zbr-m: a new zigbee routing · pdf filezbr-m: a new zigbee routing protocol 17 aggregation....

International Journal of Computer Science and Applications, Technomathematics Research Foundation Vol. 10, No. 2, pp. 15 – 32, 2013

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ZBR-M: A NEW ZIGBEE ROUTING PROTOCOL

MOHAMED KASRAOUI

IRSEEM / ESIGELEC Saint-Etienne-du-Rouvray, France

[email protected]

ADNANE CABANI


[email protected]

JOSEPH MOUZNA


[email protected]

Various wireless technologies have been designed to assist with the resource management in a typical supply chain. Wireless communication systems could be a solution easily deployable, helping to improve the supply chain management and to reduce the overall cost of the system. For logistic applications, the ZigBee technology can be used in order to manage and track goods. This technology based on the IEEE 802.15.4 standard is, actually, used for the design of wireless sensor network (WSN) architecture. Several applications using this type of networks require the interconnection of a considerable number of nodes. That’s why an efficient routing protocol should be deployed by taking into account WSNs constraints. The purpose of this work is to study the routing mechanisms already defined by the ZigBee standard and to improve the existing ZBR protocol by proposing a new protocol called ZBR-M which highlights the scalability.

Keywords: ZigBee, IEEE 802.15.4, Hierarchical Tree Routing protocol, NS2, end-to-end delay.

1. Introduction

Wireless Sensor Networks (WSNs) designate a very bright solution for a large number of application scenarios and especially for the logistics applications. Many researchers have focused in this field and some of them are interested on deploying sensor networks in supply chain management [Evers and Havinga (2007)] that gave rise to use ZigBee standard. Zigbee is a new Wireless sensor network technology based on the IEEE 802.15.4 standard [ZigBee Alliance (2004)]. Its use in Wireless Sensor Networks (WSNs) has aroused a great interest in the research community and its deployment will be increasing

Mohamed KASRAOUI, Adnane CABANI and Joseph MOUZNA 16

in the near future. The lifetime and the scalability are the most frequent issues in its deployment. In order to increase the efficiency and scalability of communication, we have improved the ZigBee routing protocol in a large scale network. The new proposed protocol called ZBR-M computes the shortest path between source and destination nodes by requesting the neighbor’s nodes instead of following the tree topology. The result of first simulations shows that ZBR-M protocol reduces the end-to-end delay average and increases the packet delivery ratio compared to the basic routing protocol. However, it consumes more energy caused by the high number of broadcasted messages. In order to reduce the energy consumption, we propose to pass the criticality as a parameter in the message type. Hence, and we can select many routing protocols (AODV, ZBR or ZBR-M) for each one. We choose to organize this paper into three sections. We begin in the first section by an overview of routing protocols in wireless sensor and ZigBee networks. We reserve the second section for a comparative study of ZigBee routing protocols proposed in the literature. In the third section, we present our approach and simulation results analysis. In the last section, we conclude our work an present some perspectives.

2. Overview

In this section, we present a state of the art of routing protocols proposed in the literature to carry out routing analysis in sensor networks. The data transmission in a wireless sensor network can be done in two ways: i) A direct transmission method is possible when nodes are close to each other since the received signal is not too attenuated; ii) The sending by routing protocol based on intermediate nodes, subject to the weakening of the signals. The nodes act both as a client and server, relaying the packets to ensure their final destination.

2.1. Classification of Routing Protocols According to the Network Structure

In WSNs, routing protocols are classified into three types such as flat routing protocol, hierarchical routing protocol and geographic routing protocol.

2.1.1. Flat routing protocol

It is difficult to assign global identifiers to each node of a sensor network given the large number of deployed nodes [Akkaya and Younis (2005a)]. This absence of a global addressing scheme with the random deployment of sensor nodes makes it hard to select a specific set of sensor nodes that to be queried. Therefore, data is generally transmitted from each sensor node deployed in the region with significant redundancy [Akkaya and Younis (2005b)]. This redundancy penalizes in terms of energy consumption. Thus, this thinking leads to the use of a routing for the selection of a group of nodes and data

ZBR-M: A New Zigbee Routing Protocol

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aggregation. The recipient requests by its target regions and waits to receive data from sensors located in the selected region.

