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Showing papers on "Sensor node published in 2019"


Journal ArticleDOI
TL;DR: Firefly with cyclic randomization is proposed for selecting the best cluster head for wireless sensor network and the network performance is increased in this method when compared to the other conventional algorithms.
Abstract: Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

182 citations


Journal ArticleDOI
TL;DR: It is shown that the proposed scheme ensures security even if a sensor node is captured by an adversary, and the proposed protocol uses the lightweight cryptographic primitives, such as one way cryptographic hash function, physically unclonable function, and bitwise exclusive operations.
Abstract: Industrial wireless sensor network (IWSN) is an emerging class of a generalized WSN having constraints of energy consumption, coverage, connectivity, and security. However, security and privacy is one of the major challenges in IWSN as the nodes are connected to Internet and usually located in an unattended environment with minimum human interventions. In IWSN, there is a fundamental requirement for a user to access the real-time information directly from the designated sensor nodes. This task demands to have a user authentication protocol. To satisfy this requirement, this paper proposes a lightweight and privacy-preserving mutual user authentication protocol in which only the user with a trusted device has the right to access the IWSN. Therefore, in the proposed scheme, we considered the physical layer security of the sensor nodes. We show that the proposed scheme ensures security even if a sensor node is captured by an adversary. The proposed protocol uses the lightweight cryptographic primitives, such as one way cryptographic hash function, physically unclonable function, and bitwise exclusive operations. Security and performance analysis shows that the proposed scheme is secure, and is efficient for the resource-constrained sensing devices in IWSN.

182 citations


Journal ArticleDOI
TL;DR: The aim of the addressed problem is to develop a moving horizon estimator such that the estimation error is ultimately bounded, and a sufficient condition is established to ensure the ultimate boundedness in terms of a matrix inequality.
Abstract: This paper is concerned with the moving horizon estimation problem for a class of discrete time-delay systems under the Round-Robin (RR) protocol. The communication between the sensor nodes and the remote state estimator is implemented via a shared network, where only one sensor node is permitted to transmit data at each time instant for the purpose of preventing data collisions. The RR protocol is utilized to orchestrate the transmission order of sensor nodes, under which the selected node obtaining access to the network could be modeled by a periodic function. A lifting technology is introduced to reformulate the system model into a linear system without delays. The aim of the addressed problem is to develop a moving horizon estimator such that the estimation error is ultimately bounded. A sufficient condition is established to ensure the ultimate boundedness in terms of a matrix inequality. Within the established theoretical framework, two optimization problems are proposed to calculate the corresponding estimator parameters according to two different performance requirements (e.g., the smallest ultimate bound and the fastest decay rate). Finally, simulation examples are given to illustrate the effectiveness of the estimator design scheme.

148 citations


Journal ArticleDOI
TL;DR: Simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.
Abstract: In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T–S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T–S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

138 citations


Journal ArticleDOI
TL;DR: An IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology is proposed.

130 citations


Journal ArticleDOI
TL;DR: The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant.
Abstract: This paper is concerned with the recursive filtering problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive filtering approach.

114 citations


Journal ArticleDOI
TL;DR: This letter deals with the age of information (AoI) for a sensor network with wireless power transfer (WPT) capabilities, where a sensor node harvests energy from radio frequency signals and transmits by using all the available energy without further energy management.
Abstract: In this letter, we deal with the age of information (AoI) for a sensor network with wireless power transfer (WPT) capabilities. Specifically, we study a simple network topology, where a sensor node harvests energy from radio frequency signals (transmitted by a dedicated energy source) to transmit real-time status updates. The sensor node generates an update when its capacitor/battery becomes fully charged and transmits by using all the available energy without further energy management. The average AoI performance of the considered greedy policy is derived in closed form and is a function of the capacitor’s size. The optimal value of the capacitor that maximizes the freshness of the information, corresponds to a simple optimization problem requiring a 1-D search. The derived theoretical results provide useful performance bounds for practical WPT networks.

