Goal-oriented Communication (GoC) is a new paradigm that activates data transmission only when it is instrumental for the receiver to achieve a certain goal. This leads to the advantage of reducing the frequency of transmissions significantly while maintaining adherence to the receiver’s objectives. However, GoC scheduling also opens a timing-based side channel that an eavesdropper can exploit to estimate the state of the system. This type of attack sidesteps even information-theoretic security, as it exploits the timing of updates rather than their content. In this work, we study such an eavesdropping attack against pull-based goal-oriented scheduling for remote monitoring and control of Markov processes. We provide a theoretical framework for defining the effectiveness of the attack and propose possible countermeasures, including three heuristics that provide a balance between the performance gains offered by GoC and the amount of leaked information. Our results show that, while a naive GoC scheduler allows the eavesdropper to correctly guess the system state about 60% of the time, our heuristic defenses can halve the leakage with a marginal reduction of the benefits of goal-oriented approaches.

Artificial Intelligence (AI) is a key component of$\mathbf{6 G}$networks, as it enables communication and computing services to adapt to end users' requirements and demand patterns. The management of Mobile Edge Computing (MEC) is a meaningful example of AI application: computational resources available at the network edge need to be carefully allocated to users, whose jobs may have different priorities and latency requirements. The research community has developed several AI algorithms to accomplish this goal, but it has neglected a key aspect: learning is itself a computationally demanding task, and considering free training results in idealized conditions and performance in simulations. In this work, we consider a more realistic framework that explicitly accounts for the cost of learning, presenting a new algorithm to dynamically select when to train a Deep Reinforcement Learning (DRL) agent that allocates resources in a MEC facility. Our method is highly general, as it can be directly applied to any scenario involving a training overhead, and it can approach the same performance as an ideal learning agent even under realistic training conditions.

This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed to maximize harvested energy under fluctuating environmental conditions. We validated our model against a prototype batteryless IoT node, conducting experiments under three distinct illumination scenarios. Results show a strong correlation between analytical and measured supercapacitor voltage profiles, confirming the proposed model’s accuracy.

The Internet of Things (IoT) has become increasingly relevant in the context of large-scale asset monitoring, revolutionizing the way data is collected and analyzed. In addition to low power consumption and long-range coverage, these applications require precise time synchronization to generate accurate data to assess the structural health of critical assets. Meeting these synchronization requirements is challenging because the devices are battery-powered, deployed in extensive environments, and limited in computational capabilities. LoRaWAN, a widely adopted standard for long-range wireless networks, addresses some of these challenges. In fact, its Class-B operational mode introduces a time synchronization mechanism based on a beacon broadcasting system. However, the energy-intensive nature of this configuration can be inefficient when devices rely on limited power sources. To address this, we introduce a predictive mechanism to dynamically adjust the beacon period in LoRaWAN Class-B networks. Extensive experimentation proves that our approach reduces the average inter-beacon synchronization error by three orders of magnitude compared to the LoRaWAN standard. This improvement in timing accuracy enables more efficient energy usage, extending device battery life by more than three times relative to standard configurations. Notably, these gains are achieved with a lightweight implementation requiring only 2.5 KB of memory. Overall, our solution enhances both synchronization performance and energy efficiency in an established standard, making it a practical and cost-effective solution for IoT-based monitoring systems.

Pragmatic or goal-oriented communication can optimize communication decisions beyond the reliable transmission of data, instead aiming at directly affecting application performance with the minimum channel utilization. In this paper, we develop a general theoretical framework for the remote control of finite-state Markov processes, using pragmatic communication over a costly zero-delay communication channel. To that end, we model a cyber-physical system composed of an encoder, which observes and transmits the states of a process in real-time, and a decoder, which receives that information and controls the behavior of the process. The encoder and the decoder should cooperatively optimize the trade-off between the control performance (i.e., reward) and the communication cost (i.e., channel use). This scenario underscores a pragmatic (i.e., goal-oriented) communication problem, where the purpose is to convey only the data that is most valuable for the underlying task, taking into account the state of the decoder (hence, the pragmatic aspect). We investigate two different decision-making architectures: in pull-based remote control, the decoder is the only decision-maker, while in push-based remote control, the encoder and the decoder constitute two independent decision-makers, leading to a multi-agent scenario. We propose three algorithms to optimize our system (i.e., design the encoder and the decoder policies), discuss the optimality guarantees ofs the algorithms, and shed light on their computational complexity and fundamental limits.

The rapid development of Virtual Reality (VR) technology has led the industry and research community to look at its major challenges with increased interest. The main challenge in ensuring a high Quality of Experience (QoE) for users is represented by cybersickness, a phenomenon similar to motion sickness experienced by many VR users, while at the same time, the high data rates needed by VR require the definition of traffic models for network optimization. These two problems are intertwined, but have never been studied jointly before due to the lack of suitable datasets. In this paper, we present Questset, the first dataset designed for this purpose. Questset contains over 40 hours of VR traces from 70 users playing commercially available video games, and includes both traffic data for network optimization, and movement and user experience data for cybersickness analysis. Therefore, Questset represents an enabler to jointly address the main VR challenges in the near future.

The use of Wake-Up Radio (WUR) in Internet of Things (IoT) networks can significantly improve their energy efficiency: battery-powered sensors can remain in a low-power (sleep) mode while listening for wake-up messages using their WUR and reactivate only when polled. However, polling-based WUR may still lead to wasted energy if values sensed by the polled sensors provide no new information to the receiver, or in general have a low Value of Information (VoI). In this paper, we present Wake-Up with Awareness of VoI and Energy (WAVE), a scheme that combines the benefits of ID- and content-based WUR techniques by adapting to the update VoI. We analyze the trade-off between the tracking error and the battery lifetime of the sensors, showing that WAVE can provide fine-grained control of this trade-off and significantly increase the battery lifetime of the node with a minimal Mean Squared Error (MSE) increase.

In this paper we introduce SVRUM: a cost-effective sensor platform to be mounted on Vulnerable Road User (VRU) vehicles (bicycles, e-bikes, kicks scooters) for the detection of road anomalies. SVRUM consists of a 3-axis accelerometer, a short-range sonar sensor, and a GPS module, which are connected to an Arduino board. We used SVRUM to collect data for eight different types of road anomalies (now publicly available to the community) and test various data analysis techniques, including machine learning algorithms, to identify road anomalies. The results are auspicious and demonstrate the potential of SVRUM in enhancing road safety for VRUs.

This paper discusses the geometrical features and wideband performance of the beam with maximal ratio combining coefficients for a generic multi-antenna receiver. In particular, in case the channel is a linear combination of plane waves, we show that such a beam can be decomposed in a linear combination of beams pointed in the direction of each plane wave, and we compute how many directions can be effectively utilized. This highlights that such a beam is better exploiting the spatial diversity provided by the channel, and therefore it is expected to be more robust to disruptions. Moreover, we compute the achieved Signal-to-Noise-Ratio for a wideband receiver, showing that it is wthin 2dB of the one of other methods. Finally, we provide some insights on the robustness of the method by simulating the impact of the blockage of one multipath components.

The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections. Consequently, solving theeffective communicationproblem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to a robot controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches.

