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The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07)

PERFORMANCE STUDY OF RECONFIGURATION ALGORITHMS IN CLUSTER-TREE TOPOLOGIES FOR WIRELESS SENSOR NETWORKS Francisco J. Claudios*, Rico Radeke*, Dimitri Marandin*‡, Petia Todorova+ and Slobodanka Tomic^ *Technische Universität Dresden, + Fraunhofer FOKUS, ^ Forschungszentrum Telekommunikation Wien ‡ corresponding author ABSTRACT This paper presents a performance study of two new proposed strategies for the reorganization of a cluster-tree and its address space: proactive and reactive algorithms. The goal of the proactive reconfiguration is to minimize the average number of hops between the possible source and destination pairs. It is achieved by attempting to reconnect nodes to a parent node with the highest possible level. The goal of the reactive reconfiguration is to reorganize the cluster ondemand when a node wishing to join the cluster cannot do it, because the possible parents exhausted their free address space. Our simulation results show that with an optimized reconfiguration a higher performance in terms of connection time and connectivity of the cluster-tree can be achieved. I.

INTRODUCTION

Wireless sensor networks (WSN) are distributed systems of nodes with sensing, data processing and storage capability, wireless-communication interfaces and limited power. They are used for surveillance and control applications in a diverse range of micro and macro environments, such as wild life habitats, urban environments, technical and biological systems and structures [1,2]. One of the central research topics in wireless sensor networking is the design of energy efficient protocols optimized for the constraints of sensor nodes and for the requirements of the data dissemination in the network. In the past, many energy efficient solutions have been proposed and analyzed, especially hierarchical clustering topologies, because of the quadratic relation between transmission range and transmission energy. Significant energy savings can be realized by using clustered networks with short range radios and single-hop routing within clusters and multi-hop routing between clusters [3]. Topology changes occur in dynamic WSNs when nodes disconnect or connect from/to all or parts of it’s neighbours forced by changing of locations of nodes or adding/removing of nodes [4]. Modification of the cluster structure in the presence of topology changes leads to performance degradation in the network. For that reason, a dynamic cluster reconfiguration is required. Recently, the IEEE approved a standard [5] for medium access layer (MAC) and physical layer (PHY) for low-rate wireless personal area networks (LR-WPAN IEEE 802.15.4) [6]. Basically, IEEE 802.15.4 WPAN is composed of one PAN coordinator and a set of devices. The PAN coordinator is the primary controller of the network. It is the one responsible for the initiating of the network. According to their capabilities and available resources IEEE 802.15.4 devices can be full functional devices (FFD) or reduced functional devices (RFD). The ZigBee network layer

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specification proposes as one possibility the IEEE 802.15.4 cluster tree as an addressing and routing structure for energy efficient routing of messages. In this paper we focus on mechanisms for creation and reconfiguration of cluster tree sensor networks based on IEEE 802.15.4 and ZigBee standards. These mechanisms are based on the cluster tree addressing scheme [6] and an implementation of a low-overhead routing protocol with a small number of control messages resulting in small routing delay and the ability to make routing decisions extreme quickly. Our main contributions are the proposed novel simple and energy efficient proactive and reactive reconfiguration algorithms, handling the reorganization of a cluster-tree and its address space. Our aim is the performance analysis of the algorithms in term of connection time and connectivity degree of the cluster-tree. The remainder of the paper is organized as follows. Section 2 highlights the cluster-tree creation. Section 3 analyses clustertree addressing and routing scheme implementation issues based on ZigBee standards. Section 4 describes adaptive reconfiguration strategies. Section 5 is devoted to reconfiguration based on handover of children nodes and presents the two new reconfiguration algorithms. Section 6 provides simulation results. Finally, section 7 concludes the paper and offers directions for further studies. II. CLUSTER-TREE CREATION According to the IEEE 802.15.4, the process of cluster-tree formation starts when a FFD which has a capability to start a network (a potential cluster head) decides to start a new Personal Area Network (PAN). It advertises this decision and starts processing join-requests from other nodes. This FFD is called a PAN coordinator in [5]. The PAN coordinator is the principal controller of an IEEE 802.15.4-based network, being responsible for network formation and maintenance. This cluster head selects a channel and a PAN ID and starts distributing addresses. Very common assumption is that a network is static during the initial configuration phase. An initial cluster tree configuration ends when either all nodes wanting to join a network get the corresponding addresses or the address space is exhausted. After this initial phase a network enters a maintenance phase in which the network has to adapt to dynamic changes such as appearance/disappearance of nodes, traffic changes etc. Formation of a cluster tree takes the heterogeneous capabilities of nodes into account. In a cluster-tree topology only one PAN coordinator may be active. A set of routers can also have capability to become a PAN coordinator if needed. We refer to these routers as potential cluster heads. Other routers are simple routers.

