Large-scale Sensor Networks Research Articles

A Multi-Featured Actor-Critic Relay Selection Scheme for Large-Scale Energy Harvesting WSNs

In this letter, we focus on the design of a relay selection scheme in large-scale energy-harvesting wireless sensor networks. Considering the dynamic nature required for practical networks, multiple features, including queuing state, energy level, channel quality, and location, are involved in modeling. By introducing reinforcement learning techniques, a novel relay selection scheme based on an actor-critic algorithm with linear function approximation is proposed to improve network reliability while also taking into account the transmission delay and energy efficiency. The proposed scheme can be implemented independently in each source to maximize the data delivery ratio. Such a distributed scheme is more stable and scalable than centralized structures. The Lagrangian formula is applied to satisfy the constraints on hops and energy efficiency. Compared with traditional timer-based and Q-learning-based schemes, our simulation results show that our proposed scheme achieves good performance in terms of network reliability and obtains higher energy efficiency and lower transmission delay.

Scalable asynchronous localization algorithm with mobility prediction for underwater wireless sensor networks

With the exploration of ocean resources, underwater wireless sensor networks have become a new research focus. Localization is one of most important technologies in the application of underwater wireless sensor networks. Because of the complex of underwater environment, it is difficult to realize the precisely synchronous localization in the underwater wireless sensor networks especially the large-scale networks with mobility. To solve this problem, in this paper, we propose a scalable asynchronous localization algorithm with mobility prediction. In this localization algorithm, the large-scale underwater wireless sensor network architecture is designed. Considering the node mobility, the node motion model is built based on the tidal mobility. By analyzing the asynchronous communication between the anchor nodes and ordinary nodes, the initial position of ordinary nodes is obtained and the future position of ordinary nodes is predicted and updated. The precisely located ordinary nodes are upgraded to new reference nodes and the new reference nodes locate the other unknown ordinary nodes together with the anchor nodes until the localization tasks of all ordinary nodes are completed. Simulation results confirm that the proposed localization algorithm can achieve accurate localization of mobile nodes in large-scale underwater wireless sensor networks with asynchronous clock.

Confident Information Coverage Hole Prediction and Repairing for Healthcare Big Data Collection in Large-Scale Hybrid Wireless Sensor Networks

In the Internet of Things (IoT) for smart healthcare applications, sensors collect a vast amount of healthcare data, while coverage significantly affects the Quality of Service (QoS). In wireless sensor networks (WSNs), the QoS as well as the network lifetime are dramatically degraded with the increment of coverage holes, especially in large-scale hybrid WSNs (LS-HWSNs) where big data are collected by thousands of sensors distributed in a wide monitored area. In a LS-HWSN, two crucial problems, i.e., covering the wide area without coverage holes and designing an energy-efficient manner for dispatching mobile sensors to repair coverage holes, need to be solved. We study the problems from the cutting point of confident information coverage hole repairing (CICHR). To this end, based on the confident information coverage (CIC) model, a CIC hole predicting (CICHP) algorithm, centralized energy-efficient repairing (CEER) algorithm, and distributed energy-efficient repairing (DEER) algorithm are developed. The CICHP algorithm can predict the prior information of CIC holes (CICHs) by using the period-by-period energy consumption information of sensor nodes. Based on the prior information of CICHs, two repairing algorithms: 1) CEER and 2) DEER can schedule mobile sensors to repair CICHs beforehand. Simulation results show that the proposed algorithms can significantly improve the QoS and extend the network lifetime of LS-HWSNs.

Source localization in large-scale asynchronous sensor networks

The problem of uncooperative source localization and synchronization in asynchronous sensor networks has been considered previously in a centralized manner, where all the raw measurements are delivered to a processing center. However, for large-scale sensor networks the transmission of raw measurements over the networks requires considerable communication overhead, besides being vulnerable to transmission failure and interference, and the computational load at the processing center is rather heavy. A more suitable candidate to this problem is the distributed estimation considered in this paper, where each sensor node communicates with its neighboring nodes only, and the parameters are estimated at each sensor via information cooperation. In order to decouple the unknown source positions and sensor clock offsets, we propose to update them iteratively based on the belief propagation (BP) framework. To reduce the complexity of parameter update at each sensor, the sigma point based estimation method is adopted. Theoretical analyses concerning the computational complexity and communication overhead are carried out. Simulation results demonstrate that the proposed method could estimate the source positions distributively with much lower complexity and communication overhead compared with the centralized methods at the cost of acceptable performance degradation.

