The use of environmental energy is now emerging as a feasible energy source for embedded and wireless computing systems such as sensor networks where manual recharging or replacement of batteries is not practical. However, energy supply from environmental sources is highly variable with time. Further, for a distributed system, the energy available at its various locations will be different. These variations strongly influence the way in which environmental energy is used. We present a harvesting theory for determining performance in such systems. First we present a model for characterizing environmental sources. Second, we state and prove two harvesting theorems that help determine the sustainable performance level from a particular source. This theory leads to practical techniques for scheduling processes in energy harvesting systems. Third, we present our implementation of a real embedded system that runs on solar energy and uses our harvesting techniques. The system adjusts its performance level in response to available resources. Fourth, we propose a localized algorithm for increasing the performance of a distributed system by adapting the process scheduling to the spatio-temporal characteristics of the environmental energy in the distributed system. While our theoretical intuition is based on certain abstractions, all the scheduling methods we present are motivated solely from the experimental behavior and resource constraints of practical sensor networking systems.

Power management is an important concern in sensor networks, because a tethered energy infrastructure is not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric such as residual battery suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions, at multiple nodes. In this case it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy harvesting sensor network, prototyped for this work.

refers to the problem of judicious application of various low power techniques based on runtime conditions in an embedded system to minimize the total energy consumption. To be effective, often such decisions take into account the operating conditions and the system-level design goals. DPM has been a subject of intense research in the past decade driven by the need for low power in modern embedded devices. We present an overview of the formal methods that have been explored in solving the system-level DPM problem. We show how formal reasoning

Abstract—Dynamic power management (DPM) refers to the problem of judicious application of various low-power techniques based on runtime conditions in an embedded system to minimize the total energy consumption. To be effective, often such decisions take into account the operating conditions and the system-level design goals. DPM has been a subject of intense research in the past decade driven by the need for low power consumption in modern embedded devices. We present a comprehensive overview of two closely related approaches to designing DPM strategies, namely, competitive analysis approach and model checking approach based on adversarial modeling. Although many other approaches exist for solving the system-level DPM problem, these two approaches are closely related and are based on a common theme. This commonality is in the fact that the underlying model is that of a competition between the system and an adversary. The environment that puts service demands on devices is viewed as an adversary, or to be in competition with the system to make it burn more energy, and the DPM strategy is employed by the system to counter that. Index Terms—Competitive analysis, dynamic power management, low-power design, online algorithm, probabilistic model checking, stochastic policy.

This paper introduces a network simulation model for detailed evaluation of the performance of different energy management policies in a MANET. Next it presents an energy-aware network transaction protocol that dynamically redistributes the computational workload among a set of cooperative hosts within a MANET so as to improve network performance (network lifetime and service latency.) Extensive simulation data and empirical results are presented and discussed.

parameter Auctions to Page Allocation in Distributed Shared Memory Multiprocessors ” by Maunendra Sankar Desarkar has been carried out under my supervision and that this work has not been submitted elsewhere for a degree.

Abstract—Collaboration is a key word today in the mobile development world. This paper introduces a new concept and methodology in Mobile Collaboration between Pocket PCs or between Pocket PCs and Desktop PCs. Work includes a developed framework in a layered and scalable approach that can be used by developers to allow devices share their resources using remote execution of code whenever Mobile Collaborative Environment (MCE) is present. Included in the paper are sample applications showing the effect of using the framework when collaborating between a Pocket PC and a Desktop PC.