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Wireless Personal Communications

, Volume 100, Issue 4, pp 1537–1551 | Cite as

Statistical Multipath Queue-Wise Preemption Routing for ZigBee-Based WSN

  • Ihsan Ullah
  • Hee Yong Youn
Article
  • 397 Downloads

Abstract

Nowadays, wireless sensor network (WSN) is an important component in IoT environment, which enables efficient data collection and transmission. Since WSN consists of a large number of sensor nodes, network congestion can easily occur which significantly degrades the performance of entire network. In this paper a novel scheme called SMQP (Statistical Multipath Queue-wise Preemption) routing is proposed to balance the load and avoid the congestion for ZigBee-based WSN. This is achieved by employing statistical path scheduling and queue-wise preemption with multiple paths between any source and destination node. NS2 simulation reveals that the proposed scheme significantly improves the QoS in terms of delivery ratio, end-to-end delay, and packet delivery ratio compared to the representative routing schemes for WSN such as ad hoc on-demand distance vector and ad hoc on-demand multipath distance vector scheme.

Keywords

Congestion Wireless sensor network Multipath routing Load balancing Packet scheduling Real-time Non-real-time 

1 Introduction

Wireless sensor network (WSN) consists of a number of sensor nodes, and nowadays it is widely used in various application domains. WSN is imperative for timely and accurate detection of critical events in any target area especially when the access is restricted. Unlike wired network, it has limitations in network lifetime and communication range between the nodes. ZigBee is usually employed for WSN [1], and it is a high-level communication protocol based on IEEE 802.15.4 standard specifications. With several favorable features of ZigBee protocol including low-power and secure networking, it has been the best choice for various environments such as IoT, home automation, health care, smart energy, etc. [2]. For IoT, ZigBee has been further improved with respect to data routing and scheduling [3]. Here different types of packets are sent with different priority while fairness is considered. For this, efficient packet scheduling is needed in each node of the WSN.

In WSN congestion occurs when many nodes simultaneously transmit data, which significantly deteriorates the network performance. They increase queuing delay, packet loss, and the blocking of new connections. When some nodes or links become overloaded due to congestion, transmission failure occurs and data are lost. Network congestion or bottleneck can be avoided through multipath routing and load balancing. Multipath routing is the technique distributing the network traffic on all available paths, and thus balancing the traffic between the source and destination [4]. With ZigBee mesh topology, a node may have several paths for a packet leading to the destination. Enhanced bandwidth and reliability are the main benefits of multipath routing technique. Ad hoc on-demand multipath distance vector (AOMDV) [5, 6] is an extended version of ad hoc on-demand distance vector (AODV) [7] routing protocol which not only emphasizes optimal short path routing but also considers load balancing and distribution of network traffic via multiple paths. It provides equivalent paths of the same hop counts to the IoT gateway. It can improve the reliability of the network, reduce the number of overloaded nodes, and avoid link congestion. The paths must be disjoint, however, which unnecessarily limits the number of available alternative paths. Besides, it does not consider simultaneous usage of the paths.

In WSN the First Come First Serve (FCFS) policy [11] is typically employed for packet scheduling, where the packets are served by the order of arrival. In some situation, however, urgent packets need to be quickly delivered to the destination regardless of the order. Here the priority is decided based on various parameters of the packet such as deadline, size, and type, etc. Numerous studies on packet scheduling for WSN have been reported in the literature along with the planning of sleep–wake time [8, 9, 10, 11, 12, 13, 14]. However, most existing packet scheduling algorithms of WSN do not reflect the urgency of the packet in the scheduling [15, 16]. Even though extensive researches have been made on multipath routing, a single path of minimum cost is usually recommended among several paths while the secondary path is taken only when the primary path fails. An efficient and dynamic packet scheduler algorithm needs to be developed to prioritize the packets based on the urgency. Here the intermediate nodes require to change the order of delivery of the packets waiting in the queue based on the priority. Preemption [17, 18] is usually employed as an OS utility for the tasks running on a system. It is an effective approach for avoiding the delay of high priority tasks while allowing the system to be fairly shared among regular tasks. The preemption mechanism needs to be efficient and lightweight so that it can react fast to the dynamic system load.

