Distributed compute-in-memory (CIM) architectures are emerging as a path towards energy-efficient and highly parallel edge-class deep neural network (DNN) inference. However, the lack of a flexible, unified digital periphery limits the ability of distributed CIM systems to support integer-quantized DNN inference schemes, activation functions, and scalable tile-to-tile dataflow. We present CIM-FLEX, an integer-only periphery architecture that enables each CIM tile to autonomously perform partial-sum rescaling, asymmetric activation processing, and lightweight programmable activation functions. CIM-FLEX also supports mixed-precision integer multiply-and-accumulate (MAC) by decomposing higher-precision operations into uniform-precision partial MACs, without modifying existing CIM primitives. CIM-FLEX enables flexible tile-to-tile dataflow across distributed CIM tiles, exposing a tradeoff between parallel output generation and energy efficiency, where deeper accumulation chains reduce concurrent periphery utilization and improve overall efficiency. Numerical studies and TFLite-based LUT activation experiments validate a high signal-to-noise ratio (∼30 dB) for programmable activation functions, and a 14 nm CMOS implementation shows a 0.012 mm2 area for the CIM-FLEX.

Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems, as it enables applications such as target classification, human activity recognition, and gait analysis. In practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices, and by the phase offsets caused by their clock asynchrony. Existing approaches haveseparatelyaddressed these two aspects, either assuming clock-synchronous moving devices or asynchronous static ones. In fact, jointly tackling device motion and clock asynchrony is extremely challenging, as the Doppler shift from device movement differs for each propagation path and the phase offsets are time-varying. In this work, we present AsyMov, a method to estimate the bistatic Doppler frequency of a target and its velocity in ISAC setups featuringmobile and asynchronousdevices. It leverages the channel impulse response at the receiver, by originally exploiting the invariance of phase offsets across propagation paths and the bistatic geometry, where the target Doppler and the device velocity are jointly estimated by a newly proposed alternating minimization algorithm. Moreover, it can be seamlessly integrated with device velocity measurements obtained from onboard sensors (if available), for enhanced reliability. Here, AsyMov is thoroughly characterized by way of theory (Cramér-Rao bound), simulation, and experiments, implementing it on an IEEE 802.11ay testbed and testing it on multiple setups in the 60 GHz and 28 GHz bands, including moving human subjects. Numerical and experimental results show superior performance against state-of-the-art methods and are on par with scenarios featuringstaticISAC devices.

Large language models have significantly transformed multiple fields with their exceptional performance in natural language tasks, but their deployment in resource-constrained environments like edge networks presents an ongoing challenge. Decentralized techniques for inference have emerged, distributing the model blocks among multiple devices to improve flexibility and cost effectiveness. However, energy limitations remain a significant concern for edge devices. We propose a sustainable model for collaborative inference on interconnected, battery-powered edge devices with energy harvesting. A semi-Markov model is developed to describe the states of the devices, considering processing parameters and average green energy arrivals. This informs the design of scheduling algorithms that aim to minimize device downtimes and maximize network throughput. Through empirical evaluations and simulated runs, we validate the effectiveness of our approach, paving the way for energy-efficient decentralized inference over edge networks.

Integrated Sensing And Communication (ISAC) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, applies to different frequencies, and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum.

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated fromreflectedpackets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of$94\%$and$90\%$, respectively.

Multi-user MIMO is a key component of modern wireless networks. As such, investigating the related security weaknesses is a compelling necessity. A major issue unveiled by existing work is that adversaries can “poison” the channel information feedback reported to the beamformer to decrease the performance experienced by a legitimate user. Prior work, however, assumes that the feedback is reported in an uncompressed fashion, which is not the case in current wireless standards such as Wi-Fi or 5G. In this work, we first show that assuming uncompressed feedback leads to overestimating the attack effectiveness by up to 60%. Next, we formulateACFP(Adversarial Compressed Feedback Problem), a novel non-convex constrained optimization problem to find the compressed feedback that maximizes a victim’s bit error rate (BER) while satisfying maximum power constraints. We proposeWHACK(Wireless Harmful Adversarial Compressed feedbacK), a new algorithm to solveACFPand find the malicious compressed feedback based on the convexity of the objective function and constraint using a nonlinear conjugate gradient method.WHACKhas been prototyped and extensively evaluated with off-the-shelf Wi-Fi devices. Experimental results show that it maximizes the victim’s BER, while modifying less than 60% of the feedback. Our dataset and code are available.

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development ofjoint communication and sensingtechnology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introducestime-varyingtiming and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enablingpracticalbistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.

Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where inter-node communication is the main bottleneck. Although computationally demanding, Newton-type (NT) methods have been recently advocated as enablers of robust convergence rates in challenging DO problems where edge devices have sufficient computational power. Along these lines, in this work$w$e propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization. The proposed technique is integrated with the recent SHED algorithm, from which it inherits appealing features like the small number of required Hessian computations, while being bandwidth-versatile at a bit-resolution level. Our empirical evaluation against competing approaches shows that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.

Thanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation accuracy that can be achieved through commercial devices allows implementing human localization and tracking strategies but is insufficient to effectively design human vital signs monitoring applications due to the limited frequency and spatial diversity. We pledge to release our implementations for further investigations.1

In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than$95\%$, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes [1].

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move. By capturing unique imperfections in off-the-shelf radio circuitry, RFP techniques can identify wireless devices directly at the physical layer, allowing low-latency low-energy cryptography-free authentication. However, existing Wi-Fi RFP techniques are based on software-defined radio (SDRs), which may ultimately prevent their widespread adoption. Moreover, it is unclear whether existing strategies can work in the presence of MU-MIMO transmitters – a key technology in modern Wi-Fi standards. Conversely from prior work, DeepCSI does not require SDR technologies and can be run on any low-cost Wi-Fi device to authenticate MU-MIMO transmitters. Our key intuition is that imperfections in the transmitter’s radio circuitry percolate onto the beamforming feedback matrix, and thus RFP can be performed without explicit channel state information (CSI) computation. DeepCSI is robust to inter-stream and inter-user interference being the beamforming feedback not affected by those phenomena. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment, where 10 MU-MIMO Wi-Fi radios emit signals in different positions. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%. The identification accuracy remains above 82% when the device moves within the environment. To allow replicability and provide a performance benchmark, we pledge to share the 800 GB datasets – collected in static and, for the first time, dynamic conditions – and the code database with the community.

A well established method to detect and classify human movements using Millimeter-Wave (mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response (CIR). This is currently done in the literature either using special-purpose radar sen-sors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and commu-nication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with re-spect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.

