Vote-based Clustering Algorithm in Mobile Ad-hoc Networks Fei Li1 , Shile Zhang1 , Xin Wang1 , Xiangyang Xue1 , and Hong Shen2 1
Department of Computer Science, Fudan University, 200433 Shanghai, China {021021107,0024131,xinw,xyxue}@fudan.edu.cn 2 Department of Information Systems, JAIST, Japan
[email protected]
Abstract. Unlike current clustering methods, the presented vote-based clustering (VC) algorithm not only uses node location and ID information, but also battery time information. In VC, each mobile host (MH) counts Hello messages from its neighbors. At the same time it calculates its own vote that is the weighted sum of the normalized number of valid neighbors and its normalized remaining battery time. The one with higher vote than its neighbors will be selected preferentially as a cluster head (CH). When the number of dominated MHs of a CH is more than a balance threshold, neither of new coming MHs will be permitted to participate in the current cluster. Analysis and simulation results show that VC method can improve cluster structure than Lowest ID (LID) algorithm and Highest Degree (HD) algorithm.3
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Introduction
A MANET is a multi-hop wireless network in which mobile hosts (MHs) communicate without the support of a wired backbone [1]. In a MANET, the network topology changes frequently, the control overhead is very large and a wireless link is easy to break down. So how to reduce the number of control packets and repair a wireless route becomes very important. However, people can only get a tradeoff between the above two ambivalent objects. Clustering is such an effective method, which is a common method in a communication network topology description, and used to group network nodes into clusters. It provides a convenient framework for the development of important features such as code separation (among clusters), channel access, routing, power control, virtual circuit support and bandwidth allocation. With an underlying cluster platform, non-ordinary MH can be the dominant forwarding nodes. In comparison with fixed communication networks, clustering in MANET turns difficult. Because of node mobility and wireless link weakness, more control information must be paid to clustering a MANET. A representative of each cluster 3
This work was supported in part by NSFC-60003017, NSFC-60373020, 8632001AA114120, 863-2002AA103065, SRF for ROCS. SEM, Shanghai Municipal RD Foundation under contracts 035107008, 03DZ15019 and 03DZ14015, and Youth Foundation of Fudan University under No.EXH6286301.
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is named as a cluster head (CH) and a MH belonging to more than 2 clusters at the same time is called a gateway. Other members are called ordinary MHs. Generally a cluster is defined by its CH’s transmission range. Cluster architecture in MANET may be with or without CHs in every cluster [2]. CH-based clustering can reduce storage and exchange information of ordinary MHs. In clusters without CHs, every MH has to store and exchange more topology information, thus the bottleneck of CHs can be eliminated. CH Y. Yi and M. Gera partitioned 2 approaches to construct a MANET cluster platform, i.e. active clustering and passive clustering [4]. In active clustering, MHs cooperate to elect CHs by periodically exchanging information, regardless of data transmission. On the other hand, passive clustering suspends clustering algorithm until the data traffic commences [4]. It exploits on-going traffic to propagate ”cluster-related information” (e.g., the state of a node in a cluster, the IP address of the node) and collects neighbor information through promiscuous packet receptions. Thus, it eliminates setup latency and major control overhead of active clustering required collecting neighbor information. Recently multipoint relays (MPRs) are often used in clustering to reduce the number of gateways in active clustering. MPR Hosts are selected to forward broadcast messages during the flooding process [5]. This technique substantially reduces the message overhead as compared to a classical flooding mechanism, where every node retransmits each message when it receives the first copy of the message. Using MPRs, the Optimized Link State Routing (OLSR) protocol can provide optimal routes, and at the same time minimize the number of control messages flooded in the network [6]. present a novel vote-based clustering (VC) algorithm A good clustering method should be able to partition a MANET quickly with little control overhead. Because of node mobility, it is difficult to construct the best clustering structure in a MANET. To this end, two distributed clustering algorithms are considered. They are Lowest ID algorithm (LID) [7]. and Highest Degree algorithm (HD) [8]. Both of them belong to passive clustering. In LID algorithm, each node is assigned a distinct ID. Periodically, the node broadcasts the list of nodes that it can hear (including itself). The lowest-ID node in a neighborhood is elected as the CH. LID method has the following 4 rules: (1) A node which only hears nodes with ID higher than itself is a CH. (2) The lowest-ID node that a node hears is its CH, unless the lowest-ID specially gives up its role as a CH (deferring to a yet lower ID node). (3) A node which can hear two or more CHs is a gateway. (4) Otherwise, a node is an ordinary node. In HD algorithm, the highest degree node in a neighborhood becomes the CH. The algorithm is described below: (1) Each node broadcasts the list of nodes that it can hear (including itself). (2) A node is elected as a CH if it is the most highly connected node of all its ”uncovered” neighbor nodes (in case of a tie, lowest ID prevails).
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(3) A node which has not elected its CH yet is an ”uncovered” node, otherwise it is a ”covered” node. (4) A node which has already elected another node as its CH gives up its role as a CH. An optimized cluster protocol about LID was proposed in [2]. It ensures MHs who do not receive Hello messages during a certain time can issue a new cluster or participate in an existing cluster after a while. LID method is a quick clustering method, which only uses 2 Hello message periods to get the cluster structure. Also it provides a more stable cluster formation than HD method. In HD style even if one link drops due to node movement, the current CH may fail to be re-elected again. HD method can get fewer clusters than LID. It is very helpful in a large-scale network. In current clustering schemes, stability, quantity and convergence are of very importance. However, fewer clusters don’t always mean better. A CH dominates so many mobile nodes that its resources (e.g. computing, bandwidth, and etc.) will be exhausted soon. So the control of cluster scale is very important. On the other hand, under mobile computing environment, apart from position and ID, power is another important factor for one MH. The fore-mentioned clustering methods didn’t mention cluster scale and power factor, we do it in this paper. The rest of the paper is organized as follows: in Section 2, the vote-based clustering algorithm is presented, which includes 3 parts: vote-based partition algorithm, mobile management method and cluster load balance method. Performance simulation and analysis are shown in Section 3. Finally, conclusions and future work are given in Section 4.
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Vote-based clustering algorithm
In MANET clustering, we should consider not only position and ID but also other factors. LID is a quick clustering method, which only uses 2 Hello message periods to get the cluster structure. In LID, only ID information was used to distinguish every MH. Obviously, it did not make use of MHs’ position information and cannot get few clusters. HD method needs 3 Hello message periods, for that each MH must know its neighbor host’s neighbors’ number. It uses position information and ID information of one MH. Fig. 1 shows a simple comparison of HD clustering and LID clustering. In a MANET including 15 mobile hosts, LID method gets 6 clusters but HD method gets only 4 clusters. The virtue of VC is using every MH’s mutual location information. On the other hand, Both of LID and HD method cannot trade off among different clusters. Maybe one CH dominates fewer MHs, but another CH holds more MHs. We want to eliminate this possibility to avoid one CH be exhausted. ID is used to distinguish every MH anywhere, anytime. By position information, we know a MH’s one-hop neighbors, two-hop neighbors, etc. In our case, considering the relation between power consumption of one CH and the number of its dominated MHs, we select the lasting time of MH’s battery
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Fig. 1. A simple comparison of HD clustering and LID clustering
as one performance parameter. Our algorithm is based on two important performance factors, neighbors’ number and remaining battery time of every MH. Then we use voting method to select cluster head and determine members of a cluster. 2.1
Network Model
A MANET can be divided into several overlapped clusters. A cluster is composed of a subset of nodes, which can communicate directly with a cluster head and with each other. Hereby the scenario is modelled as an undirected graph G(V, E) where V is the set of all MHs in the network and E is the set of all links (i, j) where i, j ∈ E. Each link signifies that two MHs are within transmission range of each other. Let Si be the set of all nodes that can be reached by node i. We assume every link is bi-directional so that link (i, j) exists if and only if j ∈ Si . The topology of G is the set of nodes and edges. Each MH has a unique identifier (ID) number, which is a positive integer. The basic information inside the network is Hello message, which is transmitted in the common channel. Every MH acquires information from neighbor hosts’ periodic Hello message. We assume that only when the 2 MHs lie inside the mutual transmission range, they can communicate directly with each other, i.e. a bi-directional link exists. One another important information for every MH is its battery lasting time, which is a positive integer. 2.2
Vote-based clustering scheme
In our proposal, we consider the clustering architecture with cluster heads. It is also assumed that one MH can only participate in a unique cluster at the same time, so Fig. 2 shows this case and the communication procedure between
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Fig. 2. Communication Procedure between Cluster2 and Cluster5
Fig. 3. Hello message format
2 clusters. A cluster is tagged with its Cluster ID number, e.g. Cluster2 and Cluster5 in Fig. 2. The proposed vote-based clustering protocol includes 3 parts: vote-based partition algorithm, mobile management method and cluster load balance method. 2.2.1 Vote-based partition method Making use of node location information and power information, we introduce the concept of ”vote” and proposed vote-based partition algorithm shown later. Fig. 3. shows the Hello message format in Vote-based clustering algorithm (VC). MH ID item is MH’s own ID and CH ID item is MH’s CH IDVote item means MH’s vote value, i.e. weighted sum of number of valid neighbors and remaining battery time. Option item is used to realize cluster load balance in part 3. V ote = w1 × (n/N ) + w2 × (m/M ).