In this paper a novel scheme for packet scheduling is proposed to balance the load and avoid the congestion for ZigBee-based WSN. It employs priority-based multipath routing with preemption. Here the packets are spread on multiple paths so that the delay time and drop ratio of the packets can be minimized. The packets are categorized into different priorities; (1) the first priority of routing control packet, (2) the second priority of real-time packet, and (3) the third priority of all other packets. Each node maintains three queues hosting the packets of different priority before forwarding. Unlike AOMDV, it assigns different path according to the type (priority) of the packet while multiple paths are used to balance the load. Furthermore, statistical path scheduling approach is adopted where the path associated with the higher priority can be used for lower ones if the packets of higher priority do not exist. Consequently, more efficient packet scheduling and routing can be achieved. In typical scheduling of computer and communication, preemption is usually applied to the tasks. The proposed scheme employs preemption between the queues during the path allocation. It is thus called the SMQP (Statistical Multipath Queue-wise Preemption) routing scheme. The main contributions of the paper are summarized as follows:
  • Statistical path scheduling and queue-wise preemption for maximizing the bandwidth utilization

  • Multipath routing for different type packets for supporting high efficiency and QoS

  • Generic queuing model applicable to the analysis of packet control scheme for WSN

The rest of the paper is organized as follows: in Sect. 2, the work related to packet scheduling for WSN is discussed. The proposed scheme on packet scheduling for WSN is presented in Sect. 3. Section 4 discusses the simulation results, and the conclusion is made in Sect. 5.

2 Related Work

In this section the work related to packet scheduling for WSN-based ZigBee protocol is presented. The packet forwarding scheme in Reliable Real-Time Protocol (R2TP) [19] is based on the time metric, where the main focus is to achieve reliable transmission by duplicating the packets and using multiple paths. The velocity monotonic scheduling (VMS) [20] considers the end-to-end deadline of each packet before transferring a packet toward the destination by reflecting the local urgency. Compared with non-prioritized packet scheduling technique, VMS reduces the packet drop ratio by giving higher priority to the packets requesting higher velocity. Here data are prioritized based on the distance from the source node to the base station (BS) and deadline. If the deadline of a particular task expires, it is dropped at an intermediate node.

The Priority-based Congestion Control Protocol (PCCP) scheme [21] prioritizes both source and transit traffic, with some restriction in the process of sensed data in each node. The RACE scheme [22] follows the Bellman–Ford algorithm to find the paths between the source and destination showing minimum traffic load and delay. It decides the priority of a packet using the earliest deadline first (EDF) policy, and uses a prioritized MAC protocol. When the channel becomes idle, it modifies the initial wait time. The Adaptive Double Ring Scheduling (ADRS) scheme [23] divides the traffic into two queues according to the priority decided by the EDF policy. The scheduler dynamically switches between two queues based on the deadline of newly arrived packets.

In [24] the number of queues of a node is decided according to its location in the network. Since the packet generated from the node far from the BS usually takes relatively long time to reach the BS, the order of the delivery of the packets is changed in the intermediate nodes. For this, the queue and the position inside the queue for each packet are decided according to the priority. In the scheduling algorithm for WMSN [25], the buffer space of intermediate nodes is divided into four queues to hold three different types of video frames and one regular data frames. The video frames are given higher priority with the scheduling in round-robin fashion. Data frames are transmitted in FCFS discipline when the first three queues are empty.

Considering the types (priorities) of typical packets of WSN, the proposed scheme classifies them into three types; control, real-time, and regular packet. This will let the control packets traverse the network most swiftly while significantly reducing the end-to-end delay of real-time packets. The proposed scheme is presented next.

3 The Proposed Scheme

In this section the proposed scheme for ZigBee-based WSN is presented. It prioritizes the packets to reduce the drop rate of high priority packets, and effectively utilizes the resource of the network and thus minimizes the average waiting time and end-to-end delay. It also tries to avoid congestion by employing multipath routing which in turn increases the delivery ratio of the packets.

3.1 Design Goals

The design goals of the proposed scheme are (1) prioritization of the packets based on the type, (2) minimization of the drop ratio of control and real-time packets against regular type packets, (3) employment of multiple queues with statistical scheduling to avoid congestion and minimize energy consumption while ensuring high delivery ratio of packets under the fairness policy.

In wireless and wired communication, the end-to-end delay of packet transmission is regarded as one of the important quality metrics, especially for real-time applications. If buffer overflow and congestion occur in the network, packet loss rate is increased. Also, network throughput and energy efficiency of sensor nodes are decreased. In order to decrease packet loss rate and queuing delay, network congestion needs to be avoided.