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.

In this work, we tackle the energy consumption problem of edge computing technology looking at two key aspects: (i) reducing the energy burden of modern edge computing facilities to the power grid and (ii) distributing the user-generated computing load within the edge while meeting computing deadlines and achieving network level benefits (server load balancingvsconsolidation and reduction of transmission costs). In the considered setup, edge servers are co-located with the base stations of a mobile network. Renewable energy sources are available to power base stations and servers, and users generate workload that is to be processed within certain deadlines. We propose a predictive, online and distributed algorithm for the scheduling of computing jobs that attains objectives (i) and (ii). The algorithm achieves fast convergence, leading to an energy efficient use of edge computing facilities, and obtains in the best case a reduction of 50% in the amount of renewable energy that is sold to the power grid by heuristic policies and that is, in turn, used at the network edge for processing.

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections, and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively, based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical toward achieving robustness and accuracy in multiuser settings. Our technique is tested both on a single-target public data set, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.

The service function chaining paradigm links ordered service functions via network virtualization, in support of applications with severe network constraints. To provide wide-area (federated) virtual network services, a distributed architecture should orchestrate cooperating or competing processes to generate and maintain virtual paths hosting service function chains while, guaranteeing performance and fast asynchronous consensus even in the presence of failures. To this end, we propose a prototype of an architecture for robust service function chain instantiation with convergence and performance guarantees. To instantiate a service chain, our system uses a fully distributed asynchronous consensus mechanism that has bounds on convergence time and leads to a (1 - 1/e)-approximation ratio with respect to the Pareto optimal chain instantiation, even in the presence of (non-byzantine) failures. Moreover, we show that a better optimal chain approximation cannot exist. To establish the practicality of our approach, we evaluate the system performance, policy tradeoffs, and overhead via simulations and through a prototype implementation. We then describe our extensible management object model and compare our asynchronous consensus's overhead against Raft, a recent decentralized consensus protocol, showing superior performance. We furthermore discuss a new management object model for distributed service function chain instantiation.

Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs.

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, atruntime, from theraw physical control channelof anoperative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this article, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in anonlineandunsupervisedfashion.

ECMO (Extra Corporeal Membrane Oxygenation) is a technique that supports vital functions through extracorporeal circulation, by raising blood oxygenation, reducing hematic carbon dioxide values (CO2), increasing cardiac output and acting on body temperature. In conditions of severe respiratory and/or cardiac insufficiency, it allows the heart and lungs to rest by activating their ventilatory and pump functions. There are several ways to connect the ECMO system with the vascular system. This work studies the difference between central and peripheral cannulations by means of Computational Fluid Dynamics.

We consider a Multiaccess Edge Computing (MEC) network where distributed servers have energy harvesting (e.g., solar) and storage (e.g., batteries) capabilities. Energy from a connected power grid is also available, in case that harvested from ambient sources is scarce or absent. Network processors are deployed according to a given network topology, across two tiers, and computing tasks are flexibly allocated depending on considerations related to load balancing, energy consumption (for communication and computing) and energy purchases from the power grid. Specifically, an on-line optimization problem, exploiting a predictive control approach, is formulated to minimize the monetary cost incurred in the energy purchases from the power grid, by dispatching the computation jobs to those servers that have enough energy and computation resources. Our proposed framework uses forecasts of exogenous processes, such as the amount of energy harvested and job arrivals, which are estimated on the fly to steer the allocation of computation jobs to the servers.

Mobility may degrade the performance of next-generation vehicular networks operating at the millimeter-wave spectrum: frequent loss of alignment and blockages require repeated beam training and handover, thus incurring huge overhead. In this paper, an adaptive and joint design of beam training, data transmission and handover is proposed, that exploits the mobility process of mobile users and the dynamics of blockages to optimally trade-off throughput and power consumption. At each time slot, the serving base station decides to perform either beam training, data communication, or handover when blockage is detected. The problem is cast as a partially observable Markov decision process, and solved via an approximate dynamic programming algorithm based on PERSEUS [2]. Numerical results show that the PERSEUS-based policy performs near-optimally, and achieves a 55% gain in spectral efficiency compared to a baseline scheme with periodic beam training. Inspired by its structure, an adaptive heuristic policy is proposed with low computational complexity and small performance degradation.

Here, we present a mobility prediction framework for 5G mobile systems. Our work stems from the intuition that mobility in vehicular networks is highly correlated, and such correlation can be captured by advanced neural network designs to anticipate the users' point of attachment. To prove this, we combine Markov chains with recurrent and convolutional neural networks, training them on mobility trajectories estimated by the received radio signal from mobile millimeter-wave devices. The proposed framework is decentralized, i.e., user trajectories are independently learned by each base station. In this paper, various problems are pragmatically tackled and solved, such as dealing with imbalanced datasets, as some trajectories are under represented, and obtaining a mobility classifier whose accuracy increases as new mobility samples are collected.The proposed technique is assessed using emulated traces obtained through the SUMO mobility simulator for the city of Cologne. Numerical results show accuracies higher than 88% in the prediction of the next serving base station from 4 seconds before the handover is performed. Mobility (next base station) predictors like the ones presented here are key for network management purposes within 5G networks, e.g., to proactively allocate communication and edge computing resources.

Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

Energy Harvesting (EH) and Multi-access Edge Computing (MEC) are here combined to build energy-sustainable mobile networks. We consider an edge infrastructure shared among several mobile operators and equipped with a solar EH farm for energy efficiency purposes together with an edge MEC server for low-latency computation, where two main goals are pursued: (i) to maximally and fairly exploit the available resources at the edge, allotting them among Base Stations (BSs) belonging to different operators; and (ii) to decrease the monetary cost incurred by energy purchases from the power grid. To do so, we devise an online framework combining Artificial Neural Network (ANN)-based pattern forecasting that learns energy harvesting and traffic load profiles over time, and Model Predictive Control (MPC)-based adaptive algorithms. Numerical results, obtained with real-world harvested energy, traffic load, and energy price traces, show that our proposal effectively reduces the amount of purchased energy from the electrical grid by more than 50% with respect to the case where no EH is considered, and by about 30% with respect to the case where the optimization is performed disregarding future energy and traffic load forecasts. Moreover, it is capable of reducing the energy consumption related to edge computation by about 20% with respect to two benchmark policies.

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a technique that supports vital functions through extracorporeal circulation, by raising blood oxygenation, reducing blood carbon dioxide values (CO2), increasing cardiac output and acting on body temperature. In conditions of severe respiratory and/or cardiac insufficiency, it allows to put the heart and lungs at rest by compensating for their ventilatory and pump functions. The correct distribution of blood flows between the upper and lower parts of the body is of considerable importance to avoid the onset of serious diseases in the patient. This study presents a mathematical model for the analysis of blood flows according to the configuration of the connection between the ECMO system and the vascular system.

