Abstract

The low‐power wide‐area network (LPWAN) technologies, such as LoRa, Sigfox, and NB‐IoT, bring new renovation to the wireless communication between end devices in the Internet of things (IoT), which can provide larger coverage and support a large number of IoT devices to connect to the Internet with few gateways. Based on these technologies, we can directly deploy IoT devices on the candidate locations to cover targets or the detection area without considering multihop data transmission to the base station like the traditional wireless sensor networks. In this paper, we investigate the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS). In the problems, we consider both the placement and scheduling of IoT devices to monitor all targets (or the whole detection area) such that all targets (or the whole area) are (or is) continuously observed for a certain period of time. The objectives of the problems are to minimize the total energy consumption of the IoT devices. We first, respectively, propose the mathematical models for the MTPS and MAPS problems and prove that they are NP‐hard. Then, we study two subproblems of the MTPS problem, minimum location coverage (MLC), and minimum energy consumption scheduling deployment (MESD) and propose an approximation algorithm for each of them. Based on these two subproblems, we propose an approximation algorithm for the MTPS problem. After that, we investigate the minimum location area coverage (MLAC) problem and propose an algorithm for it. Based on the MLAC and MESD problems, we propose an approximation algorithm to solve the MAPS problem. Finally, extensive simulation results are given to further verify the performance of the proposed algorithms.

Highlights

  • The Internet of things (IoT) is a flourishing paradigm in the scenario of modern wireless telecommunications, which has been provided a wide diversity applications for all walks of life in modern time, such as home automation, transportation, industry, agriculture, mobile device applications [1], and smart systems [2]

  • We study the problems of the minimum energy consumption of IoT devices for target coverage through placement and scheduling (MTPS) and minimum energy consumption of IoT devices for area coverage through placement and scheduling (MAPS), where we consider both the placement and scheduling of IoT devices to monitor all targets or the entire monitoring area in a region such that all targets or the whole area are or is continuously observed for a certain period of time and the total energy consumption of all available IoT devices is minimized

  • In [23], Berman et al defined the maximum lifetime coverage problem (MLCP) problem as a sensor network life problem (SNLP) and proposed an approximation algorithm with a performance ratio of 1 + ln n to solve the problem based on the minimum weigh sensor coverage problem (MWSCP) which aims at finding the minimum total weight of sensors to cover a certain set of targets, where n is the number of deployed sensors

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Summary

Introduction

The Internet of things (IoT) is a flourishing paradigm in the scenario of modern wireless telecommunications, which has been provided a wide diversity applications for all walks of life in modern time, such as home automation, transportation, industry, agriculture, mobile device applications [1], and smart systems [2]. In [14], Mini et al considered both the deployment locations and scheduling of the given IoT devices to maximize the network lifetime with the required coverage level. They deployed all available IoT devices to cover targets randomly without considering their candidate sites. (1) We propose two new practical models of minimizing the total energy consumption of all IoT devices by placing and scheduling them for continuously observing all targets or the entire detection area for a certain period of time.

Related Works
Model and Problem Definitions
Mathematical Formulation for the Problems
Algorithm for the MTPS Problem
Algorithm for the MAPS Problem
Simulations
Conclusion
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