Optimizing the Threshold-Value for Counter-Based Broadcast Scheme in MANETs A. Mohammed1, M. Ould-Khaoua1, L. M. Mackenzie1, J. Abdulai1 and L. M. Jabaka2 1
Department of Computing Science University of Glasgow Glasgow, G12 8QQ, UK Email: {maminuus, mohamed, jamal, lewis}@dcs.gla.ac.uk 2
Department of Mathematics Usmanu Danfodiyo University, Sokoto P.M.B. 2346, Sokoto State, Nigeria. Email:
[email protected] Abstract- Broadcasting is a fundamental and frequently used operation in mobile ad hoc networks (MANETs). Flooding, the process in which each node retransmits every uniquely received packet exactly once is the simplest and most commonly used mechanism for broadcasting in MANETs. Despite its simplicity, it can result in high redundancy, contention and collision, collectively known as broadcast storm problem. Counter-based broadcasting scheme has been proposed to overcome the broadcast storm problem in MANET. It relies on a threshold value to decide whether or not to forward a broadcast packet. The selection of an optimal counter threshold-value of this scheme is critical to improving saved rebroadcast without degrading reachability. Previous research work has focused on determining the best counter threshold-value based on low traffic injection conditions. In this paper, we investigate the best counter threshold value for optimal performance over a wide range of standard metrics in various network traffic injection rates. Simulation results reveal new insights in the performance of the counter-based broadcasting scheme and demonstrate its sensitivity to the selected threshold-values. These results are useful for optimally setting the counter threshold-value when designing efficient counter-based broadcasting schemes. Keywords-Broadcasting, MANETs, Flooding, Broadcast Storm Problem, Saved Rebroadcast
I. INTRODUCTION Mobile ad-hoc networks (MANETs) are special type of wireless networks that comprises of wireless mobile nodes (MNs) that cooperate to form a network without relying on fixed infrastructure or central administration [1]. Scenarios that might benefit from MANETs technology include but not limited to, rescue/emergency operations in natural or environmental disasters areas, special operations during law enforcement activities, tactical missions in hostile and/or unknown territories, and commercial/academic gatherings such as conferences, exhibitions and workshops [2, 3]. Thus, these networks have become increasingly important in view of their promise of ubiquitous connectivity beyond traditional fixed infrastructure networks. Broadcasting is a means of diffusing a message from a source node to all other nodes in the network. It is a fundamental operation in MANETs and a building block for most network layer protocols. It provides an important
ISBN: 1-9025-6016-7 © 2007 PGNet
platform for route establishment functionality in a number of unicast routing protocols. For example, many unicast routing protocols such as Dynamic Source Routing (DSR)[4], Ad Hoc On Demand Distance Vector (AODV)[5], Zone Routing Protocol (ZRP)[6], and Location Aided Routing (LAR)[7] use broadcasting or its derivative to establish routes. Other unicast routing protocols, such as the temporally-ordered routing algorithm (TORA) [8], use broadcasting to transmit an error packet for invalid routes. Broadcasting is also heavily utilised in sensor networks for collecting and disseminating critical information, such as temperature, pressure and noise level [9]. Currently, these protocols typically rely on a simplistic form of broadcasting called flooding, in which each MN retransmits every unique received packet exactly once. Although flooding is simple and easy to implement, however it often causes unproductive and harmful bandwidth congestion (called the broadcast storm problem in [10-12]) and wastes node resources[11]. Nevertheless, researchers during the past few years have proposed other approaches [3, 10, 12-16] to mitigate the broadcast storm problem. These protocols aim to minimize the number of retransmissions while ensuring that a broadcast packet is delivered to each node in the network. In this paper we examine counter-based broadcast method as one of the proposed solutions in literature to alleviate the above problem caused by Flooding. Counter-based broadcast schemes for MANETs were first proposed in [11] and further investigated in [10, 12, 16]. In counter-based schemes, every mobile node relies on a predetermined counter threshold-value C, to decide whether or not to rebroadcasts a packet. These schemes do not require global topological information of the network in order to make rebroadcast decision. Thus, these schemes are localized and can considerably reduce the number of retransmission predominant in flooding. One major challenge in counter-based schemes is how to select an optimal counter threshold-value, C, that can optimize the performance of counter-based schemes in terms of saved rebroadcast, reachability and end-to-end delay. Most counter-based schemes assumed a counter thresholdvalue of 3 or 4. It has been shown in [11] that a threshold-value of 3 or 4 can save many rebroadcasts in a dense network while achieving a reachability ratio comparable to that of flooding.
