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Energy-Efficient Capture of Stochastic Events based on On-line Scheduling Scheme

: We improve the off-line scheduling scheme of existing wireless sensor network. Firstly, we introduce Bayesian statistical method in synchronous wireless sensor network. Then we let duration and interval, the reflection of characteristics of stochastic events, obey exponential distribution. Next we make Bayes posterior estimation on parameter. Based on Bayesian estimate, we obtain the analytical solution of the capture probability of stochastic events and sensor energy efficiency of capture events. Finally, we propose an on-line scheduling scheme for synchronous wireless sensor networks. This paper compares and analyzes the simulation experiments in on-line scheduling scheme and off-line scheduling scheme and the results show that compared with off-line scheduling scheme with constant distribution parameter values, on-line scheduling scheme can effectively reduce the probability of missing stochastic events and increase the probability of capturing events, further save energy consumption of wireless sensor network and extend network lifetime.


Introduction
The Internet of Things (IoT), the currently most popular research area, refers to real-time monitoring and connecting objects through various sensor devices and technologies to collect event information, and then having the access to the network it finally realizes the ubiquitous connection between object and object as well as object and people. Wireless Sensor Networks (WSN) is the key technology for the development of IoT [1][2]. WSN is composed of hundreds of small sensors, and each sensor is capable of perceiving, collecting, calculating and forwarding event information [3]. Events do not always occur and appear randomly, but their randomness can be described by two random variables, the event staying time at Point of Interest (PoI) and the interval between events. If the event duration is short but the interval is long, and the sensor is always in active state, which is bound to cause waste energy. Therefore, how to schedule the active and inactive state of the sensor in WSN, save the energy of sensor network as much as possible, and extend sensor network lifetime is a hot issue in current research.
At present, the sleeping scheduling issue in WSN can be divided into three categories: (1) Randomness without considering stochastic events [4][5][6], that is, to find non-overlapping subsets covered by sensors and let them work alternately so as to save energy and extend network lifetime. (2) Considering the randomness of stochastic events interval but not considering the randomness of duration of stochastic events [7][8][9][10], the sensor scheduling scheme is designed only based on the randomness of interval between events arrival. (3) Considering the randomness of interval between stochastic events arrival as well as the randomness of duration of stochastic events [11][12][13][14]. The sensor inactive state scheduling problem is transformed into an energy-oriented optimization problem, and the algorithm is designed to solve the issue. In the latter two categories, it is usually assumed that the interval and duration of stochastic events are subject to exponential distribution, where the distribution parameter is a given constant, and the inactive period of sensor is also constant. This mode is called an off-line scheduling scheme.
From a statistical point of view, the distribution parameter given by the off-line scheduling scheme are estimated based on historical data, and once this parameter is given at a fixed value, it remains unchanged regardless of posterior sample. However, in practical applications, event fluctuations may occur due to various reasons. For example, samples obtained in different time periods may be generated by exponential distributions with different distribution parameters. This requires the scheduling scheme of sensor to adjust distribution parameters based on the sample, otherwise huge errors would occur. Therefore, based on the research made by He et al. (2012) [15],we study on-line scheduling issue of sensors in synchronous WSN, and proposes a real-time adjustable sensor inactive period scheduling scheme to make it more in line with randomness of stochastic event, more adaptable and further saves energy consumption of network.
The main research contents of this paper are as follows: (1) Firstly, by introducing Bayes statistics method to deal with previous off-line scheduling problem in synchronous WSN. Bayesian statistics method is used to estimate interval 3 / 19 parameters of stochastic events and distribution parameters of duration and Bayes estimated value with real-time adjustment is obtained. (2) Then, the relationship between capture probability of stochastic events, the energy efficiency of sensor to capture stochastic event, and Bayesian estimated value of distribution parameter is discussed, and the relation between periodic schedule and Bayesian estimated value of distribution parameter is obtained. (3) Finally, the sensor periodic schedule that can be adjusted in real time in synchronous WSN is proposed, that is, an on-line scheduling scheme.
The contributions of this paper can be summarized as follows: (1) Since Bayesian estimation can be iteratively repeated, the method in this paper can be used to study the periodic sensor scheduling with real-time adjustment. (2) By increasing Bayesian estimation of distribution parameters, the randomness of stochastic events can be more accurately characterized.

