Conformant planning is a computationally challenging task that generates an action sequence to achieve goal condition with uncertain initial states and non-deterministic actions. The generate-and-complete (in short, GC) approach shows superior performance on conformant planning, which iteratively enumerates the solution of a planning subproblem for a single initial state and attempts to extend it for all initial states until a conform solution is found. However, two major drawbacks of the GC approach hinder its performance: the computational overhead due to state exploration and the insertion of many redundant actions. To overcome the above drawbacks, we improve both verification and completion procedures. Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF.

With the continuous development of vehicular networks, the security of in-vehicle data is receiving more and more attention. Different from traditional cryptographic techniques, the solution of physical layer security offers the advantages of lower algorithmic complexity and reduced computational demands on devices. This paper addresses the issue of resource allocation for vehicular networks in the presence of eavesdroppers. We formulate the joint optimization problem of power control and spectrum allocation under the quality of service requirements for vehicle-to-infrastructure (V2I) links with high capacity and vehicle-to-vehicle (V2V) links with low latency while ensuring the security of V2V transmission. To cope with the fast-changing channel state information in the high-mobility vehicular scenario, the optimization problem is transformed into a Markov decision process and solved by a multi-agent deep deterministic policy gradients (MADDPG) based deep reinforcement learning (DRL) algorithm. Simulation results show that the proposed MADDPG-based algorithm can effectively ensure high capacity of V2I links while ensuring the low latency of V2V secure transmission.

Intrusion detection is crucial for safeguarding intelligent vehicle systems, aiming to identify abnormal network traffic and operational anomalies. Traditional methods primarily focus on spatial features of attacks, often neglecting temporal dynamics essential for detecting complex, evolving threats. Additionally, the effectiveness of existing techniques is limited by the scope and quality of available datasets, reducing their ability to detect novel, unseen attacks. To address these challenges, this article introduces a Transformer-based transfer learning intrusion detection system (TIDS), designed to capture and analyze spatiotemporal sequence features from vehicle data. TIDS generates high-dimensional feature representations of intricate intrusion patterns, improving the detection of known attack types through instance-based transfer learning, enhancing domain adaptability. Moreover, we proposed a novel self-supervised box classification method that enhances the system’s capability to detect previously unknown attacks, thereby increasing the overall robustness of the intrusion detection process. Comparative experiments demonstrate that TIDS outperforms traditional methods in detection speed and accuracy across various intrusion scenarios, effectively responding to emerging threats in intelligent vehicle networks.

Vehicular edge computing (VEC) is an efficient solution to alleviate the limitations of local computing resources in vehicular networks. However, the high mobility of vehicles and the dynamic variability of network topologies make it significantly challengeable. In this work, we make a fusion of model-driven and data-driven intelligence to design a multi-agent deep reinforcement learning (DRL) solution for task offloading in urban VEC networks. First, computational models for task queue, transmission, computation, energy consumption, and expense are meticulously developed for the VEC network that integrates communication and computation. Vehicular tasks vary in type, urgency, size, and timeframe, leading to different latency requirements. Tasks may be executed locally within the vehicle, at a server after V2I offloading via cellular communications, or in a neighboring vehicle after V2V offloading via millimeter-wave (mmWave) communications. Each of these options incurs different levels of latency, energy consumption, and expense. Second, based on these models and the utility function that combines latency, energy consumption, and expense, an optimization problem for task offloading is formulated. This problem can be interpreted as a Markov decision process with a carefully designed reward function. Third, to address the offloading problem, we propose a multi-agent proximal policy optimization-based task and target selection algorithm (MAPPO-TTSA). This algorithm also utilizes convolutional neural networks to extract features from large-scale states, thereby enhancing their correlation. Fourth, comprehensive training is performed on the observational data to determine the optimal parameters for predicting task offloading. Finally, extensive experiments are conducted, and simulation results are provided to demonstrate that the proposed intelligent task offloading scheme offers significant advantages in terms of average task completion delay and utility level across various scenarios.

Vehicular platoon, a cutting-edge technology in the realm of intelligent transportation systems, holds the promise of transforming vehicle operations on roadways. By fostering seamless communication among vehicles and leveraging advanced automation, vehicular platoon enables vehicles to travel closely together, thereby reducing aerodynamic drag, optimizing fuel consumption, and improving road safety. However, due to their open and highly dynamic characteristics, the existing platoon communication schemes face efficiency and privacy challenges. These challenges stem from a substantial overhead on the Trusted Authority (TA) and vehicle sides during platoon communication, and a lack of multi-dimensional privacy preservation (e.g., identity privacy, location privacy, attribute privacy, and reputation value privacy) for platoon vehicles. To overcome these challenges, in this paper, we propose an efficient and multi-dimensional privacy-preserving platoon communication (EMPPC) scheme. Specifically, platoon formation is cloud-assisted and relies on the location, attribute, and reputation value of vehicles. We introduce a secure reputation value ciphertext comparison (SRCC) protocol during platoon leader selection, and present an attribute verification and matching (AVM) algorithm during platoon followers matching. Theoretical analysis and simulation evaluation demonstrate that the EMPPC scheme can offer multi-dimensional privacy preservation, and it is secure and efficient for platoon communication.

