With the evolution of modern networks, network operators are able to flexibly deploy Service Function Chains (SFCs) in accordance with network policy requirements to achieve desired purposes. At the same time, to reduce the latency of processing packets through SFCs, an increasing number of network operators are designing SFCs to operate in parallel. In addition, many service functions are designed to be stateful, making the processing behavior of packets state-dependent, but the state is complex and arbitrary, which introduces greater complexity and uncertainty and makes the verification of SFCs behavior more difficult. To verify the correctness of stateful parallel SFCs, a formal verification method named SPV is proposed in this paper. SPV first defines the correctness attribute of stateful parallel SFCs, and then uses Coloured Petri Nets (CPN) for offline modeling of stateful parallel SFCs. This model describes the topological structure and behavior of the stateful parallel SFCs and enhances the expressiveness of the CPN model with Standard ML Language (ML) functions. In experiments, by analyzing the reachability graph and state space of the stateful parallel SFC's CPN model, the correctness attribute of the stateful parallel SFCs are verified, completing the correctness verification of stateful parallel SFCs. Moreover, these results also demonstrate the potential of CPN-based modeling for the verification of SFCs in stateful networks.

To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25% at low SNR regimes, compared with the benchmark schemes.

Multiple unmanned aerial vehicles (UAVs) computation offloading plays a major role in the sixth generation (6G) mobile networks for resolving conflicts between computation-intensive or time-sensitive tasks and sensor nodes (SNs) with limited capabilities. Emerging artificial intelligence businesses such as multimodal data confusion and multi-video streams processing bring new challenges to UAV-aided communication and computation networks due to their multi-point and streaming characteristics. This paper focuses on a multi-UAV aided multi-point streaming tasks scenario where the SNs generate subtasks comprising various workflows randomly in a service period. We model the sequential relationship between multi-point tasks and time constraints for different workflows and aim to minimize the average energy consumption of all SNs while satisfying different time constraints by jointly optimizing the UAV-SN association policy and UAV deployment locations. To address the non-convexity problem, we decompose the problem into two subproblems, which we then solve alternately with the Markov approximation algorithm and convex optimization until convergence. Numerical results show that the proposed algorithm can effectively lower the average energy consumption of all SNs by a maximum of 35.6% compared to other schemes while maintaining a successful service ratio of 93%.

Drug repurposing is a promising strategy to find new usage for existing drugs outside of their original purpose. We have recently developed a computational framework ASGARD that employs single cell RNA-sequencing (scRNA-seq) to repurpose drugs. However, scRNA-seq lacks the spatial information of the tissue, an important determinant of the heterogeneity within the tissue micro-environment. In this paper, we propose Spatial Transcriptomics to Aid Drug-reposition Recommendation (STADS) framework, a new drug repurposing computational framework that employs Spatial Transcriptomics (ST) datasets with gene expression in situ. STADS reduces the ST gene expression by spatially-aware embedding, integrates multiple healthy and diseased samples, identifies the matched spatial domains between disease and controls, then repurposes drugs by a novel drug score that considers differentially expressed genes between disease and control samples among all matched spatial domains. We apply STADS to a ST dataset of patients with Hepatocellular Carcinoma and demonstrate its superior performance to ASGARD. STADS is a promising personalized drug repurposing prediction method using ST data.

In this paper, we focus on an intelligent reflecting surface (IRS) assisted anti-jamming unmanned aerial vehicle (UAV) communication system, where a UAV collects data from a ground user (GU) in the presence of multiple jammers with imperfect channel state information (CSI), and an IRS is deployed to strengthen the desired signal while suppressing the interference by adjusting the phase shifts of its reflecting elements properly. We aim at maximizing the energy efficiency (EE) of the system via jointly optimizing the UAV trajectory, velocity, and the IRS’s phase shift matrix. To tackle the formulated non-convex problem, we propose an efficient iterative algorithm by applying the alternating optimization (AO), semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. Simulation results demonstrate that compared to the baselines, the proposed scheme can considerably improve the EE of the system.