The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.

The convergence of communication and computing has led to the emergence of multi-access edge computing (MEC), where computing resources (supported by virtual machines (VMs)) are distributed at the edge of the mobile network (MN), i.e., in base stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with energy harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called Energy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and Quality of Service (QoS) provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% to 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

Future vehicular communication networks call for new solutions to support their capacity demands, by leveraging the potential of the millimeter-wave (mm-wave) spectrum. Mobility, in particular, poses severe challenges in their design, and as such shall be accounted for. A key question in mm-wave vehicular networks is how to optimize the trade-off between directive Data Transmission (DT) and directional Beam Training (BT), which enables it. In this paper, learning tools are investigated to optimize this trade-off. In the proposed scenario, a Base Station (BS) uses BT to establish a mm-wave directive link towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off, a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where the system state corresponds to the position of the MU within the road link. The goal is to maximize the number of bits delivered by the BS to the MU over the communication session, under a power constraint. The resulting optimal policies reveal that adaptive BT/DT procedures significantly outperform common-sense heuristic schemes, and that specific mobility features, such as user position estimates, can be effectively used to enhance the overall system performance and optimize the available system resources.

Multi-Access Edge Computing (MEC) is a paradigm for handling delay sensitive services that require ultra-low latency at the access network. With it, computing and communications are performed within one Base Station (BS) site, where the computation resources are in the form of Virtual Machines (VMs) (computer emulators) in the MEC server. MEC and Energy Harvesting (EH) BSs, i.e., BSs equipped with EH equipments, are foreseen as a key towards next generation mobile networks. In fact, EH systems are expected to decrease the energy drained from the electricity grid and facilitate the deployment of BSs in remote places, extending network coverage and making energy self-sufficiency possible in remote/rural sites. In this paper, we propose an online optimization algorithm called ENergy Aware and Adaptive Management (ENAAM), for managing remote BS sites through foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 56% to 66% through the scaling of computing resources, and keeps the server utilization factor between 30% and 96% over time (with an average of 75%). Notable benefits are also found against heuristic energy management techniques.

In this paper, we consider energy cooperation in an Internet of Things (IoT) smart city scenario. We assume the presence of interconnecting energy harvesting IoT gateways (GWs), that are endowed with energy harvesting capabilities and whose role is to collect and aggregate data from field sensing devices. Energy cooperation complements and balances the energetic needs to those devices that are neither connected to the power grid, nor satisfactorily served by energy harvesting due to the instability of ambient energy arrivals. The proposed solution entail energy transfers from energy rich gateways to energy scarce ones, i.e., those which are not connected to the power grid. To identify the optimal energy transfer/allocation scheme, we formulate a convex optimization problem that finds the optimal solution for heterogeneous smart systems. With this energy allocation technique, the gateways are unlikely to run out of energy during operation and the gap between energy offer and demand among interconnected gateways is kept to a minimum. We also quantify the performance of the proposed energy transfer policies as a function of network parameters, including: the amount of traffic generated by sensing devices, the number of smart services in the system, and the number of gateways that are connected to the power grid.

Energy self-sufficiency is of prime importance for future mobile networks. The design of energy efficient and possibly self-sustainable base stations is key to reduce their impact on the environment, and diminish their operating expense. As a solution to this, we advocate base station deployments featuring energy harvesting and storage capabilities. Each base station can acquire energy from the environment, promptly use it to serve the local traffic or keep it in its storage for later use. In addition, a power packet grid (DC power lines and switches) is utilized to enable energy transfer (energy routing) across base stations, compensating for imbalance in the harvested energy or in the load. Most of the base stations are offgrid, i.e., they can only use the locally harvested energy and that transferred from other network elements, whereas some of them are ongrid, i.e., they can also purchase energy from the electrical grid. We formulate the optimal energy allocation and routing as a convex optimization problem with the goals of improving the energy self-sustainability of the network, while achieving high energy transfer efficiencies under dynamic load and energy harvesting processes. An optimal assignment based on the Hungarian method is also presented. Our numerical results reveal that the proposed convex policy: (i) substantially improves the energy self-sustainability of the system, (ii) decreases its outage probability to nearly zero, even when a small number of base stations are connected to the electrical grid, and (iii) the amount of energy purchased from the electrical grid per served user is respectively decreased of three and eight times with respect to using the Hungarian policy and a scenario where the energy exchange among base stations is not permitted.

The Internet of Things (IoT) is being used to monitor a wide range of physical phenomena. In this paper, we are concerned with the extraction of features from the gathered IoT signals and, specifically, with the online estimation of their rate-distortion relationship. This information is in fact key to the configuration and adaptation of data compression and in-network processing protocols and, in turn, is deemed a prime functionality for IoT networks. The point is that lossy compression can be often applied at the sources to save transmission energy, while meeting application requirements in the reconstruction quality. This task is however signal- and time-dependent as different signals are usually characterized by different relations and the signal statistics may also change as a function of time. Here, we first formulate the rate-distortion estimation task, framing it as a classification problem. Hence, we consider the following clustering algorithms from the literature: multilayer perceptron, support vector machine, random forest and linear discriminant analysis, and use them to automatically assess rate-distortion curves in an online fashion and from a small number of signal samples. These algorithms are compared in terms of classification accuracy, training time and memory footprint. Numerical results reveal that, although the problem is inherently complex, the careful combination of feature extraction and classification tools makes it possible to reach high classification accuracies using only a few signal features (e.g., from one to four). The best algorithm (random forest) also entails a short training time and, if properly tuned, has a modest memory footprint.

Wireless Power Transfer (WPT) technology offers unprecedented opportunities to future cellular systems, making it possible to wirelessly recharge the mobile terminals as they get sufficiently close to the Base Stations (BSs). Here, we investigate the tradeoffs involved in the recharging process as multiple mobile users move across the cellular network, by systematically measuring the charging efficiency (i.e., amount of energy transferred as opposed to that transmitted) accounting for different mobility models, speeds, frequency range and inter-BS distance. We consider dense cellular deployments, where power is transferred to the mobile users through beamforming and scheduling techniques. At first, a genie is utilized to devise optimal charging schedules, where user locations and the residual energy in their batteries are exactly known by the controller. Hence, several heuristic policies are proposed and their performance is compared against that of the genie-based approach in terms of transfer efficiency and fraction of dead nodes (whose battery is completely depleted). Our numerical results reveal that: i) an even allocation of resources among users is inefficient, whereas even a rough estimate of their location allows heuristic policies to perform close to the genie-based approach, ii) mobility matters: group mobility leads to higher efficiencies and an increasing speed is also beneficial and iii) WPT can substantially reduce the number of dead nodes in the network, although this comes at the expense of constantly transmitting power and transfer efficiencies are very low under any scenario.

