Dedicated Sensor Network Research Articles

An underground drip water monitoring network to characterize rainfall recharge of groundwater at different geologies, environments, and climates across Australia

Abstract. Understanding when and why groundwater recharge occurs is of fundamental importance for the sustainable use of this essential freshwater resource for humans and ecosystems. However, accurately capturing this component of the water balance is widely acknowledged to be a major challenge. Direct physical measurements identifying when groundwater recharge is occurring are possible by utilizing a sensor network of hydrological loggers deployed in underground spaces located in the vadose zone. Through measurements of water percolating into these spaces from above, we can record the potential groundwater recharge process in action. By using automated sensors, it is possible to precisely determine when recharge occurs (which event, month, or season and for which climate condition). Combined with daily rainfall data, it is possible to quantify the “rainfall recharge threshold”, the amount of rainfall needed to generate groundwater recharge, and its temporal and spatial variability. Australia's National Groundwater Recharge Observing System (NGROS) provides the first dedicated sensor network for observing groundwater recharge at an event scale across a wide range of geologies, environments, and climate types representing a wide range of Australian hydroclimates. Utilizing tunnels, mines, caves, and other subsurface spaces located in the vadose zone, the sensors effectively record “deep drainage”, water that can move beyond the shallow subsurface and root zone to generate groundwater recharge. The NGROS has the temporal resolution to capture individual recharge events, with multiple sensors deployed at each site to constrain the heterogeneity of recharge between different flow paths, and to quantify (including uncertainty bounds) rainfall recharge thresholds. Established in 2022, the network is described here together with examples of data being generated.

Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenarios

Mobile Crowdsensing has become an important paradigm in the last decade for on-demand monitoring scenarios in Smart Cities and vehicular networks, when the deployment of a dedicated sensor network is no longer affordable. To foster the participation of a large user base, it is common to reward them on top of the amount and the quality of data provided. Regardless of the MCS policy adopted, this requires the crowdsourcer to keep track of the participants. Since the contributed data inherently carries sensitive spatio-temporal information, privacy problems arise if a malicious entity gains access to it; still, in some cases, the spatio-temporal precision is crucial for the benefit of the application and cannot be distorted. In this paper we propose a privacy preserving framework for opportunistic MCS scenarios that includes data collection and rewarding phases. The framework both retains the precision of spatio-temporal information and limits the sensitivity of information disclosed through an algorithm that clusters the data points into low correlated sets. The framework is agnostic about how correlation is calculated, and we propose three exemplary correlation functions. We evaluate our framework against six real world datasets, assessing its efficacy and envisioning its implementation in practical deployments.

A Crowd-Aided Vehicular Hybrid Sensing Framework for Intelligent Transportation Systems

The development of Internet of Things (IoT) techniques enables the paradigm shift in traffic data collecting. In traditional practices of transportation system's constructions, traffic-related information is collected based on dedicated sensor networks, which are not only coverage-limited but also cost-consuming. With the enrichment of the concepts concerning “social sensors” and “social transportation”, Sparse mobile crowdsensing (MCS) emerges as a promising sensing paradigm to collect data from only a few subareas by recruiting vehicles or mobile users with portable devices and to infer the data in unsensed subareas with acceptable errors at a low-cost manner. However, in real-world sensing campaigns, the Sparse MCS systems often fail to collect data from any subareas of interest since the assumption about sufficient participants is not always realistic. To be specific, the recruitment of participants is often limited by interest deficiency, privacy awareness, and distribution biases. To handle this problem, we introduce the dedicated sensing vehicles (DSVs) e.g., drones or driverless vehicles into traditional Sparse MCS to improve subarea coverage and inference performance. To achieve effective collaboration among DSVs and mobile users, we first design a crowd-aided vehicular hybrid sensing framework, which defines the order of task assignment for DSVs and mobile users as well as the budget allocation. In terms of DSVs route planning, we propose a three-step strategy, including optimal route searching, fused route selection, and final route determination. Moreover, mobile users are selected based on a proposed novel selection strategy. Experimental findings on two real-world datasets validate the effectiveness (with less inference error) of the hybrid sensing framework and the proposed strategies, in comparison with the user-only/DSV-only framework and five baselines. Results reveal important implications of applying the hybrid sensing paradigm in intelligent transportation systems to enhance data collection.

A Distributed Compressive Data Gathering Framework For Mobile Crowdsensing

Existing work on data gathering in mobile crowdsensing (MCS) usually assumes that the motion of the participants is either known or predictable via a mobility model. As such, the organizers can tell them when/where to sense, ensuring the data can be gathered from the target area with a minimized number of participants. Knowing exactly where the participants go, however, is not trivial, since the movements of the participants are in fact autonomous and random. In this paper, we investigate a common compressive data gathering framework for MCS, without trying to predict how a specific participant moves. We notice a key observation: while the participants move autonomously in the target sensing area, each trajectory itself provides a random, and thus valuable coverage for a sensing task. With compressive sensing (CS), these random trajectories can be utilized to exploit the spatio-temporal data correlation. Given this, we first introduce a novel idea of virtual sensor network to define the target sensing area, and then propose a distributed CS-based encoding algorithm to compress the data in the target area such that the number of the participants involved in data gathering can be reduced. We further propose a round-based CS reconstruction algorithm to exploit the inter-round data correlation and improve the accuracy of the data reconstruction. Experimental results using real sensor readings show that the proposed scheme achieves better performance than existing ones with dedicated sensor networks.

