The ZigBee Alliance uses the IEEE 802.15.4 layers to build a complete protocol stack for the implementation of wireless sensor networks. ZigBee specifies.
Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Topology formation in IEEE 802.15.4: cluster-tree characterization F. Cuomoa, S. Della Lunaa, E. Cipollonea, P. Todorovab, T. Suihkoc a b c
University of Roma “Sapienza”, Via Eudossiana, 18, 00184 Rome, Italy
Fraunhofer FOKUS, Kaiserin - Augusta-Allee 31, D-10589 Berlin, Germany
Technical Research Centre of Finland, P.O. Box 1000, FIN-02044 VTT, Finland which will constitute the infrastructure to sense and affect the physical environment [2] [3]. While, in line with the family of IEEE 802.11 standards, the proposal focuses only on PHY and MAC layers, upper layers of the protocol stack are defined by the ZigBee Alliance [4]. ZigBee specifications are an important building block for a standard WSN protocol stack, designed to support a variety of network organisation approaches and a variety of higher-layer protocols. ZigBee specifies the network layer for star, tree and peer-to-peer topologies. Starting from these, more complex topologies can be formed (known as cluster-tree topologies). This cluster-tree structure is also at the basis of the distributed algorithm for network address assignment. The goal of this paper is to investigate the topology formation strategies in IEEE 802.15.4 networks. Our aim is the simulation and performance analysis of the IEEE 802.15.4 association procedure which drives the topology formation phase. In the cluster-tree topology, a tree rooted at the PAN coordinator (the sink) is created based on the MAC parent-child relationships between IEEE 802.15.4 devices. Our contribution is in the analysis of both networks where a single sink is present and networks where multiple sinks are used. Special attention is given to the evaluation of cluster-tree height statistics as well as the statistics of the number of children per node. It is to be noticed that a detailed analysis of the characteristics of the topology formed with IEEE 802.15.4 is missing in the literature. On the contrary, a characterization of these networks can provide guidelines for a practical implementation of network formation in WSNs. The remainder of the paper is organised as follows. Section II briefly describes the IEEE 802.15.4 topology formation and association procedure. Section III is devoted to the topology characterization of IEEE 802.15.4 networks. It describes the simulation model and provides simulation results. Section IV concludes the paper.
Abstract— The IEEE 802.15.4 standard defines a set of procedures to set-up a Low-Rate Wireless Personal Area Network where nodes self-organize into a logical communication structure through which data can be routed, hop by hop, from sources to destinations. The network formation of the IEEE 802.15.4 does not impose constraints on the topology. The ZigBee Alliance uses the IEEE 802.15.4 layers to build a complete protocol stack for the implementation of wireless sensor networks. ZigBee specifies the network layer for star, tree and peer-to-peer topologies. Starting from these, more complex cluster-tree topologies can be formed. To control the network topology ZigBee fixes the maximum number of routers and end-devices that each router may have as children and also fixes the maximum depth of the tree. To better understand the importance of these constraints we simulate and analyze the IEEE 802.15.4 formation procedure in different network settings (single-sink and multisink scenarios). The goal is to provide guidelines for the practical implementation of ZigBee network formation with the aforementioned constraints.
I. INTRODUCTION Wireless Sensor Networks (WSN) are conceived as a plethora of small devices (sensors) distributed over an area of interest where some specific phenomena must be monitored. WSN construction includes the physical deployment and organization of logical topology. In several application scenarios sensor nodes are responsible for selforganizing into a logical communication network structure through which data can be routed, hop by hop, from sources to destinations. In general, the destination of the data is a specific node (commonly named sink) which has the role of collecting the data measured by all the network sensors. For scalability reasons, in large WSNs, constituted by hundreds of sensors, nodes may refer to multiple sinks or may organize themselves in clusters giving to some key nodes, called Cluster Heads (CH), the role of collecting the data of the cluster and route them towards the sink via a hop-by-hop path on other CHs. Recently, IEEE approved the 802.15.4 standard for medium access layer (MAC) and physical layer (PHY) for low-rate wireless personal area (LR-WPAN) networks [1]. The IEEE 802.15.4 is designed to wirelessly interconnect ultra low-cost sensors, actuators, and processing devices,
II. GENERAL DESCRIPTION OF IEEE 802.15.4 TOPOLOGY FORMATION
An 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 responsible for initiating the network operations, and may be mains powered. According to their capabilities and available
This work has been performed in the framework of the IST-4-027738 NoE CRUISE (http://www.ist-cruise.eu/), which is partly funded by the European Union.
