International audience

To address the challenges of high uncertainty, inter-subject variability, and inefficiency multi-feature utilization in motor imagery electroencephalogram (MI-EEG) classification, this study proposes amultiviewtransferTakagi-Sugeno-Kang (TSK) fuzzy classifier withsoftvariable embedded anddiscriminativestructural preservation (MVT-TSK-SVDS). First, a transfer learning mechanism incorporating soft variable embedding in the consequent part is developed. This mechanism establishes cross-domain correlations via a shared consequent and representation matrix. Within this framework, soft variable embedding and low-rank constrained discriminative learning work in concert to effectively capture supervision information and cross-domain relationships. Second, a local-global structural preservation term incorporating graph embedding and low-rank constraint is implemented to maintain local discriminative information from source domain while integrating global geometric patterns across all data. Third, a multiview adaptive learning framework is designed to address feature representation diversity and information loss during knowledge transfer. MVT-TSK-SVDS dynamically optimizes view-specific contributions through entropy maximization criterion while ensuring collaborative decision via consistency constraints. Experimental results validate strong generalization between and across datasets. Our model achieves 62.16% and 72.71% accuracy in cross-subject tasks on BCI-IV 2a and OpenBMI, respectively. In cross-dataset evaluations, it attains 62.75% accuracy on BCI-IV 2a to OpenBMI and 65.08% accuracy on OpenBMI to BCI-IV 2a, respectively.

Accurate long-term traffic prediction in Low Earth Orbit (LEO) satellite networks is essential for proactive resource allocation and congestion avoidance, yet remains challenging due to highly dynamic topologies, intermittent connectivity, and scarce real traffic data. Existing approaches are largely limited to short-term prediction or assume static spatial dependencies, making them inadequate for non-stationary LEO environments. To address these challenges, this paper proposes DST-DEformer, a dynamic spatial–temporal Transformer framework that jointly models evolving inter-satellite topology and multi-scale temporal dependencies. Specifically, a topology-adaptive graph convolution module captures time-varying spatial correlations, while a dual temporal encoder decouples long-term global trend modeling from short-term local fluctuation learning. In addition, a hybrid simulation–calibration framework is developed to generate realistic satellite traffic by incorporating orbital dynamics, demographic information, and real-world traffic trends. Extensive experiments on simulated LEO satellite traffic and the PEMS08 benchmark show that DST-DEformer consistently outperforms state-of-the-art methods in long-term prediction, achieving 4%-13% reductions in MSE and MAE and significantly slower error accumulation as the prediction horizon increases. These results demonstrate the effectiveness and robustness of DST-DEformer for long-term traffic prediction under dynamic network topologies.

The rapid development of Low Earth Orbit (LEO) satellite networks has made in-orbit computing more feasible, offering a solution for processing real-time, diverse user tasks. Compared with traditional cloud computing in ground cloud computing center, directly computing on the LEO satellite can significantly reduce task-processing delay. However, challenges remain, including the limited sensing and computing capabilities of satellites, high delays in processing task requests, and frequent switching of control domains due to the relative movement between LEO satellites and nodes in other orbits. To address these challenges and improve task management, computing is treated as a Virtual Network Function (VNF), managed by Software-Defined Networking (SDN) controllers. This paper proposes a distributed SDN controller-based computing framework, where task information is forwarded to SDN controllers, which then use a task scheduling strategy to allocate tasks to suitable computing nodes for processing. To support the implementation of this framework, we first propose a heuristic SDN controller placement strategy that uses a tiling method to divide the LEO satellite network into SDN control domains and places the controller at the midpoint of each domain Then, we propose a Double Deep Q-Network (DDQN) algorithm for in-orbit task scheduling, which adaptively optimizes task scheduling strategy to minimize task-processing delay and ensure a high task completion rate. Finally, Simulations are conducted in two parts to evaluate the framework. The first part validates the DDQN-based task scheduling strategy, achieving significant reductions in task-processing delay and improved task completion rates compared to conventional strategies. The second part assesses the impact of SDN control domain shape and size on task-processing delay, confirming domain size as the dominant factor influencing delay.

