A Novel Shortcut Tree Routing Method Based on Load-balanced in ZigBee Wireless Sensor Networks

International Journal of Information and Computer Science (IJICS) Volume 4, 2015 doi: 10.14355/ijics.2015.04.010

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A Novel Shortcut Tree Routing Method Based on Load-balanced in ZigBee Wireless Sensor Networks Li Zhixin1, Zhu Zhangqing*2, Xin Bo3 Department of Control and System Engineering, Nanjing University, No.22. Hankou Road, Nanjing 210093, China lizhixinsuccess@163.com; *2zzq-hf@163.com; 3xinbo@nju.edu.cn

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Abstract ZigBee Tree Routing has two major problems; the first problem is its more hop-counts as compared to the reactive routing and another one is the hotspot problem. In this paper, we propose an improved routing algorithm called Load-balanced Shortcut Tree Routing (LSTR) which utilizes the information of neighbors within two hops. In LSTR, the intermediate or source node can find the optimal next hop node which has the smallest remaining hop-count to the destination within its transmission range. In the process of data transmitting, node’s depth, node’s residual energy and energy threshold are taken into account. The simulation result shows that this algorithm can transmit data efficiently, balance the energy of network and prolong network’s lifetime.

Keywords ZigBee Network; Tree Routing Algorithm; 2-hop Neighbor

Introduction ZigBee/IEEE 802.15.4 is a global hardware and software standard designed for Wireless Sensor Networks (WSN) which is composed of a large number of low-powered, self-organizing and small size sensor devices, called sensor nodes, which are capable of collecting, storing, processing environmental information, and communicating with neighboring nodes [1]. The nodes can be deployed on the ground, in the air, under water, on bodies, in vehicles, and inside buildings. The monitoring area may be inaccessible terrains or harsh environment, which needs the sensor network possessing cooperative and self-heal capabilities. On the other hand, the ZigBee WSN must have robust routing path which can enhance the reliability of the network. There are two kinds of routing protocol in Zigbee WSN, cluster tree routing and the reactive routing protocol which is derived from AODVjr. The Cluster Tree Routing is widely used in many resource-limited devices and applications, since it does not require any routing table and route discovery overhead to send a packet to the destination. However, the tree routing cannot provide the optimal routing path because of the fundamental limitation that a packet must follow the tree topology. Moreover, because the nodes closer to the coordinator need to transport larger quantity of data and their energy would be worn out quickly and the energy of network will be unbalanced. This is also called “hotspot problem”.

ZigBee reactive routing protocol provides the optimal routing path for the arbitrary source and destination pair through flooding the Route Request Command Frame (RREQ) in the network [2]. It requires the route discovery processing for each communication pair, so the route discovery overhead and the memory consumption proportionally increases with the number of traffic sessions increases. Moreover, the flooding mechanism wastes a large amount of bandwidth and device energy, and might cause a “broadcast storm” problem [3].

Relate Work For the ZigBee standard, there have been several significant methods and thoughts proposed to solve the problems of ZigBee tree routing and AODV routing. NTR is a routing strategy proposed by L. Dan, Q.Zhihong[4, 5]. This protocol was firstly tried to find the common neighbor nodes between the source and destination by using the

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neighbor table; then it used a proposed strategy to select the neighbor that was nearest to the destination as the next hop. In the literatures[6, 7], the hops from source node to destination node can be calculated by using hierarchical tree algorithm. Based on this idea, if a node could obtain two nodes’ addresses, it could calculate the hops between two nodes. The introduction of 1-hop neighbor table provides the network addresses of the surrounding nodes. The hops to the destination can be separately calculated based on these address information.

According to the comparison of these hops, the source or intermediate node determines the optimal path to the destination in a larger scope. Then the Shortcut Tree Routing (STR) protocol is proposed in [6], which uses the 1hop neighbor table to find the optimal next hop node that has the smallest remaining hop counts to the destination.

The routing strategy of Enhanced Tree Routing protocol (ETR) [8] is similar to STR, with the difference that ETR defines tree index as a tool to simplify the route calculations. In the above protocols, nodes try to find shortcut paths by searching the neighbor table and selecting the neighbor which is the nearest to the destination as the next hop. Although, using shorter paths can reduce energy consumption, the approaches don’t consider energybalancing problem of the nodes and load-balancing problem of the network. On the other hand, although they have mentioned to use 1-hop neighbor to improve Tree Routing in [7, 8], the scope of 1-hop neighbor is very limited as compared to 2-hop neighbor, and the effect is not obvious. Another approach that considers the load balancing in the ZigBee network is introduced in [9, 10]. This improved routing protocol is based on the energybalancing and includes an optimization algorithm to make sure that the routing is locally optimal. The algorithm uses the residual energy of nodes to avoid selecting nodes with low residual energy. Although it concentrates on balancing energy consumption in the network to improve network lifetime, it doesn’t consider hop counts and routing cost as an optimization criterion.