(1)
w1, w2: Weighted coefficient of location factors and battery time, respectively, n: Number of neighbors, N : Network size or the Maximum of members in a cluster, m: Remaining battery time, M : The maximum of battery time remaining battery time. m is a characteristic parameter for every MH. Each MH consumes the battery energy anytime. Each CH spends more than a common MH, obviously. The algorithm includes the following steps: (1) Each MH sends a Hello message randomly during a Hello cycle. If a MH is a new user to the MANET, it reset ”CH ID” item. That means the MH does not belong to any cluster and does not know whether it has neighbor hosts. (2) Each MH counts how many Hello messages it can receive during a Hello period, and considers the number of received Hello messages as its own n. (3) Each MH sends another Hello message, in which ”vote” item is set to its own vote value and got from Equation 1.
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(4) Recording Hello message during 2 Hello cycles, each MH knows the sender with highest vote and not belongs to any existing cluster is its cluster head. It set its next sending Hello message item ”CH ID” to the cluster head’s ID value. One noticeable issue is when two or more mobile nodes receive the same number of hello packets, the one who owns the lower ID will be prior to others. Following the above-mentioned approach, every MH knows its cluster head ID after 2 Hello message periods. That is to say, we can finish clustering scheme during 2 Hello cycles. We also know that the cluster head sends a Hello message, in which ”MH ID” is the same as ”CH ID”. 2.2.2 Mobile management method All moving MHs can be classified into 2 kinds by their current status. For a moving cluster member, if it receives a Hello message with bigger vote from another CH or non-CH host, the latter will become its new cluster head. For a moving CH host, it uses the same method to participate in a new cluster. However in this case, all its dominated mobile hosts must start a new cluster discovery process. Once a member host finds its CH turns a member host by analyzing the received Hello message, it will reset the ”CH ID” item to 0. Using this kind of mobile management method, real-time modification of the cluster structure can be realized. 2.2.3 Adaptive cluster load balance method In LID or HD clustering scheme, one cluster head can be exhausted when it serves too many MHs. It is not good and the CH becomes a bottleneck. So we proposed an adaptive cluster load balance method. In Table1, there is an ”Option” item. If a sender MH is a cluster head, it will set the number of its dominated MHs as ”Option” value. When a sender MH is not a cluster head or it is undecided (CH or non-CH), ”Option” item will be reset to 0. When a CH’s Hello message shows its dominated MHs’ number exceeds a threshold (the maximum number one CH can manage), there will not be any new MH participate in this cluster. As a result, we can eliminate the CH bottleneck phenomenon and optimize the cluster structure. As stated in the above description, VC can get load balance between various clusters. Thus, resource consumption and information transmission will be distributed to all clusters, not only to some certain clusters. On the other hand, the consideration of battery lasting time can help us to get a steady cluster structure. Because in VC method, through inducting weighted battery lasting time, the probability of a MH without enough battery energy will be reduced.
