Approximate Data Collection

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.

A key barrier in the adoption of Wireless Sensor Networks (WSNs) is achieving long-lived and robust real-life deployments. Issues include: reducing the impact of transmission loss, node failure detection, accommodating multiple sensor modalities, and the energy requirement of the WSN network stack. In systems where radio transmissions are the largest energy consumer on a node, it follows that reducing the number of transmissions will, in turn, extend node lifetime. Research in this area has led to the development of the Dual Prediction Scheme (DPS). However, the design of specific DPS algorithms in the literature have not typically considered issues arising in real world deployments. Therefore, this thesis proposes solutions to enable DPSs to function in robust and long-lived real-world WSN deployments. To exemplify the proposed solutions, Cogent-House, an end-to-end open-source home environmental and energy monitoring system, is considered as a case study. Cogent-House was deployed...

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed.

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed.

In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime—a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.