| Cognitive wireless sensor network is a new type of intelligent wireless radiocommunication network. Combination of sensor networks and cognitive radiotechnology solve the contradiction between high-speed communication requirementsand the shortage of radio spectrum resources in WSNs, and provides an effectivesolution for wireless communication. However, when the cognitive radio technologybrings some convenience to us, it also brings additional challenges. Sensor nodes incognitive wireless sensor networks can detect idle spectrum resources in theenvironment that can be used to transmit data, which makes the wireless sensornetwork link capacity changes at any time. Therefore, it is a problem that needs to besolved for cognitive wireless sensor networks to improve the time-varying velocitydistribution and save the limited energy of nodes efficiently. Additionally, in order topromote the application of wireless sensor networks, it is an important research focusto improve the data processing speed of sensor nodes and ensure the accuracy of theresults for cognitive wireless sensor networks.Based on the full understanding of the present research status on cognitivewireless sensor network energy-efficient cross-layer optimization at home and abroad,we in-depth study network throughput, network lifetime of inter-layer optimizationand algorithms in WSNs by joint transport layer, network layer, MAC layer andphysical layer. The main research work is as follows:1) Focusing on the problem that increasingly tensions wireless spectrum resourcelimits the development potential of wireless sensor network, this paper introduces anew wireless cognitive technology that is also a combination of wireless sensornetworks and cognitive radio technology, which not only can improve the freeunlicensed spectrum resource utilization, but also ease the unlicensed spectrumshortage problem. However, this new network is facing a characteristic oftime-varying channel capacity and limited energy in traditional WSNs, which is caused by dynamic spectrum resources.Therefore, we study a utility optimization problem for joint rate allocation andnetwork life with constraints of time-varying spectrum and limited energy. In addition,we develop a distributed random gradient-based algorithm and prove the convergenceits performance. Without knowing the probability distribution of authorized spectrumstate, this algorithm just needs to know the available spectrum stage of the currenttime. The simulation results not only demonstrate the convergence of the algorithm,but also show cognitive radio technology and multi-path routing choices can improvethe effectiveness of the joint rate allocation and network lifetime optimization.2) On the assumption that there is no interference to authorized users, sensornodes transfer data by using the idle authorized spectrum resources that are perceivedby cognitive radio. This not only can improve spectrum resource utilization, but alsoimprove network throughput. Since the authorized user appears randomly, thetransmission link capacity of sensor node also change. However, the practicalapplication put forward higher requirements to the real time for WSNs, which requirethe sending rate quickly convergence to an optimal value along with the change oflink’s state. With a disadvantage of slow convergence and step-size selections, thispaper solve the problem of energy-efficient cross-layer optimization by using theprimal-dual scaled gradient algorithm according to the actual requirement. Under thesame conditions, we compare the traditional dual gradient algorithm and theprimal-dual scaled gradient algorithm. It is shown by simulations that the primal-dualscaled gradient algorithms can quickly converge to the global optimum intime-varying network environment with a speed, which are three orders of magnitudefaster than the primal-dual scaled gradient algorithm. |
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