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J. Sens. Actuator Netw., Volume 1, Issue 3 (December 2012), Pages 166-320

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Research

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Open AccessArticle Low Cost Multisensor Kinematic Positioning and Navigation System with Linux/RTAI
J. Sens. Actuator Netw. 2012, 1(3), 166-182; doi:10.3390/jsan1030166
Received: 17 July 2012 / Revised: 28 August 2012 / Accepted: 9 September 2012 / Published: 28 September 2012
Cited by 3 | PDF Full-text (393 KB) | HTML Full-text | XML Full-text
Abstract
Despite its popularity, the development of an embedded real-time multisensor kinematic positioning and navigation system discourages many researchers and developers due to its complicated hardware environment setup and time consuming device driver development. To address these issues, this paper proposed a multisensor kinematic
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Despite its popularity, the development of an embedded real-time multisensor kinematic positioning and navigation system discourages many researchers and developers due to its complicated hardware environment setup and time consuming device driver development. To address these issues, this paper proposed a multisensor kinematic positioning and navigation system built on Linux with Real Time Application Interface (RTAI), which can be constructed in a fast and economical manner upon popular hardware platforms. The authors designed, developed, evaluated and validated the application of Linux/RTAI in the proposed system for the integration of the low cost MEMS IMU and OEM GPS sensors. The developed system with Linux/RTAI as the core of a direct geo-referencing system provides not only an excellent hard real-time performance but also the conveniences for sensor hardware integration and real-time software development. A software framework is proposed in this paper for a universal kinematic positioning and navigation system with loosely-coupled integration architecture. In addition, general strategies of sensor time synchronization in a multisensor system are also discussed. The success of the loosely-coupled GPS-aided inertial navigation Kalman filter is represented via post-processed solutions from road tests. Full article
Open AccessArticle Group-based Motion Detection for Energy-Efficient Localisation
J. Sens. Actuator Netw. 2012, 1(3), 183-216; doi:10.3390/jsan1030183
Received: 18 June 2012 / Revised: 22 August 2012 / Accepted: 11 October 2012 / Published: 19 October 2012
Cited by 2 | PDF Full-text (3051 KB) | HTML Full-text | XML Full-text
Abstract
Long-term outdoor localization remains challenging due to the high energy profiles of GPS modules. Duty cycling the GPS module combined with inertial sensors can improve energy consumption. However, inertial sensors that are kept active all the time can also drain mobile node batteries.
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Long-term outdoor localization remains challenging due to the high energy profiles of GPS modules. Duty cycling the GPS module combined with inertial sensors can improve energy consumption. However, inertial sensors that are kept active all the time can also drain mobile node batteries. This paper proposes duty cycling strategies for inertial sensors to maintain a target position accuracy and node lifetime. We present a method for duty cycling motion sensors according to features of movement events, and evaluate its energy and accuracy profile for an empirical data trace of cattle movement. We further introduce the concept of group-based duty cycling, where nodes that cluster together can share the burden of motion detection to reduce their duty cycles. Our evaluation shows that both variants of motion sensor duty cycling yield up to 78% improvement in overall node power consumption, and that the group-based method yields an additional 20% power reduction during periods of low mobility. Full article
(This article belongs to the Special Issue Feature Papers)
Open AccessArticle Range-Free Localization in Wireless Sensor Networks with Neural Network Ensembles
J. Sens. Actuator Netw. 2012, 1(3), 254-271; doi:10.3390/jsan1030254
Received: 19 September 2012 / Revised: 8 November 2012 / Accepted: 15 November 2012 / Published: 28 November 2012
Cited by 4 | PDF Full-text (934 KB) | HTML Full-text | XML Full-text
Abstract
In wireless sensor networks (WSNs), the location information of sensor nodes are important for implementing other network applications. In this paper, we propose a range-free Localization algorithm based on Neural Network Ensembles (LNNE). The location of a sensor node is estimated by LNNE
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In wireless sensor networks (WSNs), the location information of sensor nodes are important for implementing other network applications. In this paper, we propose a range-free Localization algorithm based on Neural Network Ensembles (LNNE). The location of a sensor node is estimated by LNNE solely based on the connectivity information of the WSN. Through simulation study, the performance of LNNE is compared with that of two well-known range-free localization algorithms, Centroid and DV-Hop, and a single neural network based localization algorithm, LSNN. The experimental results demonstrate that LNNE consistently outperforms other three algorithms in localization accuracy. An enhanced mass spring optimization (EMSO) algorithm is also proposed to further improve the performance of LNNE by utilizing the location information of neighboring beacon and unknown nodes. Full article
