Mobile Edge Computing (MEC) offloads service functionalities from central cloud to edge network and process user requests there, which reduces service latency and alleviates cloud burden. Only partial services can run on edge nodes with limited resource capacity. Both time varying and heterogeneity of services users requesting introduce great challenges for the resource utilization of edge nodes and user quality of service (QoS). Edge cooperation with joint optimization emerges to cope with this problem for MEC service provider. Recent researches focus on the non-cooperation or partial cooperation among edge nodes in local area network (LAN), their benefits are only explored on a small scale, and the users still face with resources waste and high service. This paper jointly optimizes service placing and task scheduling in MEC based on edge utility maximization and full cooperation of edge nodes in LAN. Edge full cooperation can place as many types of services as possible and capture more user requests in edge network so as to reduce the overall delay and edge energy consumption. Further considering the individual user QoS, we formularize the rewards in the edge utility to promote the local processing of user tasks. The joint optimization is a mixed integer nonlinear program problem which is NP-hard with high computational complexity. Therefore, we design a two-layer iterative strategy (TI-ST) based on Gibbs sampling and linear programming, which has polynomial computation complexity and has provably near optimal performance. Experimental results demonstrate the effectiveness of the proposed scheme when compared with the benchmark schemes.

With the development of big data and network technology, the rapidly growing data amount brings a great challenge to the performance optimization of Internet of Marine things (IoMaT). Both cutting down the redundant data communication and ensuring reliable communication of necessary data are the key issues IoMaT facing. Most existing studies focus on periodical and continuous-time data transmission mode of the source nodes once some data collection task starts, not considering decreasing the data transmission frequency efficiently in the source node. This study investigates the essential characteristics of marine data, formulates the attributes and collection characteristics (CC) of marine data, and modeling the customized data transmission scheme (DTS) based on the CC for the sake of avoiding the redundant data transmission and minimize the data transmission frequency in nature. Furthermore, two specific DTSs are present in terms of various attributes and CC of marine data to not only meet the different requirements of marine monitoring, but also reduce the network traffic. The simulation results show that the proposed schemes can effectively reduce the data transmission frequency and the energy consumption, prolong the network lifetime, and improve the data packet delivery ratio in IoMaT.

MOTIVATION: The significance of long non-coding RNAs (lncRNAs) in many biological processes and diseases has gained intense interests over the past several years. However, computational identification of lncRNAs in a wide range of species remains challenging; it requires prior knowledge of well-established sequences and annotations or species-specific training data, but the reality is that only a limited number of species have high-quality sequences and annotations. RESULTS: Here we first characterize lncRNAs in contrast to protein-coding RNAs based on feature relationship and find that the feature relationship between open reading frame length and guanine-cytosine (GC) content presents universally substantial divergence in lncRNAs and protein-coding RNAs, as observed in a broad variety of species. Based on the feature relationship, accordingly, we further present LGC, a novel algorithm for identifying lncRNAs that is able to accurately distinguish lncRNAs from protein-coding RNAs in a cross-species manner without any prior knowledge. As validated on large-scale empirical datasets, comparative results show that LGC outperforms existing algorithms by achieving higher accuracy, well-balanced sensitivity and specificity, and is robustly effective (>90% accuracy) in discriminating lncRNAs from protein-coding RNAs across diverse species that range from plants to mammals. To our knowledge, this study, for the first time, differentially characterizes lncRNAs and protein-coding RNAs based on feature relationship, which is further applied in computational identification of lncRNAs. Taken together, our study represents a significant advance in characterization and identification of lncRNAs and LGC thus bears broad potential utility for computational analysis of lncRNAs in a wide range of species. AVAILABILITY AND IMPLEMENTATION: LGC web server is publicly available at http://bigd.big.ac.cn/lgc/calculator. The scripts and data can be downloaded at http://bigd.big.ac.cn/biocode/tools/BT000004. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Limited bandwidth capacity and battery power are the unique characters of Underwater Sensor Networks (UWSNs). First this study introduces a competition scheme based on delay time. This scheme provides a selection method of relay nodes considering the limited bandwidth capacity. Then a heterogeneous nodes distribution strategy is proposed to balance the energy consumption of the whole UWSN. The ratio between the node initial energy of the adjacent annuluses is analyzed in a circular UWSN. Simulation results validate this design.

Lifetime prolonging is one significant research issue in underwater acoustic sensor networks (UASNs). First this paper analyzes the relationship between the receiving energy consumption and the transmission energy consumption in the acoustic communication of UASNs. The routing tree is built up on the factor of optimal transmission range. Then a hybrid data transmission mechanism based on energy level is proposed to balance energy consumption. The mechanism combines one-hop and multi-hop data transmission to underwater sink considering the current energy level of adjacent nodes. An optimal classification number of energy level has been evaluated through theoretical analysis. Our design will help prolong the lifetime of whole UASN. The simulation results of UASN's lifetime and the energy consumption of sensor nodes have proved the efficiency of the Energy-Level-based hybrid Transmission (ELT) mechanism.

Abstract Lifetime prolonging is one of the most significant issues in the research on underwater acoustic sensor networks (UASNs). Unbalanced energy consumption influences greatly the network lifetime. First this study discusses a probability‐based energy balance (PEB) scheme. The sensor nodes report the data to the sink by single‐hop direct transmission (DT) or by multi‐hop transmission (MT) under the probabilities. A centralized probabilities finding algorithm (PFA) can find a set of transmission probabilities to better balance the energy consumption. Then, a sub‐optimal distance (SOD)‐based data transmission scheme is proposed which is a distributed scheme and operates on each sensor node. It optimizes the slice width and selects the relays near the optimum transmission range. Simulations show that the two schemes can save more energy and prolong the network lifetime efficiently. Copyright © 2008 John Wiley & Sons, Ltd.