IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision
Queue Management Based Congestion Control in Wireless Body Sensor Network Md. Samiullah, S.M.Abdullah, A.F.M. Imamul Hoq Bappi and Shahed Anwar
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[email protected] Abstract—We consider the issue of designing a transport layer protocol for energy efficient congestion control and reliable data transfer in wireless body sensor network. The proposed protocol focuses on efficient management of queue to provide reliability and reduce packet loss. This protocol achieves energy efficiency to a greater extent by reducing packet loss. The protocol achieved greater throughput by ensuring reliability in the network. It is capable of supporting multiple applications in the same network by introducing unique packet sequence number. We present the design and implementation of the protocol and evaluate the protocol with different scenarios and network characteristics.
I. INTRODUCTION Wireless Sensor Networks (WSN) generally consist of spatially distributed autonomous sensors which can cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutions. In Wireless Body Sensor Networks (WBSN) sensor nodes are used to collect and process healthcare related data. A Sensor node has the ability to sense, perform simple computations and communicates among its peer nodes or directly to a base station which is a special master node that further analyze and report to the central server. Instead of having any pre-determined topologies, sensor nodes construct and dynamically maintain the structure of the network through wireless communication. Two different transmission links are used to transmit data in WBSN. One is used to transmit data among sensor nodes and another pass data to the central server. Central server creates useful reports from these data and forwards to health experts. Data packet generation may be continuous or event based depending on the application. Wireless sensor networks are suitable for different applications, e.g. traffic monitoring, plant monitoring, health monitoring and infrastructure monitoring. However, the proposed protocol is related to wireless sensor technology for health monitoring applications called Body Sensor Networks (BSN). Use of WBSN in healthcare monitoring is gaining popularity. It offers personalized healthcare services to the patient whilst promising to alleviate pressure on the overburdened healthcare system by improving daily disease management. It enables automatic emergency, trend detection which facilitates self-care [1]. Technical challenges those faced by WBSN are interoperability, invasion of privacy, data consistency, interference, scalability, hardware constraints, network topology, environment, power consumption and transmission media, congestion and reliability issues. Until now lots of works have been done to meet the above stated challenges. We specially focused on controlling congestion to provide reliability and greater throughput. In addition, our protocol meets the challenges of network topology maintaining, scalability and lesser power consumption. There are various protocols those control congestion. The proposed protocol controls congestion and provides reliability. Rest of the paper is organized in the following way. Next section will discuss the background and related
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works. Section III presents the proposed protocol. In describing the proposed protocol, we first introduce the basic components and then describe the protocol operation and how it performs accordingly. In section IV simulation results are presented and analyzed. Section V concludes the paper. II. BACKGROUND STUDY In networking environment congestion is a classical problem. Link or node level congestion can occur which may cause packet loss. As a consequence packet retransmission is required which increases energy dissipation. Energy loss can hamper the usual functionalities of sensor nodes in WBSN. A. Congestion In WBSN, congestion causes overall channel quality to degrade and loss rate to raise, leads to buffer drops and increased delays which causes reduction in throughput [2]. Some wireless technologies follow a mechanism of retransmitting lost packets. When more new packets are sent along with the retransmitted packet then it must be handled by intermediate nodes thus increasing the amount of data to be sent, which is the opposite of what should be done during congestion. B. Causes of Congestion Congestion can occur for various reasons. In [3], authors have discussed four basic reasons of congestion. Firstly, congestion can occur due to signal attenuation in distant communication. Secondly, interference at listening node makes the network congested which causes packet drop. Thirdly, due to self-interference, that is, a node’s transmission interferes with itself at the receiver and inadvertently congestion occurs. Finally, packet loss because of queue overflows due to mismatch of incoming and outgoing data rate which is for having mixed link with different bandwidth in wireless networks. C. Types of Congestion Congestion in wireless networks is slightly different from that of wired networks. Generally, they can be classified into two categories. First type consists of nodes which are within the range of one another and transmitting simultaneously, reduces throughput. Another type is within a particular node, queue, or buffer used to hold packets to be transmitted, overflows similar to wired network.