Literature review
In the application of IoT, wireless sensor is widely used in a variety of monitoring scenarios due to its low price, small size and strong functions, such as precision agriculture [16][17][18], environmental monitoring [19][20][21], target detection [22][23][24], etc. Due to the complex geographical environment of the monitored target area, it is often necessary to use drone to throw a large number of wireless sensors so that the area is completely covered by the sensor. However, the location of wireless sensor placed is random, and it is likely that the perception range of some sensors in the target area is completely covered by its neighbor activation, that is, causing redundant nodes.
In the study of target coverage issue in most WSN [4,6], the randomness of events at coverage points is not taken into account. Only sensor subsets that can cover the target without overlapping in the region where the sensor is densely populated are considered, allowing subset to work alternately to maximize save energy and extend WSN lifetime. But the problem is that once coverage subset of the sensor is selected, the sensor is always working and certainly causes waste. Ren et al. (2014) [7] considered the interval between events occurrence while ignoring events duration at PoI. The authors assumed that the interval of event columns is independently and identically distributed random variables and these random variables obey exponential distribution with determined parameters, and studied the inactive state scheduling issue of sensor coverage subset. Cheng et al. Unlike previous literature, this paper discusses on-line adjustable sensor periodic schedule problem in synchronous WSN where the distribution parameters of random variables are not determined. Moreover, by conducting simulation experiments, it is proved that the on-line scheduling scheme in sensor sleep cycle proposed in this paper is more adaptable and energy-saving than off-line scheduling scheme.

Models and assumptions
This paper assumes that a sufficient amount of sensors are randomly placed into a planar area to capture stochastic events occurred within the target area and events randomly occur at a given PoI. We use Boolean network model [28] to analyze whether a stochastic event is captured by the sensor: if the distance between the location of the activated sensor and the location of PoI where the stochastic event occurs is less than the perception radius r of sensor, the stochastic event is captured by the sensor. Otherwise, it cannot be captured by the sensor. The energy consumed by the sensor per unit time active and inactive state is k1, k2, respectively. The energy consumption of the sensor to change between active/inactive states is c. The average arrival rate of stochastic events of PoI is ν. In a single scheduling period, a single sensor has two state transitions, and the sensor can be independently scheduled to switch. The duration X and the interval time Y of different stochastic events are independently and identically distributed. Stochastic events at different PoI are also independent. The symbols used in this article and their meanings are shown in Table   1.

3.1Prior distribution of stochastic events
Unlike off-line scheduling scheme, this paper first gives Bayesian estimation of duration and interval distribution parameter describing the randomness of event.
Lemma 1: Stochastic event duration variable X obeys exponential distribution with parameter −1 , and its probability density function is where θ>0 is average duration of stochastic events.

Lemma 2:
Stochastic event interval variable Y obeys exponential distribution with parameter −1 , and its probability density function is where β>0 is average interval between stochastic events.
In order to obtain the Bayesian estimation of average duration of stochastic events, it is first necessary to determine the prior distribution of average duration of eventsθ. In Bayesian statistical theory, the inverse Gamma distribution IG(α, λ) is usually regarded as the prior distribution of unknown parametersθ [29]. The density where α > 0 , > 0 are two undetermined parameters. The mathematical expectation E(θ) and variance D(θ) are respectively From Eq. (4) and (5) we can get We use the following method to estimate parameters α, λ. Based on exponential Then make Eq. Similarly, the prior distribution of average interval of stochastic events is π(β).