2.1.2. Hierarchical routing protocol

These protocols are adopted to allow the system to cover a wider area without degradation of service. The principal aim of hierarchical routing is to reduce energy consumption and routing cost of sensor nodes by making them within a cluster in order to perform aggregation and reduce the number of messages transmitted to the base station [Kim et al. (2007)]. This routing is based primarily on the gateway nodes. In fact, ordinary nodes know that if the recipient is not in their immediate vicinity, they just send the request to the gateway. In turn, it will forward the request to the target node.

2.1.3. Geographic routing protocol

Routing protocols use location-based information service for discovery of routing and data transmission [Li et al. (2009)]. They allow the directional transmission of information to avoid the flooded data across the network. Therefore, the routing cost will be reduced and the routing algorithm will be more optimized. In addition, the use of network topology based on location information of nodes provides easily control and management of network. The disadvantage of these routing protocols is that each node must know the locations of other nodes.

2.2. Scalability in WSNs

In this section, we introduce some routing protocols for sensor networks and we examine the scaling of the network protocol with increasing the number of nodes. In the case of SPIN (Sensor Protocol for Information via Negotiation), the negotiation introduces the network overhead and each node disseminates its message descriptor to all other nodes [Kulik et al. (2002)]. Similarly to Directed Diffusion protocol [Intanagonwiwat et al. (2000)], many control messages have to be exchanged which increase the routing cost. Whatever these two routing protocols have proven their performance for small networks, so, they have to be suitable in a large network without they induce an overload penalizing. Rumor routing protocol [Braginsky and Estrin (2002)] based on the detection of specific events can only work when the number of events is low. However, if we want to expand the network, the number of events will increase significantly which is disabling. For the TTDD (Two Tier Data Dissemination) [Luo et al. (2003)], the construction and the permanent maintenance of the grid structure for each base station import a considerable excess of traffic which depend of the network size. In addition, the protocol supports that nodes know their exact location which requires a reliable tracking system. The problem of reliability of the tracking system persists not only for the algorithm TTDD but also for any protocol which requires geographic information. This type of protocols allows a directional transmission which broadens the area used by the data and it requires that each node has knowledge of the position of other sensor nodes. This


knowledge only remains valid for networks containing a large number of sensors due to the limited capacity of the nodes. In conclusion, hierarchical protocols are the best suited to cover a wider such as LEACH [inzelman et al. (2000)], TEEN [Manjeshwar and Grawal (2001)] and HEED [Younis and Fahmy (2004)]. Due to the dynamic clustering and the data aggregation techniques used by these protocols [Villas et al. (2011)], these techniques maintain an equitable distribution of energy consumption into network by sharing the role of cluster-head between the different nodes. Table 1 shows some examples that use these techniques.

Table 1. Routing protocols for sensor networks.

Routing protocols

Type QoS data aggregation

SPIN FLAT - Directed Broadcast

FLAT -

EAR FLAT - - CADR FLAT - - COUGAR FLAT - LEACH Hierarchical - PEGAGIS Hierarchical - AQUIRE FLAT - - TEEN Hierarchical - MCF FLAT - - HEED Hierarchical - - MECN Geographic - - GAF Geographic - - TTDD Hierarchical - - GEAR Geographic - - Rumor FLAT - SPEED Geographic - GBR FLAT - SAR Geographic -

3. ZigBee Network Routing

By default, ZigBee uses a combination of two routing protocols. One is hierarchical tree routing (ZBR) protocol and another is Ad Hoc On-demand Distance vector (AODV) protocol.

