Scheduling recurring tasks in energy harvesting sensors

Audet, David; Macmillan, Neil A.; Marinakis, Dimitri; Wu, Kui

doi:10.1109/infcomw.2011.5928823

Cited by 21 publications

(10 citation statements)

References 11 publications

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“…Audet et al [105] present two energy-aware techniques, termed Smooth to Average Method (STAM) and Smooth to Full Utilization (STFU), for scheduling a set of periodic tasks. The aim is to combine the two techniques with known scheduling algorithms to reduce the likelihood of energy violations while meeting tasks deadlines.…”

Section: Task Allocationmentioning

confidence: 99%

Wireless Sensor Networks with Energy Harvesting

Basagni¹,

Naderi²,

Petrioli³

et al. 2013

Mobile Ad Hoc Networking

134

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This chapter covers the fundamental aspects of energy harvesting-based 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 harvesters 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 self-sustaining and with low environmental impact. 703 704 WIRELESS SENSOR NETWORKS WITH ENERGY HARVESTING hardware, protocol stack design, localization and tracking techniques, and energy management [1].Research on WSNs has been driven (and somewhat limited) by a common focus: energy efficiency. Nodes of a WSN are typically powered by batteries. Once their energy is depleted, the node is "dead." Only in very particular applications can batteries be replaced or recharged. However, even when this is possible, the replacement/ recharging operation is slow and expensive and decreases network performance. Different techniques have therefore been proposed to slow down the depletion of battery energy, which include power control and the use of duty cycle-based operation. The latter technique exploits the low power modes of wireless transceivers, whose components can be switched off for energy saving. When the node is in a low power (or "sleep") mode its consumption is significantly lower than when the transceiver is on [2,3]. However, when asleep the node cannot transmit or receive packets. The duty cycle expresses the ratio between the time when the node is on and the sum of the times when the node is on and asleep. Adopting protocols that operate at very low duty cycles is the leading type of solution for enabling long-lasting WSNs [4]. However, this approach suffers from two main drawbacks: (1) There is an inherent tradeoff between energy efficiency (i.e., low duty cycling) and data latency, and (2) battery operated WSNs fail to provide the needed answer to the requirements of many emerging applications that demand network lifetimes of decades or more. Battery leakage and aging deplete batteries within a few years, even if they are seldom used [5,6]. For these reasons, recent research on long-lasting WSNs is taking a different approach, proposing energy harvesters combined with the use of rechargeable batteries and super-capacitors (for energy storage) as the key enabler to "perpetual" WSN operations.Energy-Harvesting-based WSNs (EHWSNs) are the result of endowing WSN nodes with the capability of extracting energy from the surrounding environment. Energy harvesting can exploit different sou...

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Section: Task Allocationmentioning

confidence: 99%

Wireless Sensor Networks with Energy Harvesting

Basagni¹,

Naderi²,

Petrioli³

et al. 2013

Mobile Ad Hoc Networking

134

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“…However, none of these energy management solutions investigate scheduling in sustainable sensor networks where environmental energy can change dynamically. The most closely related works are Dewdrop [30], STAM [31], and virtual battery [32]. Dewdrop and STAM only schedule recurring tasks based on the environmental energy.…”

Section: Related Workmentioning

confidence: 99%

DEOS: Dynamic energy-oriented scheduling for sustainable wireless sensor networks

Zhu

Mohaisen

et al. 2012

2012 Proceedings IEEE INFOCOM

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Energy is the most precious resource in wireless sensor networks. To ensure sustainable operations, wireless sensor systems need to harvest energy from environments. The timevarying environmental energy results in the dynamic change of the system's available energy. Therefore, how to dynamically schedule tasks to match the time-varying energy is a challenging problem. In contrast to traditional computing-oriented scheduling methods that focus on reducing computational energy consumption and meeting the tasks' deadlines, we present DEOS, a dynamic energy-oriented scheduling method, which treats energy as a first-class schedulable resource and dynamically schedules tasks based on the tasks' energy consumption and the system's real-time available energy. We extensively evaluate our system in indoor and outdoor settings. Results indicate that DEOS is extremely lightweight (e.g., energy consumption overhead in the worst case is only 0.039%) and effectively schedules tasks to utilize the dynamically available energy. I. INTRODUCTIONTo ensure sustainable operations of wireless sensor networks, environmental energy harvesting has been regarded as the right solution for long-term applications. However, the harvested energy is usually not sufficient to allow the sensor nodes to keep active all the time. Therefore, the bottleneck in sustainable wireless sensor networks is energy instead of computing and communication capabilities. What is worse is that the availability of energy in a sustainable sensor network is intermittent and varies over time. Thus it becomes important to shift the focus from the constraint of computation and communication to the energy constraint and explore a method, which dynamically schedules tasks based on energy.In this paper, we propose dynamic energy-oriented scheduling -a method that allocates tasks to resources based on the system's instantaneous available energy and the tasks' energy consumption. The novel features are (i) the ability to decompose and recombine tasks to eliminate redundant operations, and (ii) the concurrent execution to simultaneously utilize multiple resources (e.g., sensing and communication). In addition, admission control is applied to ensure the execution of high priority tasks when there is insufficient energy. It is a challenging task to build an energy efficient and light weight scheduling method because the scheduling operation also consumes energy. This calls for a design that reduces scheduler overhead. It should be noted that dynamic energyoriented scheduling achieves a balance between the system's available energy and the energy consumption of tasks in real time. Such a realtime balance makes our work on energyoriented scheduling unique and novel. More specifically, our

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“…Scheduling tasks on EH-WSN node has been an active topic of research [1], [4], [5], [6]. Moser et al [4] propose an optimal scheduling algorithm called Lazy Scheduling Algorithm (LSA).…”

Section: Introductionmentioning

confidence: 99%

“…We model the incoming energy as a stochastic process [6], [7]. Since we consider future energy arrivals in the system, the problem at hand is to dynamically optimize the energy expenditure while maximizing the utility, given the variations in harvesting energy.…”

Section: Introductionmentioning

confidence: 99%

Optimal task scheduling policy in energy harvesting wireless sensor networks

Rao

Prasad

Niemegeers

2015

2015 IEEE Wireless Communications and Networking Conference (WCNC)

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Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for longlasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of energy harvested highly varies spatially and temporally. Moreover the ambient source cannot be assumed to be continuously available. When harvested energy is in excess, it is desirable that the nodes take up higher loads. The nodes should switch to highly energy efficient schemes when the energy is not sufficient. Hence harvesting-aware scheduling of tasks is required. The two most important challenges for harvesting-aware scheduling are (a) to determine the amount of energy to be expended in a time slot, and (b) to utilize this energy for execution of tasks maximally. To increase energy utilization for task execution, we decompose application level tasks into subtasks, some of which can be executed concurrently. In this article, we propose a dynamic optimization model, based on Markov Decision Process (MDP) that takes into account priorities and deadlines of the tasks, and stored and harvested energy to derive an optimal scheduling policy. Since the complexity of the MDP is intractable in realtime, we propose a greedy scheduling policy. We compare its performance with the optimal policy.

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Scheduling recurring tasks in energy harvesting sensors

Cited by 21 publications

References 11 publications

Wireless Sensor Networks with Energy Harvesting

Wireless Sensor Networks with Energy Harvesting

DEOS: Dynamic energy-oriented scheduling for sustainable wireless sensor networks

Optimal task scheduling policy in energy harvesting wireless sensor networks

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