106 citations


Journal ArticleDOI
TL;DR: A new method called particle swarm optimization based selection (PSOBS) is proposed to select the optimal rendezvous points and the simulation results show the superiority of PSOBS as compared with WRPBS, but it increases the packet loss rate in comparison withWRPBS.
Abstract: One of the most effective approaches to increase the lifetime of wireless sensor networks (WSNs), is the use of a mobile sink to collect data from sensor. In WSNs, mobile sinks implicitly help achieving uniform energy-consumption and provide load-balancing. In this approach, some certain points in the sensors field should be visited by the mobile sink. The optimal selection of these points which are also called rendezvous points is a NP-hard problem. Since hierarchical algorithms rely only on their local information to select these points, thus the probability of selecting an optimal node as rendezvous point will be very low. To address this problem, in this paper, a new method called particle swarm optimization based selection (PSOBS) is proposed to select the optimal rendezvous points. By applying PSO, the proposed method is capable of finding optimal or near-optimal rendezvous points to efficient management of network resources. In the proposed method, a weight value is also calculated for each sensor node based on the number of data packets that it receives from other sensor nodes. The proposed method was compared with weighted rendezvous planning based selection (WRPBS) algorithm based on some performance metrics such as throughput, energy consumption, number of rendezvous points and hop count. The simulation results show the superiority of PSOBS as compared with WRPBS, but it increases the packet loss rate in comparison with WRPBS.

91 citations


Journal ArticleDOI
TL;DR: These methods extend one-class support vector machines to tensor space and improve the accuracy and efficiency of anomaly detection while retaining the intrinsic structure of big sensor data.
Abstract: Various types of sensor data can be collected by the Internet of Things (IoT). Each sensor node has spatial attributes and may also be associated with a large number of measurement data that evolve over time; therefore, these high-dimensional sensor data are inherently large scale. Detecting outliers in large-scale IoT sensor data is a challenging task. Most existing anomaly detection methods are based on a vector representation. However, large-scale IoT sensor data have characteristics that make tensor methods more efficient for extracting information. The vector-based methods can destroy original structural information and correlation within large-scale sensor data, resulting in the problem of the “curse of dimensionality,” and some outliers hence cannot be detected. In this paper, we propose a one-class support Tucker machine (OCSTuM) and an OCSTuM based on tensor Tucker factorization and a genetic algorithm called GA-OCSTuM. These methods extend one-class support vector machines to tensor space. OCSTuM and GA-OCSTuM are unsupervised anomaly detection approaches for big sensor data. They retain the structural information of data while improving the accuracy and efficiency of anomaly detection. The experimental evaluations on real data sets demonstrate that our proposed method improves the accuracy and efficiency of anomaly detection while retaining the intrinsic structure of big sensor data.

90 citations


Journal ArticleDOI
TL;DR: Simulation performance based results indicates the effectiveness of MEACBM routing protocols by comparing it with other contemporary cluster based routing protocols in terms of network lifetime, stability, throughput, number of CHs and number of dead nodes.
Abstract: Routing in Wireless Sensor Networks (WSNs) is the most significant and the challenging issue for the researchers in terms of enhancing its performance in terms of network lifetime, energy efficiency, scalability, connectivity, throughput, etc. They have the incredible capability to interact and gather data from any physical environment with help of routing protocols. Many routing protocols based solutions have been proposed in the recent years for accomplishing the preferred level of performance in WSNs for these issues. The hierarchical heterogeneous cluster based energy efficient routing protocols are more efficient as compared to flat and location based routing protocols due to the presence of nodes heterogeneity in terms of energy level of sensor nodes which enhances the lifetime of the network. The most recent trend that extensively enhances the functionality and the performance of WSNs is the use of mobile sensor nodes. In this paper, the authors proposed a novel concept regarding mobile sensor nodes is proposed called Mobile Energy Aware Cluster Based Multi-hop (MEACBM) routing protocol for hierarchical heterogeneous WSNs which selects CHs on the basis of newly proposed probability equation which selects only that sensor node as Cluster Head (CH) which has the highest energy among other sensor nodes by introducing a new term S(i).E in the equation. It considers hierarchical heterogeneous clustering considering three levels of sensor nodes; multi-hoping for inter-cluster communication and connectivity of sensor nodes within the whole network area. In MEACBM, after the deployment of sensor nodes and formation of clusters, the whole network area is divided into sectors and inside each sector a mobile sensor node is placed which act as Mobile Data Collector (MDC) for collecting data from CHs. This technique helps in significantly reducing the energy consumption of sensor nodes for transferring information to the Base Station (BS). Simulation performance based results indicates the effectiveness of MEACBM routing protocols by comparing it with other contemporary cluster based routing protocols in terms of network lifetime, stability, throughput, number of CHs and number of dead nodes.

89 citations


Proceedings ArticleDOI
01 Jul 2019
TL;DR: This work proposes a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks and demonstrates the feasibility and effectiveness via a use case of fall detection using recurrent neural networks.
Abstract: Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90% and an average recall over 95% in fall detection.

