Research On Efficient Routing And Scheduling Algorithms In Internet Of Things

Internet of Things(IoT)is considered to be the third revolution in the development of the information technology industry following the computer and the Internet.Sensor-based communication components are widely deployed to monitor real-time physical phenomena and send the information to network to enable control of entities in the physical world.In large-scale IoT,densely distributed sensor nodes will sense massive amounts of data,and massive data transmission will lead to great energy consumption.Therefore,how to reduce energy consumption is a key issue in IoT.On the other hand,for novel control applications in IoT,the timeliness of information plays a crucial role.The more fresh the information,the better for precise control of such IoT applications.Therefore,how to maintain the freshness of information is another key issue in IoT.In order to solve the above two key problems,the main contribution of this thesis is given as follows:(1)Considering the energy efficiency in large-scale IoT,an energy-efficient clustering routing algorithm is proposed for load balancing.Considering the non-uniform traffic distribution,an uneven cluster formation scheme is designed for load balancing and energy efficiency.Moreover,a distributed cluster head rotation mechanism is proposed to balance energy consumption within each cluster.As for long distance transmission,a dynamic multi-hop routing algorithm among cluster heads is designed based on a proposed distance-and-energy-aware cost function to avoid the energy hole problem.Simulation results show that the performance of our proposed routing algorithm is competitive in terms of network lifetime,throughput and energy efficiency.(2)For effective monitoring and control of entities in physical world,a joint optimization of data sampling and link scheduling algorithm is proposed for data freshness.We consider a multi-hop IoT with multiple data flows and investigate an age minimization problem by jointly optimizing data sampling at the sources and link scheduling along the path of each data flow.The formulated problem falls in the form of an integer nonlinear program.To solve the problem efficiently,an efficient scheduling algorithm with scalibility and low complexity is proposed.Compared with traditional delay-oriented scheduling,simulation results show that AoI-oriented scheduling performs better in delivering data in a timely manner.Moreover,we demonstrate that by jointly optimizing link scheduling and sampling time among data sources,network-level data freshness can be further improved.