Green wireless networks are characterized by devices that are pervasively deployed and that harvest energy from the surrounding environment. Devices are also endowed with low-power triggering techniques (e.g., wake-up radios) to obviate costly idle communication times. In this paper, we present a novel data forwarding strategy for green wireless networks that fully exploits the self-powered wake-up radio capabilities of the network nodes. The proposed strategy, named WHARP for Wake-up and HARvesting-based energy-Predictive forwarding, sends data to their destination by making decentralized and proactive decisions based on forecast energy and expected traffic. The performance of WHARP has been compared to that of the Energy Harvesting Wastage-Aware (EHWA) strategy through GreenCastalia-based simulations. Results show that our approach delivers up to 72% more packets, 1.6 times faster, and consuming 58% less energy than EHWA. This is obtained through a learned selection of forwarder relays allowing WHARP nodes to be operational 98% of the time: A 30% improvement over EHWA.

Energy-harvesting techniques for low-power embedded devices are opening up new opportunities for the design and optimization of security protocols for Green Wireless Sensor Networks. In this paper, we focus on scenarios where the energy resources of nodes in the network are heterogeneous, and propose a network-level solution that leverages the heterogeneity of harvesting capabilities to reduce the energy consumption of performing costly security operations. Our proposed distributed protocol, called HELIOS (Harvesting- EnabLed computatIon Outsourcing Scheme), allows nodes with scarce energy availability to outsource resource-demanding cryptographic operations to nodes that are harvesting power in excess, resulting in a significant reduction of their energy consumption.

This work is motivated by a general question: can micro-scale energy-harvesting techniques be exploited to support low-cost standard security solutions on resource-constrained devices? We focus on guaranteeing integrity and authentication in Internet of Things (IoT) and Wireless Sensor Network (WSN) applications. In this article, we propose techniques to make ECDSA signatures low cost and implementable on resource-constrained devices. By combining precomputation techniques and energy-harvesting capabilities of modern sensor nodes, we achieve significant improvement over prior works. In addition, we show that the cost of ECDSA signatures can be reduced by up to a factor 10 by using harvesting-aware optimizations.

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications.

Wake-up-radio-based sensing systems make use of radio- triggering techniques and ultra-low power wake-up receivers (WuRs) to enable on-demand asynchronous network wake ups. Thanks to this, they have the potential to achieve low latency data collection at minimum energy cost, thus meeting the challenging lifetime and quality-of-service demands of emerging Internet of Things (IoT) and Wireless Sensor Networks (WSNs) applications. However, the fact that nodes can be remotely activated on-demand makes wake-up-radio-based networks vulnerable to energy exhausting attacks. In this paper, with a focus on practical implementation and validation, we present a full-fledged solution to counteract Denial-of-Sleep (DoS) attacks to wake-up-radio-based sensing systems. A core component of our proposed solution is a key exchange protocol based on Elliptic Curve Cryptography (the Fully Hashed MQV protocol), which we use in conjunction with implicit certificates.

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.

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.

Sensor mission assignment involves 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 and are powered in part by uncontrollable environmental sources, which impose quite a different energy model. In this article we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, specifically tailored for energy-harvesting mission-centric WSNs. To assess the performance of our proposed solution we have interfaced TelosB nodes with solar cells and performed extensive experiments to derive models and traces of solar energy acquisition. We use such real-life traces in our simulations. A comparative performance evaluation between EN-MASSE and other schemes previously proposed in the literature has shown that our solution significantly outperforms existing energy-harvesting-unaware mission assignment schemes. Moreover, using our analytical model as a benchmark, we also show that the profit earned by EN-MASSE is close to the optimum. Finally, we have implemented our proposed solution in TinyOS and experimentally validated its performance, showing the effectiveness of our approach.

Structural health monitoring is a vital tool to help engineers improving the safety of critical structures, avoiding the risks of catastrophic failures. Wireless sensor networks (WSNs) are a very promising technology for structural health monitoring, as they can provide a quality of monitoring similar to conventional (wired) SHM systems with lower cost. In addiction, WSNs are both non-intrusive and non-disruptive and can be employed from the very early stages of construction.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.

The increasing popularity of micro-scale energy-scavenging techniques for wireless sensor networks (WSNs) is opening new opportunities for the development of energy-autonomous systems. To sustain perpetual operations, however, environmentally-powered motes must adapt their workload to the stochastic nature of ambient power sources. Energy prediction algorithms, which forecast the source availability and estimate the expected energy intake in the near future, are precious tools to support the development of proactive power management strategies. In this work, we propose Pro-Energy-VLT, an enhancement of the Pro-Energy prediction algorithm that improves the accuracy of energy predictions, while reducing its memory and energy overhead.

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%.

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.