1. Introduction
In urban systems for a smart city using Wireless Sensor Network (WSN) technologies, real-time environment monitoring is essential to provide helpful information that can be used for urban management [
1]. To acquire data from multiple sensors deployed on urban monitoring systems, sensing technologies such as Bluetooth and radio-frequency identification are essential [
2]. In addition, WSN-based sensing communication has been developed rapidly and applied in many areas [
3]. Users with mobile devices (e.g., smart phones, smart watches, and smart tablets) are connected to a WSN, and many static sensors are sparsely deployed in the urban systems [
4]. Mobile devices are used to relay the data from sensors to a cloud server and should satisfy event reliability such that the quantity of sensing data is large enough to be utilized [
5]. Furthermore, reliable data acquisition at numerous urban monitoring places from the macroscale to the microscale has been required. In urban monitoring systems with a WSN, a large number of scattered sensors conduct sensing tasks and multihop networking over a temporarily configured ad hoc network. To communicate with sensors and relay the data to the cloud server, it is important to maintain the battery power of mobile devices [
6]. Although the battery power efficiency of mobile devices has increased, people spend more time and use more data via their mobile devices than ever before [
7]. As mobile device users have increased along with data-heavy mobile applications, the overall battery power consumption of mobile devices has also increased [
8]. Therefore, mobile devices must maintain their battery power above a certain level to communicate with sensors and the cloud server in urban systems. Here, it is challenging for mobile devices to determine the communication with sensors and the cloud server.
Currently, air quality is one of the essential real-time monitored indexes in urban systems (e.g., particulate matter, oxide gases, carbon monoxide, etc.), and many countries have deployed urban sensing and monitoring infrastructure to provide useful information to their citizen [
9]. In the urban-scale monitoring infrastructure, each sensor measures the air quality, weather, and noise parameters sent to a cloud server using mobile devices [
10]. However, few sensors are deployed in urban systems so that their data show rough information. Furthermore, mobile devices cannot acquire data in some environments (e.g., not enough battery power for mobile devices, users in vehicles).
In this paper, we propose a data acquisition system with the optimization of the battery power consumption of each mobile device. After receiving the cloud server request, each mobile device accepts the request and conducts the data acquisition considering its battery power consumption. The game theory model is formulated to minimize battery power consumption while acquiring more data than the desired level. Furthermore, a Nash equilibrium is derived via the best response strategy algorithm. To evaluate our system, we implement Particulate Matter (PM) sensors to monitor air quality. PM sensors are deployed in various environments, sending the data to mobile devices. The contributions of this paper can be summarized as follows: (1) we model the minimization of the battery power consumption in mobile devices and optimize the model; (2) based on a game theoretic method, we derive the optimal strategy, which implies that the strategy can be adopted by the real urban system; and (3) we present and analyze the evaluation results under various environments. Experimental results show that the proposed system acquires more data than the data acquisition threshold and consumes less battery power than the target battery power status.
The remainder of this paper is organized as follows. In
Section 2, we summarize the related work. In
Section 3, we elaborate on the system model. In
Section 4, we formulate the system model as a game theoretic process. The optimization for the game theoretic process is described in
Section 5. Evaluation results are discussed in
Section 6, and our concluding remarks are provided in
Section 7.
2. Related Work
Studies on urban sensing to acquire microscale data have been reported in the literature [
11,
12,
13]. The authors in [
11] utilized geotagged tweets, sensing data for urban temperature analysis, and investigated the relationships between monitored temperatures and heat-tweets using a statistical model based on copula modeling methods. In [
12], an integrated geovisualization framework was proposed. This framework was used to analyze the complex patterns of an urban microclimate for real-time wireless sensor network data. In the proposed algorithm, a Bayesian method and a hyper ellipsoidal model were used to analyze the data in the urban microclimate, as well as a smart city environment. The authors in [
13] suggested a decentralized data fusion framework. The proposed framework was utilized for microscale monitoring systems in urban monitoring environments using a sensor network. Furthermore, an urban air pollution monitoring scenario demonstrated the proposed framework.
Data relaying using mobile devices was utilized in [
14,
15,
16]. In [
14], the authors proposed a hybrid protocol for delivering data from sensors to mobile devices and analyzed the performance of the protocol to derive the probability model of the data delivery. Then, the authors derived the performance of the parameters and the effectiveness of the model. In [
15], the authors presented a mobile-cloud middleware for opportunistic mobile sensing using smart phones. This middleware allows dynamically downloading and installing sensor-specific transcoding modules using the mobile device as the sensor type. The authors in [
16] modeled data pre-forwarding as an optimization problem to improve the performance of opportunistic data collection with smartphones. Then, a formal network model and a mechanism for data pre-forwarding were proposed, and the optimal solution was derived. Finally, the authors evaluated a small laboratory testbed with scenarios based on smartphone users.