A Robust Energy Optimization and Data Reduction Scheme for IoT Based Indoor Environments Using Local Processing Framework

The extensive growth in popularity of Internet of Things (IoT) has led to the generation of massive amount of data from several heterogeneous sensory devices. This has also led to the increase in energy consumption by these connected devices. Smart buildings are one such platform which are equipped with several micro-controllers and sensors, generating a huge amount of redundant information at their data acquisition level. As a result, real-time applications may not be efficiently executed due to latency delays at the cloud service end. This requires several devices at cloud service end to execute the massive amount of data generated by these sensors, which does not satisfy green computing criteria. In this context, a novel local processing mechanism (LPM) is proposed, which favors an improved IoT service architecture for smart buildings. From the perspective of green computing, the proposed LPM framework facilitates reduction of manifolds at data acquisition level of sensor nodes. This paper also addresses the concept of optimal use of sensors in a wireless sensor network (WSN) and estimates costs corresponding to non-Poisson and Poisson arrival of data packets at local processor using the well-known queuing model. We also provide an efficient algorithm for smart buildings using our expert Markov switching (EMS) model, which is a well known probabilistic model in the field of artificial intelligence (AI) for subjectively validating real sensory data sets (viz., temperature, pressure, and humidity). Further, it has been analyzed that the proposed EMS algorithm outperforms several other algorithms conventionally used for determining the state of large-scale dynamic sensor networks. The service cost of proposed model has been compared with conventional model under various stress conditions viz., arrival rate, service rate, and number of clusters. It is observed that the proposed model operates well by leveraging green computing criteria. Thus, in the aforementioned context, this paper provides thing-centric, data-centric, and service-oriented IoT architecture.

Localization of Networks on 3D Terrain Surfaces

The majority of current research on sensor network localization focuses on wireless sensor networks deployed on two-dimensional (2D) plane or in three-dimensional (3D) space, very few on the 3D terrain surface. However, many real-world applications require large-scale sensor networks deployed on the surface of a complex 3D terrain. Compared with planar and 3D network localization, terrain surface network localization generates unique and fundamental hardness. We explore 3D surface network localization with terrain models. A digital terrain model (DTM), available to the public with a variable resolution of up to one meter, is a 3D representation of a terrain's surface. It is commonly built using remote sensing technology or from land surveying and can be easily converted to a triangular mesh. Given a sensor network deployed on the surface of a 3D terrain with one-hop distance information available, we can extract a triangular mesh from the connectivity graph of the network. The constraint that the sensors must be on the known 3D terrain's surface ensures that the triangular meshes of the network and the terrain's surface overlap and approximate the same geometric shape. The basic idea of the localization algorithms is to map the two triangular meshes extracted from the connectivity graph of a sensor network and the DTM of its deployed terrain surface to the plane. The two meshes mapped to the plane can be easily aligned if the location information of anchor nodes is available. We introduce a fully distributed algorithm to construct a well-aligned mapping between the two triangular meshes in the plane based on anchor nodes information. However, accidents may happen on anchor nodes. We then introduce an anchor-free algorithm to extract feature points with geometric properties intrinsic to surface distances and independent of the embedding of the two meshes in 3D. The matched feature points induce transformations to align the two meshes in the plane. With the aligned triangular meshes of a network and its deployed terrain surface, each sensor node of the network can easily locate reference grid points from the DTM of the terrain to calculate its own geographic location. We carry out extensive simulations under various scenarios to evaluate the overall performance of the proposed algorithms with different factors such as the one-hop distance measurement error, the resolution of a DTM, and the performance of the algorithm in the situation of connectivity only.

A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks.

Distributed Direct Position Determination

The direct position determination (DPD) algorithms have been shown to outperform conventional two-step methods in localization accuracy under poor observation conditions (e.g., low signal-to-noise ratios or short signal observation durations). In this paper, we consider the distributed DPD of a static source in large-scale sensor networks. Different from the centralized DPD (CDPD) algorithms where all the raw signal samples are transmitted globally to a fusion center to solve for the source position, in our method the signal samples are only exchanged locally between neighboring sensors, and the source position is estimated using the proposed DPD algorithm distributively. The problem-specific cost function, which is different from the cost function of CDPD, is first derived. To obtain the cost function at each sensor, we propose to transmit the low-dimensional sufficient statistics across the sensor networks instead of the original high-dimensional raw signal samples, and to reconstruct the cost function via simple interpolations. Simplification of cost function evaluation is also developed to further reduce complexity. Finally, source position is estimated at each sensor efficiently via the importance sampling algorithm. Simulation results demonstrate the superior performance of the proposed method over existing adaptive distributed DPD as well as the two-step algorithms under poor observation conditions, and its computational efficiency is significantly improved compared with the CDPD.

