Unmanned Aerial Vehicles (UAVs), as an aerial extension of the Internet of Things (IoT) sensing layer, have played an increasingly important role in applications such as environmental monitoring, disaster assessment, and precision agriculture. These tasks can be uniformly abstracted as Coverage Path Planning (CPP), which aims to achieve efficient scanning and surveying while ensuring complete coverage of single or multiple disconnected regions. While single-region CPP has been extensively studied, in multi-region settings existing methods often rely on predefined coverage patterns to guarantee completeness, which to some extent limits their flexibility. Meanwhile, constraints on UAV energy and onboard computation impose higher performance requirements on planning methods. To address these challenges, this paper targets an energy-constrained multi-UAV cooperative scenario and proposes a cross-layer, energy-constrained path optimization framework based on multi-agent reinforcement learning (CLMPO-EC). Specifically, the framework organizes the overall task into two layers—CPP and multi-agent path planning—and, on this basis, integrates Back-and-Forth Planning (BFP) with multi-agent reinforcement learning under a centralized training and distributed execution paradigm to construct a unified, interactive, and structured environmental model. CLMPO-EC further introduces a lightweight cross-layer connection network that propagates raw state information to higher layers to enhance learning efficiency. In addition, building on BFP, an entrance–exit exploration factor is proposed to dynamically adjust the exploration probability of regional entrances and exits in CPP according to the training phase and batch, thereby improving the efficiency of searching for optimal solutions. Theoretical analysis and experimental results demonstrate that the proposed method achieves superior performance in terms of optimality and efficiency.

The slime mold algorithm (SMA) is a metaheuristic optimization algorithm that simulates the foraging behavior of slime molds. Compared to other optimization algorithms, SMA has fewer parameters, faster convergence speed, and stronger optimization capabilities. However, the standard SMA uses two randomly selected individuals to guide the search direction of the population, which results in excessive randomness during the search process. This can lead to the loss of valuable information and waste computational resources. To overcome these limitations, this study proposes an enhanced slime mold algorithm (S2SMA) based on a spiral sensing search mechanism. The main contributions of this study are as follows: Firstly, a fitness–distance balanced oscillation search mechanism is introduced to solve the issue of lack of guidance in the individual oscillatory search phase in the original SMA, thus enhancing the global exploration ability of the algorithm. Secondly, the spiral sensing search mechanism is introduced, reshaping the random redistribution behavior in SMA. This aims to fully utilize the effective information in the existing population, improve search efficiency, and enhance population diversity. Finally, the computational logic of SMA is restructured based on the existing parameters, improving the algorithm’s performance while avoiding additional computational overhead. To validate the effectiveness of the proposed S2SMA, experiments were conducted on 71 test instances from the IEEE CEC2017 and IEEE CEC2021 benchmark sets, as well as three engineering problems. The algorithm was compared with classical algorithms, high‐performance algorithms, and advanced SMA variants. Experimental results show that S2SMA outperforms the classical algorithms, high‐performance algorithms, and other SMA variants in terms of both performance and robustness, demonstrating its potential application in engineering optimization.

Drunk-driving is an important factor causing road traffic accidents and deaths, which deserves a lot of research. However, most current methods for detecting drunk-driving depend on customized hardware or require users’ active participation, making it impractical to monitor blood alcohol content (BAC) during driving. This article introduces BACFuse, a device-free, contactless, and noninvasive system utilizing smartphone in driving environments, which achieves relatively high accuracy in drunk-driving monitoring by integrating various voice sensing modalities. BACFuse first captures vocal cord vibration from ultrasonic signals, then records voice commands from audio signals. BACFuse combines the ultrasonic signals with audio signals and effectively detects drunk-driving and BAC. A key enabler lies in our modeling of latent interaction between acoustic and ultrasonic signals to mitigate ambient noise, realizing noise-resistant drunk-driving detection. Additionally, we propose an effective modules within the co-attention method to fuse the multimodal signals, further enhancing the accuracy of drunk-driving detection. We conduct extensive experiments to evaluate BACFuse’s performance on 20 participants in safe laboratory experiments. The results demonstrate that our system achieves BAC measurement with an MAE of 2.13 mg/dl, showing promise for future in-car driving management paradigms.