Within a recent line of research, age of information is supported as an alternate network performance metric with respect to throughput or delay, to evaluate the performance of medium access techniques, especially for remote sensing applications. Analytical investigations based on game theory have shown how selfish players can behave efficiently in random access systems if they are driven by AoI-based objectives. We extend this kind of reasoning to the case of a slotted ALOHA system with capture. We present a fully analytical derivation of the general framework and its main results. We provide a quantitative characterization for the strength of capture in relation to the efficiency of the resulting Nash equilibrium, which provides extremely useful insights for a distributed system management. We apply our analysis to some scenarios of interest, in particular the case of exponentially distributed powers, for which we obtain a closed-form relationship. We highlight the impact of the system parameters, specifically the cost coefficient and the capture threshold, towards achieving an efficient allocation that represents an equilibrium for the network management. It is ultimately shown that, when capture is strong, as quantified through precise conditions (the system is driven towards a Nash equilibrium achieving near-optimal performance).

Over the past few years, the concept of Virtual Reality (VR) has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by the VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation, focusing on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode may have significant frame size fluctuations, impacting NS optimization. We then exploited the models to dynamically determine requirements in an NS scenario, providing the required QoS while minimizing resource usage.

COVID-19 had substantial effects on the IoT community which designs systems for safety: the urge to face masks worn by everyone, the analysis of crowds to avoid the spread of the disease, and the sanitization of public environments has led to exceptional research acceleration and fast engineering of the related solutions. Now that the pandemic is losing power, some applications are becoming less important, while others are proving to be useful regardless of the criticality of COVID-19. The Safe Place project is a prime example of this situation (DATE23 MPP category: final stage). Safe Place is an Italian 3M euro regional industrial/academic project, financed by European funds, created to ensure a multidisciplinary choral reaction to COVID-19 in critical environments such as rest homes and public places. Safe Place consortium was able to understand what is no longer useful in this post-pandemic period, and what instead is potentially attractive for the market. For example, the detection of face masks has little importance, while sanitization does have much. This paper shares such analysis, which emerged through a co-design process of three public Safe Place project demonstrators, involving heterogeneous figures spanning from scientists to lawyers.

In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks. In many scenarios, the learning task is performed in the Cloud, while experience samples are generated directly by edge nodes or users. Therefore, the learning task involves some data exchange which, in turn, subtracts a certain amount of transmission resources from the system. This creates a friction between the need to speed up convergence towards an effective strategy, which requires the allocation of resources to transmit learning samples, and the need to maximize the amount of resources used for data plane communication, maximizing users' Quality of Service (QoS), which requires the learning process to be efficient, i.e., minimize its overhead. In this paper, we investigate this trade-off and propose a dynamic balancing strategy between the learning and data planes, which allows the centralized learning agent to quickly converge to an efficient resource allocation strategy, while minimizing the impact on QoS. Simulation results show that the proposed method outperforms static allocation methods, converging to the optimal policy (i.e., maximum efficacy and minimum overhead of the learning plane) in the long run.

The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slice requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agent training is carried out following the Advantage Actor Critic algorithm, which permits to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.

Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum, thus making it possible to wirelessly approach several emerging use cases, such as eXtended Reality (XR), telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service (QoS), that only contention-free scheduling algorithms can guarantee. In this paper, we propose a framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3 (ns-3), a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the trade-offs between contention-based and contention-free access strategies.

Over the last few years, the Deep Reinforcement Learning (DRL) paradigm has been widely adopted for 5G and beyond network optimization because of its extreme adaptability to many different scenarios. However, collecting and processing learning data entail a significant cost in terms of communication and computational resources, which is often disregarded in the networking literature. In this work, we analyze the cost of learning in a resource-constrained system, defining an optimization problem in which training a DRL agent makes it possible to improve the resource allocation strategy but also reduces the number of available resources. Our simulation results show that the cost of learning can be critical when evaluating DRL schemes on the network edge and that assuming a cost-free learning model can lead to significantly overestimating performance.

The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment. Among them, Light Detection and Ranging (LiDAR) sensors are witnessing a surge in popularity as their application to vehicular networks seem particularly promising. LiDARs can indeed produce a three-dimensional (3D) mapping of the surrounding environment, which can be used for object detection, recognition, and topography. These data are encoded as a point cloud which, when transmitted, may pose significant challenges to the communication systems as it can easily congest the wireless channel. Along these lines, this paper investigates how to compress point clouds in a fast and efficient way. Both 2D- and a 3D-oriented approaches are considered, and the performance of the corresponding techniques is analyzed in terms of (de)compression time, efficiency, and quality of the decompressed frame compared to the original. We demonstrate that, thanks to the matrix form in which LiDAR frames are saved, compression methods that are typically applied for 2D images give equivalent results, if not better, than those specifically designed for 3D point clouds.

Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data privacy. At the same time, growing interest is being devoted to the energetic sustainability of machine learning. At the intersection of these trends, in this paper we focus on the energetic characterization of machine learning at the edge, which is attracting increasing attention. Unfortunately, calculating the energy consumption of a given neural network during inference is complicated by the heterogeneity of the possible underlying hardware implementation. In this work, we aim at profiling the energetic consumption of inference tasks for some modern edge nodes by deriving simple but accurate models. To this end, we performed a large number of experiments to collect the energy consumption of fully connected and convolutional layers on two well-known edge boards by NVIDIA, namely, Jetson TX2 and Xavier. From these experimental measurements, we have then distilled a simple and practical model that can provide an estimate of the energy consumption of a certain inference task on these edge computers. We believe that this model can prove useful in many contexts as, for instance, to guide the search for efficient neural network architectures, as a heuristic in neural network pruning, to find energy-efficient offloading strategies in a split computing context, or to evaluate and compare the energy performance of deep neural network architectures.

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR’s interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.

LoRaWAN is a Low Power Wide Area Network technology featuring long transmission ranges and a simple MAC layer, which can support sensor data collection, control applications and reliable services thanks to the flexibility offered by a large set of configurable system parameters. However, the impact of such parameters settings on the system’s performance is often difficult to predict, depending on several factors. To ease this task, in this paper, we provide a mathematical model to estimate the performance of a LoRaWAN gateway serving a set of devices that may or may not employ confirmed traffic. The model features a set of parameters that can be adjusted to investigate different gateway and end-device configurations, making it possible to carry out a systematic analysis of various trade-offs. The results given by the proposed model are validated through realistic ns-3 simulations that confirm the ability of the model to predict the system performance with high accuracy, and assess the impact of the assumptions made in the model for tractability.

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design anad hoctracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. We analyze the trade-off in setting the main parameters of the communication system and Adaptive Data Rate (ADR) scheme, showing how our tracking system can handle large swarms of drones at distances up to 4 km. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a swarm of UAVs, significantly outperforming baseline tracking approaches.

Many IoT scenarios, such as smart cities, wild life monitoring, or smart agriculture, involve thousands of battery-powered devices. The disposal and replacement of such batteries represent an important economical and environmental cost. To realize Green IoT solutions, it is therefore desirable to adopt battery-less energy-neutral devices that can harvest power from renewable sources, such as solar or wind energy and store it in much more sustainable capacitors. The limited and inconstant energy supply and the limited energy storage capacity of such devices, however, require special care in the design of communication and computational processes, which have a major impact on the energy consumption of the devices. In this work, we explore multiple elements that could affect the device energy and communication capabilities of LoRaWAN devices. We propose and compare different energy-aware packet transmission algorithms, and test them in a scenario where values for the harvested power are collected from real testbeds. We show that the number of successfully transmitted packets can be doubled by using an energy-aware design approach.

