Energy-aware Sparse Sensing of Spatial-temporally Correlated Random Fields

This dissertation focuses on the development of theories and practices of energy aware sparse sensing schemes of random fields that are correlated in the space and/or time domains. The objective of sparse sensing is to reduce the number of sensing samples in the space and/or time domains, thus reduce the energy consumption and complexity of the sensing system. Both centralized and decentralized sensing schemes are considered in this dissertation. Firstly we study the problem of energy efficient Level set estimation (LSE) of random fields correlated in time and/or space under a total power constraint. We consider uniform sampling schemes of a sensing system with a single sensor and a linear sensor network with sensors distributed uniformly on a line where sensors employ a fixed sampling rate to minimize the LSE error probability in the long term. The exact analytical cost functions and their respective upper bounds of these sampling schemes are developed by using an optimum thresholding-based LSE algorithm. The design parameters of these sampling schemes are optimized by minimizing their respective cost functions. With the analytical results, we can identify the optimum sampling period and/or node distance that can minimize the LSE error probability. Secondly we propose active sparse sensing schemes with LSE of a spatial-temporally correlated random field by using a limited number of spatially distributed sensors. In these schemes a central controller is designed to dynamically select a limited number of sensing locations according to the information revealed from past measurements,and the objective is to minimize the expected level set estimation error.The expected estimation error probability is explicitly expressed as a function of the selected sensing locations, and the results are used to formulate the optimal sensing location selection problem as a combinatorial problem. Two low complexity greedy algorithms are developed by using analytical upper bounds of the expected estimation error probability. Lastly we study the distributed estimations of a spatially correlated random field with decentralized wireless sensor networks (WSNs). We propose a distributed iterative estimation algorithm that defines the procedures for both information propagation and local estimation in each iteration. The key parameters of the algorithm, including an edge weight matrix and a sample weight matrix, are designed by following the asymptotically optimum criteria. It is shown that the asymptotically optimum performance can be achieved by distributively projecting the measurement samples into a subspace related to the covariance matrices of data and noise samples

Energy and relevance-aware adaptive monitoring method for wireless sensor nodes with hard energy constraints

© 2024 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Traditional dynamic energy management methods optimize the energy usage in wireless sensor nodes adjusting their behavior to the operating conditions. However, this comes at the cost of losing the predictability in the operation of the sensor nodes. This loss of predictability is particularly problematic for the battery life, as it determines when the nodes need to be serviced. In this paper, we propose an energy and relevance-aware monitoring method, which leverages the principles of self-awareness to address this challenge. On one hand, the relevance-aware behavior optimizes how the monitoring efforts are allocated to maximize the monitoring accuracy; while on the other hand, the power-aware behavior adjusts the overall energy consumption of the node to achieve the target battery life. The proposed method is able to balance both behaviors so as to achieve the target battery life, at the same time is able to exploit variations in the collected data to maximize the monitoring accuracy. Furthermore, the proposed method coordinates two different adaptive schemes, a dynamic sampling period scheme, and a dual prediction scheme, to adjust the behavior of the sensor node. The evaluation results show that the proposed method consistently meets its battery lifetime goal, even when the operating conditions are artificially changed, and is able to improve the mean square error of the collected signal by up to 20% with respect to the same method with the relevance-aware behavior disabled, and of up to 16% with respect the same algorithm with just the adaptive sampling period or the dual prediction scheme enabled. Consequently showing the ability of the proposed method of making appropriate decisions to balance the competing interest of its two behaviors and coordinate the two adaptive schemes to improve their performance.This study was supported by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR 2019 DI 075 to David Arnaiz). The founder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep-convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep-convection parameterization and the target model without deep-convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that the total mass, heat and momentum are conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep-convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread, leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded

Deterministic scheduling for energy efficient and reliable communication in heterogeneous sensing environments in industrial wireless sensor networks

The present-day industries incorporate many applications, and complex processes, hence, a large number of sensors with dissimilar process deadlines and sensor update frequencies will be in place. This paper presents a scheduling algorithm, which takes into account the varying deadlines of the sensors connected to the cluster-head, and formulates a static schedule for Time Division Multiple Access (TDMA) based communication. The scheme uses IEEE802.15.4e superframe as a baseline and proposes a new superframe structure. For evaluation purposes the update frequencies of different industrial processes are considered. The scheduling algorithm is evaluated under varying network loads by increasing the number of nodes affiliated to a cluster-head. The static schedule generated by the scheduling algorithm offers reduced energy consumption, improved reliability, efficient network load management and improved information to control bits ratio

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs