Nga Dang

QuARES: Quality-aware data collection in energy harvesting sensor networks

Renewable energy technology has become a promising solution to reduce energy concerns due to limited battery in wireless sensor networks. While this enables us to prolong the lifetime of a sensor network (perpetually), unstable environmental energy sources bring challenges in the design of sustainable sensor networks. In this paper, we propose an adaptive energy harvesting management framework, QuARES, which exploits an applications tolerance to quality degradation to adjust application quality based on energy harvesting conditions. The proposed framework consists of two stages: an offline stage which uses prediction of harvested energy to allocate energy budget for time slots; and an online stage to tackle the fluctuation in time-varying energy harvesting profile. We implemented the application and our framework in a network simulator, QualNet. In comparison with other approaches (e.g., [9]), our system offers improved sustainability (low energy consumption, no node deaths) during operation with data quality improvement ranging from 30–70%. QuARES is currently being deployed in a campus-wide pervasive space at UCI called Responsphere[11].

Energy Budget Management for Energy Harvesting Embedded Systems

In battery-powered embedded systems, the limit of battery charge creates a challenge in scheduling tasks to meet both their deadlines and Quality of Service (QoS) requirements. Harvesting energy from the surrounding environment continuously eliminates the concern of limited battery charge. However, the uncertainty in availability of energy brings challenges in embedded systems. In this paper, we propose an energy management technique to maximize QoS of the system. Our technique is composed of two steps: an offline step and an online step. In the offline step we use frame-based energy harvesting prediction in one harvesting period, in order to find the best QoS level for the tasks and maximize the energy utilization. The information provided from the offline step guides the online scheduler to decide about job scheduling at run-time to minimize the QoS violation. We compared our scheduler with other approaches and on average we reduce the violation count by 22%.

Adapting data quality with multihop routing for energy harvesting wireless sensor networks

Renewable energy technology is a viable and promising solution toward achieving self-sustainable low power wireless sensor networks. However, the uncertainty and fluctuations in energy availability require a sophisticated energy management scheme, i.e., energy demand of each sensor node at any time does not exceed its available energy. In this paper, we propose to continuously adapt the energy requirements of sensor nodes based on availability of renewable energy sources, network routing needs and application quality constraints - addressing these trade-offs is our distinctive contribution in this paper. We present a novel algorithm to find the optimal uniform data quality for approximated data collection in a multihop energy-harvesting wireless sensor network (EH-WSN). Our approach guarantees routing sustainability in the network and has significantly less failed data queries (<;2%) as compared to a state-of-the-art energy-harvesting-aware routing protocol [10][11] which is not aware of data quality.

Modeling and control battery aging in energy harvesting systems

Energy storage is a fundamental component for the development of sustainable and environment-aware technologies. One of the critical challenges that needs to be overcome is preserving the State of Health (SoH) in energy harvesting systems, where bursty arrival of energy and load may severely degrade the battery. Tools from Markov process and Dynamic Programming theory are becoming an increasingly popular choice to control dynamics of these systems due to their ability to seamlessly incorporate heterogeneous components and support a wide range of applications. Mapping aging rate measures to fit within the boundaries of these tools is non-trivial. In this paper, a framework for modeling and controlling the aging rate of batteries based on Markov process theory is presented. Numerical results illustrate the tradeoff between battery degradation and task completion delay enabled by the proposed framework.

Multi-level QoS Support with Variable Window Size in Weakly Hard Real-Time Systems

Weakly hard real-time model has been successfully used for scheduling in firm real-time systems where some deadline misses are tolerable. The tolerable deadline misses are specified using a (m, k) tuple, which means that tasks are desired to meet m deadlines in any k consecutive task invocations. This model is adopted to provide QoS in many applications such as control systems or multimedia applications. In this paper, we show that multiple QoS levels can be provided using (m, k) constraint with variable window size k. Even though the size of window k is changed at run time, it is shown that the QoS level with minimum ratio of m/k is guaranteed for evenly distributed pattern. Using this guarantee, we show that higher utilization with equal or higher QoS can be achieved in overload management.

