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Wireless Sensor Network, 2010, 2, 293-299 doi:10.4236/wsn.2010.24040 Published Online April 2010 (http://www.SciRP.org/journal/wsn) Copyright © 2010 SciRes. WSN A Deterministic Protocol for Permutation Routing in Dense Multi-Hop Sensor Networks Alain Bertrand Bomgni1, Jean Frédéric Myoupo2 1Department of Computer Science, University of Yaounde 1, Yaounde, Cameroon 2Department of Computer Science, University of Picardie Jules Verne, Amiens, France E-mail: alain.bomgni@yahoo.fr, jean-frederic.myoupo@u-picardie.fr Received January 16, 2010; revised February 20, 2010; accepted March 4, 2010 Abstract A large variety of permutation routing protocols in a single-hop Network are known in the literature. Since they are single hop, there is always a wireless link connecting two nodes. One way to solve this problem in a multiple hop environment is to partition nodes into clusters, where a node in each cluster called clusterhead is responsible for the routing service. In this paper, we propose a hybrid clustering mechanism to perform permutation routing in multi-hop ad hoc Networks. We first propose to partition the network in single-hop clusters also named cliques. Secondly, we run a local permutation routing to broadcast items to their local destinations in each clique. Next we partition the clusterheads of cliques with the hierarchical clustering technique. We show how the outgoing items can be routed to their destination cliques. We give an estimation of the number of broadcast rounds in the worse case. More precisely, we show that solving the permutation routing problem on a multi-hop sensor network need 2 (1) ()2 max max (/ )kO k HUB HUB np k in the worse case. Where n is the number of the data items stored in the network, p is the number of sensors, |HUBmax| is the number of sensors in the clique of maximum size and k is the number of cliques after the first clustering. Finally, simulation results show that our algorithm performs better than the naïve multiple gos- siping. To the best of our knowledge, it is the first algorithm for permutation routing in multi-hop radio net- works. Keywords: Permutation Routing Problem, Wireless Sensor Networks, Hierarchical Clustering, Clique 1. Introduction Wireless sensor networks (WSN for short) can be de- ployed to provide continuous surveillance over an area of interest, referred to as a sensor field [1,2]. Wireless sen- sor nodes perform collaborative work [3] via wireless communication channels to retrieve information about targets that appear in the sensor field or to exchange some information. Higher-level decision making can then be carried out based on the information received from the sensor nodes. These networks can be deployed in inhospitable terrain or in hostile environments to pro- vide continuous monitoring and information. In some applications, a sensor may need to get all its own data items that should hep him to accomplish its mission. In such applications and for confidential reasons, during the deployment, a sensor may hold items which are not nec- essary its own. A permutation routing between sensors permits that every sensor can recover its own items in order to perform the task that has been assigned to him. There are two types of wireless networks: Single hop wireless networks in which each station can transmit or communicate directly with any other station. All the sta- tions use the same channel to communicate, and the mes- sage broadcast by one of the stations on the common channel is simultaneously heard by all other stations. In the multi-hop wireless networks intermediate nodes are used to route message from the source to the destination. In this paper only multi-hop wireless networks are con- sidered. Permutation Problem: Consider a wireless sensor net- work of p stations with n items pretitled on it (WSN(n, p) for short). Each item has a unique destination which is one of the p stations. Each station has a local memory of size in which items are stored. It is important to note that in general, some of the (/ )On p/np /np A. B. BOMGNI ET AL. 294 items stored in the station, say i, have not i as destination station. And even, it can happen that none of these items belongs to it. On the other hand, the situation in which initially all items in i belong to i can also occur. The permutation routing problem is to route the items in such a way that for all i, 1 i p, station i contains all its own items at final. /np 1.1. Related Work The number of studies specifically targeted to permuta- tion routing in single hop wireless networks has grown significantly: It is shown in [4] that the permutation rout- ing of n items pretitled on wireless sensor network of p stations and k channels with k < p, can be carried out efficiently if /2kp. In [5], we solve the problem showing how the above restriction can