0-7695-3113-X/08 $25.00 © 2008 IEEE DOI 10.1109/PERCOM.2008.26
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resources, IEEE 802.15.4 devices can be full-function devices (FFD) or reduced-function devices (RFD). Two main types of network topology are considered in IEEE 802.15.4, namely, the star topology and the peer-topeer topology [5]. In the star topology, a master–slave network model is adopted. A FFD takes up the role of the PAN coordinator that administers network operation; other nodes can be RFDs or FFDs and communicate directly with the PAN coordinator. In the peer-to-peer topology, a FFD can talk to other FFDs within its radio range and can relay messages to other FFDs outside of its radio coverage through an intermediate FFD, forming a multihop network. RFDs can communicate only with FFDs. Concerning the topology formation, the IEEE 802.15.4 group defined a mechanism to support a PAN coordinator in channel selection when starting a new WPAN, and a procedure, called association procedure, which allows devices to join a WPAN. A PAN coordinator wishing to establish a new WPAN searches for a suitable channel among the ones available within the frequency band. The channel selection is performed by the PAN coordinator through the Energy Detection (ED) scan which returns the measure of the peak energy in each channel. It is to be noticed that the standard only provides the ED mechanism but it does not specify channel-selection logics. The operations accomplished by a device to discover an existing WPAN and to join it can be summarised as follows: i) search for available WPANs; ii) select the WPAN to join; iii) start the association procedure with the PAN coordinator or with another FFD device, which has already joined the WPAN. The discovery of available WPANs is performed by scanning channels and by searching available coordinators1. Two different types of scan for the association phase are proposed: 1. Passive scan: in beacon-enabled networks the associated FFDs periodically transmit beacon frames; the information on the available WPANs can be directly derived by eavesdropping the wireless channels and by capturing the beacon frames; 2. Active scan: in non-beacon-enabled networks the beacon frames are not periodically transmitted by coordinators; a device scanning the channels shall explicitly request beacon frames (by a beacon request command) and shall wait for replies from the available coordinators. After the scan of the channels, a list of available WPANs is used by the device to choose the network to try to connect with. In the standard, no specific procedure to select a WPAN is provided and so, this selection among potential coordinators is open for different implementations. The device sends an association request frame to the coordinator by means of which the WPAN was discovered. The association phase ends with a successful association response command frame to the requesting device. This procedure results in a set of MAC association relationships 1 Coordinators are the PAN coordinator or FFDs already connected to a WPAN.
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between devices, named in the following parent-child relationships. The totality of these relationships forms a tree rooted at the PAN coordinator. The general topology is called cluster-tree. The coordinators (FFD devices) of the clusters form a tree structure, and they act as intermediate aggregators and routers of data between different devices. Each router is an association point for other routers and a number of end-devices (RFDs) which do not participate in routing (Figure 1). Levels in a tree are defined as the distances, in terms of hops, from the nodes to the relevant sink. The node at level 0 is the PAN coordinator, nodes at level 1 are the children of the PAN coordinator and so on. In the following we call low levels the levels closest to the sink while high levels are the ones close to the leaves of the tree. PAN coordinator FFD
0
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RFD
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1 CH
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2 Cluster a
Cluster b
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Figure 1 - A generic structure of WPAN organized in a cluster tree topology; the level of each node is indicated by the number in the circle.
