Research On Channel Assignment In Wireless Sensor Networks

With the development of Wireless Sensor Networks (WSNs), there are more and more applications of WSNs emerging. This must result in that many WSNs with different functions coexist in the same area. Since the sensor node density is already very high in WSNs, the trend of coexistence is doomed to significantly increase the sensor node density of an area and thus aggravate the communication interference between sensor nodes. Therefore, traditional single channel communication protocols cannot handle this increasing interference, and introducing multi-channel techniques and designing channel assignment algorithms become a necessity to reduce the interference.Through there are a great deal of solid research on channel assignment in wireless networks, there are new challenges encountered by channel assignment research in WSNs, i.e., sensor nodes have limited energy, limited capacity of computing and storing, and limited communication ability. Therefore, channel assignment algorithms should be designed to have low energy consumption, high efficiency of reducing interference, and low complexity. This thesis has the following work to overcome these challenges:This thesis proposed a high efficient static assignment algorithm. Through static assignment brings little extra overhead, it usually results in low efficiency of reducing interference. A static assignment repeated game was constructed in this thesis, and a game based static channel assignment algorithm (GBCA) was designed according to the best response dynamics. GBCA was first designed in the case of tree-routing structure networks, and then extended to general routing structure networks. Theoretical analysis revealed that GBCA can converge into a Nash Equilibrium in polynomial time, and also bounded the sub-optimality of GBCA. The simulation results showed that GBCA got very good network performance in terms of throughput, delivery ratio, channel access delay, and energy consumption, which means that GBCA achieved high efficiency of reducing interference.This thesis also designed a light-weight dynamic assignment algorithm. In general, dynamic assignment has high efficiency of reducing interference, but it needs lots of information exchanges between sensor nodes, which will result in large extra overhead to the networks. In this thesis, a dynamic assignment repeated game was constructed, and a regret matching based dynamic channel assignment algorithm (RMCA) was designed. RMCA was proved to converge into a Correlated Equilibrium. The simulation results revealed that RMCA achieved very good performance of reducing extra overhead, and satisfactory network performance. Real experiments were also conducted to evaluate RMCA, and the results indicated RMCA can get good performance in real world.