Troubleshooting millimeter-wave (mm-wave) wireless networks is complex due to the directionality of the communication. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and identifying them is crucial to improve network deployments. To this end, access to lower-layer information is important. However, commercial off-the-shelf mm-wave wireless devices typically do not provide such information. Even if they would, detecting effects such as deafness based on information of a single node that forms part of the network is typically hard. In this paper, we present the design and evaluation of an external sniffing device that can infer the aforementioned performance issues only using narrowband physical layer energy traces. Our sniffer does not need to decode any data, resulting in a simple but effective approach which also preserves privacy and works on encrypted networks. Our key contribution is a machine learning framework which enables automated energy trace analysis while coping with the non-stationarity of the traces. We evaluate its performance in practice using off-the-shelf wireless devices operating in the 60 GHz band. Our results show that the above framework correctly infers physical layer events in virtually all cases, thus providing valuable information to troubleshoot issues in mm-wave networks.

Modern wearable IoT devices enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed.

Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN- and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures.

Distributed generators (DGs), coupled with suitable control and communication infrastructures, are expected to play a key role in improving the efficiency of electricity grids. In this paper, we focus on low-voltage and single-phase microgrids exploring the interplay of distributed power loss reduction and communication. We select representative power-loss reduction algorithms from the state of the art and provide design rules for the required networking strategies in the presence of lossy communication links, assessing the impact of communication as well as electrical grid features. Toward this end, we devise a novel statistical cosimulation (electricity grid, communication, and control) framework that faithfully mimics the characteristics of real-world microgrids in terms of communication and grid topologies, power demand, and distributed generation from solar sources. Our numerical results highlight the role of communication procedures and the differences among the selected optimization techniques for power loss reduction, assessing their convergence rate and quantifying the impact of communication failures, line impedance estimation error, communication and electricity grid topologies, network size, and number of DGs.

Recent advances in energy harvesting devices and low-power embedded systems are enabling energetically self-sustainable wireless sensing systems able to sense, process, and wirelessly transmit environmental data. In such systems, energy resources need to be judiciously allocated to processing and transmission tasks to guarantee high-fidelity reconstruction of the phenomenon under observation while keeping the system operational over extended periods of time. Within this context, this paper addresses the problem of designing efficient policies to control the task of lossy data compression for wireless transmission over fading channels in the presence of a stochastic energy input process and a replenishable energy buffer. As a first contribution, the transmission and energy dynamics of a sensor node implementing practical lossy compression methods are modeled as a constrained Markov decision problem (CMDP). Then, an algorithm is designed to derive optimal compression/transmission policies through a Lagrangian relaxation approach combined with a dichotomic search for the Lagrangian multiplier, while also obtaining theoretical results on the optimal policy structure. Furthermore, a thorough numerical evaluation of optimal and heuristic policies is conducted under different scenarios. Finally, the impact of practical operating conditions, including perfect versus delayed channel state information and power control, is evaluated.

Wearable Internet of Things (IoT) devices permit the massive collection of biosignals (e.g., heart-rate, oxygen level, respiration, blood pressure, photo-plethysmographic signal, etc.) at low cost. These, can be used to help address the individual fitness needs of the users and could be exploited within personalized healthcare plans. In this letter, we are concerned with the design of lightweight and efficient algorithms for the lossy compression of these signals. In fact, we underline that compression is a key functionality to improve the lifetime of IoT devices, which are often energy constrained, allowing the optimization of their internal memory space and the efficient transmission of data over their wireless interface. To this end, we advocate the use of autoencoders as an efficient and computationally lightweight means to compress biometric signals. While the presented techniques can be used with any signal showing a certain degree of periodicity, in this letter we apply them to ECG traces, showing quantitative results in terms of compression ratio, reconstruction error and computational complexity. State of the art solutions are also compared with our approach.

Self-sustainability is a crucial step for modern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies that make the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources. Our framework sheds new light into the design of pragmatic schemes for the control of energy-harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity, the harvester size, the information transmission rate, and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state-of-the-art solutions, and describing the impact of relevant design choices in terms of achievable network throughput and battery-level dynamics.

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data-gathering protocol for multihop wireless sensor networks with energy-harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable tradeoff between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost.

Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, we evaluate a number of selected lossy compression methods from the literature and extensively analyze their performance in terms of compression efficiency, computational complexity, and energy consumption. Specifically, we first carry out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and wavelet-based models , by looking at their performance as a function of relevant signal statistics. Second, we obtain formulas through numerical fittings to gauge their overall energy consumption and signal representation accuracy. Third, we evaluate the benefits that lossy compression methods bring about in interference-limited multihop networks, where the channel access is a source of inefficiency due to collisions and transmission scheduling. Our results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay, and increased reliability performance.

In this paper, we present the implementation and performance evaluation of security functionalities at the link layer of IEEE 802.15.4-compliant IoT devices. Specifically, we implement the required encryption and authentication mechanisms entirely in software and as well exploit the hardware ciphers that are made available by our IoT platform. Moreover, we present quantitative results on the memory footprint, the execution time and the energy consumption of selected implementation modes and discuss some relevant tradeoffs. As expected, we find that hardware-based implementations are not only much faster, leading to latencies shorter than two orders of magnitude compared to software-based security suites, but also provide substantial savings in terms of ROM memory occupation, i.e. up to six times, and energy consumption. Furthermore, the addition of hardware-based security support at the link layer only marginally impacts the network lifetime metric, leading to worst-case reductions of just 2% compared to the case where no security is employed. This is due to the fact that energy consumption is dominated by other factors, including the transmission and reception of data packets and the control traffic that is required to maintain the network structures for routing and data collection. On the other hand, entirely software-based implementations are to be avoided as the network lifetime reduction in this case can be as high as 25%.

In this work we present a link abstraction model for the simulation of downlink data transmission in LTE networks. The purpose of this model is to provide an accurate link performance metric at a low computational cost by relying solely on the knowledge of the SINR and of the modulation and coding scheme. To this aim, the model combines Mutual Information-based multi-carrier compression metrics with Link-Level performance curves matching, to obtain lookup tables that express the dependency of the Block Error Rate on the SINR values and on the modulation and coding scheme being used. In addition, we propose a 3GPP-compliant Channel Quality Indicator evaluation procedure, based on the proposed Link Abstraction Model, to be used as part of the LTE Adaptive Modulation and Coding mechanisms. Finally, we discuss how these contributions have been tested, validated and integrated in the ns-3 simulator. The link abstraction model described in this paper has been included in the official ns-3 distribution since release 3.14.