3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy-efficient connectivity option to the rapidly expanding market of the Internet-of-Things (IoT) devices. To unleash its full potential, end devices are expected to work in a plug-and-play fashion, with zero or minimal configuration of parameters, still exhibiting excellent energy efficiency. We performed the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our findings prove that parameters' settings do impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Furthermore, we investigated which device-reported metadata metrics better reflected performance and implemented an algorithm to automatically identify device state in the current time-series logs. We worked with two major western European operators to provide a measurement-driven analysis of energy consumption and network performance of two popular NB-IoT boards under different parameter configurations. We observed that energy consumption is mostly affected by the paging interval in connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters' settings can yield great energy savings.

In this article, we evaluate the performance of a LoRaWAN network in industrial scenarios where different Industrial Internet of Things (IIoT) end nodes communicate to a central controller in order to provide monitoring and sensing information to optimize the efficiency of industrial processes and reduce costs. In particular, we consider confirmed and unconfirmed traffic, multigateway deployments, the usage of different classes of devices, and a nonstandard channel plan. Furthermore, we analyze the higher-layer impact of different models of LoRa PHY layer with industrial channel models. We show that, with proper configuration, LoRaWAN is able to serve IIoT sensing applications with a packet success rate over 90%, providing at the same time limited communication delays.

Next-generation wireless networks are expected to enable new applications with strict latency constraints. However, existing transport layer protocols are unable to meet the stringent Quality of Service (QoS) requirements on throughput and maximum latency: excessive queuing due to capacity-oriented congestion control inflates end-to-end latency well beyond interactivity deadlines. In this work, we propose a novel framework that evolves best-effort communications into reliability- and latency-aware communications for QoS-sensitive applications. The new protocol, named High-reliability latency-bounded Overlay Protocol (HOP), provides a novel combination of packet-level Forward Error Correction (FEC) and multipath scheduling to compensate for capacity drops and meet pre-defined QoS requirements. More specifically, the sender splits the data and the associated redundancy between the paths by using a stochastic forecast of their future capacity and decides the amount of redundancy necessary to meet the application’s requirements without clogging the connections. We compare HOP’s performance with state-of-the-art multipath protocols in ns-3 simulations using both synthetic and live network traces, and confirm that our scheme can reliably deliver high-throughput data, reducing the number of late blocks by 2 to 5 times with respect to optimized Multipath TCP (MPTCP).

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications.

The recently proposed QUIC protocol has been widely adopted at the transport layer of the Internet over the past few years. Its design goals are to overcome some of TCP's performance issues, while maintaining the same properties and basic application interface. Two of the main drivers of its success were the integration with the innovative Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control mechanism, and the possibility of multiplexing different application streams over the same connection. Given the strong interest in QUIC shown by the ns-3 community, we present an extension to the native QUIC module that allows researchers to fully explore the potential of these two features. In this work, we present the integration of BBR into the QUIC module and the implementation of the necessary pacing and rate sampling mechanisms, along with a novel scheduling interface, with three different scheduling flavors. The new features are tested to verify that they perform as expected, using a web traffic model from the literature.

LoRaWAN is an emerging low-power wide-area network (LPWAN) technology, which is gaining momentum thanks to its flexibility and ease of deployment. Conversely to other LPWAN solutions, LoRaWAN indeed permits the configuration of several network parameters that affect different network performance indexes, such as energy efficiency, fairness, and capacity, in principle making it possible to adapt the network behavior to the specific requirements of the application scenario. Unfortunately, the complex and sometimes elusive interactions among the different network components make it rather difficult to predict the actual effect of a certain parameters setting, so that flexibility can turn into a stumbling block if not deeply understood. In this article, we shed light on such complex interactions for a single-gateway (GW) system by analyzing the effect of some built-in features and configurations, including the GW's limitations in terms of duty cycle and the number of parallel reception paths, the number of allowed retransmissions for confirmed traffic, and the preconfigured data rate used in downlink transmissions. The simulation-based analysis reveals various tradeoffs and highlights some inefficiencies in the design of the LoRaWAN standard. Furthermore, we show how significant performance gains can be obtained by wisely setting the system parameters, possibly in combination with some novel network management policies (e.g., enabling selective prioritization of downlink transmissions at the GW).

Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.

Jamming may become a serious threat in Internet of Things networks of battery-powered nodes, as attackers can disrupt packet delivery and significantly reduce the lifetime of the nodes. In this work, we model an active defense scenario in which an energy-limited node uses power control to defend itself from a malicious attacker, whose energy constraints may not be known to the defender. The interaction between the two nodes is modeled as an asymmetric Bayesian game where the victim has incomplete information about the attacker. We show how to derive the optimal Bayesian strategies for both the defender and the attacker, which may then serve as guidelines to develop and gauge efficient heuristics that are less computationally expensive than the optimal strategies. For example, we propose a neural-network-based learning method that allows the node to effectively defend itself from the jamming with a significantly reduced computational load. The outcomes of the ideal strategies highlight the tradeoff between node lifetime and communication reliability and the importance of an intelligent defense from jamming attacks.

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios.

The growing penetration of on-board communication units is enabling intelligent vehicles to share their sensor data with cloud computing platforms as well as with other vehicles. Although this unlocks the possibility of a variety of emerging applications, the massive amount of data traffic in vehicular networks is expected to pose a big challenge in the long term. In this paper, we shed light on the potential of value-anticipating networking to tackle this issue. A vehicle sending a piece of information first anticipates the value of that information for potential receivers. When the network is congested, the sender may defer or even cancel transmissions of less valuable information, so that important information can be delivered to receivers more reliably. We investigate the applicability of this concept to cooperative perception, where vehicles exchange processed sensor data over vehicle-to-vehicle (V2V) networks to collaboratively improve coverage and accuracy of environmental perception. Through simulations based on realistic road traffic, we show that value-anticipating V2V communications can significantly improve the performance of cooperative perception under heavy network load.

The Long Term Evolution (LTE) standard enables, besides cellular connectivity, basic automotive services to promote road safety through vehicle-to-infrastructure (V2I) communications. Nevertheless, stakeholders and research institutions, driven by the ambitious technological advances expected from fully autonomous and intelligent transportation systems, have recently investigated new radio technologies as a means to support vehicular applications. In particular, the millimeter wave (mmWave) spectrum holds great promise because of the large available bandwidth that may provide the required link capacity. Communications at high frequencies, however, suffer from severe propagation and absorption loss, which may cause communication disconnections especially considering high mobility scenarios. It is therefore important to validate, through simulations, the actual feasibility of establishing V2I communications in the above-6 GHz bands. Following this rationale, in this paper we provide the first comparative end- to-end evaluation of the performance of the LTE and mmWave technologies in a vehicular scenario. The simulation framework includes detailed measurement-based channel models as well as the full details of MAC, RLC and transport protocols. Our results show that, although LTE still represents a promising access solution to guarantee robust and fair connections, mmWaves satisfy the foreseen extreme throughput demands of most emerging automotive applications.

In recent years, the use of drones to map environments and survey them for items of interest such as forest fires, landslides or wild animals has gained traction in various research communities. However, the need for a human pilot or a pre-planned flight path severely limits the effectiveness of the drones, especially when a whole swarm is used. In this work, we propose a model of the drone survey problem and apply three well-known reinforcement learning strategies, showing that the performance loss due to the lack of explicit optimization and pre-programmed knowledge of the system statistics is negligible in the swarm scenario.