In ZigBee-based WSN only the sensor nodes already joined the network can receive or send messages to the coordinator and other nodes. As shown in Fig. 1, ZigBee network consists of three types of nodes which are ZigBee Coordinator (ZC), ZigBee Router (ZR), and ZigBee End Node (ZEN). There exist various topologies adopted for WSN including star, mesh, and tree. With tree topology, ZigBee network is formed with one ZC and multiple ZRs.
Fig. 1

The structure of ZigBee-based WSN

The key purpose of multipath routing [26, 27] is to avoid overload of some nodes and reduce congestion of the network. Figure 2 shows an example of source–destination paths between the sender, ‘S’, to the destination, ‘D’. The three paths are denoted as P1, P2, and P3, respectively.
Fig. 2

The multipath structure of the SMQP scheme

In the proposed scheme three queues, Q1, Q2, and Q3, of different queuing discipline are used for accommodating the packets of each of three priorities, respectively. Each of them is associated with a path such as Q1 with P1, Q2 with P2, and Q3 with P3. Here statistical path scheduling is applied such that if Q1 is empty, the packets in Q2 are distributed to P1 and P2. Similarly, if both Q1 and Q2 are empty, the packets in Q3 are distributed to all the three paths. With the proposed approach, different types of packets are forwarded to the receiver simultaneously on multiple paths, maximizing the path utilization. Delivery ratio, end-to-end delay, and packet drop ratio are the performance metrics taken to evaluate the proposed scheme.

3.2 Packet Scheduling

Refer to Fig. 3. Q1 and Q3 employ the FCFS scheduling discipline, while Q2 is based on the priority. To further enhance the performance, the preemption policy between the queues is employed. Packet service time is defined as time interval between the packet arrival at MAC layer and successful transmission to the next hop. In the proposed scheme, the packets from Q1, Q2 and Q3 are forwarded in the ratio of 2:2:1 toward the next hop.
Fig. 3

The structure of the queue and routing of the proposed scheme

3.3 Performance Modeling

The analytical model of the proposed scheme is based on M/M/c queuing model [28, 29]. It is employed to evaluate the performance of the proposed scheme in terms of delivery ratio and end-to-end delay of the packets. The variables used in the model are listed in Table 1.
Table 1

The variables of used in the model

λ i

Mean arrival rate of the packet of priority_i (i = 1,2,3)

μ i

Mean service rate of the packet of priority_i (i = 1,2,3)

W

Wait time of a packet in the queue

δ

Delay time of a packet in a node

L Q

Number of packets in queue_Q

β n

Probability of a node having n packets

Note that the arriving packets are of one of the three priorities. Assuming that the packets move in the network in the Poisson process,
$$\lambda = \lambda_{1} + \lambda_{2} + \lambda_{3} \left\{ {\begin{array}{*{20}l} {\lambda_{1} \to Q_{1} } \hfill \\ {\lambda_{2} \to Q_{2} } \hfill \\ {\lambda_{3} \to Q_{3} } \hfill \\ \end{array} } \right.$$
(1)
$$\mu = \mu_{1} + \mu_{2} + \mu_{3} \left\{ {\begin{array}{*{20}l} {\mu_{1} \to P_{1} } \hfill \\ {\mu_{2} \to P_{2} } \hfill \\ {\mu_{3} \to P_{3} } \hfill \\ \end{array} } \right.$$
(2)

Using the model of the target WSN, the values of the network control parameters are decided to avoid congestion and increase the QoS of the network. The key parameters are the traffic generation rate and service rate in each node, packet scheduling rate, etc.

The waiting time of a packet can be estimated by Eq. (3).
$$W = \frac{{\left( {L_{{Q_{1} }} + L_{{Q_{2} }} + L_{{Q_{3} }} } \right)}}{{\left( {\lambda_{1} + \lambda_{2} + \lambda_{3} } \right)}}$$
(3)
The delay time of a packet in a node, δ, includes the wait time in the queue and the service time as follows.
$$\delta = W + \lambda /\mu$$
(4)
As shown in Fig. 3, the packets continuously enter a node with the rate of λ and are lined up in the queues for the service at the rate of μ. The 3D state transition diagram of a node is shown in Fig. 4. Here State_(i,j,k) indicates that the node has i, j, and k packets in Q1, Q2, and Q3, respectively, while the size of each queue is N.
Fig. 4