The cluster structure can be used by the upper layers to carry on specific functions (e.g. addressing, data aggregation). In the following we call this cluster-tree simply tree. To have a comprehensive overview of IEEE 802.15.4 standard and its performance a significant paper is the one by Zheng and Lee [6]. This paper describes the WPAN ns-2 simulator for IEEE 802.15.4 (see also the paper in [7]) and conducts several sets of experiments to study its various features. While the objective of the work in [6] is an extensive evaluation of the IEEE 802.15.4 performance by varying MAC parameters, here we concentrate on the results of the topology formation. We used the above-mentioned simulator for the single sink scenario (refer to the paper in [7] for a description of the simulator) while we extended it to model the multiple sinks case. Performance analysis of the formed networks in terms of capabilities in supporting data transfer and data gathering are reported in [8]. An analytic model based on this analysis that can be used to optimize the network formation parameters (e.g. the tree depth) is reported in [9].
III. TOPOLOGICAL CHARACTERIZATION OF THE IEEE 802.15.4 NETWORKS The adopted ns-2 software includes implementations of the IEEE 802.15.4 physical and MAC layers, both developed by Zheng and Lee [7]. To create a large set of tree-based topology realizations using IEEE 802.15.4-compliant formation procedures, we extended the ns-2 tool in order to consider also multisink/multi-channel scenarios. With this enhanced tool we create topologies of disjoint trees. We selected the 2.4 GHz band where 16 different channels can be used. Since no specific channel and WPAN/coordinator selection algorithms have been defined in the standard, we implemented two new mechanisms in ns2. As for the selection of one of the 16 channels, the PAN coordinator adopts the following rules: • if the same peak energy is detected on all the channels, the channel is selected randomly among them; • if different peak energies are measured on the channels, the selected channel is the one with lowest peak energy; if there are many channels having the same lowest value, the channel is randomly selected among them. As for the WPAN/coordinator selection by a device, it randomly selects the coordinator among those for which the measured link quality is bounded between the maximum detected and γ times this value (with γ = 0.8). In general, therefore, we introduce a random mechanism in order to prevent a bias in the selection of certain channels/coordinators. A. Simulation model We considered a network scenario consisting of N sensor nodes and S sinks (PAN coordinators). The sensor nodes are randomly deployed in a square of side L and area A = L2. All nodes are static (no nodes’ mobility or nodes’ failure/death is envisaged), they are FFDs and they are configured to act as possible coordinators in the WPAN allowing association from other devices. The transmission range of a device is R (see Table I). The considered scenarios are representative of indoor WSNs used in small offices. At the MAC level it is assumed that beacon frames can collide. Data packets can collide too, since they are transmitted in accordance to the CSMA-CA protocol as defined in the 802.15.4 standard. The sink is configured in beacon-enabled-mode, while all other nodes operate in non-beacon-enabled-mode. The Beacon Interval (BI) and the active Superframe Duration (SD) depend on the Beacon Order (BO) and the Superframe Order (SO): BI = aBaseSuperframeDuration * 2BO, SD = aBaseSuperframeDuration * 2SO, where aBaseSuperframeDuration = aBaseSlotDuration*aNumSuperframeSlots, and aBaseSlotDuration = 0.960 ms, aNumSuperframeSlots = 16.
