In figure 5, we compared the total energy consumption under different numbers of sensor nodes (average arrival rate 0.05) over a mesh network. In this simulation, there are no certain data sources. Instead, every sensor node will sense data and forward it to the sink. The total energy consumption of LMAC is 28% of that of SMAC when the network size is small. However, when the network size is larger, the total energy consumption of LMAC is 59% of that of SMAC. The difference of energy consumption is smaller because there are too many data packets flowing in the network. Thus, no matter when a sensor node wakes up, there are a lot of data packets ready to be sent in buffer. In figure 6, only the leaf nodes on the left and bottom edges will sense data over a grid network topology. In this setup, we can clearly see that the energy consumption of LMAC is much less than that of SMAC and PMAC. The total energy consumption of LMAC is 23% of that of SMAC and 50% of that of PMAC when the traffic load is small. In figure7, we show that the energy consumption of the busiest sensor node in the network. In figure 8, we show the total energy consumption under different arrival rate.
The energy consumption of LMAC is smaller more when the arrival rate is small. The
Sending Power 0.25 w
Table. 2. Simulation Setup Parameters
performance difference is more obvious when the source nodes are only leaf nodes on edges. In figure 11, we show the total energy consumption under different latency constraints. LMAC indeed consume less energy when the latency constraint is bigger.
But when the latency constraint is larger than a threshold, the total energy consumption of LMAC is fixed because the idle listening caused by inaccurate prediction no longer exists. Or the other restrictions such as buffer size, transmission rate will bound the minimal energy consumption.
Figure 5. Total energy consumption under different numbers of sensor nodes
Figure 6. Total energy consumption under different numbers of sensor nodes &
Figure 8. Total energy consumption under different arrival rates
Figure 7. Energy consumption of the busiest sensor node under different numbers of sensor nodes
Figure 9. Total energy consumption under different arrival rates & Source nodes on edge
Figure 10. Energy consumption of the busiest sensor node under different arrival rates
Figure 11. Total energy consumption under different latency constraint
Figure 12. States of latency constraint failures
PMAC SMAC
9 122.22% 243.43%
16 42.86% 155.82%
25 21.78% 92.73%
36 29.06% 84.18%
49 16.34% 68.73%
LMAC PMAC SMAC
0.001 0.0189 0.0460 0.2044 0.002 0.0235 0.0718 0.2079 0.005 0.0382 0.1161 0.2293 0.01 0.0558 0.1548 0.2546 0.02 0.1113 0.2250 0.2730
Table 3. Extra energy consumption compared to LMAC under different numbers of nodes
MAC Protocol Arrival
Rate
Table 4. Average Energy Consumption per packet under different arrival rates MAC Protocol
Number of Nodes
CHPATER 5
CONCLUSION AND FUTURE WORK
This paper considers the reasons causing idle listening and tries to avoid them by sacrificing latency to increase the arrival prediction accuracy. LMAC outperforms energy-consumption over SMAC and PMAC under a loose latency constraint and a light traffic load in the wireless sensor network environment. It can operate successfully in the high traffic load and the high latency constraint as well.
In a light traffic load scenario, prediction-based protocols and constant sleep wake-up schedule protocols will suffer more energy wastage penalty from idle listening due to the uncertainty of incoming data. LMAC prevents the uncertainty by increasing the length of sleep cycles to guarantee the existence of transmission when a sensor node wakes up. This method greatly reduces the total energy consumption. Moreover, in a loose latency constraint setup, LMAC can use the extra latency to increase the prediction accuracy while other protocols do not take latency constraint factor into consideration.
The most important future work in LMAC is to initiate Bulk parameter. In chapter 3, we present many initial options of Bulk parameters. However, these initial options for Bulk parameters can only be done manually. These methods try to please some specific
sensor nodes but they can not guarantee the option is optimal for the network topology.
We will try to find an algorithm to give every sensor node a weight that stands for its energy consumption rank.
An improved time synchronization is another work that needs to be done in the future. In LMAC, sensor nodes will sleep for a fixed time slot and then wake up. A
sensor node which has more than one child does not have the cooperative action of its children. What the children do is to compete for the access of medium. The future work is to solve this problem by either giving sensor nodes a cooperation scheme or trying to stagger the transmission like DMAC.
CHPATER 6 REFERENCE
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APPENDIX
Publication
Ang-His Lee; Ming-Hui Jing; Cheng-Yan Kao;
“LMAC An Energy-Latency Trade-off MAC Protocol for Wireless Sensor Networks”
International Symposium on Computer Science and its Applications, 2008