3.1. AODV (Ad hoc On Demand Distance Vector) Protocol

AODV is a reactive protocol, i.e., the network stays silent until a connection is requested. If a node wants to communicate with another one, it broadcasts a request to its neighbors who re-route the message and safeguard the node from which they received the message [M.A.N.W.G (2003)]. If a node receives a message and it has an entry corresponding to the destination in its routing table, it returns a RREP through the reverse-path to the


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requesting node. So, the source sends its data through this path to the destination with the minimum number of hops.

3.2. ZBR (ZigBee Routing Protocol)

In a tree topology, the role of the ZigBee coordinator is to establish the network and configure all parameters. During the establishment phase, the ZigBee coordinator determines the maximum number of nodes, which are (Cm) and (Rm). Whereas Cm is the maximum number of children and ‘Rm’ is the maximum number of routers a parent may have as children. In addition, each node has an attribute called "depth" which is the minimum number of hops to reach the coordinator using only parent-child link. The ZigBee coordinator itself has a depth of zero and it determines the maximum tree depth of the network (Lm). The path is constructed as follows: the source node checks if the recipient is one of its descendants because it knows the network address in the block of its child node. Otherwise, the source sends the data to its parent. The parent node also sends the data to its parent until we get to a parent node of the destination node. The downlink of information is provided through a technique for determining the successor based on the ZigBee router address, depth and address of the node [Bidai et al. (2011)].

3.3. Other routing protocols

Many researchers enhanced ZigBee routing protocols basing on ZBR and AODV protocols. Among of them, Authors in [Nefzi and Song (2007)] proposed a new routing protocol called M-HTR. It is based on the ZigBee hierarchical addressing scheme to find the shortest path from the source to the destination. In addition, authors in [Ha et .al (2007)] presented an enhanced hierarchical routing protocol for ZigBee called EHRP. All these approaches present an improvement of Zigbee Routing protocol in terms of efficiency and reliability to find the routing path. However, they consume more energy than the basic ZBR because of the additional computational algorithms. In other hand, Yaze et .al in [Yaze et .al (2012)] and Xu et .al in [Xu et .al (2010)] present an improvement algorithms of AODV called respectively E-AODV and AODV_D. They reduce the overall energy consumption and extend the network life comparing to the basic AODV. Among all these protocols, M-HTR is the most similar to the one developed by our approach. In section V, we will present a comparison between them.

4. Performance Evaluation of ZigBee Sensor Networks

4.1. Routing protocol comparison

In this section, a full scan of hierarchical routing protocols was performed and compared with AODV. So, this comparative study will provide at the same time the performance of all routing mechanisms of the standard Zigbee. The largest network considered is described in this Section. It is formed by one ZigBee coordinator (ZC) and 200 ZigBee


routers (ZR). Since simple nodes don't participate in routing, they aren’t considered in the simulation. The network is fully connected and each node hears only its direct neighbors. The maximum depth (Lm) is equal to 6 and the maximum number of children by parent (Cm) is 7. In the beginning of simulation, the idea was to extend the network and increasing the depth of the tree. Then, we studied the send data to the node "0". This allowed discerning evolutionary of end-to-end delay.

Fig. 1. Network layout The important simulations parameters used to compare between ZBR and AODV protocols are summarized in the following table.

Table 2. Parameter values.

Technology Zigbee Protocol ZBR / AODV Scenario Dimension (x,y) 80*80 Buffer node size 50 packets Propagation TwoRayGround Data packet size 80 bytes Number of nodes 7-->200 MAC Protocol IEEE 802.15.4


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4.2. Simulation results

4.2.1. Performance Study of End-to-end Delay

In terms of the average "end-to-end delay", the behavior of AODV routing protocol is similar to the Hierarchical routing in the first depth. The process of route discovery doesn’t use time to establish the path. Only a RREQ and a RREP are exchanged before forwarding packets of data. However, the difference between end-to-end delays increases as the number of nodes is increased. If the depth of the transmitter increases, the number of control messages exchanged during the search path to the destination will be amplified. This could introduce an additional burden that leads collisions in the network and additional delays. Figure 2 shows the simulation results of the ZBR in comparison to the AODV protocol in terms of average end-to-end delay.

