Seamlessly unifying communication and sensing, sixth-generation (6G) networks are poised to transform into intelligent platforms with high spectral–energy efficiency and real-time environmental awareness. In the low-altitude economy, unmanned aerial vehicles (UAVs) enable air–ground integrated sensing and communication (ISAC) for applications such as logistics and inspection, yet most studies focus on single-UAV or homogeneous-agent designs. In contrast, this paper proposes a multi-UAV cooperative ISAC system that enables heterogeneous-agent collaboration between multiple UAVs and a ground base station (BS) for joint target sensing, tracking, and communication. The system is formulated as a posterior Cramér–Rao bound (PCRB) minimization problem under communication performance constraints, utilizing joint trajectory–beamforming optimization. To tackle the NP-hard nature of this problem, we design a curriculum-based heterogeneous-agent proximal policy optimization (C-HAPPO) algorithm, where curriculum learning guides progressive policy refinement and Kronecker/QR decomposition mitigates action dimensionality. Simulation results show that the proposed approach achieves more than a 30% improvement in sensing performance, faster convergence, and higher tracking accuracy than existing baselines, demonstrating its scalability and effectiveness for complex multi-UAV ISAC scenarios.

In the wake of the swift evolution of technologies, such as the Internet of Things (IoT), the global data landscape is undergoing an exponential surge, propelling DNA storage into the spotlight as a prospective medium for contemporary cloud storage applications. This article introduces a semantic artificial intelligence-enhanced DNA storage (SemAI-DNA) paradigm, distinguishing itself from prevalent deep learning (DL)-based methodologies through two key modifications: 1) embedding a semantic extraction module at the encoding terminus, facilitating the meticulous encoding and storage of nuanced semantic information and 2) conceiving a forethoughtful multireads filtering model at the decoding terminus, leveraging the inherent multicopy propensity of DNA molecules to bolster the system fault tolerance, coupled with a strategically optimized decoder’s architectural framework. Numerical results demonstrate the SemAI-DNA’s efficacy, attaining 2.61 dB peak signal-to-noise ratio (PSNR) gain and 0.13 improvement in structural similarity index (SSIM) over conventional DL-based approaches.

With the advancement of nanotechnology, the Bio-Internet of Things(B-IoT) and nanonetworks have become important research focuses. Compared to electromagnetic communication, molecular communication is not constrained by device size and has outstanding low-power consumption and biocompatibility, making it a promising approach for realizing nanonetworks. However, when multiple nanomachines are present, using the same type of information molecule leads to severe inter-user interference. Since the available types of information molecules are limited, simply increasing their number is not feasible and also raises the detection complexity at the receiver. In this paper, we propose a non-negative signal-based code-division multiple access (NCMA) scheme that enables communication among multiple nanomachines using only one type of information molecule. By assigning unique orthogonal codewords to different users and employing a non-uniform sampling and quantization scheme, the proposed NCMA effectively distinguishes information from different users and enhances interference resistance. Simulation results demonstrate that this scheme can support up to 16 nanomachines communicating with each other using only a single type of information molecule, while significantly reducing inter-symbol interference, making it a viable and effective communication solution for B-IoT.

The exponential increase of data has motivated advances of data storage technologies. As a promising storage media, deoxyribonucleic acid (DNA) storage provides a much higher data density and superior durability, compared with state-of-the-art media. In this article, we provide a tutorial on DNA storage and its role in molecular communications (MCs). First, we introduce the fundamentals of DNA-based MCs and storage (MCS), discussing the basic process of performing DNA storage in MCS. Furthermore, we provide tutorials on how conventional coding schemes that are used in wireless communications can be applied to DNA-based MCS, along with numerical results. Finally, promising research directions on DNA-based data storage in MCs are introduced and discussed in this article.

