Study On Game Theory-based Spectrum Sharing Model In Wireless Sensor Network

As the use and scale of Wireless Sensor Network(WSN)has grown,the lack of transmission spectrum has become a key factor affecting WSN QoS.The current situation of utilizing spectrum resources is that most spectrum is assigned to licensed users(i.e.,primary users),leaving only a small part of unlicensed spectrum for public use.Unlicensed spectrum is exhausted by wireless communication users,while licensed spectrum is always idle.Therefore,to solve the problem of spectrum scarcity,an effective solution is licensed users share idle spectrum with WSN by Cognitive Radio(CR).A node or cluster head in WSN plays a role of a cognitive user(CU),sensing the surrounding licensed user,and then conducts the spectrum trading with them.Our research content is just the process of spectrum trading and to find a spectrum sharing scheme.For a centralized Wireless Sensor Network,scheme 1 proposes a spectrum sharing model based on Bertrand game and Multiple Knapsack problem.First,as a cognitive user,the base station plays a Bertrand game with the monopolists(the primary users)to gain spectrum access on behalf of the cluster heads.Then,the base station allocates the spectrum band to the cluster heads using the Ant Colony Optimization-based Multiple Knapsack problem(ACO-MKP).After obtaining the spectrum,each cluster head programs its nodes' spectrum according to a timing sequence.Simulation results show that the Bertrand game model is a good choice for determining spectrum pricing and that the ACO-MKP algorithm is an efficient way to allocate spectrum dynamically.Scheme 1 has additional requirements for spectrum supply and demand,network model,and users,they are limiting the application scenario.To obtain the practicability,adaptability,and compatibility,we propose scheme 2.With the expansion of WSN application environment and coverage area,sensor nodes or link structures are ofen heterogeneous.Considering a heterogeneous WSN(HWSN),scheme 2 proposes a strategic bargaining spectrum sharing model.The sensors of HWSN are discrepant and under different wireless environment,which leads to various signal-to-noise ratio(SNR)for the same or different licensed users.As cognitive user,sensor node bargains with licensed users about the spectrum price.In each round of bargaining,licensed users are allowed to adaptively adjust to the best spectrum price to maximize the profit.Then,each cognitive user makes the best response and informs licensed users of "bargain" and "warn".Through finitely many rounds of bargaining,this scheme can obtain a Nash bargaining solution(NBS),where all licensed and unlicensed users reach an agreement.The simulation results demonstrate that scheme 2 can quickly find a NBS and all players in the game prefer to be honest.Compared with existing schemes,scheme 2 outperforms existing schemes within a certain range,in terms of fairness and the trade success probability.