Measuring Occupants’ Behaviour for Buildings’ Dynamic Cosimulation

Measuring and identifying human behaviours are key aspects to support the simulation processes that have a significant role in buildings’ (and cities’) design and management. In fact, layout assessments and control strategies are deeply influenced by the prediction of building performance. However, the missing inclusion of the human component within the building-related processes leads to large discrepancies between actual and simulated outcomes. This paper presents a methodology for measuring specific human behaviours in buildings and developing human-in-the-loop design applied to retrofit and renovation interventions. The framework concerns the detailed building monitoring and the development of stochastic and data-driven behavioural models and their coupling within energy simulation software using a cosimulation approach. The methodology has been applied to a real case study to illustrate its applicability. A one-year monitoring has been carried out through a dedicated sensor network for the data recording and to identify the triggers of users’ actions. Then, two stochastic behavioural models (i.e., one for predicting light switching and one for window opening) have been developed (using the measured data) and coupled within the IESVE simulation software. A simplified energy model of the case study has been created to test the behavioural approach. The outcomes highlight that the behavioural approach provides more accurate results than a standard one when compared to real profiles. The adoption of behavioural profiles leads to a reduction of the discrepancy with respect to real profiles up to 58% and 26% when simulating light switching and ventilation, respectively, in comparison to standard profiles. Using data-driven techniques to include the human component in the simulation processes would lead to better predictions both in terms of energy use and occupants’ comfort sensations. These aspects can be also included in building control processes (e.g., building management systems) to enhance the environmental and system management.

Experimental study on occupants' interaction with windows and lights in Mediterranean offices during the non-heating season

The modelling of human behaviour is an important challenge for the building sector, as the actions of users have significant impacts on both energy consumption and comfort assessment. In the search for a comprehensive understanding of the behaviour of occupants, many researchers have directed their efforts towards determining typical patterns and developing models to predict human-building interactions. This study investigates the behaviour of building users during the summer season in offices in Mediterranean climate. Studies focusing on this area are still lacking, despite their importance for cooling loads. A survey is conducted using a dedicated sensor network to monitor environmental variables, and to determine the presence of people and their interactions with windows and lights in three offices. The driving factors for the actions of users are assessed and behavioural models are proposed. The results indicate that interactions with windows and lights are driven by both time-related events and environmental factors, confirming previous findings. A comparison of the proposed models with others developed for different climate zones suggests that interactions with windows are affected by the geographic area, while light switching behaviour seems to be very similar for the different case studies. A simplified approach for the consideration of different user-device interactions is also proposed. This novel method, developed to evaluate the interactivity between users and building systems, is based on a coefficient of interactivity, CI. Both the behavioural models and the simplified approach could be introduced into future simulations to improve predictions of energy use in buildings.

Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring

Much attention has been focused on the research of structural health monitoring (SHM), since it could increase the safety and reduce the maintenance costs of engineering structures. In recent years, wireless sensor network (WSN) has been explored for adoption to improve the centralized cable-based SHM system performances. This paper presents a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring to validate the efficiency of the multi-agent technology. Through the cooperation of six different agents for SHM applications, the distributed wireless sensor network can automatically allocate SHM tasks, self-organize the sensor network and aggregate different sensor information. In the evaluation work, the strain gauge and PZT sensors are used to monitor strain distribution change and joint failure of an experimental aluminum plate structure. A dedicated sensor network platform including the wireless strain node, wireless PZT node and wireless USB station is designed for the evaluation system. Based on the hardware platform, the multi-agents software architecture is defined. The multi-agent monitoring principle and implementation in the validation work for two typical kinds of structure states are presented. This paper shows the efficiency of the multi-agent technology for WSN based the SHM applications on the large aircraft structures.

Energy Efficient Signal Detection in Sensor Networks Using Ordered Transmissions

Signal detection is one application for which dedicated sensor networks have been proposed. Joint signal processing and communication design of such networks has been of great interest recently. We consider new approaches where transmissions can be ordered and halted when sufficient evidence is accumulated. We demonstrate that these approaches require, on average, fewer sensor transmissions and that the savings can be significant in cases of interest. We describe a highly efficient approach which saves transmissions over either the optimum unconstrained energy approach or censoring while achieving the same error probability as these approaches. The average number of transmissions saved (ANTS) is lower bounded by a quantity proportional to the number of sensors employed provided a well-behaved distance measure between the sensor distributions is sufficiently large. For such cases, the ANTS over the optimum unconstrained energy approach is shown to be larger than half the number of sensors employed. Numerical results for a mean-shift hypothesis testing problem with Gaussian noise show significant savings even for smaller values of the distance measure.