Empathetic response generation necessitates the integration of emotional and intentional dynamics to foster meaningful interactions.Existing research either neglects the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy, or resorts to large language models (LLMs), which incur significant computational overhead.In this paper, we introduce ReflectDiffu, a lightweight and comprehensive framework for empathetic response generation.This framework incorporates emotion contagion to augment emotional expressiveness and employs an emotion-reasoning mask to pinpoint critical emotional elements.Additionally, it integrates intent mimicry within reinforcement learning for refinement during diffusion.By harnessing an intent twice reflect mechanism of Exploring-Sampling-Correcting, ReflectDiffu adeptly translates emotional decision-making into precise intent actions, thereby addressing empathetic response misalignments stemming from emotional misrecognition.Through reflection, the framework maps emotional states to intents, markedly enhancing both response empathy and flexibility.Comprehensive experiments reveal that ReflectDiffu outperforms existing models regarding relevance, controllability, and informativeness, achieving stateof-the-art results in both automatic and human evaluations.

In multi-platform mobile crowdsensing (MCS), platforms attract mobile workers to participate in sensing tasks and collect data through incentive mechanisms to provide data-driven services. However, existing studies often focus exclusively on either competition or cooperation mechanisms between platforms, overlooking scenarios where both coexist. Additionally, most studies prioritize optimizing social welfare or market fairness, neglecting the core role of platforms as data service providers in a free market and the impact of their dominant market position, which limits the utility of platform benefit optimization. In addition, the issue of privacy protection has not received sufficient attention. To this end, this paper proposes a two-stage hybrid mechanism (TS-HM) with the goal of maximizing the utility of the platform, including a decentralized multi-agent reinforcement learning pricing mechanism (DC-PM) and a two-substage cooperative matching mechanism (CMM). In the first stage, the DC-PM mechanism is used to help the platform learn the optimal pricing strategy under privacy-preserving conditions by modeling the pricing and worker contribution problem as a multi-leader-multi-follower stackelberg game; and in the second stage, the CMM mechanism is used to guarantee the matching stability and further enhance the platform utility. Experimental simulations demonstrate that the DC-PM mechanism effectively achieves rapid convergence of pricing strategies while ensuring privacy protection, and the CMM mechanism excels in matching stability and platform performance improvement. In general, the TS-HM mechanism outperforms existing approaches by increasing the total utility of the platform by an average of approximately 12. 65%, significantly increasing the effectiveness of the platform and showcasing its strong advantages.

As the demand for computing in remote areas increases, Low Earth Orbit (LEO) satellites are transitioning from merely serving as communication roles to becoming providers of computing services. LEO satellite edge computing is being integrated into satellite networks, by placing servers on LEO satellites for task processing, to enable in-orbit computing. A key issue is how to optimally place servers in a constellation to support in-orbit computing of tasks effectively. This work proposes a LEO satellite server placement method that minimizes the average response latency of tasks processed on servers while reducing constellation construction costs by strategically selecting a subset of satellites in a constellation for server placement. First, we utilize real-world datasets from remote area devices and remote sensing satellites to derive the task distribution. To enable a unified representation of in-orbit computing tasks (ICT), we develop a unified model that standardizes task data from both sources. Second, we formulate the server placement process as an optimization problem that incorporates both spatial and temporal characteristics of the satellite network, aimed at minimizing the average response latency of ICT processing. To solve this problem, we design an Improved Genetic Algorithm with Simulated Annealing (IGASA) to search for the effective server placement. Simulation results demonstrate that our method achieves superior performance in minimizing the average response latency of task processing compared to baseline server placement methods and converges rapidly.