be left. Datta in [6] derived a fault tolerant permutation routing protocol of n items pretitled on mobile Ad-hoc network of p stations and k channels MANET(n, p, k) for short. He also as- sumed that /2kp and in the presence of faulty stations some data items are lost. We came out with our work in [7] presenting a fault tolerant protocol which avoids the lost of items. The first energy-efficient per- mutation routing appeared in [8]. A more efficient en- ergy-efficient permutation routing protocol was pre- sented in [9]. In [10] Walls et al. propose an optimal permutation routing on mesh networks. Another ap- proach as an application of an initialization algorithm appeared in [11]. All these approaches assume that the WSN is a single hop networks. Our former work in [12] presents a randomized algorithm for the same problem in multi-hop network with high probability. 1.2. Our Contribution We consider a WSN (n, p) with n items, p stations. We first propose to partition the network into single-hop clusters also named cliques. Secondly, we run a local permutation routing to broadcast items to their local des- tinations in each clique. Next we partition the cluster- heads of cliques with the hierarchical clustering tech- nique. We show how the outgoing items can be routed to their destination cliques. We give an estimation of the upper bound of the number of broadcast rounds in the worse case. The rest of this work is organized as follows. Some definitions and the environment considered in this work are presented in Section 2. In Section 3, we present some useful preliminaries. The permutation routing is de- scribed in Section 4 followed by the simulation results in Section 5. A conclusion ends the paper. 2. Basic Definitions A Wireless Sensor Network is a set S of n radio trans- ceivers or sensors which can transmit and/or receive messages from each other. The time is assumed to be slotted and all sensors have a local clock that keeps syn- chronous time. In any time slot, a sensor can tune into one channel and/or broadcast on at most one channel. A broadcast operation involves a data packet whose length is such that the broadcast operation can be completed within one time slot. So, in the WSN with Collision De- tection (CD for short), the status of an n-station WSN channel is: -NULL: If no station broadcasts on the channel in the current slot, -SINGLE: If exactly one station broadcasts on the channel and -COLLISION: If two or more stations broadcast on the channel in the current time slot. Also, all the communications are performed at time slots boundaries i.e. the duration operation is assumed to be sufficiently short. 1) All communications are over wireless links. A wire- less link can be established between a pair of nodes only if they were within wireless range of each other. Two sensors that have a wireless link, will be said to be 1-wireless hop away from each other. They are also called neighbors. 2) Each sensor belonging to a cluster is a resident of that cluster. Hence, this sensor may in a given time unit, broadcast a message to its neighbours. Definition 1 Let us consider p stations 1, 2,..., p which communicate in a multi-hop wireless sensor network WSN (n, p). We suppose that we have n items in the system. Then each station of a WSN (n, p) is assumed to have a local mem- ory of size at least. (/ )Onp Definition 2 We suppose that the n items denoted a1, a2, ..., an are pretitled on a WSN(n, p) such that for every i, 1 i p, station i stores the items. Each item has a unique destination station. It is important to note that hereafter a station knows the destination of items it holds. In fact the data item it hols is a couple (a(v), v). Where a(v) is the real data item belonging to sensor v. For every v, 1 v p, let hv be the set of items whose destination is sensor v. /np The permutation routing problem is to route the items in such a way that for all v, 1 v p, sensor v contains all the items in hv. Consequently, each hv must contain exactly items (see Figure 1 for an example). /np 3. Preliminaries We now describe some tools that we will use to derive our permutation routing algorithm. A practical way of tack- ling the permutation routing problem would be to build a hierarchical structure above the network in order to si- mulate a sort of backbone made up of nodes which are Copyright © 2010 SciRes. WSN A. B. BOMGNI ET AL.295 Figure 1. Permutation routing with n= 40 and p=8. more “adapted” than others. This is precisely the goal of clustering. This methodology has already proven its efficiency in the past. In sensor networks the sensor nodes can be partitioned into clusters by their physical proximity to achieve better efficiency, and each cluster may elect a clusterhead to coordinate the nodes in the cluster. Certain references say that clustering with at most two hops is said to be node-centric[13], whereas clustering with over two hops is called cluster-centric [14-16]. In node-centric approach, clusterheads are first elected and a procedure indicates how to assign other nodes to different clusters. In Cluster-centric approaches, clusters are first formed, and each cluster then elects its clusterhead. Such approaches require that all the nodes in one cluster agree on the same membership before electing their clusterhead. We now summarize two clus- tering schemes that will be helpful to describe our per- mutation routing protocol. 