This paper describes the design, implementation and evaluation of a search and rescue system called CenWits. CenWits uses several small, commonly-available RF-based sensors, and a small number of storage and processin...
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Generally the energy of each node in wireless sensornetworks is restricted, energy efficient and energy evenly distributed route protocol is indispensable to prolong the lifetime of the network. Although Low-Energy A...
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We propose an energy-efficient hybrid data collection architecture based on controllably mobile infrastructure for a class of applications in which sensornetworks provide both low-priority and high-priority data. Hig...
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ISBN:
(纸本)9781424415014
We propose an energy-efficient hybrid data collection architecture based on controllably mobile infrastructure for a class of applications in which sensornetworks provide both low-priority and high-priority data. High-priority data require a data delivery scheme with low latency and high fidelity. Meanwhile low-priority data may tolerate high-latency data delivery. Our approach exploits the design of a network that supports a hybrid data delivery scheme to enhance the network performance and reduces total network energy usage. In our system design two delivery schemes are deployed for purposes of comparison. The first is the traditional ad hoc approach to deliver high-priority data with high fidelity and low latency. The second presents a controllable infrastructure in the sensor field, which acts as low-priority data collection agent. Through simulations, we show that our proposed approach can provide substantial energy saving in this class of sensor application compared to the traditional multihop approach used alone.
Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless sensornetworks (WSNs). ML leverages the power of data analysis and pattern recognition to detect ...
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ISBN:
(纸本)9798350372977;9798350372984
Machine Learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless sensornetworks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSNs. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.
This paper proposes an enhanced pattern discovery algorithm for data streams processing of sensornetworks, in order to improve the performance of SPIRIT. The new algorithm adapts the optimized correction for tracking...
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In this paper we describe the recent development of a wireless sensor network to monitor human motion and stream relevant events over the network. The events are locally generated based on accelerometer, magnetometer ...
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ISBN:
(纸本)9781424415014
In this paper we describe the recent development of a wireless sensor network to monitor human motion and stream relevant events over the network. The events are locally generated based on accelerometer, magnetometer and gyroscope data, taken by a sensor node, which is worn by a person. A reduced radio transmission range enables implicit coarse-grain localization. To minimize the number of radio messages, the events indicate changes in the person's motion state. This paper describes a case study, where walking steps are detected using realtime wavelet analysis of accelerometer data, which can be used for finer grain localization purposes. To enable the reliable transfer of every event with a minimal radio packet overhead, a delay tolerant network using an implicit acknowledge protocol has been implemented and tested.
A wireless sensor network, is in information technology infrastructure capable of processing theoretically unlimited sequences of data commonly called as data streams. dA heterogeneous sensor network consists of sensi...
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ISBN:
(纸本)9783642011115
A wireless sensor network, is in information technology infrastructure capable of processing theoretically unlimited sequences of data commonly called as data streams. dA heterogeneous sensor network consists of sensing nodes creating the streams of atomic data items and clustering nodes joining several data streams into one stream of complex data items and performing the preliminary data processing. This work investigates the microprogramming implementation of join operation at the clustering nodes. We define a data stream join operation and we prove that it can be computed in an incremental way. Then, we propose a number of optimisation techniques for its processing. The micro implementation of join operation on 8-bit microprocessor controller with memory and interfacing ports is derived from the incremental processing algorithm. We describe architecture of firmware implementation of join operation and we propose a network implementation of a number of join modules. The experimental results include testing of join operation on two and many data streams.
We propose a novel algorithm for sensor self-localization in cooperative wireless networks where observations of relative sensor distances are available. The variational message passing (VMP) algorithm is used to impl...
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ISBN:
(纸本)9781457705953
We propose a novel algorithm for sensor self-localization in cooperative wireless networks where observations of relative sensor distances are available. The variational message passing (VMP) algorithm is used to implement a mean field solution to the estimation of the posterior probabilities of the sensor positions in an R-2 scenario. Extension to R-3 is straight-forward. Compared to non-parametric methods based on belief propagation, the VMP algorithm features significantly lower communication overhead between sensors. This is supported by performance simulations which show that the estimated mean localization error of the algorithm stabilizes after approximately 30 iterations.
sensornetworks support flexible, non-intrusive and fine-grained data collection and processing and can enable online monitoring of data center operating conditions as well as autonomic data center management. This pa...
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ISBN:
(纸本)9781424437511
sensornetworks support flexible, non-intrusive and fine-grained data collection and processing and can enable online monitoring of data center operating conditions as well as autonomic data center management. This paper describes the architecture and implementation of an autonomic power aware data center management framework, which is based on the integration of embedded sensors with computational models and workload schedulers to improve data center performance in teens of energy consumption and throughput. Specifically, workload schedulers use online information about data center operating conditions obtained from the sensors to generate appropriate management policies. Furthermore, local processing within the sensor network is used to enable timely responses to changes in operating conditions and determine job migration strategies. Experimental results demonstrate that the framework achieves near optimal management, and in-network analysis enables timely response while reducing overheads.
The Internet of Things concept is leaving its toddler age where essentially it is a research issue and becoming a key player in many current applications where Smart Cities are perhaps its greatest exponent. In such r...
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ISBN:
(纸本)9789897580017
The Internet of Things concept is leaving its toddler age where essentially it is a research issue and becoming a key player in many current applications where Smart Cities are perhaps its greatest exponent. In such realworld scenarios, efficient large scale machine-to-machine communication is of utmost importance. However, the current 6LoWPAN standard, proposed by the IETF, has some efficiency problems which can make its application to the Internet of Things very difficult to scale up. This work transforms the existent 6LoWPAN implementation enabling a data-centric solution that will overcome the current viability issues of 6LoWPAN in these networks through the integration of an in-network data processing aggregation mechanism. The proposed data aggregation mechanism increases dramatically the sustainability and network lifetime of 6LoWPAN based sensornetworks, contributing directly to the Internet of Things revolution.
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