In this study, we tackle the challenge of parcel damage detection and present YTCNet to improve both accuracy and real-time performance. We enhance the Yolov5 algorithm by integrating C3TR modules into the architecture to capture more comprehensive feature information. Furthermore, we introduce the CBAM attention module to focus on critical areas in parcel images, enhancing the model’s feature extraction capabilities. By effectively combining C3TR and CBAM modules, we successfully developed an efficient parcel damage detection algorithm. Our experimental results demonstrate the superior performance of YTCNet in accuracy and real-time processing compared to various object detection models across multiple parcel damage scenarios. Our research significantly contributes to the logistics supply chain industry by aligning with IoT development. It enables practical applications to more effectively identify parcel damage, thereby enhancing customer satisfaction.

The express delivery industry has become integral to modern social life, but supply chain parcels, especially those made of corrugated cardboard, are at risk of damage during transportation. Although corrugated cardboard boxes offer some impact resistance, they can still experience deformation and penetration damage. To address this issue, we propose a hybrid model called Parcels-DNet. Parcels-DNet adopts a lightweight feature extraction backbone network, enabling deployment in resource-constrained scenarios like mobile or embedded devices. Additionally, our experimental results demonstrate that Parcels-DNet effectively captures the features of parcel deformation and penetration damage. This improves the safety and efficiency of express supply chain parcel transportation, offering greater convenience and economic benefits for the logistics industry.

Security vulnerabilities, illustrated by the menace of track traffic or vehicle hacking, present a substantial risk to the Controller Area Network (CAN) bus, enabling unauthorized remote access and intrusion. Nevertheless, existing vehicle intrusion detection models encounter challenges in capturing temporal aspects, compromising the preservation of temporal attributes in the data. Addressing this predicament, we propose a novel Vehicle Intrusion Detection System (PTIDS) model based on Principal Component Analysis (PCA) and Transformer architecture, equipped with multi-type classification decisions. This model utilizes the PCA algorithm to preprocess the data and employs a multi-head self-attention mechanism to simulate the continuous input of the real-world environment in vehicle or track traffic data. Experimental results demonstrate that the PTIDS model outperforms traditional neural networks regarding accuracy, precision, recall, and F1-score for vehicle intrusion detection, confirming the importance of temporal variables in intrusion detection. However, owing to the high accuracy and low discrimination of the PTIDS model on the Car Hacking dataset, we migrated the model to the M-CAN and B-CAN intrusion datasets and conducted ablation experiments. The results reveal that the model achieved an accuracy of 90.14% on the M-CAN dataset, surpassing other models by 54.5%, demonstrating robust generalization ability.

This paper focuses on the issue of resource allocation in vehicular networking, specifically in scenarios where vehicle-to-vehicle (V2V) links coexist with vehicle-to-infrastructure (V2I) links that utilize pre-allocated spectrum resources for communication. Different link has different quality-of-service (QoS). The V2I links commonly provide high data rate service, while the V2V links usually offer reliable safety message exchange service. However, due to the high mobility of vehicles, the communication network topology is constantly changing, which imposes a significant challenge on ensuring QoS for both V2I and V2V links with minimal overhead. To address this, we propose a joint spectrum and power allocation scheme based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which has demonstrated excellent performance in multi-intelligent cooperation scenarios and can facilitate the learning process for agents without requiring global information. Furthermore, the D-MAPPO and C-MAPPO based allocation schemes are presented for two different cases, discrete finite power choices and continuous power, respectively. To deal with the non-stationarity during training for multi-agent cooperation, the method of general advantage estimation is utilized to enhance the robustness of proposed algorithms. Finally, simulation results are given to indicate that our schemes can effectively ensure a high capacity of V2I links while guaranteeing stringent low latency and high reliability for V2V links.

In order to improve the security of IoT information systems, this paper proposes the Blockchain-based Framework for Securing IoT Information (BFSII), which is built on consortium blockchain and the edge IoT architectures. This paper addresses data security in smart hotels as a research scenario. The majority of data generated by IoT devices in smart hotels contains users' private information, which is susceptible to alteration and leakage during transmission and storage. The BFSII solution leverages the decentralized nature of blockchain to enhance data traceability and tamper-proof capabilities. And it uses edge IoT architecture and consortium blockchain to improve system operational efficiency. Sensitive data generated by IoT devices are protected in BFSII. The experiment's findings show that BFSII can boost smart hotel system security while maintaining operational effectiveness. The information management system of smart hotels is provided with an inventive and secure solution by the BFSII framework.

