Search

The important role of microRNA (miRNA) in human diseases has been confirmed by some studies. However, only using biological experiments has greater blindness, leading to higher experimental costs. In this paper a high-efficiency algorithm based on a variety of biological source information and applying a combination of a convolutional neural network (CNN) feature extractor and an extreme learning machine (ELM) classifier is proposed. Specifically, the semantic similarity of diseases, the gaussian interaction profile kernel similarity of the four biological information of miRNA, disease, long non-coding RNA (lncRNA) and environmental factors (EFs), and the similarities of miRNAs are fused together. Among them, miRNAs similarity is composed of miRNA target information, sequence information, family information, and function information. Then, the dimensionality of the data set is reduced by the autoencoder (AE). Finally, deep features are extracted through CNN, and then the association between miRNA and disease is predicted by ELM. The experimental results show that the average AUC value based on the multi-biological source information (MSCNE) model is 0.9630, which can reach higher performance than the other classic classifier, feature extractor mentioned and the other existing algorithms. The results show the MSCNE algorithm is effective to predict the correlation of miRNA-disease.

Cloud-edge heterogeneous network is an emerging technique built on edge infrastructure, which is based on the core of cloud computing technology and edge computing capabilities. The joint problem of computation offloading, cache decision, and resource allocation for cloud-edge heterogeneous network system is a challenging issue. In this paper, we investigate the joint problem of computation offloading, cache decision, transmission power allocation, and CPU frequency allocation for cloud-edge heterogeneous network system with multiple independent tasks. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the transmission power and CPU frequency constraint of the tasks. The constraint of computing resource and cache capacity of each Access Point (AP) are considered as well. The formulated problem is a mixed integer non-linear optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Reinforcement Learning (RL) and Sequential Quadratic Programming (SQP). In the upper level, given the allocated transmission power and CPU frequency, the task offloading decision and cache decision problem is solved using Deep Q-Network method. In the lower level, the optimal transmission power and CPU frequency allocation with the offloading decision and cache decision is obtained by using SQP technique. Simulation results demonstrate that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting; and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.

Cyber-physical systems (CPSs) realize the automatic control of entities through computing systems and networks. Stability is an important factor in CPS for system upgrading and troubleshooting. Traditional analysis methods focus on simulation and formal analysis, which have two major limitations: first, the current state information of CPS is difficult to obtain; second, most CPS face the state space explosion problem. These problems can be avoided and a good analysis can be provided based on empirical data. The main work of this article is summarized as follows: first, a phase space reconstruction method is designed to divide the dataset into several subsequences with the same shape; second, we propose a stability analysis method based on correlation dimensions. Results indicate that the proposed approach can obtain a stable correlation dimension. CPS perform better if the correlation dimension is maintained within a certain range; otherwise, a destabilizing factor exists. The proposed stability analysis has less complexity and running time.

A growing number of studies have confirmed the important role of microRNAs (miRNAs) in human diseases and the aberrant expression of miRNAs affects the onset and progression of human diseases. The discovery of disease-associated miRNAs as new biomarkers promote the progress of disease pathology and clinical medicine. However, only a small proportion of miRNA-disease correlations have been validated by biological experiments. And identifying miRNA-disease associations through biological experiments is both expensive and inefficient. Therefore, it is important to develop efficient and highly accurate computational methods to predict miRNA-disease associations. A miRNA-disease associations prediction algorithm based on Graph Convolutional neural Networks and Principal Component Analysis (GCNPCA) is proposed in this paper. Specifically, the deep topological structure information is extracted from the heterogeneous network composed of miRNA and disease nodes by a Graph Convolutional neural Network (GCN) with an additional attention mechanism. The internal attribute information of the nodes is obtained by the Principal Component Analysis (PCA). Then, the topological structure information and the node attribute information are combined to construct comprehensive feature descriptors. Finally, the Random Forest (RF) is used to train and classify these feature descriptors. In the five-fold cross-validation experiment, the AUC and AUPR for the GCNPCA algorithm are 0.983 and 0.988 respectively.

In recent years, the miRNA is considered as a potential high-value therapeutic target because of its complex and delicate mechanism of gene regulation. The abnormal expression of miRNA can cause drug resistance, affecting the therapeutic effect of the disease. Revealing the associations between miRNAs-drug resistance can help in the design of effective drugs or possible drug combinations. However, current conventional experiments for identification of miRNAs-drug resistance are time-consuming and high-cost. Therefore, it’s of pretty realistic value to develop an accurate and efficient computational method to predicting miRNAs-drug resistance. In this paper, a method based on the Support Vector Machines (SVM) to predict the association between MiRNA and Drug Resistance (SVMMDR) is proposed. The SVMMDR integrates miRNAs-drug resistance association, miRNAs sequence similarity, drug chemical structure similarity and other similarities, extracts path-based Hetesim features, and obtains inclined diffusion feature through restart random walk. By combining the multiple feature, the prediction score between miRNAs and drug resistance is obtained based on the SVM. The innovation of the SVMMDR is that the inclined diffusion feature is obtained by inclined restart random walk, the node information and path information in heterogeneous network are integrated, and the SVM is used to predict potential miRNAs-drug resistance associations. The average AUC of SVMMDR obtained is 0.978 in 10-fold cross-validation.

Geographic routing is a research hotspot of the Internet of Vehicles (IoV) and intelligent traffic system (ITS). In practice, the vehicle movement is not only affected by its characteristics and the relationship between the vehicle and position but also affected by some implicit factors. Pointing to this problem, we combine the vehicle moving position probability matrix, the vehicle position association matrix, and the implicit factors to study the influence of vehicle position potential features and vehicle association potential features and propose a routing algorithm based on vehicle position (RAVP) analysis, which can obtain the more accurate vehicle prediction trajectory. Then, the vehicle distance is obtained based on the vehicle prediction trajectory. By the normalization of vehicle distance and cache, the vehicle data forwarding capability is obtained and the transmission decision is made. Simulation results show that the proposed algorithm outperforms the other three routing algorithms in terms of packet delivery ratio, average end-to-end delay, and routing overhead ratio.

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy- and spectral-efficiency with $\alpha$ -fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed-integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for $\alpha >0$ and $\alpha =0$ , respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings.

The mode selection and resource allocation are important issues in Device-to-Device (D2D) communication, which have been investigated in single Base Station (BS) cellular network. However, the joint problem of the base station selection, mode selection and resource allocation is a challenging issue. Because the mode selection, base station and resource allocation are inter-coupled with each other. The joint problem of D2D User Equipments (DUEs) mode selection, base station selection, channel allocation and power allocation remains an open problem. In this paper, the scenario with multiple BSs in D2D heterogeneous networks is considered, and the joint problem of DUEs mode selection, base station selection, channel allocation and power allocation is studied. The objective is to maximize the system energy efficiency. We consider the DUEs multiplex the cellular user uplink resource. Meanwhile, the constraint of the total transmission power of the DUEs, and the constraint of the load balancing of the BSs are considered. The joint problem of mode selection, base station selection and resource allocation is formulated. The formulated problem is a non-convex mixed-integer optimization problem. In order to handle the formulated problem, a joint mode selection, base station selection and resource allocation algorithm based on particle swarm optimization is proposed. Numerical results demonstrate that the proposed algorithm can improve the system energy efficiency and obtain the desired target.

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate the device-to-device (D2D) user equipments (DUEs) multiplexing cellular user equipments (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is to maximize the average energy efficiency of all D2D links, in the case of guaranteeing the quality of service of CUEs and the EH constraints of the D2D links. The resource allocation problems contain the EH time slot allocation of DUEs, power and spectrum resource block (RB) allocation. In order to tackle these issues, we formulate an average energy efficiency problem in EH-DHNs, taking into consideration EH time slot allocation, power and spectrum RB allocation for the D2D links, which is a nonconvex problem. Furthermore, we transform the original problem into a tractable convex optimization problem. We propose joint the EH time slot allocation, power and spectrum RB allocation iterative algorithm based on the Dinkelbach and Lagrangian constrained optimization. Numerical results demonstrate that the proposed iterative algorithm achieves higher energy efficiency for different network parameters settings.

With the rapid increase of smart mobile devices and the improvement of people's demand for wireless communication, the insufficient system capacity and shortage of spectrum resources is gradually emerging. The current wireless communication technology is facing enormous challenges. Device-to-Device (D2D) technology has become a key technology in the future 5G network, with its flexible working mode, low energy consumption, low latency and high capacity, etc. At the same time, D2D communication devices are usually powered by batteries and have a limited lifetime. The lack of spectrum resources and the single power supply for D2D User Equipment (DUE) have become an important issue for mobile communication. Energy harvesting (EH) can provide the power supply. The joint problem of mode selection and resource allocation is challenging issues in energy harvesting D2D heterogeneous networks (EH-DHNs). In this paper, mode selection and resource allocation problem with DUEs multiplexing cellular user equipments (CUEs) uplink spectrum resources for EH-DHNs is investigated. Our object is to maximize the system throughput, and the energy harvesting constraint and the quality of service of CUEs are considered. In order to tackle these issues. The mode selection and resource allocation system throughput maximization problem in EH-DHN is formulated. The formulated problem is solved by the convex optimization theory and greedy strategy. We proposed a Mode Selection and Resource Allocation algorithm (MSRA). In order to illustrate the advantage of the MSRA algorithm, we compared it with other algorithms. We also analyzed the convergence of the MSRA and the performance with different system configurations on system throughput. Comparison shows that the MSRA algorithm can improve system throughput effectively.

Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency.

Cognitive radio (CR) is a state-of-the-art technology to solve the spectrum shortage problem for emerging wireless services, which include the CR-enabled vehicular ad hoc networks (CR-VANETs) for vehicle-to-road side unit (RSU) communications. With the increasing demands for high data rate and more reliable mobile services, orthogonal frequency division multiple access (OFDMA) has been often used in such systems. Energy efficiency is an important issue in OFDMA CR-VANETs due to the concern of green communications to transmit the required data in the shortest time, without affecting primary users. In this paper, we proposed an adaptive solution to minimize the overall energy consumption of CR-VANETs as well as maintaining the service quality of Vehicle-to-RSU uplink communications. This goal has been achieved by the means of dynamically selecting different spectrum access schemes for CR-enabled vehicles with relays. Considering the inter-vehicle distances and location information, we formulated a mixed-integer nonlinear constrained optimization problem. A heuristic algorithm based on the greedy strategy and bisection method is then used to solve the formulated problem and it has been evaluated through extensive simulations for different upload data sizes and available communication durations. The acquired results substantiate the efficiency of the proposed solution in terms of energy consumption.

The mode selection and resource allocation are important issues in device-to-device (D2D) communication. There are very little research works considering the joint problem of base station selection and load balance of base station with mode selection and base station selection in multiple BSs cellular systems or heterogeneous cellular systems, which is a challenging issue. In this paper, the joint problem of D2D User Equipments (DUEs) mode selection, base station selection, channel allocation and power allocation is investigated. A mode selection, base station selection and resource allocation problem is formulated. The goal is to maximize the system energy efficiency. We consider the DUEs multiplex the cellular user uplink resource. Meanwhile, the total transmission power constraint of the DUEs, and the load balancing constraint of the BSs are considered. The formulated problem is NP problem, which is hard to solve. In order to solve the formulated problem effectively, a joint mode selection, base station selection and resource allocation algorithm based on particle swarm optimization (PSO) is proposed. Numerical results demonstrate that the proposed algorithm can effectively improve the system energy efficiency.

Mobile edge computing (MEC) is a promising technique to enhance the computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling and resource allocation for MEC systems is a challenge issue. In this paper, we investigate the problem of partial offloading scheduling and resource allocation for mobile edge computing systems with multiple independent tasks. Our goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraints of the tasks. The execution delay of tasks running in MEC and mobile edge devices are both considered. The energy consumption of both the tasks computing and task data transmission are considered as well. In order to tackle these issues, we formulate an energy-efficient and low-delay partial offloading scheduling and power allocation problem in single-user MEC systems, which is a non-convex mixed-integer optimization problem. A two-level alternation method framework based on decomposition optimization strategy is proposed. Furthermore, we propose Joint Partial Offloading scheduling and power Allocation (JPOA) iterative algorithm based on Lagrangian constrained optimization and Johnson method. Numerical results demonstrate JPOA algorithm achieves the most noticeable delay performance with a large energy consumption reduction.