With an increasing number of sensor devices connected to industrial systems, the efficient and reliable aggregation of sensor data has become a key topic in Industrial Internet of Things (IIoT). Computing First Networks (CFN) are emerging as a promising technology for aggregating vast quantities of IIoT data. However, existing CFN data collection frameworks are usually centralized, which overly rely on third-party trusted authorities and fail to fully schedule and utilize limited computing resources. More critically, that is prone to trust and security issues. In this paper, considering the heterogeneity and data security in complex industrial scenarios, we propose a blockchain-based and multi-edge CFN collaborative IIoT data hierarchical collection framework (ME-CIDC) to collect massive IIoT data securely and efficiently. In ME-CIDC, a blockchain-driven resource allocation algorithm is proposed for inter-domain CFN, which achieves distributed and efficient task scheduling and data collection by constructing multiple blockchains. A self-incentive mechanism is designed to encourage inter-domain nodes to contribute resources and support the operation of the inter-domain CFN. We also propose an efficient double-layered data aggregation algorithm, which distributes computational tasks across two layers to ensure the efficient collection and aggregation of IIoT data. Extensive simulation and numerical results demonstrate the effectiveness of our proposed scheme.

Abstract One of the most important problems in traffic safety is providing effective collision warnings in intersection areas. In this paper, we propose a Clothoid Curve-based Intersection Collision Warning scheme (CICW) in the Internet of Vehicles. In CICW, we first present a clothoid curve-based vehicle trajectory prediction model. In this model, vehicles can establish the trajectory prediction equations by themselves. Each vehicle solves the equations based on its internal state information, electronic map, GPS data and neighbour vehicles’ state information derived from periodical beacons. The vehicle then predicates the crossing points of the predicted trajectory between itself and the neighbour vehicles. Based on the reference points, it further obtains the earliest possible collision location and then issues a warning. Extensive simulation results show that the performance of the proposed scheme achieves higher collision warning accuracy and a lower error warning ratio compared to existing schemes.

Beetle Antennae Search algorithm is a kind of intelligent optimization algorithms, which has the advantages of few parameters and simplicity. However, due to its inherent limitations, BAS has poor performance in complex optimization problems. The existing improvements of BAS are mainly based on the utilization of multiple beetles or combining BAS with other algorithms. The present study improves BAS from its origin and keeps the simplicity of the algorithm. First, an adaptive step size reduction method is used to increase the usability of the algorithm, which is based on an accurate factor and curvilinearly reduces the step size; second, the calculated information of fitness functions during each iteration are fully utilized with a contemporary optimal update strategy to promote the optimization processes; third, the theoretical analysis of the multi-directional sensing method is conducted and utilized to further improve the efficiency of the algorithm. Finally, the proposed Enhanced Beetle Antennae Search algorithm is compared with many other algorithms based on unbiased test functions. The test functions are unbiased when their solution space does not contain simple patterns, which may be used to facilitate the searching processes. As a result, EBAS outperformed BAS with at least 1 orders of magnitude difference. The performance of EBAS was even better than several state-of-the-art swarm-based algorithms, such as Slime Mold Algorithm and Grey Wolf Optimization, with similar running times. In addition, a WSN coverage optimization problem is tested to demonstrate the applicability of EBAS on real-world optimizations.

The energy problem is one of the most important challenges in the application of sensor networks. With the development of wireless charging technology and intelligent mobile charger (MC), the energy problem can be solved by the wireless charging strategy. In the practical application of wireless rechargeable sensor networks (WRSNs), the energy consumption rate of nodes is dynamically changed due to many uncertainties, such as the death and different transmission tasks of sensor nodes. However, existing works focus on on-demand schemes, which not fully consider real-time global charging scheduling. In this article, a novel dynamic charging scheme (DCS) in WRSN based on the actor-critic reinforcement learning (ACRL) algorithm is proposed. In the ACRL, we introduce gated recurrent units (GRUs) to capture the relationships of charging actions in time sequence. Using the actor network with one GRU layer, we can pick up an optimal or near-optimal sensor node from candidates as the next charging target more quickly and speed up the training of the model. Meanwhile, we take the tour length and the number of dead nodes as the reward signal. Actor and critic networks are updated by the error criterion function of R and V. Compared with current on-demand charging scheduling algorithms, extensive simulations show that the proposed ACRL algorithm surpasses heuristic algorithms, such as the Greedy, DP, nearest job next with preemption, and TSCA in the average lifetime and tour length, especially against the size and complexity increasing of WRSNs.

Reputation system is an important means to build trust, aid decision making of users, and sustain user loyalty in the context of online services. However, different users inherently have different preferences, and so it is impossible that all users rate services with the same criteria. Thus, aggregating cardinal ratings into reputation will potentially lead to unreliable and misleading result, which makes the impossibility of interpersonal utility comparisons should be considered in reputation systems. In this paper, we propose a novel reputation measurement mechanism that aggregates ordinal user preferences rather than cardinal ratings into reputation. By extending the majority rule naturally, dominance relationship between services is defined based on ordinal preferences. Then, reputation measurement is modelled as a problem to find a ranking that indicates the dominance relationships among services. A directed acyclic graph is constructed based on the dominance relationships of services pairs and then the ranking of services is found from the graph. We prove our method satisfies some basic criteria that a reasonable reputation measurement method should satisfy in the context of the impossibility of interpersonal utility comparisons. We also conduct a comprehensive experimental study and performance analysis to evaluate the effectiveness and efficiency of the proposed method.

