Abstract

Ambient energy harvesting is a solution to mitigate the typical finite energy supply of sensor nodes in wireless sensor networks (WSNs). On the one hand, the uncertainty of energy availability in energy harvesting systems makes network protocol design challenging. On the other hand, the fact that energy is continuously replenished opens up avenues for protocol design based on prediction of future energy arrivals. One of the key application scenarios for sensor networks is task allocation, in which a central entity allocates tasks to a set of geographically distributed sensor nodes to accomplish an overall objective. In this work, we consider a scenario in which the sensor nodes are equipped with devices capable of harvesting ambient energy, e.g., solar panels to harvest the Sun’s energy, and focus on energy-aware strategies for adaptive task allocation. We decompose the static task allocation problem into two phases: scheduling of the task graph and task mapping to the appropriate sensor nodes. The solution objectives are to minimize the makespan and maximize the fairness in energy-driven task mapping (i.e., energy-balancing), while satisfying the task precedence constraints and energy harvesting causality constraints. We employ a novel energy prediction model which incorporates seasonal changes in solar energy harvesting as well as sudden weather changes. In case of an error in available energy prediction, a dynamic adaptation phase runs to avoid violation of the task allocation objectives. The performance of our proposed algorithms, in terms of energy-balancing and scheduling length, is evaluated through simulation and compared with other approaches, including a genetic algorithm as a baseline. We achieve more balanced residual energy levels across the network while attaining a near optimum scheduling length. By utilizing the dynamic adaptation phase, for certain runs of simulation, the missing ratio, which is the percentage of times in which the task allocation fails due to a temporal shortage of energy availability, is dramatically decreased.

Highlights

  • A sensor network, which is a network of collaborating embedded devices with capabilities of sensing, computing, and communicating, is used to run specific applications

  • We address the task allocation problem which allocates and schedules a set of tasks represented by a task graph to a set of geographically distributed sensor nodes to achieve an overall system objective

  • We propose a novel task allocation algorithm for energy harvesting wireless sensor networks that operates in two phases: task scheduling of directed acyclic graph (DAG) and task mapping to the solar-powered sensor nodes

Read more

Summary

Introduction

A sensor network, which is a network of collaborating embedded devices with capabilities of sensing, computing, and communicating, is used to run specific applications (such as target tracking and event detection). There is some existing work on resource management for real-time energy harvesting embedded systems in the literature In some works, such as [14, 15], dynamic voltage scaling policies are used to reduce the energy consumption; they may violate the scheduling length constraint for the case of task allocation to the multiple sensor nodes. We propose a novel task allocation algorithm for energy harvesting wireless sensor networks that operates in two phases: task scheduling of DAG and task mapping to the solar-powered sensor nodes. To predict the harvesting energy in the slot, the algorithm uses the values of power measurement e(j) ∈ E T of the current day.

Decision variables
Proposed algorithm for task allocation in EH-WSNs
Online dynamic adaptation stage
Computational complexity
Multi-objective genetic algorithm for task allocation
Simulation results for the energy harvesting prediction algorithm
Discussion on results for energy-balancing and scheduling length objectives
Findings
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call