Utility-based Scheduling Algorithm for Wireless Multi ...

2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): MAC and Cross-Layer Design

Utility-based Scheduling Algorithm for Wireless Multi-media Sensor Networks Han Hao, Ke Wang, Hong Ji, Xi Li, Heli Zhang Beijing University of Posts and Telecom., P.R. China E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract—Wireless Sensor Networks (WSNs) have been widely deployed in monitoring and surveillance areas. With the improvement of sensor nodes’ processing capability, multi-media transmission in WSNs has become a new trend. Since multi-media has strict Quality of Service (QoS) requirements, which calls for efficient scheduling algorithm that could guarantee the end-to-end delay and reduce the energy consumption. Therefore, in this paper we propose a utility-based scheduling algorithm that could enhance the energy efficiency and meet the QoS requirements of multimedia. Our proposed scheduling algorithm includes two steps: firstly, a semi-Markov model is deployed to predict the arrival rate of multi-media; secondly, a utility function is designed which based on the consideration of energy consumption and the QoS constraints. Simulations show that, compared to conventional algorithms, our proposed scheduling algorithm performs better in terms of packet loss rate due to buffer overflow and end-to-end delay. Keywords- wireless sensor networks, multi-media, packet scheduling, QoS, semi-Markov

I.

INTRODUCTION

Wireless Sensor Networks (WSNs) have been widely deployed in monitoring and surveillance in recent years. With the development of microchips, sensor nodes gained more power capacity and processing ability. Some sensor nodes are equipped with multi-media devices, such as cameras that are capable of retrieving video and audio streams [1]. Thus the wireless sensor networks can be used for various application areas such as smart homes, automated parking advice and health care delivery. The multi-media data have strict Quality of Service (QoS) requirements for the end-to-end delay that data should be transmitted before the expiration of deadline. On the other hand, the battery-powered nodes are difficult to replace batteries after deployed [2]. Thus the wireless sensor networks are also highly effected by energy consumption. Efficient scheduling algorithms can reduce sensors’ energy consumption and data transmission delay, which called up wide attention from researchers. Multi-media data have strict QoS requirements, so the efficient architecture for WSNs transmitting multi-media is star topology. Data from sensor nodes need to be transmitted to the sink node, and the sink node has to decide which sensor node will be scheduled based on an algorithm. The multi-media streams generated by sensors have to reach the sink node within a specific time period or before the expiration of deadline [3], so the sink node needs to decide the delivery order of data packets in their ready queue based on their importance and delivery deadline [4]. Specifically, in a real-time system with burst traffic, sensors with finite buffer need to maintain enough free storage space before the incoming data in case of losing data.

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Extensive research for scheduling packet of sensor nodes has been conducted. Some algorithms concentrated on energy saving. A predictive-wakeup MAC protocol for wireless sensor networks was proposed to save energy by predict receiver wake up times in [5], while an MAC-Protocol for Dense Wireless Sensor Networks was proposed in [6] to increase the lifetime of WSNs by reducing the energy consumption in overhearing. The authors in [7] proposed a kind of adaptive/periodic on–off scheduling algorithm which sensor nodes use only local information to make scheduling decisions to reduce energy consumption. On the other hand, researchers also paid attention to the end-to-end delay. The authors in [8] proposed a priority-based scheduling algorithm which classify the data into three levels. It can reduce the transmission delay of real time data packet. Additionally, [9] proposed a dynamic fuzzy logic based packet scheduling algorithm to make it more flexible. [10] Also classified the data into real-time data and no-real-time data. Packets were prioritized based on the location of sensor nodes and the algorithm achieved high performance of end-toend delay. Moreover, a Priority-based low-power algorithm was given in [11] to reduce the transmission delay by multilevel and power consumption at the same time with finite sensor buffer. However, to transmit the multi-media data with high QoS in WSNs, we have to consider the application’s priority and energy consumption at the same time. Moreover, we should also take sensor’s limited buffer size into consideration which can reduce packet loss caused by buffer overflow [12]. So the scheduling algorithm for sensor nodes considering energy consumption and end-to-end delay at the same time with finite buffer needs to be formulated carefully. To address the above problem, a utility-based scheduling algorithm for wireless sensor networks has been proposed in this paper. The algorithm could make the decision based on not only the energy consumption and buffer status of nodes, but also the QoS requirements of data. The proposed scheduling algorithm can be concluded as follows. We first make a prediction of the incoming data rate by a semi-Markov (SMM) model. Then we design a utility function considering energy consumption, buffer size and QoS requirements of data at same time to adjust the priority of nodes. The algorithm formulates the scheduling problem into a utility maximization. The simulation results show that the proposed algorithm can get better performance in end-to-end delay and packet loss rate. The rest of the paper is organized as follows: Section II presents some related works. We describe the system model for the WSNs. Our packet scheduling algorithm are presented in Section III. Section IV simulates our algorithm with Crankshaft in life time, delay and packet loss. Section V makes a conclusion of the paper. II.

