Abstract
This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.
Highlights
IntroductionWith the rapid development of the Internet of Things (IoT), a growing number of sensor nodes are required to access wireless networks and arousing a large number of computation-intensive and latency-sensitive applications [1,2,3], which brings crucial challenges to resource-constraint devices
We focus on a simultaneous wireless information and power transfer (SWIPT)-aware fog computing system with power splitting (PS) receiver architecture
The mode selection associated with the positions of the sensor for fixed hybrid access point (HAP) and fog server (FS) is discussed, which shows that when the sensor is close to the HAP or the FS, the fog offloading mode is a better choice, but for the rest of positions, local computing should be selected in order to achieve a lower energy requirement at the sensor
Summary
With the rapid development of the Internet of Things (IoT), a growing number of sensor nodes are required to access wireless networks and arousing a large number of computation-intensive and latency-sensitive applications [1,2,3], which brings crucial challenges to resource-constraint devices. Optimally designing such a SWIPT-aware fog computing system faces some challenges, since to fully explore the potential performance of the system, the communication, the computation, and the energy resources have to be efficiently utilized together As these resources are coupled together, which is difficult to handle, to this end, we study a three-node system model, where a sensor harvests energy and receives information from a HAP through PS receiver architecture. The sensor is able to process the received information itself (local computing mode) or offload the task to the nearby FS (fog offloading mode) with the harvested energy For such a model, we desire to theoretically derive the inner relationships among the different parameters associated with different kind of resources, and some fundamental questions are going to be answered, e.g.,. Eu the time length of transmission frame the time length of energy harvesting in the local computing mode the time length of energy harvesting in the fog offloading mode the time used for local computing the time used for task offloading from the sensor to the FS the transmit power of the HAP the number of antennas at the HAP the RF signal symbol transmitted by the HAP the beamforming vector the noise received at the receiver the complex channel vector from the HAP to the sensor the power splitting factor in the local computing mode the power splitting factor in the fog offloading mode the energy conversion efficiency of the EH circuit the achievable information rate at the sensor the system frequency bandwidth the energy requirement for decoding per bit the achievable information rate associated with the offloading the transmit power at the sensor the receiver’s noise power the maximal available transmit power the number of logic operations per bit the complex-valued channel coefficient from the sensor to the FS the minimum information transmission rate requirement from the HAP to the sensor the maximum number of the operations per second at the sensor the harvested energy at the sensor the required energy for information decoding at the sensor the local computing energy requirement the energy required for task offloading at the sensor the total required energy at the sensor
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