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Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman-Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

Electric vehicles (EVs) provide sustainable and eco-friendly transportation. However, long-range driving is challenging due to limited battery capacity. While charging stations can replenish the battery, they are not as widely deployed as gas stations, causing the battery depletion of EVs. This paper presents a novel optimal charging strategy for EV long-range driving. The strategy offers EV drivers the optimal charging instructions for a given trip. A new finite-horizon Markov decision process (FH-MDP) problem is formulated to minimize the travel time of an EV while preventing its battery depletion, by optimally selecting the charging stations and charging times along the trip. By confirming the monotonicity and subadditivity of the FH-MDP, we prove the existence of a monotone deterministic Markovian policy for the optimal charging decision and reveal the optimal charging time is monotone regarding the remaining battery level and driven distance. We also reveal that the optimal charging time only changes when either of two thresholds regarding the remaining battery level or driving distance is met. By comparing its state with the thresholds, the EV can make optimal decisions with linear complexity. Simulations corroborate that our algorithm can save travel time by at least 12.6% under our simulation settings, compared to alternative methods.

Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated $M$ -player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the $\alpha $ -utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

Full-duplex (FD) integrated sensing and communication (ISAC) has great potential in future vehicular networks. However, the FD requirement and the ISAC functions make the receiver processing extremely complicated, particularly when multiple transmissions are uncoordinated. In this paper, we study frequency-hopping (FH) based receivers in an FD ISAC system, where the arrivals of backscattered signals from one node may overlap with those of signals from another node. To mitigate the interferences caused by the overlapping signals, we consider two receiver options based on either conventional communications or frequency-modulated continuous-wave radars, and two signal modulations based on either fast FH or un-slotted ALOHA FH. Based on the different signal modulations, we develop two parameter estimation schemes via using FH-decoding and de-chirp operations, respectively. To further improve the sensing accuracy, we proceed to propose an iterative algorithm, which refines the estimates of all parameters via using short-time-Fourier transform and maximizing the received power in desired frequency bands. After obtaining all channel parameters in sensing, bilateral communications between two nodes are realized by differential phase-shift keying. Finally, simulation results are provided and verify that the proposed FD ISAC can obtain parameters in high resolution and realize robust communication links.

Data sharing is essential for future autonomous vehicles in vehicular networks. Building trust in the shared data can be challenging among mutually unknown vehicles in an open and distributed environment. Blockchain is the key to establish trusted data sharing in a decentralized vehicular environment. However, the low efficiency (e.g., transaction per second, TPS) of blockchain is the bottleneck for the efficient data sharing. Moreover, the highly dynamic topology, the imperfect wireless links, and the limited radio coverage may further slow down the blockchain consensus process. To improve the efficiency and trustworthiness of data sharing in a vehicular network, we propose a sharding-enabled vehicular blockchain system. Specifically, the blockchain-enabled vehicles are dynamically allocated to different shards based on their geographical locations so that transactions are processed in parallel. We design an efficient and trusted sharding-enabled blockchain consensus mechanism by integrating a reputation-assisted intra-shard consensus and a vehicle-assisted inter-shard consensus. The reputation assistance in intra-shard consensus is designed to defend against the collusive false-block attack that may happen in a small-scale shard environment. The vehicle-assisted inter-shard consensus is designed to tackle the issues caused by defective roadside units (RSUs). Simulation results demonstrate the improved performance on efficiency and trustwor-thiness of the proposed system. In addition, trusted data sharing can be established even under the conditions of malicious vehicles or attacked RSUs.

Joint Communications and Sensing (JCAS) in mobile networks are typically based on Orthogonal Frequency Division Multiplexing (OFDM) systems. For time-varying channels, large Doppler frequencies in OFDM JCAS can cause notable intercarrier interference, which has not been considered for sensing. In this letter, we propose a frequency-domain sensing framework for OFDM JCAS systems. We first derive a frequency-domain closed-form expression of the received signals, to characterise the delay and Doppler frequency impact within and across OFDM blocks. We then develop intra-block and inter-block sensing algorithms, based on the expression. The framework is further completed with exemplified pilot design and periodogram sensing algorithm. Simulation results demonstrate the effectiveness of the proposed framework.

