Despite the rapid proliferation of Internet of Things applications driving widespread wireless sensor network (WSN) deployment, traditional WSNs remain fundamentally constrained by persistent energy limitations that severely restrict network lifetime and operational sustainability. Wireless rechargeable sensor networks (WRSNs) integrated with wireless power transfer (WPT) technology emerge as a transformative paradigm, theoretically enabling unlimited operational lifetime. In this paper, we investigate a heterogeneous mobile charging architecture that strategically combines an automated aerial vehicle (AAV) and a ground smart vehicle (SV) in heterogeneous deployment scenarios to collaboratively exploit the superior mobility of the AAV and extended endurance of the SV for energy distribution. We formulate a multi-objective optimization problem that simultaneously addresses the dynamic balance of heterogeneous charger advantages, charging efficiency versus mobility energy consumption trade-offs, and real-time adaptive coordination under time-varying network conditions. This problem presents significant computational challenges due to its high-dimensional continuous action space, non-convex optimization landscape, and dynamic environmental constraints. To address these challenges, we propose the improved heterogeneous agent trust region policy optimization (IHATRPO) algorithm that integrates a self-attention mechanism for enhanced complex environmental state processing and employs a Beta sampling strategy to achieve unbiased gradient computation in continuous action spaces. Simulation results demonstrate that IHATRPO achieves a 51% performance improvement over the original HATRPO, significantly outperforming state-of-the-art baseline algorithms while substantially decreasing sensor node mortality rate and improving charging system efficiency.

Autonomous aerial vehicles (AAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the Line of Sight (LoS) dominance in air-to-ground (A2G) communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This article explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-AAV network systems. Specifically, the AAV swarm forms an AAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a tradeoff tradeoff between the effectiveness of CB-based interference mitigation and the energy conservation of AAVs. Thus, by optimizing the excitation current weights and hover position of AAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multiobjective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic nondominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by AAVs to nonreceiving BSs.

The widespread deployment of dual-camera systems has laid a solid foundation for practical applications of infrared (IR)-RGB cross-modality person reidentification (ReID). However, the inherent modality differences between RGB and IR images cause significant intra-class variances in the feature space for individuals of the same identity. Current methods typically employ various network architectures for the image style transfer or extracting modality-invariant features, yet they overlook the information extraction from the most fundamental spectral semantic features. Based on the existing approaches, we propose a multi-spectral semantic alignment (MSSA) architecture aimed at aligning fine-grained spectral semantic features across both intra-modality and inter-modality perspectives. Through modality center semantic alignment (MCSA) learning, we comprehensively mitigate differences in identity features of different modalities. Moreover, to attenuate the discriminative information unique to a single modality, we introduce the modality reliability intensification (MRI) loss to enhance the reliability of identity information. Finally, to tackle the challenge that inter-modality intra-class disparities surpass inter-modality inter-class differences, we leverage the dynamic discriminative center (DDC) loss to further bolster the discriminability of reliable information. Through an extensive experiments conducted on SYSU-MM01, RegDB, and LLCM datasets, we demonstrate the substantial advantages of the proposed MSSA over other state-of-the-art methods.

Feature selection aims at reducing the number of attributes while achieving a high classification accuracy in machine learning. In this paper, we design a fitness function to jointly reduce the number of the selected features and enhance the accuracy. Then, we propose a chaotic binary dragonfly algorithm (CBDA) with several improved factors on the conventional dragonfly algorithm (DA) for developing a wrapper-based feature selection method to solve the fitness function. Specifically, the CBDA introduces three improved factors that are the chaotic map, evolutionary population dynamics mechanism and binarization strategy to make the algorithm more suitable for the problem. Experiments are conducted to evaluate the performance of the proposed CBDA on 24 well-known data sets from the UCI repository, and the results demonstrate that the proposed CBDA outperforms other comparative algorithms on the majority of the tested data sets.

