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Sensor-cloud is a developing technology and popular paradigm for various applications. It integrates wireless sensor into a cloud computing environment. On the one hand, the cloud offers extensive data storage and analytical and processing capabilities not available in sensor nodes. On the other hand, data distribution (such as time synchronization and configuration files) is always an important topic in such sensor-cloud systems, which leads to a rapid increase in energy consumption by sensors. In this paper, we aim to reduce the energy consumption of data dissemination in sensor-cloud systems and study the optimization of energy consumption with time-varying channel quality when multiple nodes use the same channel to transmit data. Suppose that there is a certain probability that the nodes send data for competing channel. And then, they decide to distribute data in terms of channel quality for saving energy after getting the channel successfully whether or not. Firstly, we construct the maximization problem of average energy efficiency for distributing data with delay demand. Then, this maximization problem transferred an optimal stopping problem which generates the optimal stopping rule. At last, the thresholds of the optimal transmission rate in each period are solved by using the optimal stopping theory, and the optimal energy efficiency for data distribution is achieved. Simulation results indicate that the strategy proposed in this paper can to some extent improve average energy efficiency and delivery ratio and enhance energy optimization effect and network performance compared with other strategies.

With the rapid development of sensor networks, different wireless sensor networks are quickly becoming popular. Wireless sensors equipped with a variety of wireless interfaces such as Wi-Fi, Bluetooth, and cellular networks have powerful wireless communication capabilities. In particular, by combining wireless sensor networks and the cloud, the concept of sensor-cloud systems appeared [

In the nodes’ communication of sensor-cloud systems, the wireless channel’s quality changes randomly with time because of its essential attributes, multipath propagation, and environmental interference and so on. If one node (sensor/sink) is selected to disseminate data when the channel quality is in good condition, the energy consumption generated in data dissemination will be effectively reduced. In some applications, multiple nodes use the same channel to distribute data, but only one node is allowed to use it in a given time period which will cause transmission collisions and data distribution failure if multiple (more than one) nodes use one channel to distribute data at the same time. In order to reduce energy waste caused by transmission collisions, the sending nodes must detect the channel service condition by successfully receiving a response signal from the receiving nodes within a given time when they distribute data to the receiving nodes [

In this paper, we consider the data distribution delay requirements in sensor-cloud systems. And it is also consistent with the realistic situation that the random varying channel quality has a certain holding time. The channel variation model is used in [

Therefore, this paper proposes an optimal energy efficiency distribution strategy for data distributing in sensor-cloud systems when the node competes for the same channel. And the maximum distribution delay constraint of the data distribution is also considered. In sensor-cloud systems, the sending node participates in channel competition with a certain probability, observes the channel quality after successfully competing to the channel, then decides whether or not to distribute data based on the channel quality condition. The optimal energy efficiency moment is selected for the data distribution so as to realize energy saving. Optimal energy efficiency means that the average amount of the data distribution per unit of energy consumption reaches a maximum. The problem of stopping observation and distributing data through the sending node constantly observing the channel quality to select a good channel quality is an optimal stopping rule [

The paper is organized as follows. Section

Data distribution is always a hot topic in distributing networks, and its applications are applied widely in various networks, such as ad hoc networks, social networks, and wireless sensor networks. Researchers pay extensive attention to data distribution applications and their research goals include reducing distribution energy consumption, increasing throughput, and reducing delay.

At present, the integration of wireless sensor networks and the cloud have been proposed in many architectures and research frameworks [

In [

On the other hand, another research work has been mentioned in different literature, such as in [

In [

In general, the researchers addressed the issue of data dissemination in different network backgrounds, such as ad hoc networks and social networks, and used different optimization methods, such as the optimal stopping theory, game theory, and heuristic algorithm. But the final goal is to reduce data dissemination energy consumption and optimize network performance. However, some researches do not consider the problems of data transmission delay, while others do not take into account random variation of channel quality.

In [

In a sensor-cloud system, such as the sink node sends a

A data distribution model for sensor-cloud system.

The conflicting of the transmission and the failure of the data distribution will appear if multiple sinks use the same channel to disseminate data and multiple (more than one) sensors are enclosed in the transmission interference range. However, the data transmission will be successful if only one sink uses the channel to transmit data at the same time. The channel will be idle if there is no data to be transmitted. In order to detect the status of the channel, the sending sink transmits data to the receiving sensor for duration of

One round of channel competition and data dissemination process.

It is assumed that the number of sending nodes using the same channel is

The total times of expected channel competition is

Under normal circumstances, data dissemination is subject to a certain deadline in practical applications and the data not yet transmitted within the deadline will be discarded. This paper assumes that the maximum data delay is

According to the Shannon formula, when the sending node’s transmission power consumption is given, the channel quality is better and the transmission rate is bigger. That means the more data is disseminated by the sending node at the same time, and the more data is transmitted by using the same power. Defining the energy efficiency is the amount of data bits that can be disseminated in per unit of joule energy. Our goal is to obtain the maximum or optimal energy efficiency when the sending node competes successfully to the channel for data dissemination and to improve the energy efficiency of data dissemination with guarantee of data transmission delay.

