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Due to the limited energy of the sensor nodes, the unreasonable clustering routing algorithm will cause node premature death and low utilization of energy efficiency in wireless sensor network (WSN). In Adaptive Thresholdsensitive Energy Efficient Network (APTEEN), the assignments of the cluster head (CH) are much heavier than other nodes. The CH unbalanced energy dissipation between nodes that make them die prematurely. Ant colony algorithm can avoid this problem, so this paper presents a double cluster heads Adaptive Threshold-sensitive Energy Efficient Network based on ant colony (ADCAPTEEN). ADCAPTEEN optimizes the cluster head election method compared with APTEEN. It suggests that one master cluster head (MCH) and one vice cluster head (VCH) will be selected in each cluster. The double cluster heads (DCH) can co-work on data collection, fusion, transition, etc. To make routes more stable and energy efficient, this paper proposes a Multiple Adaptive Threshold-sensitive Energy Efficient Network based on Ant-colony (AMAPTEEN). It is the optimization of ADCAPTEEN. And CH selects intermediate node (IM_node) multiple times with ant colony algorithm per round in each cluster, and this way forms multiple route transmission data. Simulation in OPNET proves that compared with APTEEN, ADCAPTEEN reduces energy dissipation, improves node survival rate, and extends network life cycle. AMAPTEEN delays the time of node death, balances energy consumption, and extends network lifetime further operating in the same settings compared with ADCAPTEEN. The proposed two algorithms have good scalability, and they are suitable for large-scale network.

In order to improve the energy efficiency of cognitive wireless sensor network, this paper introduces threshold-sensitive energy efficient sensor network (TEEN) routing protocol into cognitive wireless sensor network. To make routing and spectrum more stable, this paper presents advanced threshold-sensitive energy efficient sensor network (A-TEEN), which is the optimization of TEEN. A-TEEN optimized the cluster head election method compared with TEEN. Simulation result shows that compared with low-energy adaptive clustering hierarchy, TEEN increases the energy efficiency and extends life cycle of cognitive wireless sensor network. A-TEEN improves the energy efficiency and lifetime further operating in the same settings compared with the regular TEEN.

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