SYSTEM MODEL

In this paper, we consider a WSNs in a star topology. Sensor nodes are all connected to the sink node. We divide each sensor into three

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2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): MAC and Cross-Layer Design

activity levels, each level has different rates of packet arrival. A semiMarkov model has been used to describe the switch of different levels. The TDMA based WSNs is organized into a star topology, which has a sink node and N sensor nodes. Each sensor node has one hop to the sink node. Sensors are all full charged and the battery is not rechargeable. Time is divided into slots with fixed duration. Only one node can be scheduled in a slot. The selected user transmits all its data to the sink node and clears its buffer. Each node costs the energy PA in state report and PT when it is selected to transmit data. Fig.1 details the system model of the WSNs. We define sensor node n has the buffer size Bn and energy capacity En . while Bn (t ) and En (t ) represents the buffer occupied and energy remain for node n in slot t .The data in node n have strict deadline Dn and Dn (t ) stands for the waiting time slots in the scheduler. M sensors are deployed for no real time applications which are not sensitive to the end-to-end delay. Other N − M sensors are deployed for multi-media sensors which deliver the real time data with strict deadline. Data will loss if it would not be scheduled before the expiration of deadline or the buffer is full. Therefore the multi-media sensors must have lower end-to-end delay and packet loss rate.

arrival rate DRi (t ) at slot t , which follows Gaussian distribution. When the DRi (t ) ∈ [ DRi min , DRi max ] , the node stay in Si (t ) .When a state transition occurs, the model is described by a state transition matrix, whose element Pij denotes the probability of transition from state i to state j . Because of the explicit modelling of the state duration time distribution, Pii = 0 for all i . Finally, Pi denotes the initial probability of the model being in state Pi .

N 













Fig.2 semi-Markov models

Fig. 2 illustrates the state chain for a three-state SMM. The state i = 1, 2,3 lasts for k instants (expressed by the 1-probability transitions), where is explicitly extracted according to state duration distribution di [k ] , Then, the next state j ≠ i is selected by means of the transition matrix.

Sink node

III.

UTILITY-BASED SCHEDULING ALGORITHM

This paper makes a prediction of the incoming data to reduce the packet loss caused by buffer overflow. The proposed algorithm also utilizes the status information of sensor buffer and energy remaining to improve the performance of end-to-end delay and packet loss rate. We can estimate the parameters of the 3 states SMM by an observation sequence [13]. λ and Pij can be given by an EM

Sensor nodes

Fig.1 System model





N

Gateway

Buffer size

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According to the results in [13], the bit rate of the media stream varies based on the sensor activity level. Each level will last for a period and Poisson distribution properly trades off for accuracy in the traffic model. Therefore we assume the duration of each activity level follows the Poisson distribution. We use Si (t ) to describe the level i at slot t . λ is the average duration of each Si (t ) . Then the di [k ] shows the probability of state Si (t ) holding k times. Thus the di [k ] follows.

e − λi λik (1) k! The activity level of the sensor is changing all the time. To represent this behaviour, we describe it by a semi-Markov models. Each state represents an activity level. We define the activity into three states: idle state S1 , instability state S2 and busy state S3 . In the idle state, no activity was detected by sensor and no data was conducted. Both real event and noise can lead to the instability state. The difference is the real event can change the state to busy state rapidly while the state will back to idle state if it is noise. In the busy state, a large number of data was conducted and need to be sent in time. Each state has a data

algorithm. We define K ni (t ) as the times the sensor n has already in the state Si . Then we get the SPni = =

Pni (k ≥ ( K ni (t ) + 1)) Pni (k ≥ K ni (t )) ∞

¦

d i [k ]

k = K ni ( t )

∞

¦

(2) di [k ]

k = K ni ( t ) +1

to describe the possibility of the node n stay in the state Si . Then we can deduce the BAni (t + 1) = ¦ (1 − SPni ) Pij DR j max + SPni DRi max (3)

di [k ] =

j

as the max data in t + 1 slot. If Bn (t ) + BAni (t + 1) ≥ Bn , it means buffer will be overflow next time slot. If the sensor would not be selected, packet will loss caused by buffer overflow, so we have to select node n and transmit the data immediately. We assume the buffer situation level B n (t ) of node n in slot t . B (t ) is modelled as:

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n

2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): MAC and Cross-Layer Design