3.2Bayesian posteriori estimate of distribution parameters
First, an observation valueθis generated from Eq.(3) prior distribution π(θ), and then an observation value 1 , 2 , … , of theduration of n events is generated from Eq.(1) conditional distribution P(X|θ)., where x ( = 1,2, … , ) represents the duration of ith stochastic event. The joint conditional density function of the sample is Multiplying the prior distribution π(θ) by the sample distribution P(X|θ), we can get the kernel of posterior distribution Since the inverted Gamma distribution is a conjugate distribution, the posterior distribution of inverted Gamma distribution is still inverse Gamma distribution, that is, Eq. (10) is still the kernel of inverted Gamma distribution. Assuming that the loss function is a squared loss function, then Bayes estimation value of θ is the mean value of posterior distribution π(θ│X) . From Eq. (4) and (5) we can get Therefore, Bayes posterior estimated value of exponential distribution parameter −1 obeyed by stochastic event duration variable X is 1/ .
Similarly, Bayes posterior estimated value of exponential distribution parameter −1 obeyed by stochastic event duration variable Y is 1/ .

Event capture performance analysis
The following section analyses the probability that each sensor captures stochastic events occurring in PoI and the energy efficiency of the sensor in capturing stochastic events in periodic schedule (q, p) of sensors in synchronous WSN.
Synchronous WSN mean that all sensors use the same periodic schedule (q, p) and the active and inactive state of all sensors are synchronized, where p is the length of time the sensor is scheduled, q is the length of time the sensor is in active state, and p-q is the length of time the sensor remains inactive.

Capture probability of events in Bayesian estimation of distribution parameters
The following theorem can be obtained according to the Bayesian estimate of

Energy efficiency of capture events under Bayesian estimates of distribution parameters
It can be seen from Theorem 1 and Theorem 2 that under periodic schedule (q, p), the number of events that arrives each PoI and are captured by sensors is where The energy consumption rate per sensor is Since one sensor may cover multiple PoIs and one PoI may be covered by > 0 > 0 0 < = ≤ 1 (25) Then use the dichotomy to find zero approximation value of Eq. (24) and (25), that is, the specific steps of on-line adjustable periodic schedule are as follows: Step1: For each set of 1/ Bayes n , 1/ Bayes n , set a Pc or QE value, determine the interval [p1, p2] to make sure F(p1) F(p2)<0, set accuracy.
Therefore, based on dichotomy and Eq. (24) and (25), on-line scheduling scheme of synchronous WSN under different exponential distribution parameters of Bayesian estimates, stochastic event capture probability and capture event energy efficiency can be obtained.

Parameter settings
A large number of sensors are randomly placed into 20*20m target area in the synchronous WSN. Table 2 shows the relevant experimental parameters.  When θ −1 =1, as shown in Fig.1, in different duty cycles q/p, the capturing probability of stochastic events Pc decreases rapidly as the scheduling duration p of sensor in inactive state increases, that is, the probability of missing stochastic events increase. When the scheduling sensor duration remains the same, the probability of stochastic events capture increases rapidly with the increase of duty cycle, thereby effectively reducing the probability of missing stochastic events. scheme has a longer scheduling duration p and a longer active state duration q compared to that in off-line scheduling scheme, indicating stochastic events that cannot be captured in off-line scheduling scheme can still be captured in the on-line scheduling scheme proposed in this paper, that is, the on-line scheduling scheme can capture more stochastic events. When the duty ratio q/p=0.4, as shown in Fig. 4 and Fig. 5, as the energy efficiency QE of sensor capture event gradually increases, the scheduling duration p and active state duration q of the sensor in both scheduling schemes increase rapidly, that is, the sensor active state duration becomes longer p and q in on-line scheduling scheme change more rapidly compared to that in off-line scheduling scheme. In addition, when the energy efficiency is the same, the on-line scheduling scheme is corrected in real time compared with the constant off-line scheduling scheme, that is, p and q are changed with real time. Therefore, we can get the following conclusions: (