The use of mobile phones while driving has become a major source of distraction to drivers, leading to a large number of car accidents. In this paper, we study the problem of automatically detecting driver phone calls by monitoring smartphone activities and utilizing the vehicle on-board unit. The challenges to overcome include: i) passenger phone calls should be allowed while the calls of the driver should be blocked; ii) the detection mechanism should be phone position-independent and phone owner-independent as the driver may put the smartphone at any position in the front row and make calls via an earphone, or the driver may borrow a passenger's phone to make a call; iii) the in-vehicle environment is noisy resulted from the operating engine, the music the driver and passenger may listen to, and the conversation between passengers and/or the driver; and iv) the computational cost at the smartphone should be light as realtime phone call detection is expected to effectively block an ongoing call to and from the driver. To overcome these challenges and achieve our objective of detecting driver phone calls, we take advantage of the uniqueness of individual's voice features. Through a short period of learning stage, our proposed system can recognize the driver's voice from the collected audio data. Combined with the smartphone's call state, our scheme can determine whether the driver is participating in the current phone call or not. Our strategy takes into account the complicated in-vehicle environment, and the proposed algorithm does not rely on the location of the phone within the vehicle nor the ownership of the smartphone, as the most existing driver phone call detection mechanisms do. We develop a client-server based system with the smartphones being the clients and the vehicle on-board unit being the server. To validate our mechanism, we perform extensive real-world experiments under different scenarios. The results demonstrate a high probability of detecting driver phone calls with a small false alarm rate.

Mobile social networks have been increasingly popular with the explosive growth of mobile devices. Mobile users are allowed to interact with potential friends within a certain distance. Motivated by this feature, many exciting applications have been developed, yet the challenge of privacy protection is thus aroused. In this paper, we propose an efficient customized privacy preserving friend discovery mechanism, which not only protects the privacy of users' profile, but also establishes a verifiable secure communication channel between matched users. Besides, the initiator has the freedom to set a customized request profile by choosing the interested attributes and giving each attribute a specific value. Moreover, the request profile's privacy protection level is customized by the initiator according to his/her own privacy requirements. We also consider the collusion attacks among unmatched users. To the best of our knowledge, this is the first work to address such a security threat. Our protocol guarantees that only exactly matched users are able to communicate with the initiator securely, while little information can be obtained by other participants. To increase the matching efficiency, our design adopts the Bloom filter to efficiently exclude most unmatched users. As a result, our design effectively protects the profile privacy and efficiently decreases the computational overhead. Security analysis and performance evaluation are conducted to justify the superiority of our protocol.

Attribute based encryption (ABE) has been applied to many applications nowadays [1][2] and it effectively achieves a fine grained access control. Even the encryptor needs only one encryption operation, and all the decryption operations are distributed to the receiver's end, the computational cost of encryption is still impractical when there is a large amount of encryption with different access structures. In this paper, we examine existing techniques that optimize the decentralized attribute based encryption scheme [3], such as a better construction of e Linear Secret-Sharing Scheme (LSSS) matrix, pre-processing of scalar multiplication and pairing, multi-pairing and so on. We proposed the deployment of offline pools to improve the real-time operations. We proposed the method to construct offline pools and designed algorithms to achieve better hitting rate of offline pool topples. We evaluated the optimization techniques, the result shows that there is a 45 times of performance improvement in the encryption (6-8 times for the decryption) after we applied all the real-time optimization techniques mentioned in our paper. With deployment of the offline pools, the optimization can be improved at least 100-200 times than without offline pools.

Cognitive radio networking allows the unlicensed secondary users to opportunistically access the licensed spectrum as long as the performance of the licensed primary users does not degrade. This dynamic spectrum access strategy is enabled by cognitive radio coupled with spectrum sensing technologies. Due to the imperfection of wireless transmissions, collaborative spectrum sensing (CSS) has been proposed to significantly improve the probability of detecting the transmissions of primary users. Nevertheless, current CSS techniques are sensitive to malicious secondary users, leading to a high false alarm rate and low detection accuracy on the presence of the primary users. In this paper, we present several robust collaborative spectrum sensing schemes that can calculate a trust value for each secondary user to reflect its suspicious level and mitigate its harmful effect on cooperative sensing. Our approach explores the spatial and temporal correlations among the reported information of the secondary users to determine the trust values. Extensive simulation study has been performed and our results demonstrate that the proposed schemes can guarantee the accuracy of the cooperative sensing system with a low false alarm rate when a considerable number of secondary users report false information.

