The rapid implementation of Internet of Things (IoT) technologies in industrial and air quality monitoring applications has resulted in large amounts of data being acquired. In addition, the integration of the artificial intelligence of things (AIoT) enables decision-making at edge nodes, involving the execution of data-driven models close to the data source. Data loss has become a limiting factor in AI-based applications. One way to address missing data is to leverage data from co-existing sensors, which are potentially correlated, to impute missing values. This article evaluates a set of missing value imputation (MVI) techniques in an edge node environment, where constraints on data availability and computational complexity must be met. Specifically, models such as multiple imputation by chained equations (MICE), k-nearest neighbors (KNN), and models using variational autoencoders (VAE) are evaluated. A comprehensive evaluation is presented covering different scenarios of missing data in terms of the percentage of missing values, bursts of missing values, and the size of the data windows stored on edge nodes. This evaluation uses real sensor data from an air quality monitoring network and an industrial sensor network. The results show that the VAE is able to obtain good imputation performances (R 2 greater than 0.90) while providing good uncertainty quantification (UQ) estimates with around 90%–95% of samples falling within the estimated confidence intervals. Moreover, a transfer learning-based VAE has been shown to adapt to the non-stationary nature of IoT sensing signals, and all methods have proven to be efficient in terms of execution time for the edge setting. • Missing value imputation for edge IoT applications. • Window-based models for the edge. • Variational autoencoders (VAE) for imputing missing values in IoT. • Uncertainty quantification (UQ) using heteroscedastic VAEs. • Transfer learning-based VAE for imputation in the edge.

The use of low-cost sensors (LCS) in Internet of Things (IoT) networks offers a promising way to improve air quality monitoring. However, there is a major concern regarding their long-term accuracy due to continuous data drift, which requires frequent data recalibration. To address this, we present window-based uncertainty drift detection and recalibration (W-UDDR), a unified system that automates the entire process. Our system uses a Bayesian approach with Gaussian processes (GP) to automatically and accurately detect when sensor output needs to be corrected. To achieve this, the uncertainty of the estimates is quantified using predictive confidence intervals alongside a window system that detects the need for recalibration in real time. We validate our approach using an air quality real-world sensor deployment, systematically assessing key performance metrics such as the frequency of recalibration and the required sample size. Our results show that W-UDDR successfully triggers automatic events when it detects drifts, achieving significant long-term accuracy improvements ranging from 40% to 95%. Hence, this tool provides an automated real-time mechanism to facilitate long-term sensor deployment maintenance.

Recently, the use of Internet of Things (IoT) technologies has been widely adopted, enabling large-scale monitoring and data acquisition. Together with the so-called artificial intelligence of things (AIoT), data-driven models are to be executed close to the data source. Nevertheless, one potential problem is the quality of the data used to feed these models. Data loss is a common issue in these systems and can hinder the use of monitoring data and the application of artificial intelligence (AI) models. This paper evaluates a set of missing value imputation techniques, emphasizing their application in an edge setting. Specifically, offline and sliding window variants are evaluated to meet data availability and computational complexity criteria. The benchmarking has been performed using the data of a real IoT air quality monitoring platform. The results demonstrate that the windowed variants can approach the offline performance using reduced sliding windows of 25 to 100 samples per sensor.

A digital twin (DT) is a virtual representation of a physical object or system created using data obtained through numerical simulation or sensor measurements from Internet of Things (IoT) technologies, among others. These measurements allow the virtual model to be specific to a particular object or system, enabling diagnosis, predictive, and prescriptive maintenance. In this paper, we present the development of a DT for an IoT monitoring network with applications in Industry 5.0. To achieve this, we employ reduced-order models (ROM) based on the proper orthogonality decomposition (POD) technique to reconstruct a field of interest, e.g., temperature in a cold room or on a pig livestock farming, from a reduced set of optimally placed sensors. The results demonstrate that optimal placement can achieve low reconstruction errors even when only a few sensors are deployed - for example, three or four.

Intelligent computing at the IoT edge allows tasks that are normally performed in the cloud to be performed at the IoT node, enabling low-latency applications and more efficient control and management of services. Among the tasks that can be performed on the IoT edge is improving data quality, such as outlier detection. This task is challenging because IoT nodes have fewer computational and storage resources than the cloud. In this paper, we propose an outlier detection framework adapted to IoT edge nodes that is lightweight, consumes few resources, adapts to changes in signal trends, and has the ability to provide real-time responses. The framework presented is based on the use of two windows. A sliding window for the current data, which captures the short-term changes in the signal, and a window that stores a summary of historical values, which captures whether the values are within the long-term range of the signal. We show how models using two windows reduce the number of false positives compared to models using only one window. A version for near real-time applications is also proposed, which improves detection by identifying trend changes in the signal at the cost of delaying the decision by a few samples.