The Internet of Things (IoT) is rapidly spreading, reaching a multitude of different domains, including personal health care, environmental monitoring, home automation, smart mobility, and Industry 4.0. As a consequence, more and more IoT devices are being deployed in a variety of public and private environments, progressively becoming common objects of everyday life. It is hence apparent that, in such a scenario, cybersecurity becomes critical to avoid threats like leakage of sensible information, denial of service (DoS) attacks, unauthorized network access, and so on. Unfortunately, many low-end IoT commercial products do not usually support strong security mechanisms, and can hence be target of-or even means for-a number of security attacks. The aim of this article is to provide a broad overview of the security risks in the IoT sector and to discuss some possible counteractions. To this end, after a general introduction to security in the IoT domain, we discuss the specific security mechanisms adopted by the most popular IoT communication protocols. Then, we report and analyze some of the attacks against real IoT devices reported in the literature, in order to point out the current security weaknesses of commercial IoT solutions and remark the importance of considering security as an integral part in the design of IoT systems. We conclude this article with a reasoned comparison of the considered IoT technologies with respect to a set of qualifying security attributes, namely integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, self organization.

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user traffic periods has been proved to be an effective energy saving strategy. However, this strategy has strong interactions with interference coordination (IC) mechanisms, making it convenient to address both tasks simultaneously. The motivation of this paper is to develop a self-optimization algorithm capable of jointly controlling energy saving and IC mechanisms using an online learning approach. Our proposal is based on a contextual bandit formulation that, among other challenges, implies discovering the most energy-efficient control actions while satisfying a predefined level of Quality of Service (QoS) for the users. We propose a two-level framework comprising a global controller, in charge of a group of macro cells, and multiple local controllers, one per macro cell. The global controller implements a novel algorithm, referred to as the Bayesian Response Estimation and Threshold Search (BRETS), that is capable of learning, for each control action, its feasibility boundaries in terms of QoS and its energy consumption as a function of the aggregated user traffic. The algorithm comes with a bound on its expected convergence time. The local controllers translate the control actions learned by the global controller into local decisions. Our numerical results show that BRETS is only 1% less efficient than an ideal oracle policy, clearly outperforming other benchmark algorithms.

The availability of cheap and easy-to-install sensors is bolstering the development of monitoring applications in IoT scenarios. Although there is a need for periodical measurements of the tracked signals to guarantee an accurate representation at the receiver, choosing an appropriate duration for the reporting window is not trivial. In fact, the energy restrictions of many devices and the interference caused by other users call for longer reporting windows. However, this causes a higher reconstruction error due to the lower sampling resolution, which may be unacceptable in some applications. We propose a probabilistic random channel access scheme for battery-powered devices which monitor time-correlated phenomena and report their measurements to a fusion center. Our goal is to minimize the energy consumption of the sensors, while guaranteeing that the error in the signal estimate at the receiver does not exceed a chosen threshold. We exploit Markov chains and stochastic geometry to characterize the interference caused by the other devices. The numerical evaluation proves that our scheme is scalable and may be used in highly dense scenarios, and that it outperforms other state-of-the-art approaches, which do not consider the impact of interference on both the energy consumption and the accuracy of data representation.

As the capacity and reliability of mobile networks increases, so does the demand for more responsive end-to-end services: applications such as augmented reality, live video conferencing, and smart or autonomous vehicles require reliable, throughput-intensive end-to-end communications with strict delay constraints. Only consistently reliable delivery of data flows well within human interactivity deadlines will enable a truly immersive user experience. To enable data delivery within pre-defined deadlines, controlled on demand by an application or its user, we propose and demonstrate a novel transport-layer protocol for explicit latency control called latency-controlled end-to-end aggregation protocol (LEAP). The LEAP splits a data flow with quality of service (QoS) constraints into multiple subflows that are delivered over multiple parallel links (e.g., Wi-Fi and LTE in a standard smartphone, WiGig, and 5G in the near future). The subflow data rates are set based on a novel proactive forecasting of the achievable channel capacity, subject to application-specific QoS constraints. Cross-path encoding and redundancy adaptation are then used to deliberately balance the trade-off between maximum throughput, required delay, and minimum reliability as function of application/user-specific input parameters. When compared to leading state-of-the-art transport protocols in live network experiments, LEAP exhibits a superior capacity to reliably provide a high and stable throughput with bounded latency, both in wired and wireless scenarios. The LEAP is also the first protocol to allow applications to explicitly set their priorities, giving them the freedom to set the operating point in the trade-off between throughput, latency, and reliability.

LoRaWAN is gaining momentum in the arena of IoT connectivity technologies thanks to the low cost, ease of deployment, and support for adaptable transmission rates and bidirectional communications. The research community has then been working to develop suitable analytical models that can be instrumental in the study of this technology. In this work we propose a mathematical model that makes it possible to accurately estimate the packet success probability of a LoRaWannetwork in presence of bi-directional traffic, i.e., both uplink (UL) and downlink (DL) transmissions, and that accounts for the most critical features of the LoRa chipset and the LoRaWanstandard. The proposed model, furthermore, makes it possible to study the effect of different parameters configurations, thus offering a valid tool to investigate possible improvements to the system configuration. The proposed model is first validated by comparison with some accurate simulation results and, then, its potential is exemplified by analyzing the system settings that yield the best network performance.

In this paper, we propose a new method for jointly compressing EEG and EMG biosignals based on the so-called cortico-muscular coherence, a function that takes into account the simultaneous frequency changes of the brain and the muscles activity, and can be used, e.g., to classify different kinds of movement. It is shown that this method increases the achievable compression rate compared to transmitting EEG and EMG samples separately, while trading-off with the accuracy of the classification. This can be exploited in several kinds of life and health applications e.g., motor rehabilitation and drivers attention monitoring; it could be especially useful for low-power wireless technologies, such as Bluetooth Low Energy or IEEE 802.15.6, whose transmission resources are limited.

This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.

This paper addresses the joint problem of energy saving and interference coordination in heterogeneous networks (HetNets) using a contextual bandit formulation. We propose a semi-distributed scheme consisting of a learning agent and local controllers. The learning agent comprises a neural network (NN) classifier and a Multi-Armed Bandit (MAB) algorithm. The NN classifier is dynamically trained to choose a subset of configurations (i.e., feasible configurations in terms of QoS) based on the context information (network state). Then, the MAB algorithm picks one control (i.e., global configuration parameters) among those selected by the NN classifier, with the aim of improving the energy efficiency. These global configurations are interpreted by the local controllers on each network sector. This scheme allows the learning agent to progressively learn the best policy by observing the network state and the performance of the chosen configurations in terms of energy consumption and QoS. Our numerical results show an energy saving close to 20% with respect to a default policy and an improvement of 13% with respect to addressing energy saving and interference coordination separately.

Monitoring applications are gaining a lot of momentum in the Internet of Things (IoT), bolstered by the availability of cheap and easy-to-install sensors. Often, random access schemes are preferred to coordinated ones because they are more flexible and have no synchronization overhead. The possibility of collisions with other packets and the limited energy availability of the battery-powered nodes demand strategies to reduce the number of transmissions. This, however, is counterbalanced by the need to accurately monitor the process of interest, which requires a sufficient amount of sensed data. In this study, we propose a novel compression and transmission strategy with the objective of prolonging sensors' lifetime while guaranteeing a desired reconstruction accuracy of the tracked data. We consider the effect of both sensing and transmissions on the energy consumption, and adapt the sampling and transmission rates based on the target estimation accuracy and on the probability of packet losses caused by the interference from other sensors.

Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications. However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels. Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity. These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design. In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services. The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.

So far, research on Smart Cities and self-organizing networking techniques for fifth-generation (5G) cellular systems has been one-sided: a Smart City relies on 5G to support massive machine-to-machine (M2M) communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this paper, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in heterogeneous networks and dynamically manage mobility management entity (MME) loads to reduce handover completion times.

The presence of many battery-powered sensors in the Internet of Things paradigm calls for the design of energy-aware protocols. Source coding techniques make it possible to save some energy by compressing the packets sent over the network, but at the cost of poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining a scheduling strategy with the twofold goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a time division multiple access-based access scheme that efficiently allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry energy costs. We consider both full and statistical knowledge of the wireless channels and derive communication policies for the two cases. The overall problem is structured in modules and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters taken from real sensors.

The role of mobile devices, like smartphones or tablets, is becoming more and more important in everyday life, at the point that their unavailability due to early or unexpected battery discharge is perceived as a serious issue. Therefore, there is an urge for smart and efficient battery management algorithms that can prolong the duration of the battery charge. To this end, a reliable prediction of the battery discharging process would represent a precious tool to enable energy-efficiency optimization mechanisms. In this paper, we address this challenge by considering different machine learning techniques to provide an accurate and user-dependent prediction of the discharging time of a mobile device and, eventually, we propose a Deep Neural Network model that provides the best performance. Unlike previous solutions proposed in the literature, our method exploits space-time data from the device operating system (Android) to learn the specific battery usage pattern of the user, thus offering a customized prediction of the discharge process. We show that such model outperforms the other machine-learning methods considered in this study, and achieves much better performance than the deterministic linear fitting methods widely used in commercial devices.

On a droplet-based Labs-on-Chip (LoC) device, tiny volumes of fluids, so-called droplets, flow in channels of micrometer scale. The droplets contain chemical/biological samples that are processed by different modules on the LoC. In current solutions, an LoC is a single-purpose device that is designed for a specific application, which limits its flexibility. In order to realize a multi-purpose system, different modules are interconnected in a microfluidic network — yielding so-called Networked LoCs (NLoCs). In NLoCs, the droplets are routed to the desired modules by exploiting hydrodynamic forces. A well established topology for NLoCs are ring networks. However, the addressing schemes provided so far in the literature only allow to address multiple modules by re-injecting the droplet at the source every time, which is a very complex task and increases the risk of ruining the sample. In this work, we address this issue by revising the design of the network nodes, which include the modules. A novel configuration allows the droplet to undergo processing several times in cascade by different modules with a single injection. Simulating the trajectory of the droplets across the network confirmed the validity of our approach.

Several studies assert that the random access procedure of the Long Term Evolution (LTE) cellular standard may not be effective whenever a massive number of simultaneous connection attempts are performed by terminals, as may happen in a typical Internet of Things or Smart City scenario. Nevertheless, simulation studies in real deployment scenarios are missing because many system-level simulators do not implement the LTE random access procedure in detail. In this paper, we propose a patch for the LTE module of ns-3, one of the most prominent open-source network simulators, to improve the accuracy of the routine that simulates the LTE Random Access Channel (RACH). The patched version of the random access procedure is compared with the default one and the issues arising from massive simultaneous access from mobile terminals in LTE are assessed via a simulation campaign.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their effectiveness for a wide range of real-time industrial communication applications. Nonetheless, the introduction of the important IEEE 802.11n amendment, which is commonly implemented in commercial devices, has not been adequately addressed in this operational framework yet. IEEE 802.11n encompasses several enhancements at both physical (PHY) and medium access control (MAC) layers that may bring considerable improvements to the performance of WLANs deployed in real-time industrial communication systems. To this regard, in this paper, we present a thorough investigation of the most important IEEE 802.11n features, addressing in particular, specific performance indicators such as timeliness and reliability, which are crucial for industrial communication systems. To this aim, after an accurate theoretical analysis, we implemented a suitable experimental setup and carried out several measurement sessions to obtain an exhaustive performance assessment. The outcomes of these experiments, on one hand, revealed that the adoption of IEEE 802.11n can actually provide significant improvements to the performance of the IEEE 802.11 WLAN in the industrial communication scenario. On the other hand, the assessment allowed to select, among the various options of IEEE 802.11n, the parameter settings which may ensure the best behavior in this specific (and demanding) field of application.

Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information.

In this work we address the problem of optimal resource allocation in the uplink of a wireless cellular network with Rayleigh fading channels, where the aim is to minimize the average total energy spent for packet delivery. The devices are assumed to transmit over orthogonal resources where the total energy used for each resource is modeled as the sum of the transmit energy and an overhead circuit energy. We first derive the optimal allocation for the single-user case when varying our assumptions on both the Channel State Information available at the transmitter and the Automatic Repeat reQuest capabilities. Then, we generalize the analysis to the multi-user case and compare the results obtained for the different scenarios.

In this paper we present an easy-to-handle model for the analysis and simulation of droplet-based microfluidic networks. The model is capable to capture the "macroscopic" dynamics of the droplets in a purely microfluidic network, i.e., to determine their path across the circuit according to the fluidic parameters of the system and the inputs at the boundaries. We start with a quick overview of microfluidic basics and recall its main governing rules. We then describe in detail the proposed iterative model, which is finally tested and validated by means of experiments.

In recent past, researchers have suggested the idea of extending information theory to the microfluidic domain. Along this stream, a few solutions have been proposed to support logic and computing functions as well as simple communications in droplet-based microfluidic systems. Of course, pursuing this objective, requires a deep knowledge of microfluidic basics and relies on the use of an appropriate information coding strategy. Accordingly, in this paper we introduce a possible scheme for information coding in microfluidic devices, evaluate its performance by means of some preliminary experimental results and draw a number of considerations.

A number of scheduling algorithms for LTE downlink have been proposed and evaluated leveraging the flexibility of the resource allocation in both the time and the frequency domain. However, the existing literature falls short when it comes to schedulers that provide throughput guarantees. In this paper, we contribute to fill this gap by implementing a scheduling algorithm that provides long-term throughput guarantees to the different users, while opportunistically exploiting the instantaneous channel fluctuations to increase the cell capacity. We perform a thorough performance analysis comparing this algorithm with the other well known algorithms by means of extensive ns-3 simulations, both for saturated UDP and TCP traffic sources. The analysis makes it possible to appreciate the difference among the scheduling algorithms, and to assess the performance gain, both in terms of cell capacity and packet service time, obtained by allowing the schedulers to work in the frequency domain.

The spreading of video streaming services in the last few years is presenting new challenges in wireless networking; Video Rate Adaptation (VRA) is a technique that optimizes the bandwidth usage by adapting video quality as network conditions change. We propose two Quality of Experience (QoE) aware algorithms that perform VRA while guaranteeing user satisfaction.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their effectiveness for a wide range of real-time industrial communication applications. Nonetheless, the enhancements at the PHY and MAC layers introduced by the IEEE 802.11n amendment have not yet been adequately addressed in the context of industrial communication. In this paper we investigate the impact of some IEEE 802.11n new features on some important performance figures for industrial applications, such as timeliness and reliability.