Content-sharing is one of the major applications of vehicular networks. To fully utilize the spectrum and the connection time, rate-compatible codes are required when sharing content. In this paper, we present a simple and flexible method to construct low-complexity rate-compatible codes for content sharing. We first present a novel construction framework for rate-compatible codes via recursive block Markov superposition transmission (rBMST). In the proposed construction, the shared content is partitioned into equal-length data chunks and transmitted directly, while their replicas are taken as the inputs of a given number of parallel systematic encoders to generate parity-check chunks. These parity-check chunks are then transmitted in parallel in a recursive block Markov superposition transmission manner. The proposed construction is flexible in the sense that codes with arbitrary rates can be obtained by adjusting the number of parallel rBMST encoders and the number of randomly punctured bits. We show that the simplest construction, using repetition to generate the parity-check chunks, leads to high-performance and low-complexity rate-compatible rBMST (RC-rBMST) codes. Specifically, the extrinsic information transfer (EXIT) chart analysis shows that asymptotic thresholds of the repetition-based RC-rBMST (RB-RC-rBMST) codes are within 0.25 dB of the channel capacities for a wide range of coding rates. Numerical results are presented to confirm the advantages of the RB-RC-rBMST codes in performance and complexity. Particularly, the RB-RC-rBMST codes perform as well as BMST-R codes but with much lower computational complexities.

One primary challenge in the high-speed train (HST) communication is that the wireless signal suffers from severe attenuation while it penetrates the sealed carriage of the train. In this letter, we show that such a nuisance can provide an opportunity of improving the uplink capacity when a full-duplex (FD) mobile relay (MR) is furnished on the train. To this end, by introducing the self-interference cancellation (SIC) level, we present an uplink channel model for the HST communication with FD MR. Then the uplink capacity is analyzed under either user equipment power constraint or total power constraint. By comparing with time division scheme, we can derive the required SIC level for the FD scheme to bring capacity improvement, which is confirmed by numerical results. Furthermore, theoretical analysis shows that, for a given SIC level, the uplink channel with severer carriage attenuation has higher capacity, which is also validated by numerical results.

Structural properties of array-based non-binary low-density parity-check (NBLDPC) codes are studied in this paper. First, we characterize graphical substructures induced by codewords of symbol weight six in array-based NBLDPC codes defined by parity-check matrices with column weight three. We also reveal necessary conditions for these graphical substructures to incur weight-6 codewords. Such conditions can be used to select nonzero elements for avoiding weight-6 codewords or reducing the multiplicity of weight-6 codewords. Second, we show that there exist weight-7 codewords in array-based NBLDPC codes defined by parity-check matrices with column weight three. As a byproduct, we find that the graphical substructure induced by a weight-7 codeword takes the graphical substructure induced by the related weight-6 codewords as a subgraph. Third, we show that there may exist codewords with symbol weight four, six, and seven in array-based NBLDPC codes defined by parity-check matrices with column weight two. These results enrich the structural analysis of array-based LDPC codes. In addition, simulation results show the performance advantage of array-based NBLDPC codes.

We are concerned with the optimal detector design for the all-bit-line MLC flash memory. We provide a channel model of the MLC flash memory, where the channel parameters are mathematically tractable. Then we present an optimal maximum a-posteriori sequence detector. The optimal detector can be executed over a trellis whose branch metrics can be computed by using Fourier transforms of analytically computable characteristic functions (corresponding to likelihood functions). The soft-output detectors for both simple one-dimensional channel models and more realistic page-orientated two-dimensional channel models are derived. Simulation results show not only that the soft-output detector has the same hard-output bit-error-rate performance as some previously known detectors did, but that the soft-output detector outperforms previously known detectors by a gain of 0.23 dB.

This paper is concerned with the Gaussian relay channel (GRC) under the one-code constraint, where the source and the relay utilize the same code to send message. An advantage of such one-code constraint is that the error propagation resulting from re-encoding can be mitigated as the relay can forward directly the decoded “codeword” to the destination. The maximal achievable rate of the considered GRC is derived using the technique of superposition block Markov encoding based on the single code. Moreover, the forward-backward (FB) decoding strategies over the sliding window are developed both at the destination and at the relay. When LDPC codes are applied to the GRC system, a practical FB message passing decoding algorithm is presented. Simulation results show that the decoding performance can be improved as the window length increases and a small length (no greater than 4) is good enough for the FB decoding, and that re-encoding at relay may degrade the decoding performance at the destination.

