Joint Sensing and Communication-Rate Control for Energy Efficient Mobile Crowd Sensing

TL;DR

This paper proposes an energy-efficient rate control method for mobile crowd sensing that optimizes sensing and transmission rates over time, considering busy periods and buffer constraints, to reduce energy consumption.

Contribution

It introduces a novel decomposition approach and efficient algorithms for joint sensing and communication rate control in mobile crowd sensing with energy minimization.

Findings

The proposed method effectively reduces energy consumption in simulations.

The decomposition approach simplifies the complex optimization problem.

Buffer constraints are successfully integrated into the rate control strategy.

Abstract

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing enables controllable sensing and transmission processes for multiple types of data in a single device. In this paper, a typical user is considered that is required to sense and transmit data to a server, while it is assumed to remain busy and incapable of sensing data during an interval. An optimization problem is formulated to minimize the energy consumption of data sensing and transmission by controlling the sensing and transmission rates over time, subject to the constraints on the sensing data sizes, transmission data sizes, data casualty, and sensing busy time. This problem is highly challenging, due to the coupling between the rates as well as the existence of the busy time. To deal with this problem, we first show that it can be equivalently decomposed into two subproblems, corresponding to a search for the amount of data size that needs to be sensed before the busy time (referred to as the height), as well as the sensing and transmission rate control given the height. Next, we show that the latter problem can be efficiently solved by using the classical string-pulling method, while an efficient algorithm is proposed to progressively find the optimal height without the exhaustive search. Moreover, the solution approach is extended to a more complex scenario where there is a finite-size buffer at the server for receiving data. Last, simulations are conducted to evaluate the performance of the proposed design.