This letter proposes a virtual-array-based waveform optimization scheme for broadband joint radar-communication systems. We first formulate a problem of minimizing Cramér-Rao bound (CRB) of radar subject to communication constraints. To reduce the high complexity of directly solving the problem, we allocate the virtual arrays to users according to their communication thresholds and minimize the largest eigenvalue of the CRB matrix. Then, the optimization problem can be solved by semi-definite programming but the solutions are not always available. We finally propose a low-complexity solution that minimizes the CRB first and then transforms the optimization results to meet the constraints on communications. Simulation results are provided and verify the effectiveness of the proposed scheme.

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming. It is non-trivial to perform holistic selections of users, data streams, and modulation-coding modes for all subchannels, and RIS configuration in a downlink multiuser OFDMA system under jamming attacks, because of a mixed-integer program nature and difficulties in acquiring the channel state information (CSI) of the channels to and from the RIS and from an uncooperative jammer. We propose a new deep reinforcement learning (DRL)-based approach that learns through changes in the data rates of the users to reject jamming and maximize the sum rate. The key idea is to decouple the continuous RIS configuration from the discrete selections of users, data streams, subchannels, and modulation-coding modes. Another critical aspect is that we show the optimal selections almost surely follow a winner-takes-all strategy. Accordingly, the new DRL framework learns the RIS configuration with a twin-delayed deep deterministic policy gradient and takes the winner-takes-all strategy to evaluate the reward, thereby reducing the action space and accelerating learning. Simulations show the framework converges fast and fulfills the benefit of the RIS. With no need for the CSI of the channels to and from the RIS and from the jammer, the framework offers practical value.

Uplink sensing in perceptive mobile networks (PMNs), which uses uplink communication signals for sensing the environment around a base station, faces challenging issues of clock asynchronism and the requirement of a line-of-sight (LOS) path between transmitters and receivers. The channel state information (CSI) ratio has been applied to resolve these issues, however, current research on the CSI ratio is limited to Doppler estimation in a single dynamic path. This paper proposes an advanced parameter estimation scheme that can extract multiple dynamic parameters, including Doppler frequency, angle-of-arrival (AoA), and delay, in a communication uplink channel and completes the localization of multiple moving targets. Our scheme is based on the multi-element complex Taylor series of the CSI ratio that converts a nonlinear function of sensing parameters to linear forms and enables the applications of traditional sensing algorithms. Using the truncated Taylor series, we develop novel multiple-signal-classification grid searching algorithms for estimating Doppler frequencies and AoAs and use the least-square method to obtain delays. Both experimental and simulation results are provided, demonstrating that our proposed scheme can achieve good performances for sensing both single and multiple dynamic paths, without requiring the presence of a LOS path.

IoT provides ubiquitous connectivity and pervasive intelligence. Challenges arise from the data-intensive applications pertaining to IoT. The major contribution of this article is a new asymptotically optimal, fully decentralized, real-time framework which seamlessly integrates wireless computation offloading and fog computing in IoT networks with random traffic variations over space and time. Other enabling techniques are discussed to address practical challenges, such as outdated incentive for the cooperation of multiple service providers, massive access requests within limited system bandwidth, and computing acceleration. The new framework is able to provide ubiquitous computing for continuously increasing IoT services. Numerical experiments indicate that ubiquitous fog computing can substantially improve throughput, reduce computing latency, and cut off signaling overhead, without compromising the optimality of network operations.

