With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

Edge computing is fundamental to filling the various quality-of-service needs for Industrial Internet of Things (IIoT) applications. However, introducing edge computing to IIoT inevitably results in hybrid intrusion problems and fails to satisfy the security demands of IIoT. Fortunately, Lagrange coded computing has emerged as a low-complexity and low-overhead solution for resisting hybrid intrusions during data offloading and processing. However, how to make decentralized, accurate, and real-time encoding/offloading/decoding decisions remains challenging. This article designs a fully decentralized training and decision-making framework to address the joint secure data offloading and resource allocation problem against hybrid intrusions for dynamic and uncertain IIoT, attempting to minimize the long-run energy and delay costs while improving the data confidentiality, integrity, and availability. It is proposed a fully decentralized multiagent actor–critic-based secure data offloading (FM-SDO) algorithm to solve the secure data offloading subproblem, wherein each industrial end device utilizes its local information to learn and execute its policy independently. This algorithm improves the structure of actor and critic networks and designs a multiagent alternant updating mechanism to increase learning accuracy, convergence, and stability. Based on the received offloading decisions of each device, each edge server leverages the Lagrange multiplier approach and Karush–Kuhn–Tucker condition to make fast and decentralized resource allocation decisions. Finally, we employed an IIoT intelligent production line platform named iCandyBox to test the performance of the FM-SDO algorithm. Experiment results suggest that the FM-SDO algorithm effectively reduces the total energy and delay costs while increasing the capability of resisting hybrid intrusions.

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. However, ensuring privacy within this quantum computing necessitates innovative approaches. Existing research has introduced the concept of quantum differential privacy (QDP) to protect data privacy in quantum computing by leveraging quantum noise. Yet, this method faces limitations due to the fixed and uncontrollable nature of the inherent noise, which directly affects the privacy budget of QDP. Addressing this critical gap, our study proposes a novel approach that utilizes quantum error correction (QEC) techniques not only to mitigate quantum computing errors but also to adjust QDP protection levels precisely. By selectively applying QEC to single or multiple qubit gates, we introduce a method to manipulate the quantum noise error rate effectively. Moreover, we derive a new formula for calculating the overall error rate in a quantum circuit and the adjusted privacy budget after QEC operation. Through extensive numerical simulations, we validate the efficacy of utilizing QEC in tuning privacy protection levels within quantum computing.

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

MOTIVATION: The modification of N4-acetylcytidine (ac4C) in RNA is a conserved epigenetic mark that plays a crucial role in post-transcriptional regulation, mRNA stability, and translation efficiency. Traditional methods for detecting ac4C modifications are laborious and costly, necessitating the development of efficient computational approaches for accurate identification of ac4C sites in mRNA. RESULTS: We present DPNN-ac4C, a dual-path neural network with a self-attention mechanism for the identification of ac4C sites in mRNA. Our model integrates embedding modules, bidirectional GRU networks, convolutional neural networks, and self-attention to capture both local and global features of RNA sequences. Extensive evaluations demonstrate that DPNN-ac4C outperforms existing models, achieving an AUROC of 91.03%, accuracy of 82.78%, MCC of 65.78%, and specificity of 84.78% on an independent test set. Moreover, DPNN-ac4C exhibits robustness under the Fast Gradient Method attack, maintaining a high level of accuracy in practical applications. AVAILABILITY AND IMPLEMENTATION: The model code and dataset are publicly available on GitHub (https://github.com/shock1ng/DPNN-ac4C).

Constructing underwater acoustic sensor networks (UASNs) for data collection has gradually become an effective ocean exploration and exploitation method. However, the interference of the underwater environment and the limited capacity of underwater communication equipment increase the difficulty of information interaction, posing a challenge to secure data transmission strategies for UASNs. Therefore, this study proposes a safe and reliable data transmission scheme based on reinforcement learning-aided two-stage trust evaluation (RLTST) to overcome the problems mentioned above. This article proposes a distinct self-trust concept, different from traditional trust mechanisms. A node self-trust evaluation method based on Q-learning is designed in the first stage, which defects compromised nodes actively. In the second stage, the trustworthiness of data is calculated based on the real data received, followed by backtracking the transmission path of untrustworthy data to identify malicious nodes. Finally, the results show that our proposed scheme is more effective in malicious node detection and improves data collection reliability.