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IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision D. Mechanism of Congestion Control Congestion control schemes are usually composed of two components: congestion detection and congestion avoidance. Congestion avoidance can be divided into feedback and sending-rate control. Congestion can be determined by checking queue length. It can also be indirectly detected by monitoring the trend of throughput or response time. Congestion detection can be processed in intermediate nodes or receivers. There are several mechanisms for congestion control such as feedback dependent congestion control, rate controlled congestion avoidance, congestion avoidance in wireless network, congestion detection using queue length. E. Existing Protocols Discussion of this section is based on the previous work on congestion control in wireless network. Congestion Detection and Avoidance (CODA) employs two mechanisms to detect congestion cooperatively. First, intermediate nodes need to be measured workload and infer congestion by comparing it with a maximum throughput threshold applicable to CSMA. If intermediate nodes detect local congestion, “Open-Loop Hop-by-Hop Back Pressure” is used to regulate the congestion. Another mechanism in CODA is making the sink regulate the sources in a closed loop manner. CODA suggests that the rate adjustment should be Additive Increase Multiplicative Decrease (AIMD), which has the disadvantage of being more biased towards source closer to the sink. A transport protocol called Event-to-Sink Reliable Transport (ESRT) is proposed in [4]. By studying the relationship between reporting rate and reliability, the authors figured out an optimal operating region. The sink node regulates the sources reporting frequency to make the system operate in the optimal region. The sink uses congestion feedback from sensors node to broadcast a notification to reduce reporting frequency. The effectiveness of this method is dependent on the persistence of congestion and the feedback latency. It works in centralized manner in congestion control that is its idea is motivated by the fact that only sink is interested in reliable detection of event and controlling the flow of data. In [5], the authors proposed Pump Slowly Fetch Quickly (PSFQ), a reliable transport protocol suitable for a new class of reliable data applications emerging in wireless sensor networks. PSFQ takes a different approach and supports a simple, robust and scalable transport that is customizable to meet the needs of different reliable data applications. The authors observed that in the context of sensor networks, data that flows from sources to sinks is generally tolerable of loss. On the other hand, however, data that flows from sinks to sources for the purpose of control or management is sensitive to packet loss. Rate-Controlled Reliable Transport (RCRT) is the design and implementation of a transport protocol that ensures reliable delivery of data from a collection of sensors to a base station, while avoiding congestion collapse. The authors in [6] foresee that future sensor network deployments will be multiuser systems, with concurrently executing applications.
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In [7], a new Priority-based Congestion Control Protocol (PCCP), which employs packet, based computation to optimize congestion control for a WSN. PCCP enables cross-layer optimization. Specifically authors proposed to exploit packet interval time and packet service time to produce a measure of congestion. PCCP designs a novel Priority-base Rate Adjustment algorithm (PRA) employed in each sensor node in order to guarantee both flexibility and throughput, where each sensor node is given priority index. Use of priority index provides PCCP with high flexibility in weighted fairness. The whole concept of PCCP can be divided into three parts: Intelligent congestion detection (ICD), Implicit Congestion Notification (ICN), Priority-based Rate Adjustment (PRA). III. PROPOSED PROTOCOL Sensor nodes have limited power and computational capacity. In designing the protocol we keep in mind these factors and introduce several new terminologies such as BP, QS and four different types of nodes. Four types of nodes are Source node (SoN), it generates packets and injects in the network, Intermediate node (IN) which acts as relay that is buffer and forward packets, Source with intermediate node (SIN), a special type of node that not only generate packet but also behave like a relay, Sink node (SN), and finally the destination node. In the proposed protocol, many to one implementation is considered that is one or more source nodes and a single node to process it that is sink node. We assume that sink node have greater processing power for complex calculation on incoming data. The proposed distributed congestion control protocol detects and eventually recovers from congestion using queue occupancy of the intermediate nodes. Every node in the network participates on congestion detection and control mechanism. The proposed protocol checks the queue length of its successor nodes and itself to determine congestion level. When packets are generated, an extra field, Queue Status (QS), is attached with the packets. The field is checked and possibly updated in every intermediate node depending on local queue occupancy and its successor nodes queue occupancy. QS is a single bit congestion control approach. The value of QS determines the intensity level of congestion which is initially set to zero to indicate moderate queue occupancy. Packet sending rate is determined based on queue occupancy of current node and its successor node. Congestion is considered as moderate or extreme depending on the queue occupancy. If the queue is fifty percent filled then it is considered as moderate, otherwise extreme congestion is considered. Following pseudo code illustrates how to update QS value. If (QOCCcurrnode ≥ £) QSprev := QS; set QS := 1; If( QSprev != QS) Create and Send BP; Else QS := 0;
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IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision Current nodes queue occupancy is compared with a constant threshold ‘£’. To find out queue occupancy the following equation is used. Q
O
(1)
The constant threshold (‘£’) is set to 0.5 in this network model. If the QOCCnode is greater than or equal to 0.5, then moderate or rigorous level of congestion is considered. Depending on the value of QSnode, it will generate a feedback message. A. Backpressure Message Back pressure message (BP) is the indication of congestion in the network, which contains the dynamically configured sending rate, is fed back to the predecessor node. Unlike Additive Increase Multiplicative Decrease (AIMD), it uses constant packets per second with current node’s QOCC. Calculation of packet per second (pps) is defined by the following equation: 1.0
(2)
Where α is pps. By receiving the BP message, corresponding node update its sending rate. If the network is congested it will slow down its sending rate, depending on the Ratepps indicated in the BP message. The message is recursively passed towards the source node and controls the flow of packets in the network. Next section illustrates the effectiveness of the proposed protocol by varying parameters of the wireless body sensor networks those are buffer drop ratio, delivery ratio, energy dissipation for different network scenario. IV. PERFORMANCE EVALUATION C/C++ programming language is used to evaluate the performance of the proposed protocol. The protocol is implemented in the context of transport layer. While simulating, we keep in mind that the underlying network layer rudimentary behavior can choose any node suits best to itself for forwarding and routing purpose.