Optimization problems for energy consumption have been proposed [
6,
7,
8]. In [
6], the authors proposed adaptive scheduling algorithms to enhance the energy efficiency of mobile devices in cellular networks considering user performance. The authors designed an algorithm to minimize the total energy cost for data transfers subject to mobile user performance constraints. The hybrid energy optimization was formulated and demonstrated to validate the energy efficiency of the proposed algorithm. The authors in [
7] examined the trends of the energy power consumption of mobile devices for data transfer and mobile networks based on a top-down energy intensity estimate and public data. Energy consumption for mobile data transfer was analyzed from the perspective of the life cycle, examining both direct and indirect energy use. The authors in [
8] proposed a service-specific and end-to-end energy consumption model to investigate smartphone applications and conducted a sensitivity analysis on different usage patterns. Furthermore, the authors suggested energy-efficient solutions to reduce the service energy consumption.
In spite of the above issues, there is no existing work that optimizes the overall battery power consumption of mobile devices in a mobile-based sensing scheme. In [
5], the authors proposed a reliable event data acquisition system for mobile-assisted urban monitoring named urban reliable event transport (uRET). uRET was designed for reliable event transmission from sensors to a cloud server using mobile devices. The authors in [
5] showed that uRET can provide a high delivery success ratio and event reliability accomplishment ratio in a dynamic environment. However, this work did not consider the battery power consumption of mobile devices. Our work aims to optimize the battery power consumption of mobile devices while satisfying the data acquisition threshold. We propose a novel energy-efficient data collection model for mobile devices based on the users’ battery status and duration.
4. Game Theoretic Formulation
In this section, we formulate a game model to achieve a decision process for the reliable data acquisition system. The game model is composed of a set of players, a set of strategies used by a player, and a payoff for each strategy [
17]. Note that, because the mobile devices are the players in the game model, the terms player
i and mobile device
are used interchangeably. The important notations for the game model are summarized in
Table 1.
4.1. Player
Mobile device
is a player in the game model, where
.
D is a set of mobile devices
denoted as
. In addition,
represents all players except player
. In the PM monitoring system,
stays in the event area denoted by
with moving velocity
. Mobile device
receives the number of data denoted by
from
, where
denotes PM sensor
j. Let
denote the probability of the data loss ratio between mobile device
and PM sensor
. After receiving the data from
, mobile device
sends the data to the cloud server. We assume that mobile device
sends all the acquired data to the cloud server. Therefore, the quantity of acquired data from PM sensor
during
is given by:
Furthermore,
represents the initial battery power status of mobile device
, which the cloud receives at first. When mobile device
accepts the request from the cloud server during
, it consumes its battery power
to send the data to the cloud server received from sensor
. Hence, the total remaining battery power of mobile device
during
is defined as follows:
4.2. Strategy
is a strategy of player i. can be represented as , where means that mobile device does not accept the connection of PM sensor ’s request, whereas indicates that mobile device accepts the connection of PM sensor ’s request. Then, a set of strategies S can be denoted by .
4.3. Payoff Function
To maintain a battery power consumption below a certain level during the data acquisition, we define the battery power consumption function as
. If mobile device
accepts the request from the cloud server (i.e.,
= 1), it consumes battery power
during
.
Each mobile device
chooses its strategy
to acquire the data threshold. We define a payoff function as
. This function represents the total acquisition data of mobile device
. If mobile device
receives the request and accepts it (i.e.,
), the sensor-mobile communication between
and
is processed with probability
. After receiving data
from
in its staying time
, mobile device
transmits the data to the cloud. Therefore,
can be represented by Equation (
4).
7. Conclusions
In this paper, the battery power issue for PM monitoring in an urban system is addressed. We design a game theoretic optimization problem for the battery power consumption of mobile devices. Mobile devices and PM sensors on the embedded system are implemented in various environments. In the proposed system, the cloud server performs the optimization algorithm and sends the request to the mobile devices. Furthermore, the mobile devices accept the optimal strategy derived from the cloud server via the best response dynamics and Nash equilibrium. The performance evaluation results demonstrate that mobile devices in the proposed system accept the request and thus acquire more data than the threshold, and a low battery power consumption can be guaranteed. In future work, we will consider more scenarios such as multiple sensors in urban systems and the dynamic mobility of users. In sensor-mobile communication, not only broadcast mode, but also unicast mode will be considered. Furthermore, to achieve a high reliability for the data acquisition, additional constraints will be adopted in the game theory model.