For a limited scale self-organized multi-agent system operating in environments with unknown task distributions, one challenge is to reduce the task response time via efficiently combining task exploration and allocation, another challenge is to improve the task completion rate via unlocking the potential of network cooperation in task allocation. However, in the existing studies, task allocation is generally regarded as an independent issue for known task distribution environments, rarely combined with task exploration, also hardly solving the conflict between the multi-hop network cooperation and mobility flexibility of agents. In view of this, this paper proposes a network connectivity-aware deep reinforcement learning method for task exploration and allocation in limited scale multi-agent systems (NCADRL4TEA). This method divides the task environment into regions and integrates task exploration with task allocation via two policies: a leaving policy to guide global task exploration among regions according to the distribution of agents and tasks, and a stay policy to guide local task allocation within each region according to the multi-hop network cooperation performance between agents. Further, in the stay policy, a network connectivity-aware task allocation optimization model is provided, which leads agents in the same region to cooperate with each other via multi-hop intermittent network connectivity and flexibly adjust their locations until the optimal multi-hop network cooperation performance is achieved. The experimental results verify that NCADRL4TEA can reduce the task response time in combination of task exploration and allocation, and improve the task completion rate in network cooperation.

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers’ willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group’s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers’ willingness to collaborate, and the diminished motivation among workers will consequently reduce the group’s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers’ cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%.

Mobile crowd sensing (MCS) has attracted extensive attention as a promising method for environmental sensing and data collection. However, due to the increasing computational tasks, bandwidth, and computing pressure, traditional “Cloud-Edge–End” MCS is insufficient to efficiently offload sensing data in real-time for data aggregation. Therefore, we consider a novel MCS framework based on the “Cloud-Enhanced-Edge–End,” the framework uses MCS idle users as edge nodes (ENs) to assist edge servers to enhance computing power and reduce delay and energy consumption. To achieve efficient data aggregation in the “Cloud-Enhanced-Edge–End” MCS framework, we first consider the multiobjective optimization of delay and energy consumption to establish a utility function. Second, addressing the shortcomings of traditional optimization algorithms, which often struggle with complex decision spaces and dynamic environments, we propose a MCS offloading (MCSOL) algorithm based on deep Q-network (DQN). The algorithm uses reinforcement learning to adaptively offload computing tasks to the optimal ENs or servers to maximize utility function to reduce delay and energy consumption. Our experimental results reveal that, in comparison with the other five strategies like only offloading data to base stations and ENs, MCSOL enhances data aggregation performance within the range of 30%–60%.

In mobile crowd sensing (MCS), most of the research work does not consider the dropout situation of the workers, resulting in a lower completion rate of the tasks. To decrease the performance loss caused by the dropout of workers, one solution is to prevent workers from dropping out in advance. However, this solution cannot be widely applied to some emergent scenarios and avoid dropouts strictly. In this paper, we consider another solution to respond to worker dropouts actively and innovatively propose a two-stage incentive framework, namely the Game and Reverse Auction based Incentive Mechanism (GRAIM), which aims at motivating dropout workers to submit perceived data and effectively recruiting high-quality idle workers to complete all unfinished tasks, including the tasks left behind by the dropout workers and other incomplete tasks. In the first stage, a bargaining game-based reward allocation method (BGRA) is proposed to incentivize the perceived data submission of dropout workers with a reward equilibrium between the platform and the dropout workers. In the second stage, worker recruitment is modeled as a multi-armed bandits (MAB) issue in a reverse auction, and the extended upper confidence bound (EUCB) algorithm is proposed for the platform to recruit high-quality idle workers to perform unfinished tasks. The experimental results show that compared with the state-of-art mechanisms, our proposed GRAIM increases social welfare by about 15%~ 23%, reduces average reward by about 16% ~ 25%, and increases task completion rates by about 8% ~ 20%.

Task allocation, which can be divided into offline task allocation and online task allocation, is a significant issue in mobile crowdsensing (MCS). Unlike offline task allocation, in the online task allocation scenario, since participants arrive at the service dynamically, the quantity of participants in a specific time and space is uncertain, hence it could affect the quality and efficiency of task completion. However, due to the difficulty of predicting the quantity of real-time participants in a specific time and space accurately, the existing studies of online task allocation lack deep consideration of the quantity of participants. Therefore, this article investigates a participant-quantity-aware online task allocation problem. First, in view of the difficulty of predetermining the participant quantity in MCS, an fuzzy time-series analysis (FTSA) method is developed to predict the participant quantity available for each task in a specific time and space. Then, according to the predicted quantity, two reasonable attributes for each task, including the task’s threshold on participant’s sensing ability and the reward provided for participants to execute the task, can be calculated separately. On this basis, considering the participant’s willingness, the participant’s sensing ability, the sensor types of the participant’s device, and the participant’s time coverage jointly, we design an online task allocation algorithm based on an improved genetic algorithm (OTAGA) to allocate an appropriate set of tasks to each participant who arrives in real time, so as to maximize the platform utility and minimize the movement cost of the participant. Simulation results show that the proposed method is effective in terms of the accuracy of prediction, the platform utility and the movement cost of the participant.