License-assisted access (LAA) to unlicensed spectrum is a potential solution to improve the resource availability and system scalability of 5G/ B5G networks. Challenges arise from coexistence between LAA and incumbent systems, especially the ubiquitous IEEE 802.11 WiFi systems. This article demonstrates that the coexistence can be substantially improved by leveraging learning-based clustering of small base stations (SBSs), referred to as learning-assisted clustered access (LACA), and improving the interoperability between licensed and unlicensed access. Fast signaling and a centralized control plane of licensed access can facilitate clustering SBSs for coordinated access to the unlicensed spectrum, hence reducing the number of parties contending for access and alleviating contention. Appropriate clustering of SBSs is important to the efficiency of LACA in 5G/B5G networks. LACA can quickly converge with strong locality, facilitating the coordination of the SBSs, for example, cooperatively connecting multiple users and conducting beamforming. Analytic evaluation and numerical tests confirm the improved coexistence through enlarged LAA-WiFi capacity regions, as well as reduced transmission delays.

Licensed-assisted-access (LAA) is used to extend the LTE link into the unlicensed band. How to guarantee the quality-of-service (QoS) for LTE devices in the unlicensed band is a challenging problem due to the listen-before-talk contention access in 5-GHz unlicensed bands. In this letter, we quantitatively analyze the medium access control delay for tagged LAA eNBs and propose a delay-guaranteed admission control scheme. We consider the freezing time of busy slots caused by collision or successful transmission, and introduce the exponential backoff mechanism for delay analysis. Validated by simulation results, our method provides important insights into the system admission performance and fairness of access.

The last few years have seen a high volume of sophisticated cyber-attacks leading to financial instability and privacy breaches. This reveals the need for a Network Intrusion Detection System (NIDS) that is more robust, effective, and capable of learning complex relations of network flows. Existing machine learning approaches are barely effective for picking new attacks in IoT traffic, primarily due to treating flows independently. Recently, Graph Neural Networks (GNNs) have gained popularity due to their ability to model underlying topology in the form of nodes and edges. We propose a GNN-based NIDS that maximizes the possibility of involving structural characteristics of normal and malicious network traffic. We construct a multi-edged graph structure that captures full communication between any pair of IoT nodes at the edge level. This novel GNN model combines the benefits of spectral and spatial GNNs tailored to process and learn from complex multigraph-structured information. The proposed GNN model is adjusted to work on multi-edges and multi-dimensional edge features. Extensive experimental analysis using evaluation metrics is carried out on four benchmark datasets, demonstrating the competence of our proposed scheme compared to the state-of-the-art GNN models. Our proposed model shows about 2% to 5% improvement in accuracy, precision, F1 and recall offering less training time and model size in the considered datasets. The proposed model has achieved high accuracy ranging from 98.91% to 99.96%, with precision ranging from 98.86 to 99.96, recall ranging from 98.91% to 99.96%, and F1-score ranging from 98.74% to 99.95% in the four benchmark datasets.

Quality of service (QoS) and queue management are critical issues for the broadcast scheme of IEEE 802.11p systems in vehicular ad hoc networks (VANETs). However, existing 1- dimensional (1-D) Markov chain models of 802.11p systems are unable to capture the complete QoS performance and queuing behavior due to the lack of an adequate finite buffer model. We present a 2-dimensional (2-D) Markov chain that integrates the broadcast scheme of the 802.11p system and the queuing process into one model. The extra dimension, which models the queue length, allows us to accurately capture the important QoS measures, delay and loss, plus throughput and queue length, for realistic 802.11p systems with finite buffer under finite load. We derive a simplified method to solve the steady state probabilities of the 2-D Markov chain. Our 2-D Markov chain model is the first finite buffer model defined and solved for the broadcast scheme of 802.11p systems. The 2-D model solutions are validated by extensive simulations. Our analyses reveal that the lack of binary exponential backoff and retransmission in the 802.11p system results in poor QoS performance during heavy traffic load, particularly for large VANETs. We demonstrate that our model provides traffic control guidelines to maintain good QoS performance for VANETs.

Some semi-dry Flue Gas Desulfurization ash (SDFGDA) were taken from sinter gas of steel factory and been oxidized at experimental condition. Oxidized ash, granulated blast-furnace slag (GBFS) and steel slag were grinded and used to prepare cementing materials. Scanning electron microscope (SEM) was employed to identify the samples in order to investigate their hydration structure. The result shows that cementing materials have good hydration properties and mechanical properties, the initial and final setting time are qualified. It is recommended to control the Blaine fineness, mount of SDFGDA, ratio of GBFS/steel slag, mount of clinker to be 4500cm2/g, 19%, 1:4, 20%, respectively, when prepare eco-cement with those materials.