Fig.1 Network scenario for simulation
12 nodes are considered in Fig.1, which are chronologically ordered from 0 to 11. Node number 11 is considered as sink. The edges in the directed graph
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represent virtual links. Generally these links remain invisible in WBSN. Though the WBSN links are being constructed dynamically, for simplicity we consider them fixed. In the simulation scenario, we considered all links in the network has identical bandwidth and all nodes transfer packets in the maximum rate. A. Performance Metrics Several factors those are needed to evaluate the performance of the proposed protocol are discussed below. Buffer Drop Rate: The ratio of dropped packets due to buffer overflow to the number of sent packets. Delivery Ratio: The number of unique packets successfully received at sink with respect to the number of packets sent by the sources. Energy Dissipation: Amount of energy dissipated per unit time, measured in Joule. B. Simulation Result The simulation results are shown as experienced while working with graphs having different type of complexities. As the whole simulation is done in C/C++, several experiments have been performed to verify protocol’s efficiency and checked with several network infrastructures.
Fig.2 Drop ratio with varying number of source(s) and buffer space
The proposed protocol is intended to reduce packet drop. Increment of buffer size is a solution to minimize drop ratio which is described in the Fig.2. Buffer size is considered from 1000 to 1400 packets. The remarkable fact about the result is that, by increasing buffer size up to one third, drop ratio can be reduced up to fifty percent.
Fig.3 Drop ratio with varying queue size and number of source node
Since in practice, the sensor nodes have few kilobytes of internal memory which should be used effectively for
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IEEE/OSA/IAPR International Conference on Informatics, Electronics & Vision processing functionalities and buffer space. The experimental result shown in Fig.3, describes that packet drops decreases linearly when buffer size increases in a limited range as from 1000 to 1600 packets.
which reduces overhead of the data packet. REFERENCES [1]
[2]
[3]
[4]
[5]
Fig.4 Delivery ratio with varying number of source(s) and packet per second (pps).
Delivery ratio is a crucial performance measurement of a protocol. Delivery ratio indicates number of unique packets received at the sink node. The proposed protocol recognized that if the number of source increased there is a ruthless packet drop. The amount of drop is inversely proportional to successful packet delivery. In the simulation environment 85 to 90 percent packet delivery rate is found.
[6]
[7]
Val Jones, Valerie Gay and Peter Leijdekkers, “Body Sensor Networks for Mobile Health Monitoring: experience in Europe and Australia,” in Proc. 4th Int Conf. on Digital Society, ICDS 2010, pp. 204-209. Bret Hull, Kyle Jamieson and Hari Balakrishnan, “Mitigating Congestion in Wireless Sensor Networks,” in Proc. of the 2nd Int conf. on Embedded networked sensor systems, 2004, pp. 134-147. Cheng Tien Ee and Ruzena Bajcsy, “Congestion control and fairness for many-to-one routing in sensor networks,” in Proc. of the 2nd Int conf. on Embedded networked sensor systems, 2004, pp. 148-161. I. A. Y. Sankarasubramaniam, A. Ozgur, “ESRT:Event-to-Sink reliable transport in wireless sensor networks,” in Proc. of the 4th ACM Int symposium on Mobile ad hoc networking & computing, 2003, Vol. 10, pp. 177-188. Chieh-Yih Wan, Andrew T. Campbell and Lakshman Krishnamurthy, “PSFQ: A Reliable Transport Protocol for Wireless Sensor Networks”, in Proc. of the 1st ACM Int workshop on wireless sensor networks and application, 2002, pp. 1-11. Jeongyeup Paek and Ramesh Govindan, “RCRT: Rate-Controlled Reliable Transport for Wireless Sensor Networks,” in Proc. of the 5th Int conf. on Embedded networked sensor systems, 2007, pp. 305-319. S. M. D. Y. H. C. Wang, B. Li, “Upstream Congestion Control in Wireless Sensor Networks Through Cross-Layer Optimization,” J. of IEEE on Selected Areas in Comunications, vol. 25, May 2007, pp. 786-795.
Fig.5 Average Energy Dissipation
The power dissipation is one of the major concerns in wireless body sensor network. Since the size of sensors are too small and the power management strategy is not well established. Fig.5 represent power dissipation ratio with respect to the number of source nodes. Significant decrease of power dissipation is achieved in this simulation environment. V. CONCLUSION Primary consideration of the proposed protocol is to control congestion in WBSN. Effective congestion detection and control mechanisms are needed to eliminate congestion from network. As the sensor node has limited power and computational capability, designing protocol for body sensor network is not only difficult but also challenging. The proposed protocol addresses the above issues admirably. Here single bit is used to indicate the level of congestion in the network
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