The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect of the use of sensed data in decision-making processes, digital twins , and other applications. Emphasis has been placed on the development of machine learning (ML) and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology . Many technologies such as graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. This survey focuses on graph-based models for data quality control in monitoring sensor networks. In addition, it introduces the technical details that are commonly used to provide powerful graph-based solutions for data quality tasks in sensor networks, such as missing value imputation, outlier detection , or virtual sensing. To conclude, different challenges and emerging trends have been identified, e.g., graph-based models for digital twins or model transferability and generalization.

The introduction of high- and low-cost Internet of Things (IoT) sensors in air quality monitoring networks, in addition to providing a cost-effective solution for monitoring pollutant levels, also brings with it the challenge of ensuring data reliability. These sensors can present anomalies in the data and identifying them is a challenging task. In this article, we propose a spatiotemporal correlation recurrent autoencoder anomaly detection (STC-RAAD) architecture that unlike the other existing architectures, in addition to the temporal correlation present in the sensor data, also involves information from the neighboring sensors in the monitoring network for better reconstruction. The performance of STC-RAAD is compared with the other methods on two different real data sets, for two categories of anomalies, outperforming the other existing approaches with an average increase of 38% for the detection rate and 36% for the precision.

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from neighboring sensors is a key technique for maintaining network data quality, with examples including filling in missing data with correlated neighboring nodes, creating virtual sensors, or correcting a drifting sensor with neighboring sensors that are more accurate. This article proposes a signal reconstruction framework for air pollution monitoring data where a graph signal reconstruction model is superimposed on a graph learned from the data. Different graph signal reconstruction methods are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that the scalability of the framework can be improved with simple methods, such as partitioning the network using a clustering algorithm.

Existing air pollution monitoring networks use reference stations as the main nodes. The addition of low-cost sensors calibrated in-situ with machine learning techniques allows the creation of heterogeneous air pollution monitoring networks. However, current monitoring networks or calibration techniques have limitations in estimating missing data, adding virtual sensors or recalibrating sensors. The use of graphs to represent structured data is an emerging area of research that allows the use of powerful techniques to process and analyze data for air pollution monitoring networks. In this article, we compare two techniques that rely on structured data, one based on statistical methods and the other on signal smoothness, with a baseline technique based on the distance between nodes and that does not rely on the measured signal data. To compare these techniques, the sensor signal is reconstructed with a supervised method based on linear regression and a semisupervised method based on Laplacian interpolation, which allows reconstruction even when data is missing. The results, on data sets measuring O3, NO2, and PM10, show that the signal smoothness-based technique behaves better than the other two, and used together with the Laplacian interpolation is near optimal with respect to the linear regression method. Moreover, in the case of heterogeneous networks, the results show a reconstruction accuracy similar to the in-situ calibrated sensors. Thus, the use of the network data increases the robustness of the network against possible sensor failures.

This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models.

This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O3). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal-oxide O3sensors, 25 electro-chemical O3sensors, 25 electro-chemical NO2sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.

The remarkable advances in sensing and communication technologies have introduced increasingly low-cost, smart and portable sensors that can be embedded everywhere and play an important role in environmental sensing applications such as air quality monitoring. These user-friendly wireless sensor platforms enable assessment of human exposure to air pollution through observations at high spatial resolution in near-realtime, thus providing new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. However, data quality from such platforms is a concern since sensing hardware of such devices is generally characterized by a reduced accuracy, precision, and reliability. Achieving good data quality and maintaining error free measurements during the whole system lifetime is challenging. Over time, sensors become subject to several sources of unknown and uncontrollable faulty data which comprise the accuracy of the measurements and yield observations far from the expected values. This paper investigates calibration of low-cost air quality sensors in a real sensor network deployment. The approach leverages on the availability of sensor arrays in a wireless node to estimate parameters that minimize the calibration error using fusion of data from multiple sensors. The obtained results were encouraging and show the effectiveness of the approach compared to a single sensor calibration.

