In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL.

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.

Ultradense networks are widely regarded as a promising solution to explosively growing applications of Internet of Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-OMA (NOMA) are considered under base station (BS) clustering. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computation resources, and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control, and computation offloading are performed for minimizing the overall energy consumed by all IMDs. It is noteworthy that multistep computation offloading is concentrated to balance the network loads and fully utilize computation resources. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computation complexity, and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under strictly satisfying constraints of latency and security cost.

Current mainstream biometric user authentication approaches are based on passive measurements of the subject's characteristics, and usually come with less-than-satisfactory accuracy. This paper takes a unique approach to biometric authentication. Specifically, instead of training a machine learning algorithm to recognize a legitimate user, the paper proposes a hybrid type of training, in which the legitimate user is also trained to use a customized instance of the machine. The user thus achieves a level of artificially-induced expertise to interact with the machine, which makes the user easier to recognize. We implement this concept in a mouse-based user authentication system, in which we produce customized machine instances by introducing an angle offset to the standard mouse. Human subjects then rely on their kinesthetic intelligence to achieve motor learning and visual-motor adaptation to the modified mouse. We design a 7-week IRB-approved experiment, collect data from 18 human subjects over this period, and evaluate the proposed approach with two existing state-of-the-art mouse-based authentication schemes. We find that, in both schemes, our approach significantly outperforms the baseline in which a regular unaltered mouse is used. Somewhat surprisingly, results also show that our approach improves the authentication performance even when both legitimate and non-legitimate users are trained to exactly the same instance of customized machine (i.e., the same mouse angle offset). In addition, we also observe that users can generally maintain their learned expertise even after one week of washout, which further demonstrates the practicality of the approach. Finally, we present a practical strategy to manage the enrollment of users in such a proposed system.

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.

Biometric user authentication is at the core of multifactor authentication, and mouse-based biometric authentication comes at no additional cost for most computer systems. This paper describes a mouse-based user authentication scheme, called MAUSPAD, which uses a novel progress-adjusted dynamic time warping (PADTW) algorithm, along with a segmentation algorithm, to accurately and meaningfully measure the differences between observed data and reference data. By introducing a new concept, which we call progress, into standard DTW, the new PADTW can have better control of the warping and mapping process and hence is more suitable for comparing time-stamped spatial sequences such as mouse cursor movements. Furthermore, in order to preserve the important but transient details in the cursor movement (which may be critical in identifying a specific user), we apply a segmentation algorithm to divide each reference cursor movement into multiple smaller segments, and measure the differences between cursor movements at the segment level. Evaluation results on two mouse-behavior datasets show that MAUSPAD yields the best overall performance among tested schemes, and demonstrate the effectiveness of PADTW over DTW, and segmentation over non-segmentation. The processing techniques developed herein can be extended to applications that rely on sequence comparison, and where relevant sequence information spans multiple semantic domains.

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach.

The energy reduction for wireless systems becomes more and more important due to its impact on the operation cost and global carbon footprint. In this paper, we design two kinds of energy-efficient association schemes under an open loop power control for uplink heterogeneous cellular networks (HCNs), which are formulated as problems with maximizing sum energy efficiency (EE) and EE utility respectively. In them, the second scheme integrates with the load balancing level and user fairness. Since the first problem is in a simple form, we can easily solve it without any iteration. As for the second problem, we first introduce a dual variable to decouple the constraint and then develop a distributed algorithm using dual decomposition. In addition, we also give some convergence proofs for the proposed algorithms. In the simulation, we investigate the influences of different parameters on the association performance of designed association schemes.

As one of the most popular services over online platforms, social recommendation has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is item recommendation over high speed social media streams. Existing stream recommendation techniques are not effective for handling social users with diverse interests. Meanwhile, approaches for recommending items to a particular user are not efficient when applied to a huge number of users over high speed streams. In this paper, we propose a novel framework for the social recommendation over streams. Specifically, we first propose a novel Bi-Layer Hidden Markov Model (BiHMM) that adaptively captures the users' behaviors and their interactions with influential official accounts to predict their long-term and short-term interests. Then, we design a new probabilistic entity matching scheme for identifying the relevance score of a streaming item to a user. Moreover, we propose a novel index scheme called CPPse-index for improving the efficiency of our solution. Extensive tests are conducted to prove the superiority of our approach in terms of the recommendation quality and time cost.