A Mobile-Beacon-Based Iterative Localization Mechanism in Large-Scale Underwater Acoustic Sensor Networks

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Efficient scheduling of a mobile charger in large-scale sensor networks

Schedule a mobile charger to replenish energy to sensor nodes for the wireless sensor networks has attracted great attention recently, due to its efficiency and flexibility. Some existing works study the mobile charger scheduling problem by considering that only the depot can recharge or replace the battery for the mobile charger. However, for large-scale wireless sensor networks, the mobile charger is energy inefficient or even may run out of energy during the travel for charging. In this paper, we consider the scenario that there are some service stations in the network area which can be used to replace the battery for the mobile charger, and we study the problem of minimizing the number of used batteries for a mobile charger to charge a wireless sensor network (MBA). We first consider a special case of the MBA problem, in which the depot is the only service station, and we present an approximation algorithm to address it. Then we propose an approximation algorithm for the MBA problem with the assumption that the distance of any two service stations is limited. And finally, we consider the general MBA problem and propose an approximation algorithm. We validate the performance of our algorithms by extensive simulations, and the results show that our proposed algorithms are promising.

The AirSensor open-source R-package and DataViewer web application for interpreting community data collected by low-cost sensor networks

While large-scale low-cost sensor networks are now recording air pollutant concentrations at finer spatial and temporal scales than previously measured, the large environmental data sets generated by these sensor networks can become overwhelming when considering the scientific skills required to analyze the data and generate interpretable results. This paper summarizes the development of an open-source R package (AirSensor) and interactive web application (DataViewer) designed to address the environmental data science challenges of visualizing and understanding local air quality conditions with community networks of low-cost air quality sensors. AirSensor allows users to access historical data, add spatial metadata, and create maps and plots for viewing community monitoring data. The DataViewer application was developed to incorporate the functionality and plotting functions of the R package into a user-friendly web experience that would serve as the primary source for data communication for community-based organizations and citizen scientists.

A comprehensive review of deep learning applications in hydrology and water resources.

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

Multilinear Plus Sparse Based Tensor Completion for Long-Term Operating Large-Scale and Heterogeneous Sensor Networks

Data integrity and correctness are essential for many internet of things applications. This work addresses the data completion problem for long-term operating, large-scale and heterogeneous wireless sensor networks (WSNs), using tensor completion technique. In most previous works, the data models only involve single temporal and single spatial dimensions, ignoring the higher-order intrinsic structures. Besides, most existing schemes use low-rankness and sparsity in a fixed domain to describe the data correlation, but neither can fit well with all the temporal, spatial and attribute correlation. To address these issues, we first propose a higher-order tensor model for data from WSNs, namely multilinear plus sparse (MPS) model, which has multiple temporal, multiple spatial and one attribute modes. The core idea is to describe the temporal-spatial-attribute correlation by the sparsity in the multilinear transform domain. Appropriate sparsifying transforms can be flexibly determined according to the specific natures of each mode. For the temporal and spatial modes, analytical transforms are adopted considering computational efficiency and addressing continuity feature. For the attribute mode, we utilize MPS to propose a robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm based principle component analysis (robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA) algorithm to adaptively learn the sparsifying transform for various heterogeneous networks. Finally, based on MPS and the learned transform, we develop an efficient tensor completion algorithm using alternating direction method of multipliers, namely MPS-TC. The experimental results on real-life WSN data verify that the proposed robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA algorithm can learn an attribute transform more robustly than the conventional PCA and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PCA algorithms; and the proposed MPS-TC algorithm outperforms the state-of-the-art tensor completion algorithms in terms of signal-to-noise ratio and root mean square error.

Krill herd based TSP approach for mobile sink path optimization in large scale wireless sensor networks

In wireless sensor networks (WSN), the establishment of large-scale sensor networks has always needed attention. One of the many challenges is to set up an architecture that is different from the rest and find mechanisms that can efficiently scale up with the growing number of nodes that may be ess ential to ensure sufficient coverage of large areas under study. Concurrently, these new architectures and mechanisms are supposed to maintain low power consumption per node to comply with energy guaranty acceptable network lifetime. The researchers utilized numerous Data collection techniques for the prompt data aggregation, yet still those outcomes the node with path failures. To solve this issue, the mobile sink is being extensively used for data aggregation in large scale wireless sensor networks (WSNs). This technique avoids imbalances in energy consumption due to multi-hop transmission but might lead to extended delay time. In this paper, our focus is on shortening the length of the mobile sink’s travelling path to reduce the delay time during data gathering in large scale WSN. To achieve this, the mobile sink visits the cluster heads in an optimized path instead of sensors one by one. Here Hierarchical clusters are efficiently formed by modified K- means with outlier elimination and node proximity and residual energy based second level clustering algorithm. Next, we determine the optimal path for the mobile sink by formulating KH based Travelling Salesman Problem solving optimization algorithm. This technique proposed reduces not only the length of the path travelled by the mobile sink but also lessens the computational effort that is required for travelling-path planning and enhances the lifetime of nodes. And to ensure aggregation accuracy in cluster heads iterative filtering is implemented. Our experimental results show the proposed algorithm shortens the tour length by 40–60 percent compared to Bacterial foraging optimization-based TSP algorithm. Also delivers better results compared to other’s in terms of the computational effort, time, energy use, and enhances the network lifetime.

