We design several experiments with C language to evaluate the performance of our multicast
protocol. First, we build a ZigBee network. The network resides in a 35 × 35 m2square
re-gion, where hundreds of sensor nodes are randomly deployed. The full energy of each node
is approximately 100 joules. The transmission power, transmission rate, and transmission
power of each node are set to 6 meters, 250 kbps, and 50 mW , respectively. The multicast
group members are randomly selected among the nodes in the network. Besides, since
Zig-Bee defines that the multicast group members are physically separated by a hop distance of
no more than MaxNonMemberRadius, we set the parameter to 5 in our simulation
experi-ments. We adopt IEEE 802.15.4 MAC protocol with unslotted CSMA/CA algorithm as our
MAC protocol. Next, we demonstrates our protocol performance against ZigBee multicast
in each experiment.
Fig. 1 shows the impact of the network size on the total number of packets incurred by
our protocol and ZigBee. The network size varies from 100 to 500 nodes and we evaluate
the total number of packets transmitted by our protocol against ZigBee. We randomly select
10 nodes as multicast members. In each multicasting, we will randomly choose one node as
multicast source among these 10 members. Apparently, our protocol produces much fewer
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Figure 1: Comparison on the number of packets against ZigBee
packets than ZigBee multicast regardless of the network size. In addition, as the network size
increases, the number of packets incurred by ZigBee multicast increase greatly from roughly
250 to 650 packets, while the number of packets incurred by our protocol increase slightly
from 40 to 70 packets. It is not difficult to understand the results because ZigBee
multi-cast exploits regional flooding to deliver the multimulti-cast packet. Each member node floods the
multicast packet to the sub-network bounded by MaxNonMemberRadius hops. Moreover,
without any acknowledgement mechanism, each node receiving the multicast packet blindly
broadcasts the packet for 3 times. Therefore, ZigBee multicast incurs a large number of
packets during the multicast packet propagation. The larger network size causes more nodes
to participate in the packet propagation, each of which performs the regional flooding and
the blind retransmissions and thereby largely increases the number of packets transmitted.
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Figure 2: Comparison on transmission latency against ZigBee
On the other hand, our protocol takes advantage of the coverage-over-cost ratio to reduce the
number of forwarders. Besides, our protocol provides an overhear-based acknowledgement
mechanism to further reduce the packets resulting from retransmissions. Thus, our
proto-col incurs much fewer packets and, when the network size grows, the increased amount of
packets is little.
We also evaluate the latency of the packet propagation. The transmission latency is
measured as the time elapsed when the multicast packet initiated by the multicast source is
received by all the group members. As shown in Fig. 2, both of the latencies for ZigBee
multicast and our protocol are decreasing as the network size increases. With the growth of
the network size, the number of each node’s neighbors increases, and some of them might
be capable of reaching more members within MaxNonMemberRadius hops. Therefore, the
total length of the packet delivery path might be shorter, so the latency decreases. The latency
of our protocol is shorter than ZigBee multicast. As we mentioned above, ZigBee multicast
exploits regional flooding to deliver the multicast packets and relies on blind retransmissions
as an acknowledgement mechanism. These two reasons lead to extremely heavy traffic, so
the probability of packet collision increases. If a node transmits a packet, and the packet
collides with others, the node has to wait until its retransmission time arrives to retransmit
the packet, and the probability of packet collision is still high at that time. In contrast, there
is no such problem in our protocol since the traffic produced by our protocol is much lighter.
Therefore, the latency for our protocol is shorter than ZigBee multicast.
Fig. 3 and Fig. 4 show the impact of the group size on the total number of packets and
transmission latency. We fix the network size to 300 nodes, and vary the group size from 10
to 50 members. It is not surprising to see that as the group size increases, both the number of
packets and transmission latency increase because it needs more forwarders and takes more
time to deliver the multicast packet to the group members when the network size grows.
Therefore, both the number of packets and the latency are increasing. In spite of the growth
of the network size, our protocol still outperforms ZigBee in the number of packets and the
transmission latency.