Based on the review of existing source location privacy protection technologies and research on underwater data transmission, numerous scholars have performed extensive work in the field of source location privacy protection. Further, current methods for protecting the source node location privacy in Internet of Underwater Things (IoUT), especially in underwater acoustic sensor networks (UASNs), suffer from several issues, including high data transmission energy consumption, short network lifespan, and inability to ensure data accuracy. Moreover, current common security research on UASNs considers only passive attacks and has fewer countermeasures for active attacks. To address these challenges, this article proposes a source location privacy protection algorithm based on polyhedral phantom routing in UASNs (PPR-USLP). First, the polyhedral phantom routing algorithm based on platonic solids is employed to introduce phantom nodes, which increases path diversity and protects the privacy of the source node, thereby thwarting passive attacks from adversaries and prolonging the life cycle of the network. Second, suitable relay nodes are selected by considering the peripheral status of the nodes, the data are collected by autonomous underwater vehicles (AUVs) to reduce the waiting time of sensor nodes and reduce the energy consumption of data transmission. Furthermore, to ensure data integrity and defend against active attacks from adversaries, this article combines error correction coding, which enhances network resilience and security while improving throughput and achieving load balancing. The simulation results demonstrate that PPR-USLP exhibits favorable performance in terms of energy consumption, safety time, and data accuracy, effectively safeguarding the privacy of the underwater source locations.

Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.

Federated learning (FL) has been envisioned as a promising distributed learning framework for next-generation wireless communication systems. FL introduces new challenges in system design, since users need to consider the local processing optimization in addition to traditional communication resources allocation. In this paper, we aim to address this challenge by considering a wireless-powered FL network with multiple users, where non-orthogonal multiple access (NOMA) is employed for uplink transmission. A latency minimization problem is formulated, requiring to jointly optimize the power and time allocation for all FL phases together with the local processing computation frequency at each user. An one-dimensional search algorithm (ODSA) is proposed to obtain the optimal solution for the formulated non-convex problem. Presented numerical results demonstrate that the proposed scheme outperforms its orthogonal counterpart.

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

Underwater acoustic sensor networks (UASNs) are effective instruments for monitoring marine environments and surveying seabed resources, it is important to improve their security protection, including source location privacy protection. Numerous strategies have been presented by researchers to strengthen location privacy, however, the majority of these plans have expensive energy costs. Therefore, a backbone-network-construction-based multiautonomous underwater vehicle (AUV) collaboration source location privacy protection (BNCSLP) algorithm has been enhanced to address this issue. First, the nodes in the network are split into various clusters, and an entire network is segmented into various regions. The backbone network is built using clusters that house the source. To prevent the adversary’s tracking, the AUV alternately chooses alternative backbone nodes and relays the source and fake data. Then, the cluster head determines whether to update the clusters by calculating how similar the data are with nearby clusters. The nearest neighbor technique is used by the updated cluster to anticipate the data and to reduce the energy utilization of forwarding the data packets, resulting in that there is less chance of data packets being intercepted and the source location being revealed. Finally, the AUV replans the trajectory, which shortens the AUV’s traveling path because only fewer cluster heads need to be accessed, decreasing the time it takes for data to be transmitted.

The position of the source is sensitive and critical information in underwater acoustic sensor networks (UASNs). In this study, a network coding-based scheme called the stratified source location privacy protection scheme (SSLP-NC) with autonomous underwater vehicle (AUV) is suggested for a strong adversary that can decode data. First, for the adversary with passive attacks, several fake source selection algorithms are suggested for two circumstances where the source is in the shallow and deep sea, respectively. Each node then utilizes a pseudo-random number generator to create sequences on a regular basis so that the key data can be delivered to the sink without interference. Then, for the adversary with the active attack, the node encrypts the source and fake data using the pre-existing pseudo-random number sequence as an encoding vector to thwart the adversary’s decryption. Further, this work develops a relay node selection approach for transmitting the encoded data, which increases the variety of the data transmission pathways. Finally, this study includes a hole avoidance strategy that uses nodes or an AUV to address the potential hole issue. The simulation demonstrates that the SSLP-NC successfully fends off an adversary that can decode data packets, and performs better than the EECOR and DBR-MAC algorithms in terms of network safe time and packet delivery rate.

It is predicted that the number of Internet of Things (IoT) devices will be more than 75 billion by 2025, where a large portion of IoT devices will be long-range (LoRa) powered by batteries. The battery lifetime limitations and the spectrum shortage have been the main problems in realizing LoRa wide area network (LoRAWAN) for the devices in hard-toreach areas. The dynamic spectrum access technique has gained tremendous research interest as a promising paradigm due to its outstanding performance in improving spectrum efficiency. How to realize intelligent resource allocation (RA) to avoid collisions among IoT devices with low energy consumption is an important problem in LoRaWAN. However, either synchronization and prior information estimation, such as channel state information (CSI), are required, or the energy consumption of LoRa devices is not considered in related work, which may decrease the energy efficiency of the LoRa devices. In addition, the necessary prior information may be challenging to obtain in future networks. To address these issues, we propose a deep Q learning-based RA (DQLRA) method for LoRaWAN. In our proposed method, the gateway (GW) trains the deep neural network (DNN) only based on the transmission state, i.e., transmission failure or success, and the corresponding device number of each LoRa device. Then, each LoRa device can make decisions based on its device number and ACK or NACK information using the trained DNN. Synchronization and prior information estimation are not required in our proposed method, which may improve the energy efficiency of IoT devices. Simulation results show that the proposed method can achieve the optimal frame success rate (FSR) in most scenarios.