Fig. 2. End-to-end delay as a function of nodes depth In conclusion, to get a long-range network (a great depth of the tree), the basic hierarchical routing is more efficient because the shortest path to the root is generally that which follows the Parent-Child Relationship in a Tree Network. On the other side, AODV must discover the route before sending data. Thus, the time delay is the most important factor to performance degradation of a network using the AODV routing protocol.

4.2.2. Packet Delivery Ratio (PDR)

In this section we analyze the performance of two protocols (ZBR and AODV) according to Packet Delivery Ratio.


Fig. 3. Packet Delivery Ratio as a function of nodes depth

Figure 3 shows that Packet Delivery Ratio decreases when increasing network size. Increasing the duration of simulation, from a certain threshold (the death of the first node), the delivery rate drops down. This is due to the death of nodes near the destination stretched by other nodes.

4.2.3. Network Life-Span (Lifetime)

The evaluation metric in this section is the network life-span [Chang and Tassiulas (2000)]. All nodes have a limited energy. The traffic circulates between two randomly chosen nodes. For both protocols, we followed the evolution of the network until the time of the first node failure. Table 3 shows that the network using hierarchical routing base is less durable. The path is pre-established; it can exchange more traffic in a shorter time.

Table 3. The time of the first node failure.

Protocol ZBR AODV Death time (sec) 84.39 sec 89.40 sec

The traffic flow is interrupted by the failure of the first node. In fact, the excess energy caused by the self-organization phase remains negligible. The important factor in the energy depletion caused by the static nature of hierarchical routing protocol that route only through parent-child relationship. So, one path is used by all traffic which rapidly depletes the residual energy of some nodes compared to others. It's that after a while, the routing to the destination is no longer possible. All descendant nodes of the death node become isolated from the rest of the network. This is a real handicap of hierarchical routing core.

4.3. Synthesis

The simulation results should be taken as a relevant indication of the behavior of these two routing protocols and not as an accurate representation of its behavior in real environments, given several constraints simulation namely the size of the field nodes, distribution the number of nodes, the type of traffic, the simulation time, etc. We have analyzed the delay and delivery packet ratio of two routing protocol in ZigBee network. It has been shown that ZBR provides shorter average of end to end


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delay and performs better in terms of delivery packet ratio. The good delay performance of ZBR led us to think about improving it to support real time applications. In fact, the worst case and energy consumption analysis showed that ZBR has a great potential of improvements. So, the next work presented in this paper is to ameliorate the ZBR routing.

5. Improvement of the ZBR Algorithm

5.1. Modified Algorithm

In this section, we present our proposal to improve the tree routing. Let us consider a source node "S" with an identifier "SourceNodeID" which wants to transmit a packet "P" to a destination node "D" with an identifier "FinalDstNodeID". Let Depth (x) the depth of the node "x" in the tree and V(x) denotes the neighbor list of node "x". In the case of hierarchical routing, arriving at a router node "R" with an identifier myNodeID, R computes the Cskip using Equation (2) and "P" will be treated as follows

Algorithm 1 : Description of ZBR protocolIfDisadescendantofnodeR

then

userulegivenbythisequation 1 tofindtheNextHop

1 1

1

∗

Else

1 1 1

1 Cm Rm CmRm1 Rm

2

If we restrict ourselves to the ZigBee routing, and a node belonging to the path that goes down, all its descendants can no longer send their data to the base station. In addition,


after analyzing the behavior of the protocol for some simulated cases, we found that the packet must travel to the first common parent between the transmitter and receiver in order to descend into the tree and reach its target and even though the nodes are close to each other in depth. So we thought of a horizontal exploration of the tree which increases the probability of finding an alternative route to the destination with the shortest jumps without necessarily need to borrow parent-child relationship while keeping the profit from the simplicity of the routing hierarchy [Qiu et al. (2007)]. The modified ZBR algorithm is as follows

