Method For Sensor Networks Research Articles

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis

In order to solve the abnormal behaviors in wireless sensor networks, such as attacks, intrusions, node failures, and data anomalies, a data anomaly detection method for sensor networks based on DNN algorithm and cluster analysis is proposed. The deep neural network is introduced into the wireless sensor network, and each wireless sensor data is described by the neuron to construct the neural network element model. The traditional neural network model is improved, and the neural network model of the wireless sensor is used to realize the fusion and extraction of the data collected by the wireless sensor network. The clustering technology is used to complete the abnormal data judgment of the nodes, and at the same time, the spatial correlation of the sensing data between neighboring nodes is used to filter the noise data, extract the abnormal event information, and assist the system decision-making. The results show that when the number of neighbor nodes increases, the number of nodes with similar physical locations in the optimal neighbor node set gradually increases, and the accuracy rate is also continuously improved. However, if too many nodes are selected, the algorithm will use the data of nodes with a longer physical distance to vote, which will lead to an increase in the error rate. Therefore, when determining the number of neighbor nodes, 25%-30% of the total number of nodes in the positioning scenario can be selected. Therefore, the algorithm can effectively detect and distinguish environmental noise and abnormal events in the network.

An experimental evaluation of localization methods used in wireless sensor networks

<span class="fontstyle0">The problem of localization in wireless sensor networks has received considerable attention from researchers over the past decades. Several methods and algorithms have been proposed to solve this problem. The effectiveness of these algorithms depends on the accuracy of the estimated positions and the information required to calculate the coordinates. In this paper, we propose to evaluate four of the most commonly used localization methods in sensor networks. Our study considers a mathematical description of the studied methods in order to evaluate their complexity, and then a practical implementation on the simulation tool Cooja. We evaluate the performance of the studied methods as a function of the number of deployed sensor nodes and their degree of mobility in terms of several performance metrics. The objective is to reveal the most suitable localization method for a particular case of deployment. Improvement proposals are also provided to improve the most relevant localization method for the investigated study.</span> <br /><br />

Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things

The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%–30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%–50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm’s target loss rate and tracking errors are about 0~10% and 1.5%~2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking.

A nonlinear outlier detection method in sensor networks based on the coordinate mapping

This paper designs a nonlinear outliers detection method based on the coordinate mapping. Because data in different coordinate systems have specific attributes, the data coordinates in different coordinate systems can be transformed by coordinate mapping. Then the stream data features in a sensor network can be extracted accurately by principal component analysis to improve the detection accuracy of abnormal data points. Clustering of convection data features is implemented to shorten the time of subsequent detection by rapidly classifying data. Finally, the difference point factor is used to detect the nonlinear outliers in the sensor network. Experimental results show that the maximum detection accuracy of this method can reach 97%, the maximum detection time required is only 15 s, and the maximum miss rate of this method is 1.32%, indicating that this method can effectively detect nonlinear anomaly points.

A nonlinear outlier detection method in sensor networks based on the coordinate mapping

A nonlinear outlier detection method in sensor networks based on the coordinate mapping

Intelligent data processing methods in sensor networks of mobile and autonomous objects

In the near term, tasks related to monitoring mobile geographically distributed objects with the aim of efficiently managing them are acquiring great importance. An example of such tasks is the monitoring the state of given number of vehicles of agricultural enterprise, when it becomes necessary to promptly transfer given amount of data with high requirements for the level of their reliability. The use of the infrastructure of cellular mobile networks for this is not suitable for a number of reasons for rural areas. The paper proposes an approach based on the use of high-speed dynamic sensor networks. This makes it possible to carry out relay transmission of data between objects, including mobile ones. However, the issues of building flexible data transfer protocols in networks of this kind, providing a significant data transfer rate and taking into account the priority of the transmitted information and its features, still remain open. At the same time, for mobile objects in conditions of non-stationarity of the parameters of communication channels, the problems of ensuring the required reliability of data are of particular importance. To solve the last problem, it is proposed to use the permutation decoding method based on the cognitive metaphor.

