Abstract Future Internet of Things (IoT) applications will require that billions of wireless devices transmit data to the cloud frequently. However, the wireless medium access is pointed as a problem for the next generations of wireless networks; hence, the number of data transmissions in Wireless Sensor Networks (WSNs) can quickly become a bottleneck, disrupting the exponential growth in the number of interconnected devices, sensors, and amount of produced data. Therefore, keeping a low number of data transmissions is critical to incorporate new sensor nodes and measure a great variety of parameters in future generations of WSNs. Thanks to the high accuracy and low complexity of state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are potential candidates to optimize the data transmissions in WSNs at the finest level because they facilitate for sensor nodes to avoid unnecessary transmissions without affecting the quality of their measurements. In this work, we present a sensor network model that uses statistical theorems to describe the expected impact of DPSs and data aggregation in WSNs. We aim to provide a foundation for future works by characterizing the theoretical gains of processing data in sensors and conditioning its transmission to the predictions’ accuracy. Our simulation results show that the number of transmissions can be reduced by almost 98 in the sensor nodes with the highest workload. We also detail the impact of predicting and aggregating transmissions according to the parameters that can be observed in common scenarios, such as sensor nodes’ transmission ranges, the correlation between measurements of different sensors, and the period between two consecutive measurements in a sensor.
The works above inspired our model for data transmissions. However, different from our proposal, they focused only on data aggregation and did not evaluate the effects of DPS in WSN . Concerning the evaluation of DPS , were the first authors to introduce statistical methods to choose which prediction model better fits a certain environment @cite_28 . They created a formula to estimate the Prediction Cost ( @math ), which considers the percentage of transmitted measurements ( @math ) and the user desired level of accuracy ( @math ) @cite_28 . More recently, an extended formula was designed and implemented in real sensor nodes to compare the savings using several prediction methods, such as the , ES and ARIMA methods @cite_31 @cite_24 . The new formula is more generic than the original proposed and also considers the prediction models' memory footprint ( @math ) as a significant computational cost for sensor nodes:
@cite_28 @cite_31 @cite_24
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In this paper, a framework for dynamic high-dimensional hypothesis testing in wireless sensor networks is presented. The sensor nodes (SNs) collect and transmit to a fusion center (FC), in a distributed fashion, compressed measurements of a time-correlated hypothesis vector. The FC, based on the measurements collected, tracks the hypothesis vector, and feeds back minimal information about the uncertainty in the current estimate, which enables adaptation of the SNs' data collection and transmission strategy. The policy of the SNs is optimized with the overall objective of minimizing the detection error probability, under sensing and transmission cost constraints incurred by each SN. A Bernoulli approximation on the detection error is employed, which enables a significant reduction in the optimization complexity and the design of scalable estimators based on sparse approximation recovery algorithms. Simulation results demonstrate that, for a target 5 detection error, the adaptive scheme attains 90 and 50 cost savings with respect to a memoryless scheme which does not exploit the time-correlation and a non-adaptive one, respectively. We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and or quantization noise. In particular, we introduce a decentralized scheme for least-squares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms. Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach. This two-part paper presents a feedback-based cross-layer framework for distributed sensing and estimation of a dynamic process by a wireless sensor network (WSN). Sensor nodes wirelessly communicate measurements to the fusion center (FC). Cross-layer factors such as packet collisions and the sensing-transmission costs are considered. Each SN adapts its sensing-transmission action based on its own local observation quality and the estimation quality feedback from the FC under cost constraints for each SN. In this second part, low-complexity myopic sensing-transmission policies (MPs) are designed to optimize a trade-off between performance and the cost incurred by each SN. The MP is computed in closed form for a coordinated scheme, whereas an iterative algorithm is presented for a decentralized one, which converges to a local optimum. The MP dictates that, when the estimation quality is poor, only the best SNs activate, otherwise all SNs remain idle to preserve energy. For both schemes, the threshold on the estimation quality below which the SNs remain idle is derived in closed form, and is shown to be independent of the number of channels. It is also proved that a single channel suffices for severely energy constrained WSNs. The proposed MPs are shown to yield near-optimal performance with respect to the optimal policy of Part I, also in this issue, at a fraction of the complexity, thus being more suitable for practical WSN deployments.