A constrained relay placement problem satisfying application requirements in terms of network lifetime and end-to-end (e2e) delay in Wireless Sensor Networks (WSN) is investigated in this paper. The network and the traffic are adequately modeled considering uniform node deployment and low data rate periodic traffic generation. An optimization problem is defined to obtain the minimum number of relays to be deployed, at each level of the network, in order to fulfil network duty-cycle and e2e delay constraints under anycast forwarding based on the wake-up period parameter of the duty-cycle MAC protocol. Since the optimization problem is non-convex, an alternative and efficient algorithm for relay node placement called EDC-RP (Energy-Delay Constrained Relay Placement) is introduced. The comparison of the proposed node deployment strategy with state-of-the-art relay placement methods demonstrates the gain of the heuristic in terms of deployment cost (number of relays) over other solutions while fulfilling the application constraints.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy-constrained, delay-sensitive wireless sensor networks is a conflicting multiobjective optimization problem. We investigate the problem from a game theory perspective, where the two optimization objectives are considered as game players. The cost model of each player is mapped through a generalized optimization framework onto protocol-specific MAC parameters. From the optimization framework, a game is first defined by the Nash bargaining solution (NBS) to assure energy consumption and e2e delay balancing. Secondy, the Kalai-Smorodinsky bargaining solution (KSBS) is used to find an equal proportion of gain between players. Both methods offer a bargaining solution to the duty-cycle MAC protocol under different axioms. As a result, given the two performance requirements (i.e., the maximum latency tolerated by the application and the initial energy budget of nodes), the proposed framework allows to set tunable system parameters to reach a fair equilibrium point that dually minimizes the system latency and energy consumption. For illustration, this formulation is applied to six state-of-the-art wireless sensor network (WSN) MAC protocols: B-MAC, X-MAC, RI-MAC, SMAC, DMAC, and LMAC. The article shows the effectiveness and scalability of such a framework in optimizing protocol parameters that achieve a fair energy-delay performance trade-off under the application requirements.

Optimizing energy consumption and end-to-end (e2e) packet delay in energy constrained distributed wireless networks is a conflicting multi-objective optimization problem. This paper investigates this trade-off from a game-theoretic perspective, where the two optimization objectives are considered as virtual game players that attempt to optimize their utility values. The cost model of each player is mapped through a generalized optimization framework onto protocol specific MAC parameters. A cooperative game is then defined, in which the Nash Bargaining solution assures the balance between energy consumption and e2e packet delay. For illustration, this formulation is applied to three state-of-the-art wireless sensor network MAC protocols; X-MAC, DMAC, and LMAC as representatives of preamble sampling, slotted contention-based, and frame-based MAC categories, respectively. The paper shows the effectiveness of such framework in optimizing protocol parameters for achieving a fair energy-delay performance trade-off, under the application requirements in terms of initial energy budget and maximum e2e packet delay. The proposed framework is scalable with the increase in the number of nodes, as the players represent the optimization metrics instead of nodes.

As the evolution of multi-core multi-threaded processors continues, the complexity demanded to perform an extensive trade-off analysis, increases proportionally. Cycle-accurate or trace-driven simulators are too slow to execute the large amount of experiments required to obtain indicative results. To achieve a thorough analysis of the system, software benchmarks or traces are required. In many cases when an analysis is needed most, during the earlier stages of the processor design, benchmarks or traces are not available. Analytical models overcome these limitations but do not provide the fine grain details needed for a deep analysis of these architectures. In this work we present a new methodology to abstract processor architectures, at a level between cycle-accurate and analytical simulators. To apply our methodology we use queueing modeling techniques. Thus, we introduce Q-MAS, a queueing based tool targeting a real chip (the Ultra SPARC T2 processor) and aimed at facilitating the quantification of trade-offs during the design phase of multi-core multi-threaded processor architectures. The results demonstrate that Q-MAS, the tool that we developed, provides accurate results very close to the actual hardware, with a minimal cost of running what-if scenarios.

Wireless nodes such as smart-phones in which the WiFi wireless card is continuously on, consume battery energy in just a few hours. Moreover, in many scenarios, an always-on wireless card is useless because there is often no need for transmission and/or reception. This fact is exacerbated in Delay/Disruptive Tolerant Network (DTN) environments, in which nodes exchange Delay Tolerant Objects (DTO) when they meet. Power Saving Management (PSM) techniques enable the lifetime of the nodes to be extended. This paper analyses the trade-offs that appear when wireless nodes periodically turn off the wireless card in order to save battery in DTN environments. The paper shows the conditions in which a node can switch off the battery without impacting the peer-to-peer contact probability, and those in which this contact probability is decreased. For example, it is shown that node lifetime can be doubled while keeping the peer-to-peer contact probability equal to one. But, further increase of the node lifetime quickly decreases peer-to-peer contact probability. Finally, the impact of power savings in DTO dissemination time is also analyzed.

Vehicular Ad Hoc Networks are networks characterized by intermittent connectivity and rapid changes in their topology. This paper addresses car-to-road communications in which vehicles use Access Points (AP) in a Delay Tolerant Network architecture. Results show how the combination of a Delay-Cooperative ARQ mechanism reduces packet losses and in conjunction with a Carry-and-Forward cooperative mechanism improves performance parameters in terms of total file transfer delay and number of AP needed to download files.

