Vijay Raghunathan

Energy-aware wireless microsensor networks

This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy-aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.

Design considerations for solar energy harvesting wireless embedded systems

Sustainable operation of battery powered wireless embedded systems (such as sensor nodes) is a key challenge, and considerable research effort has been devoted to energy optimization of such systems. Environmental energy harvesting, in particular solar based, has emerged as a viable technique to supplement battery supplies. However, designing an efficient solar harvesting system to realize the potential benefits of energy harvesting requires an in-depth understanding of several factors. For example, solar energy supply is highly time varying and may not always be sufficient to power the embedded system. Harvesting components, such as solar panels, and energy storage elements, such as batteries or ultracapacitors, have different voltage-current characteristics, which must be matched to each other as well as the energy requirements of the system to maximize harvesting efficiency. Further, battery non-idealities, such as self-discharge and round trip efficiency, directly affect energy usage and storage decisions. The ability of the system to modulate its power consumption by selectively deactivating its sub-components also impacts the overall power management architecture. This paper describes key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and presents the design, implementation, and performance evaluation of Heliomote, our prototype that addresses several of these issues. Experimental results demonstrate that Heliomote, which behaves as a plug-in to the Berkeley/Crossbow motes and autonomously manages energy harvesting and storage, enables near-perpetual, harvesting aware operation of the sensor node.

Emerging techniques for long lived wireless sensor networks

In recent years, sensor networks have transitioned from being objects of academic research interest to a technology that is frequently being deployed in real-life applications and rapidly being commercialized. However, energy consumption continues to remain a barrier challenge in many sensor network applications that require long lifetimes. Battery-operated sensor nodes have limited energy storage capability due to small form-factors, or operate in environments that rule out frequent energy replenishment, resulting in a mismatch between the available energy budget for system operation and the required energy budget to obtain desired lifetimes. This article surveys several techniques that show promise in addressing and alleviating this energy consumption challenge. In addition to describing recent advances in energy-aware platforms for information processing and communication protocols for sensor collaboration, the article also looks at emerging, hitherto largely unexplored techniques, such as the use of environmental energy harvesting and the optimization of the energy consumed during sensing.

Adaptive duty cycling for energy harvesting systems

Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely: (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment; (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint; and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used

Harvesting aware power management for sensor networks

Energy harvesting offers a promising alternative to solve the sustainability limitations arising from battery size constraints in sensor networks. Several considerations in using an environmental energy source are fundamentally different from using batteries. Rather than a limit on the total energy, harvesting transducers impose a limit on the instantaneous power available. Further, environmental energy availability is often highly variable and a deterministic metric such as residual battery capacity is not available to characterize the energy source. The different nodes in a sensor network may also have different energy harvesting opportunities. Since the same end-user performance may be achieved using different workload allocations at multiple nodes, it is important to adapt the workload allocation to the spatio-temporal energy availability profile in order to enable energy-neutral operation of the network. This paper describes power management techniques for such energy harvesting sensor networks. Platform design considerations as well as power scaling techniques at the node-level and network-level are described

A survey of techniques for energy efficient on-chip communication

Interconnects have been shown to be a dominant source of energy consumption in modern day System-on-Chip (SoC) designs. With a large (and growing) number of electronic systems being designed with battery considerations in mind, minimizing the energy consumed in on-chip interconnects becomes crucial. Further, the use of nanometer technologies is making it increasingly important to consider reliability issues during the design of SoC communication architectures. Continued supply voltage scaling has led to decreased noise margins, making interconnects more susceptible to noise sources such as crosstalk, power supply noise, radiation induced defects, etc. The resulting transient faults cause the interconnect to behave as an unreliable transport medium for data signals. Therefore, fault tolerant communication mechanism, such as Automatic Repeat Request (ARQ), Forward Error Correction (FEC), etc., which have been widely used in the networking community, are likely to percolate to the SoC domain. This paper presents a survey of techniques for energy efficient on-chip communication. Techniques operating at different levels of the communication design hierarchy are described, including circuit-level techniques, such as low voltage signaling, architecture-level techniques, such as communication architecture selection and bus isolation, system-level techniques, such as communication based power management and dynamic voltage scaling for interconnects, and network-level techniques, such as error resilient encoding for packetized on-chip communication. Emerging technologies, such as Code Division Multiple Access (CDMA) based buses, and wireless interconnects are also surveyed.

Heliomote: enabling long-lived sensor networks through solar energy harvesting

The crucial need for long-lived and autonomous operation has elevated power and energy consumption to primary optimization metrics during wireless sensor network design. While most work in the field of power management and low-power design has focused on optimizing the energy consumer (i.e., the sensor node, including its hardware, software, applications, and network protocols), very little work has targeted the energy supply system itself. A practical approach to alleviating the problem of limited battery resources in sensor nodes is the use of environmental energy harvesting. Solar energy harvesting, in particular, holds significant promise since photovoltaic conversion techniques are now mature enough to permit the development of cheap and small, yet reasonably efficient, solar panels. Our demonstration showcases our recent research in designing solar energy harvesting systems, as well as harvesting aware performance scaling algorithms and network protocols [1].

Power management for energy-aware communication systems

System-level power management has become a key technique to render modern wireless communication devices economically viable. Despite their relatively large impact on the system energy consumption, power management for radios has been limited to shutdown-based schemes, while processors have benefited from superior techniques based on dynamic voltage scaling (DVS). However, similar scaling approaches that trade-off energy versus performance are also available for radios. To utilize these in radio power management, existing packet scheduling policies have to be thoroughly rethought to make them energy-aware, essentially opening a whole new set of challenges the same way the introduction of DVS did to CPU task scheduling. We use one specific scaling technique, dynamic modulation scaling (DMS), as a vehicle to outline these challenges, and to introduce the intricacies caused by the nonpreemptive nature of packet scheduling and the time-varying wireless channel.

Exploiting radio hierarchies for power-efficient wireless device discovery and connection setup

We propose the coordinated use of multiple heterogeneous wireless technologies to optimize the latency and power consumption of mobile systems during device discovery and connection setup. We present several multi-radio connection establishment techniques, which exploit the wide disparity in the power and performance characteristics of different radios to create an efficient and flexible communication subsystem. Our techniques enable mobile devices to combine the strengths of these diverse technologies, thereby powering down higher power radios when their capabilities are not needed and using a hierarchical radio structure for device discovery and connection setup. Experiments using a prototype multi-radio device show that the use of radio hierarchies results in significant power savings, and often improves connection setup latency as well.

Modulation scaling for real-time energy aware packet scheduling

Portable wireless communication systems operate on a limited battery supply, and energy efficiency is therefore crucial. Voltage scaling techniques have been proposed to lower the energy consumption of embedded processors and real-time operating systems have incorporated these schemes in their task scheduling engine. However, the actual data transmission itself constitutes a major portion of the total energy consumption in these wireless communication systems. In this paper, we extend the scaling notion to the realm of wireless communications and propose a novel technique called modulation scaling to decrease the energy consumed during data transmission. Modulation scaling trades off energy consumption against transmission delay and as such, introduces the concept of energy awareness in communications. We investigate how modulation scaling can be exploited to design a dynamic power management engine at the level of the radio. This engine coordinates the packet transmission schedule while optimizing energy efficiency. We demonstrate such a power management module for real-time traffic and show that it reduces the energy consumption of data transmissions by up to 50 % through smart traffic scheduling.