Imaging using S-wave seismic data based on elastic media holds the potential to address certain critical issues that conventional P-wave imaging cannot resolve, thereby offering new opportunities for oil and gas exploration. In this article, we have developed a feasible S-wave reverse time migration (RTM) imaging technique. Based on nonconversion elastic wave theory, we introduce auxiliary variables to construct a first-order pure S-wave equation, which is used to simulate the extrapolation of source wavefields in S-wave RTM. In addition, we employ the first-order velocity-stress equation for backward propagation and use decoupled elastic operators for decoupling of receiver wavefields, and utilize vector cross-correlation imaging conditions to obtain scalar imaging results. Compared with traditional P-wave RTM, the proposed S-wave RTM in this article addresses the limitations of traditional P-wave RTM in processing S-wave seismic data, demonstrating its advantages in reacting to reservoir fluid in the field of oil and gas exploration.

In environments lacking infrastructure, such as the battlefield, a diverse array of intelligent devices continues to evolve. These devices face challenges in flexibly addressing resource allocation and information transmission within their communication range, necessitating exploration of business awareness and resource identification technologies. Addressing these issues hinges on solving object identification problems related to address, identity, content, service, location, and resource. To tackle these challenges, we propose a Mult-IP object identification mechanism that serves as the foundation for information exchange, task allocation, and service provisioning among heterogeneous communication entities. Additionally, we design a multi-dimensional resource and service intelligence matching mechanism based on object identification. Building upon this framework is the intent-driven control algorithm AntEdge which dynamically senses task states using information encapsulated in Mult-IP packets and optimizes task packet transmission according to business intentions. Finally, leveraging the Mult-IP object identification mechanism enables task offloading to achieve timely completion of tasks within limited timeframes. Simulation experiments demonstrate that our proposed algorithm effectively enhances task completion ratios.

With no infrastructure, the battlefield environment has characteristics such as device heterogeneity, uneven resource distribution, and dynamic node movement. Additionally, the sensing tasks produced by devices are complex and delay-sensitive, making it challenging to complete the tasks within the time limit if a centralized unified scheduling strategy is employed. In order to fully utilize the resources in the battlefield environment, optimize the load situation in the battlefield scenario, and guarantee the stability of task offloading, this paper proposes a scheduling strategy based on computing-aware and task classification (CATC). We first design a distributed computing-aware mechanism to ensure the detection of surrounding resources and load conditions by the generating task devices. Based on this, we classify the battlefield tasks into multi-attribute tasks according to the actual situation, and develop an offloading strategy for different task types, taking into account computation, storage, transmission, and load situation. The simulation results demonstrate that the proposed CATC strategy has a high task completion rate and can satisfy the definite delay requirements of delay-sensitive tasks.

Velocity and reflectivity models are fundamental outcomes obtained from seismic exploration, enabling the identification of subsurface structures and physical properties. Two primary methods for obtaining these high-resolution results are two-way wave equation-based full waveform inversion (FWI) and least-squares reverse time migration (LSRTM). Despite sharing a similar least-squares inversion framework, FWI and LSRTM present challenges in effectively combining them to generate both velocity and reflectivity models simultaneously due to their different modeling engines and dependencies on data components. In this study, we propose a novel joint migration inversion (JMI) approach by incorporating a two-way full-wavefield modeling engine parameterized by the subsurface velocity and reflectivity. By providing adjoint velocity and reflectivity sensitive kernels, this JMI can produce high-quality velocity and reflectivity models concurrently. The method developed in this study has the potential to directly process seismic data containing full-wavefield information, reducing data preprocessing complexity and avoiding potential damage to valid signals during the processing. Through a synthetic data test and a benchmark test, we demonstrate the effectiveness of our JMI approach. Moreover, when compared with conventional approaches for velocity building and imaging, our method yields velocity and reflectivity models with higher resolution and improved consistency.

In marine exploration, conventional migration algorithms based on primary reflections often have poor angular illumination, especially in shallow areas and complex salt boundaries. Source density is the primary controller of angular illumination. It is well-known that source density is relatively sparse in a typical marine seismic acquisition, such as in a towed streamer survey. In contrast, imaging of multiples treats each receiver as a virtual source, mimicking a high-density source survey. Imaging of multiples can provide additional angular illumination and help to improve subsurface imaging. The extra illumination provided by multiple reflections enhances angle-domain common-image gathers (ADCIGs), a component of velocity model building and amplitude versus angle (AVA) analysis. Our main objective is to illustrate the advantages of angular illumination from imaging of multiples in the angular domain. To achieve this purpose, we propose a workflow for calculating high-quality ADCIGs for imaging of multiples. The workflow mainly contains up-going and down-going wavefield decomposition and the stabilized Poynting vectors for calculating more accurate imaging angles. To illustrate the accuracy of the proposed workflow, a simple model is used to calculate angle gathers for imaging of multiples. Then, the Sigsbee2b model and a field dataset from the Gulf of Mexico are used to compute angle gathers by imaging primaries and multiples to demonstrate the improved angular illumination achieved when multiples are also used for imaging. The comparison results show that the angle gathers obtained by imaging of multiple has better angular illumination, especially in shallow areas and complex salt boundaries.

The formulation of conventional least-squares reverse time migration (LSRTM) starts with the forward modeling process; as a result, the migration operator of it presents as a migration process with a cross correlation imaging condition (CCIC). Since the imaging results produced by CCIC usually contain undesirable components (e.g., strong backscattering noise), it can be assumed that the primary target of the conventional LSRTM is to fit input data rather than produce high-quality imaging results; therefore, conventional LSRTM can be considered as a modeling-driven algorithm. To mitigate the desirable component in the imaging results, additional efforts should be spent in the process of modeling-driven LSRTM. To improve the performance of the LSRTM, we develop a migration-driven LSRTM by formulating the migration process using the inverse scattering imaging condition (ISIC) first. To guarantee the convergence of the algorithm, an adjoint modeling operator and a data precondition operator are incorporated in this migration-driven LSRTM. Since the ISIC can effectively eliminate the backscattering noise, this migration-driven LSRTM can produce high-quality images without the influence of that. After two synthetic data tests, this approach is applied to a 2-D streamer field dataset from the Gulf of Mexico. These tests indicate that, compared to the modeling-driven LSRTM, the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently.

As complementary to the imaging of primaries, multiples can be used for imaging and produce additional angular illumination. These additional illumination angles can improve imaging resolution. There are several methods to calculate the angle gathers from imaging of primaries, among which the directional vector methods are matured and have been implemented in practice. For conventional directional vector algorithms using common-shot reverse time migration, usually, only one angle with the main contribution to each imaging point is collected, which is referred to as a one-shot, one-position, and one-angle mapping strategy. This strategy is appropriate for imaging of primaries; however, the principle fails in the imaging of multiples, where more than one angle exists at an imaging point even using a shot gather. Therefore, conventional directional vector approaches cannot separate different imaging angles produced by imaging of multiples. In this article, we introduce the Poynting vector method to calculate angle gathers for imaging of multiples. Based on the one-shot, one-position, and one-angle mapping strategy, we search for the peak of modulus of Poynting vectors in a time window to differentiate imaging angles at an imaging point. The ray path diagrams and angle-gather imaging conditions are used to demonstrate the improved angular illumination from imaging of multiples. Then, the arriva -time equation is derived mathematically to demonstrate wavefield arrival time differences for different imaging angles from imaging of multiples. Based on the arrival time differences, a Poynting vector algorithm is proposed to compute more than one angle at one subsurface location for imaging of multiples. Data examples are used to validate our approach.

There are many heterogeneous intelligent devices with different resources in the field environment lacking in-frastructure. Some of these devices have insufficient computing power and power supply, seriously affecting the functioning of computing-intensive and delay-sensitive applications. To fully utilize the available computing resources scattered in the region and provide deterministic quality of service guarantees for task execution, we designed Universal Identity Tag (UIT), a universal resource identification method. UIT can identify multi-dimensional resources and extract task characteristics, which effectively supports the heterogeneous communication entities computing resource discovery process and provides a basis for task scheduling. Subsequently, we propose Computing-Aware Strategy (CAS), a task scheduling algorithm based on ad hoc cloud and computing power awareness. CAS allows intelligent devices to dynamically perceive the state of other mobile devices to optimize the task scheduling process. Therefore, regional devices can share data and collaborate on computing tasks. Simulation results show that the proposed scheme effectively reduces data traffic overhead and lowers the average service delay.