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Performance simulation and analysis
In this part, we simulate the proposed clustering protocol under C++ programming environment. The simulation network is a square plane area with 50m∗50m. There are totally N mobile hosts in the square space and N can be 10, 20, 30,
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Fig. 4. Average cluster head number with 3 methods
Fig. 5. Average cluster head change with 3 methods
Fig. 6. Variance of cluster size with 3 methods
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and up to 150. Each mobile host stays in the space randomly at the start. Every MH will move at a proportional rate between 0 ∼ 5m/s, and at a random direction. If they move to the boundary, they will be bounced back. The Hello message is sent at 5ms period. When using VC with load balance method, the threshold for a dominating MH is defined as 15. Each simulation lasts out 1 minute. The initial battery time of each MH is a random value between 0 and 1, 3, or 5 minutes. We tested some parameters using LID method and VC method, respectively. From the fore description, it is easy to see that VC without load balance and without battery time limit is HD method indeed. The parameters include number of cluster heads, average change of cluster heads and variance of cluster size. 3.1
Number of cluster heads
We count the number of cluster heads every 1s and compute the arithmetic average every 5 simulations. In our simulation, an isolated MH will not become a cluster head, because it cannot communicate with any other mobile host. Fig. 4 illustrated the average cluster head number in the MANET. For a medium-scale network, VC method can reduce clusters obviously. When the network is very sparse, VC method cannot play very well. However, we know that in that case clustering will be not a good mechanism at all. In addition, with the network scale increased, VC with load balance will result in more clusters even than LID. It is because it can save a cluster heads resource to avoid its premature exhaustion. 3.2
Average change of cluster heads
If we define A and B as aggregate of cluster heads in previous test moment and current test moment, respectively, the change of cluster heads equation holds: δ =| (A ∪ B− | A ∩ B).
(2)
We computed the arithmetic average of every 5 simulations. Fig. 5 illustrated average change of cluster heads. Since LID only uses not node location information but ID information, so its cluster status is steadier than VC. Obviously, VC with load balance is worse than VC without load balance about this parameter when network size exceeds a certain scale. When N is bigger than 70, in VC with load balance method cluster heads change very frequently. 3.3
Variance of cluster size
We recorded current cluster size every second, Ci , i = 1, 2, . . . , M (M is number of cluster heads). We define C and D as below: C=
M X i=1
Ci /M.
(3)
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Fig. 7. Average change of CHs with different battery time proportion
D=
M X
(Ci − C)2 /M.
(4)
i=1
Fig. 6 illustrated variance of cluster size, D. Simulation results show that LID methods variance of cluster size is less than VC method. From the above 3 figures, we know that VC method can optimize cluster structure by reducing cluster head number. The cost is more cluster head change and higher variance of cluster size. 3.4
Average change of CHs with different battery time proportion
Like in 3.2, we can get the average of CHs when w2 is equal to 1, 0.5 and 0.9, respectively. It is noticeable in the simulation, at every Hello period, a common MHs battery time drops down at a constant space. For a CH, its battery time decreases in proportion to the number of current dominated MHs. Fig. 7 illustrated average change of cluster heads with different battery time proportion. When w2 is equal to zero, it means battery time is not considered in clustering. As w2 is increased step by step, battery time takes a more important role in clustering. The curve shows that the more proportion battery time owns, the steadier the cluster structure turns. If w2 is equal to 1, the VC method only uses battery time information, not position information.
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Conclusions and Future work
In this paper we present a novel vote-based clustering algorithm for MANET. Unlike current clustering method, VC not only uses node location information and ID information, but also battery time information. In VC method, each MH counts Hello messages from its neighbors. At the same time it calculates its own vote that is the weighted sum of the normalized number of valid neighbors and its normalized remaining batter time. The one with higher vote than its neighbors will be selected preferentially as a CH. When
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the number of dominated MHs of a CH is more than a balance threshold, neither of new coming MHs will be permitted to participate in the current cluster. Analysis and simulation results show that VC method can improve cluster structure than LID and HD algorithm. At first, VC can get less cluster number than LID. Secondly, VC can support adaptive cluster size balance to avoid one CH of being exhausted, better than LID and HD. Thirdly, VC can get steadier cluster structure than HD, since it uses battery information. As to LID and VC, on the one hand, LID uses fewer clusters since it only uses ID information, which is constant for every MH. On the other hand, VC improves stability of cluster structure by inducting battery information. Any MH with little battery time may be not selected as a CH. We will study VC-based routing further. In current method, cluster structure maybe is changed even if only one MH comes or leaves, since many MHs’ vote is changed. Apparently, it is not very good. In next-step work, we will focus on boosting up VC’s robustness. We will also study VC-based routing and multicast algorithms. In clustering, we reduce the effect of node mobility and link state on cluster structure and make cluster structure repaired with little spending. However in routing, we hope discovery in time and little cost to get a route quickly. We ever used spine structure in multicast [9] and will use VC structure in multicast later.
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