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REALTIME SCHEDULING FOR ENERGY HARVESTING SENSOR NODES
"... Abstract Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if realtime res ..."
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Cited by 27 (3 self)
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Abstract Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if realtime responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling. 1.
Realtime scheduling with regenerative energy
 In Proc. of the 18th Euromicro Conference on RealTime Systems (ECRTS 06
, 2006
"... This paper investigates realtime scheduling in a system whose energy reservoir is replenished by an environmental power source. The execution of tasks is deemed primarily energydriven, i.e., a task may only respect its deadline if its energy demand can be satisfied early enough. Hence, a useful sc ..."
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Cited by 26 (9 self)
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This paper investigates realtime scheduling in a system whose energy reservoir is replenished by an environmental power source. The execution of tasks is deemed primarily energydriven, i.e., a task may only respect its deadline if its energy demand can be satisfied early enough. Hence, a useful scheduling policy should account for properties of the energy source, capacity of the energy storage as well as power dissipation of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we state and prove optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Furthermore, an offline schedulability test for a set of periodic or even bursty tasks is presented. Finally, we validate the proposed theory by means of simulation and compare our algorithms with the classical Earliest Deadline First Algorithm. 1.
ABSTRACT Adaptive Power Management in Energy Harvesting Systems
"... Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal ..."
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Cited by 26 (7 self)
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Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and timevarying amount of available energy. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal online controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor node. 1.
EnergyEfficient, Utility Accrual RealTime Scheduling Under the Unimodal Arbitrary Arrival Model
 in ACM Design, Automation, and Test in Europe (DATE
, 2005
"... We present an energyefficient realtime scheduling algorithm called EUA∗, for the unimodal arbitrary arrival model (or UAM). UAM embodies a “stronger ” adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, ..."
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Cited by 23 (5 self)
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We present an energyefficient realtime scheduling algorithm called EUA∗, for the unimodal arbitrary arrival model (or UAM). UAM embodies a “stronger ” adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, and the multicriteria scheduling objective of probabilistically satisfying utility lower bounds, and maximizing systemlevel energy efficiency. Since the scheduling problem is intractable, EUA ∗ allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands, in polynomialtime. We establish that EUA ∗ achieves optimal timeliness during underloads, and identify the conditions under which timeliness assurances hold. Our simulation experiments illustrate EUA∗’s superiority. 1.
Procrastination Scheduling in Fixed Priority RealTime Systems
 In Proceedings of the Language Compilers and Tools for Embedded Systems
, 2004
"... Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms ..."
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Cited by 20 (2 self)
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Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms to compute the maximum procrastination intervals for tasks scheduled by either the fixed priority or the dual priority scheduling policy. We show that dual priority scheduling always guarantees longer shutdown intervals than fixed priority scheduling. We further combine procrastination scheduling with dynamic voltage scaling to minimize the total static and dynamic energy consumption of the system. Our simulation experiments show that the proposed algorithms can extend the sleep intervals up to 5 times while meeting the timing requirements. The results show up to 18% energy gains over dynamic voltage scaling.
EnergyEfficient, Utility Accrual Scheduling under Resource Constraints for Mobile Embedded Systems
 in ACM International Conference on Embedded Software
, 2004
"... We present an energyefficient, utility accrual, realtime scheduling algorithm called the Resourceconstrained EnergyEfficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function (TUF) time constraints, resource dependencie ..."
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Cited by 17 (9 self)
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We present an energyefficient, utility accrual, realtime scheduling algorithm called the Resourceconstrained EnergyEfficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function (TUF) time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including activity (timeliness) utility bounds that are probabilistically satisfied. Further, ReUA targets mobile embedded systems where systemlevel energy consumption is also a major concern. For such a model, we consider the scheduling objectives of (1) satisfying the statistical performance requirements; and (2) maximizing the systemlevel energy efficiency. At the same time, resource dependencies must be respected. Since the problem is NPhard, ReUA makes resource allocations using statistical properties of application cycle demands and heuristically computes schedules with a polynomialtime cost. We analytically establish several timeliness and nontimeliness properties of the algorithm. Further, our simulation experiments illustrate the algorithm's effectiveness.
Energy aware dynamic voltage and frequency selection for realtime systems with energy harvesting,” in
, 2008
"... In this paper, an energy aware dynamic voltage and frequency selection (EADVFS) algorithm is proposed. The EADVFS algorithm adjusts the processor’s behavior depending on the summation of the stored energy and the harvested energy in a future duration. Specifically, if the system has sufficient ene ..."