This paper proposes a low coordination overhead cooperative automatic repeat request (ARQ) scheme with an integrated frame combiner, which exploits space diversity and cooperation between neighbouring nodes. In channels with a strong line of sight (LOS) component and low signal-to- noise ratio (SNR), the maximum achievable throughput of the proposed protocol is many times higher than for other ARQ schemes. For non LOS scenarios, the cooperative ARQ without frame combiner achieves the best efficiency results, and the overhead introduced by the frame combiner mechanisms leads to results which can be even below the classical ARQ mechanism.

We address the design of high-speed packet buffers for Internet routers. We use a general DRAM/SRAM architecture for which previous proposals can be seen as particular cases. For this architecture, large SRAMs are needed to sustain high line rates and a large number of interfaces. A novel algorithm for DRAM bank allocation is presented that reduces the SRAM size requirements of previously proposed schemes by almost an order of magnitude, without having memory fragmentation problems. A technological evaluation shows that our design can support thousands of queues for line rates up to 160 Gbps.

The explosive and robust growth of the Internet owes a lot to the "end-to-end principle", which pushes stateful operations to the end-points. The Internet grew both in traffic volume, and in the richness of the applications it supports. The growth also brought along new security issues and network monitoring applications. Edge devices, in particular, tend to perform upper layer packet processing. A whole new class of applications require stateful processing.In this paper we study the impact of stateful networking applications on architectural bottlenecks. The analysis covers applications with a variety of statefulness levels. The study emphasizes the data cache behavior. Nevertheless, we also discuss other issues, such as branch prediction and ILP. Additionally, we analyze the architectural impact through the TCP connection life. Our results show an important memory bottleneck due to maintaining the states. Moreover, depending on the target of the application, the memory bottleneck may be concentrated within a set of packets or distributed along the TCP connection lifetime.

In this paper we study the properties of a new packet trace compression method based on clustering of TCP flows. With our proposed method, the compression ratio that we achieve is around 3%, reducing the file size, for instance, from 100 MB to 3 MB. Although this specification defines a lossy compressed data format, it preserves important statistical properties present into original trace. In order to validate the method, memory performance studies were done with the Radix Tree algorithm executing a trace generated by our method. To give support to these studies, measurements were taken of memory access and cache miss ratio. For the time, the results have showed that our proposed method provides a good solution for packet trace compression

In this paper, we address the design of a future high-speed router that supports line rates as high as OC-3072 (160 Gb/s), around one hundred ports and several service classes. Building such a high-speed router would raise many technological problems, one of them being the packet buffer design, mainly because in router design it is important to provide worst-case bandwidth guarantees and not just average-case optimizations. A previous packet buffer design provides worst-case bandwidth guarantees by using a hybrid SRAM/DRAM approach. Next-generation routers need to support hundreds of interfaces (i.e., ports and service classes). Unfortunately, high bandwidth for hundreds of interfaces requires the previous design to use large SRAMs which become a bandwidth bottleneck. The key observation we make is that the SRAM size is proportional to the DRAM access time but we can reduce the effective DRAM access time by overlapping multiple accesses to different banks, allowing us to reduce the SRAM size. The key challenge is that to keep the worst-case bandwidth guarantees, we need to guarantee that there are no bank conflicts while the accesses are in flight. We guarantee bank conflicts by reordering the DRAM requests using a modern issue-queue-like mechanism. Because our design may lead to fragmentation of memory across packet buffer queues, we propose to share the DRAM space among multiple queues by renaming the queue slots. To the best of our knowledge, the design proposed in this paper is the fastest buffer design using commodity DRAM to be published to date.

We can define a software router as a general-purpose computer that executes a computer program capable of forwarding IP datagrams among network interface cards attached to its I/O bus. This paper presents a parametrical model of a PC based IP software router. Validation results clearly show that the model accurately estimates the performance of the modeled system at different levels of detail. On the other hand, the paper presents experimental results that provide insights about the detailed functioning of such a system and demonstrate the model is valid not only for the characterized systems but for a reasonably range of CPU, memory and I/O bus operation speeds.

We study tree networks of discrete-time queues loaded with periodic traffic sources. By using the so-called Benes method, exact closed-form expressions are obtained for the queue length distributions. The models developed can be used to study the superposition of periodic sources emitting bursts of cells in ATM networks. The results obtained show the significant effect that this kind of traffic can have on the performance of these systems.

This paper determines packet level performance measures of a MAC protocol for a wireless ATM local area network. A key characteristic of the MAC protocol is the identifier splitting algorithm with polling, a contention resolution scheme used to inform the base station about the bandwidth needs of a mobile station when no piggybacking can be used. We consider higher layer packets that are generated at the mobile station and investigate the influence of the traffic characteristics of the packet arrival process on the efficiency of the protocol and on the delay that packets experience to access the shared medium.