Orchestrated application quality and energy storage management in solar-powered embedded systems

While energy harvesting technology is a promising solution toward achieving self-sustainable low power systems, the efficient energy storage for these energy harvesting systems is still a challenge because of high self-leakage (e.g., supercapacitors) and limited life cycles (e.g., batteries). In this work, we propose an adaptive quality-aware energy management middleware framework for energy harvesting embedded systems. Our hybrid energy storage model takes into consideration the battery life cycle, supercapacitor self-leakage, and power loss in the harvesting circuit. The framework has an offline planning phase and a runtime adaptation phase. By incorporating abstract models for battery state of health (SoH) and supercapacitor self-leakage, the offline stage determines the budget for charging and discharging distribution of each storage component and accordingly adapts the application quality of service (QoS). The runtime adaptation phase dynamically adjusts the charging and discharging distribution to the dynamic changes in energy harvesting profile. In comparison with related work, our proposed framework is able to capture the lifetime and characteristics of the energy storage components more accurately during adaptation and hence, resulting in a more sustainable system with realistic QoS.

A Unified Stochastic Model for Energy Management in Solar-Powered Embedded Systems

Energy harvesting from environments such as solar energy are promising solutions to tackle energy sustainability in embedded systems. However, uncertainties in energy availability, non-ideal characteristics of harvesting circuits, energy storage (battery or supercapacitor), and application demand dynamics add more complexity in the system. We present a unified model based on discrete-time Finite State Markov Chain to capture the dynamicity and variations in both the energy supply from solar irradiance and the energy demand from the application. In this paper, we exploit the temporal and spatial characteristics of solar energy and propose a deterministic profile with stochastic process to reflect the fluctuation due to unexpected weather condition. Optimal policy to maximize expected total QoS is derived from the presented model using a probabilistic dynamic programming approach. Compared to a state-of-the-art deterministic energy management framework, our proposed approach outperforms in term of QoS and energy sustainability (with less shutdown time) of the system.

Distributed flow optimization control for energy-harvesting wireless sensor networks

This paper proposes a distributed flow-based routing technique in energy-harvesting wireless sensor networks (EHWSNs) in order to balance the energy consumptions by sending packets assigned to routers that are sent from sensors to base stations. The objective of the flow optimization problem is to minimize the total load factors of all the nodes and wireless links, which leads to sustainable management of the sensor networks that exploit renewable power from energy harvesting systems. We propose a novel algorithm based on tie-set graph theory where the underlying graph of an EHWSN is divided into a set of independent loops to significantly reduce the topological complexity, which simplifies the flow optimization problem to be solved in a distributed manner. Simulation experiments against the shortest-path and multi-path algorithms demonstrate that optimized packet flows by the proposed method realize the sustainable EHWSNs and maintain the useful life of storage devices with modest increase in total energy consumption by routings.

Harvesting-aware adaptive energy management in solar-powered embedded systems

Solar-powered embedded systems are challenged by variations and uncertainties in energy availability due to seasonal changes and weather conditions. Such systems need to deploy various schemes to adapt their energy consumption in order to achieve energy sustainability. We target solar-powered real-time systems with adaptive QoS model. A holistic middleware framework for energy management that orchestrates DVFS and application QoS in solar-powered real-time systems is proposed. Extensive experiments are carried out to show the effectiveness and the robustness of our technique on two case studies on a solar-powered smart camera system. Simulation results show an improvement of 19%-50% in terms of total QoS compared to a DVFS framework with fixed QoS model for real time embedded systems.

Energy Harvesting for Sustainable Smart Spaces

Abstract Energy sustainability is a challenge in making smart spaces truly pervasive. Renewable energy technologies have become a promising solution to reduce energy concerns that arise due to limited battery in wireless sensor networks, the backbone of many smart spaces. While this enables us to prolong the lifetime of a wireless sensor network (perpetually), the realization of such sustainable micro-scale energy harvesting system is challenging due to the unstable nature of environmental energy sources and demanding requirements of applications. In this chapter, we show the model of micro-scale energy harvesting system and research efforts both in designing low-power efficient hardware platforms and in creating software components to operate micro-scale energy harvesting systems at their maximum potentials. We highlight the need of deployment study of micro-scale energy harvesting systems in addition to designing and operating research and propose middleware architecture for a unified structured way to optimize energy harvesting system performance.