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Figure 3: Impact of group size on the number of packets
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Figure 4: Impact of group size on the transmission latency
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Figure 5: Comparison on network lifetime when the first node death occurs
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Figure 6: Comparison on the number of successful multicasting when first node death occurs
22 24
Residual energy after 8000 multicasts
x axis
y axis Residual energy(Joule)
Figure 7: Residual energy of each node after 8000 multicasts in ZigBee
65
Residual energy after 8000 multicasts
x axis
y axis Residual energy(Joule)
Figure 8: Residual energy of each node after 8000 multicasts in our protocol
Next, we conduct several experiments to verify the load balance effect of our protocol.
We deploy 100 nodes in the network with 10 members randomly chosen. First, we evaluate
the time elapsed and the number of packets successfully delivered to the members when the
first node having exhausted its energy appears, i.e., the first node death. In each multicast
session, the multicast source is randomly chosen among the members, and the multicast
sessions are initiated after the previous one has ended. As shown in Fig. 5, the network
lifetime of our protocol is more than 2.78 times longer than that of ZigBee multicast, when
the first node death occurs. The number of multicast sessions which successfully deliver
the multicast packets to all members until the first node death occurs is shown in Fig. 6.
Our protocol successfully delivers 1.79 times more packets to the members. Fig. 7 and
Fig. 8 show each node’s residual energy after 8000 multicasting under ZigBee multicast
and our protocol. The average residual energy of the nodes after 8000 ZigBee multicasts is
31.2 joules, while the average residual energy is 81.5 joules by using our protocol. Also,
the energy consumption is more balanced using our protocol. The results shown in Fig. 5 to
Fig. 8 prove the effectiveness of our idea of taking into account each node’s residual energy
when choosing forwarders. As a matter of fact, because the nodes with more residual energy
have higher probability to forward the packets, the packet delivery path dynamically adapts
0
Figure 9: Impact of link stability on the delivery ratio
to the instant network condition as the energy of each node depletes. As a result, the energy
consumption is evenly distributed among the nodes in the network, and not only the network
lifetime is extended but also the single-node-failure problem is avoided.
Finally, we study the reliability of our protocol. Similarly with the previous experiments
for load balance, we also deploy 100 nodes in the network with 10 members randomly
cho-sen. We evaluate the delivery ratio under different link stability. The delivery ratio is
mea-sured as the percentage of the number of multicast packets successfully delivered to all the
group members. The link stability represents the probability that a packet is successfully
received by the nodes within the senders’ communication range. As shown in Fig. 9, when
the link stability is greater than 90%, our protocol is able to achieve completely successful
delivery, while ZigBee only reaches 90% due to the heavy traffic caused by the regional
flooding and the blind retransmissions. Clearly, in both our protocol and ZigBee multicast,
as the link stability is getting more unstable, the delivery ratio is lower. The delivery ratio in
ZigBee multicast is much lower because it suffers from the ineffectiveness of the blind
re-transmission. We know that ZigBee multicast relies on the blind retransmissions to increase
its reliability. However, when the link is unstable, the blind retransmissions might not take
effect because they might not be successfully sent to the receivers. As you can see, when
the link stability is worse than 70%, the delivery ratio of ZigBee multicast drops to under
75%. The delivery ratio even drops to only 30% when there is a half chance that the packet
transmission fails. On the other hand, our protocol is able to achieve more than 85% delivery
ratio when the link stability is greater than 70%.
5 Conclusions
We have studied the energy-efficient multicast problem in WSNs. Due to the limited power
resource of sensor nodes, energy-efficient multicast is a critical issue in WSNs. ZigBee is a
cost-effective wireless networking solution with the features including low data-rates,
low-power consumption, security, and reliability. Although ZigBee is widely adopted in WSNs,
ZigBee multicast is energy-inefficient and unreliable. Many other approaches have been
pro-posed, but they fail to achieve energy efficiency and load balance at the same time. Moreover,
these proposed approaches do not support dynamic member joining and leaving. In this
pa-per, we present a ZigBee compatible, energy-efficient, load-balanced, and reliable multicast
protocol which supports dynamic member joining and leaving. Our protocol adopts a
prob-abilistic anycast mechanism to realize multicast communication. As the network topology
changes or the node’s energy depletes, our protocol can adapt to the instant network
condi-tion since it considers the coverage-over-cost ratio as well as the residual energy among the
candidate forwarders. Therefore, our protocol is able to achieve energy efficiency and load
balance at the same time. Simulation results show that our protocol provides longer network
lifetime and outperforms ZigBee in energy consumption, latency, and reliability.
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