This paper investigates the maximal ratio combining (MRC) performance of an amplify and forward (AF) relay system in Nakagami- m fading environments. The study considers a general scenario with distinct m fading parameters for the following three links, source to relay link, and source to destination link and relay to destination link. We derive new closed form expressions for the statistics of important performance metrics, including the moment generating function, outage probability, higher order moments of equivalent signal to noise ratio (SNR), ergodic capacity, and average symbol error probability (SEP) of common modulation types. In particular, we focus on analytical SEP expressions in the context of an additive white generalized Gaussian noise (AWGGN). As an active area of research, generalized noise receives much attention for its flexible model. However, analytical performance of modulation scheme in generalized noise type has not been found in open literature for AF relaying with MRC despite its practical usefulness. Without the help of analytical solutions, the SEP in generalized noise can only be obtained by a large number of repeated simulation experiments. Therefore, we present the general SEP expression by using special Fox’s H function. Simulation results verify the accuracy of our theoretical analysis and show that the diversity order of MRC criterion linearly depends upon Nakagami parameters of three links.

Network alignment aims at finding a bijective mapping between nodes of two networks. Due to its wide application in various fields (e.g., Computer Vision, Data Management, Bioinformatics, and Privacy Protection), researchers have proposed many network alignment algorithms, most of which rely on a set of pre-mapped seeds. However, it is challenging to identify an initial credible set of seeds solely with structural information. In this paper, by exploiting the observation that a true mapping leads to a large portion of consistent edges among the mapped nodes, we formally define the credibility of a mapping as its deviation from a random one. This enables us to measure the credibility of an initial set of seeds. We also present DeepMatching which is a seed identification framework for social network alignment. First, we represent the nodes of the two mapping networks with their structural feature vectors by employing graph embedding techniques. Second, we obtain an initial mapping of the nodes based on the obtained vectors by leveraging point set registration methods. Third, we develop a heuristic algorithm to extract a credible set of seed from the initial mapping. Finally, we utilize the extracted seed set as input of an efficient propagation-based algorithm for large scale network alignment. We conduct extensive experiments to evaluate the performance of DeepMatching, and the results clearly demonstrate its effectiveness and the efficiency.

Radar and communication (RadCom) systems have received increasing attention due to their high energy efficiency and spectral efficiency. They have been identified as green communications. This paper is concerned with a joint estimation of range‐Doppler‐angle parameters for an orthogonal frequency division multiplexing (OFDM) based RadCom system. The key idea of the proposed method is to derive different factor matrices by the tensor decomposition method and then extract parameters of the targets from these factor matrices. Different from the classical tensor decomposition method via alternating least squares or higher‐order singular value decomposition, we adopt a greedy based method with each step constituted by a rank‐1 approximation subproblem. To avoid local extremum, the rank‐1 approximation is solved by using a multiple random initialized tensor power method with a comparison procedure followed. A parameterized rectification method is also proposed to incorporate the inherent structures of the factor matrices. The proposed algorithm can estimate all the parameters simultaneously without parameter pairing requirement. The numerical experiments demonstrate superior performance of the proposed algorithm compared with the existing methods.

This paper is devoted to an investigation of an exact average symbol error probability (SEP) for amplify and forward (AF) relaying in independent Nakagami‐m fading environments with a nonnegative integer plus one‐half m, which covers many actual scenarios, such as one‐side Gaussian distribution (m = 0.5). Using moment generating function approach, the closed‐form SEP is expressed in the form of Lauricella multivariate hypergeometric function. Four modulation modes are considered: rectangular quadrature amplitude modulation (QAM), M‐ary phase shift keying (MPSK), M‐ary differential phase shift keying (MDPSK), and π/4 differential quaternary phase shift keying (DQPSK). The result is very simple and general for a nonnegative integer plus one‐half m, which covers the same range as integer m. The tightness of theoretical analysis is confirmed by computer simulation results.

This study considers an improved decode and forward (DF) protocol in an orthogonal frequency division multiplexing‐based cooperative system, where the improved DF protocol implies that the source node is allowed to emit the same symbol in the second phase as that in the first phase, irrespective to whether the relay is idle or not. Because rate provision is one of the main design goals in wireless network, the authors construct an optimisation problem to improve the overall sum rate of the system and propose a joint power allocation and subcarrier pairing algorithm. Total power constraint and individual power constraints at the source node and the relay node will be treated differently. Both theoretical analysis and simulation results demonstrate that the authors' proposed joint algorithm for the improved DF protocol drastically harvests remarkable gains from the extra repeat transmission in the second phase and is superior to other existing methods.

Physical‐layer security from an energy‐efficient perspective is of crucial importance in cognitive radio (CR). A CR network is considered where a secondary user (SU) coexists with a primary user in the presence of an eavesdropper and channel fading. Secure energy efficiency (EE) maximisation problems are formulated in secure green CR based on the condition that a minimum secrecy capacity (SC) of a SU is guaranteed. A peak interference power constraint and an average (ATP)/peak transmit power (PTP) constraint are imposed in the SU's Tx. Using fractional programming and the Lagrange dual method, energy‐efficient optimal power allocation strategies are proposed to efficiently solve the secure EE maximisation problems. It is shown that the secure EE of the SU achieved under the ATP constraint is higher than that obtained under the PTP constraint. The tradeoff is elucidated between the secure EE and the SC of the SU.