The three-dimensional state transition diagram of a node

Recall that the proposed scheme employs three queues having different packet arrival rate λ1, λ2 and λ3, and different service rate of μ1, μ2 and μ3, respectively. The 3D transition diagram aggregates them as
$$\left( {\lambda_{1} ,\lambda_{2} ,\lambda_{3} } \right) = \left\{ {\begin{array}{*{20}l} {\left( {\lambda_{1} ,0,0} \right) = \left( {0,0,0} \right),\left( {1,0,0} \right),\left( {2,0,0} \right), \ldots ,\left( {N,0,0} \right)} \hfill \\ {\left( {0,\lambda_{2} ,0} \right) = \left( {0,0,0} \right),\left( {0,1,0} \right),\left( {0,2,0} \right), \ldots ,\left( {0,N,0} \right)} \hfill \\ {\left( {0,0,\lambda_{3} } \right) = \left( {0,0,0} \right),\left( {0,0,1} \right),\left( {0,0,2} \right), \ldots ,\left( {N,0,0} \right)} \hfill \\ \end{array} } \right.$$
and
$$\left( {\mu_{1} ,\mu_{2} ,\mu_{3} } \right) = \left\{ {\begin{array}{*{20}l} {\left( {\mu_{1} ,0,0} \right) = \left( {N,0,0} \right),\left( {2,0,0} \right),\left( {1,0,0} \right), \ldots ,\left( {0,0,0} \right)} \hfill \\ {\left( {0,\mu_{2} ,0} \right) = \left( {0,N,0} \right),\left( {0,2,0} \right),\left( {0,1,0} \right), \ldots ,\left( {0,0,0} \right)} \hfill \\ {\left( {0,0,\mu_{3} } \right) = \left( {0,0,N} \right),\left( {0,0,2} \right),\left( {0,0,1} \right), \ldots ,\left( {0,0,0} \right)} \hfill \\ \end{array} } \right.$$
With different types of packets arriving and serving simultaneously,
$$\left( {\lambda_{1} ,\lambda_{2} ,\lambda_{3} } \right) = \left( {0,0,0} \right),\left( {1,1,1} \right),\left( {2,2,2} \right), \ldots ,\left( {N,N,N} \right)$$
and
$$\left( {\mu_{1} ,\mu_{2} ,\mu_{3} } \right) = \left( {N,N,N} \right),\left( {2,2,2} \right),\left( {1,1,1} \right), \ldots ,\left( {0,0,0} \right)$$
In queuing network, each node of the network can be considered as a queue connected to each other via several paths. The packets arriving at a node in the proposed ZigBee network are continuously forwarded to the destination node through three different queues in the node. Therefore, this process can be regarded as a composition of three independent birth–death process, and thus the utilization of a node will be as following equations which are derives from the M/M/c queuing theory [29].
$$\uprho = \left( {\frac{{\lambda_{1} + \lambda_{2} + \lambda_{3} }}{{\mu_{1} + \mu_{2} + \mu_{3} }}} \right)$$
(5)
The probability of no packet in a node can be determined by
$$\beta_{0} = \left[ {\mathop \sum \limits_{n = 1}^{P - 1} \frac{\left( \rho \right)}{n!}^{n} + \frac{{\rho^{P} }}{{P!\left( {1 - \rho } \right)}}} \right]^{ - 1}$$
(6)
Here P represents the number of paths from a node to the next node. Let us denote n the number of packets in a node. If there exist fewer packets than the number of paths between them, the equation is given as
$$\beta_{n} = \beta_{0} \left[ {\frac{{\left( {P\rho } \right)^{n} }}{\left( n \right)!}} \right]\quad {\text{for}}\quad n \le P$$
(7)
If the number of packets is larger than P, then the probability can be given as
$$\beta_{n} = \beta_{0} \left[ {\frac{{\left( {P\rho } \right)^{n} }}{{P^{n - P} P!}}} \right]\quad {\text{for}}\quad n > P$$
(8)
The number of packets in a node is a measure indicating the possibility of congestion, and the following equation shows the average number of packets in a queue [29].
$$L_{Q} = \beta_{0} \frac{{\left( {\frac{{\lambda_{1} + \lambda_{2} + \lambda_{3} }}{{\mu_{1} + \mu_{2} + \mu_{3} }}} \right)^{P} \rho }}{{P!\left( {1 - \rho } \right)^{2} }}$$
(9)
From Eq. (9) the total member of packets in a node, N t , is decided as
$$N_{t} = \left( {\lambda_{1} + \lambda_{2} + \lambda_{3} } \right)\left( {L_{{Q_{1} }} + L_{{Q_{2} }} + L_{{Q_{2} }} } \right)$$
(10)
The model of the arrival, service, and departure process of the packets can be used for deciding the rate of data generation in each sensor node, size of the queue, scheduling rate, etc., which are important to maximize the performance of the network. The procedure of the proposed scheme is summarized as follows.