Tracking System and Its Application in Unmanned Automobile Navigation Based on Sparse Photoelectric Sensor Network

Sensor tracking technology has broad prospects of application in the fields of smart home and environmental protection. The passive motion tracking method of sensor networks can realize the perception of location, temperature and other information without carrying sensor nodes. A sparse network tracking system based on infrared sensor nodes is proposed in this study, which can control the running automobiles with unmanned navigation. On the basis of the theory of diffraction, the way of spreading for wireless received signal strength (RSS) can be divided into "scattered waves" and "diffracted waves," which can be regarded as two components of infrared sensing wireless signals so as to further propose the RSS indicators of "long-term testing value" and "short-term test value." Based on these indicators, a measurement model based on diffraction effects and scattering effects is proposed, and an improved particle filter algorithm is used to update the motion tracking. The hardware design of each module in an unmanned vehicle includes the main controller, tracking circuit, serial port circuit, motor control circuit and infrared sensor control circuit of the car. In the experiment, the measurement accuracy of the tracking system based on the sparse infrared photoelectric sensor was first tested. In the simulation experiment, the long-term test value, the short-term test value and the actual measurement value were compared respectively. The test results show that the theoretical RSS value and the actual test result can be matched. Moreover, the infrared photoelectric tracking system is used to design the navigation control system of unmanned cars, helping the car to drive automatically through obstacle avoidance test and tracking obstacle avoidance test.

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

Aiming at solving the problem of the concentration signal hardly being compressive sensed directly in nonlinear gas diffusion environment, a compressed sensing direction of arrival (DOA) estimation method according to the acoustic characteristics of the gas leakage was proposed for source localisation. Firstly, the corresponding compressed sensing matrix and the DOA estimation model was established. Then, the sparse Bayesian recovery algorithm was designed for the DOA estimation of gas leakage acoustic source. Finally, the simulation results show that the proposed method could achieve an accurate DOA estimation of one or more gas leakage acoustic sources, and the sparse Bayesian recovery algorithm could effectively improve the estimation accuracy and robust performance with fewer amounts of samples compared to the orthogonal matching pursuit (OMP) algorithm.

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

Gas leakage acoustic source localisation with compressed sensing method in sensor networks

Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.

GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks

In this paper, we focus on image quality assessment (IQA) in sensor networks and propose a novel method named gradient magnitude and variance pooling (GMVP). The proposed GMVP follows a two-step framework. In this first step, we utilize gradient magnitude to compute the local quality, which is efficient and responsive to degeneration when the images are transmitted by sensor networks. In the second step, we propose a weighted pooling operation, i.e., variance pooling, which explicitly considers the importance of different local regions. The variance pooling operation assigns different weights to local quality map according to the variance of local regions. The proposed GMVP is verified on two challenging IQA databases (CSIQ and TID 2008 databases), and the results demonstrate that the proposed GMVP achieves better results than the state-of-the-art methods in sensor networks.

An Error Control Method with Linear Block Code in Sensor Networks

An error control method is investigated and obtained by combination of the modulation and linear block code in sensor networks. The constructed coding scheme is multistage decoded using a new low-complexity soft-decision decoding method in sensor networks. Computer simulations of the proposed method are presented and compared with the reunion junction technique. The proposed method shows a better performance than the reunion junction technique. The low complexity of the method allows the use of long linear block codes. It is possible to construct an efficient bandwidth multilevel code on signal set partitioning. A wide range of design tradeoffs are possible with multilevel codes. Composite codes are possible using component codes.

Multi-hop Fuzzy Routing For Wireless Sensor Network With Mobile Sink

Using clustering methods in sensor networks improves their efficiency and management. Moreover, it brings with it the main parameter in sensor networks i.e. decrease in energy consumption. To transfer data to a sink in a network having many nodes, multi-hop methods have to be used. Also in many of the cases, there is a mobile sink (or user) in a network environment. In this paper, using fuzzy logic we present a routing method which divides the network into groups which are like clusters and has the ability of supporting the mobile sink. This routing method has been compared with other routing and clustering methods and the results show the efficiency of the mentioned method.