This paper proposes a multiple-antenna non-orthogonal multiple access scheme including channel state information (CSI) quantization and feedback, user clustering, signal superposition coding, transmit beamforming, and successive interference cancellation at receivers under a general limited CSI feedback framework for frequency duplex division systems. Given a combination of system parameters, we conduct a mathematically strict performance analysis of the considered system, and obtain a closed-form lower bound on the ergodic rate of each user without assuming any extreme for system parameters, which has never been obtained before. Then, we jointly optimize two key parameters, i.e., transmit power and the number of feedback bits allocated to each user, and propose low-complexity closed-form solutions. Finally, numerical results are presented to verify our theoretical results and to illustrate the advantages in accuracy and performance over some existing related results under practical conditions.

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.

We present a secure multi-user multi-antenna transmission framework based on artificial-noise-aided linear zero-forcing beamforming, with limited channel state information feedback from multiple distributed legitimate users (LUs). The secrecy performance of the proposed scheme is analytically investigated and optimized. We develop a new accurate closed-form expression of a lower bound on the ergodic secrecy rate (ESR) of each LU without assuming asymptotes for any system parameter. To make system design tractable, we develop another lower bound on ESR which is so analytically amenable that it enables one to not only extend the results of the previous related works but also explore some untouched aspects of the well known artificial-noise-aided scheme. We derive the optimized power allocation coefficient to message-bearing signals which maximizes the latter ESR lower bound. Furthermore, we theoretically study respectively the impacts of the two main parameters, i.e., transmit power P and the number of feedback bits of each LU B, on the power allocation coefficient, and show the asymptotic results for the high-power and high-quantization-resolution systems. We also develop a sufficient condition on P and B under which a positive ESR of each LU can be achieved. We study some important parameters called the minimum required transmit power (MRTP) and the minimum required number of feedback bits (MRFBs) for each LU to achieve a positive ESR or to achieve a target ESR, which have rarely been touched before. Besides, we propose the algorithms to obtain the MRTP and MRFBs. Numerical results are also provided to verify our theoretical results.

We provide in this paper a comprehensive solution to the design, performance analysis, and optimization of a multi-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communications under a general limited channel state information (CSI) feedback framework for frequency division duplex mode. We design a general framework including user clustering, joint power and bits allocation, CSI quantization and feedback, signal superposition coding, transmit beamforming, and successive interference cancellation at receivers. Then, we conduct a mathematically strict performance analysis of the considered system, and obtain a closed-form lower bound on the ergodic rate of each user in terms of transmit power, CSI quantization accuracy and channel conditions. For exploiting the potentials of multiple-antenna techniques in NOMA systems, we jointly optimize two key parameters, i.e., transmit power and the number of feedback bits allocated to each user, and propose low-complexity closed-form solutions. Moreover, through asymptotic analysis, we reveal the interactions between the main system parameters and their impacts on the joint power and feedback bits allocation result, and hence show some guidelines on the system design. Finally, numerical results validate the correctness of our theoretical analysis and demonstrate the advantages of the proposed algorithms over the most related state of the art.

Although there have been extensive works on artificial-noise-aided (AN-aided) secure transmission schemes for multi-antenna systems, there is still lack of study on the AN-aided scheme employing the widely used linear zero-forcing beamforming (ZFBF) for systems with multiple distributed users. Particularly, there is no analytical secrecy performance for general system settings with neither perfect nor imperfect channel state information of the legitimate users at the transmitter (CSIT). This paper considers the AN-aided ZFBF based on the quantized CSIT for secure communication in the downlink multiuser multi-antenna systems with an external multi-antenna eavesdropper. We develop an approximated closed-form lower bound on the ergodic rate of each legitimate user (LU), and also a closed-form upper bound on the maximum achievable ergodic rate for each LU's messages over the eavesdropper's channel without assuming any asymptotes for system parameters. Then, an approximated closed-form lower bound on the ergodic secrecy rate of each LU follows. Simulation results validate our analytical secrecy performance results and also the effectiveness of AN in enhancing the secrecy performance of linear ZFBF.

