Adaptive Resource Allocation Algorithms For Data And Energy Integrated Networks Supporting Internet of Things

According to the forecast, there are around 2.1 billion IoT devices connected to the network by 2022. The rapidly increased IoT devices bring enormous pressure to the energy management work as most of them are battery-powered gadgets. What’s more, in some specific scenarios, the IoT nodes are fitted in some extreme environment. For example, a large-scale IoT pressure sensor system is deployed underneath the floor to detect people moving across the floor. A density-viscosity sensor is deployed inside the fermenting vat to discriminate variations in density and viscosity for monitoring the wine fermentation. A strain distribution wireless sensor for detecting the crack formation of the bridge is deployed underneath the bridge and attached near the welded part of the steel. It is difficult for people to have an access to the extreme environment. Hence, the energy management work, namely, replacing batteries for the rapidly increased IoT sensors in the extreme environment brings more challenges. In order to reduce the frequency of changing batteries, the thesis proposes a self-management Data and Energy Integrated Network (DEIN) system, which designs a stable and controllable ambient RF resource to charge the battery-less IoT wireless devices. It embraces an adaptive energy management mechanism for automatically maintaining the energy level of the battery-less IoT wireless devices, which always keeps the devices within a workable voltage range that is from 2.9 to 4.0 volts. Based on the DEIN system, RF energy transmission is achieved by transmitting the designed packets with enhanced transmission power. However, it partly occupies the bandwidth which was only used for wireless information transmission. Hence, a scheduling cycle mechanism is proposed in the thesis for organizing the RF energy and wireless information transmission in separate time slots. In addition, a bandwidth allocation algorithm is proposed to minimize the bandwidth for RF energy transmission in order to maximize the throughput of wireless information. To harvest the RF energy, the RF-to-DC energy conversion is essential at the receiver side. According to the existing technologies, the hardware design of the RF-to-DC energy converter is normally realized by the voltage rectifier which is structured by multiple Schottky diodes and capacitors. Research proves that a maximum of 84% RF-to-DC conversion efficiency is obtained by comparing a variety of different wireless band for transmitting RF energy. Furthermore, there is energy loss in the air during transmitting the RF energy to the receiver. Moreover, the circuital loss happens when the harvested energy is utilized by electronic components. Hence, how to improve the efficiency of RF energy utilization is considered in the thesis. According to the scenario proposed in the thesis, the harvested energy is mainly consumed for uplink transmission. a resource allocation algorithm is proposed to minimize the system’s energy consumption per bit of uplink data. It works out the optimal transmission power for RF energy as well as the bandwidth allocated for RF energy and wireless information transmission. Referring to the existing RF energy transmission and harvesting application on the market, the Powercast uses the supercapacitor to preserve the harvested RF energy. Due to the lack of self-control energy management mechanism for the embedded sensor, the harvested energy is consumed quickly, and the system has to keep transmitting RF energy. Existing jobs have proposed energy-saving methods for IoT wireless devices such as how to put them in sleep mode and how to reduce transmission power. However,they are not adaptive, and that would be an issue for a practical application. In the thesis, an energy-saving algorithm is designed to adaptively manage the transmission power of the device for uplink data transmission. The algorithm balances the trade-off between the transmission power and the packet loss rate. It finds the optimal transmission power to minimize the average energy cost for uplink data transmission, which saves the harvested energy to reduce the frequency of RF energy transmission to free more bandwidth for wireless information.