The surge in global population is compelling a shift towards smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of internet of things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensors network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies such as WSN, radio frequency identification, cloud computing, middleware systems and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business and marketability.

Wireless energy harvesting (EH) is a promising solution to prolong lifetime of power-constrained networks such as military and sensor networks. The high sensitivity of energy transfer to signal decay due to path loss and fading, promotes multi-antenna techniques like beamforming as the candidate transmission scheme for EH networks. Exploiting beamforming in EH networks has gained overwhelming interest, and lot of literature has appeared recently regarding this topic. The objective of this paper is to point out the state-of-the-art research activity on beamforming implementation in EH wireless networks. We first review the basic concepts and architecture of EH wireless networks. In addition, we also discuss the effects of beamforming transmission scheme on system performance in EH wireless communication. Furthermore, we present a comprehensive survey of multi-antenna EH communications. We cover the supporting network architectures like broadcasting, relay, and cognitive radio networks with the various beamforming deployment within the network architecture. We classify the different beamforming approaches in each network topology according to its design objective such as increasing the throughput, enhancing the energy transfer efficiency, and minimizing the total transmit power, with paying special attention to exploiting the physical layer security. We also survey major advances as well as open issues, challenges, and future research directions in multi-antenna EH communications.

Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the potential of challenging large-scale problems in conventional massive multiple-input-multiple-output (CM-MIMO) systems. This introduces the concept of intelligent massive MIMO (I-mMIMO) systems. Due to the surge of application of different ML techniques in the enhancement of mMIMO systems for existing and emerging use cases beyond fifth-generation (B5G) networks, this article aims to provide an overview of the different aspects of the I-mMIMO systems. First, the characteristics and challenges of the CM-MIMO have been identified. Secondly, the most recent efforts aimed at applying ML to a different aspect of CM-MIMO systems are presented. Thirdly, the deployment of I-mMIMO and efforts towards standardization are discussed. Lastly, the future trends of I-mMIMO-enabled application systems are presented. The aim of this paper is to assist the readers to understand different ML approaches in CM-MIMO systems, explore some of the advantages and disadvantages, identify some of the open issues, and motivate the readers toward future trends.

The placement of Unmanned Aerial Vehicle (UAV) based aerial Base station (BS) has emerged as a viable solution to the contemplated high traffic demands of the future wireless networks. Nevertheless, the practical realization of UAVs for wireless coverage provisioning demands efficient utilization of its limited onboard energy reserves. This paper studies the power-domain Non-Orthogonal Multiple Access (NOMA) scheme for energy-efficient placement of the UAV-BS. The objective of maximized information bits per unit energy consumption of the UAV-BS is developed assuming a multi-Quality of Service (QoS) scenario. Also, the efficiency analysis is carried out considering the composite of energies consumed for the signal transmission and hover operation of the UAV-BS. The formulated Non-Linear Fractional Problem (NLFP) as a function of transmission power and altitude is solved by employing an alternating optimization technique based on a nested Dinkelbach structure. An investigation into the user pairing scheme also sheds new light on its effect upon the constrained feasible altitude range of the UAV-BS. Consequently, a computationally efficient approach is devised to render solution space based on the identified subset of user pairs. The numerical analyses of the proposed scheme signify a substantial increase in the energy efficiency of the NOMA based UAV-BS in comparison to the baseline scheme of Orthogonal Multiple Access (OMA). Thus, the improved energy efficiency enables enhanced coverage for the UAV-BS.

We consider a scenario where multiple pairs of users exchange information within pair, with the help of a dedicated multi-antenna relay. The protocol integrates the idea of analogue network coding in mixing two data streams originating from the same user pair, together with the spatial multiplexing of the data streams originating from different user pairs. The key feature of the protocol is that it enables both the relay and the users to participate in interference cancellation. We propose several beamforming schemes for the multi-antenna relay and evaluate the performance using information theoretical metrics such as ergodic capacity, outage probability and diversity and multiplexing tradeoff. Analytical and simulation results justify that the ergodic capacity, outage probability and diversity and multiplexing tradeoff of the proposed beamforming schemes outperform comparable schemes.

Localization is an apparent aspect of a wireless sensor network, which is the focus of much interesting research. One of the severe conditions that needs to be taken into consideration is localizing a mobile target through a dispersed sensor network in the presence of physical barrier attacks. These attacks confuse the localization process and cause location estimation errors. Range-based methods, like the received signal strength indication (RSSI), face the major influence of this kind of attack. This paper proposes a solution based on a combination of multi-frequency multi-power localization (C-MFMPL) and step function multi-frequency multi-power localization (SF-MFMPL), including the fingerprint matching technique and lateration, to provide a robust and accurate localization technique. In addition, this paper proposes a grid coloring algorithm to detect the signal hole map in the network, which refers to the attack-prone regions, in order to carry out corrective actions. The simulation results show the enhancement and robustness of RSS localization performance in the face of log normal shadow fading effects, besides the presence of physical barrier attacks, through detecting, filtering and eliminating the effect of these attacks.