The future of the Internet of Things (IoT) holds great promise, particularly in the realm of healthcare, where the concept of Bio-IoT (B-IoT) has gained significant attention. B-IoT involves the coordination of monitoring and treatment within the human body using bio-implants that require communication. However, how to efficiently communicate among bio-implants is seldom studied. Molecular communication (MC), which uses molecules as information carriers, is a novel communication method of nano-devices for its excellent bio-compatibility and low energy consumption. In every part of the body, there is a micro-circulation network (MCN) responsible for substance exchange which can be utilized as a channel to deliver information efficiently by bio-implants. However, since the structure of MCN is complicated and the characteristics of blood flow vary, there is not yet a mature channel modeling on MCN, making it impossible to design and evaluate the performance of B-IoT. In this article, we address the need for efficient communication channels in B-IoT by exploring the potential of MCNs in MC. We have fully analyzed the characteristics of MCN and blood flow and derived the mathematical model of channel impulse response. We also built a simple end-to-end communication model based on MCN and analyzed its error probability and mutual information from a communication perspective. The numerical results have shown that MCN is an effective communication channel of MC for B-IoT in the scale of${\mu }m$and mm.

The development of the intelligent Internet of Things has facilitated the adoption of high-efficiency Multiple Targets Tracking (MTT) in many civil security applications. However, existing MTT technologies cannot offer full capability in accurate and real-time MTT for civil security. Many attractive applications in the next-generation wireless network, like Unmanned Aerial Vehicle (UAV) swarm, are envisioned to be exploited for enhanced MTT with the advantage of flexibility. Nonetheless, highly dynamic moving targets impose some new challenges. UAVs cannot always perform expected cooperative tracking in conventional architectures as well. To address these problems, we design a tiered Digital Twin-assisted tracking framework in this paper, which leverages multi-grained imitation for real-time and accurate MTT. We imitate a coarse-grained MTT to ensure a high successful tracking ratio. We then design a fine-grained imitation with a reaction-diffusion mechanism to explore the feasible cooperators based on trajectory prediction. Hardware-in-the-loop simulations demonstrate that our tiered framework can reduce 66.7% of the system latency overhead compared to the conventional DDPG benchmark while improving the successful tracking ratio by 30.6%.

A novel nanobiosensing framework named “in vivo computation” has been proposed recently, where the challenge of early tumor detection is overcome from an optimization perspective. The biological gradient field (BGF) triggered by the tumor lesion is viewed as the optimizable objective function with the tumor site being the global optimum. The externally manipulable nanorobots playing the role of agents are manipulated in the search space (i.e., the vascular network of high-risk tissue). Several computational strategies have been proposed to realize tumor targeting by overcoming the in vivo constraints which focused on the tumor detection process without any emphasis on the nanorobots aggregation at the tumor. In this paper, we focus on the utilization rate of agents, which means to improve the percentage of nanorobots that detect the tumor site after it has been found by the first arrival agent (i.e., the nanorobot that detects the tumor at the earliest), and the solution set search, which means to find the tumor region as whole as possible. This process is interpreted as the exploitation process of in vivo computation. An exploitation approach named center-aided weak priority evolution strategy (CWP-ES) is developed for the setting of nanorobot moving direction in this paper. In the approach, a direction generated by the center of agents that have found the tumor mixed with the direction generated by the weak priority evolution strategy (WP-ES) proposed in the previous work is used to steer the motion of nanorobots that have not detected the tumor. Several numerical experiments are performed in a 3D search space to show the effectiveness of this novel computational nanobiosensing approach in three BGF landscapes with different degrees of optimization complexity.

We have recently proposed a framework of in vivo computation (IVC) which transforms the early tumor sensing problem into a computational problem. In the framework, a tumor-triggered biological gradient field (BGF) guides the swarm-intelligence-assisted targeting process, where externally manipulable and trackable magnetic nanorobots act as computational agents for the optimization procedure. As BGF can be viewed as an objective function which is utilized to define the fitness landscape for the agents, the inherent attributes of BGF are critical to the IVC process. All our previous investigations are based on the hypothesis that the BGF landscape remains time invariant during the tumor-targeting process, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will naturally lead to time-dependent variation of BGF, which means that the targeting process should be modeled as a dynamic function optimization problem. Based on this consideration, we focus on dynamic IVC by considering different variation patterns of BGF in this article. Two computational intelligence strategies named “swarm-based learning” and “individual-based learning” are proposed for dealing with the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategies. In addition, the above process is conducted in a 3-D search space, where the tumor vascular network is generated by an invasion percolation algorithm, which is more realistic compared to the 2-D search space in our previous works.

Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are firstly realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.

