Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires huge cost of deploying and maintaining communication infrastructures. In this paper, we propose an Unmanned Aerial Vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data is first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links and integration with edge computing.

Recent proliferation of Internet of Things (IoT) demands large scale connectivity among smart IoT devices over a vast geographical area. However, limited radio range and lack of scalability of conventional wireless sensor networks do not allow a wide area connectivity among IoT devices. To address these challenges, Low-Power Wide-Area Networks (LPWANs) are emerging to provide long-range communication capability with low-power consumption of the end devices. Nevertheless, given the demand in delivering an increasingly large volume of data generated by IoT devices, the direct data transmission model is not suitable due to its poor network lifetime. Therefore, in this work, a multi-hop data routing method is proposed for LPWANs. Since multi-hop data transmission faces several challenges such as increased data latency, higher interference, and reduced data throughput (i.e., poor bandwidth utilization), we propose a reinforcement learning method to address those challenges. The proposed method updates the Q-matrix of the network at varying discrete time instants and selects relay devices in such a way that maximizes the cumulative reward value between selected device-gateway pairs. The applicability and effectiveness of the proposed method are illustrated over both simulated LPWAN testbed and real field data sets. The obtained results clearly demonstrate the improved network performance in terms of energy efficiency and QoS of the proposed method as compared to various existing methods.

In this paper, we study the impacts of co-channel interference (CCI) and channel estimation error (CEE) on the performance of a cellular multiuser two-way relay network. The network comprises of one $N_{a}$-antenna base station (BS), M single-antenna users, and one L-antenna relay with $N_{r}$ number of interferers. In particular, by employing antenna selection at the relay, we propose beamforming and antenna selection transmission schemes at the BS with user scheduling in the presence of CEE and CCI. Then, we derive the tight closedform lower bound expressions for the overall outage probability under Nakagami-m fading. We also deduce the asymptotic outage expressions in the high signal-to-noise ratio regime, from which the impacts of CEE and CCI on the achievable diversity order are highlighted. Moreover, using the asymptotic expressions, three optimization problems to minimize the outage probability are formulated and solved: 1) optimal power allocation (OPA) with fixed relay location. Specifically, we develop two OPA approaches, namely, OPA I and OPA II. OPA I allocates the total power appropriately at each terminal, whereas OPA II allocates the fixed half of the total power at the relay and remaining power to base station and users, 2) optimal relay location with fixed power allocation, and 3) jointly optimal power allocation and relay location. Our results reveal that the OPA I outperforms OPA II, irrespective of different channel/system parameters, interferers, and relay locations. In addition, we demonstrate that the effect of interferers at the relay can be ignored if we allocate sufficient power to the base station and users. It is also shown that the CEE and CCI can severely degrade the outage performance, and even reduce the diversity order to zero. Further, it is highlighted that the impact of CCI can be alleviated by increasing the number of relay antennas. The numerical and simulation results validate our analytical findings, and also demonstrate that the antenna selection scheme offers comparable outage performance as beamforming scheme.

Time synchronization and node localization over a wireless sensor network (WSN) is a vital problem in applications such as Internet of Things (IoT) and context-aware pervasive systems. Addressing this problem leads to an efficient data fusion, power scheduling, bandwidth utilization, geographic routing, time-based channel sharing, and clustering among sensor nodes. In a WSN, sensor nodes utilize multi-hop data transmission model towards the data transfer. Utilization of large number of hops for the data transfer leads to poor time synchronization resulting in erroneous node location estimates. In this letter, we make use of a recent development in social networks termed as small world characteristics and propose a novel method of time synchronized node localization over a small world WSN (SW-WSN). The results are obtained via simulations over a medium scale WSN. The performance of the proposed method is evaluated by conducting detailed analysis of time synchronization and node localization errors over the SW-WSN. The results obtained illustrate that the proposed small world model yields improved localization results when compared to results obtained over a regular WSN.

In this paper, we investigate the performance of secure cellular multiuser two-way amplify-and-forward (AF) relay networks, wherein one $N_{a}$ -antenna base station (BS) exchanges information bidirectionally with ${K}$ single-antenna users (USs) by the assistance of a single-antenna relay node in the presence of a single-antenna passive eavesdropper. We employ user selection coupled with antenna selection (AS) scheme at the BS so as to maximize the end-to-end signal-to-noise ratios (SNRs). In the considered system, by employing two possible combining techniques at the eavesdropper, viz., maximum ratio combining (MRC) and selection combining (SC), we derive the novel tight closedform expressions for the secrecy outage probability (SOP) under Rayleigh fading channels. We present an asymptotic secrecy diversity order analysis of the considered system for the large transmit power, and demonstrate that the eavesdropping reduces the secrecy diversity order from min( $N_{a}$ , ${K}$ ) to 1. Further, we present some useful insights into the impact of eavesdropper and number of antennas and users on the SOP performance. It is shown that the eavesdropper has a severe impact on the SOP performance, regardless of number of antennas and users. The numerical and simulation results corroborate our theoretical findings, and demonstrate the influence of eavesdropper and relay positions on the SOP performance. We show that the SOP performance improves significantly when the eavesdropper is far away from the BS and USs. It is also shown that the SOP performance enhances noticeably if the relay location is chosen optimally, for different eavesdropper positions and/or different values of antennas and users. Moreover, our results also reveal that only coding gain rather than diversity gain is achieved by increasing $N_{a}$ and ${K}$ , because of the dominance of information leakage by the first time phase. Furthermore, results illustrate that the SOP performance under SC technique employed at the eavesdropper outperforms the MRC based technique for the broad range of transmit power.

This paper considers an Internet-of-Things (IoT) network where a multi-antenna access point (AP) and several single-antenna IoT devices (IoDs), operating under the full-duplex (FD) mode, communicate with each other bidirectionally through the help of a wireless-powered FD relay. In particular, the power splitting (PS) protocol is adopted at the amplify-and-forward (AF) relay to implement a simultaneous wireless information and power transfer (SWIPT) receiver. For such a multiuser FD-IoT network assisted by a wireless-powered two-way relay, we derive exact overall outage probability (OOP) expressions and tight closed-form expressions for the ergodic sum rate (ESR) under generalized Nakagami-m fading channels. Furthermore, aiming to maximize the energy efficiency (EE), we obtain optimal power allocation (OPA) under the total power and rate threshold constraints. Finally, extensive numerical and simulation results are presented to corroborate our analytical findings.