Mobile Edge Computing (MEC) has solved a sharp increase in data volume caused by various emerging network applications. The edge data center is an essential part of MEC, which connects the edge of the network and the backbone network. Faced with a complex network environment, edge data centers suffer low bandwidth resource utilization and high network latency. This paper proposes Twin Delayed Deep Deterministic policy gradient based Routing Optimization (TRO) algorithm to improve the performance of edge data centers. The TRO algorithm uses Deep Reinforcement Learning (DRL) and Software-Defined Networking (SDN) to achieve routing optimization from two aspects of bandwidth utilization and load balancing. Experiments demonstrate that compared with other algorithms, the TRO algorithm proposed in this paper significantly improves network throughput and reduces average packet latency and average packet latency error.

Aiming at the problem of network performance degradation caused by the characteristics of mobile ad hoc network, this paper proposes a cooperative packet forwarding algorithm based on game combination and third-party altruism. First of all, we analyze the static game among nodes, and calculate the forwarding probability of nodes in the case of static game through the theorem of Nash equilibrium. Then, for the sake of solving the shortcomings of static game, we introduced the self-game model. Next, this paper puts forward the definition of node's benefit, which is vague in many studies, and proposes the concept of incentive factor based on third-party altruism to improve node collaboration. Finally, using the static game and self-game, combined with the incentive factor to calculate the node forwarding probability, the data packet cooperative forwarding algorithm is designed. The simulation results demonstrate that the proposed algorithm improves the performance indicators of MANET such as packet delivery ratio, average transmission delay, throughput and routing overhead.

In recent years, sensor multimodal time series data fusion has attracted widespread attention. One of the key challenges is to extract features from multimodal data to obtain shared representations, which combines the characteristics of time series data to further improve prediction performance. To solve this problem, based on Stacked Sparse Auto-Encoder (SSAE) and Long Short-Term Memory (LSTM), we propose a multimodal time series data fusion model called SSAE-LSTM. SSAE mines the inherent correlation features of multimodal data to extract a good shared representation, which is used as the input of the LSTM neural network to perform data fusion processing. Experiments on real time series datasets demonstrate that SSAE-LSTM can obtain a good shared representation of multimodal data to predict the future development trend. Compared with other neural networks, SSAE-LSTM has better performance in Precision, Accuracy, Recall, F-score and so on.

We propose a task distribution algorithm based on agent correlation to enhance the comprehensive research on the distribution sensing tasks in mobile sensor networks. First, in the proposed algorithm, the score and feature factors of the mobile agents are considered comprehensively in a direct correlation model, which is constructed by combining the direct and indirect correlation samples. Second, we introduce a mobility model based on the exponential distribution, and obtain a calculation method of probability parameter λ according to the analysis presented in this article. Finally, we integrate the constructed correlation model and mobility model; which are then applied to the distribution algorithm. By experimental research, the task distribution algorithm based on relationships of agents we proposed, has a precision improvement of 57.23% over MTPS, which is a fine-grained multitask allocation framework algorithm, and 12.31% of that to sequential MF, which is a recommendation algorithm based on collaborative filtering by exploiting sequential behaviors. These results indicate that the proposed algorithm improves the task distribution performance significantly, and offers a more accurate and reliable service.

With the popularization of intelligent hardware, wireless sensor networks have led to mobile crowdsensing (MCS) systems, which provide solutions for large-scale and complex urban data collection. Task distribution is the most important part of intelligent hardware applications. MCS can improve the task distribution efficiency by accurately predicting the location of a perceived user for task distribution. This paper proposes a task location estimator based on a variable-order Markov time window sensing (TEMTWS) algorithm. This method is based on time window modeling, and the association between user tasks is established by sensing the historical track data of user execution tasks. First, the task execution frequency and task vector are calculated, and the organizer at each position is selected. To obtain more perceptual users, similarity estimation is performed on the users and organizers within the time window, and users with high relevance are grouped into the same cluster. An experiment is conducted with the Gowalla dataset to verify the algorithm. The results show that the proposed algorithm outperforms the standard Markov K-means algorithm and K-means-GA algorithm in terms of the prediction accuracy.