Performance evaluation of CoAP proxy virtualisation in cloud‐assisted sensor networks

In this study, the authors have modelled the performance of a scalable proxy virtualisation framework to support an appropriate level of quality of service (QoS) for delay sensitive applications in cloud-based large-scale sensor networks. The proposed model leverages the functionalities of a constrained application protocol (CoAP) to expose a standard interface for transparently decoupling of the applications from servers and ensuring the implementation of the custom QoS metrics through virtualisation techniques. The model, combined with the prediction-based dynamic resource allocation approach, enables the computation of the number of requests that can be supported for a given service rate and required QoS. It also features the evaluation of different performance metrics, particularly, the probability distribution of the response time and the ability to identify the optimal balance between the resource cost and the service performance. The numerical results obtained from the proposed analytical model can be used to help design and optimise industrial cloud-based sensing systems.

Data Collection Method of Large Scale WSNS Mobile Node Based on Compressed Sensing and Intelligent Optimization

Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency, a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed. Firstly, by analyzing the relationship between the network partitioning and the node deployment, an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection. Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay. Finally, the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data, and reduce network traffic and network task delay, and better balance network node energy consumption.

Guided Identification of Failed Nodes Based on Failure Presentation

Sensor nodes and Internet of Things devices may experience long-term loss of functional roles. The objective of this article is to efficiently identify the failed sensors in a large-scale sensor network. The problem addressed is overcoming information constraints in identifying the location of a failed sensor using only local interactions between low-cost sensors and a network maintainer. The proposed method in this article allows the sensors to self-organize into a functional structure in which a supporting sensor set facilitates the autonomous maintainer (AM) to find the failed sensors. The supporting sensor set presents the failure information to the AM, which then rescales its search area for quick discovery of the failed sensor. Both a qualitative analysis and simulation evaluation are conducted to verify the effectiveness of the proposed approach. The results show that an appropriate presenting area of the failed sensor facilitates fast and localized discovery of failed sensors by the AM.

Analytic Sensor Rules for Optimal Distributed Decision Given $K$-Out-of-$L$ Fusion Rule Under Monte Carlo Approximation

With the development of large-scale sensor networks, there is an increasing interest for high dimensional distributed decision fusion problem in statistical decision, machine learning, control theory, etc. In this article, we investigate the optimal sensor quantized rules of the distributed decision under a given fusion rule for conditionally dependent and high dimensional sensor observations. We provide a Monte Carlo importance sampling method to reduce the computational complexity of the distributed decision fusion. For the K-out-of-L rule, we derive a group of analytic sensor rules based on the necessary and sufficient condition of the optimal sensor rules. In term of efficiency, it is significantly better than the traditional iterative search algorithms, such as the Riemann sum approximation iteration algorithm. The analytic solution is also a general result since it does not rely on the specific K value of the K-out-of-L rule, the specific conditional probability density function and the dimension of sensor observations. Thus, it can be applied to multisensor decision fusion problems with high dimensional dependent sensor observations. For the general fusion rules rather than the K-out-of-L rule, the Monte Carlo Gauss-Seidel algorithm is also provided. The numerical examples demonstrate the effectiveness of the analytic solution and the Monte Carlo Gauss-Seidel algorithm.

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks.

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime.

Research on Cost-Balanced Mobile Energy Replenishment Strategy for Wireless Rechargeable Sensor Networks

In order to maximize the utilization rate of the Mobile Wireless Chargers (MWCs) and reduce the recharging delay in large-scale Rechargeable Wireless Sensor Networks (WRSNs), a type of Cost-Balanced Mobile Energy Replenishment Strategy (CBMERS) is proposed in this paper. Firstly, nodes are assigned into groups according to their remaining lifetime, which ensures that only the ones with lower residual energy are recharged in each time slot. Then, to balance energy consumption among multiple MWCs, the moving distance as well as the power cost of the MWC are taken as constraints to get the optimal trajectory allocation scheme. Moreover, by further adjusting the amount of energy being replenished to some sensor nodes, it ensures that the MWC have enough energy to fulfill the recharging task and return back to the base station. Experiment results show that, compared with the Periodic recharging strategy and the Cluster based Multiple Charges Coordination algorithm (C-MCC), the proposed method can improve the recharging efficiency of MWCs by about 48.22% and 43.35%, and the average waiting time of nodes is also reduced by about 55.72% and 30.7%, respectively.