3.1. A Clustering Scheme in Cliques Our approach uses one of the protocols from [17,18] to partition network into clusters (cliques). The figure blow illustrates a network in which each clique is a single hop sub network. Each clique is a single hop network. 3.2. Hierarchical Clustering Banerjee and Khuller [14] proposed a clustering algo- rithm for multi-hop sensor networks. Their clustering scheme is motivated by the need to generate an applicable hierarchy for multi-hop wireless environment. Their method yields a multi-stage cluster- ing. To reach their goal they construct a depth first search tree such that each level is composed of cluster- heads of the immediate low level. These clusters are by definition disjointed and the number of the nodes in a cluster remains between k and 2k for some integer k. Figure 3 shows a hierarchical clustering of a network of 25 sensors with k = 3. 4. Permutation Routing ProTocol We recall that we have p sensors and n data items preti- tled in these p sensors. Hence each sensor has a locale memory of size . The time is slotted Our Ap- proach to provide permutation routing in multi-hop sen- sor network consists of the following five phases. (/ )Onp 4.1. Phase 1: Clustering Procedure in Cliques The sensors run one of the protocols in [17,18] to create (a) (b) Figure 2. (a) Network with 11 sensors; (b) Resulting cluster formation in cliques. Figure 3. Hierarchical clustering with k = 3. Copyright © 2010 SciRes. WSN A. B. BOMGNI ET AL. 296 cliques like clusters. We assume that this phase yields k cliques (clusters), hence k cluster heads named CHclique-i for the clusterhead of clique i. In fact clique i is a hub for its local sensors and will be named HUB(i). The role of clusterhead CHclique-i, is to collect all data items whose destination sensors are in i. Note HUBmax the clique that contains the maximum of sensors. Initially HUB(i) may contain outgoing items i.e. items whose destinations are not in HUB(i). Thus the goal of the phases that follow is to describe a mechanism that permit to each sensor to collect all its own data items. 4.2. Phase 2: Local Broadcasts in Cliques The idea of this phase is similar to the protocol sin- gle-channel-routing in [4]. The broadcast item here is a couple (a(v), v) where a(v) is the data item belonging to sensor v. It can be summarized as follows. CHclique-i in- vites each node of HUB(i) to broadcast one by one the data items it holds. In each slot, the sensor whose iden- tity matches the destination of the item being broadcast copies the item in its local memory. If no sensor of the clique is the destination of the broadcast data item then the clusterhead (CHclique-i) copies it in its outgoing_ local_memory. Note that the cluster head has the IDs of all the residents of its cluster. The broadcasts are carried out on cliques. So the clique with the great number of sensors should hep to estimate the total broadcast rounds of this phase. At the end of this phase all data items that do not belong to the sensors of a clique are saved by the clusterhead. The goal now is to route them to their final destinations. 4.3. Phase 3: Hierarchical Clustering The clustering in cliques of the first phase gives k cliques, thus k clusterheads. Now we focus only on these clusterheads and consider a network, say G’, whose sen- sors reduce to these k clusterheads. Clearly |G’| = k. Par- tition this network as in Subsection 3.2 using the hier- archical clustering such that each resulting cluster con- tains at least sensors and at most k sensors. Hence the partition will give only one cluster, and thus one clusterhead. This clusterhead knows the IDs of all residents of its cluster, i.e. the IDs of the other k-1 sen- sors (see Figure 4). /2k 4.4. Phase 4: Ordering Sensors for Broadcasting Outgoing Data Items The clusterhead of the above hierarchical clustering, say CHhierarchi knows all the residents of its cluster, thus has their IDs named CHHUB(i) in phase 1. CHhierarchi or Figure 4. An example hybrid clustering: The first stage shows an example of clustering in cliques. The second stage shows the hierarchical clustering of G’. CHHUB(1) broadcasts one by one the IDs of the CHHUB(i), 1≤ i≤ k in the network G’. Each CHHUB(i) counts the number of IDs that are less than its own ID. At the end the end of the process each CHHUB(i) knows its rank in the set of the IDs in G’. 