Cooperative routing has been shown to be an effective technique to improve the throughput/delay performance of multi-hop wireless ad hoc networks. In addition, suitable cooperation selection policies also allow for a reduction of the overall energy expenditure. In a previous study, we proposed a centralized algorithm to obtain optimal cooperation selection policies in multi-hop networks with the aim of minimizing a linear combination of energy and delay costs. In this paper, we look at this problem from a different angle, devising three online and fully distributed algorithms which only exploit local interactions for the selection of the cooperators. The first technique selects at each hop a fixed number of nodes having the minimum distance with respect to the destination. The second one adopts a look-ahead strategy, which selects a fixed number of nodes at each hop, according to their expected advancement toward the destination. The third technique utilizes a more refined look-ahead strategy, which dynamically adjusts the number of nodes that cooperate at each hop. Numerical results are thus presented for the proposed techniques, comparing them against the optimal centralized strategy and competing algorithms from the literature. These results indicate that our techniques improve upon existing distributed approaches and achieve close-to-optimal performance.

In this paper we discuss security procedures for constrained IoT devices. We start with the description of a general security architecture along with its basic procedures, then discuss how its elements interact with the constrained communication stack and explore pros and cons of popular security approaches at various layers of the ISO/OSI model. We also discuss a practical example for the establishment of end-to-end secure channels between constrained and unconstrained devices. The proposed method is lightweight and allows the protection of IoT devices through strong encryption and authentication means, so that constrained devices can benefit from the same security functionalities that are typical of unconstrained domains, without however having to execute computationally intensive operations. To make this possible, we advocate using trusted unconstrained nodes for the offloading of computationally intensive tasks. Moreover, our design does not require any modifications to the protocol stacks of unconstrained nodes.

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

In the past few years, a large number of networking protocols for data gathering through aggregation, compression and recovery in Wireless Sensor Networks (WSNs) have utilized the spatio-temporal statistics of real world signals in order to achieve good performance in terms of energy savings and improved signal reconstruction accuracy. However, very little has been said in terms of suitable spatio-temporal models of the signals of interest. These models are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering algorithms. In this paper, we address this gap by devising a mathematical model for real world signals that are correlated in space and time. We thus describe a method to reproduce synthetic signals with tunable correlation characteristics and we verify, through analysis and comparison against large data sets from real world testbeds, that our model is accurate in reproducing the signal statistics of interest.

The main goal of the SWAP project is that of designing, implementing and ultimately testing a new breed of wireless sensor nodes with energy scavenging capabilities. Our design will include novel energy scavenging hardware as well as network protocols and algorithms. In this paper, we summarize the outcomes of the first year of the project as well as the way forward to the further phases. In particular, we analyze the state of the art in the main research areas: energy efficient communication protocol design, ultra-low-power hardware design and most advanced harvesting techniques. For what concerns the future phases of the project we elaborate on the adoption of statistical predictive models for the energy description, we account for game theoretic approaches for distributed optimization and we apply our considerations on the most modern standards for wireless sensor networks communication. We review the state of the art on hardware components, to provide a shortlist of the most efficient building blocks of the SWAP platforms as well as a draft version of the schematics of the module. Finally, we provide a brief overview on the latest energy harvester for miniature scale devices and we argue on the feasibility of a hybrid solar-electromagnetic harvesting module.

In this paper we consider wireless cooperative multihop networks, where nodes that have decoded the message at the previous hop cooperate in the transmission toward the next hop, realizing a distributed space-time coding scheme. Our objective is finding optimal cooperator selection policies for arbitrary topologies with links affected by path loss and multipath fading. To this end, we model the network behavior through a suitable Markov chain and we formulate the cooperator selection process as a stochastic shortest path problem (SSP). Further, we reduce the complexity of the SSP through a novel pruning technique that, starting from the original problem, obtains a reduced Markov chain which is finally embedded into a solver based on focused real time dynamic programming (FRTDP). Our algorithm can find cooperator selection policies for large state spaces and has a bounded (and small) additional cost with respect to that of optimal solutions. Finally, for selected network topologies, we show results which are relevant to the design of practical network protocols and discuss the impact of the set of nodes that are allowed to cooperate at each hop, the optimization criterion and the maximum number of cooperating nodes.

The main contribution of this paper is the implementation and experimental evaluation of a signal reconstruction framework for Wireless Sensor Networks (WSNs). We design WSN-Control, an architecture to control a WSN from an external server connected to the Internet. Within such architecture, we implement a compression and recovery technique that combines Principal Component Analysis (PCA) and Compressive Sensing (CS) to reconstruct signals with many components from a sensor field through the collection of a relatively small number of samples, i.e., through incomplete representations of the actual signal. Overall, our experimental results show that a careful use of CS recovery is effective and can lead to a fully automated system for data gathering and reconstruction of real world and non-stationary signals in WSNs. In detail, WSN-Control effectively recovers signals showing some temporal and/or spatial correlation, from a relatively small number of samples, even below 20%, keeping the relative reconstruction error smaller than 5 · 10-3. Signals with more irregular and quickly varying statistics are also recovered, even though the reconstruction error becomes highly dependent on the number of collected samples. CS minimization is obtained through the recently proposed NESTA optimization algorithm. Our implementation of CS recovery is available.

This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Furthermore, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of fountain codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language. At the end of this paper, the effectiveness of SYNAPSE++ is demonstrated through experimental results over actual multihop deployments, and its performance is compared with that of Deluge, the de facto standard protocol for code dissemination in WSNs. The TinyOS 2 code of SYNAPSE++ is available at http://dgt.dei.unipd.it/download.

In this paper we consider practical dissemination algorithms exploiting network coding for data broadcasting in ad hoc wireless networks. For an efficient design, we analyze issues related to the use of network coding in realistic network scenarios. In detail, we quantify the impact of random access schemes, as used by IEEE 802.11, on the performance of network coding. In such scenarios, deadlock situations may occur where the delivery process stops and some of the nodes never gather the required packets. To tackle this problem, we propose a proactive mechanism (called proactive network coding) which adapts its transmission schedule according to the decoding status of neighboring nodes. This scheme can detect when nodes need additional packets in order to decode and acts accordingly. We finally investigate the behavior of network coding schemes in multi-rate environments, where we propose a distributed heuristic approach for the selection of data rates.