On the other hand a larger threshold of C > 6 will provide less saving of rebroadcasts in a sparse network but behave almost like flooding in terms of rechability. However, the studies in [10-12] have determine the threshold-value in the context of light traffic injection rate scenario. Several previous studies [12, 16, 17] have used the threshold- value suggested in [11]. Nevertheless, these studies assumes different traffic conditions (i.e. moderate and high traffic) that have not been considered in [11]. In this paper, we investigate the optimal counter threshold value for counterbased broadcast scheme and its derivatives in MANETs under a wide range of traffic conditions (i.e. low, moderate and high traffic loads). The results reveal the sensitivity of the performance of the counter-based broadcast scheme to the selection of the threshold-value under different traffic conditions. Moreover, an optimal counter threshold-value C has been determined and is different from that reported in [11]. The remainder of this paper is organized as follows. Section 2 presents an overview of the counter-based broadcasting scheme while Section 3 outlines some previous research work related to our study. Section 4 presents performance evaluation. In Section 5 we show our simulation results and finally the paper is concluded in Section 5.
II. COUNTER-BASED BROADCAST SCHEME Ni et al [11] have shown an inverse relationship between the number of times a packet is received at a particular node and the probability of that node being able to reach additional coverage area on a rebroadcast [17]. This result is the foundation of their Counter-Based broadcast scheme. Specifically, a counter c is used to keep track of the number of times the broadcast message is received. When a broadcast packet is heard for the first time, the node initializes its c to 1 and waits for a random number of time slots (RAD). The node increments its c by one each time it receives the same packet until the RAD expires. The node compares the c with a predefined threshold value, C. If c < C, the node rebroadcast the packet. Otherwise the packet is dropped.
III. RELATED WORK This section sheds some light on the research work related to counter-based broadcasting scheme. Ni et al [11] introduced the counter-based scheme after analysing the additional coverage of each rebroadcast when receiving n copies of the same packet. The predefined threshold C is the key parameter in this approach. They showed that about 67% of the rebroadcasts could be saved when choosing a C value of 3 or 4 while the amount of saving decreases sharply if C > 6, especially in sparse network. In their follow-on work [12], they proposed an adaptive counter-based scheme in an attempt to improve the savedrebroadcast without degrading the network reachabilty.
Here each node can dynamically adjust its threshold value C based on its number of neighbour. Specifically, they extend the fixed threshold C to a function C(n), where n is number of neighbours of the node. In this approach there should a neighbour discovery mechanism to estimate the current value of n. This can be obtained through periodic exchange of ‘Hello’ packets among mobile nodes. Recently, Zhang and Agrawal [16] have described a dynamic probabilistic broadcast scheme. The scheme combines the characteristics of counter-based and probabilistic broadcast methods. The authors have implemented the scheme for route discovery process using AODV as base routing protocol. The rebroadcast probability P is dynamically adjusted according to the value of the local packet counter, c, at each mobile node. Therefore, the value of P changes when the node moves to a different neighbourhood; for example, in sparser area, the rebroadcast probability is large compared to denser area. The packet counter is used as density estimates (i.e. a high value implies that the number of neighbours is high, and a low value corresponds to a small number of neighbours). The results from [12] were used to set the counter threshold-value, C, for this scheme.
IV. PERFORMANCE EVALUATION This section evaluates the performance of counter-based scheme [11] using different threshold-values. We implement the algorithm using the packet level ns-2(v.2.29) simulator [18] and provide a side by side comparison of the five threshold-values for counter-based scheme. The aim is compare the threshold values over a range of network conditions, such as node densities, node mobility and traffic rates, in order to identify areas where each threshold value achieves the best performance. To attain these objectives we focus on three sub studies which are outlined below. However, the network parameters outlined in Table 1 remains constant for all studies and are commonly used in the previous studies [11, 12, 14, 19]. A. Study 1- Network Density The aim of this sub-study is to quantify the effects of node density on the counter threshold values. To achieve this, the number of mobile nodes in the network area is varied from 25 to 150. A broadcast packet origination rate of 10 packets per second was used. Similarly, a static network and Null MAC object provided in ns-2 [18] was used to avoid any effects that mobility and congestion may have on the counter threshold values. To avoid the effects of mobility and packet collision, a static (i.e. zero mobility) and a contention-free (using Null Mac) network have been assumed in this sub-study.