We have recently proposed a novel framework of in vivo computation by transforming the early tumor detection into an optimization problem. In the framework, the tumor-triggered biological gradient field (BGF) provides aided knowledge for the swarm-intelligence-assisted tumor targeting process. Our previous investigations are based on the hypothesis that the BGF landscape is time-invariant, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will bring about time-dependent variation of BGF. Thus, we focus on dynamic in vivo computation by considering different variation patterns of BGF in this paper. A computational intelligence strategy named “swarm-based learning strategy” is proposed for overcoming the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategy. In addition, the above process is conducted in a three-dimensional search space, which is more realistic compared to the two-dimensional search space in our previous work.

DNAs exist in nature and could provide solutions to computing, storage, and communications as existing ones approach their physical limits. Plenty of research work has been conducted on DNA-based computing, data storage and molecular communications (MCs), but largely in isolation. There is a lack of a unified place where the triples are put together to be discussed. This article aims to filling in this gap by providing an overview of each triplet. Starting from an overall description of DNA features and their reading and writing in practical terms, this article goes on to describe each of the three from three aspects: 1) requirements and differences from current electronic-dominating technologies; 2) their working principles; and 3) practical considerations. Recent advancement in each area is summarized and discussed. Furthermore, this article intends to call for researches that go beyond the boundary of each and encourages interconnection and joint research among the three. It proposes a molecular information and communication technology (ICT) system architecture with all its three components underpinned by DNAs. This article also identifies and discusses some new future directions, such as joint coding for storage and communications, directional DNA-based MCs, interfaces between molecular DNA systems and electronic systems. It is hoped that this article can spark more joint research across computing, storage, and communications in this exciting field of DNA-based molecular ICT systems.

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep${Q}$-learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep$Q$-network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

Considering not only reducing the energy consumption based on the fixed energy source but also capturing new energy by RF energy harvesting (EH) for Internet-of-Things (IoT) devices, an integrated energy-efficient strategy for IoT devices is proposed in this article. On the one hand, a node sampling scheduling algorithm based on matrix completion is designed. All the sampling data are handled as matrix elements. The basic idea is to reduce the sampling data and then reconstruct the complete data set with the technology of matrix completion by using the spatial-temporal correlation of all the sampling data. Thus, the IoT nodes can keep dormant more instead of working on data sampling. The energy consumption can be significantly reduced with very little data loss. On the other hand, an adaptive RF energy management strategy is introduced. Based on the self-designed data and energy integrated network (DEIN) system, both data and energy are transferred between the DEIN gateway (DEING) and the DEIN Node (DEINN). With the adaptive energy management strategy, they both automatically switch between the wireless information transfer (WIT) mode and the wireless energy transfer (WET) mode. Combining energy saving with EH, the energy efficiency can be greatly enhanced. The proposed integrated solution aims to decrease the energy consumption of IoT devices and provide them with constant new energy by RF EH. Thus, their battery lives can be prolonged. Both the effectiveness and the efficiency of the proposed integrated strategy have been validated with the simulation.

This article studies an unmanned aerial vehicle (UAV)-enabled edge-cloud system, where UAV acts as a mobile edge computing (MEC) server interplaying with remote central cloud to provide computation services to ground terminals (GTs). The UAV-enabled edge-cloud system implements a virtualized network function, namely, mobile clone (MC), for each GT to help execute their offloaded tasks. Through such network function virtualization (NFV) implemented on top of the UAV-enabled edge-cloud system, GTs can have extended computation capability and prolonged battery lifetime. We aim to jointly optimize the allocation of resource and the UAV trajectory in the 3-D spaces to minimize the overall energy consumption of the UAV. The proposed solution, therefore, can extend the endurance of the UAV and support reliable MC functions for GTs. This article solves the complicated optimization problem through a block coordinate descent algorithm in an iterative way. In each iteration, the allocation of resource is modeled as a multiple constrained optimization problem given predefined UAV trajectory, which can be reformulated into a more tractable convex form and solved by successive convex optimization and Lagrange duality. Second, given the allocated resource, the optimization of the trajectory of rotary-wing/fixed-wing UAV can be formulated into a series of convex quadratically constrained quadratically program (QCQP) problems and solved by the standard convex optimization techniques. After the block coordinate descent algorithm converges to a prescribed accuracy, a high-quality suboptimal solution can be found. According to the simulation, the numerical results verify the effectiveness of our proposed solution in contrast to the baseline solutions.