Fuzzy$c$-means (FCM) clustering is a promising method to handle uncertainties in data clustering. However, the traditional FCM and most of its variants cannot address incomplete inputs. To this aim, a novel fuzzy clustering framework is put forward to perform highly accurate clustering on incomplete data. It adopts twofold ideas: 1) Utilizing a nonnegative latent factor model to prefill the missing data in the inputs by rigidly extracting involved entities’ latent features, where the principle of a minibatch gradient descent algorithm is incorporated into a single latent factor-dependent, nonnegative and multiplicative update algorithm to accelerate the convergence rate; and 2) integrating the distribution of inputs and the weights of local features into the objective function through sparse self-representation and weighting allocation to focus on crucial features. In this way, a NLF analysis-incorporated and feature-weighted fuzzy double$c$-means clustering (NF$^2$D) method is achieved, where the data distribution and instance correlation are simultaneously considered with care. Experiments on 12 real-world datasets including both data and images with different missing rates show that the proposed NF$^2$D method has a significant superiority over state-of-the-art fuzzy clustering methods.

The booming of mobile technologies and Internet of Things (IoTs) have facilitated the explosion of wireless devices and brought convenience to people's daily lives. Coming with the explosive growth of wireless devices, incompatibility of heterogeneous wireless technologies hindered the growing demands for everything connected. And spectrum sharing among heterogeneous wireless technologies has led to severe Cross-Technology Interference (CTI), which is a vital obstacle for network reliability and spectrum utilization. Researches in recent years have shown that Cross-Technology Communication (CTC) turns out to be a promising solution with broad perspective for the coexistence of heterogeneous wireless technologies. However, due to the physical layer incompatibility of WiFi and Bluetooth Low Energy (BLE), the researches about CTC between these two most wildly used wireless technologies are limited by now. In this paper, we propose WiBWi, a payload encoding-based bidirectional CTC scheme between BLE and WiFi, which can achieve near-optimal throughput and powerful robustness. For uplink, i.e., BLE to WiFi communication, WiBWi leverages a novel extended WiFi preamble detection rule and probabilistic inference based encode mapping to achieve fast and reliable communication. For downlink, i.e., WiFi to BLE communication, WiBWi introduces an encoding mapping scheme in the sight of BLE receiver with little modification to accomplish high throughput and robustness. Extensive evaluation shows that WiBWi can offer near-optimal throughput (near the maximum throughput of BLE) and extremely low bit error rate (less than 1%).

Abstract The unstable link quality in wireless sensor networks (WSNs) directly affects the success rate of data transmission. The retransmission mechanism is one of the commonly used methods to solve this problem. However, too many retransmissions could lower the communication efficiency. Therefore, to reduce retransmissions while guaranteeing the communication reliability in WSNs, this study introduces the NarrowBand Internet of Things (NB‐IoT) technology, and builds a network including both sensor and NB‐IoT nodes. The NB‐IoT node is designed to support both the radio frequency and the NarrowBand communication modes; thus it can communicate with both sensor nodes and NB‐IoT base stations. Further, the communication reliability metrics considering both the link quality and the number of retransmissions in different communication modes are defined, based on which, an adaptive communication reliability algorithm is proposed to switch the communication modes of nodes. The simulation results verify that the proposed algorithm can achieve higher success rate with less end‐to‐end delay and flexibly control the cost on NB‐IoT communication.