Algorithm 2 : Description of ZBR-M protocolIfDisadescendantofnodeR

then

userulegivenbythisequation 1 tofindtheNextHop

1 1

1

∗

else ifDisadescendantofN V x using 2 thenNext‐Hop Nelse

The principle of modified routing algorithm is: The transmitting node checks if the destination is one of its descendants. If so, it sends it according to the basic hierarchical routing. If this is not the case, it sends requests to all of its one-hop neighbors of the same depth in the tree after estimate the round-trip delay of the message and initiates the timestamp. Each neighbor receiving the message verifies even if the recipient is one of its descendants. If so, the neighbor sends an acknowledgment to the sender and takes care of routing the message. Otherwise, the neighbor drops the message. At the sending node, if the timer expires without receiving anything, the message is transmitted to the parent. Comparing ZBR-M to the M-HTR [Nefzi.B et .al (2007)] protocol, each router broadcasts a request ’Rq’ to all its neighbors to check if the destination is one of its


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descendants. ZBR-M is considered as a cooperative computation system between all neighbors in order to determine the less cost path reaching the destination. Whereas in M-HTR, each router uses the neighbor’s tree table and it computes all the related equations used to search the destination node. Hence, M-HTR is considered more expensive than ZBR-M in terms of the routing overhead. Contrary to routers using M-HTR which require the update of neighbors table, in ZBR-M based on neighbors requests, routers do not need to use the tree table. This is suitable to our logistic applications which deploy high dynamic networks. Moreover, when several neighbors reply the ZBR-M router by saying “D is one descendant”, the latter sends the data to the first replying neighbor as the next hop. This makes our algorithm faster and avoids waiting all the replies. In difference with M-HTR router that should choose the neighbor with the highest depth and it should wait all the equation results. We assume that ZBR-M provides better performance than M-HTR in terms of energy consumption and memory storage. Unlike our approach, M-HTR did not satisfy both scalability and mobility. To prove our assumption, it would be necessary to simulate M-HTR and compare it with our protocol.

5.2. Example

As an example, let us consider the network illustrated in figure 4 where node 7 sends data to node 5.

Fig. 4. Illustration example of the modified ZBR Node 7 checks that node 5 is not a descendant. So it sends requests to its neighbors (8) check if node 5 is one of their descendants. If it does not receive any reply before an expiration time, it sends data to its parent (node 3). The latter checks if the destination is


a descendant of its neighbors (0,2,4) by broadcasting one request. Then, it sends data to the first replying neighbor (0 or 2) without comparing the different paths. All the requests are sent hop by hop and they could cause potential flooding and traffic problems. In order to prevent flooding and network collisions, we delimit the requests perimeter by the neighbors at one hop.

5.3. Performance Analysis

In this section, we present an empirical investigation on the performance of ZBR-M in ZigBee wireless communication. Before physical implementation, we started by the simulation to validate our proposed algorithm ZBR-M. Our simulation had looked at the ZigBee performance of two sizes. In first time, we started with one Coordinator, 6 Rotors and 6 End-Devices and all traffic has been routed from end devices to the network coordinator. In second time, we increased the number of nodes to 200 and we analyzed the worst cases.

Fig. 5. Network layout Every node in network layout generates one data packet every 1 minute, starting at a randomly picked initial packet generation time. The important simulations parameters, that were included, are summarized in the following table.


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Table 4. Parameter values.

Technology Zigbee Protocol ZBR / ZBR-M

MAC/PHY 802.15.4 Channel Wireless channel

Propagation TwoRayGround Topology 100*100

Number of nodes 7-100-200

5.4. Simulation Results

We have developed the ZBR-M under OPNET simulator [OPNET (2005)] using the same network and parameters as in last section. 50 simulations are run and in each randomly chosen node, traffic goes between this node and the coordinator. We realized different simulation scenarios, among of them we used a network composed of 7 nodes and another one composed of 200 nodes. The average results of the comparison between the basic ZBR and ZBR-M are shown in tables 5 and 6.