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Cited by 15 (0 self)
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In this paper, an energy aware dynamic voltage and frequency selection (EADVFS) algorithm is proposed. The EADVFS algorithm adjusts the processor’s behavior depending on the summation of the stored energy and the harvested energy in a future duration. Specifically, if the system has sufficient energy, tasks are executed at full speed; otherwise, the processor slows down task execution to save energy. Simulation results show that when the utilization is low, the EADVFS algorithm gives a deadline miss rate that is at least 50 % lower than the one given by the lazy scheduling policy. Similarly, when the workload is low, the minimum storage size is reduced by at least 25%. 1.
Dynamic reconfiguration in sensor networks with regenerative energy sources
 In DATE ’07: Proceedings of the conference on Design, automation and test in Europe
, 2007
"... In highly power constrained sensor networks, harvesting energy from the environment makes prolonged or even perpetual execution feasible. In such energy harvesting systems, energy sources are characterized as being regenerative. Regenerative energy sources fundamentally change the problem of power s ..."
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Cited by 13 (0 self)
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In highly power constrained sensor networks, harvesting energy from the environment makes prolonged or even perpetual execution feasible. In such energy harvesting systems, energy sources are characterized as being regenerative. Regenerative energy sources fundamentally change the problem of power scheduling for embedded devices. Instead of the problem being one of maximizing the lifetime of the system given a total amount of energy, as in traditional battery powered devices, the problem becomes one of preventing energy depletion at any given time. Coupling relatively computationally intensive applications, such as video processing applications, with the constrained FPGAs that are feasible on power constrained embedded systems, makes dynamic reconfiguration essential. It provides the speed comparable to a hardware implementation, but it also allows the dynamic reconfiguration to meet the multiple application needs of the system. Different applications can be loaded on the FPGA, as the system’s needs change over time. The problem becomes how to schedule the dynamic reconfiguration to appropriately make use of the regenerative energy source, to ensure the proper availability of energy for the system over time. In this paper, we present a methodology for carrying out dynamic reconfiguration for regenerative energy sources, based on statistical analysis of tasks and supply energy. The approach is evaluated through extensive simulations. Additionally, we have evaluated our implementation on our regenerative energy, dynamically reconfigurable prototype, known as the MicrelEye. Our approach is shown to miss 57.7 % less deadlines on average than the current approach for reconfiguration with regenerative energy sources. 1.
LAZY SCHEDULING FOR ENERGY HARVESTING SENSOR NODES
"... Abstract The paper studies the case of a sensor node which is operating with the power generated by an environmental source. We present our model of an energy driven scheduling scenario that is characterized by the capacity of the node’s energy storage, the deadlines and the power dissipation of the ..."
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Cited by 13 (1 self)
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Abstract The paper studies the case of a sensor node which is operating with the power generated by an environmental source. We present our model of an energy driven scheduling scenario that is characterized by the capacity of the node’s energy storage, the deadlines and the power dissipation of the tasks to be performed. Since the execution of these tasks requires a certain amount of energy as well as time, we show that the complexity of finding useful scheduling strategies is significantly increased compared to conventional realtime scheduling. We state online scheduling algorithms that jointly account for constraints arising from both the energy and time domain. In order to demonstrate the benefits of our algorithms, we compare them by means of simulation with the classical Earliest Deadline First Algorithm. 1.
Wireless sensor networks with energy harvesting
 MOBILE AD HOC NETWORKING: CUTTING EDGE DIRECTIONS
, 2013
"... This chapter covers the fundamental aspects of energy harvestingbased wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With th ..."
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Cited by 13 (8 self)
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This chapter covers the fundamental aspects of energy harvestingbased wireless sensor networks (EHWSNs), ranging from the architecture of an EHWSN node and of its energy subsystem, to protocols for task allocation, MAC, and routing, passing through models for predicting energy availability. With the advancement of energy harvesting techniques, along with the development of small factor harvester for many different energy sources, EHWSNs are poised to become the technology of choice for the host of applications that require the network to function for years or even decades. Through the definition of new hardware and communication protocols specifically tailored to the fundamentally different models of energy availability, new applications can also be conceived that rely on “perennial ” functionalities from networks that are truly selfsustaining and with low environmental impact. Wireless sensor networks (WSNs) have played a major role in the research field of multihop wireless networks as enablers of applications ranging from environmental