Computation offloading from a mobile device to the edge server is an emerging paradigm to reduce completion latency of intensive computations in mobile-edge computing (MEC). In order to satisfy the delay-sensitive computing tasks, offloading time, including task uploading time, task execution time, and results downloading time is adopted as the computational performance metrics for offloading nodes that perform offloaded computing tasks for mobile devices. Therefore, how to minimize the offloading time by selecting an optimal offloading node in MEC is of research importance. This work first investigates a MEC system consisting of mobile devices and heterogeneous edge severs that support various radio access technologies. Then, based on the available bandwidth of heterogeneous edge severs and the location of mobile devices, an optimal offloading node selection strategy is formulated as a Markov decision process (MDP), and solved by employing the value iteration algorithm (VIA). Finally, extensive numerical results demonstrate the effectiveness of the proposed strategy over classic strategies in terms of offloading time.

As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost.

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers' mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers' competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit.

Summary Feature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.

The emergence of computation offloading can meet the real‐time requirements of computing tasks with intensive computing demands. In this study, the authors use opportunistic communication to construct a network framework for opportunistic edge computing (OEC) to perform computation offloading. Specifically, OEC forms a computing resource pool near the edge servers in the edge layer by gathering idle computing resources. Firstly, the state of the system is defined by the attributes of the computing task, the execution location of the computing task and the location of the terminal device in OEC. Then the computation offloading time is calculated and learned by selecting different offloading nodes. Finally, an optimal offloading node selection strategy based on the Q‐learning algorithm is obtained. Extensive simulations show that the proposed strategy consumes the minimum computation offloading time compared with benchmark algorithms in aspects of the amount of uploaded data, the total number of CPU cycles of the task and the number of computing tasks.

Resourced‐constrained task allocation is a fundamental research problem in wireless sensor networks. While existing approaches mainly allocate the task to a single sensor node, this study proposes a novel algorithm, in which tasks are allocated to a pair of collaboratively working sensor nodes based on their intimacy. Specifically, the intimacy level of two nodes is modelled based on their link quality and preference degree. This basic idea is that each node pair should be allocated with a task whose task level (e.g. measured by the computing intensity) could match with the intimacy level of this node pair, so that each task can be executed collaboratively and efficiently. Considering that a node connecting with multiple nodes, it may be allocated with redundant tasks (tasks with the same task level), and these tasks need to be adjusted to avoid redundant task execution. Simulation results show that the proposed algorithm not only can improve the task allocation efficiency but also can balance the network energy consumption.

Network connectivity, with its significant application value for data transmission and node cooperation, has drawn a great concern in recent years. Facing the heterogeneity and complexity of the IoT system, the connectivity determination between nodes in the network is a big challenge. In view of this, this article proposes a generic and efficient connectivity determination method for IoT applications. This method first characterizes the connectivity parameters of nodes, including the direct connection probabilities between nodes, the degree centrality, and the betweenness centrality of nodes, and based on them, then constructs a node connectivity random graph (NCRG) and splits the NCRG into separate components. Furthermore, it converts the connectivity between nodes located in different components into the connectivity between these components and provides an algorithm to determine their connectivity. Specifically, three testing rules are defined in the algorithm to rank the testing priorities of these components and testing edges between these components. The simulation results show that the proposed method can efficiently achieve high accuracy with less cost.

Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns. Matrix factorization (MF) technique is an efficient method to extract the useful latent factors (LFs) from the LF model, which directly gives rise to the so-called MF model. However, most MF models cannot maintain some frequently encountered constraints such as nonnegativity of LFs and the symmetry of the target network. In addition, in spite of its potential capability of obtaining the effectiveness of both the computation and the storage, the currently developed double factorization (DF)-based model still suffers from the problem of the low prediction accuracy due to the limited amount of LFs. To address the above problems, a novel MF model is proposed in terms of the triple-factorization (TF) technique, thereby leading to TF-based symmetric and nonnegative latent factor (SNLF) models. Compared with the traditional DF-based SNLF model, the proposed TF-based SNLF model is equipped with: 1) constraints on symmetry and nonnegativity; 2) desirable performance with high accuracy; 3) the convergence of the algorithm; and 4) fairly low storage and computational complexity. Furthermore, in order to reduce overfitting so as to further improve the model performance, regularization is precisely considered into the proposed TF-based SNLF model. Experiments on real datasets show that the proposed TF-based SNLF model has a whelming ability of improving the estimation accuracy for the missing data as well as guaranteeing the symmetry of the target network and the nonnegativity of LFs at a little expense of the computation and storage burden. Moreover, it is easy to be implemented for the data analysis.