B (t ) B n (t ) = n Bn

(4)

B n (t ) reflects the occupied rate of the sensor to reduce the packet loss caused by the finite buffer. The higher B (t ) n

means the sensor buffer have fewer space and the sensor should be scheduled with a higher priority, so that the sensor has enough space for the incoming data to prevent the packet loss. Then we define the energy situation level E n (t ) of node n in slot t as: α En + (1 − α ) En (t ) (5) E n (t ) = En Where the α ∈ (0,1) is a parameter to change the proportion of E (t ) to influence E (t ) . For instance, if the α n

n

is closer to 0, the equation may degenerate into En (t ) − 1 , so the E n (t ) cannot reflect the remaining energy. When the α is closer to 1, the equation may degenerates into E (t ) (6) E n (t ) = n En The E (t ) can totally reflects the remaining energy of the n

sensor. We can flexible change the α whether we want a long network life time or an extreme performance for transmitting data. The α can also limit the scale of E n (t ) . We can deduce that α u ° n (8) ® ° En (t ) > v °¯ En Where u and v range from 0 to 1. Without the condition B n (t ) ≥ u , a sensor with tiny data would contend for the channel. It costs energy in reporting the node’s state. Meanwhile, the sensor with low B n (t ) has less probability of buffer overflow. Thus, such a state report is energy inefficient. For our system model, if the sensor is in the busy state, the duration will follows Poisson distribution which means the data was continue conducted. The buffer will rapidly reach the condition and the condition (8) would not bring in too much delay. If the sensor is in the instability state caused by noise, it will transfer between idle and instability state. It is hard to reach the condition (8), so it would not waste resource. Besides, the condition E n (t ) ≥ v makes our scheduling algorithm more practical, because the node with little residual energy would lose its function. Due to the low power condition, it is hard to send all the data to the sink node in a slot while other sensor must wait for scheduling in the sink node. Selecting this kind of sensor to transmit data is another way of wasting resource. So if the energy level is below v , we assume the sensor is dead. Considering the 3 conditions. For the node which satisfies (8), it calculates the utility function B (t ) D n (t ) U n (t ) = n (9) E n (t ) We can easily deduce: 1 1 U n (t ) < < (10) α + v −αv α In addition, we consider some extreme conditions to improve the U n (t ) to make it more practical. When the urgent level D (t ) is close to 1, it should be selected to avoid packet n

loss caused by overtime. However, if there is only few data in the buffer and the sensor is in a high power condition, we will get a small B n (t ) and a large E n (t ) . As a result, the U n (t ) in this condition is not large enough compared to those condition are all in the middle level. This decision will cause packet loss for the system. So we define the B (t ) D n (t ) | D n (t ) − β | + D n (t ) − β (11) U n (t ) = n + E n (t ) 2 | D n (t ) − β | α When the data is urgent enough, the last addition item will up to 1 / α which ensures the U n (t ) achieves larger than not urgent situation. While the item equals to 0 when the data is not that urgent, the equation (11) degenerates to (9) which is smaller than 1 / α . This measure ensures that scheduler will select the sensor with extreme urgent data first and select the sensor considering the comprehensive level in the normal condition. Then we have to change the report condition to ensure this kind of node update its information. The added condition is D n (t ) − β > 0 (12)

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2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): MAC and Cross-Layer Design

The update condition can be sum up to (8) or (12). Each node update their Bn (t ) , En (t ) and Dn (t ) to the scheduler. The scheduler will calculate U n (t ) for all nodes. Then the scheduled node is: U n* (t ) = arg max(U n (t )) (13) i

Subject to uBn < Bn (t ) < Bn vEn < En (t ) < En 0 < Dn (t ) < Dn

(14) (15) (16)

The user n* (t ) with maximal announcement among all users which satisfies (8) or (12) will be selected in slot t . The solution is given below for the utility-based algorithm. Algorithm utility based algorithm Step 1 Initialize En ˈ Bn and Dn for each node Step 2 Data collection Step 3 Judge the reporting requirements (8) or (12) Update parameters to the scheduler Step 4 B n (t ) , E n (t ) and D n (t ) Step 5 If node n satisfied Bn (t ) + BAni (t + 1) ≥ Bn Schedule node n and return to step 3 Step 6 Calculate utility function (11) for each node Solve the optimization problem. Step 7 U n* (t ) = arg max(U n (t ))

