Energy Management in Wireless Networked Embedded Systems

Introduction

Real-time systems have undergone an evolution in the last several years in terms of their number and variety of applications, as well as in complexity. A natural result of these advances, coupled with those in sensor techniques and networking, have led to the rise of a new class of applications that fall into the distributed real-time embedded systems category (Loyall, Schantz, Corman, Paunicka, & Fernandez, 2005; Report, 2006). Recent technological advancements in device scaling have been instrumental in enabling the mass production of such devices at reduced costs. As a result, applications with a number of internetworked embedded systems have become prominent. At the same time, there has been a need to move from stand-alone real-time unit into a network of units that collaborate to achieve a real-time functionality. Extensive research has been carried out to achieve real-time guarantees over a set of nodes distributed over wired networks (Siva Ram Murthy & Manimaran, 2001). However, there exist a number of real-time applications in domains, such as industrial processing, military, robotics and tracking, that require the nodes to communicate over the wireless medium where the application dynamics prevent the existence of a wired communication infrastructure. These applications present challenges beyond those of traditional embedded or networked systems, since they involve many heterogeneous nodes and links, shared and constrained resources, and are deployed in dynamic environments where resource contention is dynamic and communication channel is noisy (Report, 2006, Loyall et al., 2005). Hence, resource management in embedded real-time networks requires efficient algorithms and strategies that achieve competing requirements, such as time sensitive energy-efficient reliable message delivery. In what follows, we discuss some applications in this category, and discuss their requirements and the research challenges.

Safety-critical mobile applications running on resource-constrained embedded systems will play an increasingly important role in domains such as automotive systems, space, robotics, and avionics. The core controlling module in such mission critical applications is an embedded system consisting of a number of autonomous components. These components form a wireless (ad hoc) network for cooperatively communicating with each other to achieve the desired functionality. In these applications, a failure or violation of deadlines can be disastrous, leading to loss of life, money, or equipment. Hence, there arises a need to coordinate and operate within stringent timing constraints, overcoming the limitations of the wireless network. For example, robots used in urban search and rescue missions cooperate together and with humans in overlapping workspaces. For this working environment to remain safe and secure, not only must internal computations of robots meet their deadlines, but timely coordination of robots behavior is also required (Report, 2006). Other such medium-scale distributed real-time embedded applications include target tracking systems that perform surveillance, detection, and tracking of time critical targets (Loyall et al., 2005), or a mobile robotics application where a team of autonomous robots cooperate in achieving a common goal such as using sensor feeds to locate trapped humans in a building on fire. Other more passive applications include the use of networked embedded systems to monitor critical infrastructure such as electric grids (Leon, Vittal, & Manimaran, 2007). These applications need to meet certain real-time constraints in response to transient events, such as fast-moving targets, where the time to detect and respond to events is shortened significantly. In surveillance systems, for example, communication delays within sensing and actuating loops directly affect the quality of tracking. While providing real-time guarantees is the primary requirement in these applications, mechanisms need to exist to meet other crucial system needs such as energy consumption and accuracy (Rusu, Melhem & Mosse, 2003). In most cases, there are tradeoffs involved in balancing these competing requirements.