The paper is organized as follows. Section III discusses hierarchical tree routing. Section IV gives a detailed description of load-balanced shortcut tree routing protocol. In section V, we evaluate the performance. Finally, in section VI, we present the paper concludes and the work for the next-phase.

Hierarchical Tree Routing The network address assignment in the ZigBee specification includes hierarchical tree address allocation scheme which is defined in ZigBee2007 and stochastic address allocation scheme which is defined in ZigBeePro[11].

A hierarchical tree network starts from a ZigBee coordinator acting as the root of the tree. A ZigBee coordinator or router can act as a parent device and accept association from other devices in the network. A device connected to a parent device is known as a child device[12]. The messages intended for a child can be routed through its parent. A ZigBee end device can act only as a child because it lacks the routing capability. The network depth is defined as the minimum number of hops to reach the ZigBee coordinator[13]. The ZigBee coordinator itself has a depth of zero. Application developers are allowed to use their own address allocation method. When the ZigBee coordinator starts establishing the network, if the nwkUseTreeAddrAlloc attribute is set to TRUE, the coordinator will use the default distributed addressing scheme. In the distributed addressing, the ZigBee coordinator provides each potential parent with a sub-block of network addresses. The parent will assign these addresses to its children.

The ZigBee coordinator determines the maximum number of children allowed for each parent. If the nwkUseTreeAddrAlloc is set to FALSE, the APL layer will provide user-defined addressing to the NWK layer. The default address allocation simply uses the depth and maximum number of children to allocate the addresses. The parameters that affect the address allocation are the following: Lm: The network maximum depth (nwkMaxDepth).

Cm: The maximum number of children a parent can accept (nwkMaxChildren). Rm: The maximum number of routing-capable children a parent can accept (nwkMaxRouters ). d: The depth of a device in a network [14].

To determine the address of the remaining devices, a simple function Cskip(d) is introduced:

1 + Cm × (Lm − d − 1)  Cskip (d) 1 + Cm − Rm − Cm × Rm Lm − d − 1  1 − Rm 

,if Rm = 1 (1)

, otherwise

The address of each end device and router is assigned using the following equations:

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= Ak Aparent + Cskip (d) • (k − 1) + 1

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(1 ≤ k ≤ Rm) if node k is Router

A = Aparent + Cskip (d) • Rm + n (1 ≤ n ≤ Cm-Rm) node n is Endevice n

(2) (3)

For a ZigBee router with address Am at depth d, the destination with address Ad is a descendant if:

Am < Ad < Am + Cskip (d − 1)

(4)

When the destination is a descendant of the receiving device, the address of the next hop node is:

if A d is child of A m ,  Ad  An =   Ad − Am − 1  otherwise.  Am + 1 +  Cskip (d)  Cskip (d)   

(5)

Hierarchical tree routing is based on the block address allocation mechanism, so each device has an address space to distribute to their children[15]. When a device has no capability of route discovery, it simply follows the hierarchical tree by comparing the destination address. Tree routing algorithm uses address calculation of two nodes to determine the relationship between parent and child, and choose next hop. The algorithm just uses two addresses to identify a jump, and it may have the problem that the path may not be optimal, as Fig.1 shows:

d

s

FIG.1 EXAMPLE OF HIERARCHIAL TREE ROUTING

Improved Algorithm Designing Our objective is to provide a near optimal routing path like the reactive routing protocol as well as to maintain the advantages of cluster tree routing such as no route discovery overhead and little memory consumption for the routing table. In contrary to the cluster tree routing which only uses tree links connecting the parent and child node, LSTR exploits the neighbor nodes by focusing that there exist the neighbor nodes shortcutting the tree routing path in the mesh topology.

The main point of LSTR is: firstly, we use the beacon frame to finish the establishment of 2-hop neighbor table. The table is defined as follow: id

nbrAddr1

nbrAddr2

LQI

Epower

depth

nbrAddr1 is 1-hop neighbor’ s address nbrAddr2 is 2-hop neighbor’ s address Link Quality Indicator (LQI) indicates the quality of the received signal and the result should be represented as an integer ranging from 0 to 255.

Epower is the residual energy value of neighbor node.

Routers set up 2-hop neighbor table by broadcasting beacon frame in cycles. In receiving the other neighbors’ beacon frame, the node extracts the information to join its 2-hop neighbor table. We assume that each node has an up-to-date 2-hop neighbor table.