we compare the network lifetime which we define as the number of dead nodes is less than 0.9 N . In Fig 3, we compare the proposed algorithm with Crankshaft in life time. The WSNs that uses proposed algorithm keeps alive for 25309 slots, while sensors are all dead at 26761 slots using the Crankshaft. Even if Crankshaft saves the power consumption in user’s state report, our scheduling algorithm efficiently selects the node in lower energy level to reduce the energy consumption for waiting the scheduler. After being served, the sensor cuts off its following channel requests until it meets the requirements of the buffer. All the measures enable our strategy to achieve almost the same network life time compared with Crankshaft. We compare the end-to-end delay for the 10 multi-media sensors in 30 sensors’ network in Fig 4. After 4000 slots, the end-to-end delay became stable. The average end-to-end delay for the proposed algorithm achieves 6 time slots which decreases 25% end-to-end delay to the Crankshaft. The proposed algorithm outperforms the Crankshaft. Because we design an urgent level model to measure the data, and the model gives high priority to the real time data with small Dn and considers the time waiting in the scheduler. The more time you wait, the more chance you will be scheduled. 40

30

i

Select U n (t ) to transmit data Return step 3

Number of Nodes

IV.

SIMULATION RESULTS

We set up the simulation based on a WSNs with N = 30 sensors which has N − M = 10 real time application sensors in it. We compare our algorithm with Crankshaft in network lifetime, end-to-end delay and the packet loss rate. Crankshaft divides time into frames, with each frame divided into N slots. Each user has its exclusive time slot in one frame to communicate with the sink node. This algorithm totally conserves the energy consumed in users’ state report. Thus, Crankshaft almost achieves the lower bound of power consumption in network scheduling. It offers us a good standard to judge our scheduling algorithm’s performance. The simulation lasts 4 × 10 4 slots and each slot’s duration is 500 ms. The energy capacity En is 1Wh and the buffer size Bn is 100 Kbytes. Initially, each user is fully charged and its data in buffer uniformly distributed in [20, 40] Kbytes. User’s energy cost of reporting its condition PA is 5 × 10 −5 Wh and the energy consumed in data transmission PT=2.5 × 10−4 Wh . We set α = 0.9 , β = 0.9 , u = 0.5 and v = 0.1 respectively. Each user’s data growth in buffer per slot follows the SMM model. In the simulation, we define the matrix p to ensure p1 = 0.6 , p2 = 0.1 and p3 = 0.3 . The duration t1 and t3 follows the Poisson distribution with λ = 8 while t2 with λ = 4 .We define dead node as the user with Esi (t ) < v . Then

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Fig. 3 Life time of WSNs 10 Proposed Algorithm Crankshaft 9

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Fig.4 Average end-to-end delay

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2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC): MAC and Cross-Layer Design

V. 10

In this paper, a packet scheduling algorithm is proposed to achieve high performance of end-to-end delay and packet loss rate. The algorithm can predict the incoming data rate by a SMM and the utility function of the algorithm considers the buffer condition, energy condition and QoS requirements at the same time. Measures also have been taken to deal with some extreme situation which may cause packet loss. In simulations, our proposed algorithm performs better than the Crankshaft in end-to-end delay and packet loss, while it achieves almost the same performance in network life time.

Proposed Algorithm Crankshaft

9

Average Packet Loss Rate (%)

8 7 6 5 4 3 2

ACKNOWLEDGMENT

1

The research is supported by National Science and Technology Major Project of the Ministry of Science and Technology ˄Grant No.2014ZX03003013-004˅

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REFERENCES

Fig.5 Packet loss rate [1]

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8 Average Packet Loss Rate (%)

CONCLUSION

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[3]

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Fig.6 Packet loss for different node numbers

We also count the packet loss within 2 ×104 time slots for 30 sensors. In Fig.5, we can see the proposed algorithm can reduce the packet loss rate compared to the Crankshaft. After 12000 slots the packet loss rate became stable and the proposed algorithm achieves 3% while the Crankshaft stays at 6%. We design a buffer monitoring model which can report the situation of sensors’ buffer. The model ensures that the sensor with more data in the buffer has high priority to be scheduled to reduce the packet loss caused by buffer overflow. Moreover, we compare the 2 algorithms in average packet loss rate with different sensor numbers in Fig.6. The Sensor number N increases from 15 to 30 with 10 multi-media sensors in it. As N = 15 , the traffic load is not heavy so there is not too much data stack in the scheduler. As N increases from 20 to 25, the packet loss in the network with Crankshaft increases rapidly, while the network traffic increment does not have a clear effect on our algorithm’s performance. When N increases to 30, the packet loss with the proposed algorithm increases on account of heavy load. But compared to the Crankshaft, the proposed algorithm still reduce 50% packet loss. So our algorithm is more efficient to deal with buffer overflow and flexible for the heavy traffic load.

[7]

[8]

[9]

[10]

[11]

[12]

[13]

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