Assume that the sending node successfully obtains the channel in the

Here,

In order to reduce energy consumption, each sending node maximizes its own energy efficiency to achieve optimal energy efficiency. Therefore, the optimal energy efficiency

Each sending node competes for the channel with probability

After the sending node receives the data with the maximum delay of

In (

It is called one data dissemination, which refers to the process from the sending node competing for the channel to winning the competition and disseminating data. Indeed, one data distribution includes one or more rounds of channel competition and one data dissemination.

According to the law of large numbers, (

Here,

According to (

Furthermore, formula (

Formula (

Therefore, the average energy efficiency maximization problem of (

The aim of (

The average energy efficiency maximization problem of formula (

The sending node takes part in the channel competition in each period

Here,

Proposition 1. When

Proof. According to (

Since the data has the maximum delay

According to (

As a result, the transmission rate threshold at which the sending node stops the channel competition and starts disseminating data at

So there is

Similarly, the transmission rate threshold obtained from

In summary, the transmission rate threshold of the transmission sensor at time

According to the optimal myopia stopping rule in Subsection

Here,

Given the cumulative dissemination function

The transmission rate expected value and the expected time length of the sending node data dissemination at the time

The expected value of transmission data, the expected energy consumption, and the expected detection total energy consumption of the sending node from 1 to

Therefore, the flowchart of OEDDBOS is shown in Figure

The flowchart of OEDDBOS.

The above algorithm uses the Newton iteration method to calculate the optimal average energy efficiency

The optimal rate threshold

This section compares the optimal energy efficiency data dissemination strategy based on optimal stopping theory proposed in this paper with other data dissemination strategies in related studies, then analyzes and evaluates the average energy efficiency and the average transmission rate of each strategy. The strategies being compared in this paper include the following three.

Energy-Efficient Optimization for Distributed Opportunistic Scheduling Strategy (EEODOS).

Energy Efficient Data Dissemination Strategy Based on Game Theory (EEDDBG).

Randomly Competing Data Dissemination Strategy (Random).

Simulation experiment parameter values.

Parameter | Description | Value |
---|---|---|

Bandwidth (Hz) | 10^{7} | |

Noise power spectral density (W/Hz) | 10^{-7} | |

Channel gain variance correlation value | 1 | |

Transmission power (W) | 0.1 | |

Channel gain | 0 ~ 4 | |

Peak of main signal amplitude | 1 | |

Number of sending sensor (s) | 5 | |

Cycle of base station dissemination data (s) | 4 | |

Amount of data distributed by the base station at each time (kB) | 144 | |

The maximum delay of data (s) | 10 | |

Channel quality holding time (s) | ||

Channel competition period (s) | ||

Transmission time length of EEODOS (s) | ||

Pe | Reference power consumption (W) |

According to the analysis in Section

The relationship between the optimal average energy efficiency

First of all, when the amount of the data to be distributed

Energy efficiency is the ratio of the total amount of data disseminated by the sending node to the total energy consumption. And the average energy efficiency refers to the average energy efficiency of each sending node, which represents the average amount of data dissemination per unit of energy consumption as well as the data dissemination efficiency by the unit energy consumption. The higher the average energy efficiency is, the greater amount of data dissemination per unit of energy consumption is achieved and the more energy is saved. Figure

The comparison of the average energy efficiency of different strategy. (a) Average energy efficiency of different quantities of data distributed

In Figure

The transmission rate is the ratio of the amount of data being successfully disseminated by the sending node to the amount of data to be disseminated. Because data has the maximum delay requirement, the data that exceeds the delay will be discarded. The average transmission rate is the average of the transmission rate of each transmission node. It represents the proportion of the data that is disseminated within the delay requirement in the network. And the average transmission rate is also the probability of successful data dissemination. The greater the average transmission rate, the less data discarded due to timeout. Figure

The mean comparison of the average transmissibility for five strategies.

The description of Figure

In sensor-cloud systems, multiple sensors/sinks usually use the same channel to transmit data at the same time in the transmission interference range, which will bring transmission collisions and result in data transmission failure. Aiming at reducing energy consumption for data dissemination in sensor-cloud systems, this paper mainly focuses on the energy consumption optimization problem with time-varying channel quality when multiple sensors use the same channel to disseminate data. The sensor sends data with a certain probability to compete for channel. The sensor decides whether or not to distribute data in terms of channel quality for saving energy after getting channel successfully. We construct the maximization problem of average energy efficiency for distributing data with delay demand. Then, this maximization problem is transferred to an optimal stopping problem which generates the optimal stopping rule. At last, the thresholds of the optimal transmission rate in each period are solved by using the optimal stopping theory, and the optimal energy efficiency for data distribution is achieved. Simulation results indicate that the strategy proposed in this paper can to some extent improve average energy efficiency and delivery ratio and enhance energy optimization effect and network performance compared with other strategies.

The figure’s data used to support the findings of this study are included within the article.

The authors declare no conflict of interest.

This study was funded by the National Natural Science Foundation of China under Grant Nos. 61562006 and 61772233 and in part by the Natural Science Foundation of Guangxi Province under Grant Nos. 2013GXNSFGA019006 and 2016GXNSFBA380181.