In cognitive radio networks (CRNs), secondary users need to first discover neighbours and form communication links, referred to as the rendezvous process. Rendezvous between any two secondary users can only be achieved on the same channel. However, the nature of the CRN makes this a challenging problem. Specifically in CRN, not only the network is multi- channel, but the channels available at different nodes may be different. While most of the existing works study pair-wise rendezvous and design channel hopping sequence, in this paper, we focus on the performance improvement of the rendezvous process based on the existing channel hopping sequences with multiple users in CRN. We propose a new strategy, called Group Based Strategy (GBS) to achieve the acceleration, which is flexible to incorporate the existing sequence generation algorithms. Our basic idea is to group the encountered users and schedule rendezvous for them. With the purpose to increase rendezvous diversity, other users or groups can join the group if they get the group rendezvous information. Experiments are conducted to evaluate the proposed scheme. Overall, the performance can be improved by more than 50% under symmetric model or asymmetric model using our accelerating strategy.

The continuous demand for content-rich multimedia is pushing for high-speed and secure transmission approaches. In recent years, linear network coding (LNC) has been shown to be a promising technology to improve network throughput, transmission reliability, and information security. In this paper, we study the optimal transmission topology construction and LNC design for a secure multiple-source multicast to deliver the same content with integral link rates, which can be equivalent to the secure multicast problem with a virtual source, i.e., the integer secure virtual-source multicast (ISVM) problem. The objectives of the ISVM problem include the following: 1) satisfy the weakly secure requirements, 2) maximize the secure multicast rate (SMR), and 3) minimize the transmission cost when the SMR is maximized. First, we analyze the necessary and sufficient condition that there exist a transmission topology with integral link rates and a secure LNC that can achieve a given SMR$R$. Then, we model the ISVM problem as an integer linear programming based on the theoretical analysis and design an efficient transmission topology construction algorithm to solve the ISVM problem by utilizing the Lagrangian relaxation and subgradient method. We also analyze the size of finite field required to construct thedeterministic LNCfor a secure virtual-source multicast and the probability that the virtual-source multicast is weakly secure when usingrandom LNCin the ISVM problem. Finally, we design upper and lower bounds for the ISVM problem and conduct extensive simulations to compare the performance of the proposed algorithms with these two bounds.

Obtaining spectrum information efficiently is key to cognitive radio networks (CRNs). The database-driven approach has emerged recently as an alternative or supplement for spectrum sensing, and has been quickly adopted by the government and the industry. Within database-driven CRNs, master devices obtain spectrum information by direct connection to a spectrum database, while slave devices can only access spectrum information indirectly via masters. Out-of-band connections with a second network interface which do not depend on the primary spectrum channels can be used for the communication of spectrum information among masters and slaves. Alternatively, the in-band approach completely based on primary spectrum channels can be used, which eliminates the need for out-of-band connections and eases the adoption of the database-driven spectrum sharing. In this paper, we study the in-band bootstrapping process for database-driven multi-hop CRNs, where master/slave devices form a multi-hop networks and slaves need multi-hop communications to obtain spectrum information from the master during bootstrapping. We propose several protocols to reduce the bootstrapping time and protocol overhead. According to the analysis and simulation results, our proposed protocols can greatly improve the performance.

Independent spanning trees (ISTs) on networks have applications in networks such as reliable communication protocols, the multi-node broadcasting, one-to-all broadcasting, reliable broadcasting and secure message distribution. However, there is a problem on ISTs on graphs: If a graph G is n-connected (n ≥ 1), then there are n ISTs rooted at an arbitrary vertex on G. This problem has remained open for n ≥ 5. In this paper, we consider the construction of ISTs on Möbius cubes—a class of hypercube variants. An O(N log N) recursive algorithm is proposed to construct n ISTs rooted at an arbitrary vertex on the n-dimensional Möbius cube Mn, where N = 2n is the number of vertices in Mn. Furthermore, we prove that each IST obtained by our algorithm is isomorphic to an n-level binomial-like tree with the height n + 1 for n ≥ 2.

The dimensions of twisted cubes in the original definition of twisted cubes are only limited to odd integers. In this paper, we first extend the dimensions of twisted cubes to all the positive integers. Then, we introduce the concept of the set of restricted faulty nodes into twisted cubes. We further prove that under the condition that each node of the n-dimensional twisted cube TQnhas at least one fault-free neighbor its restricted connectivity is 2n - 2, which is almost as twice as that of TQnunder the condition of arbitrary faulty nodes, the same as that of the n-dimensional hypercube. Moreover, we give an O(N log N) fault-free unicast algorithm, where N denotes the node number of TQn-1. Finally, we give the simulation result of the expected length of the fault-free path gotten by our algorithm.