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We set the BO = SO = 7. The fact that BO=SO assures that no inactive part of the superframe is present. The selection of the value of these two parameters equal to 7 is the result of an empirical evaluation of the impact on the network formation performance. A low value of this parameter implies a great probability of collisions of beacon frames, since these would be transmitted very frequently by coordinators. On the other hand, a high value of the BO (the standard fixes BO ≤ 15) introduces a significant delay in the time required to perform the MAC association procedure, since the duration of the channel scan (part of the association procedure) is proportional to BO. Therefore, our choice represents a compromise between the probability of collision among beacon frames and the duration of the MAC association procedure. Further, we assume the following attenuation model in which the power attenuation between nodes follows an inverse power law, and random channel fluctuations: La = k0 + k1 ln r + s, where La is the loss in dB in signal strength at distance r from the transmitter, k0 and k1 are constants, and s is the random fluctuation, which follows a normal distribution with zero mean and variance σ2s [10]. In the simulations, a packet is successfully received if the attenuation does not exceed a given value Lamax which is related to the transmitter power and the receiver sensitivity. In particular, we set k0 = 40, k1 = 13.03 m-1, σ2s = 3.5 and Lamax = 95.6 dB [10]. We carried out M different simulation runs and each time an independent nodes deployment was considered. In case of single sink scenarios the sink is always located in the centre of the monitored region. In case of multiple sinks these are randomly deployed in the area. TABLE I - SIMULATION PARAMETERS AND SETTINGS Single sink simulations parameters Number of nodes, N 100 Side of the square area, L 150 m Number of sinks, S 1 Radio transmission range, R 50 m Sink position Centre of the area Number of simulation runs, M 100 Multiple sink simulations parameters Number of nodes, N 500 Side of the square area, L 1000 m Number of sinks, S 50 Radio transmission range, R 150 m Sinks positions Random deployment in the area Number of simulation runs, M 200 Common simulations parameters Beacon Order 7 Superframe Order 7 Attenuation threshold (Lamax) 95.6 dB
For each simulation run we collected the structure of S cluster-trees resulting from the IEEE 802.15.4 topology formation mechanism. In all formed trees we recorded all the parent-child relationships, the number of nodes at each level and the tree height.
B. Simulation results We characterized the network topologies resulting from the different simulation runs in accordance to the following metrics: • η_mean: the mean number of children per parent, computed for each tree level, and averaged on all the formed M*S trees; • C_max: the maximum number of children per parent, computed for each tree level and measured over all the formed M*S trees. • T: the tree height, computed as the maximum tree depth for all M*S trees. We first evaluated the association procedure without constraints on the tree parameters in order to capture the key topological characteristics. Then we measured also a topology performance when the tree height is constrained. Figures 2-6 refer to the single sink case. Figure 2 shows the probability density function of the tree height, T, for the M simulated WPANs. The mean value of T is about 7.7, and more than 95% of the trees have T ≤ 9. The maximum measured value is T = 11. Notice that, given the size of the side of the area, the number of hops (by using the maximum transmission range R) on the diagonal of the square area is 4,24. So a value of T =11 is five times the maximum distance (2,12 hops) that a node may have from the sink in the considered scenario. The reason why much more hops are generated depends on the different factors (i.e., collisions., link quality measurements, etc) that influence the association procedure as defined by the IEEE 802.15.4.
packets transmission. The consequence is an inefficient use of the channels. As a general comment it is convenient to limit the value of T but the selection of the appropriate value is not trivial and could impact the network connectivity as shown in the following. In Figures 3 and 4 we report the mean number (η_mean) and the maximum number (C_max) of children per parent, as a function of the parent level. It can be noted that η_mean is about 4 and 5 for the level 0 and level 1 nodes, while it reduces to 1 or less than 1 for levels ≥ 2.
Figure 3 - Mean number of children per parent as a function of the parent level with 95% confidence interval, single sink scenario.
Figure 4 - Maximum number of children per parent as a function of the parent level, single sink scenario.
This behaviour is caused by the location of the PAN coordinator that associates the most of the nodes in the centre of the area at low levels. From Figure 3 it emerges that trees widen at levels close to the sink (levels 0 and 1), and stretch out at levels distant from the sink. Since here we do not consider constraints on the maximum number of children per node, we measured (see Figure 4) C_max = 9 and C_max = 15 for level 0 and level 1 respectively, and decreasing values from level 2 to level 11 (the average value of C_max is 5.75). The combination of the results of Figures 3 and 4 suggests that the selection of a unique value of the maximum number of children to be constrained for all network nodes (the parameter Cm of the
Figure 2 - Probability density function of the tree height T, single sink scenario.