The wide flexibility of LTE resource allocation scheme has led to the definition of various schedulers that attempt to maximize the quality of the service offered to the different users, depending on their channel conditions. Unfortunately, providing service guarantees in dynamic channel conditions typically requires a cost in terms of spectral efficiency of the transmission resource allocation. In this work, we investigate this tradeoff and propose a resource allocation scheme that adapts the service level guarantees to the average channel conditions of the users, in order to provide fair resource allocation among users with homogeneous channel conditions, while improving the cell spectral efficiency. A performance analysis is carried out by comparing the proposed scheduler with other well known schedulers. Results show that the proposed method can improve the cell capacity, while guaranteeing long-term throughput fairness among users of the same class. In addition, we analyze the short-term throughput provided by the proposed scheduler and provide a semi-analytical model to assess the gap with respect to the long-term performance.

Microfluidics is a multidisciplinary field of research that deals with elementary hydraulic circuits with channels of micrometer size. At this scale, fluids exhibit very specific patterns that cannot be observed at the macro-scale. In particular, vortex forces become negligible, so that the behavior of fluids in the circuit becomes easily controllable and predictable. This technology is currently used in medicine and chemistry to perform specific tasks, such as blood analysis, DNA sequencing, and others. The interest on this technology has been increasing over the last few years and, recently, microfluidic circuits capable of performing simple logical operations have been proposed and experimentally tested, paving the way to a new research branch known as microfluidic networking. In this paper we analyze the design of a microfluidic network with a bus topology, where multiple microfluidic machines are connected to a main channel by means of passive switching elements, realized as T-junctions with bypass (shunt). We mathematically model the system and find the rules to be followed for proper design and dimensioning of such a microfluidic network. We then propose a preliminary performance analysis that gives some insights into the complex interrelations among the different elements of the microfluidic network.

The exponential growth of video traffic in mobile networks calls for the deployment of advanced video admission control (VAC) and resource management (RM) techniques in order to provide the best quality of experience (QoE) to the end user according to the available network resources. The degradation of the QoE perceived by the user when reducing the source rate of a video typically depends on the content of the video itself. In this paper, we analyzed the QoE of a group of test video sequences encoded with H.264 advanced video codec at different rates, i.e., quality levels. The QoE is objectively expressed in terms of the average structural similarity (SSIM) index. Based on empirical results, we propose a 4-degree polynomial approximation of the SSIM as a function of the coded video rate. We hence propose to tag each video with these polynomial coefficients that provide a compact description of its specific SSIM behavior, and to use this information in VAC and RM algorithms to optimally manage a shared transmission medium. As a proof of concept, we report selected simulation results that compare QoE-aware and QoE-agnostic algorithms in a scenario with a single link shared by multiple concurrent video flows.

Several techniques for wireless networking, such as opportunistic spectrum access, or self-healing networks, may be seen as using a form of cognition, meaning that they mimic reasoning processes of intelligent beings. We propose to expand this cognition-based process by exploiting the parallel processing power of the infrastructure, so as to go beyond cognition as is meant by these approaches. We leverage novel approaches, taken from cognitive science and artificial intelligence, involving not only supervised but also unsupervised learning, and we envision their application to systems for video over wireless. The transmission of multimedia content, and its adaptation to the condition of the communication infrastructure, i.e., the wireless channel or the content delivery network, are envisioned as particularly critical steps for the development of latest generation mobile networks. For this scenario, we propose and evaluate a video classifier based on a Restricted Boltzmann Machine that tries to extract abstract features of videos from the analysis of the sizes of a few coded frames. These features can then be exploited by the communication network itself to optimize video transmission based on its content.

“Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

The efficient management of the supply and demand in electricity networks is becoming a pivotal issue with important fallbacks both in the technological and financial domains. An interesting topic in this domain is the use of large batteries at the end users premises to reduce the average cost of energy supply, by storing energy when its cost is low and releasing it when the cost is high. In this paper, we wish to gain insights on the impact of some system model parameters, such as battery capacity, charge/discharge rate, power request process, and cost functions, on the cost saving that can be achieved by some selected energy storage algorithms. The study shows that the battery capacity has a direct and rather linear impact on cost reduction, while the effect of charge/discharge rates is less straightforward to predict. Furthermore, we show that, with piecewise, convex and non-decreasing cost functions, the optimal energy storage strategy has a threshold structure, where the number of thresholds depend on the shape of the cost function and the constraints of the battery.

In this paper, we address the problem of computing the probability that r out of n interfering wireless signals are "captured," i.e., received with sufficiently large Signal to Interference plus Noise Ratio (SINR) to correctly decode the signals by a receiver with multi-packet reception (MPR) and Successive Interference Cancellation (SIC) capabilities. We start by considering the simpler case of a pure MPR system without SIC, for which we provide an expression for the distribution of the number of captured packets, whose computational complexity scales with n and r. This analysis makes it possible to investigate the system throughput as a function of the MPR capabilities of the receiver. We then generalize the analysis to SIC systems. In addition to the exact expressions for the capture probability and the normalized system throughput, we also derive approximate expressions that are much easier to compute and provide accurate results in some practical scenarios. Finally, we present selected results for some case studies with the purpose of illustrating the potential of the proposed mathematical framework and validating the approximate methods.

A batch resolution algorithm (BRA) is a channel access policy used by a group of nodes (the batch) that simultaneously generate a packet for a common receiver. The aim is to minimize the batch resolution interval (BRI), i.e., the time it takes for all nodes in the batch to successfully deliver their packet. Most of existing BRAs require immediate feedback after each packet transmission, and typically assume the feedback time is negligible. This conjecture, however, fails to apply in practical high rate wireless systems, so that the classical performance analysis of BRAs may be overoptimistic. In this paper we propose and analyze a novel BRA named Adaptive Batch Resolution Algorithm with Deferred Feedback (ABRADE), which waives the immediate feedback approach in favor of a deferred feedback method, based on a framed ALOHA access scheme. The frame length is optimized by using a dynamic programming technique in order to minimize the BRI, under the assumption that the batch size is known. Successively, we remove this assumption by coupling ABRADE with a batch size estimate module. The new algorithm, called ABRADE+, is compared against the best performing BRAs based on the immediate feedback paradigm, showing better performance both in case of partial and no prior knowledge of the batch multiplicity.

This paper proposes a novel method for solving localization problems leveraging on node position constraints. This consists in mapping n wireless nodes onto n predefined positions in a map. The problem may be solved by first applying standard localization algorithms to get an initial estimate of the node positions in the area and, successively, mapping each estimated position to the closest admissible point in the map. Results can be improved by applying algorithms that are explicitly designed to manage the available information for the constrained problem. In this study, we propose three algorithms, based on a greedy, multi dimensional scaling, and belief propagation approach, respectively. The algorithms are analyzed and compared by using synthetic data. Results reveal that the belief propagation approach, suitably modified to account for the position constraints, outperforms the other algorithms in all the considered settings.

In this paper, we address the problem of computing the probability that r out of n interfering signals can be correctly received in a random access wireless system with capture, capable of performing up to K successive interference cancellation iterations. We provide an expression for the probability distribution of the number of captured packets that is scalable with n and r. We also provide an approximate expression for the mean number of captured signals, which gives the system throughput as a function of K and n. The approximation is much easier to compute than the exact mean and provides good results for reasonable values of K. Finally, we present some selected results for a case study with the purpose of illustrating the potential of the proposed mathematical framework, and validate the accuracy of the approximate method for the computation of the system throughput.