We set an energy threshold value Q, on the basis of residual energy state of the sensor node. When node residual energy is smaller than Q, it means the battery energy of this node is not sufficient, in the process of data transmitting, this node should be avoided.

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Assume that the initial energy value of node is E, the energy threshold equation is:

Q=

1 1 × E× ×∂ t di + 1

(6)

t means the network operation time, di is the depth of i, ∂ is a coefficient, which is used to balance the speed that the threshold decreases which comes from the increase of the depth of the network.

Secondly, after finishing the establishment of neighbor table, node could use it to relay the data packet, choose one of neighbor nodes as the next hop node when the remaining tree hops to the destination can be reduced. Using the address information which is provided by 2-hop neighbor table, node could determine the optimal path in a larger scope, and generally will reduce the overhead of relaying the data packet. Specific algorithm is as follow: 1) If the destination is the node’s 1-hop neighbor, send directly to destination node.

2) If the destiantion is the descendant or parent node, according to the tree algorithm routing to send.

3) If it does not fit the above two kinds of circumstances, we compute the remaining tree hops from an arbitrary source to a destination using ZigBee address hierarchy and tree structure as discussed in previous section. In other words, the remaining tree hops can be calculated using tree levels of source node, destination node, and their common ancestor node, because the packet from the source node goes up to the common ancestor, which contains an address of the destination, and goes down to the destination in Cluster Tree Routing. Table 1 describes the algorithm to find ancestors’ network address at each tree level.

TABLE 1 ALGORITHM TO FIND ANCESTORS AT EACH TREE LEVEL

Find_Ancestors(devAddr) Input: devAddr- device’s network address Output: level(devAddr)- devAddr’s tree level A(devAddr)- nerwork address of the devAddr’s ancestors at each level A(devAddr,0) = 0 for i=0 to Lm -1 if (A(devAddr, i) =devAddr) return i, A(devAddr) endif rtrOrder = (devAddr-A(devAddr,i)-1)/Cskip[i] if(rtrOrder < Rm) A(devAddr, i+1)= A(devAddr,i)+ Cskip[i]. rtrOrder +1 else if(rtrOrder >Rm) A(devAddr, i+1)= devAddr endif endfor

The reason that we find the common ancestors of the device is we want to compute the tree routing cost between a source and a destination. The relevant definitions are as follows: level (u) : tree level of node u.

A(u): {A(u,i) |A(u,i) is the network address of u’s ancestor at tree level i, i <level(u)}.

LCA(s, d): the lowest common ancestor between source node s and destination d. Since the network address of device is contained in its ancestors’ address space in lower tree levels, we can find the rtrOrder, which is the router capable child order k. Since the routing hops from S to LCA(s, d) and from LCA(s, d) to D can be calculated using different of tree levels, the tree routing cost from s to d can be calculated by the equation “level(s) + level(d) 2level(LCA(s, d))”.

Table 2 describes the load-balanced shortcut tree routing (LSTR) algorithm to find the next hop node’s network address. When node’s residual energy Epower < Q, it means the battery energy of this node is not sufficient, the node’s working state is unstable, and should be avoided in the process of transmission. We can weigh the importance of each criterion based on our goal by using coefficients α, β and γ (0≤α, β, γ≤1, α+ β+ γ=1). In LSTR, a

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source or an intermediate node selects the next hop node having the smallest TotalCost to the destination. This procedure can effectively balance the network traffic by evenly distributing the workload of intermediate nodes and by relieving the congestion of nodes. Also, it can reduce delay by reducing the hop counts. Furthermore, LSTR improves the packet delivery ratio, because it selects link with higher quality. The routing path selection in LSTR is decided by individual node in a distributed manner, and LSTR is fully compatible with the ZigBee standard that applies the different routing strategies according to each node’s status. The LSTR algorithm basically follows ZigBee Tree Routing, but chooses one of 1-hop neighboring nodes or 2-hop nodes as the next hop node when the remaining hops to the destination can be reduced.

