in Sensor Networks (IPSN/SPOTS) [Hu et al. 2005]. The paper features newer results on improving the lifetime of the sensor network for cane-toad monitoring through harvesting-aware sensor duty cycling algorithms.

A key goal of mobile computing is untethering devices from wires, making them truly portable. While mobile devices can make use of wireless communication for network connectivity, they are still dependent on an electrical connection for continued operation. This need for tethering to available electricity significantly limits their range, usefulness, and manageability. Environmental energy harvesting—collecting energy from the sun, wind, heat differentials, and motion—offers the prospect of unprecedented, large-scale deployments of perpetual mobile systems that never need to be recharged. However, programming these systems presents new challenges: perpetual systems must adapt dynamically to available energy, delivering higher service levels when energy is plentiful, while consuming less energy when energy is scarce. This paper presents eFlux, a high-level energy-aware programming language and associated runtime system that specifically targets perpetual mobile systems. eFlux programmers build programs from components written in C or NesC and label flows through the program with different energy-states. The deployed program then adapts to current energy levels by changing energy states, turning flows on and off and adjusting their rates. We demonstrate eFlux’s utility and portability with two perpetual applications deployed on widely different hardware platforms: a solar-powered web server for remote, ad-hoc deployments, and a GPS-based location tracking sensor that we have deployed on a threatened species of turtle as well as on automobiles. 1

Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensively in wireless networks. However, little work has yet been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asynchronously. In this type of network, abroadcastingpacketisrarelyreceivedbymultiplenodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. Combined with unreliable links, flooding in low-duty-cycle networks is a new challenging issue. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. The key idea is to make probabilistic forwarding decisions at a sender based on the delay distribution of next-hop nodes. Only opportunistically early packets are forwarded using links outside the energy optimal tree to reduce the flooding delay and redundancy in transmission. To improve performance further, we propose a forwarder selection methodtoalleviatethehiddenterminalproblemandalink-qualitybased backoff method to resolve simultaneous forwarding operations. We evaluate Opportunistic Flooding with extensive simulation and a test-bed implementation consisting of 30 MicaZ nodes. Evaluation shows our design is close to the optimal performance achievable by oracle flooding designs. Compared with improved traditionalflooding, ourdesignachievessignificantly shorter flooding delay while consuming only 20 % ∼ 60 % of the transmission energy invarious low-duty-cycle network settings.

To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakageprone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settings — indoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.

Structural health monitoring of railway bridges is an essential but currently expensive proposition. Current systems employing data loggers and analyzers are both labour intensive for setup and require trained manpower for deployment and usage. In this work we propose a system design for remote, on-demand railway bridge monitoring system which is easier to deploy and autonomous in its operation. The system is low cost, as off the shelf equipments are used and expensive sensing equipments are replaced with Micro Electronic Mechanical Systems (MEMS) based sensors and Wireless Sensor Networks (WSN) replacing the cabling and data logging system for data transportation and collection. Current structural health monitoring systems are essentially wired solution or proprietary single hop wireless solutions. These systems are non-scalable and difficult to deploy on most of the structures. In contrast our approach using independent nodes attached with appropriate sensors and batteries for power make them easy to deploy without hassles of cabling. Using wireless data transmission and multi-hop data transport our solution is highly scalable. Use of low

DuraCap is a solar-powered energy harvesting system that stores harvested energy in supercapacitors and is voltage-compatible with lithium-ion batteries. The use of supercapacitors instead of batteries enables DuraCap to extend the operational life time from tens of months to tens of years. DuraCap addresses two additional problems with micro-solar systems: inefficient operation of supercapacitors during cold booting, and maximum power point tracking (MPPT) over a variety of solar panels. Our approach is to dedicate a smaller supercapacitor to cold booting before handing over to the array of larger-value supercapacitors. For MPPT, we designed a bound-control circuit for PFM regulator switching and an I-V tracer to enable self-configuring over the panel’s aging process and replacement. Experimental results show the DuraCap system to achieve high conversion efficiency and minimal downtime. Categories and Subject Descriptors C.3 [Special-purpose and application-based systems]: Real-time and embedded systems

Energy harvesting capabilities enable totally untethered operation of mobile and ubiquitous systems for extended periods of time without requiring battery replacement. This paper examines technical issues with solar energy harvesting. First, maximum power point tracking (MPPT) techniques are compared in terms of solar cell model, tracking source, and controller style. For energy harvesting in conjunction with energy storage, this paper compares batteries and supercapacitors, and discusses trade-offs between complexity of charging circuitry and efficiency. Recent techniques for handling cold booting are also examined in terms of both hardware and software solutions. This paper assumes mainly small-scale photovoltaic sources, although many techniques apply to other sources as well. Together, the increase efficiency is expected to enable more compact, lower cost energy harvesters to bring longer, more stable operation to the systems.

Abstract: Starting from the features and impact of energy leakage in ultra-capacitor powered systems, this demonstration highlights the design of a capacitor-only Twin-Star node, and a leakage-aware energy synchronization methodology.

Abstract—Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes. However, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42 % on the average while significantly improving the network utility. I.

Abstract—Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, time variations in recharging rate presents a new challenge. In this paper, we first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes when a DAG routing structure is given. Then, we analytically study the key properties of the optimal DAG(s) and propose a mechanism for constructing a DAG that can support high network utility. Moreover, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42 % on the average while significantly improving the network utility. I.