The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07)

In a network which is organically and autonomically growing (opposite to fully pre-defined networks), several potential cluster head routers may autonomously start their own PANs. A reason to start a new PAN may be that no nodes are available which could permit to join an existing PAN. Each potential cluster head has to make the decision on whether to start a new cluster-tree or to join an existing cluster-tree in autonomous fashion. In a heterogeneous network only a subset of nodes can initiate a new network; hence, a number of nodes may stay uncovered. The global objective of the network self-organization in cluster-trees is to provide full coverage and therefore the quality of this process can be qualified by accessing the number of trees, the size of the trees, the energy consumption in the trees and the number of nodes which stays uncovered.

need some improvement especially in cases where nodes in the locality of each other are organized in two branches of the tree and instead of the direct communication a multi-hop communication is used. To alleviate this problem a gateway concept may be introduced to enhance a tree with some mesh links. It is important that the tree is established and maintained according to the traffic demands. Cluster Head Level 0 Level 1 Level 2 Level 3

III. CLUSTER-TREE ADDRESSING AND ROUTING SCHEME According to the ZigBee standard, a cluster-tree topology uses a hierarchical addressing and routing structure for energyefficient data collection from the nodes with sleeping schedules. One of the FFD devices takes the role of a PAN coordinator and starts a new network. This node has an address 0 and is in the root of a tree it initiates. Other FFDs assume the router role and connect to the tree, thus providing connection points for other FFDs and for RFDs. The routers joining the network at the cluster head (the children of the cluster head) are 1-level routers. Each i-level router manages the communication with its children at (i+1)-level and is referred to as a coordinator of this part of the cluster. A cluster tree is bounded with a number of children nodes per router and with the maximum allowed tree depth. A cluster-tree depth defines the number of hierarchical levels at which nodes may connect. The node at the depth D is a coordinator for its children nodes at the level D+1, and the cluster tree is often organized as a beacon-controlled structure. Consequently, a node at the level D establishes a super-frame in which it communicates with its children nodes at the level D+1. A level D node communicates with its parent node at the level D-1 within the active part of the super-frame established by the (D-1) –level node. Efficient addressing of nodes in a network significantly simplifies the routing. In a tree structure messages are forwarded up the tree from the source node to the first common parent of both the source node and destination node, and then down the tree to the destination node. The addressing scheme in which the addresses of nodes in each branch belong to the contiguous address range is a part of the ZigBee network layer. Here the address range is first splitted at all nodes at level 1. These nodes further divide the assigned address space at the level 2 nodes and so on. When a message is forwarded, a simple check if an address is in the address space of the coordinator decides on whether the next hop is up the tree or down the tree. This routing scheme

Figure 1: Cluster Tree Structure. For smaller networks a single cluster network with one cluster head may be a realistic study case. For large networks a potential realistic architecture may be the one with more than one cluster head, and consequently more than one cluster-tree interconnected together. In both cases the cluster-tree is a basic structure, which needs efficient and adaptive mechanisms for cluster forming and reconfiguration. IV. CLUSTER TREE ADAPTIVE RECONFIGURATION Reconfiguration deals with the potential inefficiency of the cluster-tree addressing and routing due to the organic growth of a cluster tree as devices are added to or removed from the network. The cluster-tree addressing scheme divides the address space in contiguous intervals and assigns them to the tree branches. This may lead to the state where some parts of the address space are being unused and some are being fully exhausted preventing further adding of nodes, which may be critical for RFDs. In this case, reconfiguration may be considered as a necessary autonomic process. In other cases, where the tree is sub-optimal concerning the data flows, reconfiguration is needed for topology and energy consumption optimization process. Adaptive reconfiguration is a distributed mechanism during which the cluster-tree topology is rearranged and/or the distribution of the address space is improved. For example the reconfiguration could provide an address space for active nodes which were unable to join the network and get a unique address within the original address structure. Further more, reconfiguration may aim at balancing the traffic load, energy consumption or the tree depth. Triggers for reconfigurations may be classified into events related to coverage, connection or traffic monitoring.

The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07)

Coverage monitoring process detects the exhaustion of the address space. A router which sent a join-request message but did not get join-reply may either decide to start a new PAN, if it is a potential coordinator, or stay unconnected and periodically request joining. An end-device will stay unconnected and silently wait for a possibility to join. The PAN coordinator or a router in a tree needs to detect that an active node was not able to join and should start reconfiguration. Connection monitoring detects that the parent or a child is not replying any more. If a node has lost a parent it must search for a new parent to get a new address or a new address space. A node with children will need to disassociate all nodes of his branch, which can then start searching for new parents autonomously. Similarly, a node that detects that its child and a potential root of a branch is not responding any more, can assume that the lost address space is free for new associations. Traffic monitoring can detect that routing on the tree is not efficient and may trigger reconfiguration. Depending on which node in the network starts the reconfiguration and how extensive the reconfiguration is we classify reconfiguration strategies within four groups. 1. Cluster head switch: The current cluster head triggers reconfiguration by selecting and activating a new cluster head. 2. Changing the maximal number of children per node: The current coordinator propagates a new value for the maximum number of children per node (Cm) to each node in a tree and rebuilds the tree. 3. Splitting/merging cluster trees: A node detecting a problem takes a role of an additional coordinator and creates its own tree without changing the rest of the structure of the current cluster. Both trees need to be interconnected. In case of small clusters it would be also interesting to merge these into a single cluster. 4. Handover of children nodes: A node detecting a problem asks one or some children nodes if they can connect to a different parent node (or cluster). If possible a child node releases the connection with the parent node and connects to another, more suitable one. The first three groups need some cluster wide communication and therefore overhead to plan and control this kind of reconfiguration. The fourth group of reconfiguration offers possibilities to reconfigure and optimize the cluster tree based on local decisions. In our further investigations we focus solely on this kind of reconfiguration due to energy consumption aspects in sensor networks. V. HANDOVER OF CHILDREN NODES Reconfiguration based on handover of children relies on detection of one of the following events: 1.

a router detects that it has no free addresses for a node who wants to connect

2.

a node being in a lower level of the cluster tree detects a possible parent with free addresses in a higher level of the cluster tree

3.

a node without children detects that it is in a high level of the cluster tree (and therefore blocking addresses)

