To keep pace with the current rapid evolution of mobile data requirements, IEEE 802.11 was evolved to provide more desirable performance to fulfill the needs of fifth-generation (5G) and Internet of Things (IoT) networks. It provides two different access contention-based schemes; Distributed Coordination Function (DCF) which not differentiates between different services, and Enhanced Distributed Channel Access (EDCA) which provides differentiation between various services through four priority Access Categories (ACs). The dilemma of the conventional IEEE 802.11 networks is the static assignation of parameters in DCF and EDCA regardless of the number of associated stations and no matter what kind of service is required by each station (i.e., the activity of ACs). Consequently, this led to a significant degradation in the performance of the network, especially in the case of ultra-dense load network. Therefore, in this paper, we introduce a novel algorithm for EDCA considering a dynamic assignation of Arbitration Inter-Frame Space Number (AIFSN) and guidance Contention Window (CW) depending on the number of associated stations and ACs activeness status. Based on the analytical models of EDCA, a game-theoretic method is proposed to make each associated station adapts its transmission probability within the guidance CW. The purpose of guidance CW is a pre-stage to detect the selfish stations which pick up a very low CW to maximize its throughput regardless of the overall network throughput. Simulation results show that the proposed game-based algorithm can obtain higher performance than the standard 802.11 networks in terms of normalized throughput, data dropped during retransmissions limit threshold exceeding, and mean average delay for sensitive delay applications.

Reconfigurable intelligent surface (RIS) is considered a promising technology for dense Internet of Things (IoT) networks to enhance spectral efficiency and coverage due to its lower energy consumption and decreased hardware cost. However, channel estimation is a significant challenge for two reasons; first, RIS reflects signals without any signal processing capabilities, and second, it contains large numbers of reflected elements, making the pilot overhead extremely high. In this paper, we propose a two-phased RIS channel estimation framework for MIMO uplink transmissions with lower pilot overhead. In phase I, we take advantage of the fact that RIS elements reflect all IoT devices through the same BS-RIS channel and propose a dual-link time division duplex (TDD) pilot transmission scheme for estimating the BS-RIS channel where the pilots are transmitted from BS to RIS and then reflected back to BS. Afterward, we propose an algorithm to obtain the BS-RIS channel from the dual link BS-RIS-BS cascaded channel. In phase II, we estimate the user channels between RIS and devices based on the knowledge of the BS-RIS channel obtained in phase I. Simulation results indicate that the proposed framework attained a lower normalized mean square error (NMSE) and higher sum rate than other channel estimation frameworks with lower pilot overhead and complexity overhead.

Kirchhofffs Law Johnson Noise (KLJN)-based thermal noise modulation (Ther-Mod) represents a viable answer for secure Internet of Things (IoT) communication at ultra-low power levels. However, the conventional symmetric KLJN systems are not reliable, unless a large number of noise samples are employed, giving rise to a high latency and a low effective bitrate. We propose P-Ther-Mod, a new asymmetric KLJN-based modulation scheme, which utilizes a four-resistor structure in order to improve the bit error rate (BER) without imposing additional noise samples per bit. Theoretical bit error probability (BEP) expressions are developed for both wired and wireless IoT channels where the additive white Gaussian noise (AWGN) and Rayleigh fading models are considered. Simulation results demonstrate that the proposed P-Ther-Mod technique reduces the noise samples by up to 35 % (e.g., reaching BER =10-5 at N = 35 compared to BER = 10-5 at N = 50 in existing methods), and achieves BER. 10-8 at N = 200, improving previous approaches by 5 orders of magnitude. The asymmetric approach allows balancing higher bit rates by decreasing oversampling of noise. Furthermore, optimizing the detection threshold parameter (delta representing signal-to-noise ratio (SNR) in detection) enhances robustness and enables 7 dB savings in the SNR even under the fading channel. These developments render P-Ther-Mod a reliable, secure and scalable approach for IoT deployments with a vulnerable environment to interference, such as industrial sensor networks and wearable devices.

Machine type communication (MTC), additionally called machine-to-machine (M2M) communications, broadly is referring to a number of cooperating machines exchange sensed data or information and make decisions with slight or zero human intervention. M2M technology will let a massive number of devices to be interconnected over the internet leading to a rapid development in recent time, which can be a promising enabling key for many areas, particularly for the internet of things (IoT) and 5th generation (5G) networks. This chapter presents a summary and state of art of M2M communications characteristics, taxonomy, applications, different technologies for deploying of M2M communications, and future challenges.