4.5. Phase 5: Broadcasting Outgoing Data Items 4.5.1. Step1: Collecting the Rest of the Data Items of a HUB In this phase CHHUB(i) or CHhierarchi invites the sensors of G’ with the lowest ID to initiate the broadcasts. It then broadcasts its outgoing data items one by one every two slot to the sensors in G. More precisely, when an item is broadcast in slot , the next slot + 1 is not used and the next broadcast in carried out in slot + 2. It accompa- nies its last item with a special mark indicating that it is its last outgoing item. On receiving this last item, the sensor of second rank takes over the broadcasts. The senor third rank takes over the broadcasts when it re- ceives the last item from the one of second rank and so on till the sensor of the higher rank is reached. In this phase, an item whose destination matches with the ID of a sensor in clique-i is saved by CHHUB(i) in its local memory. During this process all sensors which are not in G’ are inactive except gateway nodes. At the end of this phase the rest of the data items of HUB(i) is saved in the local memory of CHHUB(i). 4.5.2. Step2: Broadcasts of the Rest of the Data Items of a HUB In HUB(i), CHHUB(i) broadcasts one by one the data items collected in step one above and whose destinations are sensors of HUB(i). The protocol is summarized by the pseudo code below: Copyright © 2010 SciRes. WSN A. B. BOMGNI ET AL.297 Protocol-Permut-Routing- muti-hop-Wsn INPUT: wsn of p sensors in which a sensor may not have its own data items OUPUT: wsn in which each sensor has its own data items Begin 1. run the maximum clique formations protocol in [17 or 18] to obtain HUBS 2. in parallel on HUBS, run the protocol single-channel- routing of [4] for single hop wsn 3. run the hierarchical clustering algorithm in [14] with k = |G’/2| on the dominating set, G’ derived from 1. 4. Broad casts of outgoing data Items 4.1 Order sensors for broadcasting outgoing data items 4.2 collect the rest of data items of a HUB 4.3 broadcasts of the rest of the data items of a HUB END Lemma Let p sensors in a multi-hop sensor network (n, p) with n items pretitled on it. Without clustering broadcast rounds, the permutation routing problem can be solved in 2 (1) ()2 max max (/ )kO HUBHUB k np k in the worse case. Proof: 1) Evaluation the broadcast rounds of phase 2 Since HUBmax detains the maximum number of sen- sors, it should need the maximum number of broadcast rounds in this phase. Therefore local broadcasts in cliques need O(|HUBmax|) broadcast rounds (see [4]). 2) Evaluation the broadcast rounds of phase 4 There are k sensors in G’ (kG').The clusterhead of G’ knows all its residents and must broadcast their Ids. Therefore k broadcast rounds are necessary for this sce- nario. However the last broadcasted ID has to travel through the network diameter, i.e. k in the worse case. Hence broadcast rounds are necessary to achieve this phase in the worse case. k2 3) Evaluation the broadcast rounds of phase 5 It is important to note that the upper bound of the num- ber of items stored in each outgoing_local_memory is max (/ ) HUB np . Since we have to broadcast all data items of the outgoing_local_memories, max (/ ) HUB np broadcast rounds are necessary to broadcast all items from these outgoing_local_memories. The special mark indi- cating the end of broadcasts for a sensor is broadcast k × (diameter of G’) times. It reduces to k2 in the worse case. Therefore Step1 of this phase is completed in 2 (/ )max npk HUB k broadcast rounds in the worse case. Step2 takes max (/ broadcast rounds in the worse case. Therefore this phase is completed ) HUB np 2 (1) max (/ )k HUB k np broadcast rounds in the worse case. Summing up these numbers of broadcast rounds, we get the result. 5. Simulation Results In this section, we present simulations results of the clustering algorithm to show the influence of the heu- ristic used to choose the clusterhead. These benchmarks have been run on a laptop (Pentium-m 1.7 GHz, 1 GO RAM, Windows XP SP 2, Cygwin 1.5.19) and pro- grammed in C++. Our main problem has been to estab- lish suitable experiments conditions. Our main issue was to set up a quite realistic mobility scenario. As WSNs are supposed to be used in rescue services, we can assume that nodes are static. Random numbers have been obtained using the well-known Mersenne Twister random numbers generator. All nodes are assumed to have the same transmission range. The experiments take place in a geographic square area of side L. In the simulation depicted by the Figure 2, we measure the instantaneous average number of clusters generated by the algorithm with up to 300 nodes. The presented curves are the average of 100 experiments. We have made the common assumption that two nodes are neighbors if and only if their Euclidean distance is lower than 1 Km. The nodes are in a square, which the length is L = 2 km. In our implementation, the MAC layer is managed in such a way that a node can only receive one message at a time, yielding delays in the clustering process and so maintaining always a high number of clusters. 