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach in the existing literature is by approximating the UAV trajectory with piecewise-linear path segments connected via a finite number of waypoints in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper anewandgeneralframework for UAV trajectory and communication co-design with flexible number of waypoint optimization variables (calleddesignablewaypoints) or theirsub-pathrepresentations. First, we propose aflexible path discretizationscheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, we propose a novelpath compressionscheme, which treats the UAV trajectory as a signal and compresses its path representation based on the basis decomposition. Specifically, the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths (which are generally less than the number of designable waypoints) weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed sensors, and numerically show that the proposed flexible path discretization and path compression schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.

In wireless sensor networks (WSNs) enabled by fixed ground infrastructure, sink nodes located at the margin of the coverage area often suffer from bad communication channels. In this paper, we use UAV-carried aerial base station (ABS) to assist ground base station (GBS) for data collection from sink nodes in WSN, and focus on the co-deployment of ABS and GBS from aspects of coverage partition and bandwidth resource allocation, aiming at minimizing the total energy consumption for uplink transmission of all the sensor nodes in WSN. In particular, we first derive the estimated energy consumption with the sink nodes' density instead of their exact positions. Then, to tackle the non-convexity of the original problem, we decomposing it into two subproblems, which are alternately solved to iteratively improve the sum energy consumption. Numerical results show that the proposed design significantly reduces the energy consumption of all sink nodes as compared to benchmark schemes.

Blockchain technology is adopted by many research to provide underlying security protection for the booming Inter-net of Things (IoT) industry. Nonetheless, due to some aspects of IoT, such as resource-constrained devices, heterogeneous mass devices, and real-time transactions, the traditional blockchain consensus algorithm is not suitable for the scene of blockchain-IoT integration. This paper proposes a Dual Vote Confirmation based Consensus (DVCC) mechanism for the Blockchain-IoT integration (BlockIoT). First, we adopt a role division approach to solve the problem of limited resources of devices in IoT. Second, multi-dimensional credit score, dual confirmation mechanism, classified random selection mechanism, and credit score based incentivization are designed to improve the security and fairness of the consensus. Third, a voting mechanism is proposed to improve the delay performance and avoid energy waste. Finally, the experimental results show that DVCC gains better delay and throughput performance on a large scale while guaranteeing the security and fairness of the consensus.

With their high mobility and flexible configuration, unmanned aerial vehicles (UAVs), play important roles in the development of intelligent transportation in smart cities. Due to the complex three-dimensional (3D) flight environments, obtaining an optimal collision-safe flight path is a challenge with the limited computational capability of UAVs. In this paper, we propose a UAV trajectory design method for obstacle avoidance, which could be used in UAV intelligence navigation. First, we propose a cell-varying JPS method in hexagonal cells to integrate the aerodynamic constraints into trajectory designing in a two-dimensional (2D) plane. Second, we develop the cell-varying JPS with modified pruning and jumping rules and extend this modified version to trajectory-planning in the three-dimensional (3D) environments. Our simulation results demonstrate that the proposed cell-varying JPS achieves the reduction up to 4.2% in path length compared with the existing method while guaranteeing a safe flight.

In order to adapt to different vehicle densities, a path connectivity probability based cross-layer routing handoff mechanism is proposed under software defined vehicle ad hoc networks (SDVN). In this mechanism, we employ the derived path connectivity probability model to predict the density degree of a path, which will be used to decide whether to use base stations (BSs) assisted routing or V2V cross-layer routing. Particularly, in the process of performing V2V cross-layer routing, we utilize the information such as connectivity probability, transmission rate, and load to further improve the effectiveness of the proposed mechanism. Simulation results show that our proposed routing algorithm achieves significant gains in terms of delivery ratio and average delay, compared with several existing routing protocols.

Wireless sensor network (WSN) has attracted a lot of interest for its wide range of practical applications. The energy-resource limitation of WSN demands transmissions between sensor nodes to be high efficiency. Random linear network coding (RLNC) exploits the broadcast and distributed nature of WSN to improve the throughput of network. The throughput gain brought by RLNC grows up as the generation size increased, while the decoding complexity has a cubic growth, being unacceptable for the energy-resource limited WSN. In this paper, we propose a segment linear network coding (SLNC) scheme which reduces the decoding complexity dramatically through adding constraints to the encoding coefficients and dividing a complex matrix inverse operation into several simple ones at receiver. Proved by theoretical analysis and simulation results, we show that SLNC achieves a fairly low decoding complexity with seldom increase on the overhead of network.