Journal: Sensors (Basel, Switzerland)
The main objective of this paper is to investigate the effects of routing protocols on wireless sensor and actuator networks (WSANs), focusing on the control system response and the energy consumption of nodes in a network. We demonstrate that routing algorithms designed without considering the relationship between communication and control cannot be appropriately used in wireless networked control applications. For this purpose, an ad-hoc on-demand distance vector (AODV) routing, an IEEE 802.15.4, and a building-temperature control system are employed for this exploration. The findings from our scenarios show that the AODV routing can select a path with a high traffic load for data transmission. It takes a long time before deciding to change a new route although it experiences the unsuccessful transmission of packets. As a result, the desirable control target cannot be achieved in time, and nodes consume more energy due to frequent packet collisions and retransmissions. Consequently, we propose a simple routing solution to alleviate these research problems by modifying the original AODV routing protocol. The delay-threshold is considered to avoid any congested connection during routing procedures. The simulation results demonstrate that our solution can be appropriately applied in WSANs. Both the energy consumption and the control system response are improved.
This paper is a theoretical analysis of mirror tilt in a Michelson interferometer and its effect on the radiant flux over the active area of a rectangular photodetector or image sensor pixel. It is relevant to sensor applications using homodyne interferometry where these opto-electronic devices are employed for partial fringe counting. Formulas are derived for radiant flux across the detector for variable location within the fringe pattern and with varying wave front angle. The results indicate that the flux is a damped sine function of the wave front angle, with a decay constant of the ratio of wavelength to detector width. The modulation amplitude of the dynamic fringe pattern reduces to zero at wave front angles that are an integer multiple of this ratio and the results show that the polarity of the radiant flux changes exclusively at these multiples. Varying tilt angle causes radiant flux oscillations under an envelope curve, the frequency of which is dependent on the location of the detector with the fringe pattern. It is also shown that a fringe count of zero can be obtained for specific photodetector locations and wave front angles where the combined effect of fringe contraction and fringe tilt can have equal and opposite effects. Fringe tilt as a result of a wave front angle of 0.05° can introduce a phase measurement difference of 16° between a photodetector/pixel located 20 mm and one located 100 mm from the optical origin.
The problem of determining the optimal geometric configuration of a sensor network that will maximize the range-related information available for multiple target positioning is of key importance in a multitude of application scenarios. In this paper, a set of sensors that measures the distances between the targets and each of the receivers is considered, assuming that the range measurements are corrupted by white Gaussian noise, in order to search for the formation that maximizes the accuracy of the target estimates. Using tools from estimation theory and convex optimization, the problem is converted into that of maximizing, by proper choice of the sensor positions, a convex combination of the logarithms of the determinants of the Fisher Information Matrices corresponding to each of the targets in order to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. Analytical and numerical solutions are well defined and it is shown that the optimal configuration of the sensors depends explicitly on the constraints imposed on the sensor configuration, the target positions, and the probabilistic distributions that define the prior uncertainty in each of the target positions. Simulation examples illustrate the key results derived.
The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.
While photosystem II (PSII) of plants utilizes light for photosynthesis, part of the absorbed energy may be reverted back and dissipated as long-term fluorescence (delayed fluorescence or DF). Because the generation of DF is coupled with the processes of forward photosynthetic activities, DF contains the information about plant physiological states and plant-environment interactions. This makes DF a potentially powerful biosensing mechanism to measure plant photosynthetic activities and environmental conditions. While DF has attracted the interest of many researchers, some aspects of it are still unknown because of the complexity of photosynthetic system. In order to provide a holistic picture about the usefulness of DF, it is meaningful to summarize the research on DF applications. In this short review, available literature on applications of DF from PSII is summarized.
Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.
The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor’s information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient.
This paper focuses on several aspects extending the dynamical efficiency of a cantilever beam vibrating in the third mode. A few ways of producing this mode stimulation, namely vibro-impact or forced excitation, as well as its application for energy harvesting devices are proposed. The paper presents numerical and experimental analyses of novel structural dynamics effects along with an optimal configuration of the cantilever beam. The peculiarities of a cantilever beam vibrating in the third mode are related to the significant increase of the level of deformations capable of extracting significant additional amounts of energy compared to the conventional harvester vibrating in the first mode. Two types of a piezoelectric vibrating energy harvester (PVEH) prototype are analysed in this paper: the first one without electrode segmentation, while the second is segmented using electrode segmentation at the strain nodes of the third vibration mode to achieve effective operation at the third resonant frequency. The results of this research revealed that the voltage generated by any segment of the segmented PVEH prototype excited at the third resonant frequency demonstrated a 3.4-4.8-fold increase in comparison with the non-segmented prototype. Simultaneously, the efficiency of the energy harvester prototype also increased at lower resonant frequencies from 16% to 90%. The insights presented in the paper may serve for the development and fabrication of advanced piezoelectric energy harvesters which would be able to generate a considerably increased amount of electrical energy independently of the frequency of kinematical excitation.
Wireless sensor networks (WSNs) have become more and more diversified and are today able to also support high data rate applications, such as multimedia. In this case, per-packet channel handshaking/switching may result in inducing additional overheads, such as energy consumption, delays and, therefore, data loss. One of the solutions is to perform stream-based channel allocation where channel handshaking is performed once before transmitting the whole data stream. Deciding stream-based channel allocation is more critical in case of multichannel WSNs where channels of different quality/stability are available and the wish for high performance requires sensor nodes to switch to the best among the available channels. In this work, we will focus on devising mechanisms that perform channel quality/stability estimation in order to improve the accommodation of stream-based communication in multichannel wireless sensor networks. For performing channel quality assessment, we have formulated a composite metric, which we call channel rank measurement (CRM), that can demarcate channels into good, intermediate and bad quality on the basis of the standard deviation of the received signal strength indicator (RSSI) and the average of the link quality indicator (LQI) of the received packets. CRM is then used to generate a data set for training a supervised machine learning-based algorithm (which we call Normal Equation based Channel quality prediction (NEC) algorithm) in such a way that it may perform instantaneous channel rank estimation of any channel. Subsequently, two robust extensions of the NEC algorithm are proposed (which we call Normal Equation based Weighted Moving Average Channel quality prediction (NEWMAC) algorithm and Normal Equation based Aggregate Maturity Criteria with Beta Tracking based Channel weight prediction (NEAMCBTC) algorithm), that can perform channel quality estimation on the basis of both current and past values of channel rank estimation. In the end, simulations are made using MATLAB, and the results show that the Extended version of NEAMCBTC algorithm (Ext-NEAMCBTC) outperforms the compared techniques in terms of channel quality and stability assessment. It also minimizes channel switching overheads (in terms of switching delays and energy consumption) for accommodating stream-based communication in multichannel WSNs.
A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.