In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

Network coding is a technique which is particularly suitable for the dissemination of data in distributed ad hoc networks. The definition of a mathematical model that describes the interactions among nodes and, in particular, their relationship in terms of buffer subspaces is still an open and challenging problem. The contribution of this paper is an analysis of the relationship between the network topology and the subspace overlap among nodes. This analysis can be used to establish criteria for the design of packet combination policies in diverse networking scenarios. Differently from previous studies, we will explicitly take the overlap among subspaces into account through a framework comprising networks with fixed as well as mobile nodes.

The paper presents an original integrated MAC and routing scheme for wireless sensor networks. Our design objective is to elect the next hop for data forwarding by jointly minimizing the amount of signaling to complete a contention and maximizing the probability of electing the best candidate node. Towards this aim, we represent the suitability of a node to be the relay by means of locally calculated and generic cost metrics. Based on these costs, we analytically model the access selection problem through dynamic programming techniques, which we use to find the optimal access policy. Hence, we propose a contention-based MAC and forwarding technique, called cost and collision minimizing routing (CCMR). This scheme is then thoroughly validated and characterized through analysis, simulation and experimental results.

Wireless reprogramming is a key functionality in wireless sensor networks (WSNs). In fact, the requirements for the network may change in time, or new parameters might have to be loaded to change the behavior of a given protocol. In large scale WSNs it makes economical as well as practical sense to upload the code with the needed functionalities without human intervention, i.e., by means of efficient over the air reprogramming. This poses several challenges as wireless links are affected by errors, data dissemination has to be 100% reliable, and data transmission and recovery schemes are often called to work with a large number of receivers. State-of-the-art protocols, such as Deluge, implement error recovery through the adaptation of standard automatic repeat request (ARQ) techniques. These, however, do not scale well in the presence of channel errors and multiple receivers. In this paper, we present an original reprogramming system for WSNs called SYNAPSE, which we designed to improve the efficiency of the error recovery phase. SYNAPSE features a hybrid ARQ (HARQ) solution where data are encoded prior to transmission and incremental redundancy is used to recover from losses, thus considerably reducing the transmission overhead. For the coding, digital fountain codes were selected as they are rateless and allow for lightweight implementations. In this paper, we design special fountain codes and use them at the heart of SYNAPSE to provide high performance while meeting the requirements of WSNs. Moreover, we present our implementation of SYNAPSE for the Tmote Sky sensor platform and show experimental results, where we compare the performance of SYNAPSE with that of state of the art protocols.

In this paper we study the performance of the ambient networks (AN) access selection architecture. We consider heterogeneous wireless networks, where mobile terminals (MTs) own multiple radio technologies and need to remain connected while on the move. We would like to provide the MTs with seamless IP services, such as video/audio streaming, so that changes in their point of attachment will not affect the experienced streaming quality. In the first part of this paper, we present the AN architecture for access selection, along with its functional entities (FEs), their interrelations and the algorithms that are to be run within each FE. Hence, we describe our ns2 simulation framework and detail two simulation scenarios. We finally discuss, through extensive simulation results, the effectiveness of the AN architecture in reaching the above goals.

In this letter we discuss the exploitation of aggregated mobility patterns in mobile networks including heterogeneous multiple access techniques. We advocate the use of knowledge about neighboring devices to create routing groups (RGs) of adjacent nodes in order to optimize radio resource management. Basically, RGs consist of aggregated logical structures which are built and maintained at the application layer. Their use allows decreased signaling overhead between groups of nodes and access points (AP) and, at the same time, improved connectivity, which is achieved through the exploitation of technology diversity and relaying schemes. We illustrate a simple yet effective analytical model, and validate it through accurate simulation results. Finally, we show the effectiveness of the RG approach in terms of resource efficiency, throughput and multiple access performance.

Abstract This paper explores the logical device aggregation of terminals in future generation networks, where the availability of several different radio access techniques is integrated by means of common radio resource management algorithms. In particular, we investigate the creation of routing groups (RGs) among adjacent nodes, which might be beneficial in order to improve connectivity, decrease signaling overhead and increase transmission efficiency. A simple analytical approach is proposed, which allows the performance evaluation of device aggregation algorithms. We measure the performance of establishing RGs with special focus on two metrics of interest: the connectivity of the nodes and the energy consumption. Within this framework, many detailed insights are obtained and presented throughout the paper. In particular, we focus on the effectiveness of these aggregation techniques in improving network connectivity and on the cost incurred in getting the extra information needed to build and maintain group structures. In the final part of the paper, we provide simulation results which further validate our discussion and highlight additional aspects that are to be considered in real scenarios. Our work is a first step in the investigation of the effectiveness of in‐network aggregation of terminals equipped with multiple radio technologies. The results derived in the paper are encouraging and motivate further research on the topic. Copyright © 2007 John Wiley & Sons, Ltd.

This paper investigates routing strategies for mobile and heterogeneous multi-hop wireless networks. We leverage the knowledge about users mobility to improve the efficiency of route discovery and of the following data forwarding phase. In particular, we exploit group mobility behaviors, which allow us to apply a distributed on-line algorithm for the recognition of aggregated mobility patterns. Hence, we adopt a novel routing strategy that uses the aggregate structure formed within this algorithm to simplify the exchange of signaling and data messages. Finally, we demonstrate and quantify the benefits obtained with the proposed technique by means of a simulator for heterogeneous wireless networks.

In recent years, network coding has proved to be an efficient tool to disseminate data through a network. A number of practical schemes have been proposed to implement network coding also in wireless environments. Most of them are based on reactive and probabilistic random network coding and their effectiveness has been investigated under the assumption of idealized network conditions. However, recent work has shown that the benefits of such strategies decrease when applied in realistic network environments. In this paper, we propose an algorithm to efficiently disseminate data through network coding in realistic wireless networks by using a proactive approach, named ProNC. We develop a distributed and self-adaptable protocol which substantially increases the performance of network coding in practical scenarios and achieves full reliability with both low protocol overhead and low delay. We show the effectiveness of ProNC via ns-2 simulations and compare it with previously proposed schemes.

