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.

In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models.

The security and privacy of underwater acoustic sensor networks has received extensive attention recently due to the proliferation of underwater activities. This paper proposes an analytical model to investigate the eavesdropping attacks in underwater acoustic sensor networks. Our analytical framework considers the impacts of various underwater acoustic channel conditions (such as the acoustic signal frequency, spreading factor and wind speed) and different hydrophones (isotropic hydrophones and array hydrophones) in terms of network nodes and eavesdroppers. We also conduct extensive simulations to evaluate the effectiveness and the accuracy of our proposed model. Empirical results show that our proposed model is quite accurate. In addition, our results also imply that the eavesdropping probability heavily depends on both the underwater acoustic channel conditions and the features of hydrophones.

With the proliferation of wireless communication technologies, user equipments (UEs) in rural or disaster areas have data-transmission demand to upload their data to the core network. However, current networks lack coverage in rural or disaster areas due to the absence or damage of/to infrastructures. To address this issue, a promising solution is employing unmanned aerial vehicles (UAVs) as relays to assist the wireless backhaul of UEs to remote ground base stations (GBSs). For convenience, we call these networks as UAV-assisted wireless backhaul networks (UABNs). This paper aims to investigate the uplink transmission performance in UABNs. In particular, we analyze the connectivity of the two-hop uplink path from a reference UE to a remote GBS via a reference UAV. Compared with previous studies that mostly analyze single-hop transmissions, the investigation of the path connectivity of UABNs is more complex because of the location variation of UAVs as well as the complexity of the interference at the two-hop path. Considering the distribution of UEs, we exploit stochastic geometry to establish a theoretical model to analyze the path connectivity of UABNs. In our model, UEs form clusters according to a Poisson Cluster Process (PCP) and one UAV serves one UE cluster. Based on our model, the connectivity of a two-hop uplink path is finally derived by limiting the signal-to-noise-plus-interference (SINR) above a threshold. Theoretical values of the connectivity of UABNs match with simulation results, confirming the accuracy of the proposed analytical model. Our results also offer insightful implications for constructing and configuring UABNs.

In underlay cognitive radio networks (CRNs), the connectivity of secondary users (SUs) is difficult to be guaranteed due to the existence of primary users (PUs). Most prior studies only consider cognitive radio networks equipped with omni-directional antennas causing {high interference} at SUs. We name such CRNs with omni-directional antennas as Omn-CRNs. Compared with an omni-directional antenna, a directional antenna can concentrate the transmitting/receiving capability at a certain direction, consequently resulting in the less interference. In this paper, we investigate the connectivity of SUs in CRNs with directional antennas (named as Dir-CRNs). In particular, we derive closed-form expressions of the connectivity of SUs of both Dir-CRNs and Omn-CRNs, thus enabling tractability. We show that the connectivity of SUs is mainly affected by two constraints: the {spectrum availability} of SUs and the {topological connectivity} of SUs. Extensive simulations validate the accuracy of our proposed models. Meanwhile, we also show that Dir-CRNs can have higher connectivity than Omn-CRNs mainly due to the lower interference, the higher spectrum availability and the higher topological connectivity brought by directional antennas. Moreover, we also extend our analysis with consideration transmission power efficiency. The simulation results show that Dir-CRNs require less transmission power to establish links than Omn-CRNs. We further investigate the throughput capacity of SUs, which is shown to heavily depend on the connectivity of SUs.

Eavesdropping attack is one of the most serious threats in industrial crowdsensing networks. In this paper, we propose a novel anti-eavesdropping scheme by introducing friendly jammers to an industrial crowdsensing network. In particular, we establish a theoretical framework considering both the probability of eavesdropping attacks and the probability of successful transmission to evaluate the effectiveness of our scheme. Our framework takes into account various channel conditions such as path loss, Rayleigh fading, and the antenna type of friendly jammers. Our results show that using jammers in industrial crowdsensing networks can effectively reduce the eavesdropping risk while having no significant influence on legitimate communications.