Journal ArticleDOI
29 Jan 2019-Sensors
TL;DR: A fuzzy logic model for cluster head election and the Gini index is adopted to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes.
Abstract: In wireless sensor networks, the energy source is limited to the capacity of the sensor node's battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms' energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

Proceedings ArticleDOI
15 Apr 2019
TL;DR: A wearable sensor network system for Internet of Things (IoT) connected safety and health applications which incorporates multiple wearable sensors to monitor environmental and physiological parameters is presented.
Abstract: This paper presents a wearable sensor network system for Internet of Things (IoT) connected safety and health applications. Safety and health of workers are important for industrial workplace; therefore, an IoT network system which can monitor both environmental and physiological can greatly improve the safety in the workplace. The proposed network system incorporates multiple wearable sensors to monitor environmental and physiological parameters. The wearable sensors on different subjects can communicate with each other and transmit the data to a gateway via a LoRa network which forms a heterogeneous IoT platform with Bluetooth-based medical signal sensing network. Once harmful environments are detected and, the sensor node will provide an effective notification and warning mechanism for the users. A smart IoT gateway is implemented to provide data processing, local web server and cloud connection. After the gateway receives the data from wearable sensors, it will forward the data to an IoT cloud for further data storage, processing and visualization.

Journal ArticleDOI
TL;DR: This paper proposes a novel consensus-based distributed unscented Kalman filtering algorithm with event-triggered communication mechanisms that can significantly reduce unnecessary data transmissions and hence save communication energy consumption and alleviate the communication burden.

Journal ArticleDOI
TL;DR: Bat Algorithm is used to select the optimum monitoring sensor node and resulted path to reduce energy consumption and the proposed algorithm has been able to reduce the power consumption of the network and increase the lifetime of thenetwork.
Abstract: Sensor nodes spend the most of their limited energy on communicating with environmental information gathered in receivers. Hence, it is important to determine the optimal monitoring sensor nodes and information flow paths to the destination and sink in order to survive the sensor networks. Additionally, the heavy traffic load for transferring packets in nodes closer to the sink increases energy consumption and reduces battery life. It is desirable to reduce the energy between nodes and sink. The main goal is to extend the network lifetime through extending the lifetime of operating sensors as well transferring gathered data from super node to the sink. In this paper, Bat Algorithm (BA) is used to select the optimum monitoring sensor node and resulted path to reduce energy consumption. Simulation results and comparison with other algorithms show the superiority of the proposed algorithm. The simulation results of the proposed algorithm show that the proposed algorithm has been able to reduce the power consumption of the network and increase the lifetime of the network. Also, the proposed algorithm is able to outperform the comparable algorithms on average by 27%.

Journal ArticleDOI
TL;DR: A prototype WPSN testbed is built with a large-scale antenna array and it is demonstrated that a sensor node can perpetually operate up to the distance of 50 meters with self-powering, validating the potential of turning the WpsN concept into reality.
Abstract: As the era of Internet of Things (IoT) emerges, powering massive IoT devices becomes a great challenge in need of immediate attention. This challenge can be resolved by radio frequency (RF) wireless power transfer (WPT) technology that remotely supplies power to a distant sensor device. In this article, we discuss the issues arising in designing wireless-powered sensor networks (WPSNs). We have built a prototype WPSN testbed with a large-scale antenna array. By demonstrating that a sensor node can perpetually operate up to the distance of 50 meters with self-powering, we validate the potential of turning the WPSN concept into reality.

Journal ArticleDOI
TL;DR: A redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN.
Abstract: In order to give a complete description of an environment or to make a robust decision, a number of observations must be collected and combined from multiple sensor nodes. In these large collections of data, only some are useful, whereas others are redundant. This redundancy decreases performance in terms of computing overhead, excessive transmission, and covering a large space. The process of selecting and analyzing the useful information from the collection of sensed data is called mining. Mining is used to produce more consistent, accurate, and useful information than that provided by any individual sensor node. Data mining has been widely applied in many areas, such as object recognition, wireless sensor networks (WSNs), image processing, environment mapping, and localization. Nowadays, Internet of Things utilizes WSN as a necessary platform for sensing and communication of the data. For efficiency, mining of spatial and temporal data is performed on the sensed sample collected by sensor nodes. Therefore, in this paper, a redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN. Extensive simulations were conducted, and the related results showed that the proposed scheme had better performance compared to other schemes in our simulated scenarios.

Journal ArticleDOI
TL;DR: A near optimal buffer-battery-aware adaptive scheduling scheme is further proposed, in which the run-time status of the data buffer and battery are utilized and the performance of NO-BBA is close to that of Opt-JoDGE, especially when a certain delay is tolerable.

