Energy-efficient Data Acquisition Research Articles

Fly, Wake-up, Find: UAV-based Energy-efficient Localization for Distributed Sensor Nodes

A challenging application scenario in the field of industrial Unmanned Aerial Vehicles (UAVs) is the capability of a robot to find and query smart sensor nodes deployed at arbitrary locations in the mission area. This work explores the combination of different communication technologies, namely, Ultra-Wideband (UWB) and Wake-Up Radio (WUR), with a UAV that acts as a “ubiquitous local-host” of a Wireless Sensor Network (WSN). First, the UAV performs the localization of the sensor node via multiple UWB range measurements, and then it flies in its proximity to perform energy-efficient data acquisition. We propose an energy-efficient and accurate localization algorithm – based on multi-lateration – that is computationally inexpensive and robust to in-field noise. Aiming at minimizing the sensor node energy consumption, we also present a communication protocol that leverages WUR technology to minimize ON-time of the power-hungry UWB transceiver on the sensors. In-field experimental evaluation demonstrates that our approach achieves a sub-meter localization precision of the sensor nodes – i.e., down to 0.6m – using only three range measurements, and runs in 4ms on a low power microcontroller (ARM Cortex-M4F). Due to the presence of the WUR and the proposed lightweight algorithm, the entire localization-acquisition cycle requires only 31mJ on the sensor node. The approach is suitable for several emerging Industrial Internet of Things application scenarios where a mobile vehicle needs to estimate the location of static objects without any precise knowledge of their position.

A novel quantum-inspired solution for high-performance energy-efficient data acquisition from IoT networks

Accuracy in IoT data acquisition has been an indispensable need to meet the increasing demand for time-sensitive data analysis, and real-time decision-making. Conspicuously, this study proposes a Quantum Computing inspired technique of temporal space optimization for real-time big IoT applications. For this purpose, quantification of IoT sensors is performed in terms of Sensors of Interest (SoI) and Degree of Aptness (DoA) measure to minimize IoT sensor-space in real-time. The proposed methodology incorporates quantum computing-based formalization of IoT sensor parameters to present a Quantum-Temporal Minimization Algorithm. Moreover, 2 key performance indicators in terms of Data Similarity Analysis and Energy Efficiency are estimated for optimized efficacy. To evaluate the presented technique, numerous simulations are performed in real-time scenario of vehicular traffic determination over 1km of Regional National Highway using 70 WiSense nodes comprising of noise sensors, vibration sensors, and Raspberry Pi device. Acquired data comprising of 28,586 segments are stored in the Amazon EC2 cloud database for evaluation. The performance enhancement is estimated based on comparative analysis with several state-of-the-art optimization techniques. Results registered depict that significant improvements are registered for the presented technique in terms of temporal effectiveness, and performance parameters like Accuracy, Correlation Analysis, and Reliability.

A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink

Solutions for energy hole problem in wireless sensor networks (WSNs) have been excessively explored using mobile sink (MS). Although, MS provides a considerable amount of energy saving and extends network lifetime. However, MS introduces varying degree of data acquisition latency depending on the trajectory followed. Therefore, rendezvous based data acquisition methods are proposed to mitigate this issue which are aimed to provide a trade-off between energy consumption and data acquisition latency. There exists a list of surveys that focus on issues related to sink mobility such as mobility aware energy efficient data acquisition schemes, mobility aware data acquisition and routing, etc. However, none of these surveys concern about the issue of providing a trade-off between energy consumption and data acquisition latency. Therefore, this review addresses the same issue and presents a taxonomy of rendezvous based data acquisition methods along with the design goals and associated designing requirements. The methods are grouped into two categories: rendezvous point (RP) based and rendezvous area (RA) based. Furthermore, a phase-wise comprehensive overview of these methods is provided which clearly unfold the way of resolving the targeted issue. Finally, the research issues and challenges are discussed in pursuit of data acquisition by MS.

Precise, Energy-Efficient Data Acquisition Architecture for Monitoring Radioactivity Using Self-Sustainable Wireless Sensor Nodes

Measuring radiation dosage rates is becoming more and more important in many applications scenarios. Continuously monitoring radiation in contaminated and poorly accessible areas is challenging due to the frequent data collection/transmission combined with long life requirements. We present a self-sustainable wireless sensor node for low power, high precision radiation dosage rate monitoring. We propose an energy-efficient data acquisition algorithm that can reduce the energy per measurement, while guaranteeing minimal loss of precision. The proposed node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deployment of the wireless sensor nodes at suitable locations, and acquiring data logs via low-power, short-range radio communication in fly-by mode after a wake-up command. The system uses off-the-shelf components for defining the mission, drop-zone, and trajectory, for compressing data and managing communication. The node is equipped with a novel low-power nuclear radiation sensor, and has been designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management, and a nano-watt wake-up radio. Experimental results demonstrate the precision and the low energy consumption of the radiation sensor, the energy efficiency of the whole solution, and the acquisition algorithms. The node consumes only 31 $\mu \text{W}$ in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed.

Low Power Data Acquisition System for Bioimplantable Devices

Signal acquisition represents the most important block in biomedical devices, because of its responsibilities to retrieve precise data from the biological tissues. In this paper an energy efficient data acquisition unit is presented which includes low power high bandwidth front-end amplifier and a 10-bit fully differential successive approximation ADC. The proposed system is designed with 0.18 µm CMOS technology and the simulation results show that the bioamplifier maintains a wide bandwidth versus low noise trade-off and the proposed SAR-ADC consumes 450 nW power under 1.8 V supply and retain the effective number of bit 9.55 in 100 KS/s sampling rate.

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN.

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long‐term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long‐term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point‐source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption.

Adaptive Level-Crossing Sampling Based DSP Systems

Adaptive level–crossing sampling based DSP systems are able to provide energy–efficient data acquisition and transmission. The proposed sampling mechanism is a generalization of the sampling with equidistantly spaced reference levels. Two different sampling implementations are offered. Simulation of the adaptive level–crossing sampling is performed to estimate the number of reference levels and evaluate the quality of signal reconstruction. The approach is targeting non–stationary and nonlinear signals using non–conventional digital signal processing methods. High correlation between input and reconstructed signal is supporting use of small number 9–16 sampling levels. Ill. 5, bibl. 13, tabl. 4 (in English; abstracts in English, Russian and Lithuanian).

Energy conservation in wireless sensor networks: A survey

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

Data acquisition in multiple-sink sensor networks

Scalable, energy-efficient data acquisition in large sensor network deployments such as habitat monitoring is an research important problem. In several papers [1, 2], sensor networks have been modeled as having a single sink (or base-station) that acts as the data recipient for a large number of sensors (data sources) deployed over a sensor field. The sensor network might use simple querying and data collection trees for hop-by-hop query dissemination and routing of sensor responses [1] back towards the sink. Since sensors are energy-constrained devices, we wish to minimize communication energy expenditure of these sensors.