If the association tree is used for routing purposes, high values of T cause data frames to be routed along long paths towards the sink. In fact in this type of topology a hierarchical routing is performed (i.e. packets are routed towards the sink via the parent-child paths). This will influence network performance both in terms of delay and energy consumption. Shorter trees, on the contrary, can cause crowding of nodes connected at the lowest levels thus implying a high probability of MAC collisions during the
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ZigBee specifications) is delicate since it can compromise the topology characteristics. A low value of Cm could be insufficient for connecting all nodes that can be located close to the sink (as in our case where the sink is in the centre of the area) while a selection of a high value can give rise to an increment of collisions of children transmitting to the parent and an inefficient addressing scheme. As for this latter point it is to be noticed that, in the ZigBee specification, the address space that each node should reserve for its children is proportional to Cm. If the Cm is high large address spaces are assigned with the risk that these are not fully used (addresses wasted).
Figure 5 - Mean number of children per parent as a function of the parent level for tree with T = 6, 8, and 10, single sink scenario.
since they were composed only by one sink. These events are due to the considered sink density and radio parameters and occur when two or more sinks have their relative radio coverage regions largely overlapping. As shown in Figure 6 the IEEE 802.15.4 WPAN association procedure forms trees with different heights, some trees are very high. As the energy consumed per sample transmitted is strongly dependent on the number of transmissions and receptions (and therefore on the number of hops), the use of very high trees, can create an energy-inefficient situation. Thus, T should be properly controlled in order to reduce such inefficiency and a maximum value of T, T_max, should be imposed also in the case of multiple sinks.
Figure 7 - Mean number of children per parent as a function of the parent level with 95% confidence interval, multi-sink scenario.
Tree heigh T
Figure 6 - Cumulative distribution function of the tree height T, multi-sink scenario.
We also verified that the tree structure derived in Figure 3 holds independently from the tree height. Figure 5 shows the η_mean as a function of the parent level for trees with T = 6, 8 and 10. A similar tree structure appears so we can infer that the selection of an appropriate Cm can hold for different tree heights. For the multi-sink scenario, in Figure 6 we report the cumulative distribution function of the tree height, T. It can be noted that even if the maximum measured tree height is 18, more than the 90% of all collected trees have T ≤ 7 and a mean equal to 3.3. We remark that, about 20% of the trees in each simulation have not been considered in the analysis
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Then, also for the multi-sink case, we computed the mean number of children η_mean for nodes at different levels in each tree. Figure 7 shows, in the multi-sink scenario, the η_mean value for all the collected trees with the relevant 95% confidence interval. It emerges also in this multi-sink case that the trees widen at levels close to the sink (i.e, 0 and 1) since these nodes have several children, and stretch out at levels ≥ 2 where the number of children decreases as the level in the tree increases. The main conclusion from the above results is that the unconstrained topologies can give rise to very high trees (even if their probability is low). On the contrary, if we constrain T we cannot expect that all nodes are able to connect to the network. In Figure 8 we report some preliminary results on the analysis of the topology formation when T is constrained to T_max. To derive these results we implemented a procedure to select the coordinator to connect with. It is assumed that nodes record their level in the tree. During the scan procedure a node may discover different candidate coordinators. It selects one of them in accordance to the following criteria: • the node selects the coordinator(s) having the lowest level; • if there are two or more coordinators having the same (lowest) level, among them, the node selects the one with the highest value of a parameter that measures the link quality between the node and the potential coordinator;
• if there are two or more coordinators having the same (lowest) level and having the same value of the link quality, the node chooses randomly one of them. In each of the previous cases, a coordinator is selected iff it has a level less than T_max. To understand the impact of T_max on network topologies we measured the following metric: • Probability that α% of nodes is connected to the network (measured as the number of simulations where α% of the nodes become associated to a WPAN in proportion to the total number of simulations). This metric was obtained considering a multi-sink scenario with S = 20 sinks in beacon-enabled-mode and N = 1500 sensor nodes, all FFDs operating in non-beacon-enabledmode. From Figure 8, it can be noticed that only for T_max ≥ 10 it is possible to have 75% of the nodes connected while a T_max = 8 is sufficient for having connected 70% of the nodes .
