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Pro-Energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks

Energy harvesting is one of the most promising technologies towards the goal of perpetual operation of wireless sensor networks (WSNs). Environmentally-powered systems, however, have to deal with the variable behavior of ambient energy sources, which results in different amounts and rates of energy available over time. To alleviate the problem of the harvested power being neither constant nor continuous, energy prediction methods can be employed. Such models forecast the source availability and estimate the expected energy intake, allowing the system to take critical decisions about the utilization of the available energy. In this work, we present a novel energy prediction model, named Pro-Energy (PROfile energy prediction model), for multi-source energy harvesting WSNs, which is able to leverage past energy observations to provide accurate estimations of future energy availability. To assess the performance of our proposed solution, we use real-life solar and wind traces that we collected by interfacing TelosB nodes with solar cells and wind micro-turbines, as well as public available traces of solar and wind obtained from weather monitoring stations in the US. A comparative performance evaluation between Pro-Energy and energy predictors previously proposed in the literature, such as EWMA and WCMA, has shown that our solution significantly outperforms existing algorithms for both short and medium term prediction horizons, improving the prediction accuracy up to 60%.

Beyond duty cycling: Wake-up radio with selective awakenings for long-lived wireless sensing systems

Emerging wake-up radio technologies have the potential to bring the performance of sensing systems and of the Internet of Things to the levels of low latency and very low energy consumption required to enable critical new applications. This paper provides a step towards this goal with a twofold contribution. We first describe the design and prototyping of a wake-up receiver (WRx) and its integration to a wireless sensor node. Our WRx features very low power consumption (<; 1.3μW), high sensitivity (up to -55dBm), fast reactivity (wake-up time of 130μs), and selective addressing, a key enabler of new high performance protocols. We then present ALBA-WUR, a cross-layer solution for data gathering in sensing systems that redesigns a previous leading protocol, ALBA-R, extending it to exploit the features of our WRx. We evaluate the performance of ALBA-WUR via simulations, showing that the use of the WRx produces remarkable energy savings (up to five orders of magnitude), and achieves lifetimes that are decades longer than those obtained by ALBA-R in sensing systems with duty cycling, while keeping latencies at bay.

GreenCastalia: an energy-harvesting-enabled framework for the Castalia simulator

The emergence of energy-scavenging techniques for powering networks of embedded devices is raising the need for dedicated simulation frameworks that can support researchers and developers in the design and performance evaluation of harvesting-aware protocols and algorithms. In this work we present GreenCastalia, an open-source energy-harvesting simulation framework we have developed for the popular Castalia simulator. GreenCastalia supports multi-source and multi-storage energy harvesting architectures, it is highly modular and easily customizable. In addition, it allows to simulate networks of embedded devices with heterogeneous harvesting capabilities.

AGREE: exploiting energy harvesting to support data-centric access control in WSNs

This work is motivated by a general question: can energy harvesting capabilities embedded in modern sensor nodes be exploited so as to support security mechanisms which otherwise would be too demanding and hardly viable? More specifically, in this work we focus on the support of extremely powerful, but complex, fine-grained data-centric access control mechanisms based on multi-authority Ciphertext Policy Attribute Based Encryption (CP-ABE). By integrating access control policies into the (encrypted) data, such mechanisms do not require any server-based access control infrastructure and are thus highly desirable in many wireless sensor network scenarios. However, as concretely shown by a proof-of-concept implementation first carried out in this paper on TelosB and MicaZ motes, computational complexity and energy toll of state-of-the-art multi-authority CP-ABE schemes is still critical. We thus show how to mitigate the relatively large energy consumption of the CP-ABE cryptographic operations by proposing AGREE (Access control for GREEn wireless sensor networks), a framework that exploits energy harvesting opportunities to pre-compute and cache suitably chosen CP-ABE-encrypted keys, so as to minimize the need to perform CP-ABE encryptions when no energy from harvesting is available. We assess the performance of AGREE by means of simulation and actual implementation, validating its operation with real-world energy-harvesting traces collected indoors by TelosB motes equipped with photovoltaic cells, as well as public available traces of radiant light energy. Our results show that complex security mechanisms may become significantly less demanding when implemented so as to take advantage of energy harvesting opportunities.

Adaptive Rectifier Driven by Power Intake Predictors for Wind Energy Harvesting Sensor Networks

This paper presents a power management technique for improving the efficiency of harvesting energy from air-flows in wireless sensor networks (WSNs) applications. The proposed architecture consists of a two-stage energy conversion circuit: an ac-dc converter followed by a dc-dc buck-boost regulator with maximum power point tracking capability. The key feature of the proposed solution is the adaptive hybrid voltage rectifier, which exploits both passive and active topologies combined with power prediction algorithms. The adaptive converter significantly outperforms other solutions, increasing the efficiency between 10% and 30% with respect to the only passive and the only active topologies. To assess the performance of this approach in a real-life scenario, air-flow data have been collected by deploying WSN nodes interfaced with a wind microturbine in an underground tunnel of the Metro B1 line in Rome. It is shown that, using the adaptive ac-dc converter combined with power prediction algorithms, nodes deployed in the tunnel can harvest up to 22% more energy with respect to previous methods. Finally, it is shown that using power management techniques optimized for the specific scenario, the overall system overhead, in terms of average number of sampling performed per day by a node, is reduced of up to 93%.