Table 5. Experimental results for 7 nodes.

ZBR ZBR-M Delivery ratio (%) 99, 87 99,88

End-to-End Delay (ms) 8 5 Energy consumption

(mJ) 0,449 1,003

Table 6. Experimental results for 200 nodes.

ZBR ZBR-M Delivery ratio (%) 48,93 51,22

End-to-End Delay (ms) 68.5 64.7 Energy consumption

(mJ) 1,207 2,177

As it is expected, ZBR-M performance is better in terms of end to end delay and delivery packet ratio. It succeeds to eliminate the worst cases and to achieve a low end to end delay. Moreover, ZBR-M improves the basic algorithm but never degrade it. Due to the important number of packets used to discover routes, ZBR-M provides an additional cost in energy consumption. So, we have to maximize the battery life in our next work.

5.5. Proposing a design for communication protocol

The aim of this study is to reduce the energy consumption involved in the routing of packets used in ZBR-M [Kasraoui et al. (2012)]. In this algorithm, classifying the


messages to different types is the main task. To do this, the message type may be given with a variable that can determine the type of information originated from the application layer. We propose to classify the messages into three types based on the criticality. The first type is the highly critical message when a node has a very important data to send, in this case, it selects the AODV routing protocol. The second type is the normal message used when a node has a classic data to send, like the GPS location, temperature values, etc. The third type is the least important message assigned to other messages. Before sending a DATA message, the sender selects the type of ad-hoc routing protocol for wireless nodes (AODV, ZBR or ZBR-M) as shown in figure 6.

Fig. 6. The use of routing protocols depending on the message type

At application layer, each node can choose between AODV and hierarchical protocol to route the data into the network.

No

2 3

Network layer

Application layer

Select the type of the message

If HCM

ZBR-M ZBR

AODV

Type of message

Highly critical messages (HCM)

Normal message

Least important message

Messages


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For the first message type, a node chooses the AODV that uses low number of messages to conserve the capacity of the network and ensure the reception of the message by the destination nodes. However it requires more time to establish a connection and it introduces an additional cost in terms of data packet delivery ratio, routing overhead and delay.

Fig. 7. Behavior of different routing protocols At the network layer, each node can choose between ZBR and ZBR-M. If it looks at the message type, it selects the ZBR-M to route the normal messages in order to make a short delay and it selects the ZBR to route the least important message using less energy. The figure 7 shows the difference between the routing protocols (AODV, ZBR, ZBR-M) in terms of energy consumption and the end-to-end delay. For each network composed of k-nodes, the combination of the three routing protocols and mostly the use of ZBR to route the normal message are the best ways to reduce the energy consumption.

6. Conclusion

This paper provides an overview of routing protocols in wireless sensor networks (WSNs) and discusses how scalability, energy consumption and delays present important constraints for these types of networks. We were interested specifically in routing mechanism defined by the ZigBee standard. We conducted simulations to evaluate the


performance of routing protocol proposed by ZigBee Alliance while comparing it to On-Demand Routing protocol to identify the characteristics of hierarchical routing as well as its defects. The simulation results showed that the hierarchical routing core of this delay and delivery rate allowing for availability of service regardless of the size of the network did not stand for long because of its static nature. We proposed a new algorithm called ZBR-M which allows a horizontal exploration of the tree and more vertical exploration of the links between parent and child nodes. It increases the likelihood of finding an alternate path from the destination without achieving a common parent node. However, it introduces an additional energy cost compared to the basic hierarchical routing. To reduce the energy consumption introduced by ZBR-M, we proposed to classify the messages into three types based on the criticality. For each message type, so we select the appropriate routing protocol (AODV, ZBR or ZBR-M) in order to use less energy and reduce the routing cost. This combination of protocols provides scalability and adaptation to the changes in the network topology.

Acknowledgement(s)

This work is funded by European Regional Development Fund (ERDF) and by Haute-Normandie Region.

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