Proceedings ArticleDOI
01 Sep 2019
TL;DR: This work presents a system architecture and implementation that expands the possibilities of smart agriculture and farming applications with Edge and Fog computing and LPWAN technology for large area coverage by integrating artificial intelligence at the local network layer.
Abstract: The agricultural and farming industries have been widely influenced by the disruption of the Internet of Things. The impact of the IoT is more limited in countries with less penetration of mobile internet such as sub-Saharan countries, where agriculture commonly accounts for 10 to 50% of their GPD. The boom of low-power wide-area networks (LPWAN) in the last decade, with technologies such as LoRa or NB-IoT, has mitigated this providing a relatively cheap infrastructure that enables low-power and long-range transmissions. Nonetheless, the benefits that LPWAN technologies enable have the disadvantage of low-bandwidth transmissions. Therefore, the integration of Edge and Fog computing, moving data analytics and compression near end devices, is key in order to extend functionality. By integrating artificial intelligence at the local network layer, or Edge AI, we present a system architecture and implementation that expands the possibilities of smart agriculture and farming applications with Edge and Fog computing and LPWAN technology for large area coverage. We propose and implement a system consisting on a sensor node, an Edge gateway, LoRa repeaters, Fog gateway, cloud servers and end-user terminal application. At the Edge layer, we propose the implementation of a CNN-based image compression method in order to send in a single message information about hundreds or thousands of sensor nodes within the gateway's range. We use advanced compression techniques to reduce the size of data up to 67% with a decompression error below 5%, within a novel scheme for IoT data.

Journal ArticleDOI
TL;DR: This study addresses the issue of distributed event-triggered H ∞ state estimators subject to deception attacks for sensor networked systems and establishes a novel estimator network to realise the estimation of the decoupling output measurements and coupling intercommunication measurements.
Abstract: This study addresses the issue of distributed event-triggered H ∞ state estimators subject to deception attacks for sensor networked systems. A decentralised event-triggered scheme (ETS) is introduced to determine whether the sampling data of each sensor is transmitted or not, respectively. In this scheme, each sensor node is independent to decide to deliver the local measurement output through the corresponding ETS. Due to the insertion of the network, the effect of the deception attacks along with time delay and packet dropouts are considered in this study. A novel estimator network is established to realise the estimation of the decoupling output measurements and coupling intercommunication measurements. Firstly, a distributed event-triggered H ∞ estimating system with deception attacks is constructed in a mathematical model. Secondly, sufficient conditions are derived, which can ensure the stability of the designed H ∞ estimating error systems and the related parameters of the desired distributed estimators are presented in an accurate form. Finally, a simulated example is given to demonstrate the effectiveness of the designed event-triggered distributed H ∞ state estimator systems under the deception attacks.

Journal ArticleDOI
TL;DR: This work proposes and implements a new mechanism for geographic routing based on a weighted centroid localization technique, where the positions of unknown nodes are calculated using fuzzy logic method to minimize the position error of nodes and reduces the error localization average.

Journal ArticleDOI
TL;DR: The proposed Data Collection scheme based on Denoising Autoencoder (DCDA) results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a received signal strength (RSS)-based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation.
Abstract: This paper proposes a received signal strength (RSS)-based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs, energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy. Network localization is carried out by measuring the RSSs of active nodes, which are modeled based on the underwater optical communication channel characteristics. Thereafter, block kernel matrices are computed for the RSS-based range measurements. Unlike the traditional shortest-path approach, the proposed technique reduces the estimation error of the shortest path for each block kernel matrix. Once the complete block kernel matrices are available, a closed form localization technique is developed to find the location of every optical sensor node in the network. An analytical expression for the Cramer–Rao lower bound is also derived as a benchmark to evaluate the localization performance of the developed technique. The extensive simulations show that the proposed framework outperforms the well-known network localization techniques.

