اﻟﮕﻮرﯾﺘﻢ ﺧﻮﺷﻪ ﺑﻨﺪي ﮔﺮهﻫﺎي ﺣﺴﮕﺮ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺗﺮاﮐﻢ ﮔﺮهﻫﺎ در ﺷﺒﮑﻪ ﻫﺎي ﺣﺴﮕﺮ ﺑﯽﺳﯿﻢ

Sensor Node Clustering Algorithm with Respect to Node Density in Wireless Sensor Networks

در روﯾﮑﺮدﻫﺎي ﻣﺒﺘﻨﯽ ﺑﺮ ﺧﻮﺷﻪﺑﻨﺪي در ﺷﺒﮑﻪﻫﺎي ﺣﺴﮕﺮ ﺑﯽﺳﯿﻢ، ﺳﺮﺧﻮﺷﻪﻫﺎي ﻧﺰدﯾﮏ ﺑﻪ ﮔﺮه ﺳﯿﻨﮏ 1ﻣﻌﻤﻮﻻً ﺑﺎ ﺗﺮاﻓﯿﮏ رﻟﻪ ﺑﺴﯿﺎر ﺑﯿﺸﺘﺮي روﺑﻪرو ﻣﯽﺷﻮﻧﺪ و ﺑﻨﺎﺑﺮاﯾﻦ ﺑﻪﺳﺮﻋﺖ اﻧﺮژي ﺧﻮد را از دﺳﺖ ﻣﯽدﻫﻨﺪ. ﺑﺮاي رﻓﻊ اﯾﻦ ﻣﺸﮑﻞ، روﯾﮑﺮدﻫﺎي ﺧﻮﺷﻪﺑﻨﺪي آﮔﺎه از ﻓﺎﺻﻠﻪ2، ﻣﺎﻧﻨﺪ اﻟﮕﻮرﯾﺘﻢ ﺧﻮﺷﻪﺑﻨﺪي EEUC3ﮐﻪ اﻧﺪازه ﺧﻮﺷﻪ را ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻓﺎﺻﻠﻪ ﺑﯿﻦ ﮔﺮه ﺳﯿﻨﮏ و ﻫﺮ ﺳﺮﺧﻮﺷﻪ ﺗﻨﻈﯿﻢ ﻣﯽﮐﻨﻨﺪ ﭘﯿﺸﻨﻬﺎد ﺷﺪه اﺳﺖ. ﺑﺎ اﯾﻦ ﺣﺎل، ﻃﻮل ﻋﻤﺮ ﺷﺒﮑﻪ ﺑﺎ اﺳﺘﻔﺎده از ﭼﻨﯿﻦ روﯾﮑﺮدﻫﺎﯾﯽ ﺑﺴﯿﺎر واﺑﺴﺘﻪ ﺑﻪ ﺗﻮزﯾﻊ ﮔﺮه ﻫﺎي ﺣﺴﮕﺮ ﻣﯽﺑﺎﺷﺪ؛ زﯾﺮا در ﺷﺒﮑﻪﻫﺎي ﺣﺴﮕﺮ ﺗﻮزﯾﻊ ﺷﺪه ﺗﺼﺎدﻓﯽ، روﯾﮑﺮدﻫﺎ ﺗﻀﻤﯿﻦ ﻧﻤﯽﮐﻨﻨﺪ ﮐﻪ ﻣﺼﺮف اﻧﺮژي ﺧﻮﺷﻪ ﻣﺘﻨﺎﺳﺐ ﺑﺎ اﻧﺪازه ﺧﻮﺷﻪ ﺑﺎﺷﺪ. ﺑﺮاي رﻓﻊ اﯾﻦ ﻣﺸﮑﻞ، ﻣﺎ ﯾﮏ روش ﺟﺪﯾﺪ ﺑﻪ ﻧﺎم اﻟﮕﻮرﯾﺘﻢ ﺧﻮﺷﻪﺑﻨﺪي ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﺗﻮزﯾﻊ ﮔﺮه ﻫﺎ ﭘﯿﺸﻨﻬﺎد ﻣﯽﮐﻨﯿﻢ ﮐﻪ ﻧﻪﺗﻨﻬﺎ آﮔﺎه از ﻓﺎﺻﻠﻪ اﺳﺖ ﺑﻠﮑﻪ آﮔﺎه از ﺗﺮاﮐﻢ ﮔﺮه ﻫﺎ ﻧﯿﺰ ﻣﯽﺑﺎﺷﺪ. در اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي ﻣﺎ 4)DBCA(، ﺧﻮﺷﻪﻫﺎ داراي ﮔﺮه ﻫﺎي ﻣﺤﺪودي ﻫﺴﺘﻨﺪ ﮐﻪ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻓﺎﺻﻠﻪ ﺑﯿﻦ ﮔﺮه ﺳﯿﻨﮏ و ﺳﺮﺧﻮﺷﻪ ﺗﻌﯿﯿﻦ ﻣﯽﺷﻮﻧﺪ. ﻧﺘﺎﯾﺞ ﺷﺒﯿﻪﺳﺎزي ﻧﺸﺎن ﻣﯽدﻫﺪ ﮐﻪ DBCA در ﺷﺮاﯾﻂ ﻣﺨﺘﻠﻒ ﻋﻤﻠﯿﺎﺗﯽ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻃﻮل ﻋﻤﺮ ﺷﺒﮑﻪ، 25 درﺻﺪ اﻟﯽ 45 درﺻﺪ ﮐﺎرآﻣﺪﺗﺮ از اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻗﺒﻠﯽ از ﻟﺤﺎظ ﻣﺼﺮف اﻧﺮژي ﻣﯽﺑﺎﺷﺪ.

In clustering algorithms for wireless sensor networks, cluster heads close to the sink node usually encounter much more relay traffic and therefore lose energy rapidly. To address this problem in wireless sensor networks, distance-aware clustering approaches such as EEUC that adjust the cluster size according to the distance between the sink node and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes because in randomly distributed sensor networks the approaches do not guarantee that the cluster energy consumption is commensurate with the cluster size. It might be necessary, for example, for sensors to be randomly distributed over the surveillance region (e.g., via aircraft). To solve this problem in wireless sensor networks, a new method called distribution based clustering algorithm (DBCA) was proposed in the present research which is not only aware of the distance but also the density of the sensor nodes. In DBCA, clusters have limited sensor nodes that are determined by the distance between the sink node and the cluster head. The simulation results show that DBCA is 25% to 45% more efficient than previous algorithms in terms of power consumption under different operating conditions.