This paper presents a biologically inspired design and its implementation of macroscale mobile molecular communication systems. Our design of macroscale mobile molecular communication systems consists of a statically placed target and mobile nodes that autonomously move in the environment. The target keeps releasing molecules to create a concentration gradient in the environment, and mobile nodes sense the concentration gradient in the environment and move toward the target according to an algorithm inspired by bacterial chemotaxis. The proposed design of macroscale molecular communication systems is implemented using electrical sprays, mobile robots and alcohol molecules, and evaluated experientially. The proposed design and implementation provides a new tool to study mobile molecular communication systems.

Image captioning is an important problem in artificial intelligence, related to both computer vision and natural language processing. There are two main problems in existing methods: in the training phase, it is difficult to find which parts of the captions are more essential to the image; in the caption generation phase, the objects or the scenes are sometimes misrecognized. In this paper, we consider the training images as the references and propose a Reference based Long Short Term Memory (R-LSTM) model, aiming to solve these two problems in one goal. When training the model, we assign different weights to different words, which enables the network to better learn the key information of the captions. When generating a caption, the consensus score is utilized to exploit the reference information of neighbor images, which might fix the misrecognition and make the descriptions more natural-sounding. The proposed R-LSTM model outperforms the state-of-the-art approaches on the benchmark dataset MS COCO and obtains top 2 position on 11 of the 14 metrics on the online test server.

Switching is an indispensable functionality in traditional computer networks. Inspired by computer networks design, this paper investigates the switching functionality for molecular communication networks. In particular,we design channel switches for molecular communication among biological cells through calcium signaling. First, we extend mathematical models of calcium signaling by incorporating gating models of gap junction channels. Second, we show how channel switches may be designed based on the mathematical models, with numerical results demonstrating the switching functionality. Further, we discuss design issues for practical application of channel switches. This paper shows through mathematical modeling and numerical experiments that channel switches are feasible and indicates that complex molecular communication networks may be designed using channel switches.

Nervous cells and blood vessels form crucial circulating networks in human body. They are interdependent on biological level and communicate with each other via abundant interaction phenomena. These interactions are complex while worthy concerned, which may guide to implement controllable relaying communication across nervous and blood vascular heterogeneous channels. In this paper, we highlight the heterogeneous theme in area of molecular communication. We set up a basic framework based on the heterogeneous network interactions, in which two properties are proposed to make those interactions easier understood on communication level. Moreover, we establish a one-way single-threaded channel model as a case study based on the network framework, and give the mutual information expressions. We aim to explore the possibility of effective communication. The results show that settings of the adjustable system have a significant impact on the relaying performance, as well as the mutual information.

Transfer visual feature learning (TVFL), which learns compact representations for images such that we can build accurate classifier for target domain by leveraging rich labeled data in the source domain, has attracted increasingly attention recently. Previous methods mainly focus on reducing the distribution difference between domains but ignore the intrinsic hidden semantics in data. In this paper, we put forward a novel method for TVFL, called Distribution Regularized Nonnegative Matrix Factorization (DRNMF). Specifically, we employ Nonnegative Matrix Factorization (NMF) to uncover the intrinsic information in visual data, and regularize it with geometrical distribution, marginal probability distribution and conditional probability distribution. Thus, DRNMF can discover the intrinsic information, preserve the manifold structure and reducing both marginal and conditional probability distribution difference simultaneously, which all perspectives above are important for TVFL. We also propose an effective and efficient algorithm for the optimization of DRNMF and theoretically prove the convergence. Extensive experiments on three types of cross-domain image classification tasks in comparison with several state-of-the-art methods demonstrate the superiority of our DRNMF, which validates its effectiveness.

