Machine learning algorithms for wireless sensor networks: A survey

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Secure Data Storage and Searching for Industrial IoT by Integrating Fog Computing and Cloud Computing

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

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. This 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. This paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs.

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A Critical Analysis of Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks

A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications

One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.

A structure fidelity approach for big data collection in wireless sensor networks.

In wireless sensor networks (WSNs), due to the restriction of scarce energy, it remains an open challenge how to schedule the data communications between the sensor nodes and the sink to reduce power usage with the aim of maximizing the network lifetime. To face this challenge, this paper proposes a workable data communication scheme utilizing the hierarchical Least-Mean-Square (HLMS) adaptive filter. The HLMS predicting techniques are explored that predict the measured values both at the source and at the sink, sensor nodes are subsequently required only to send those readings that deviate from the prediction by an error budget. Such data reduction strategy achieves significant power savings by reducing the amount of data sent by each node. We discuss the working mechanism of HLMS in the purpose of data reduction in WSNs, analyze the mean-squared error in the two level HLMS, and design the interactive HLMS prediction algorithm implemented at sink and sensor node and the transmission protocol between them. To elaborate on our theoretical proposal, the HLMS algorithms and protocols are then evaluated by simulation. Simulation results show that our proposed scheme achieves major improvement in convergence speed compared with previous approaches, and achieves up to 95% communication reduction for the temperature measurements acquired at Intel Berkeley lab while maintaining a minimal accuracy of 0.3 °C.

Data Reduction in Wireless Sensor Networks: A Hierarchical LMS Prediction Approach

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment The effectiveness of energy–accuracy tradeoff is also validated

Adaptive Range-Based Target Localization Using Diffusion Gauss–Newton Method in Industrial Environments

Least mean squares (LMS) adaptive algorithms are attractive for distributed environment parameter estimation problems in a smart city due to the benefits of cooperation, adaptation, and rapid convergence. To obtain a reliable estimate of the network-wide parameter vector, local results can be further fused by intermediate agents in a distributed incremental way. In this paper, we propose an intelligent variable step size incremental LMS (VSS-ILMS) algorithm to solve the dilemma between fast convergence rate and low mean-square deviation (MSD) in conventional incremental LMS (ILMS) algorithms. The main idea behind our proposal is that the local step-size is adaptively updated by minimizing the MSD in every iteration, where Tikhonov regularization and time-averaging estimation methods are adopted. A theoretical analysis of proposed algorithm is presented in terms of mean square performance and mean step size in a closed form. Simulation results show that VSS-ILMS algorithm outperforms the constant step size ILMS algorithm and several classical variable step-size LMS algorithms. The derived theoretical results shows good agreement with those based on simulated data. For a practical consideration, the proposed algorithm is also verified by the model of target localization in sensor networks.

Mou Wu

Papers

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

A structure fidelity approach for big data collection in wireless sensor networks.

Data Reduction in Wireless Sensor Networks: A Hierarchical LMS Prediction Approach

Adaptive Range-Based Target Localization Using Diffusion Gauss–Newton Method in Industrial Environments

An Intelligent Adaptive Algorithm for Environment Parameter Estimation in Smart Cities