| With the development of terminal technologies and the expansion of appli-cation domain,the value and requirements of IoT(Internet of Things)are both ascending.The efficient data collection is a reliable guarantee for the develop-ment and application of IoT.The rationality of energy utilization in resource-constrained IoT directly affects the reliability of data collection and reflects it as energy efficiency.Therefore,the optimization of energy efficiency during data collection in resource-constrained IoT is of great importance.However,the information in IoT services derive from vast and ever-changing natural en-vironments and complex areas of human activities.Thus,the energy efficiency is inevitably constrained by the environment,and even suffers from artificially negative effects.To address the issue,efficient and reliable energy efficiency optimization methods with the integration of advanced technologies need to be designed,which is valuable and challenging.This dissertation takes the energy-constrained IoT as the research background and energy efficiency optimization methods during data collection as the object of study.Practical network envi-ronments are taken into consideration with advanced expansion technologies.Efficient and reliable energy efficiency optimization methods are studied ana-lytically and mathematically.The main contributions of this dissertation are as follows:(1)An energy efficiency optimization method for scenarios with a trajectory-constrained sink node is proposed.Undirected Complete Graph enhances the description of network by representing the randomly distributed node sets with a matrix of grids.Benefited from the grid model,the hierarchical characteris-tic of the network topology is clearly seen.The analytical and computational complexities are decreased.Afterwards,a grid-based energy model is estab-lished and analyzed.It is concluded that the network energy efficiency is di-rectly related to the association of energy consumption equilibrium among static sinks and the energy consumption of all data nodes,and the node grid matching decision-making problem is extracted from it.The energy efficiency optimiza-tion method is named after DEON.The DEON issue is then formulated.A heuristic genetic algorithm with manual intervention for the optimal solution of DEON is designed.A grid-based route discovery algorithm is designed and verified to find the shortest paths for data reporting.DEON solves the problem of uneven energy consumption of nodes in hot spots in the existing methods by optimizing the matching relationship of grids.The NS-3 simulation verification results show that compared with existing methods,DEON achieves increases of 20%and 30%respectively in network lifetime optimization and static sink en-ergy efficiency optimization.Also,it shows a stable superiority in the network energy consumption efficiency,thus enhancing the network robustness.(2)An energy efficiency optimization method for SWIPT(Simultaneous Information and Power Transfer)network is proposed.The influence of SWIPT on energy efficiency is analyzed and a TS-SWIPT(time-switching SWIPT)op-timization method is proposed.Afterwards,the problem of allocation of nodes'energy-transfer time slots is extracted from the model,and it is pointed out that a reasonable node energy-transfer-slot allocation strategy can effectively use node redundancy energy to optimize network energy efficiency.Finally,an energy transfer time slot optimization method ETTO is proposed to mathemati-cally describe and model the time slot allocation problem.A centralized method is designed to analyze the energy consumption of nodes and a heuristic algo-rithm based on the node energy consumption distribution is designed to find a feasible solution to the ETTO optimization problem.ETTO solves the problem of unequal energy utilization in randomly deployed nodes and uses redundant energy to optimize the energy efficiency of important nodes.NS-3 simulation verification results show that compared with DEON,ETTO reduces the redun-dancy of the residual energy and optimizes the energy utilization rate.The total network life and data collection amount can be increased from 5%to 10%with-out requiring additional energy supply.(3)An energy efficiency optimization method suitable for DWET(Ded-icated Wireless Energy Transfer)scenarios is proposed.Firstly,the impact of DWET on the energy efficiency of IoT is analyzed,and an energy efficiency optimization model based on stationary RF energy transfer nodes is proposed.Afterwards,the problem of deployment decision of energy transfer nodes is ex-tracted from the model,and it is pointed out that the rationality of the deploy-ment strategy directly affects the effective utilization of the energy resources of energy transfer nodes.Then,the index GTI is defined to reflect the data node's demand level for energy replenishment.Then,the energy node deployment op-timization method DORN is proposed to mathematically describe and model the deployment decision problem.Finally,Markov Decision Process is used to describe the deployment,and Q Learning algorithm is used to train the deploy-ment.DORN enables the limited energy transfer resource to be efficiently and avoids the waste of energy transfer resources.The NS-3 simulation verifica-tion results show that compared with the random deployment strategy,DORN increases network lifetime and data collection amount by 15%,30%and 50%respectively with 5,10 and 15 RF nodes and optimizes the energy efficiency of shot-lived nodes.(4)A detection and energy efficiency optimization method towards ma-licious behaviors of nodes is proposed.Malicious behaviors during data col-lection are modeled,including information confusion,information interception and EEA(Energy Exhaustion Attack).Based on the network energy efficiency,a hybrid intrusion detection and energy efficiency optimization method named after HIEO is proposed.HIEO utilizes the behavior characteristics of nodes to detect and locate malicious nodes.Besides,HIEO uses a lightweight gateway selection algorithm to achieve distributed energy efficiency guarantee and op-timization,and reduces the negative effect of EEA on energy efficiency,The HIEO method utilizes data packet metadata to avoid extra energy consumption of data nodes caused by malicious behavior detection mechanism.The NS-3 simulation verification results show that HIEO detects and locates malicious nodes with extremely high accuracy and reduces EEA's damage to network en-ergy efficiency.Benefited from HIEO,the real-time data collection efficiency and network energy efficiency are guaranteed and optimized indirectly. |
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