Energy conservation is crucial in wireless ad hoc sensor network design to increase network lifetime. Since communication consumes a major part of the energy used by a sensor node, efficient communication is important. Topology control aims at achieving more efficient communication by dropping links and reducing interference among simultaneous transmissions by adjusting the nodes’ transmission power. Since dropping links make a network more susceptible to node failure, a fundamental problem in wireless sensor networks is to find a communication graph with minimum interference and minimum power assignment aiming at an induced topology that can satisfy fault-tolerant properties. In this paper, we examine and propose linear integer programming formulations and a hybrid meta-heuristic GRASP/VNS (Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Search) to determine the transmission power of each node while maintaining a fault-tolerant network and simultaneously minimize the interference and the total power consumption. Optimal biconnected topologies for moderately sized networks with minimum interference and minimum power are obtained using a commercial solver. We report computational simulations comparing the integer programming formulations and the GRASP/VNS, and evaluate the effectiveness of three meta-heuristics in terms of the tradeoffs between computation time and solution quality. We show that the proposed meta-heuristics are able to find good solutions for sensor networks with up to 400 nodes and that the GRASP/VNS was able to systematically find the best lower bounds and optimal solutions.
DOCUMENT
Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE.
DOCUMENT
from the article: The demand for a wireless CO2 solution is ever increasing. One of the biggest problems with the majority of commercial available CO2 sensors is the high energy consumption which makes them unsuitable for battery operation. Possible candidates for CO2 sensing in a low power wireless application are very limited and show a problematic calibration process. This study focuses on one of those EMF candidates, which is a Ag4RbI5 based sensor. This EMF sensor is based on the potentiometric principle and consumes no energy. The EMF cell was studied in a chamber where humidity, temperature and CO2 level could be controlled. This study gives an detailed insight in the different drift properties of the potentiometric CO2 sensor and a method to amplify the sensors signal. Furthermore, a method to minimize the several types of drift is given. With this method the temperature drift can be decreased by a factor 10, making the sensor a possible candidate for a wireless CO2 sensor network.
DOCUMENT
Reliability is a constraint of low-power wireless connectivity, commonly addressed by the deployment of mesh topology. Accordingly, power consumption becomes a major concern during the design and implementation of such networks. Thus, a mono-objective optimization was implemented in this work to decrease the total amount of power consumed by a low-power wireless mesh network based on Thread protocol. Using a genetic algorithm, the optimization procedure takes into account a pre-defined connectivity matrix, in which the possible distances between all network devices are considered. The experimental proof-of-concept shows that a mean gain of 26.45 dB is achievable in a specific scenario. Through our experimental results, we conclude that the Thread mesh protocol has much leeway to meet the low-power consumption requirement of wireless sensor networks.
LINK
In the GoGreen project an intelligent home that is able to identify inhabitants and events that take place is created. The location of sounds that are being produced is an important feature for the context awareness of this system. A a wireless solution that uses low-cost sensor nodes and microphones is described. Experiments show that solutions that only use the three sensor nodes that are closest to the origin of the sounds provide the best solutions, with an average accuracy of 40 cm or less.Paper published for the ICT Open 2013 proceedings (27-28 November 2013, Eindhoven).
MULTIFILE
From Springer description: "We present the design considerations of an autonomous wireless sensor and discuss the fabrication and testing of the various components including the energy harvester, the active sensing devices and the power management and sensor interface circuits. A common materials platform, namely, nanowires, enables us to fabricate state-of-the-art components at reduced volume and show chemical sensing within the available energy budget. We demonstrate a photovoltaic mini-module made of silicon nanowire solar cells, each of 0.5 mm2 area, which delivers a power of 260 μW and an open circuit voltage of 2 V at one sun illumination. Using nanowire platforms two sensing applications are presented. Combining functionalised suspended Si nanowires with a novel microfluidic fluid delivery system, fully integrated microfluidic–sensor devices are examined as sensors for streptavidin and pH, whereas, using a microchip modified with Pd nanowires provides a power efficient and fast early hydrogen gas detection method. Finally, an ultra-low power, efficient solar energy harvesting and sensing microsystem augmented with a 6 mAh rechargeable battery allows for less than 20 μW power consumption and 425 h sensor operation even without energy harvesting."
LINK
Sensor systems can be deployed in the homes of older adults living alone for functional health assessments. Their information is very useful for health care specialists. The problem lies in developing person independent models while facing a large variability in behavior. We address this problem by, first, proposing a new feature extraction method for data from ambient motion sensors. The method uses functional similarities between houses and daily structure to extract meaningful features. Second, we propose a change-based approach for analyzing data, taking difference scores of both the sensor features and health metrics. To evaluate our approach, experiments on longitudinal data were conducted, where the relationship between sensor data and health measurements was modeled with linear regression and (nonlinear) regression forests. These experiments show that the change-based approach yields better results and that the resulting models can be used as a reliable metric for (functional) health. In addition, feature analysis can help health care specialists understand relevant aspects of behavior. Prediction of health metrics is possible even with simple sensors. With such sensors, it is possible to detect problems and health decline in an early stage. This will have great impact on clinical practice.
DOCUMENT
The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods. © 2013 IEEE.
DOCUMENT
Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
DOCUMENT
Wireless sensor networks are becoming popular in the field of ambient assisted living. In this paper we report our study on the relationship between a functional health metric and features derived from the sensor data. Sensor systems are installed in the houses of nine people who are also quarterly visited by an occupational therapist for functional health assessments. Different features are extracted and these are correlated with a metric of functional health (the AMPS). Though the sample is small, the results indicate that some features are better in describing the functional health in the population, but individual differences should also be taken into account when developing a sensor system for functional health assessment.
DOCUMENT