Open AccessArticle Estimation of Physical Layer Performance in WSNs Exploiting the Method of Indirect Observations
J. Sens. Actuator Netw. 2012, 1(3), 272-298; doi:10.3390/jsan1030272
Received: 15 September 2012 / Revised: 2 November 2012 / Accepted: 15 November 2012 / Published: 30 November 2012
Cited by 4 | PDF Full-text (402 KB) | HTML Full-text | XML Full-text
Abstract
Wireless Sensor Networks (WSNs) are used in many industrial and consumer applications that are increasingly gaining impact in our day to day lives. Still great efforts are needed towards the definition of methodologies for their effective management. One big issue is themonitoring of
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Wireless Sensor Networks (WSNs) are used in many industrial and consumer applications that are increasingly gaining impact in our day to day lives. Still great efforts are needed towards the definition of methodologies for their effective management. One big issue is themonitoring of the network status, which requires the definition of the performance indicators and methodologies and should be accurate and not intrusive at the same time. In this paper, we present a new process for the monitoring of the physical layer in WSNs making use of a completely passive methodology. From data sniffed by external nodes, we first estimate the position of the nodes by applying the Weighted Least Squares (WLS) to the method of indirect observations. The resulting node positions are then used to estimate the status of the communication links using the most appropriate propagation model. We performed a significant number of measurements on the field in both indoor and outdoor environments. From the experiments, we were able to achieve an accurate estimation of the channel links status with an average error lower than 1 dB, which is around 5 dB lower than the error introduced without the application of the proposed method. Full article
(This article belongs to the Special Issue Internet of Things: Technologies and Applications)
Open AccessArticle Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks
J. Sens. Actuator Netw. 2012, 1(3), 299-320; doi:10.3390/jsan1030299
Received: 28 September 2012 / Revised: 29 October 2012 / Accepted: 11 November 2012 / Published: 30 November 2012
Cited by 3 | PDF Full-text (420 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed
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Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our focus for variable sampling methodology is to determine the appropriate neural network topology for delay prediction and also investigate the impact of additional inputs to the neural network such as network packet loss rate and throughput. The major contribution of this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers, which are not a correct representation of delays actually experienced in a wireless network. Our results here shows that adequate prediction of the time delay series using the observer based variable sampling is able to compensate for delays in the communication loop and influences the performance of the control system model. Full article

Review

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Open AccessReview Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0
J. Sens. Actuator Netw. 2012, 1(3), 217-253; doi:10.3390/jsan1030217
Received: 4 September 2012 / Revised: 31 October 2012 / Accepted: 31 October 2012 / Published: 8 November 2012
Cited by 120 | PDF Full-text (933 KB) | HTML Full-text | XML Full-text
Abstract
The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT) ecosystem is emerging to support the process of connecting real-world objects
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The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT) ecosystem is emerging to support the process of connecting real-world objects like buildings, roads, household appliances, and human bodies to the Internet via sensors and microprocessor chips that record and transmit data such as sound waves, temperature, movement, and other variables. The explosion in Internet-connected sensors means that new classes of technical capability and application are being created. More granular 24/7 quantified monitoring is leading to a deeper understanding of the internal and external worlds encountered by humans. New data literacy behaviors such as correlation assessment, anomaly detection, and high-frequency data processing are developing as humans adapt to the different kinds of data flows enabled by the IOT. The IOT ecosystem has four critical functional steps: data creation, information generation, meaning-making, and action-taking. This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT). Full article
(This article belongs to the Special Issue Internet of Things: Technologies and Applications)
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