The present paper investigates practical algorithms to efficiently exploit random network coding for data delivery in multi-hop wireless networks. In the past few years, a great deal of work has been carried out to derive analytical results about network coding. However, only recently have researchers started to utilize the theoretical findings in practical settings. Network coding is a new paradigm for data delivery which proved to be very efficient. It is particularly suitable for wireless networks due to the inherent broadcast nature of the channel. Even though previous work dealt with practical schemes exploiting these new techniques, many issues concerning the coexistence of network coding and channel access mechanisms are still unsolved. In addition, it is still unclear how packets should be combined in order to get the highest benefits in terms of throughput, delay, and energy efficiency. Our work presents an accurate investigation of these aspects. In particular, we couple several MAC and scheduling schemes together with different network coding strategies, and compare them via extensive ns2 simulation. Finally, we propose a new timing strategy for the combination of data packets in random network coding.

The paper presents a novel integrated MAC/routing scheme for wireless sensor networking. Our design objective is to elect the next hop for data forwarding by minimizing the number of messages and, at the same time, maximizing the probability of electing the best candidate node. To this aim, we represent the suitability of a node to act as the relay by means of locally calculated and generic cost metrics. Based on these costs, we analytically model the access selection problem through dynamic programming techniques thereby devising the optimal access policy. We subsequently derive a contention-based MAC and forwarding scheme, named cost and collision minimizing routing (CCMR). Both analytical and simulation results are given to demonstrate the effectiveness of our technique by comparing its performance against state of the art solutions.

Wireless sensor network technologies enable a wide variety of applications (e.g., environmental monitoring). Such sensor networks are often deployed in regions that make it difficult to collect and redistribute the nodes for maintenance. However, there is often a need to reprogram all of the nodes in the network, either during application test phases on deployed networks, or to support software upgrades. Therefore, a reliable method of sending a relatively large amount of data to each node in the network is required to support these functions.

This paper presents integrated MAC/routing solutions for wireless sensor networks. At the MAC layer, every node accesses the channel according to its own cost by means of properly defined cost-dependent access probabilities. Costs are used to capture the suitability of a node to act as the relay and may depend on several factors such as residual energies, link conditions, queue state, etc. Our cost-aware MAC discriminates nodes right in the channel access phase by therefore assisting the forwarding decisions to be made at the routing level. In fact, nodes with high costs are ruled out from channel contention and are not considered when making routing decisions. This provides the routing layer with better relay candidates and, at the same time, decreases the number of in-range devices contending for the channel, thereby reducing interference. The proposed MAC scheme is coupled with routing over hop count (HC) coordinates. To this end, we introduce a set of rules designed to perform HC routing by exploiting first and second order neighborhood information. These are then integrated with our MAC scheme according to a cross-layer approach and their effectiveness is demonstrated by means of analysis and simulation

The paper presents IRIS, an Integrated Routing and Interest dissemination System for wireless sensor networks. The proposed protocols are designed to work under very low duty cycle operations and are jointly optimized for improved efficiency. Routing towards the sink is achieved by exploiting hop count information which is proactively distributed during the interest dissemination phase. Node densities are locally and dynamically estimated at each node and exploited at the MAC layer by means of a cost based probabilistic scheme. A cross-layer routing/MAC scheme is defined where relays to the sink are selected based on nodes' resources (including energy and queue occupancy). The proposed solution is a step towards the definition of complete, self-adapting and autonomous sensor network systems.

The focus of this paper is on routing over heterogeneous networks. We consider a scenario involving both infrastructure and infrastructureless wireless networks, where a set of mobile users are interested in communicating with several access points (APs). Multi-hop routing, possibly over heterogeneous technologies, is exploited to extend the coverage for those users that are not within the transmission range of any APs. We propose a proactive tree-based approach for the dissemination of routing information and the subsequent data forwarding towards the APs. Subsequently, we compare its performance against reactive routing algorithms for ad hoc networks. The network performance is obtained via simulation through careful modeling of the considered radio interfaces. The results indicate the superiority of proactive schemes under moderate/high traffic conditions and motivate further research

In this letter we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the m round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular the packet arrival rate, on the delay statistics

In this paper the packet delay statistics of a fully reliable selective repeat ARQ (SR ARQ) scheme is investigated. An N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be non zero, i.e., ACK/NACK messages are received at the transmitter m channel slots after the packet transmission started. The ARQ packet delay statistics is evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, procedures to derive a Markov channel description of a Rayleigh fading process are discussed and the delay statistics obtained from the Markov analysis is compared with that estimated by simulation of the SR ARQ protocol over the actual fading process. The accuracy of the delay statistics obtained from the Markov Channel representation of the actual fading process is investigated by explicitly addressing the effect of the number of states considered in the Markov channel model and the impact of the Doppler frequency. Finally, besides giving a new analysis to obtain link layer statistics over N-state Markov channels, the paper provides important considerations on the adequacy of the widely used Markov modeling approach for the characterization of higher layer performance

In this paper we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular of the packet arrival rate, on the delay statistics

In this paper we focus on routing strategies for wireless sensor networks over hop count (HC) virtual coordinates. We consider the problem of optimally delivering data packets by means of multi-hop forwarding techniques where we assume that each node in the network, upon the execution of a proper distribution algorithm, can obtain a hop count number, i.e., the minimum number of transmissions needed to get to the sink (destination) node on the shortest path. We exploit HCs in place of commonly considered geographical coordinates as a valuable indication of the direction towards the sink. Within this framework, we present localized greedy routing schemes and compare them against globally optimal solutions, where the objective is to minimize a properly defined cost function. Further, we present novel routing algorithms where the statistical knowledge of the minimum costs of second order (two hops away) neighboring nodes is used as an aid to drive the forwarding process. These statistically enhanced schemes are found to outperform both hop count greedy approaches and geographical routing of up to one order of magnitude in terms of goodness of the selected path

In this paper, we propose a novel technique for the provisioning of multicast streaming flows in 3G W-CDMA cellular systems. Our focus here is on the transmission of a downlink multicast streaming flow to the interested users in a 3G cell. For error control, we propose packet-based forward error correction (FEC) and with the transmission of a certain amount of redundancy over parallel common channels. In practice, we exploit the temporal diversity over multiple channels to strengthen the FEC scheme thereby increasing the QoS. To increase error resilience, in every channel we exploit well-known packet-based Reed Solomon like coding techniques. Moreover, appropriate time shifts of the information sent across the parallel common channels are also introduced to increase the robustness against error bursts. The performance evaluation is carried out through an analytical framework. The obtained results show the substantial benefits deriving from the usage of a multiple channel transmission technique.

The packet delay statistics of a fully reliable selective repeat (SR) ARQ scheme are investigated. A N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be finite, i.e., ACK/NACK messages are received at the transmitted m channel slots after the packet transmission is started. The ARQ packet delay statistics are evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, a procedure to derive a Markov channel description of a Rayleigh fading process is presented and the delay statistics obtained from the Markovian analysis is compared with those estimated by simulation of the ARQ protocol over the actual fading process. Finally, some discussion on the accuracy of the delay statistics obtained from the Markov channel representation of the actual fading process is reported.

