Sensing Workload Scheduling in Sensor Networks Using Divisible Load Theory

Abstract

This paper presents scheduling strategies for sensing workload in wireless sensor networks using Divisible Load Theory (DLT), which offers a tractable model and realistic approach to investigate optimal scheduling issues in distributed systems. Due to the limited energy resource it is desirable that a sensor network can complete tasks as fast as possible. Sensor nodes are coordinated to perform measuring, transmitting, and processing data. Two closely related network models are presented to illustrate how the workload is scheduled among sensor nodes so that the finish time is minimized. Closed-form solutions are derived to achieve the optimization if the source node satisfies certain utility rate, which is used to evaluate the informative ratio of sensory data. Furthermore, we present the energy model for sensor nodes in the multi-hop multi-source network topology. Finally, simulation results are presented to demonstrate the effects of different parameters such as the number of sensor nodes, measurement, communication, and processing speed on the finish time and energy consumption.

Divisible Load Theory Based Active-Sleep Workload Assignment Schemes for Wireless Sensor Networks

“…From the figure, it is seen that 1 increases with decreasing in accordance with (15). As given by (16), when is small, decreases rapidly indicating that the majority of workload is assigned to the first indexed sensor. Furthermore, all sensors in this scheme will be assigned realizable positive workloads.…”

“…When the DLT is used in WSN workload scheduling, it has to take into account the different operation schemes. For example, sensors can be arranged in clusters, and reporting can be done using multihop transmissions [16]. On the other hand, different operation schemes may impose difficulties when DLT is applied.…”

Cooperative image analysis in visual sensor networks

“…In the homogeneous scenario all links have the same effective transmission rate c i ¼ 24 Mbps. In the heterogeneous scenario the effective transmission rates are chosen uniform at random from the set of available data rates in 802.11 g (i.e., 6,9,12,18,24,36,48,54 Mbps). For simplicity, for the broadcast offloading we set c bc to the minimum effective transmission rate of the participating nodes.…”

“…To analyze the performance of distributed processing we leverage tools from the divisible load theory (DLT), which addresses the problem of load distribution in a grid of processors. DLT has been applied for different distributed systems [9], and recently also to wireless sensor networks [17,18], for processing large data whose computation may be split among different entities (i.e., in case the load is divisible).…”

A Mathematical Programming Approach to Task Offloading in Visual Sensor Networks

“…However, the main focus of these works is to minimize the task completion time without explicitly targeting the energy consumed in the process. Energy consumption is often analyzed after the computation of the optimal offloading schedule (i.e., number, identity, and sequence of sensor nodes to be used), as done in [8].…”