An analytical model for the aggregate throughput of IEEE 802.11ah networks under the restricted access window mechanism

The IEEE 802.11ah is an amendment to the IEEE 802.11 standard to support the growth of the Internet of Things (IoT). One of its main novelties is the restricted access window (RAW), which is a channel access feature designed to reduce channel contention by dividing stations into RAW groups. Each RAW group is further divided into RAW slots, and stations only attempt channel access during the RAW slot they were assigned to. In this paper, we propose a discrete-time Markov chain model to evaluate the average aggregate throughput of IEEE 802.11ah networks using the RAW mechanism under saturated traffic and ideal channel conditions. The proposed analytical model describes the behavior of an active station within its assigned RAW slot. A key aspect of the model is the consideration of the event of RAW slot time completion during a station’s backoff operation. We study the average aggregate network throughput for various numbers of RAW slots and stations in the network. The numerical results derived from our analytical model are compared to computer simulations based on an IEEE 802.11ah model developed for the ns-3 simulator by other researchers, and its performance is also compared to two other analytical models proposed in the literature. The presented results indicate that the proposed analytical model reaches the closest agreement with independently-derived computer simulations

Computational efficiency maximization for UAV-assisted MEC network with energy harvesting in disaster scenarios

Wireless networks are expected to provide unlimited connectivity to an increasing number of heterogeneous devices. Future wireless networks (sixth-generation (6G)) will accomplish this in three-dimensional (3D) space by combining terrestrial and aerial networks. However, effective resource optimization and standardization in future wireless networks are challenging because of massive resource-constrained devices, diverse quality-of-service (QoS) requirements, and a high density of heterogeneous devices. Recently, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks are considered a potential candidate to provide effective and efficient solutions for disaster management in terms of disaster monitoring, forecasting, in-time response, and situation awareness. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. In this thesis, we consider a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed integer non-linear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power splitting architecture splits the source power for communication and EH. We jointly optimize user association, the transmission power of UE, task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt k-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading with EH, and no-offloading no-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumptio

Distributed Batteryless Access Control for Data and Energy Integrated Networks: Modeling and Performance Analysis

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices. Battery-powered and batteryless wireless devices with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a three-dimensional (3D) Markov chain, we model the operating behaviors of batteryless wireless devices and an HAP in a DEIN. Apart from backoff operations of devices, the 3D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. Wireless devices consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided