The effectiveness of federated learning (FL) systems relies on extensive client participation. However, the existing incentive mechanisms often overlook the intrinsic coupling between participation and privacy strategies. This oversight leads to mismatches between incentive distributions and clients’ privacy costs and contribution quality, limiting the effectiveness of incentives for enhancing contribution quality and participation scale. To address this, DIM-NEG, a dynamic incentive model based on smart contracts and nested evolutionary games, is proposed. First, we construct a nested evolutionary game framework linking external participation with internal privacy strategies. By utilizing the internal equilibrium payoff as a feedback parameter for the external game, we achieve unified modeling of these coupled decision processes. Second, on the basis of this structure, a functionally decoupled dual-incentive lever mechanism is proposed. The server employs the strategy incentive to guide the internal strategy portfolio and the participation incentive to regulate the participation scale, enabling separate optimization of the strategy composition and overall participation level of the system via hierarchical control. Finally, utilizing blockchain-based smart contracts, we design an automated mechanism that encodes rules on-chain to resolve trust issues associated with centralized servers. A theoretical analysis and simulation results demonstrate that the DIM-NEG model achieves superior global accuracy, training efficiency, and communication cost-effectiveness, while exhibiting strong robustness in non-IID environments. The model adequately motivates users to participate in high-quality data sharing tasks and maintains system stability, thereby maximizing the overall effectiveness of the federated learning system.

RDMA, as a cutting-edge networking technology, has gained extensive adoption in large-scale data centers due to its exceptional characteristics, such as low and stable latency, high throughput and low CPU utilization. However, due to the limited on-chip memory of the RDMA Network Interface Cards (RNIC), commercial RDMA usually only supports single-path transmission and cannot fully utilize the rich parallel paths within the DCN, resulting in insufficient bandwidth utilization. Multipath transmission can improve bandwidth utilization, but the out-of-order (OoO) packets it brings negatively impacts the performance of RNICs. Recent works have attempted to address this issue by using bitmaps to record OoO packets to better support multipath transmission. However, these approaches either consume excessive memory for maintaining bitmaps, leading to poor connection scalability, or introduce high latency in bitmap sharing. Consequently, implementing efficient packet reordering in RDMA remains a challenge.

With the advancement of autonomous driving and intelligent transportation systems, there has been a notable increase in the demand for multi-modal task processing by mobile edge terminals. However, intelligent vehicles need to offload complex tasks to servers due to their constrained processing capabilities and limited storage. In this context, the multi-access edge servers which are closer to vehicle terminals, emerge as a superior alternative. Nonetheless, the heterogeneous distribution of traffic flows across time and space can lead to significant communication delays due to potential overloading of some edge servers when offloading schemes employ random resource allocation. To address these challenges, this paper proposes a region attention-based edge resource allocation (RAllo) model to allocate the computing resource by predict regional demand. A regional attention-based traffic prediction model (RATra) extracts crucial spatio-temporal information using a temporal module and a graph attention network to forecast the regional demand. As for the resource allocation, after getting the predicted demand of tasks based on RATra, the radiating-breadth-first-allocation (RBFA) algorithm is designed for task scheduling within and across regions based on task demand. Finally, the results of experiments demonstrate the superiority of RAllo in reducing time consumption and enhancing MEC server utilization compared to existing methods.

In the Internet of Connected Vehicles, caching content frequently requested by users on edge devices can reduce access latency. Particularly in high-traffic density areas, Unmanned Aerial Vehicles (UAVs) can integrate into future cellular networks to enhance the network capacity and meet increased requests. Therefore, we formulate a joint optimization problem of cooperative caching of Base Station (BS) and UAVs and UAV trajectory planning to minimize network latency while considering the limited energy and storage capacity and dynamic vehicles. First, we propose a Temporal-evolving Bipartite Graph Neural Networks (TBGN) model for traveling areas prediction of vehicles. Then, regarding the coupling of optimization variables, we propose an Energy-aware Monte-Carlo Tree Search algorithm to optimize the UAV’s service trajectory by predicted spatio-temporal vehicle density. Finally, the optimization problem degenerates into a monotonic submodular function to optimize caching decisions. We utilize real vehicle trajectories for simulations. The results show that the TBGN outperforms other advanced models in terms of mobility prediction accuracy by 7.4%, and the proposed scheme reduces average latency by 16% compared to other schemes.

With the development of the 6th generation communication technology, the service traffic of mobile communication is rapidly growing. Many new types of services usually have high requirements for computing resources and low latency constraints. They need to be offloaded to a base station (BS) with computing resources for processing. In some disaster areas, the communication system will go down due to damage to the ground infrastructure. High altitude platforms (HAPs) with extensive coverage and unmanned aerial vehicles (UAVs) with simple deployment can provide various emergency services as aerial BS. UAVs and HAP carry servers and other equipment to serve users. It is a promising technology for communication and computing services. Due to UAVs’ limited computing resources and energy, it is a challenge to deploy them effectively and fully use network resources. Therefore, a task-dynamic processing through multi-UAV cooperation (TDPUC) strategy is proposed. A improved K-means algorithm is proposed to realize the dynamic deployment, which optimizes the number of UAVs dispatched and reduces the overall energy consumption. In addition, the multi-UAV cooperation for task offloading can realize dynamic task processing under constrained energy and resources. When UAVs cooperate, the multi-agent reinforcement learning (MARL) algorithm is used to optimize resource allocation and learns online. By numerical results, the proposed TDPUC strategy can improve the service capacity of tasks by 11% on average with less energy consumption.

By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high-reliability requirements of IoV users for multimedia services. However, the edge node’s coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) how to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users and 2) how to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this article, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle’s driving trajectory and travel preference. The scheme learns the vehicle’s travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility data sets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle’s request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware in-order caching schemes.

With the development of wireless communication technology, the amount of data in the Internet of Things has increased rapidly, and the application mode has become ubiquitous. The content sharing mode has the characteristics of diversified services, diversified contents, and diversified scenarios. Content sharing from device to device (D2D) can cope with the increasing traffic pressure on cellular networks. Users can use mobile devices to transmit and share content across spaces by opportunities. However, in the content sharing process, the available cache space for users is limited, and content may delay delivery in searching cooperative sharing users. Researching effective content sharing and forwarding algorithms can improve such a transmission environment. In the content sharing process in the mobile Internet of Things, users can analyze and judge the surrounding fields according to the shared content attributes and personalized preferences and search for suitable sharing targets. This paper proposes a content sharing algorithm based on dynamic behavior and collaborative prediction (DBCPNF). It establishes a user preference model based on the user’s historical behavior, cooperation users, and content attributes. By calculating the matching degree between content attributes and user preferences, users with matching higher are adopted as target users to share content cooperatively. Through experimental analysis and comparison with other algorithms, our algorithm has the best performance on the content delivery ratio and improves the network’s overall efficiency.

Similarity measurement is an important basis in time series analysis.Among them, dynamic time warping distance (DTW) is considered to be the most effective distance measurement method.However DTW's huge computational overhead is difficult to meet the application requirements in the era of big data.Previous optimization methods often focus on reducing unnecessary calculation objects and do not involve warping distance calculation itself.After studying many related optimization algorithms, we propose a DTW matching algorithm based on key structure point alignment.By extracting the key structure points of time series and calculating the warping alignment relationship between the key structure points, the constraint range of the cumulative distance matrix of the approximate optimal warping distance from the path is mapped, which greatly reduces the amount of calculation of the distance cumulative matrix, then approximate warping distance can be calculated quickly.The experimental results show that the calculation speed of our method is significantly improved compared with the traditional algorithm in similarity matching, and it also has a good performance in classification accuracy. Keywords-time series

The multiaccess edge computing/mobile-edge computing (MEC) is becoming a key technology toward “full 5G.” However, as it gets widely used, a fundamental problem is how to support as many service requests as possible under stringent Quality-of-Service (QoS) requirements and limited communications and computing resources. In this article, we study the long-term throughput maximization problem for multicell multiuser MEC systems. Different from most of the existing works that focus on energy or latency minimization problem for a single-edge system, a novel design is proposed from the service provider’s perspective to maximize the system-wide throughput under latency bounds by jointly taking user association and resource allocation for both communications and computing into account. To capture the stochastic nature of MEC environments, a Markov decision process (MDP) is employed to model the queuing states for both mobile devices and MEC servers. By combining MDP and matching theory, a joint user association and resource allocation algorithm is given, where the resource allocation policy under given user-server association is solved. Extensive numerical results demonstrate the superiority of the proposed scheme in comparison with several existing approaches.

The International Classification of Diseases (ICD) code is a disease classification method formulated by the World Health Organization(WHO). ICD coding usually requires clinicians to manually allocate ICD codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic ICD coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0.553, Micro-AUC of 0.989 and precision at top 8(P@8) of 0.735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method.

Abstract The rapid development of the Internet of things has led to the explosive development of data in various fields. Traditional routing protocols cannot effectively handle the reception and transmission of data. This makes it difficult to exchange and transmit information in the Internet of things. Therefore, the choice of data transmission methods is particularly important. In order to solve this problem, this paper proposes a data transmission mechanism based on social relationships, namely, the mobile node social relationship quantification (MNSRQ) algorithm, which analyses the social relationship characteristics of mobile nodes in the Internet of things, extracts decision‐making features to study the dynamics of social relationship, then combines information entropy and fuzzy clustering theory to quantify the social relationship, and then selects the relay node with strong social relationship for data transmission. Theoretical analysis and experimental results show that the performance of the MNSRQ algorithm is better than previous studies. Compared with epidemic algorithm, ICMT algorithm, spray and wait algorithm, and EIMST algorithm, the MNSRQ algorithm can reduce end‐to‐end transmission delay and routing overhead while maintaining the life of the network, effectively reduce energy consumption during transmission, and maintain a high data transmission success rate, with a transmission success rate of 0.7–0.9.

The electrocardiogram sensor network (ECG-SN) is a medical monitoring system based on IoT technology, which can detect heart bioelectric signals in real time. But the ECG signal is vulnerable to human mobility and sensitive to the Age of Information (AoI). In this article, we first analyze the impact of human mobility on channel fading based on a real-world activity trace data set and design a two-state ECG work model based on the tradeoff between the ECG signal quality and energy consumption. Furthermore, an AoI model is proposed to evaluate the data timeliness. Then, an online transmission optimization algorithm is proposed to maximize the system utility by optimizing the sampling rate, transmission power, and data dropping rate. Furthermore, performance analysis presents the bounds for data buffer, battery capacity, and AoI. Numerical results show the dynamics of the system and the impact of the parameters on system performance, which verify that the proposed design has a larger utility and a smaller AoI in comparison with two benchmark schemes.

OBJECTIVE: Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a new automatic multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning in this study. PET image pre-screening can reduce the time cost of noise reduction, segmentation and lesion partitioning methods, and denoising can enhance both quantitative metrics and visual quality for better segmentation accuracy. For pre-screening, we propose a new differential activation filter (DAF) to screen the lesion images from whole-body scanning. For noise reduction, neural network inverse (NN inverse) as the inverse transformation of generalized Anscombe transformation (GAT), which does not depend on the distribution of residual noise, was presented to improve the SNR of images. For segmentation and lesion partitioning, definition density peak clustering (DDPC) was proposed to realize instance segmentation of lesion and normal tissue with unsupervised images, which helped reduce the cost of density calculation and completely deleted the cluster halo. The experimental results of clinical data demonstrate that our proposed methods have good results and better performance in noise reduction, segmentation and lesion partitioning compared with state-of-the-art methods.

The computation offloading in mobile-edge computing (MEC) systems emerges as a promising technology to enhance users' quality-of-experience over mobile devices (MDs). However, the design of computation offloading policy for MEC systems inevitably faces challenges with respect to the gap between dynamic task generation in MDs and the limited resources at an MEC server, especially for a multi-user MEC system. More specifically, whether or not offload a task to a nearby MEC server and how much communication and computing resources are allocated to the selected MDs should be carefully investigated to optimize the long-term system performance. In this paper, we handle this issue based on the Markov decision process, where collaborated resource allocations are determined according to both the queueing state of the task buffer at the MDs and the MEC server. By analyzing the average task delay of each user and the average throughput of the system, we formulate a throughput maximization problem with the constraints on delay, spectrum resource, and computing resource, and develop a throughput-optimal resource allocation policy. Simulation results show that the proposed joint communication and computing resource allocation policy is highly effective and efficient.

The measurement of cavum septum pellucidum is an important step in prenatal testing. However, this process is usually done manually, which is such a difficult and time-consuming task due to the attenuation and shadows of ultrasound images even for experienced sonographers. In this study, we propose a novel deep attention network to address this problem by segmenting and measuring the width of cavum septum pellucidum. The proposed network is based on U-net with three changes: a new channel attention module, increasing attention on relevant regions; VGGI I, adding the depth of encoder path to increase the receptive field; And post-processing to measure and diagnose the anomalies of cavum septum pellucidum. Experiments on a fetal ultrasound dataset demonstrated our proposed network achieved the highest precision of 79.5% and the largest Dice score of 77.5%. To demonstrate the generalization capacity, we also have been validated our model on the BraTs 2017 dataset, obtaining an excellent performance with the Dice score of 91.5%.

With the development of 5G network and big data and the popularity of mobile intelligent devices, the opportunistic social network has been further developed. At present, several existing routing algorithms based on node similarity use the context information of the node to select the best relay node. However, most opportunistic social algorithms only consider the social properties of nodes and ignore the importance of the similarity of the moving trajectories of the nodes. The transmission opportunity of messages in the opportunity social network is generated by the movement of the nodes, so this feature must betaken into account in the designing of the routing algorithm. Therefore, this study proposes a routing algorithm based on the triangular fuzzy layer model and multi‐layer clustering for the opportunistic social network. In this study, the authors use the fuzzy analytic hierarchy process model to analyse the social similarity and trajectory similarity to determine the best message transmission node. This study compares the other four opportunistic social network routing algorithms in the simulation environment. In general, among the five routing algorithms, the transmission rate of the TFMC algorithm is the best. The average end‐to‐end delay and average network overhead are also the lowest.

With the development of big data, the authors have witnessed the great success of the mobile internet that the number of intelligent mobile devices (MDs) has increased dramatically and the data that needs to be transmitted grows exponentially. The traditional end‐to‐end communication mechanism in social networks is difficult to satisfy enormous communication demands. Therefore, opportunistic social networks proposed that message applications should choose relay nodes to perform effective data transmission processes. Currently, some routing algorithms that utilise contextual information associated with nodes need to handle heavy computing tasks and manage large numbers of messages, which usually results in higher network overhead and network latency. Furthermore, the computing power of MDs is usually limited, message carriers may not be able to quickly find the suitable relay node because of their insufficient computing power, resulting in higher network latency and network overhead. In this study, they construct a cooperative‐routing mechanism based on node classification and task allocation for opportunistic social networks. In their proposed strategy, the reliable relay nodes can be obtained by classifying nodes according to the social attributes of the nodes. The task of node classification will be uniformly assigned to other idle nodes according to their computing power.

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

This paper studies resource allocation for a fully sustainable cooperative network, which consists of multiple Internet of Things (IoT) nodes powered by radio-frequency (RF) energy, one relay with renewable energy supplies, and one destination. Specifically, the relay forwards the data received from IoT nodes to the destination and charges the IoT nodes at the same time. A throughput maximization problem is formulated, which takes into consideration the upper bound of transmit power, stochastic energy harvesting (EH) process and channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different parameters. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the Internet of Things (IoT) technology. In BSN, prolonging the service cycle of the network is a major challenge due to the limited battery capacity and energy supply for sensors. To this end, improving energy efficiency and harvesting energy are the keys for the network to maintain sustainability. In this paper, we propose a transmission scheduling and energy harvesting strategy to manage energy supply and consumption, and build several dynamic models to capture the stochastic processes in BSN. Besides, a system utility maximization problem is formulated. Since this problem is a multiobjective mixed-integer optimization problem (MMOP) which is difficult to solve directly, we provide a solution framework where MMOP is decomposed into several subproblems by the Lyapunov optimization method. Based on this framework, we propose an online energy sustainability optimization algorithm to solve these subproblems, such as the matching problem and convex optimization problem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the appropriate sizes of the data buffer and battery capacity are derived, which can give a guidance to determine the sizes of these components. Simulation results show the impact of the system parameter on the utility and data and energy queues, and verify that the proposed strategy and methods can maintain the sustainable operation of BSN effectively.

Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases. But these methods are concentrated on specific entities of interest (lncRNAs and diseases) and they do not allow to consider networks with more than two types of entities. Considering the limitations in previous computational methods, we develop a new global network-based framework, LncRDNetFlow, to prioritize disease-related lncRNAs. LncRDNetFlow utilizes a flow propagation algorithm to integrate multiple networks based on a variety of biological information including lncRNA similarity, protein-protein interactions, disease similarity, and the associations between them to infer lncRNA-disease associations. We show that LncRDNetFlow performs significantly better than the existing state-of-the-art approaches in cross-validation. To further validate the reproducibility of the performance, we use the proposed method to identify the related lncRNAs for ovarian cancer, glioma, and cervical cancer. The results are encouraging. Many predicted lncRNAs in the top list have been verified by the biological studies.

Fog computing is a distributed computing model that can utilise the storage, analysis and processing capabilities of fog nodes near edge devices. Although fog computing can support task processing for various Internet of Things (IoT) systems, Fog‐IoT architecture faces several new challenges with the rapid development of IoT systems, especially delay‐sensitive IoT systems, such as stochastic and dynamic data arrival, optimal utility and deadline of tasks. To address these challenges, workload scheduling toward worst‐case delay and optimal utility for single‐hop Fog‐IoT architecture are studied and the workload dynamic scheduling algorithm (WDSA) is proposed. The proposed WDSA algorithm can maximise the average throughput utility while guarantees the worst‐case delay of task processing. In addition, it is online and needs no prior information about future. The algorithm performance is analysed from the perspective of optimality and worst‐case delay, demonstrating that the proposed WDSA algorithm can get an approximate optima and worst‐case delay guarantees. Finally, simulation results demonstrate that the efficiency and efficacy of this kind of the algorithm can meet the requirement.

In this study, the authors study a sustainable resource allocation scheme for delay‐sensitive applications in an energy harvesting (EH)‐cloud radio access network (CRAN). The authors formulate an optimisation problem to maximise the user equipment (UE) utility and provide them with strong delay‐guarantee by jointly considering the stochastic EH process, dynamic wireless channel state. By using the Lyapunov stochastic network optimisation technique combined with virtual queues, the authors decompose the formulated problem into four sub‐problems, including channel allocation, data dropping, UE request scheduling and energy management. Based on the solutions of these sub‐problems, a UE optimal resource allocation algorithm is proposed to maximise UE utility while guaranteeing the delay bound and the sustainability of remote radio heads. Furthermore, this algorithm does not require any prior statistical information of the system, e.g. EH process and channel state. Both theoretical analyses and simulation results demonstrate that the proposed algorithm can achieve close‐to‐optimal UE utility, bounded data buffer, delay‐guarantee, and required battery capacity for the operation of the CRAN.

In real network environment, nodes may acquire the communication destination during data transmission and find a suitable neighbor node to perform effective data classification transmission. This is similar to finding certain transmission targets during data transmission with mobile devices. However, the node cache space in networks is limited, and waiting for the destination node can also cause end‐to‐end delay. To improve the transmission environment, this study established Data Transmission Probability and Cache Management method. According to selection of high meeting probability node, cache space is reconstructed by node. It is good for nodes to improve delivery ratio and reduce delay. Through experiments and the comparison of opportunistic network algorithms, this method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.

High-resolution vibration data collection with data quality guaranteeing is important in a class of applications like industrial machine and structural health monitoring. Applying wireless vibration sensor networks (WVSNs) to this class is challenging due to severe resource constraints (e.g., bandwidth and energy). State-of-the-art data reduction approaches (e.g., signal processing, in-network aggregation) suggested to improve these constraints do not satisfy application-specific requirements, e.g., high quality of data (QoD) collection or quality of monitoring (QoM). In this paper, we propose vCollector, a general approach to vibration data collection and monitoring in a resource-constrained WVSN. We enable each sensor to reduce the amount of data (before transmission) in a decentralized manner in two stages: the data acquisition stage and data transmission stage. In the first, we propose a solution to low-complexity signal processing; each sensor analyzes signals using the fast Fourier transform (FFT) under the quadrature amplitude modulation (QAM) and then applies an idea from the Goertzel algorithm (first proposed by Goertzel in 1958) so that the sensor can reduce a significant amount of data without sacrificing the QoD. In the second stage, we propose a decision-making algorithm by which each sensor can make a decision on its acquired data (considered event-sensitive data if it has information about harmful vibrations) so that event-insensitive data communication is reduced. Evaluation results (obtained by simulations using our empirical data traces and by a real system deployment) demonstrate that vCollector significantly reduces energy consumption and guarantees QoM in a WVSN.

Prediction of Protein-RNA binding sites is one of the most challenging and intriguing problems in the field of computational biology. Here, we proposed an effectively machine learning algorithm termed PredRBR (Prediction of RNA Binding Residues), using Gradient Tree Boosting algorithm and mRMR-IFS feature selection method in combination with sequence features, structure characteristics and two categories of structural neighborhood feature for prediction of RNA binding sites in proteins. We evaluate PredRBR on the independent test dataset (RBP101), and obtain significant improvement on the prediction performance compared with other state-of-the-art approaches. In addition, we test the variable importance of diverse feature types. The results show that structural neighborhood features play a crucial role in the identification of RNA binding sites.

In this paper, we investigate the sustainable resource allocation for green Cloud Radio Access Networks (C-RAN) powered by renewable energy. Specifically, the Base Station pool (BS pool) in the C-RAN distributes data to a set of remote radio heads (RRHs) with energy harvesting (EH) capability, and allocates sub-carriers to the selected RRHs for downlink transmissions, by jointly considering the user throughput and energy sustainability performance of RRHs. To this end, we formulate a utility optimization problem, characterizing the stochastic process of energy harvesting (EH) and wireless fading channel. Based on Lyapunov optimization techniques, we decompose the formulated problem into three sub-problems, including energy harvesting, data scheduling, and sub-carrier allocation. We then propose an efficient online algorithm to obtain the maximal aggregate user utility while ensuring the stability of the data buffers and sustainability of the energy buffers. Performance analysis demonstrates that the proposed algorithm can achieve a suboptimal performance with guaranteed upper bounds on data queue and energy queue lengths. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.

BACKGROUND: Protein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA. RESULTS: Experimental results obtained from 5-fold cross-validation based on the Manesh-215 dataset show that the mean absolute error (MAE) and the Pearson correlation coefficient (PCC) of PredRSA are 9.0 % and 0.75, respectively, which are better than that of the existing methods. Moreover, we evaluate the performance of PredRSA using an independent test set of 68 proteins. Compared with the state-of-the-art approaches (SPINE-X and ASAquick), PredRSA achieves a significant improvement on the prediction quality. CONCLUSIONS: Our experimental results show that the Gradient Boosted Regression Trees algorithm and the novel feature combination are quite effective in relative solvent accessibility prediction. The proposed PredRSA method could be useful in assisting the prediction of protein structures by applying the predicted RSA as useful restraints.

In this paper, we study resource management and allocation for energy harvesting cognitive radio sensor networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate the self-sustainability of the power-limited sensors. Furthermore, cognitive radio enables access to the underutilized licensed spectrum to mitigate the spectrum-scarcity problem in the unlicensed band. We develop an aggregate network utility optimization framework for the design of an online energy management, spectrum management, and resource allocation algorithm based on Lyapunov optimization. The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading. However, a priori knowledge of any of these processes statistics is not required. Based on the framework, we propose an online algorithm to achieve two major goals: first, balancing sensors' energy consumption and energy harvesting while stabilizing their data and energy queues; second, optimizing the utilization of the licensed spectrum while maintaining a tolerable collision rate between the licensed subscriber and unlicensed sensors. The performance analysis shows that the proposed algorithm achieves a close-to-optimal aggregate network utility while guaranteeing bounded data and energy queue occupancy. The extensive simulations are conducted to verify the effectiveness of the proposed algorithm and the impact of various network parameters on its performance.

In this paper, we propose an energy-efficient location-aware clone detection protocol in densely deployed WSNs, which can guarantee successful clone attack detection and maintain satisfactory network lifetime. Specifically, we exploit the location information of sensors and randomly select witnesses located in a ring area to verify the legitimacy of sensors and to report detected clone attacks. The ring structure facilitates energy-efficient data forwarding along the path towards the witnesses and the sink. We theoretically prove that the proposed protocol can achieve$100$percent clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches$98$percent when$10$percent of witnesses are compromised. Moreover, in most existing clone detection protocols with random witness selection scheme, the required buffer storage of sensors is usually dependent on the node density, i.e.,$O(\sqrt{n})$, while in our proposed protocol, the required buffer storage of sensors is independent of$n$but a function of the hop length of the network radius$h$, i.e.,$O(h)$. Extensive simulations demonstrate that our proposed protocol can achieve long network lifetime by effectively distributing the traffic load across the network.

Structural domains are evolutionary and functional units of proteins and play a critical role in comparative and functional genomics. Computational assignment of domain function with high reliability is essential for understanding whole-protein functions. However, functional annotations are conventionally assigned onto full-length proteins rather than associating specific functions to the individual structural domains. In this article, we present Structural Domain Annotation (SDA), a novel computational approach to predict functions for SCOP structural domains. The SDA method integrates heterogeneous information sources, including structure alignment based protein-SCOP mapping features, InterPro2GO mapping information, PSSM Profiles, and sequence neighborhood features, with a Bayesian network. By large-scale annotating Gene Ontology terms to SCOP domains with SDA, we obtained a database of SCOP domain to Gene Ontology mappings, which contains ~162,000 out of the approximately 166,900 domains in SCOPe 2.03 (>97 percent) and their predicted Gene Ontology functions. We have benchmarked SDA using a single-domain protein dataset and an independent dataset from different species. Comparative studies show that SDA significantly outperforms the existing function prediction methods for structural domains in terms of coverage and maximum F-measure.

ABSTRACT To cope with the challenges existing in pervasive environments, based on the characteristics of pervasive environments, a semantic distance‐based trust model is proposed in this paper. The semantic distance between entities and between trust categories is borrowed to calculate trustworthiness more precisely. In the model, the behavior trust and capability trust are distinguished and evaluated separately; on the basis of the trust evaluations, all entities in pervasive environments make independent decisions that can maximize their own profit with the trust model. The simulation experiment results proved the effectiveness of the model in improving the interaction success ratio and efficiency between entities under pervasive environments. Copyright © 2013 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) play an increasing role in a wide variety of applications ranging from hostile environment monitoring to telemedicine services. The hardware and cost constraints of sensor nodes, however, make sensors prone to clone attacks and pose great challenges in the design and deployment of an energy-efficient WSN. In this paper, we propose a location-aware clone detection protocol, which guarantees successful clone attack detection and has little negative impact on the network lifetime. Specifically, we utilize the location information of sensors and randomly select witness nodes located in a ring area to verify the privacy of sensors and to detect clone attacks. The ring structure facilitates energy efficient data forwarding along the path towards the witnesses and the sink, and the traffic load is distributed across the network, which improves the network lifetime significantly. Theoretical analysis and simulation results demonstrate that the proposed protocol can approach 100% clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches 98% when 10% of witnesses are compromised. Moreover, our proposed protocol can significantly improve the network lifetime, compared with the existing approach.

Optimal routing in wireless mesh networks is a challenging problem considering inter- and intra-flow interference. To solve the problem, first, we define a new routing metric, expected path bandwidth (EPBW), where the varying link rate (due to wireless channel quality) and the dynamic link load (considering the inter- and intra-flow interference) have been considered to estimate EPBW accurately. Second, based on the proposed EPBW, we propose a distributed routing protocol for WMNs, aiming to maximize network throughput. We implement the proposed protocol and the routing metric EPBW in NS-2. We then design various scenarios to evaluate the protocol performance extensively using NS-2 simulation. Simulation results show that the proposed protocol and metric can substantially out-perform the state-of-the-art routing metrics, such as expected transmission count (ETX) and expected transmission time (ETT), and previous routing protocols including AODV, DSDV, and DSR.

SUMMARY The employment of batch processing in workflow is to model and schedule activity instances in multiple workflow cases of the same workflow type to optimize business processes execution dynamically. Although our previous works have preliminarily investigated its model and implementation, it is still necessary to deal with its model design problem. Process mining techniques allow for the automated discovery of process models from event logs and have received notable attentions in researches recently. Following these researches, this paper proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. Copyright © 2013 John Wiley & Sons, Ltd.

Cost function based routing has been widely studied in wireless sensor networks for energy efficiency and network lifetime elongation. Existing algorithms however have limited effects because they adopt a single cost function that does not fully capture nodal energy consumption situation. In this paper, we propose a novel Double Cost Function based Routing (DCFR) algorithm, which takes into account end-to-end energy consumption, nodal remaining energy, and energy consumption rate altogether. An extensive simulation indicates that DCFR can lead to more balanced and efficient energy usage among nodes than existing algorithms.

Activity instance oriented handling is a new means for vertical optimization of process cases. Unlike our previous batch processing mechanism in workflows, it focuses on the data characteristics of activity instances and utilizes explicit knowledge for execution optimization. This paper introduces its concept and investigates its modeling and enactment mechanisms. It uses activity instance pattern as a base to model and represent the knowledge for execution optimization. The system design for activity instance oriented handling and related algorithms are also proposed.

P2P overlay potentially provides an efficient routing architecture that is self-organizing, massively scalable, and robust in a wide area. To improve the requesting nodes' QoS routing and to reduce the bandwidth and processing consumption of the nodes in a P2P system, three correlative requirements should be considered and satisfied: (1) to make system structure to be scalable; (2) to locate and route information at a low cost and efficiently without global view of the system; (3) to make the overlay robust in front of the dynamic topologies. However, not all P2P systems are compatible with multimedia streaming. Unstructured systems are designed more specifically than structured systems for the heterogeneous Internet environment, where the nodes' persistence and availability are not guaranteed. In this paper, three correlative standards for evaluating the compatibility of a P2P system with multimedia streaming are presented. A detailed measurement study of three popular unstructured P2P overlays and our overlay called MPO is performed. Our method is to analyze performances of classical searching algorithms in various overlays. Key factors in content locations including scalability, query success rate, query messages, cost, disturbed times and fault tolerance are considered carefully. The simulation results show some characteristics in unstructured P2P overlay and prove that MPO is a highly efficient, low cost and fault tolerant overlay and a good structure for applications in multimedia streaming.

In wireless sensor networks, there exist many redundant sensor nodes, and activating only the necessary number of sensor nodes at any particular moment can save energy, while ensuring all targets covered and network connectivity. In this paper, we first introduce a distributed scheme for sink nodes to find K paths to each sensor nodes. Secondly, a shortest path-based algorithm is presented for the maximum set covers problem in wireless sensor networks. The algorithm partitions all nodes into possibly maximum disjointed sets, and the nodes in each set have all targets covered while ensuring the network connectivity. In the proposed algorithm, when constructing a cover set, the key idea is to select a node joining into the set if it has the shortest path to the nodes which is already in the set. At last, simulation is done, and the result shows that the proposed algorithm outperforms others.

A Routing Algorithm base on Trustworthy Core Tree for WSN (RATCT) is proposed in this paper aims to prolong network lifetime as well as increase network security in a hierarchical-cluster sensor network. Cluster heads with higher residual energy and trust level are elected from underlying sensor nodes. A minimum energy consumption spanning tree algorithm is used to organize all cluster heads into a trustworthy core tree with sink node as tree root. The Trustworthy core tree is expanded to cover all nodes so that each node report to sink node along a unique route. A trust model is integrated in RATCT to evaluate node's trust level and detect evil nodes. Simulation results testify to the effectiveness of the algorithm in producing a longer network lifetime and a safer network.

The topologies of Internet and Internet-based information systems have complex network properties. Designing Internet-based virtual computing environment topology with appropriate properties is significant for both the resource sharing and system performance. We analyses the topology properties of the typical P2P systems, and proposes a new topology generating method, which includes three phases, birth, growth and maturity, and supports multi-node concurrent joining in. The iVCE topology generation method can produce stable structure, with load balancing capability. Analysis of the generated topologies shows that the degree of their super-node obeys normal distribution law, the average path length between nodes shows small-world properties.

This paper studies the design of optimal location of buffer area in a movement-assisted data gathering scheme. In [7], the author only considered a network scenario where rLtR and the transmission power of sensor was fixed. This paper extends the network to a general network where the relationship between r and R is arbitrary and the transmission power of sensor is variable. In the network, the maximum transmission radius is k times of the minimum transmission radius. In order to minimize the total energy consumption of network and maximize the network lifetime, this paper employs a more accurate analytical method to select parameters of the transmission power level and location of buffer area. Finally, a conclusion is obtained that the scheme using optimized location of buffer area and transmission power excels greatly the one that employs fixed transmission power in the scenario of improving the network lifetime. Therefore, our study on the parameter selection of network can present a better guidance on the optimization of wireless sensor networks.

Energy efficient dense wireless sensor network design makes extensive use of spatial redundancy and appropriate MAC mechanism. CSMA is integrated with TDMA while filtering out the spatial correlation with a so-called HS-Sift MAC protocol. The entire sensing region is divided into three sub-areas for different channel access methods. The nodes near to the border sleep most of the time, nodes close to the event claim high channel utilisation and those lie in between compete under different priorities. A software simulation verifies effectiveness of the proposed scheme for energy consumption and access delays.