Online Energy Harvesting Prediction in Environmentally Powered Wireless Sensor Networks

The increasing popularity of micro-scale power-scavenging techniques for wireless sensor networks (WSNs) is paving the way to energy-autonomous sensing systems. To sustain perpetual operations, however, environmentally powered devices must adapt their workload to the stochastic nature of ambient sources. Energy prediction models, which estimate the future expected energy intake, are effective tools to support the development of proactive power management strategies. In this paper, we present profile energy prediction model (Pro-Energy), an energy prediction model for multi-source energy-harvesting WSNs that leverages past energy observations to forecast future energy availability. We then propose Pro-Energy with variable-length timeslots (Pro-Energy-VLT), an extension of Pro-Energy that combines our energy predictor with timeslots of variable lengths to adapt to the dynamics of the power source. To assess the performance of our proposed solutions, we use real-life solar and wind traces, as well as publicly available traces of solar irradiance and wind speed. A comparative performance evaluation shows that Pro-Energy significantly outperforms the state-of-the-art energy predictors, by improving the prediction accuracy of up to 67%. Moreover, by adapting the granularity of the prediction timeslots to the dynamics of the energy source, Pro-Energy-VLT further improves the prediction accuracy, while reducing the memory footprint and the energy overhead of energy forecasting.

Sensor-mission assignment in wireless sensor networks with energy harvesting

Sensor mission assignment concerns matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy harvesting capabilities, which impose quite a different energy model. In this paper we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, for energy harvesting WSNs. The objective of both model and EN-MASSE is to maximize the profit of the network, fully exploiting the harvesting technologies, while ensuring the execution of the most critical missions within a given target WSN lifetime. The performance of EN-MASSE is evaluated by simulations based on real solar energy traces. Our experiments show that EN-MASSE behaves very closely to the optimum provided by our model and significantly outperforms previously proposed solutions.

Energy-harvesting WSNs for structural health monitoring of underground train tunnels

The main goal of this work is to investigate the feasibility of a WSN with energy-harvesting capabilities for structural health monitoring, specifically targeting underground tunnels. To assess the energy availability in a real-life scenario, we instrumented an underground train tunnel in Rome with Telos B motes interfaced with wind micro-turbines, collecting air-flow data for more than a month. The paper analyse the collected data to quantify the energy availability in terms of typical WSN operations, including communication, storage and sensing. In addition, the energy requirements of a typical sensor for underground tunnels SHM was investigated, namely a vibrating wire strain gauge. Strain gauges are used to monitor concrete and steel deformations, which are critical factors to evaluate the stability of a tunnel and its expected shape deformation. Vibrating wire strain gauges consist of a length of steel wire, tensioned between two end-blocks embedded within the structure being studied, such that deformations of the structure will alter the tension of the steel wire. The tension of the wire is determined by using an electromagnet to excite the wire, and then by measuring its resonant frequency of oscillation.

A Novel Wake-Up Receiver with Addressing Capability for Wireless Sensor Nodes

Emerging low-power radio triggering techniques for wireless motes are a promising approach to prolong the lifetime of Wireless Sensor Networks (WSNs). By allowing nodes to activate their main transceiver only when data need to be transmitted or received, wake-up-enabled solutions virtually eliminate the need for idle listening, thus drastically reducing the energy toll of communication. In this paper we describe the design of a novel wake-up receiver architecture based on an innovative pass-band filter bank with high selectivity capability. The proposed concept, demonstrated by a prototype implementation, combines both frequency-domain and time-domain addressing space to allow selective addressing of nodes. To take advantage of the functionalities of the proposed receiver, as well as of energy-harvesting capabilities modern sensor nodes are equipped with, we present a novel wake-up-enabled harvesting-aware communication stack that supports both interest dissemination and converge casting primitives. This stack builds on the ability of the proposed WuR to support dynamic address assignment, which is exploited to optimize system performance. Comparison against traditional WSN protocols shows that the proposed concept allows to optimize performance tradeoffs with respect to existing low-power communication stacks.

CTP-WUR: The collection tree protocol in wake-up radio WSNs for critical applications

Allowing the nodes of a wireless sensor network (WSN) to turn their radio off periodically noticeably increases network lifetime. Duty cycling, however, does not eliminate idle listening, comes at the price of longer latencies and obtains lifetimes that are still insufficient for many critical applications. Using a wake-up receiver (WUR) allows actual communications on the main radio only for transmission or reception, virtually eliminating node idling. However, the range of current WUR prototypes is still significantly shorter than that of the main radio, which can challenge the use of existing WSN protocols in WUR-based networks. In this paper we present an approach to mitigate this limitation of wake-up-based networks. In particular, we show that the Collection Tree Protocol (CTP), a standard protocol for data gathering in WSNs, suitably redefined to work on WUR-endowed nodes, achieves lifetimes of several decades. This constitutes a remarkable improvement over duty cycle-based solutions, where CTP makes the network lasts only a handful of months. At the same time, our WUR-based approach obtains data latencies comparable to those obtained by keeping the main radio always on.