Design of Relay Switching to Combat an Eavesdropper in IoT-NOMA Wireless Networks

Abstract

The requirements of low latency, low cost, less energy consumption, high flexibility, high network capacity, and high data safety are crucial challenges for future IoT wireless networks. Motivated by these challenges, this study deals with a novel design of green-cooperative IoT network, which employed coupled relays consisting of one IoT relay selected for forwarding signals to multiple IoT devices while another IoT relay transmitted jamming signals to an eavesdropper. For flexibility, all IoT nodes were powered by solar energy enough to sustain themselves, in order to consume less energy. To reach low latency, the study adopted the emerging non-orthogonal multiple access technique to serve multiple IoT devices simultaneously. Furthermore, the study adopted the simultaneous wireless information and power transfer technique which transmits wireless data for information processing and energy for energy harvesting. The study sketched a novel transmission block time period framework which plotted how a signal could travel via an individual IoT model. Maximizing the achievable bit-rate of IoT devices was considered to improve network capacity and data safety as well. Aiming at enhancing secrecy performance, a rest IoT relay played a role as a friendly jammer to transmit a jamming signal to an eavesdropper using energy harvested from the power splitting protocol. The results achieved in this study showed that the proposed model satisfied the requirements of future green IoT wireless networks. Derivatives leading to closed-form expressions are presented and verified by simulation results. The investigated results demonstrated that a friendly jammer based on radio frequency and energy harvesting strongly forces the intercept probability performance of the eavesdropper towards one, while outage probability performance of IoT devices towards zero showed that the signal to noise ratio tends to infinity.

Approaching K-Means for Multiantenna UAV Positioning in Combination With a Max-SIC-Min-Rate Framework to Enable Aerial IoT Networks

“…The authors, therefore, proposed a dual-layer algorithm to optimize the power allocation factor at the inner layer and the time switching factor at the outer layer. In [34], the authors also combined NOMA and SWIPT (power splitting) and attained system throughput at legitimate devices and intercept probability at illegitimate devices by optimizing the device bit-rate threshold and power splitting factor. Wireless communication using UAVs is becoming increasingly attractive due to their flexibility in critical situations such as natural disasters [35]).…”

“…To apply (34), the study proposes Algorithm 1 to obtain Monte Carlo simulations of the outage probability at the selected UAV U u .…”

“…Algorithm 1 Adopt a max-SIC-min-rate framework to determine the outage probability at the selected UAV U u according to (34) for transmission block t and scenario Q = {(i), (ii)} Input: Initialize parameters in Tables 2 and 4. Randomly generate 10 6 samples of each fading channel over Rayleigh or Nakagami-m distribution Output: Simulated (Sim) results for outage probability at the selected UAV U u for transmission block t and scenario Q 1: Select the best received signal according to (16) 2: while W ̸ = ∅ and X ̸ = ∅ do 3:…”

“…Remark 1: The outage probability at the selected UAV U u in transmission block t and scenario Q = (i) is obtained from (34) in Theorem 1 and expressed in novel closed-form as…”

“…In scenario (ii), propagation is distributed over Nakagami-m fading channels for both stages in transmission block t. Remark 3: The outage probability at the selected UAV U u is obtained from (34) in Theorem 2, and the CDF is obtained from (8). The study therefore derives the closedform expression for the outage probability at the selected UAV U u for transmission block t and scenario (ii) as follows:…”