Figure 8 - Probability that α% of nodes are connected to the network as a function of T_max.
The analysis of the tree formation with constraints on the tree depth shows that the T_max value must be chosen with care. On one hand it is important to take into account the impact that this parameter may have in terms of network connectivity (some applications may tolerate that not all nodes are connected while others may be very sensible to the network connectivity). On the other hand if we do not impose T_max the trees may reach high depths (see Figures 2 and 6) thus impacting the network performance like energy and latency. IV. CONCLUSIONS IEEE 802.15.4 association procedures allow selforganization of wireless sensor nodes into a logical communication network structure through which data can be routed, hop by hop, from sources to destinations. Since the resulting network topology has a great impact on both routing/addressing and energy consumption in data transfer, ZigBee (responsible for the specification of the network formation) decided to constrain the topology by
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enforcing some key parameters. To investigate the topological characteristics of a WSN we simulated and analysed the detailed IEEE 802.15.4 association procedure. This kind of analysis is missing in the literature while it is the starting point to better understand how to implement the ZigBee network formation where some topological parameters shall be imposed. Our results show that the IEEE 802.15.4’s native unconstrained association procedure tends to lead to too deep trees. The results thus confirm the need for constraining the depth, controlling the number of children, and supporting various coordinator selection rules, which indeed is addressed by ZigBee. However an appropriate value of the maximum tree depth must be chosen in order to satisfy the requirements on the percentage of nodes that result connected to a WPAN at the end of the formation procedure. REFERENCES [1] Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), IEEE Std. 802.15.4, 2006. [2] J. Zheng and M. J. Lee, “Will IEEE 802.15.4 make ubiquitous networking a reality? A discussion on a potential low power, low bit rate standard”, IEEE Communications Magazine, vol. 27, no. 6, 2004, 23–29. [3] E. Callaway, P. Gorday, L. Hester, M. Gutierrez, J. Naeve, B. Heile and V. Bahl, “Home networking with IEEE 802.15.4: a developing standard for low-rate wireless personal area networks”, IEEE Communications Magazine, (40), no. 8, 2002, 70–77. [4] ZigBee Specification, ZigBee Alliance Std., 2006. Available online: http://www.zigbee.org [5] P. Baronti, P. Pillai, V. Chook, S. Chessa, A. Gotta and Y. F. Hu, “Wireless Sensor Networks: a Survey on the State of the Art and the 802.15.4 and ZigBee Standards”, Computer Communications 30, pp. 1655–1695, 2007. [6] J. Zheng and M. J. Lee, “A Comprehensive Performance Study of IEEE802.15.4”, IEEE Wpan press 2006. [7] J. Zheng and M. J. Lee, ”NS2 Simulator for IEEE 802.15.4”, 2004 Available online: http://ees2cy.engr.ccny.cuny.edu/zheng/pub/. [8] E. Cipollone, F. Cuomo, S. Della Luna, U. Monaco and F. Vacirca, "Topology Characterization and Performance Analysis of IEEE 802.15.4 Multi-Sink Wireless Sensor Networks", Med-Hoc-Net 2007, Corfu, Greece, June 13–15, pp. 196-203, 2007. [9] C. Buratti, F. Cuomo, S. Della Luna, U. Monaco, J. Orriss and R. Verdone, "Optimum Tree-Based Topologies for Multi-Sink Wireless Sensor Networks Using IEEE 802.15.4", VTC 2007Spring, 22–25, Dublin, Ireland, pp. 131-134, April 2007. [10] C. Buratti, J. Orriss and R. Verdone, “On the Design of TreeBased Topologies for Multi-Sink Wireless Sensor Networks”, NEWCOM-ACORN Workshop, Vienna, 20-22 September 2006.
