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This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network(WSN) which aims to balance the node power consumption and prolong the network lifetime as long as possible.Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol.On the one hand,considering each node’s local information such as energy level,distance to base station and local density,we use fuzzy logic system to determine one node’s chance of becoming cluster head and estimate the corresponding competence radius.On the other hand,adaptive max-min ant colony optimization is used to construct energy-aware inter-cluster routing between cluster heads and base station(BS),which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent.The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy(LEACH) and energy efficient unequal clustering(EEUC).
This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network (WSN) which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol.On the one hand, considering each node’s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node’s chance of becoming cluster head and estimate the corresponding competence radius.On the other hand, adaptive max-min ant colony optimization is used to construct energy-aware inter-cluster routing between cluster heads and base station (BS), which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering algorithm has more superior perform ance than other methods such as low energy adaptive clustering hierarchy (LEACH) and energy efficient unequal clustering (EEUC).