The relentless development of the Internet of Things (IoT) communication technologies and the gradual maturity of Artificial Intelligence (AI) have led to a powerful cognitive computing ability. Users can now access efficient and convenient smart services in smart-city, green-IoT and heterogeneous networks. AI has been applied in various areas, including the intelligent household, advanced health-care, automatic driving and emotional interactions. This paper focuses on current wireless-communication technologies, including cellular-communication technologies (4G, 5G), low-power wide-area (LPWA) technologies with an unlicensed spectrum (LoRa, SigFox), and other LPWA technologies supported by 3GPP working with an authorized spectrum (EC-GSM, LTE-M, NB-IoT). We put forward a cognitive low-power wide-area-network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. To ensure that the user can employ the AI efficiently and conveniently, we realize a variety of LPWA technologies to safeguard the network layer. In addition, to balance the demand for heterogeneous IoT devices with the communication delay and energy consumption, we put forward the AI-enabled LPWA hybrid method, starting from the perspective of traffic control. The AI algorithm provides the smart control of wireless-communication technology, intelligent applications and services for the choice of different wireless-communication technologies. As an example, we consider the AIWAC emotion interaction system, build the Cognitive-LPWAN and test the proposed AI-enabled LPWA hybrid method. The experimental results show that our scheme can meet the demands of communication-delay applications. Cognitive-LPWAN selects appropriate communication technologies to achieve a better interaction experience.

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks.

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.

Wake-up radio is a promising approach to mitigate the problem of idle listening, which incurs additional power consumption for the Internet of Things (IoT) wireless transmission. Radio frequency (RF) energy harvesting technique allows the wake-up radio to remain in a deep sleep and only become active after receiving an external RF signal to ‘wake-up’ the radio, thus eliminating necessary hardware and signal processing to perform idle listening, resulting in higher energy efficiency. This review paper focuses on cross-layer; physical and media access control (PHY and MAC) approaches on passive wake-up radio based on the previous works from the literature. First, an explanation of the circuit design and system architecture of the passive wake-up radios is presented. Afterward, the previous works on RF energy harvesting techniques and the existing passive wake-up radio hardware architectures available in the literature are surveyed and classified. An evaluation of the various MAC protocols utilized for the novel passive wake-up radio technologies is presented. Finally, the paper highlights the potential research opportunities and practical challenges related to the practical implementation of wake-up technology for future IoT applications.

Future mobile communication networks (5G) are envisioned to support a wide range of use cases with diverse service requirements. Network slicing as a key architectural enabling technology for 5G can provide users with customized services and meet the requirement of economic efficiency, where resource allocation is of vital importance. In this paper, we propose an online auction based resource allocation scheme for network slicing (ORANS) to meet the diverse service requirements and improve network economic efficiency. We first propose a bidding information construction method to provide customized bidding information for users based on service types. Next, we model the slicing resource allocation problem as an online winner determination problem, with aim to maximize the social welfare of auction participants. We then implement a resource allocation strategy and payment rule to accomplish the service-oriented optimal resource allocation. Extensive simulation experiments are conducted to evaluate the performance of the proposed ORANS and the numerical results show that ORANS significantly outperforms the greedy algorithm and the primal-dual approximation algorithm in network economic efficiency while satisfying the diverse service requirements of users.

The combination of multiple-input multiple-output (MIMO) transmission and orthogonal frequency division multiplexing (OFDM) modulation has been shown to be an effective way to substantially enhance the capacity of bandlimited optical wireless communication (OWC) systems. In this paper, we propose four OFDM-based generalized optical MIMO (GO-MIMO) techniques for intensity-modulation and direct-detection OWC systems, including OFDM-based frequency-domain generalized spatial modulation (FD-GSM), frequency-domain generalized spatial multiplexing (FD-GSMP), time-domain generalized spatial modulation (TD-GSM) and time-domain generalized spatial multiplexing (TD-GSMP). For OFDM-based FD-GSM and FD-GSMP, spatial mapping is performed in the frequency domain, while it is carried out in the time domain for OFDM-based TD-GSM and TD-GSMP. To efficiently estimate both spatial and constellation symbols in each OFDM-based GO-MIMO technique, a corresponding maximum-likelihood (ML) detection algorithm is designed. Extensive simulations are conducted to evaluate and compare the performance of OFDM-based GO-MIMO techniques under various MIMO settings in a typical indoor environment. Simulation results demonstrate that there exists an optimal GO-MIMO technique to achieve a target spectral efficiency for a given receiver location, and the obtained optimal GO-MIMO technique is also related to the specific location of the receiver.