It is necessary to develop an efficient channel selection method with low power consumption to achieve high communication quality for distributed massive IoT system. To this end, Ma et al. [1] proposed an autonomous distributed channel selection method based on the Tug-of-War (ToW) dynamics. The ToW-based method can achieve equivalent performance to UCB1-tuned [2], [3] with low computational complexity and power consumption, which is recognized as a best practice technique for solving multi-armed bandit (MAB) problems. However, Ref. [1] only considered fixed IoT devices with simplex communication.

For the realization of an Internet of Things (IoT) with high densities of nodes it is necessary that wireless communication protocols are developed that offer (1) low energy consumption, (2) simplicity of encoding and decoding, (3) an asynchronous mode of communication, and (4) an effective but simple method to deal with interference between transmissions. This paper presents the implementation, experimentation, and analysis of a protocol on the MAC sublayer that is based on the encoding of information by the silent intervals between pulses. This encoding allows for few conflicts between messages that are broadcast on the same band overlapped in time. This is demonstrated experimentally, while no adverse effect is detected on transmissions from broadcasts of a different group of nodes using the same 315 MHz band but with a different code length as coding parameter. Theoretical analysis confirms that the probability of conflicts between messages is low, even if the number of nodes increases to the order of ten thousand. This protocol facilitates the implementation of IoT nodes that are restricted in terms of hardware and energy resources.

The Internet of energy (IoE), envisioned to be a promising paradigm of the Internet of things (IoT), is characterized by the deep integration of various distributed energy systems. However, the fusion of heterogeneous IoE communication networks creates a new threat landscape. To thwart and mitigate various types of cyber threats to IoE networks, this paper proposes a novel intrusion detection system (IDS) based on a designed residual network with attention long short term memory (ReAL). Specifically, we design a light gradient boosting machine (LightGBM)-based feature selection method to identify the most useful features. Then, a residual network (ResNet) and a long short term memory neural network with an attention mechanism (ALSTM) are employed, to extract temporal patterns of network traffic events. After that, these patterns are orchestrated to identify the anomalies in IoE networks. The high effectiveness of the proposed IDS is validated on a real IoE dataset.

In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger.

Channel Hopping (CH) is a representative technique to solve the rendezvous problem for Cognitive Radio Networks (CRNs). Multiple radios technique were utilized in several latest researches on CH owing to the fact that it can significantly reduce the Time-To-Rendezvous (TTR) while the cost of the device is low. However, the radios of one unlicensed Secondary User (SU) may access same channel at the same time for most of the existing multi-radio CH protocols, which is a waste of energy. Moreover, the number of radios for the SUs is implicitly assumed same or must be more than one, which is unrealistic for heterogeneous CRNs. In this paper, an energy-efficient CH protocol, Hybrid Radio Rendezvous (HRR) protocol is proposed to address the above issues. Furthermore, theoretical analysis is presented to derive the upper bound on the Maximum TTR (MTTR) for the HRR protocol. In addition, the theoretical analysis is corroborated by extensive simulations while the simulation results show that the HRR protocol outperforms the state- of-the-art CH protocols in terms of the TTR and the energy efficiency.

Secondary Users (SUs) have capability to sense available licensed spectrum in Cognitive Radio Networks (CRNs). Hence, SUs can opportunistically access to the licensed spectrum without disturbing Primary Users (PUs). In this paper, a novel network architecture is proposed to reduce the production cost and the energy consumption for CRNs. The proposed network architecture is based on the spectral requirement of Secondary Users (SUs). In the proposed network architecture, only parts of SUs are equipped with Cognitive Radio (CR) module. In addition, a minimum number of SUs are selected to sense available licensed spectrum, which aims at reducing the energy consumption further. The minimum number of SUs selection problem is formulated as a non-linear programming problem under the constrains of energy efficiency and the real-time available spectrum information. However, the non-linear programming problem is a NP-hard problem. Hence, a distributed heuristic algorithm is proposed to calculate the near-optimal solution. The simulation results demonstrate that the proposed heuristic algorithm in the proposed network architecture outperforms the random algorithm in the proposed network architecture and traditional Cognitive Radio Ad Hoc Networks (CRAHNs) in energy efficiency.