When a connection initially begins or re-starts after a coarse timrout,- Astart ndaptivcly and repentedly resets the Slow-start Threshold (suthreslr) based on an cligihlr sending I'iitr estimation mrchanisrn proposrd in TCP Westwond. By iidapting to network conditions during the startup phase. it wndw is able to grow the congestion window (ocnh fast without incurring risk of huNw owrflow and multiple Iossrs. Simulation rxpcrirncnts show that Astart can significantly improve the link utiliiation under various bandwidth, buNrr sur;~nd round-trip propagation timrs. The mrthud avoids both under-utiliriition dur to prrmature Slowstart termination, as wcll ils multiple I~XII~S due to initinlly setting srrlire.sli too high, or. increaing nmd tin) fiat. Experiments also show that Astart uchiews good fttirnrss rind fricndlincss toward TCP NewReno. Lab measuremrnts using a FrreBSD Astart implementation are also reported in this paper, providing futrhcr evidence of the gains nchirvahlr via Astart. Kqw-orr%r-congesrionn control;.sIow-.start; rate estimution. large bundwidth ddq nehvorks I.

Efforts to provide connection oriented service over the inherently best-effort Internet started almost right after its birth. Today, there exist a multitude of solutions that have been proposed but have never been implemented due to their impracticability. We propose a practical solution for fast, low cost, scalable, and yet accurate QoS routing. We propose to use hierarchical approaches to make the scheme practical and cost-effective. At the same time, we increase network utilization and decrease inaccuracy of stale information by the use of multiple paths. An extensive simulation of the various permutations of schemes over a large set of topologies and traffic conditions validate the proposed schemes and prove conclusively that hierarchical schemes with multiple path capabilities not only result in significantly lower overhead, but also give high levels of QoS performance. This paper presents the architecture of our schemes, the multiple path computation algorithm, and the simulation results validating our claims.

The priority queueing mechanism is analysed to verify its effectiveness when applied for the support of expedited forwarding-based services in the differentiated services environment. An experimental measurement-based methodology is adopted to outline its properties and end-to-end performance when supported in real transmission devices. A test layout has been set up over a metropolitan area for the estimation of the one-way delay. The effect of the buffering architecture and of the background traffic have been evaluated, moreover a worst case analysis of the average one-way-delay that validates the experimental results is proposed.

In order to provide practical QoS services, we propose a new traffic engineering approach for low-cost and fast QoS provisioning. The new scheme utilizes network resources more evenly by exercising an enhanced QoS path computation algorithm that finds maximally disjoint multiple QoS paths. An efficient mechanism for managing computed paths and allocating calls is also discussed as a component of the system, and this path management allows fast QoS provisioning by avoiding unnecessary path recomputation. Moreover, the naive approaches to link state information acquisition in conventional QoS routing are replaced with a more effective approach dividing the link state update process into three hierarchical subprocesses. Via simulation experiments, based on an IP telephony application, we show that we can improve the performance and at the same time reduce the link overhead.