The batch resolution problem consists in arbitrating the channel access of a group of nodes in a wireless network in order to collect a single packet from each node in the shortest time. Most of existing solutions are based on the immediate feedback assumption and typically neglect or underestimate the actual time cost of feedback that can instead be significant in common wireless standards. In this paper, we propose and analyze ABRADE, which is a dynamic framed ALOHA scheme for conflict resolution in practical CSMA-based wireless networks. The core of ABRADE, in fact, is the dynamic adaption of the framed ALOHA parameters to the cardinality of the residual batch, in order to strike a balance between the control message overhead and the fraction of successful transmissions per frame. The parameters optimization is based on a dynamic programming argument that takes into account the time occupancy of successful, collided and idle slots, as well as the time cost of control messages. Compared against classical batch resolution algorithms in practical scenarios, the proposed solution yields up to 10% of throughput gain.

The first step required by the process of cognition is the intelligent observation of the environment that surrounds the actors of such a process. We present the CalRAdio-Based advanced Spectrum Scanner (CRABSS), an open platform developed to monitor the ISM 2.4--2.499 GHz band and reveal opportunities for a better utilization of the available spectrum resources. CRABSS is built through a modular approach by integrating the development platform CalRadio 1 with the Unified Link Layer API (ULLA) framework. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it takes advantage of the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs.

Abstract The first step required by the process of cognition is the intelligent observation of the environment that surrounds the actors of such a process. We present the CalRAdio‐Based advanced Spectrum Scanner (CRABSS), an open platform developed to monitor the ISM 2.4–2.499 GHz band and reveal opportunities for a better utilization of the available spectrum resources. CRABSS is built through a modular approach by integrating the development platform CalRadio 1 with the Unified Link Layer API (ULLA) framework. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it takes advantage of the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we propose a simple and standard compliant retransmission mechanism, called carrier-sense automatic repeat request (CS-ARQ), which aims at improving system performance, both in terms of throughput and energy efficiency, by avoiding useless data packet retransmissions. More specifically, in case of missed acknowledgment (ACK), the source makes use of its carrier-sensing capabilities to decide whether retransmitting the data packet or soliciting the ACK retransmission from the destination. The scheme is modeled by means of a two-state Markov chain that permits to determine closed-form expressions for the throughput and energy efficiency figures. The analysis reveals that the CS-ARQ mechanism is actually capable of significantly enhancing the system performance, in particular in some critical scenarios, while preserving standard compliancy.

This paper presents the localization of a mobile robot while simultaneously mapping the position of the nodes of a Wireless Sensor Network using only range measurements. The robot can estimate the distance to nearby nodes of the Wireless Sensor Network by measuring the Received Signal Strength Indicator (RSSI) of the received radio messages. The RSSI measure is very noisy, especially in an indoor environment due to interference and reflections of the radio signals. We adopted an Extended Kalman Filter SLAM algorithm to integrate RSSI measurements from the different nodes over time, while the robot moves in the environment. A simple pre-processing filter helps in reducing the RSSI variations due to interference and reflections. Successful experiments are reported in which an average localization error less than 1 m is obtained when the SLAM algorithm has no a priori knowledge on the wireless node positions, while a localization error less than 0.5 m can be achieved when the position of the node is initialized close to the their actual position. These results are obtained using a generic path loss model for the transmission channel. Moreover, no internode communication is necessary in the WSN. This can save energy and enables to apply the proposed system also to fully disconnected networks.

In this paper, we address the problem of computing the probability that r out of n interfering signals can be correctly received in a random access wireless system with capture. We extend previous results on the capture probability computation, and provide an expression for the distribution of the number of captured packets that is scalable with n and r. We also provide an approximate expression, that is much easier to compute and provides good results for r = 0 and r = n. Finally, we study the dependence of the system throughput performance on the multi-packet reception capabilities of the receiver.

CalRadio 1 is an open 802.11b-compatible development platform, designed and developed at UCSD with the aim of providing the research community with an open and fully reprogrammable board for experimental purposes. In this work we describe the hardware and software architecture of the board and we provide an accurate analysis of the limiting performance achieved by CalRadio 1 in comparison with commercial 802.11b wireless interfaces. The analysis offers a clear vision of the real potential and limitations of the CalRadio 1 board, pointing out the aspects of major concern for prospective developers.

Localization and tracking functionalities can benefit a number of applications. Despite the large number of algorithms and technologies that have been proposed in this context, the literature still lacks a widely accepted solution, capable of cutting a tradeoff between service quality (i.e., localization accuracy) and device/architecture cost and complexity. In this paper, we tackle the problem from a different and rather new perspective: we investigate how the localization accuracy of nodes can be ameliorated by opportunistically exchanging localization information among heterogeneous nodes that occasionally happen to be in proximity. To this end, we define a simple though accurate opportunistic meeting model and, then, we develop a mathematical framework that permits to analyze the performance of an opportunistic localization strategy based on a Maximum Likelihood argument.

In this paper, we present a mathematical framework that permits a detailed performance analysis of Bluetooth connections in fading channels. Conversely to most part of the literature, we distinguish between the transmission of useful and duplicate frames, which are handled in a different manner by the receiving unit. To this end, we define a two-state Markov Chain and we apply the renewal reward theory to determine the expressions of the throughput, energy efficiency and delay performance of the link. Although the model can be applied to any version of Bluetooth specifications, as a proof of concept we provide an accurate performance analysis of an asymmetric Bluetooth v2.0+EDR (Enhanced Data Rate) connection in typical propagation environments. The analysis reveals that best performance are (almost) always obtained by using the longest baseband frames transmitted at 2Mbps in the low-to-medium signal-to-noise ratio (SNR) region, and at 3 Mbps in the high SNR region. Furthermore, we observed that it is more fruitful assigning the master role to the destination unit. The model, hence, proves to be a valuable tool to gain insights on the aspects that have a major impact on the system performance.

In this paper we present APOS, a method for dynamically adapting the parameters of IEEE 802.11 g to the estimated system state, with the aim of enhancing the quality of a voice communication between a mobile station and a remote peer node. The system state is estimated based on a number of counters that are collected by the MAC layer of the mobile station, regarding the number of successful and unsuccessful transmission/reception events, channel busy periods and idle slots. These statistics are processed to estimate the collision probability and the signal to noise ratio at the receiver side. Hence, a mathematical model is used to get the expected end-to-end network performance in terms of throughput, delay and packet error rate, for different settings of some PHY and MAC parameters, such as the modulation/coding scheme and the retransmission limit. The setting that is estimated to maximize the quality of service for the end user is then selected. Unlike other optimization mechanisms proposed in literature, APOS is totally stand-alone and standard compliant. In fact, APOS makes use of local information that can be collected from the Network Interface Card, and no explicit interactions with the other devices in the network is required.

