Wireless sensor network is one of the wireless network which consists of many sensor nodes placed in a particular region for a certain specific functionality. Sensor nodes are generally small and have limited memory, processing resources, energy and lifetime. Limitations of sensor nodes on the energy side can affect network performance such as network lifetime and delay on the event detection. There are many methods that have been done to increase network lifetime, one of which is Clustered Shortest Geopath Routing protocal (CSGP). CSGP is a method to increase network lifetime, this method  the development of the shortest geopath routing protocol (SGP) and Low Energy Adaptive Clustering Hierarcy (LEACH). the cluster formation is based on the geographic information of the network while the cluster head selection is based on the position of the node closest to the clusters center point and the data sensing  obtained by sensor node will be sent to the cluster head which will forward them to the sink using SGP. However, this method has the limitation of cluster head selection which is not optimal and the density of network transmission on the SGP line so that the energy consumption of nodes in the path is greater than other sensor nodes.

 

Therefore, this research proposes a schema selection and turn of cluster head to determine adaptive path and data aggregation to reduce the number of network transmission. Implementation of the stages will be performed in the simulation  environment by utilizing SIDNet SWANS which is a simulator that runs over the JiST-SWANS simulator.

 

Cluster head selection scheme will be accomplished by selecting some parameters followed by evaluations so as to get the best results.Thereafter, from selected cluster head will be tested turn cluster head to find adaptive path. Furthermore, data aggregation is performed by cluster head while the aggregation process is done based on data priority level. High priority data (P1) will be sent directly to sink using SGP, whereas data with medium and low priority will be temporarily accommodated in the cluster head to certain limit. Data with medium priority (P2) until the data collected data is equal to 10, then data with low priority (P3) will be collected until the data collected is equal to 20.

 

The evaluation is  carried out 3 times then the data obtained will be turned to average and the benchmarking will be achieved using the comparison method. The network lifetime is the parameter which is analysed throughout the evaluations (second) where the  longer the first node is dead then the better while delay event detection is then opposite. The comparison method with CSGP without data aggregation and SGP without data aggregation. Table 1 depicts the results from different evaluations generated using CSGP method with data aggegation (CSGP-APA) and the comparison method.

Table 1. comparison of test result

Method

CSGP-APA

CSGP-WAPA

SGP-WAPA

Evaluation

1

2

3

avg

1

2

3

avg

1

2

3

avg

Network Lifetime

2635

2633

2640

2636

2223

2193

2218

2211.3

1159

1160

1792

1370.3

Event Detection Delay

P1

72

72

72

72

97

97

96

96.6

98

99

101

99.3

P2

83

83

83

83

105

105

105

105

103

105

111

106.3

P3

77

77

77

77

106

106

106

106

176

105

96

125.6

 

The results shown in table 1 that the CSGP method with data aggregegation (CSGP-APA) has the highest lifetime network compared with the comparison method as shown in Figure 1. Then, figure 2  shows that the CSGP with data aggregation has the smallest delay event detection value compared with the other methods. This happens since with data aggregation the number of network transmission can be reduced without affecting or ignoring data with high priority.

The results CSGP with adaptive path and aggregation data method  proved to increase network lifetime and decrease delay event detection compared to CSGP without data aggregation and SGP without data aggregation. Further research is required to handle the hole conditions in the sgp line.