4 Performance Evaluation

In this section computer simulation based on NS2 is conducted to evaluate the effectiveness of the proposed scheme in terms of delivery ratio, end-to-end delay, and packet drop ratio. Including the 802.15.4 standard and ZigBee technology [30, 31], the proposed SMQP scheme is compared with the existing AODV and AOMDV scheme, which employ priority and Deficit Round Robin (DRR) [32] scheduling, respectively. The mesh topology of ZigBee network simulated with the network animator NS2, and Table 2 lists the parameters used in the simulation.
Table 2

The parameters used in the simulation

Parameters

Value

Number of nodes

101

Simulation area

50 × 50 m2

Number of packets sent

6000

Physical and MAC protocol

IEEE 802.15.4

Antenna

Omni antenna

Traffic

CBR (constant bit rate)

Link layer type

LL

The results of simulation are compared in Table 3. Here the packets are generated randomly following the Poisson process. The traffic is generated with average rate of 5 packets per second with 5 s interarrival time, and 25, 33, 42% of the packets are of priority 1, 2, and 3, respectively. The total run time of simulation is 30 min. Notice from Table 3 that the proposed scheme allows much higher packet delivery ratio and less delay than the other two schemes.
Table 3

The comparison of the three routing schemes

Metric

AODV

AOMDV

SMQP

Packet delivery ratio (%)

80.3

86.9

93.4

Delay (ms)

301.2

261.2

184.4

In Fig. 5 the end-to-end delays of the three schemes are compared as the number of packets is increased from 400 to 2000. Compared to AOMDV and AODV, the proposed SMQP scheme significantly reduces the delay time for different types of workloads. This demonstrates that the proposed SMQP consistently outperforms the AOMDV and AODV scheme. Also, notice that its effectiveness becomes more significant as the workload increases.
Fig. 5

The comparison of end-to-end delays

Figure 6 shows the average waiting time of the packets. Observe from the figure that the waiting time of the proposed SMQP scheme is substantially smaller than those of AODV and AOMDV.
Fig. 6

The comparison of average waiting times

Figure 7 compares the packet delivery ratios of the three schemes. Observe from the figure that the ratio with the proposed SMQP is always higher than the other schemes regardless of the number of packets. This was achieved by employing different queues for different type packets and multiple paths for simultaneous routing which cause less congestion in the network.
Fig. 7

The comparison of packet delivery ratios

Figure 8 shows the number of packets lost for each type of packets by the three schemes. Note that packets might be dropped in route owing to network congestion. Traffic splitting on multipath and efficient queue management can reduce packet loss and congestion. As observed in the figure, the proposed scheme is much better than the other schemes in terms of the number of dropped packets, while the improvement for high priority packets is more significant than other type packets. This feature is quite important for providing high QoS with the target network.
Fig. 8

The comparison of the number of lost packets. a AODV, b AOMDV, c SMQP

Figure 9 compares the total number of lost packets with the three schemes. Packet loss is another metric measuring the QoS of the network. Observe from the figure that the total number of lost packets with the proposed SMQP scheme is always smaller than AOMDV and AODV. This is due to the separate queues for different type packets and splitting the network traffic into multiple paths.
Fig. 9

The comparison of the number of lost packets

5 Conclusion

In this paper a novel packet scheduling and routing scheme has been proposed for ZigBee-based WSN. It effectively reduces congestion and improves the performance of the network by employing statistical scheduling and multiple paths for different type packets. Also, queue-wise preemption technique was adopted to maximize the utilization of the links. A topology of ZigBee network was simulated using NS2, and it revealed that the proposed SMQP scheme significantly improves the network in terms of end-to-end delay and packet delivery ratio.

In the future, we will further enhance the proposed scheme with more sophisticated load balancing scheme and queuing discipline, along with the study on other performance metrics including throughput. We will also improve the queuing model of the proposed scheme accordingly. The proposed scheme will be extended to be applied to the virtualized network environment such as software defined networking (SDN). Here the schemes for dynamic path selection and load balancing will be developed.

Notes

Acknowledgements

This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00133, Research on Edge computing via collective intelligence of hyperconnection IoT nodes), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (2015-0-00914), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A6A3A11931385, Research of key technologies based on software defined wireless sensor network for realtime public safety service, 2017R1A2B2009095, Research on SDN-based WSN Supporting Real-time Stream Data Processing and Multiconnectivity), the second Brain Korea 21 PLUS project, and Samsung Electronics.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of SoftwareSungkyunkwan UniversitySuwonKorea

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