We introduce error control algorithms for multicast delivery in 3G cellular networks. For efficiency reasons, the delivery of multicast flows is usually achieved by allocating a common channel in the forward direction. This enables multiple users to be served by a single physical resource. However, different users are affected by independent channel error processes and new techniques, different from plain ARQ, have to be found to perform error recovery. These new algorithms are needed to deliver the multicast flow in an efficient manner and to enable a reliable, performant and network operator inexpensive, multicast service. Different hybrid ARQ algorithms for the error recovery of multicast flows over common channels are proposed and their performance is evaluated both analytically and by simulation. The proposed solutions have been found to be effective and advantageous over plain ARQ techniques. Results on the achievable video quality are reported for the multicast video streaming case by considering the H.263 video coding format.

The success of 3G networks depends on the possibility to attract customers to this new technology. Therefore, new services using the spectrum in an efficient manner while satisfying the customers are needed. We introduce link layer algorithms that are suitable for multicast transmission in 3G cellular systems and we present their impact on video application in terms of play-out buffer requirements. By our approach we show that hybrid ARQ solutions can be successfully employed to perform error control in the multicast transmission case. Using these schemes, performance can be increased thereby increasing system capacity and lowering the cost per served user. In the final part of the paper, these solutions are extended for the important multicast video streaming case, where new schemes are devised to avoid the throughput inefficiencies of fully reliable error recovery algorithms.

This paper presents a performance enhancing transport architecture for the satellite environment. This solution improves the network transport performance by overcoming the limits imposed by a transmission control protocol/Internet protocol (TCP/IP)-based stack suite, while maintaining the interfaces offered by it. This is an important issue since TCP/IP is widely used and most of the applications are based on it. The work starts from the state-of-the-art about the transport layer over satellite by distinguishing two alternative frameworks: the black box (BB) and the complete knowledge (CK) approaches. In the former, the network is considered as a "black box" and only modifications in the terminal tools are permitted. In the latter, the complete control of any network element is allowed so as a performance optimization procedure is possible. The proposed architecture [called Performance Enhancing Transport Architecture (PETRA)] is designed in all details using the second approach. PETRA uses network elements, called relay entities, to isolate the satellite portions in case of heterogeneous networks, while a transport layer protocol stack is used to optimize the transport of information over satellite links. A special satellite transport protocol, that is part of the transport layer protocol stack, is used over such links to perform error recovery. Simulation results show that the proposed framework significantly enhances throughput performance.

In this paper, we present an accurate analytical model for transport control protocol (TCP) over correlated channels (e.g., as induced by multipath fading) taking into account a finite round-trip delay. In particular, we develop models and analysis for studying four versions of TCP, namely, Old Tahoe, Tahoe, Reno, and New Reno. We focus on a single wireless TCP connection by modeling the correlated packet loss/error process as a Discrete Time first-order Markov chain. Our model explicitly incorporates important aspects such as slow start, congestion avoidance, fast retransmit and fast recovery. The main findings of this study are that: 1) an increasing round-trip time may significantly affect the throughput performance of TCP, especially when an independent channel is considered; 2) New Reno performs better than Reno and Tahoe when the channel is uncorrelated, whereas Tahoe's recovery strategy is the most efficient when the channel correlation is high; and 3) the maximum window size does not play a determinant role in increasing throughput performance in both correlated and independent channels. While some of these conclusions confirm what other authors have observed in simulation studies, our analytical approach sheds some new light on TCP's behavior.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. It is assumed that the sender continuously transmits packets whose error process is characterized by means of a two-state discrete time Markov channel. At the receiver these packets are checked for errors and ACK/NACK messages (assumed error-free) are sent back to the sender accordingly. The feedback message is known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been developed in order to find the exact statistics of the delays experienced by ARQ packets after their first transmission.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. It is assumed that the feedback message is known with no errors at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find exact statistics of the delays experienced by ARQ packets. In this work, in order to reduce the computational complexity of such analysis, an appropriate model is presented. The results obtained from the approximate approach are shown to be in excellent agreement with the ones derived from the exact analysis.

In this paper, we investigate the delay statistics of an aggregate of fully reliable selective-repeat ARQ packets. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. No errors are accounted for in the reverse channel. The feedback message is assumed to be known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find the exact statistics of the delays experienced by ARQ packets. This work presents an extension of the analysis that computes the delay statistics of an aggregate of ARQ packets. This is achieved without increasing the model complexity and allows useful considerations from the point-of-view of higher level protocols.

We review a multicast mobility support for cellular systems acting in a transparent way. The support is by means of multicast agents placed at each base station. This architecture makes a prediction about user movements and performs a resource reservation in advance, where the multicast tree creation is made before the radio handover. We propose a simple analytical model useful to compute the two basic performance indexes: the handover latency and the resource utilization time. This analysis has been used to set the optimal threshold parameters involved in the multicast and radio handovers.

Two TCP/IP header compression (HC) schemes for wireless networks are proposed and investigated on a WCDMA air interface. These novel schemes are compared with the classical ones by means of a TCP/IP New Reno simulator taking as input WCDMA channel traces. These channel traces are derived by using a simulator built according to the UMTS standard requirements. The RLC (radio link control) level operates in transparent mode, while overhead is added to simulate the presence of a MAC level. Some new performance metrics are introduced to evaluate the performance of HC schemes.

Abstract In this paper, we present a study on the performance of TCP, in terms of both throughput and energy consumption, in the presence of a wideband CDMA radio interface typical of third generation wireless systems. The results show that the relationship between throughput and average error rate is largely independent of the network load, making it possible to introduce a universal throughput curve, empirically characterized, which gives throughput predictions for each value of the user error probability. Furthermore, the study of the energy efficiency shows the possibility to select an optimal power control threshold to maximize the trade‐off between throughput and energy, thereby potentially achieving very significant energy gains. Copyright © 2001 John Wiley & Sons, Ltd.

In this paper we propose a mobility support for multicast systems in a radio environment. This mobility support permits to a multicast mobile host (MMH) to maintain the connection to a multicast group, when it is moving from a network to another, with low connection loss occurrence or group joint delay. With this aim, an agent-based architecture has been designed and implemented. The mobility support acts in a transparent way, without any changes in MMH software, in network protocols and in multicast system applications. This architecture makes predictions about MMH movement and performs a reservation of resource in advance, by means of a re-creation in advance of the multicast tree. The effectiveness of our proposal has been tested with simulation test fields.