A New Path-Based In-Network Join Processing Method for Sensor Networks

A New Path-Based In-Network Join Processing Method for Sensor Networks

Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures

Large and complex civil infrastructures are especially susceptible to random vibrations, whether they are from high ground accelerations, strong wind forces, or abnormal loads such as explosions. Thus, it is imperative that the robust and continuous health monitoring system needs to be designed and implemented so as to meet the demanding goals of increasing structural safety and reliability, while reducing its operating and maintenance costs. The efficiency of a structural health monitoring system (SHMS) relies on the sensitivity of the acquired data to structural changes that may be obtained by an extended sensor network on structures as shown in “Methodology developments in sensor placement for health monitoring of civil infrastructures” and [1]. Recent advances in sensing technologies have enabled the use of large numbers of sensors for health monitoring of civil infrastructures. The sensors utilized in an SHMS are required to monitor not only the structural status including the stress, displacement, and acceleration but also those influential environmental parameters, such as the wind speed, temperature, and quality of its foundation. Since a large sensor network needs to be involved in an SHMS, how to deploy it in a complicated, spatially extended structure so that the data acquired from those locations will result in the best identification of structural characteristics will be a challenging task. On the other hand, a high cost of data acquisition systems (sensors and their supporting instruments) and accessibility limitation constrain in many cases the wide distribution of a large sensor networks on a structure. Especially, many civil structures have to be tested under operational conditions, in which the sensors are not easily amenable to be removed or changed [2, 3]. It is, therefore, of increasing interests to seek rational placement and design a method of sensor networks that contain as much information as possible about the health state of a structure. This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in the integration of various aspects of SHMS applications within a broad range of disciplines. Some research topics towards this target include intelligent and efficient algorithms in the design of sensor networks; evaluation criteria for different sensor networks placement methods; inherent relationship between different sensor networks placement methods; uncertainty, sensitivity, reliability, and redundancy in sensor networks; application of large scale sensors for civil infrastructures and others related. A total of 26 peer-reviewed papers have been presented in this special issue. These include identifying the optimal number and location of sensors, as well as the robustness, which aims at maintaining the stability of the sensor network when some sensors malfunction, and so forth. Without a doubt, the papers reflect the state-of-the-art researches and developments of this subject. The Guest Editor would like to express his sincere appreciation to all the authors who have contributed their work to this special issue.

A new approach for improving lifetime in wireless sensor networks based on distributed learning automata

In wireless sensor networks, due to the strict resource limitations, consumption energy has becomes the most challenging issue. Decreasing consumption energy has a direct impact on increasing the lifetime of wireless sensor networks; data aggregation was put forward as an essential paradigm for wireless routing in sensor networks. The idea is to combine the data coming from different sources, eliminating redundancy, minimizing the number of transmissions, and thus, saving energy. For this purpose, in this paper, we propose an energy aware algorithm for the construction of virtual backbone. The proposed algorithm considers both the consumption energy and remaining energy parameters to construct the virtual backbone which increases the network lifetime. In this paper, a learning automata based data aggregation method in sensor networks was proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with minimum consumption energy for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method, computer simulations were conducted and the results were compared with the results of previous methods. The results show that the proposed method outperforms previous methods in terms of energy consumption and network lifetime. Key words: Wireless sensor networks, data aggregation, learning automata, virtual backbone.

Collaborative multi-sensor tracking in mobile wireless sensor networks

Sensor localisation and tracking is an important research topic in sensor networks. Collaboration of resource-constrained sensor nodes is extremely important to achieving substantial sensing and processing capability in the aggregate and to providing collectively reliable network behaviour in mission-critical applications. We propose a collaborative multi-sensor tracking (CMST) method for sensor networks and demonstrate through posterior Cramer-Rao bound (PCRB) analysis that sensors' mobility and collaboration can be exploited to significantly improve sensor localisation performance. We also propose a novel adaptive particle filtering algorithm AP-RPF and demonstrate its superior performance through simulation results.

Data aggregation in sensor networks using learning automata

One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may result from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata-based data aggregation method in sensor networks when the environment’s changes cannot be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.

Towards Spatial Window Queries Over Continuous Phenomena in Sensor Networks

Recent research on sensor networks has focused on the efficient processing of declarative SQL queries over sensor nodes. Users are often interested in querying an underlying continuous phenomenon such as a toxic plume, whereas only discrete readings of sensor nodes are available. Therefore, additional information estimation methods are necessary to process the sensor readings to generate the required query results. Most estimation methods are computationally intensive, even when computed in a traditional centralized setting. Furthermore, energy and communication constraints of sensor networks challenge the efficient application of established estimation methods in sensor networks. In this paper, we present an approach using Gaussian kernel estimation to process spatial window queries over continuous phenomena in sensor networks. The key contribution of our approach is the use of a small number of Hermite coefficients to approximate the Gaussian kernel function for subclustered sensor nodes. As a result, our algorithm reduces the size of messages transmitted in the network by logarithmic order, thus saving resources while still providing high-quality query results.