Journal ArticleDOI
TL;DR: A data-aware energy conservation technique that conserves 82% of energy with the error threshold of minimum level and maintains the collected data’s accuracy within the predefined error threshold.
Abstract: Wireless sensor networks (WSN) are expected to cover the major portion of the earth’s surface in the coming years. In the era of IoT, the WSN is the major data collection framework. To manage with the energy efficient data collection paradigm in WSN, numerous techniques have been suggested by the research community. In this paper, a data-aware energy conservation technique is proposed. Here, the inherent correlation between the consecutive observations of the sensor node and the data trend similarity between the neighboring sensor nodes are utilized to reduce the data transmission. A prediction-based data collection framework reduces the temporal data redundancy. ARIMA modeling is used to predict the data. The model is constructed by the (Clusterhead) CH node and is communicated to the cluster nodes. On every data collection round, the nodes compare the model predicted data and the observed data of the instant. If there is a deviation beyond the specified threshold, the nodes communicate the data difference to the CH. The data differences collected by the CH are compressed by using PCA technique. The compressed data are then sent to the sink node. Using this method, a huge portion of redundant data transmission is cut off. The method also maintains the collected data’s accuracy within the predefined error threshold. Being a data reduction-based energy conservation technique, this results in reduced data collision. This method conserves 82% of energy with the error threshold of minimum level.

Journal ArticleDOI
TL;DR: Distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a linear time-invariant discrete-time system, using its own observations and those transmitted from its neighbors in the presence of data packet drops, is studied.
Abstract: We study distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a linear time-invariant discrete-time system, using its own observations and those transmitted from its neighbors in the presence of data packet drops. This is an optimal one-step prediction problem under the framework of distributed estimation, assuming the TCP-like protocol. We first present the stationary distributed Kalman filter (DKF) that minimizes the local average error variance in the steady state at each sensor node, based on the stabilizing solution to the corresponding modified algebraic Riccati equation (MARE). The existence of the stabilizing solution to the MARE is addressed by adopting the stability margin, which can be computed by solving a set of linear matrix inequalities. Then, the Kalman consensus filter (KCF), consisting of the stationary DKF and a consensus term of prior estimates, is studied. Finally, the performance of the stationary DKF and KCF is illustrated by a numerical example.

Journal ArticleDOI
TL;DR: Simulation results demonstrate that compared with energy-aware routing, BEER, Q-Routing, and MRL-SCSO, reinforcement-learning-based routing protocol optimizes the network lifetime in three aspects and improves the energy efficiency.
Abstract: In wireless sensor networks, optimizing the network lifetime is an important issue. Most of the existing works define network lifetime as the time when the first sensor node exhausts all of its ene...

Journal ArticleDOI
TL;DR: An enhanced mechanism for developing a three-factor secure mutual authentication scheme to attain effectively the security of the remote health-care system for patient monitoring and the comparative studies of the scheme with state-of-the-art schemes are acceptable.

Journal ArticleDOI
TL;DR: MCTM uses mobile code to visit the SN s based on pre-defined itineraries while collecting necessary details about these SN s in preparation for assessing their trust, demonstrating a superior performance over a state-of-art technique that is energy-efficient management based on Software-Defined Network ( SDN ) for SN s.

Journal ArticleDOI
20 Jun 2019
TL;DR: This article presents the development of an autonomous Sigfox sensor node capable of transmitting data collected by a range of sensors directly to the cloud and can transmit data every 5 min under cloudy conditions.
Abstract: Low-power wide-area network protocols, such as LoRa, Sigfox, and NB Internet of Things, have become a popular technology for long range and limited data communication. Those protocols have been optimized for low power consumption and offer competitive subscription prices. This article presents the development of an autonomous Sigfox sensor node capable of transmitting data collected by a range of sensors directly to the cloud. The device is powered by a solar cell and can transmit data every 5 min under cloudy conditions (<5000 lx). Such a high transmission rate has not yet been reported in the literature for a fully autonomous system. Field trials have been realized by placing two sensor nodes at a vineyard in order to collect meteorological parameters.

Journal ArticleDOI
TL;DR: A cascading model of clustering WSNs by introducing the concept of sensing load and relay load is built and the impacts of model parameters on network invulnerability are discussed and the invulnerability performance of two types of WSN topologies are evaluated.
Abstract: Despite the fact that current research related to the invulnerability of wireless sensor networks (WSNs) against cascading failures has made some progress, most of it focuses on the peer-to-peer structure, which is far from the reality. In this paper, we build a cascading model of clustering WSNs by introducing the concept of sensing load and relay load. We discuss the impacts of model parameters on network invulnerability and evaluate the invulnerability performance of two types of WSN topologies, i.e., scale-free network and random network. Simulation and theoretical results show that the network invulnerability is negatively related to the proportion of cluster heads and positively related to the allocation coefficient. When the degree of each sensor node bears a linear relationship with its initial load, the network invulnerability is strongest. Moreover, to alleviate the damage level caused by cascading failures, we design capacity-expansion schemes and indicate the recommended model parameter settings for each scheme.