Concept Factorization (CF), as a variant of Nonnegative Matrix Factorization (NMF), has been widely used for learning compact representation for images because of its psychological and physiological interpretation of naturally occurring data. And graph regularization has been incorporated into the objective function of CF to exploit the intrinsic low-dimensional manifold structure, leading to better performance. But some shortcomings are shared by existing CF methods. 1) The squared loss used to measure the data reconstruction quality is sensitive to noise in image data. 2) The graph regularization may lead to trivial solution and scale transfer problems for CF such that the learned representation is meaningless. 3) Existing methods mostly ignore the discriminative information in image data. In this paper, we propose a novel method, called Robust and Discriminative Concept Factorization (RDCF) for image representation. Specifically, RDCF explicitly considers the influence of noise by imposing a sparse error matrix, and exploits the discriminative information by approximate orthogonal constraints which can also lead to nontrivial solution. We propose an iterative multiplicative updating rule for the optimization of RDCF and prove the convergence. Experiments on 5 benchmark image datasets show that RDCF can significantly out-perform several state-of-the-art related methods, which validates the effectiveness of RDCF.

Neuro-spike communication is an important branch of molecular communications and has attracted much attention recently. Seminal works on the analyses of signal processing and channel models for the synaptic communication have recently been carried out. However, these works do not consider interference. In this paper, we propose an interference model for synaptic channels with particular focus on InterSymbol Interference (ISI) and Single-Input Single-Output (SISO) channel. We have investigated the overlapping between the two consecutively signals which are sent from a presynaptic terminal to a postsynaptic terminal and their interferences. Furthermore, important parameters of synaptic communication channel that are related to the ISI are also analyzed. The relationship between channel rate region and ISI is also studied.

Spectral-based hashing (SpH) is the most used method for cross-view hash function learning (CVHFL). However, the following three problems are shared by many existing SpH methods. Firstly, preserving intra- and inter-similarity simultaneously increases models' complexity significantly. Secondly, linear model applied in many SpH methods is hard to handle multimodal data in cross-view scenarios. Thirdly, to learn irrelevant multiple bits, SpH imposes orthogonality constraints which decreases the mapping quality substantially with the increase of bit number. To address these challenges, we propose a novel SpH method for CVHFL in this paper, referred to as Kernel-based Supervised Hashing for Cross-view Similarity Search (KSH-CV). We prove that the intra-adjacency matrix is redundant given inter-adjacency matrix. Then we define our objective function in a supervised and k-ernelized way which just needs to preserve inter-similarity. Furthermore a novel Adaboost algorithm, which minimizes exponential mapping loss function for cross-view similarity search, is derived to solve the objective function efficiently while avoiding orthogonality constraints. Extensive experiments verifies that KSH-CV can significantly outperform several state-of-the-art methods on three cross-view datasets.

In Mobile Social Networks (MSNs), users with specific relationships are usually treated as a community for data sharing. However, the demand of data sharing among distributed strangers also exists. Those users that have the same interest but do not necessarily know or usually encounter each other can form a gossip community and share information. This paper proposes a data dissemination approach, i.e., the Gathering Point-aided Spreading (GPS) algorithm, which explores the encounter pattern of users and the aid of gathering points to facilitate data sharing in the gossip community. Based on the past encounter pattern, the GPS algorithm predicts the encounter probability among users and assigns the best users to carry the data for a wide spreading. Moreover, by storing a copy of data at the gathering points, GPS enables a further sharing of the data even the carriers leave the gathering points. With different utility functions, GPS can be modified into three versions (GPS-DR, GPS-DE and GPS-TR). Simulation experiments show that GPS outperforms SocialCast in both delivery ratio and delay in data sharing. In addition, among the three versions, GPS-DR and GPS-DE perform the best in terms of delivery ratio and delay respectively, while GPS-TR makes a tradeoff between them.

Molecular communication is a novel paradigm that uses molecules as an information carrier to enable nanomachines to communicate with each other. The two major components of a diffusion-based molecular communication are diffusion in the medium and the ligand-reception. In this paper, using the principles of diffusion theories and natural ligand-receptor binding mechanisms in biology, we first develop and present model for the molecular single-access channel. Then, we derive the capacity expressions of the molecular single-access channel. Furthermore, we extend it to multiple-access channel in which multiple transmitters communicate with a single receiver. The objective of this paper is to study the capacity of multiple-access channel which is effected by the parameters of diffusion channel and ligand-receptor binding mechanisms. The numerical results show that the overall channel capacity is restricted by the physical parameters of diffusion channel and ligand-receptors and some different characteristics are presented in multiple-access channel scenario comparing with single-access channel scenario.