In our work we propose two different classes of reconfiguration algorithms which we refer to as reactive and proactive. The reactive reconfiguration algorithm is triggered when a specific problem such as address space exhaustion is detected. We refer to this kind of reconfiguration also as a reactive mobility of children, where mobility is not spatial but refers to logically moving from one parent to another. The algorithm where nodes try to prevent problems (e.g. changing to other parents without specific connection problems) is called a proactive reconfiguration algorithm or proactive mobility of children. With our reactive and proactive reconfiguration algorithm we focus on finding a solution for problems after occurrence of events 1 or 2. A. Proactive reconfiguration: The goal of the proactive reconfiguration is to minimize the average number of hops between the possible source and destination pairs. To achieve this goal, the nodes, which are already in the cluster, always try to reconnect to a parent node with the highest possible level. We assume a traffic model for a sensor network where almost all communication flows between sensors and the cluster head. Therefore, minimizing the average number of hops between all nodes and the cluster head leads to minimizing traffic and energy consumption. To realize this kind of reconfiguration we extended beacons with a flag indicating the presence of free addresses at the beaconsending node. Each node monitors beacons messages from neighbouring nodes and tries to connect to the parent node with the highest level and free addresses. The level of the parent node is calculated by a node based on the source address of the beacon message and known addressing parameters (maximum number of child nodes per parent and maximum tree depth). The decision of reconfiguration and changing of a parent node is made by each node independently. B. Reactive reconfiguration The goal of the reactive reconfiguration is to reform the cluster on-demand when a node wishing to join the cluster can not do it, because the possible parents exhausted their free address space. As a result of proactive reconfiguration it can occur more often than without reconfiguration that the free address space of parent nodes at higher levels is maximally used. Our approach to resolve this problem is that parent nodes request their child nodes to reconnect to free the address space for other nodes currently wishing to connect to the cluster but are unable to do it. Complementary to the proactive reconfiguration the reactive reconfiguration uses the possibility to connect to a lower lever parent in the cluster to

The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07)

increase connectivity for all nodes. It must be used together with proactive reconfiguration to form balanced trees. Both reconfiguration methods use the same control messages in our implementation when possible. Proactive reconfiguration produces small overhead, as nodes only listen to neighbours if these offer a better (e.g. higher) level to connect to. If a suitable parent is found the child node changes to the new (e.g. better) parent node. Only two messages are used to send a connection request and a connection response. Contrarily the reactive reconfiguration algorithm uses a lot more messages. After a the first triggering connection request of a new node A to an existing node B, which has no free addresses, 5 more messages must be sent. These are a demand message to the children nodes of node B, connection requests of the children nodes if they find another parent node C, the connection response from node C back to the children node, the information about the network change back to parent node B and after all the connection response back to initial node A. Therefore the reactive reconfiguration produces more overhead.

shows great performance with almost all nodes connected right after their starting. Table 1: Simulation parameters. Simulation runs Simulation duration Simulation area size Communication range Number of nodes Placement of nodes Node start time Cluster head (CH) start time Maximum number of child nodes per parent Cm Maximum tree depth Lm

2000 40s 20x20m, 40x40m, 60x60m 10m 80 Randomly by uniform distribution in the simulation area Randomly by uniform distribution within the first 10s After all nodes, before all nodes 4 3

VI. SIMULATION RESULTS We used the implementation of the ZigBee cluster-tree addressing scheme [7] in the simulator ns-2 [8] and evaluated the optimized reconfiguration schemes as described in section 5. We compared the above mentioned optimization schemes with the non-optimized one. The simulated scenario contained 80 sensor nodes with just one node being capable of being cluster head, which is started either before or after all other nodes. The other nodes were started and placed randomly within the first 10 seconds. No data traffic was generated as the connectivity of nodes was our main research focus. Each simulation was run 2000 times for a duration of 40 seconds. We calculated the 95% confidence intervals for the mean for each set of runs. The simulation parameters are summarized in Table 1. We evaluated the following performance metrics: • • •

Number of connected nodes in the course of simulation time (Fig. 6.1 and 6.2). Number of connected nodes at the end of the simulation (Fig. 6.3) Average time to connect for all nodes which achieve a connection (Fig. 6.4)

Figures 6.1 and 6.2 show the average number of nodes connected to the cluster for different reconfiguration methods in the course of simulation. The results are shown for the 20x20m area. As can be seen in figures 6.1 and 6.2 the optimized reconfiguration schemes improve significantly the network connectivity and speed up the cluster construction. In the non-optimized reconfiguration scheme the connection request is often rejected due to the parent’s address space exhaustion. Moreover, many new nodes have to wait for a long time to get new addresses. Our optimized reconfiguration schemes decrease the cluster connection delay and increase the number of connected nodes significantly. Especially, the scenario with cluster head (CH) started before all other nodes

Figure 6.1: Average number of connected nodes, 20m x20m, the CH started before all nodes.