Wireless sensor and actuator networks (WSAN) have such superiorities of real-time sense, response, and action on the environment, but WSAN's two kinds of key member sensor and actuator both suffer the serious energy constrained problem similar to that of wireless sensor networks (WSN). Currently, the breakthrough of wireless charging technology provides a new significant opportunity to solve the energy limited problem for WSN, and many fruitful works are emerging. However, the wireless energy supplement problem of WSAN has not been addressed yet. In this paper, we explore the wireless charging issue in WSAN, and propose a mobile energy replenishment framework which can well adapt to actuators' characters such as automatous mobility, long charging duration, and high dynamic energy consumption resulted by responding the abrupt events. Through extensive simulation, we validate the effectiveness of our proposed framework, and the results show that our solution can achieve efficient mobile energy replenishment for WSAN.

Recent breakthrough progress of wireless energy transfer technology and rechargeable lithium battery technology emerge the wireless rechargeable sensor networks(WRSNs). In WRSNs, how to schedule the mobile charger to efficiently replenish energy for sensor nodes is very challenging. However, most of current existing WRSNs mobile energy replenishment schemes either cannot adapt to the dynamic and diversity energy consumption of sensors in actual environment or leave out of consideration of the fairness of charging response, which may result in sensor nodes failure due to energy depletion and low charging performance. Particularly, the nodes failure issue will get worse when there are a large number of charging requirements in the network. In this paper, we explore the node energy depletion problem in mobile charging for WRSNs and propose a node failure avoidance online charging scheme(NFAOC). To avoid the nodes failure due to energy depletion, NFAOC compares the current maximum tolerable charging delay of each request node with its shortest waiting time for charging, and then always chooses the nodes which make the least number of other request nodes suffer from energy depletion as the charging candidates. Simulation results demonstrate that NFAOC can effectively solve the node energy depletion problem with lower charging latency and charging cost in comparison with other current existing online charging schemes.

The breakthrough progress of wireless charging technology provides a significant opportunity to solve the energy constrained problem in wireless sensor networks. However, most of existing mobile energy replenishment schemes either cannot well adapt to the high diversity of energy consumption or leave out of consideration about the fairness of charging response, and thus may still suffer from non-negligible performance degradation resulted from energy starvation. Particularly when there is a large number of charging requirements, the energy starvation may bring about quite a number of sensor nodes invalid due to energy depletion. In this paper, we explore the energy starvation issue while provisioning energy for wireless sensor networks and propose a Starvation Avoidance Mobile Energy Replenishment scheme (SAMER) which can avoid energy starvation through calculating and considering the maximum tolerable latency of each charging requirement. The simulation results show that SAMER scheme can effectively solve the energy starvation problem and achieve efficient mobile energy supplement for wireless sensor networks.

As an important component of transportation system, public transportation accounts for considerable proportion in the whole traffic flow. The public transportation vehicles can be categorized to two types: ones with determinate trajectories and schedules such as bus, tramway and light rail, the others with flexible and variable running paths, such as taxis. In this paper, we firstly present a driving path prediction method based destination gathering for taxis, which can make taxis' driving paths prescient in the initial stage of carrying passengers every time. Comparing with ordinary vehicles, public transportation vehicles have such features as long time running on roads, no privacy-protection need, and thus their trajectories can been opened. Through utilizing the features above, we propose a novel Public Transportation assisted Data Delivery scheme (PTDD) used to improve the performance of data delivery of Vehicular Delay Tolerant Networks (VDTNs). Simulation results based on a real map demonstrate the effectiveness of the proposed scheme.

Due to the intermitted connectivity feature, it is rational to use multi-replica approaches so as to enhance the data transmission performance in DTMSN. But the majority of off-the-shelf multi-replica approaches usually generate too many message copies in the network, which may quickly exhaust the limited network resource of DTMSN. Therefore, an efficient and effective data delivery scheme needs to judge and weigh the benefits and costs due to the number of replica of messages. In this paper, we propose a new Replicas Adaptive Data Delivery protocol (RADD), which makes use of a self-adapting algorithm to reduce the number of redundant messages, and attains a good network performance with low network resource expenditure. Simulation results indicate that our proposed RADD reaches comparable or higher data delivery performance at the cost of the lower transmission overhead compared to several existing multi-replica schemes in DTMSN.

In wireless ad hoc networks, it is an efficient approach to utilize the dynamic pseudonyms scheme to preserve the nodes' privacy. However, in the existing works, there are some problems such as weak anonymity and synchronization difficulty when updating pseudonyms. In this paper, we propose a lightweight Dynamic Pseudonyms based Anonymous Routing protocol, called DPAR. In the proposed DPAR, the security is implemented by lightweight symmetric key cryptography and hashing operations, the pseudonyms are generated by hashing operations, and dynamically updated and synchronized by elaborate message interactions. Extensive analyses show that DPAR can achieve the routing security and the nodes' anonymity with acceptable overhead.