IoT Based Tiny Energy Harvesting System And Scheduling Algorithm

The internet of things is known as the third information technology revolution following computers and Internet.In recent years,under the background of the diversified development of the world economy,the Internet of things technology is accelerating its transformation into real productivity and bring a new wave of the digital economy to the world.Among them,wireless sensors are welcomed by all walks of life because they can be easily deployed.However,its limited power restricts the working hours of the system,and manual replacement of the battery requires a lot of manpower and material resources,which seriously affects the user experience,A better wireless power solution is to collect energy from the environment,i.e.energy harvesting.There are various kinds of micro-energy in the environment,but unlike the continuous and stable supply of wired energy,the energy harvested from the environment will be strongly heterogenous due to various factors,that is,different devices at different times and locations.There will be a huge fluctuation in the energy intensity.It is very challenging to efficiently utilize the energy with strong heterogeneity.Dynamic adjustment of the duty cycling is a widely used method to extend the working time of the system,but the existing study rarely considers the impact of the charging efficiency on the system.Energy sharing is an emerging technique to address energy limitation.,but few researchers consider the energy sharing under multi-hop conditions.Based on the above issues,this paper is mainly in the following aspects:(1)This thesis focuses on the effects of charging efficiency and harvesting energy heterogeneity on energy utilization,and designs a random duty cycling strategy to increase the utilization of collected energy.The part considers the randomness of the harvesting energy to establish a random duty cycle model,and then analyzes the three cases: offline case,online case,and correlation case,then design corresponding algorithms.Finally,the effects of charging efficiency,heterogeneity,and energy harvesting probability on system performance are analyzed,Evaluation on our real energy-harvesting system shows that the offline algorithm has the best performance,the online algorithm is closer to the actual situation,and the correlated algorithm performance is vulnerable to the influence of the heterogeneity.(2)To solve the problem of remainder energy unbalances in the network,this thesis proposes a more easily implemented multi-hop energy sharing strategy,MESS,which improves the system's energy balance.For different factors affecting the remainder energy of the device,this paper mainly considers two situations: static situation and dynamic situation,and design corresponding algorithms: static energy sharing algorithm(SESA)and dynamic energy sharing algorithm(DESA).The simulation results show that MESS can greatly improve fairness of energy consumption of whole network with a little energy consumption.(3)This thesis designs a feasible solar energy scheduling system based the Internet of Things.The system uses solar panels to provides energy to the Telos B which can collect the environment information,such as ambient temperature and humidity.The system can also achieve scheduling and management of harvesting energy and stored energy,and provide a testbed for the algorithm implementation.