Network coding-aware routing has become an effective paradigm to improve network throughput and relieve network congestion. However, to detect coding opportunities and make routing decision for a data flow, most existing XOR coding-aware routing methods need to consume much overhead to collect overhearing information on its possible routing paths. In view of this, we propose low-overhead and dynamic Coding-Aware Routing via Tree-based Address (CARTA) for wireless sensor networks (WSNs). In CARTA, a Multi-Root Multi-Tree Topology (MRMTT) with a tree-based address allocation mechanism is firstly constructed to provide transmission paths for data flows. Then, a low-overhead coding condition judgment method is provided to detect real-time coding opportunities via tree address calculation in the MRMTT. Further, CARTA defines routing address adjustments caused by encoding and decoding to ensure the flows’ routing paths can be adjusted flexibly according to their real-time coding opportunities. It also makes additional constraints on congestion and hop count in the coding condition judgment to relieve network congestion and control the hop counts of routing paths. The simulation results verify that CARTA can utilize more coding opportunities with less overhead on coding, and this is ultimately beneficial for promoting network throughout and balancing energy consumption in WSNs.

Non-negative latent factor (NLF) models have great capabilities of extracting useful knowledge from a symmetric, sparse and high-dimension (SHDiS) matrix with positive constraints. For the purpose of obtaining the NLFs, the tradition SNLF model is constructed based on the doublefactorization (DoF)-based matrix factorization (MF) technique, however it suffers from the low prediction accuracy. In order to improve the performance, an improved SNLF model in terms of triple factorization MF technique is proposed. Whereas, such a better prediction accuracy might be obtained at an evident cost of the time efficiency. Aiming at finding a fine balance between the model performance and the time cost, a novel triple factorization-like SNLF (TFL-SNLF) model is developed by introducing a pre-given non-negative symmetric matrix and a non-negative and symmetric LF matrix. Based on this established model, a single latent factor-dependent nonnegative additive gradient descent (AGD) update algorithm is designed for obtaining desired LFs. Experiments on two actual industrial data sets illustrate that the novel TFL-SNLF model can not only have a better performance of the prediction accuracy on missing data than the DoF-based SNLF model, but also is superior at the efficiency over the TrF-based SNLF. Hence, the proposed TFL-SNLF model is more applicable according to the industrial engineering.

In wireless sensor networks, the node reliability can affect the reliability of data transmission. To analyze the node reliability, this paper defines both betweenness centrality and dependency degree for a node to reflect its global reliability and local reliability, respectively. Based on the above definition, a global and local reliability-based routing (GLRR) protocol is proposed to guarantee the reliability of data transmission between a source and destination node in network. In GLRR, at first, some nodes, usually with greater betweenness centrality among their neighbors in a limited range, will be selected as the backtracking node. Moreover, all backtracking nodes then construct their backtracking paths from themselves to the source node separately, and each node on a backtracking path should calculate the dependency degree on its previous hop node. All the betweenness centrality and dependency degree will be forwarded to the source node along the backtracking paths, and be combined to design the routing metric, based on which, the source node can calculate an optimal backtracking path to forward packets to the corresponding backtracking node, meanwhile, this backtracking node will act as a new source node to launch another routing process until the packets be forwarded to the destination node. At last, the simulation results demonstrate that the proposed protocol is superior to the classical algorithms in terms of the network reliability and the network efficiency.

Recently, a new routing protocol notes as enhanced tree routing (ETR) has been designated for wireless sensor networks. However, when using the ETR to determine next-hop neighbor, only the reduced hops via them are employed, but the residual energies of one-hop neighbors are not considered. To overcome this flaw of ETR, we propose an improved ETR with energy awareness (ETREA) for sensor network. This protocol takes both the reduced hops and the residual energies into account; it is more comprehensive to determine the optimized neighbors for packet forwarding. It is shown that such an effective decision can be made when there are far more than one-hop neighbors. Simulation results show that the ETREA not only decreases the death number of nodes in network but also achieves the energy balance as well as maximizes the whole network's lifetime.