Traditional spectrum sensing methods require that a secondary user (SU) senses the spectrum at the beginning of each time slot. A closer look at the network activities of a cognitive radio network reveals that the access pattern of a primary user (PU) typically consists of a succession of transmission periods, alternating with idle periods, each of which lasts a number of time slots. Based on this observation, it becomes clear that forcing the SU to sense the channel at the beginning of each time slot is unnecessary and may lead to considerable waste of energy. The main objective of this paper is to investigate new approaches for spectrum sensing by exploring the tradeoffs between energy consumption and secondary network throughput. To this end, we propose a stochastic, energy-aware model to derive the optimal spectrum sensing interval an SU can use to dynamically determine when the next spectrum sensing should be performed. The proposed model allows an SU to adaptively derive the sensing interval based on its required quality of service and current network state, including the PU's network activities and traffic load. Extensive simulation study is performed to assess the effectiveness of our proposed approach in achieving high accuracy with reduced energy consumption. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased secondary network throughput.

Radio Frequency Identification (RFID) technology has been widely applied in many scenarios such as inventory control, supply chain management due to its superior properties including fast identification and relatively long interrogating range over barcode systems. It is critical to efficiently identify the unknown tags because these tags can appear when new tagged objects are moved in or wrongly placed. The state-of-the-art Basic Unknown tag Identification Protocol-with Collision-Fresh slot paring (BUIP-CF) protocol can first deactivate all the known tags and then collect all the unknown tags. However, BUIP-CF protocol investigates an ALOHA-like technique and causes too many tag responses, which results in low efficiency. This paper proposes a Fast Unknown tag Identification (FUI) protocol which investigates an indicator vector to label the unknown tags with a given accuracy and removes the time-consuming tag responses in the deactivation phase. FUI also adopts the classical Enhanced Dynamic Framed Slotted ALOHA (EDFSA) protocol to collect the labeled unknown tags. We then investigate the optimal parameter settings to maximize the performance of the proposed FUI protocol. Extensive simulation experiments are conducted to evaluate the performance of the proposed FUI protocol and the experimental results show that it considerably outperforms the state-of-the-art protocol.

The benefits of cognitive radio networks have been well recognized with the dramatic development of the wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interference to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the primary users. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem of cooperative relay selection is how to select a relay efficiently. But due to the potentially large number of secondary users, it is infeasible for a PU transmitter to first scan all the SUs and then pick the best one. Basically, the PU transmitter intends to observe the SUs sequentially. After observing a SU, the PU needs to make a decision on whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory, and derive the optimal stopping rule. To evaluate the performance of our proposed scheme, we conduct an extensive simulation study. The results reveal the impact of different parameters on the system performance, which can be adjusted to satisfy specific system requirements.

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network when there exist multiple primary channels. Deviated from the existing research our work focuses on a scenario in which each secondary user has the freedom to decide whether or not to participate in cooperative spectrum sensing; if not, the SU becomes a free rider who can eavesdrop the decision about the channel status made by others. Such a mechanism can conserve the energy for spectrum sensing at a risk of scarifying the spectrum sensing performance. To overcome this problem, we address the following two questions: “which action (contributing to spectrum sensing or not) to take?” and “which channel to sense?” To answer the first question, we model our framework as an evolutionary game in which each SU makes its decision based on its utility history, and takes an action more frequently if it brings a relatively higher utility. We also develop an entropy based coalition formation algorithm to answer the second question, where each SU always chooses the coalition (channel) that brings the most information regarding the status of the corresponding channel. All the SUs selecting the same channel to sense form a coalition. Our simulation study indicates that the proposed scheme can guarantee the detection probability at a low false alarm rate.

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

Body Area Networks (BANs) are expected to play a major role in the field of patient-health monitoring in the near future. While it is vital to support secure BAN access to address the obvious safety and privacy concerns, it is equally important to maintain the elasticity of such security measures. For example, elasticity is required to ensure that first-aid personnel have access to critical information stored in a BAN in emergent situations. The inherent tradeoff between security and elasticity calls for the design of novel security mechanisms for BANs. In this paper, we develop the Fuzzy Attribute-Based Signcryption (FABSC), a novel security mechanism that makes a proper tradeoff between security and elasticity. FABSC leverages fuzzy Attribute-based encryption to enable data encryption, access control, and digital signature for a patient's medical information in a BAN. It combines digital signatures and encryption, and provides confidentiality, authenticity, unforgeability, and collusion resistance. We theoretically prove that FABSC is efficient and feasible. We also analyze its security level in practical BANs.