Gentamicin bacteria residue contains high organic compound. The technology of thermochemical conversion can effectively solve the problem of bulk gentamicin residue disposal, research on pyrolysis kinetics of the reaction is the basic work for thermochemical conversion . In this paper, Pyrolysis experiments were carried out in a thermogravimetric analyzer under inert conditions and operated at different heating rates (5, 10, 20 K/min).Two different kinetic models, the iso-conversional Ozawa–Flynn–Wall (Ozawa) models and Satava method were applied on TGA data of gentamicin residue to calculate the kinetic parameters including activation energy, pre-exponential factor and Mechanism function. The results showed that: gentamicin bacteria residue lost most weight of it between 100-650 °C , about 74.23% of the whole sample can decompose under high temperature. The pyrolysis function for gentamicin residue should be G(α) =[-ln(1-α)]3.

Point-to-point FDD microwave can be a fast-to-install and cost-effective backhaul solution for mobile small cells. However, its applications have been held back, because the state-of-the-art static backhaul deployment fails to capture drastically fluctuating small cell traffic. To address this issue we introduce a new adaptive backhaul architecture that is able to adapt to changing small cell traffic. Extended from a graph theoretic clique idea, the architecture allows changes to the overall backhaul topology, and it also allows each individual backhaul link to vary its frequency to meet traffic demand. Guard bands are adaptively reserved between some of the links to suppress adjacent-frequency interference, while being efficiently used by other links for data transmission. Our case study of 10 small cells confirms that the adaptive architecture is able to increase the small cell throughput by 75 percent compared to the static backhaul architecture. It can also reduce the backhaul bandwidth requirement by 57 percent, and enhance small cells' satisfaction by 158.4 percent with respect to their allocated bandwidths.

On the basis of proximate analysis, element analysis and calorific value  that carried out on the sewage sludge, thermogravimetric analyzer (TGA) was conducted on the research of sewage sludge pyrolysis. The loss of sludge under different heating rates was comparative analyzed. And we calculated the activation energy by using Ozawa method, Satava method was also used to infer the most probable mechanism function and kinetic parameters. the result shows that sludge pyrolysis process can be divided into three parts as 200-450°C,450-600°C and above 600°C, the Reaction mechanism functions for the pre main two parts were G(α)=(1-α)-1 and  G(α)=[-ln(1-α)]2/3respectively. Its proved that Al2O3,MgO probably inhibited the decomposition while Fe2O3,CaO,ZnO promoted the degradation of organic matters under 200-450°C. CaO and Fe2O3 showed advantage while MgO, Al2O3, ZnO turned to be disadvantagble when temperature arised to 450-600°C. At the end of pyrolysis, the advantage effects order on pyrolysis : Al2O3> Fe2O3>MgO >ZnO>CaO.

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model (HMM) based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.

Abstract: Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.

An integration of Internet-of-Things (IoT) and blockchain becomes increasingly important to secure IoT data in an anti-tampering manner. Challenges arise from the immense scale of IoT and the resultant impact of network partitioning on blockchain. We design a new testbed to evaluate the impact, where resource-limited IoT devices, acting as light nodes, are an integral part of a blockchain. Our testbed is built on the Ethereum platform with non-trivial modifications on key modules. The partitioning of IoT is emulated by probabilistically dropping blocks travelling among the miners. We also propose a new discrete-time Markov chain model to validate our testbed and analyze the impact of block mining rates and network conditions on the capacity of public blockchains. The model is first formed to be non-ergodic with an infinite state space. By exploiting the eventual consistency property of blockchain, the model is collapsed to be ergodic and approximated with a finite state space and significantly improved tractability. Both the testbed and analysis reveal the blockchain capacity can be improved by accelerating the block mining rates which, however, increases stale blocks. Our analysis provides an asymptotic upper bound for the blockchain capacity.