The link failure due to the secondary users exiting the licensed channels when primary users reoccupy the licensed channels is very important in cognitive wireless mesh networks (CWMNs). A multipath routing and spectrum allocation algorithm based on channel interference and reusability with Quality of Service (QoS) constraints in CWMNs (MRIR) was proposed. Maximizing the throughput and the acceptance ratio of the wireless service is the objective of the MRIR. First, a primary path of resource conservation with QoS constraints was constructed, then, a resource conservation backup path based on channel interference and reusability with QoS constraints was constructed. The MRIR algorithm contains the primary path routing and spectrum allocation algorithm, and the backup path routing and spectrum allocation algorithm. The simulation results showed that the MRIR algorithm could achieve the expected goals and could achieve a higher throughput and acceptance ratio.

A channel utility value computing CUVC algorithm is proposed. The channel utility value contains channel usage probability and channel stability. A high reliability and low latency wireless link weight computing algorithm RL2W is proposed. On this basis, a high reliability and low latency routing and spectrum allocation algorithm based on dynamic programming in cognitive wireless mesh networks HRL2A is proposed. High reliability and low latency route is the objective of HRL2A. Firstly, HRL2A computes the channel utility value using CUVC for constructing the high reliability route. Secondly, HRL2A uses the algorithm RL2W computing wireless link weight. Thirdly, the route of high reliability and low latency is constructed based on dynamic programming, and the wireless link channel is allocated. Simulation results show that HRL2A algorithm can achieve expectation goal. The construction route not only has higher reliability, but also has lower latency. The throughput has been increased.

A data streams synopses structure FPCBF, which is based on bloom filter, is proposed in this paper. Each unit of vector BF[e] in FPCBF is a two-tuples. The transactions insert operation (InsertTran) and 1-frequent patterns pregeneration operation (PreGenFP) is defined. Then, A data streams frequent patterns mining algorithm BFFPM which is based on FPCBF is proposed in this paper. The BFFPM algorithm contains two parts: 1-frequent patterns generation and r-frequent patterns generation. The problem of computing the 1-frequent patterns generation is transformed into the problem of computing the longest common sub-sequence α i LCS of k setting identifier sequence in BF[e] in FPCBF. In the same way, the problem of computing the r-frequent patterns generation is transformed into the problem of computing the longest common sub-sequence LCS r , which is the longest common sequence of the identifier sequence α i LCS ,…, i r LCS α + of r items. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The FPCBF algorithm not only has high precision for mining frequent patterns, but also has low memory requirement

Routing and spectrum allocation is an important challenge in cognitive wireless mesh networks. A distributed routing and spectrum allocation algorithm with cooperation (DRSAC-W) in cognitive wireless mesh networks is proposed in this paper. In order to show the decrease of the average end-to-end delay with cooperation in DRSAC-W, a distributed routing and spectrum allocation algorithm without cooperation (DRSAC-WO) is proposed in this paper. Minimizing the average end-to-end delay is the objective of DRSAC-W and DRSAC-WO. Simulation results show that the proposed algorithm DRSAC-W with cooperation can alleviate the high delay due to the heterogeneity of available channels of different nodes and achieve low average end-to-end delay.

The delay tolerant mobile sensor network distinguishes itself from conventional sensor networks by several unique characteristics such as node mobility, loose connectivity, and delay tolerability. Therefore, traditional data gathering methods cannot be applied. In this paper, a novel data gathering method named average contact probability-based data delivery scheme (ACPBD) is proposed. ACPBD introduces centrality metric- average contact probability. The value of nodes average contact probability is calculated which used for choosing the next hop. In order to optimize node queue, ACPBD employs TTL to decide message's transmission and dropping for minimizing transmission overhead. Simulation results have shown that the proposed ACPBD data delivery scheme does not only achieve five times as much long network lifetime as SRAD and FAD, but also get the higher message delivery ratio with lower transmission overhead and data delivery delay than SRAD and FAD

A resource conservation wireless links weights computing function and computing algorithm (RCWC) are proposed. On this basis, a resource conservation joint multicast routing and spectrum allocation algorithm with QoS constraints in cognitive wireless mesh networks (RCDMSA) is proposed. Minimising the resource consumption of multicast tree is the objective of RCDMSA under the QoS constraints. Firstly, RCDMSA computes wireless links weights using RCWC algorithm. Secondly, RCDMSA constructs the resource conservation broadcast tree which contains source nodes and destination nodes using RCBT algorithm. Thirdly, the resource conservation multicast tree is pruned from the resource conservation broadcast tree. Simulation results show that RCDMSA algorithm can minimise the resource consumption of multicast tree, reduce overall network resource usage and increase network efficiency.