Figure 6.2: Average number of connected nodes, 20m x20m, the CH starts after all other nodes.

The 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'07)

Figure 6.3 shows the number of connected nodes at the end of the simulation for three different scenario sizes (20m x 20m, 40m x 40m, 60m x 60m) with fixed number of 80 nodes in the area (dense, medium and sparse distribution of nodes). For each density we investigated two cases: the CH switched on before all other nodes at time 0 and the CH switched on after all nodes at time 10s. The six scenarios are marked with scenario size and switch on time of the cluster head, e.g. “60x60,10” means that the cluster head is switched on at time 10s in a scenario with a size of 60 by 60 meter.

We also investigated the average time to connect a node after starting. As it is shown in figure 6.4 both reconfiguration schemes perform better than without the optimized reconfiguration. Especially, the dense network takes advantage of optimized reconfiguration mechanisms. The average connection time is reduced by 95%. In sparse network the benefit is smaller, although the optimized reconfiguration still performs better. VII. CONCLUSIONS In this paper we proposed and evaluated schemes for creation and reconfiguration of cluster-tree topologies in WSNs. These mechanisms are based on the reorganization of a cluster-tree and its hierarchical address space. The goal of the proactive reconfiguration is to minimize the average number of hops between the possible source and destination pairs. The reactive reconfiguration aims at an on-demand cluster-tree reformation when a node wants to join the network but all potential parent nodes exhausted their free address space.

Figure 6.3: Number of connected nodes at the end of simulation for different densities of nodes and different start time of CH.

The simulations have shown that both optimized reconfiguration schemes show a better performance in terms of network connection time and connectivity degree of the cluster-tree. The improvement is larger in dense sensor networks. The combination of both schemes performs only slightly better than the proactive reconfiguration, but at cost of more overhead. Thus, if the energy efficiency is important, we recommend using only the proactive reconfiguration. Our future work is to investigate cluster-trees interconnection based on gateways. VIII.

ACKNOWLEDGMENT

The work presented here is supported within the European project CRUISE (Creating Ubiquitous Sensing Environments), the EU FP6 Network of Excellence (NoE) on communication and application aspects in sensor networking. IX. REFERENCES

Figure 6.4: Mean connection time for different densities of nodes and different start time of CH. In all cases it can be seen, that the optimized reconfiguration schemes improve the connectivity by up to 20 percent. The combination of the reactive and proactive reconfiguration schemes performs slightly better than using only the proactive reconfiguration. It can be concluded, that the proposed reconfiguration schemes show a bigger improvement in the dense node distribution, as in this case many nodes can hear each other and the reconfiguration can be used to optimize the cluster tree.

[1] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, “Instrumenting the World with Wireless Sensor Networks,” Proc. Int’l Conf. Acoustics, Speech, and Signal Processing (ICASSP 2001), May 2001. [2] G.J. Pottie and W.J. Kaiser, “Wireless Integrated Network Sensors,” Communications of the ACM, vol. 43, no. 5, pp. 51-58, May 2000.J. [3] A. Bakshi and V.K. Prasanna, “Structured Communication in Single Hop Sensor Networks,” Proc. First European Workshop Wireless Sensor Networks, pp. 138-153, Jan. 2004. [4] Cormac J. Sreenan and Utz Roedig, "Wireless Sensor Networking in ENEXT," In Proceedings of the 2nd IEEE European Workshop on Wireless Sensor Networks (EWSN2005), Istanbul, Turkey, IEEE Computer Society Press, February 2005. [5] ZigBee Alliance, ZigBee Specification, June 27,2005 [6] IEEE Std 802.15.4™-2003, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), 2003. [7] P. Negro, “Cluster Tree Protocol”. internal Report, TU Dresden, 2006. [8] The network simulator ns-2 (www.isi.edu/nsnam/ns)