In this paper we derive the optimal detector for multilevel cell (MLC) flash memory channels with intercell interference (ICI). We start with the MLC channel model proposed by Dong et al. and just slightly alter the model to guarantee mathematical tractability of the optimal detectors (maximum likelihood and maximum a-posteriori sequence and symbol detectors). The optimal detector is obtained by computing branch metrics using Fourier transforms of analytically computable characteristic functions (corresponding to likelihood functions). We derive the detectors for both simple one-dimensional (1D) channel models and more realistic page-orientated two-dimensional (2D) channel models. Simulation results show that the hard-output bit error rate (BER) performance matches some previously known detectors, but that the soft-output detector outperforms previously known detectors by 0.35 dB.

A new problem formulation is presented for the Gaussian interference channels with two pairs of users, which are distinguished as primary users and secondary users, respectively. The primary users employ a pair of encoder and decoder that were originally designed to satisfy a given error performance requirement under the assumption that no interference exists from other users. In the scenario when the secondary users attempt to access the same medium, we are interested in the maximum transmission rate (defined as accessible capacity) at which secondary users can communicate reliably without affecting the error performance requirement by the primary users under the constraint that the primary encoder (not the decoder) is kept unchanged. By modeling the primary encoder as a generalized trellis code (GTC), we are then able to treat the secondary link and the cross link from the secondary transmitter to the primary receiver as finite state channels. Based on this, upper and lower bounds on the accessible capacity are derived. The impact of the error performance requirement by the primary users on the accessible capacity is analyzed by using the concept of interference margin. In the case of nontrivial interference margin, the secondary message is split into common and private parts and then encoded by superposition coding, which delivers a lower bound on the accessible capacity. For some special cases, these bounds can be computed numerically by using the BCJR algorithm. Numerical results are also provided to gain insight into the impacts of the GTC and the error performance requirement on the accessible capacity.

This paper is concerned with general interference channels characterized by a sequence of transition (conditional) probabilities. We present a general formula for the capacity region of the interference channel with two pairs of users. The formula shows that the capacity region is the union of a family of rectangles, where each rectangle is determined by a pair of spectral inf-mutual information rates. Although the presented formula is usually difficult to compute, it provides us useful insights into the interference channels. For example, the formula suggests us that the simplest inner bounds (obtained by treating the interference as noise) could be improved by taking into account the structure of the interference processes. This is verified numerically by computing the mutual information rates for Gaussian interference channels with embedded convolutional codes.

Noncontrollable finite-state channels (FSCs) are FSCs in which the channel inputs have no influence on the channel states, i.e., the channel states evolve freely. Since single-letter formulas for the channel capacities are rarely available for general noncontrollable FSCs, computable bounds are usually utilized to numerically bound the capacities. In this paper, we take the delayed channel state as part of the channel input and then define the directed information rate from the new channel input (including the source and the delayed channel state) sequence to the channel output sequence. With this technique, we derive a series of upper bounds on the capacities of noncontrollable FSCs with/without feedback. These upper bounds can be achieved by conditional Markov sources and computed by solving an average reward per stage stochastic control problem (ARSCP) with a compact state space and a compact action space. By showing that the ARSCP has a uniformly continuous reward function, we transform the original ARSCP into a finite-state and finite-action ARSCP that can be solved by a value iteration method. Under a mild assumption, the value iteration algorithm is convergent and delivers a near-optimal stationary policy and a numerical upper bound.

A new problem formulation is presented for the Gaussian interference channels (GIFC) with two pairs of users, which are distinguished as primary users and secondary users, respectively. The primary users employ a pair of encoder and decoder that were originally designed to satisfy a given error performance requirement (EPR) under the assumption that no interference exists. In the case when the secondary users attempt to access the same medium, we are interested in the maximum transmission rate (defined as accessible capacity) at which secondary users can communicate reliably without affecting the EPR under the constraint that the primary encoder (not the decoder) is kept unchanged. The relation of the accessible capacity to the capacity region of the GIFC is revealed. By modeling the primary encoder as a generalized trellis code (GTC), we are able to treat the secondary links as finite state channels. Then upper and lower bounds on the accessible capacity are derived and computed by using the BCJR algorithm. The numerical results show us either expected or interesting facts.

A non-controllable finite-state channel (FSC) is a finite-state channel in which the user can't control channel states. That is, the channel state of a non-controllable FSC evolves freely according to an uncontrollable probability law. Thus far, good upper bounds on capacities of general non-controllable FSCs remain unknown. Here we develop upper bounds that use delayed feedback and delayed state information, and propose dynamic programming methods to numerically evaluate the bounds.