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.

Connected vehicles promise to enable a wide range of new automotive services that will improve road safety, ease traffic management, and make the overall travel experience more enjoyable. However, they also open significant new surfaces for attacks on the electronics that control most of modern vehicle operations. In particular, the emergence of vehicle-to-vehicle (V2V) communication risks to lay fertile ground for self-propagating mobile malware that targets automobile environments. In this work, we perform a first study on the dynamics of vehicular malware epidemics in a large-scale road network, and unveil how a reasonably fast worm can easily infect thousands of vehicles in minutes. We determine how such dynamics are affected by a number of parameters, including the diffusion of the vulnerability, the penetration ratio and range of the V2V communication technology, or the worm self-propagation mechanism. We also propose a simple yet very effective numerical model of the worm spreading process, and prove it to be able to mimic the results of computationally expensive network simulations. Finally, we leverage the model to characterize the dangerousness of the geographical location where the worm is first injected, as well as for efficient containment of the epidemics through the cellular network.

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.

The real-world node deployment aspect is investigated, while considering cost minimization for resolving the energy hole around the sink, which represents a serious problem in typical sensor networks with uniform distribution. A novel strategy is proposed that is based on the use of two sinks and a few extra relay nodes close to the sinks' areas. The traffic is then alternatively sent to the sinks in every other cycle. As a second contribution, an efficient data collection mechanism has been developed to determine the optimal data rate that meets delay requirements of individual sensor reports and improves the network lifetime. The comparison of the proposed node deployment strategy with uniform, non-uniform geometric and linear increase node distributions demonstrates that the cost of the proposed solution is very close to that of the uniform distribution and much lower than all the others, while achieving a load balancing at the same order of the state-of-the-art solutions.

The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset is publicly available today that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region. In this paper, we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400-km2region around the city of Köln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.

We present Duo-MAC, an asynchronous cascading wake-up scheduled MAC protocol for heterogeneous traffic forwarding in low-power wireless networks. Duo-MAC deals with energy-delay minimization problem and copes with transmission latency encountered by Today's duty-cycled protocols when forwarding heterogeneous traffic types. It switches, according to the energy and delay requirements, between Low Duty cycle (LDC) and High Duty Cycle (HDC) operating modes, and it quietly adjusts the wake-up schedule of a node according to (i) its parent's wake-up time and (ii) its estimated load, using an effective real-time signal processing linear traffic estimator. As a second contribution, Duo-MAC, proposes a service differentiation through an improved contention window adaptation algorithm to meet delay requirements of heterogeneous traffic classes. Duo-MAC's efficiency stems from balancing between the two traffic award operation modes. Implementation and experimentation of Duo-MAC on a MicaZ mote platform reveals that the protocol outperforms other state-of-the-art MAC protocols from the energy-delay minimization perspective.

The large-scale adoption of vehicle-to-vehicle (V2V) communication technologies risks to significantly widen the attack surface available to mobile malware targeting critical automobile operations. Given that outbreaks of vehicular computer worms self-propagating through V2V links could pose a significant threat to road traffic safety, it is important to understand the dynamics of such epidemics and to prepare adequate countermeasures. In this paper we perform a comprehensive characterization of the infection process of variously behaving vehicular worms on a road traffic scenario of unprecedented scale and heterogeneity. We then propose a simple yet effective data-driven model of the worm epidemics, and we show how it can be leveraged for smart patching infected vehicles through the cellular network in presence of a vehicular worm outbreak.

We consider a complex (i.e., nonlinear) road scenario where users aboard vehicles equipped with communication interfaces are interested in downloading large files from road-side Access Points (APs). We investigate the possibility of exploiting opportunistic encounters among mobile nodes so to augment the transfer rate experienced by vehicular downloaders. To that end, we devise solutions for the selection of carriers and data chunks at the APs, and evaluate them in real-world road topologies, under different AP deployment strategies. Through extensive simulations, we show that carry&forward transfers can significantly increase the download rate of vehicular users in urban/suburban environments, and that such a result holds throughout diverse mobility scenarios, AP placements and network loads.

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.

Clustering techniques are proposed for developing performance of wireless sensor networks from several points of view. Multimedia nodes have different sensing region than scalar sensors that usually sense a circle or polygon regions centred by the node, and thus traditional clustering methods do not satisfy a Wireless Multimedia Sensor Network (WMSN). In this paper, a novel formation algorithm, Multi-Cluster-Membership (MCM), for multimedia node clustering is proposed. The proposed algorithm establishes clusters overlapping each other and the common members of clusters are coordinated to make cooperation among clusters. Therefore, MCM offers not only intra-cluster cooperation but also inter-cluster cooperation through coordinating members and also clusters. This differs from other coordinating ways in the fact that nodes may participate sensing in multiple clusters. We show the enhancement in power conservation efficiency of network resulted from applying MCM with respect the Single-Cluster-Membership (SCM) algorithm.

Wireless Multimedia Sensor nodes sense areas that are uncorrelated to the areas covered by radio neighbor sensors. Thus, node clustering for coordinating multimedia sensing and processing cannot be based on classical sensor clustering algorithms. This paper presents a clustering mechanism for Wireless Multimedia Sensor Networks based on overlapped Field of View (FoV) areas. Today, for random deployments, dense networks of low cost, low resolution and low power multimedia nodes are preferred than sparse cases of high cost, high resolution and high power nodes. Overlapping FoVs in dense networks causes wasting power of system because of redundant sensing of area. The main aim of the proposed clustering method is energy conservation and prolonging network lifetime. This aim is achieved through coordination of nodes belonging to the same cluster in assigned tasks, avoiding redundant sensing or processing.

A critical aspect of applications with wireless sensor networks is network lifetime. Sensing and communications consume energy particularly in wireless multimedia sensor networks (WMSN) due to huge amount of data generated by the multimedia sensors. Therefore, judicious power management and sensor scheduling can effectively extend network lifetime. In this paper we consider the problem of scheduling multimedia sensor activities to maximize network lifetime. The environment is divided in domains monitored by clusters of multimedia sensor nodes. Network lifetime increment is achieved by cooperation between multimedia sensors in two priority-based ways: Intra-cluster cooperation and Inter-cluster cooperation. We will see that the lifetime of cluster nodes is considerably increased under the proposed node selection and scheduling procedures. As for big clusters, the lifetime even is prolonged to 5.5 times with respect to the ordinary un-cooperative node awakening.

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.

We target urban scenarios where vehicular users can download large files from road-side access points (APs), and define a framework to exploit opportunistic encounters between mobile nodes to increase their transfer rate. We first devise a technique for APs deployment, based on vehicular traffic flows analysis, which fosters cooperative download. Then, we propose and evaluate different algorithms for carriers selection and chunk scheduling in carry&forward data transfers. Results obtained under realistic road topology and vehicular mobility conditions show that coupling our APs deployment scheme with probabilistic carriers selection and redundant chunk scheduling yields a worst-case 2x gain in the average download rate with respect to direct download, as well as a 10x reduction in the rate of undelivered chunks with respect to a blind carry&forward.

We consider that a given number of dissemination points (DPs) have to be deployed for disseminating information to vehicles travelling in an urban area. We formulate our problem as a maximum coverage problem (MCP) so as to maximize the number of vehicles that get in contact with the DPs and as a second step with a sufficient amount of time. Since the MCP is NP-hard, we solve it though heuristic algorithms. Evaluation of the proposed solutions in a realistic urban environment shows how knowledge of vehicular mobility plays a major role in achieving an optimal coverage of mobile users, and that simple heuristics provide near-optimal results even in large-scale scenarios.

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.