Rate adaptation for 802.11 has been deeply investigated in the past, but the problem of achieving optimal rate adaptation with respect not only to channel-related errors but also to contention-related issues (i.e., collisions and variations in medium access times) is still unsolved. In this paper we address this issue by proposing (1) a practical definition of the medium status in a multi-user 802.11 scenario in terms of channel errors, MAC collisions and packet service times, and a method for its estimation based on measurements; (2) an analytical model of the goodput performance as a function of the Medium Status; (3) a rate adaptation algorithm, called goodput optimal rate adaptation (GORA), which is based on this model. Unlike other rate adaptation schemes proposed in literature, which require either modifications to the IEEE 802.11 standard or cooperation among nodes, GORA is totally stand-alone and standard compliant. In fact, the Medium Status Estimation used by GORA is obtained by using standard MAC counters that are commonly collected by commercial MAC drivers, and no explicit interactions with the other devices in the network is required. Therefore, GORA offers the advantage of being readily deployable on real devices. The performance of GORA is evaluated through NS2 simulations which reveal that, as expected, GORA outperforms other well- known rate adaptation algorithms in several scenarios and can be used as a new reference benchmark.

This paper explores the issues connected to the transmission of three dimensional scenes over unreliable networks such as the wireless ones. It analyzes the effect of the loss of compressed data packets in a typical image-based rendering scenario, where a set of compressed images together with the corresponding depth maps are transmitted and used to generate the views required from the user at client side. The different impact on the rendered views of the geometry and texture packets is analyzed in detail, taking into account also the position of the lost packets and the warping operation. Finally we will discuss how to exploit this results in the design of an efficient network protocol for the transmission of 3D models.

In this paper, we present a mathematical framework that permits an accurate performance analysis, both in terms of average throughput and energy efficiency, of Bluetooth link performance in fading channels. Conversely to most part of the literature concerning Bluetooth performance, this analysis takes into consideration the microscopic level power-saving mechanisms introduced in Bluetooth specifications to reduce the energy consumption during active mode. The analysis makes use of a two-state Markov chain to distinguish the transmission of useful and duplicate packets by the master. Then, the average energy spent, the amount of data exchanged and the time elapsed for each transition of the Markov chain, are derived. Hence, we apply the renewal reward theory to derive the average throughput and energy efficiency of the data link for different data packet formats and transmission rates, both in AWGN and fading channels. A proof of concept is provided by applying the mathematical framework to a Bluetooth v2.0 with Enhanced Data Rate data link. The analysis permits to appreciate the advantage, in terms of throughput and energy efficiency, of the enhanced data rate packet formats over the basic rate.

In this paper we investigate the potentialities offered by IEEE 802.15.3 framework for supporting multimedia services. More specifically, we analyze the performance of some classical scheduling policies in presence of intensive heterogeneous realtime and multimedia traffic, in order to identify the most effective strategy for the considered scenarios. The analysis has been performed by using a complete 802.15.3 C++ simulator, where we have realized the different scheduling strategies upon an entirely standard-compliant round robin polling procedure. Results show that, in most cases, EDF approach offers better performance, though its margin with respect to the other strategies strictly depends on the specific scenario considered.

In this paper, we investigate some of the issues that arise when mobile nodes engage voice connections with remote peers by using a wireless ad hoc network (MANET-cell) to access the distribution core network. We focus on a specific network scenario where all communications are directed to a special node, called gateway, which in turn bridges traffic to and from the core network. We first derive a mathematical expression to estimate the maximum number of sustainable voice sessions in a single-hop cell, with multi-rate terminals. Then, we assess the accuracy of the formula through ns2 simulations, which allow us to determine the voice capacity of the system also in presence of hidden terminals. Hence, we analyze the impact of multi-hop paths on the voice capacity of the system. From this analysis, we propose a new cost metric that we apply to a modified version of the OLSR routing algorithm, named HOLSR. This algorithm has proved to be able to preserve stability in some scenarios that were unstable under other routing algorithms.

In this paper, we focus on a vehicular ad hoc network (VANET) that makes use of 802.11-like wireless interfaces for Inter Vehicular Communication (IVC). We propose a distributed position-based broadcast protocol, named Smart Broadcast (SB), that aims at i) maximizing the progress of the message along the propagation line, and ii) minimizing the re-broadcast delay. The protocol is analyzed through a mathematical model that permits to determine the optimal parameter setting for a given scenario. Simulations are then used to validate the mathematical model and to compare SB with other broadcast algorithms.

No abstract available.

Wireless communication systems are rapidly becoming a viable solution for employment at the lowest level of factory automation systems, usually referred to as either "device" or "field" level, where the requested performance may be rather critical in terms of both transmission time and reliability. In this paper, we deal with the use of wireless networks at the device level. Specifically, after an analysis of the communication requirements, we introduce a general profile of a wireless fieldbus. Both the physical and data link layers are taken directly from existing wireless local area networks and wireless personal area networks standards, whereas the application layer is derived from the most popular wired fieldbuses. We discuss implementation issues related to two models of application layer protocols and present performance results obtained through numerical simulations. We also address some important aspects related to data security and power consumption.

The standards that are supposed to play a leading role in third-generation mobile telecommunication and personal area networks in the near future are the Universal Mobile Telecommunication System (UMTS) and the Bluetooth (BT) radio technology, respectively. In this paper, we advocate that a hybrid architecture of UMTS and Bluetooth can take advantage of the complementary characteristics of these two technologies and provide a total solution for an indoor communication environment. We envision a cooperating scenario in which small Bluetooth networks (scatternets) offer basic wireless connectivity to several peripheral units scattered over small areas, while indoor UMTS supports communication among scatternets and provides wireless access to the Internet. We focus our analysis on a centralized topology, in which communication occurs only between the peripheral units and the access point. This topology can be used in many different application scenarios and represents an example of cooperation between 3G and PAN technologies. In addition to describing the architecture, we address the issue of fair capacity allocation in such a centralized topology and provide some analytic and simulation results for the topology considered.

We study the performance of TCP Westwood (TCPW), a new TCP protocol with a sender-side modification of the window congestion control scheme. TCP Westwood controls the window using end-to-end rate estimation in a way that is totally transparent to routers and to the destination. Thus, it is compatible with any network and TCP implementation. The key innovative idea is to continuously estimate, at the TCP sender, the packet rate of the connection by monitoring the ACK reception rate. The estimated connection rate is then used to compute congestion window and slow start threshold settings after a congestion episode. Resetting the window to match available bandwidth makes TCPW more robust to sporadic losses due to wireless channel problems. These often cause conventional TCP to overreact, leading to unnecessary window reduction. Experimental studies of TCPW show significant improvements in throughput performance over Reno and SACK, particularly in mixed wired/wireless networks over high-speed links. The contributions of this paper include a model for fair and friendly sharing of the bottleneck link and a Markov Chain performance model in presence of link errors/loss. TCPW performance is compared to that of TCP Reno, and analytic results are validated against simulation results. Internet and laboratory measurements using a Linux TCPW implementation are also reported, providing further evidence of the gains achievable via TCPW.

We present a performance model of TCP Westwood (TCPW), a new TCP protocol with a sender-side modification of the window congestion control scheme. TCP Westwood controls the window using end-to-end connection bandwidth share estimation, obtained by monitoring the ACK reception rate. An analytic model using Markov Chain techniques is developed in this paper, and then used to assess the performance improvements obtained using TCPW. The model takes into account the estimation and filtering method used in TCPW, as well as the following system parameters, bottleneck link bandwidth, buffer space at the bottleneck router, end-to-end propagation time, and error rate. The model reveals substantial TCPW gains over Reno whenever losses due to link or other errors are taken into consideration. The analytic model accuracy is confirmed by comparing to simulation results.