The current Internet of Underwater Things (IoUT) for marine observations and emergency responses suffers from two critical issues: 1) energy efficient and 2) timely data collection. Autonomous underwater vehicles (AUVs), serving as tools for collecting and forwarding distributed data, can deal with the unbalanced power consumption in a traditional multihop underwater communication network. However, the low speed of the AUV has not been able to guarantee the timeliness of delay-sensitive data. In this article, we introduce a hybrid data collection scheme (HDCS), taking both real-time data collection and energy efficiency (EE) issues into consideration. All sensor nodes (SNs) are first clustered based on their locations in the network. We develop an analytic expression to describe the attenuation of Value of Information (VoI), involving the relationship between the importance degree and timeliness; initial VoI could be measured by historical data. The emergency can be recognized by the presented criterion, and the transmission mode of cluster heads (CHs) in the same layer is judged by CHs themselves according to VoI. The selected CHs shall transmit the urgent data via multihop routing to avoid over attenuation of VoI. The normal data are collected by AUVs visiting all remaining CHs, and the shortest trajectory is achieved by addressing a variation of the classic traveling salesman problem (TSP). Our simulation experiments show that this mechanism can effectively increase long-term VoI while significantly improving EE.

With the rise of Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way towards an emerging paradigm, i.e., crowdsensing which facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants, and to incentivize them to improve sensing quality. In this paper, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multi-leader multi-follower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., Q-learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with Q-learning in a dueling network architecture, i.e., double deep Q network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.

The satellite-terrestrial networks (STNs) network has the characteristics of open links, node movement, dynamic network topology, and diverse collaborative algorithms, which will lead to frequent passive handovers and continuous reauthentication of user equipment (UE). This causes a waste of valuable computing and storage resources in the STNs network and seriously affects the user’s network experience. To address this, we propose an access authentication protocol with user anonymity and traceability to reduce the communication delay and signaling cost of access authentication. In addition, we also propose a hierarchical group key distribution scheme to implement cross-domain handover authentication between different groups of UE, thereby effectively avoiding reauthentication. We take strict security analysis to prove that the authentication protocol we propose is secure. Compared with the three-related existing arts, our protocol can greatly reduce the communication interaction delay and improve the efficiency of initial access and handover authentication of UE.

The Internet of Things (IoT) has such a profound impact that we have witnessed crowdsensing has emerged as the most popular sensing paradigm where participants sense and aggregate data to the platform by smart devices. However, the participants may not be willing to involve in data sensing and aggregation if they are not sufficiently compensated or their personalized private information are disclosed. In order to overcome the above issues, this paper proposes a payment-privacy protection level (PPL) game, where each participant submits his sensing data with a specified PPL while the platform chooses a corresponding payment to the participant. Additionally, we derive the Nash equilibrium (NE) point of the game. Considering that the payment-PPL model is unknown in practice, we employ a reinforcement learning technique, i.e., Q-learning to obtain the payment-PPL strategy in a dynamic payment-PPL game. We further use deep Q network (DQN), which combines a deep learning technique with Q-learning to accelerate learning speed. Through extensive simulations, we verify that our proposed algorithm using DQN achieves superior performance in terms of utilities of both platform and participants and data aggregation accuracy compared with the one using Q-learning.

Blockchain-based femtocell networks aim to build decentralized frameworks which enable easy deployment and low power consumption, thus they have been seen promising technologies to make up the coverage of cellular networks in the next generation communication system. This work aims to employ power control to support Quality-of-Service (QoS) provisioning, especially the guarantee for the transmission rate of a macrocell user (MUE) and the time delay of femtocell users (FUEs) in two-tier femtocell networks, where the MUE and FUEs share the same communication channel. We formulate the interactions among the macrocell base station (MBS) and FUEs as a Stackelberg game to maximize the utilities of MUE and FUEs by obtaining the optimal power allocation and pricing strategy. Considering the uncertainty of channel gain which is expressed as a function of transmission distance, we propose a worst-case method to transform the uncertain optimization problem into a deterministic one. We then design two algorithms by considering the dynamics of FUEs, i.e., FUEs may join and leave femtocells. Numerical results verify the convergence and superior performance of our proposed algorithms.

Delivery delay and communication costs are two conflicting design issues for mobile opportunistic networks with nonreplenishable energy resources. In this paper, we study the optimal data dissemination for resource constrained mobile opportunistic networks, i.e., the delay-constrained least-cost multicasting in mobile opportunistic networks. We formally formulate the problem and introduce a centralized heuristic algorithm which aims to discover a tree for multicasting, in order to meet the delay constraint and achieve low communication cost. While the above algorithm can be implemented by each individual node, it is intrinsically centralized (requiring global information) and, thus, impractical for real-world implementation. However, it offers useful insights for the development of a distributed scheme. The essence of the centralized approach is to first learn the probabilities to deliver the data along different paths to different nodes and then decide the optimal multicast tree by striking the balance between cost and delivery probability. In mobile opportunistic networks, even if the optimal routing tree can be computed by the centralized solution, it is the “best” only on a statistic basis for a large number of data packets. It is not necessarily the best solution for every individual transmission. Based on the above observation, we develop a distributed online algorithm using optimal stopping theory, in which in each meeting event, nodes make adaptive online decisions on whether this communication opportunity should be exploited to deliver data packets. We carry out simulations to evaluate the scalability of the proposed schemes. Furthermore, we prototype the proposed distributed online multicast algorithm using Nexus tablets and conduct an experiment that involves 37 volunteers and lasts for 21 days to demonstrate its effectiveness.