TABLE 2 LOAD-BALANCED SHORTCUT TREE ROUTING ALGORITHM

Find_NextHopAddr(dstAddr)

Input : dstAddr − network address of the destination Output : nextHopAddr − next hop address for the destination Initialize the min RouteCost and min TotalCost with ∞ Level ( dstAddr ) , A ( dstAddr ) = Find _ Ancestor ( dstAddr ) for each ( nbrAddr1 ni , nbrAddr 2 nij in nbrTable ) if ( nij == dstAddr & & Epower ≥ Q ) { min RouteCost = 2 TotalCost =

2

a

∑ ( depth k =1

+

k

b Epowerk

+

γ LQI k

) + min RouteCost

if (TotalCost ≤ min TotalCost ) min TotalCost = TotalCost nextHopAddr = nbrAddr1 ni end if } else { level ( ni ) , A ( ni ) = Find _ Ancestors ( ni ) level ( LCA ) = 0 while(level ( LCA) ≤ min(level (dstAddr ), level (ni )) & & A(dstAddr , level ( LCA) == A(ni , level ( LCA))) ++level(LCA) end while = level (dstAddr ) + level (ni ) - 2 × level ( LCA) nbrRouterCost if (nbrRouterCost ≤ min RouterCost ) min RouterCost = nbrRouteCost end if TotalCost =

2

a

∑ ( depth k =1

k

+

b Epowerk

+

γ LQI k

) + min RouteCost

if (TotalCost ≤ min TotalCost & & Epower ≥ Q ) min TotalCost = TotalCost min nextHopAddr = ni end if } end for each

Performance Evaluation In this evaluation, we conduct simulations to evaluate the performance of the proposed LSTR in comparison to the CTR and ZAODV. We compare router cost, energy consumption, packet delivery ratio, network’s lifetime and dead nodes of the three protocols. We use NS-2 as the tool of simulation experiment. The simulated network consists of 100 nodes randomly distributed in the area of 100×100m2. Node’s communication radius is 30 m. The

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coordinator is located at the center of the region and the neighbor table of each node is allowed to keep up to 14 neighbors. The network parameters are Cm = 4, Rm = 4, Lm = 7. The transmission of a packet from a randomly chosen source node to a randomly chosen destination node is called an event. For each event, the evaluation parameters are measured. The events happened sequentially that each event starts after the previous one finishes.

Therefore, there is no packet collision or channel contention during packet transmission. This allows us to focus on the performance of routing protocols by examining the hop-counts, packet delivery ratio and nodes’ life time. The simulation is based on 100 different randomly generated networks and the results are averaged.

TABLE 3 SIMULATION RESULT

CTR

ZAODV

LSTR

Packet delivery ratio

0.69

0.55

0.78

Average hop count

5.88 hops

4.03hops

4.29hops

Energy consumption

0.17

0.08

0.03

Average dead nodes Number

23.4

25.6

19.1

Network lifetime

117events

203events

312events

Table 3 compares the simulation results of CTR, ZAODV and LSTR protocol. By comparing the results, we can find out that the proposed LSTR outperforms others in terms of reliability, number of hop counts, lifetime and loadbalancing. LSTR is able to balance the energy consumption of the nodes in the network more effectively than the other two protocols. As the performance evaluation shows, the LSTR routing reduces more than 25 percent of the routing cost needed for the traditional ZigBee Tree Routing and the routing cost performance is as good as ZAODV routing.

For a random destination, the performance of the proposed algorithm depends on how many useful neighbor nodes are stored in the neighbor table, which can be optimized by applying the proper parameters of ZigBee node such as nwkMaxRouter, nwkMaxChildren,nwkMaxDepth,NWK_MAX_DEVICE_LIST of nodes. These parameters are configured in nwk_globals.c and nwk_globals.h in ZigBee protocol stack such as Z-Stack which is developed by Texas Instrument [15]. Therefore, we expect the proposed algorithm to be utilized in many ZigBee applications requiring both small memory capacity and high routing performance.

Conclusion In this paper, we have described the detour path problem, hotspot problem and load unbalanced problem of the Cluster Tree Routing. These are the fundamental problems of the traditional ZigBee tree routing protocols, which cause the overall network performance degradation. To overcome these problems, we propose LSTR that uses the 2-hop neighbor table. In LSTR, the intermediate or source node can find the optimal next hop node that has the smallest remaining hop count to the destination within its transmission range. In the process of data transmitting, node depth and node residual energy are taken into account, and the energy threshold Q is dynamically adjusted in time, trying to balance energy consumption of all nodes in the network. Simulation result shows that the LSTR can effectively avoid “hot spot” in the process of network running, transmit data more optimized, and prolong network’s lifetime. LSTR algorithm is based on proper functioning network. However, the monitoring area of ZigBee WSN may be inaccessible terrains or harsh environment, which needs the sensor networks possess cooperative and self-heal capabilities. In addition, some sensor nodes may fail due to various reasons such as power voltage changes, or environmental interference which will lead to the network partition. The next step is to research the recovery mechanism of ZigBee WSN. We plan to design a fault detection and recovery mechanism for the network based on localized tree and a recovery method for every ZigBee node.

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