TABLE 1: SIMULATION PARAMETERS Simulation Parameter Simulator used Transmission range Bandwidth Interface queue length Packet size Topology size Number of Nodes Mobility Model Maximum speed Pause times Simulation time MAC Protocol Confidence level
Value NS-2 (version 2.29) 100 meters 2Mbps 50 packets 512 bytes 600 x 600 m2 25, 50, …, 150 Random waypoint [20] 20 meter/sec 0 sec 900 sec IEEE 802.11 95%
B. Study 2 - Network Mobility This sub-study focuses on the ability of each counter threshold-value to react effectively to the node mobility. Again a contention-free network is assumed to ensure that the congestion does not affect the outcome of the study. Similar to Study 1, a packet rate of 10 packets per second was used and the number of network nodes is set to 75. The random waypoint mobility model [20] is used with zero pause time and a range of speeds from 5 to 20 meters per second. This is the most commonly used mobility model in MANETs simulation [3, 11, 12, 16, 21] and it is already implemented ns-2[18]. The decision to use zero pause time and this range of speeds is a bit subjective. Previous work shows that pause time over 20 seconds adds significant stability to dynamic networks[22]. Since we preferred to test the protocols without this added stability, we opt to use zero pause time. C. Study 3- Network Congestion The purpose of this study is to measure the effects of congestion on the counter threshold values. This study was performed using the contention based 802.11 MAC scheme. Congestion can be obtained by increasing the packet size, increasing the packet generation rate or both. We chose to fix the packet size and vary the packet generation rate because we anticipate that broadcast packets, as control type packets, to be generally small in size [23]. Thus, the payload portion of each packet was set to 64 bytes and the rate was varied from 10 packets per second to 80 packets per second. The number of nodes in the network was set to 75, which approximately represents the median value from Study 1. One might expect significantly different results using different numbers of network nodes. However, the goal of the study is to obtain a general trend for the effect of a congested network. Likewise, a static network was used to ensure that the effects of mobility do not interfere with the effects of congestion. Like the previous studies [3, 14, 16, 19, 21, 23-25], we focus on the following performance metrics:
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Reachabilty (RE) – This is the percentage of nodes that received the broadcast message to the total number of nodes in the network[21].
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Saved Rebroadcast (SRB) – This is the percentage of nodes that have received but not rebroadcast the message. Then SRB is defined as ((r – t)/r)*100, where r and t are the number of nodes that received the broadcast message and the number of nodes that transmitted the message respectively[21].
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End-to-End Delay – is defined as the time elapsed from when the source want to send a broadcast message until the moment the packet reaches its destination[3].
V. SIMULATION RESULTS The simulation output is collected using the batch mean method, where 10 simulation runs are used. In addition the mean value of each of the above parameters is collected from the simulation along with 95% confidence intervals. A. Study 1-Network Density Fig. 1(a) shows the saved rebroadcast achieved by each counter threshold values as the node density increases. The protocol using counter threshold value of C=2 has highest saved rebroadcasts of about 60% in sparse network and 71% in dense networks. On the other hand protocol using C = 6 has the least saved rebroadcast of about 5% and 10% in sparse and dense network respectively. Therefore, a lower counter threshold value means fewer nodes will retransmit the broadcast packet. Similarly, Fig. 1(b) depicts the reachability for each counter threshold values. It shows that a lower counter threshold values of C = 2, 3 results in about 90% and 95% reachability in sparse network while a higher counter threshold values of C = 4, 5, and 6 gives 98 – 99% reachability in both sparse and dense networks. To maintain a high reachability in sparse networks, a higher threshold value is required. On the other hand to maintain a high reachability in dense networks, a lower threshold can be used. Fig. 1(c) shows the end-to-end delay for different counter threshold values. Similar to the result in Fig. 1(a), lower counter threshold values results in a better end-to-end delay than higher threshold values. B. Study 2- Mobile Network This study focuses on the response of the threshold values to a highly mobile environment. Fig. 2(a), 2(b) and 2(c), shows the effects of mobility on different threshold values with regards to the performance metrics (i.e. reachability, saved rebroadcast and end-to-end delay). The figures clearly demonstrate that the node mobility has little effect
on the mentioned performance metrics for almost all threshold values. However, when C =2, we notice a degrading trend in reachability with increasing mobility. This is because the probability of rebroadcast for a lower counter threshold value is substantially low, resulting in more dropped packets.
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C. Network Congestion Fig. 3 shows that the performance of each counter threshold value degrades as the network becomes more congested. Reachability decreases by about 40% (Fig. 3b), while end-to-end delay increases to about 4.5 seconds (Fig. 3c). However, Fig. 3a shows that the saved rebroadcast decreases for counter threshold value of 2 while that of C = 3, 4 and 5 decreases at 20 – 30 packet per second rate and increases above those rate. Essentially, higher congestion prohibits redundant packets to be delivered during RAD; therefore more nodes rebroadcast. More rebroadcasts further congest the network resulting in this increasing effect. On the contrary, the saved rebroadcast of threshold value of 6 increases as congestion increases. This directly illustrates the effect of collisions and queue overflows in congested networks.
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This paper has investigated the performance merits of different counter threshold-values for counter-based broadcast scheme under a wide range of traffic conditions. The counter threshold-value has been varied over the range [2, 6] in step of 1. Results obtained from extensive simulations have shown that setting an optimal counter threshold-value for counter-based broadcast scheme has a significant effect on the overall performance. The experiments have also revealed that the optimal counter threshold-value is affected by the prevailing network conditions such as the node density, node mobility and traffic load. However, it has been observed that the performance is optimised when a counter threshold values of 2 or 3 is used in low to high node density, node mobility and traffic load. As a continuation of this work, we will explore further the performance of the counter threshold-values over all the three prevailing network conditions changing simultaneously by aggregating the parameters into trials as against looking at the conditions independently. Furthermore, we intend to propose a new broadcasting scheme that combines the advantages of both counter-based and probability-based broadcast schemes using these optimal counter threshold-values.
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