D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks

Abstract

The reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specific metrics that enable the evaluation of the model in terms of traffic savings, precision, and recall. Different model configurations are tested with two experimental cases, whose input data are extracted from an extensive set of real data. In particular, two new versions of Send–on–Delta (SoD) and Predictive Sampling (PS) have been designed and implemented in the proposed data–domain reduction for threshold–based event detection (D2R-TED) model. The obtained results illustrate the potential usefulness of analyzing different model configurations to obtain a cost–benefit curve, in terms of traffic savings and quality of the response. Experiments show an average reduction of 76% of network packages with an error of less than 1%. In addition, experiments show that the methods designed under the proposed D2R–TED model outperform the original event–triggered SoD and PS methods by 10% and 16% of the traffic savings, respectively. This model is useful to avoid network bottlenecks by applying the optimal configuration in each situation.

Data Communication Optimization for the Evaluation of Multivariate Conditions in Distributed Scenarios

“…The main background is the D2R-TED model [21]. This model provides the mathematical foundations to allow the data management of multivariate functions with thresholdbased event detection.…”

“…These sampling strategies provide an interesting approach to traffic aware methods by reducing the number of data needed for monitoring tasks. The potential of such methods when contextualising the use of data in processes with Boolean results was demonstrated in [21], in which a precision, recall, and traffic savings analysis is presented when evaluating conditional expressions with signals sampled by magnitude criteria. The study also proposes metrics to measure the effect of delays in the detection of transitions in the resulting signal, as a consequence of the magnitude-based sampling scheme.…”

“…Paper [21] delves into the effect of data resolution on the result of conditional expressions. The methodology proposes metrics to discrepancies between the obtained and actual Boolean signals in terms of precision and recall.…”

An 8.8 ps RMS Resolution Time-To-Digital Converter Implemented in a 60 nm FPGA with Real-Time Temperature Correction

“…When propagation delay changes exceeded a certain threshold, the bin-by-bin correction was launched, and LUT was updated on the threshold. Thus, the temperature correction scheme could be considered as an event-based control approach [22][23][24][25][26][27]. The proposed scheme was validated in a two-channel non-uniform multiphase (NUMP) TDC [28] using an Altera 60 nm Cyclone 10 LP FPGA and achieved a resolution of 8.8 ps root mean square (RMS) over a wide temperature range from 5 • C to 80 • C.…”

DDR-coin: An Efficient Probabilistic Distributed Trigger Counting Algorithm

“…With DTC algorithms, the number of exchanged messages can be reduced significantly (for more detailed relationship between DTC algorithms and global snapshot algorithms, please refer to the work in [ 3 ]). DTC algorithms can also be useful for monitoring specific events [ 7 , 8 , 9 , 10 , 11 ], which is one of the core functionalities in distributed systems including computational grids, cluster computing and wireless sensor networks [ 12 , 13 , 14 , 15 , 16 ].…”