5.1. Average Number of Cliques Hereafter we will need the number of cliques for the simulations. Figure 5 shows the evolution curve of the average number of cliques with respect to the number of sensors. In a square of side 2 km, the average number of cliques evolves slowly. This situation is due to the fact that, according to the broadcast range, with a little bit more than 200 sensors, the total number of cliques in this square is almost reached. Therefore the new incoming sensors should join one of the existing cliques. 5.2. Average Number of Broadcast Rounds after Clustering The number of data items is fixed to 1000. As expected, the simulation results of the Figure 6 show that there is a significant gap between the naïve gossiping heuristic and our protocol. The procedure of gossiping used here is for Copyright © 2010 SciRes. WSN A. B. BOMGNI ET AL. 298 Figure 5. Average number of cliques. Figure 6. Comparative curves of the average numbers of broadcast rounds. each sensor to broadcast its items one by one. Thus we have simultaneous broadcasts. It is important here to note that each sensor has a collision detection capabil- ity. Our protocol performs better than the above naïve heuristic. One important remark is that when the number of nodes increases, the number of broadcast rounds de- creases slowly. As we see above the number of cliques is stable when p increases. Therefore when p increases decreases (since n is fixed) and the number of items to be broadcast by each sensor decreases. /np p/n 6. Conclusion and Discussions In this paper we show that solving the permutation rout- ing problem on a multi-hop sensor network need 2 (1) ()2 max max (/ )kO HUBHUB k np kbroadcast ro- unds in the worse case. Where n is the number of the data items stored in the network, p, is the number of sensors, max HUB is the number of sensors in the clique of maximum size and k is the number of cliques after the first clustering. Finally, simulation results show that our algorithm performs better than the naïve multi- ple gossiping. To the best of our knowledge, it is the first algorithm for permutation routing in multi-hop radio networks. The reader may wonder why is the assumption about dense networks necessary. To address this point, notice that if max k HUB p then the number of broadcast rounds reduces to. And we have the same perform- ance as the one of the protocol single-channel-routing in [4], in single hop networks. )(nO However some open problems remain. The deriva- tion from the idea of this paper of a fault tolerant algo- rithm, which guarantees the delivery of data items to non faulty nodes, is to be investigated. Also, the con- struction of an energy-efficient permutation routing protocol for multi-hop ad hoc network is a challenge. Another challenging problem is to secure the protocol proposed in this paper. 7. Acknowledgment Thanks to the anonymous referees for their constructive comments and suggestions. 8. References [1] D. Estrin, R. Govindan, J. Heidemann and S. Kumar, “Next Century Challenges: Scalable Coordination in Sensor Networks,” Proceedings of the 5th annual ACM/IEEE international conference on Mobile comput- ing and networking, Seattle, 1999, pp. 263-270. [2] G. J. Pottie and W. J. Kaiser, “Wireless Integrated Network Sensors,” Communications of the ACM, Vol. 43, No.5 2000, pp. 51-58. [3] F. Zhao, J. Liu, J. Liu, L. Guibas and J. Reich, “Collabo- rative Signal and Information Processing: An Information Directed Approach,” Proceedings of the IEEE, New York, Vol. 91, No. 8, 2003, pp. 1199-1209. [4] K. Nakano, S. Olariu and J. L. Schwing, “Broadcast- Efficient Protocols for Mobile Radio Networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 10, No. 12, 1999, pp. 1276-1289. [5] F. Myoupo, “Concurrent Broadcasts-Based Permutation Routing Algorithms in Radio Networks,” IEEE Sympo- sium on Computers and Communications, 2003, pp. 1272-1278. [6] A. Datta, “Fault-Tolerant and Energy-efficient Permu- tation Routing Protocol for Wireless Networks,” Pro- ceedings of the 17th International Symposium on Parallel and Distributed Processing, Nice, 2003, pp. 22-26. [7] D. Karimou and J. F. Myoupo, “A Fault Tolerant Permu- tation Routing Algorithm in Mobile Ad Hoc Networks,” International Conference on Networks-Part II, 2005, pp. 107-115. [8] K. Nakano, S. Olariu and A. Y. Zomaya, “Energy- Copyright © 2010 SciRes. WSN A. B. BOMGNI ET AL. Copyright © 2010 SciRes. WSN 299 Efficient Permutation Routing in Radio Networks,” IEEE Transactions on Parallel and Distributed Systems, Vol. 12, No. 6, 2001, pp. 544-557. 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