Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.     

Sensor scheduling for <Emphasis Type="Italic">p</Emphasis>-percent coverage in wireless sensor networks

We study sensor scheduling problems of p-percent coverage in this paper and propose two scheduling algorithms to prolong network lifetime due to the fact that for some applications full coverage is not necessary and different subareas of the monitored area may have different coverage requirements. Centralized p-Percent Coverage Algorithm (CPCA) we proposed is a centralized algorithm which selects the least number of nodes to monitor p-percent of the monitored area. Distributed p-Percent Coverage Protocol (DPCP) we represented is a distributed algorithm which can determine a set of nodes in a distributed manner to cover p-percent of the monitored area. Both of the algorithms can guarantee network connectivity. The simulation results show that our algorithms can remarkably prolong network lifetime, have less than 5% un-required coverage for large networks, and employ nodes fairly for most cases.     

Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.     

Lifetime extension based on residual energy for receiver-driven multi-hop wireless network

An important research topic in wireless sensor networking is the extension of operating time by controlling the power consumption of individual nodes. In a receiver-driven communication protocol, a receiver node periodically transmits its ID to the sender node, and in response the sender node sends an acknowledgment, after which data transmission starts. By applying such a receiver-driven protocol to wireless sensor networks, the average power consumption of the network can be controlled, but there still remains the problem of unbalanced load distribution among nodes. Therefore, part of the network shuts down when the battery of the node that consumes the most power is completely discharged. To extend the network lifetime, we propose a method where information about the residual energy level is exchanged through ID packets in order to balance power consumption. Simulation results show that the network lifetime can be extended by about 70–100 % while maintaining high network performance in terms of packet collection ratio and delay.     

Research on contaminant sources identification of uncertainty water demand using genetic algorithm

Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.     

Bayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Network

Wireless Sensor Network monitor and control the physical world via large number of small, low-priced sensor nodes. Existing method on Wireless Sensor Network (WSN) presented sensed data communication through continuous data collection resulting in higher delay and energy consumption. To conquer the routing issue and reduce energy drain rate, Bayes Node Energy and Polynomial Distribution (BNEPD) technique is introduced with energy aware routing in the wireless sensor network. The Bayes Node Energy Distribution initially distributes the sensor nodes that detect an object of similar event (i.e., temperature, pressure, flow) into specific regions with the application of Bayes rule. The object detection of similar events is accomplished based on the bayes probabilities and is sent to the sink node resulting in minimizing the energy consumption. Next, the Polynomial Regression Function is applied to the target object of similar events considered for different sensors are combined. They are based on the minimum and maximum value of object events and are transferred to the sink node. Finally, the Poly Distribute algorithm effectively distributes the sensor nodes. The energy efficient routing path for each sensor nodes are created by data aggregation at the sink based on polynomial regression function which reduces the energy drain rate with minimum communication overhead. Experimental performance is evaluated using Dodgers Loop Sensor Data Set from UCI repository. Simulation results show that the proposed distribution algorithm significantly reduce the node energy drain rate and ensure fairness among different users reducing the communication overhead.     

Clarifying the relationship among clean energy consumption,haze pollution and economic growth–based on the empirical analysis of China's Yangtze River Delta Region

At present, the frequent occurrence of haze in China has attracted extensive attention around the world. However, there are few researches based on simultaneous equation model to evaluate the relationship among clean energy consumption, haze pollution and economic growth. Whether the relationship among clean energy consumption, haze pollution and economic growth can be clarified correctly is the core issue to achieve the goal of both haze control and high-quality economic development. The innovation of this study is to further explore the interaction mechanism among the three variables, simultaneous equation model is used to analyze the relationship of clean energy development, haze pollution and economic growth of 27 cities in the central area of Yangtze River Delta from 2006 to 2016. The estimation results show that:(i) The development of clean energy has reduced regional haze pollution and the level of economic development significantly; (ii) During the study period of this paper, haze pollution has significantly increased the consumption of clean energy, but it has hindered the level of economic development seriously. (iii) The level of economic development has increased clean energy consumption at a 1% level of significance, while also exacerbated the degree of haze pollution. Moreover, as a core variable of this paper, coal consumption has restrained clean energy development and played a positive role in accelerating economic development and haze pollution reduction.     

MapReduce framework energy adaptation via temperature awareness

MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every node. We begin by considering that no cluster is truly homogeneous with respect to energy consumption. From there we develop a MapReduce framework that can evaluate the current status of each node and dynamically react to estimated power usage. In so doing, we shift work toward more energy efficient nodes which are currently consuming less power. Our work shows that given an ideal framework configuration, certain nodes may consume only 62.3 % of the dynamic power they consumed when the same framework was configured as it would be in a traditional MapReduce implementation.     

GPS-free Positioning in Mobile Ad Hoc Networks

We consider the problem of node positioning in ad hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on GPS (Global Positioning System). Instead, the algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. Despite the distance measurement errors and the motion of the nodes, the algorithm provides sufficient location information and accuracy to support basic network functions. Examples of applications where this algorithm can be used include Location Aided Routing [10] and Geodesic Packet Forwarding [2]. Another example are sensor networks, where mobility is less of a problem. The main contribution of this work is to define and compute relative positions of the nodes in an ad hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad hoc networks.     

供给侧改革视角下中国省域生态产品有效供给及影响因素

生态产品是事关人类生命健康、经济社会建设的世界性稀缺产品.为了更好地分析生态产品有效供给问题,找出生态产品有效供给的规模效率的变化规律,本研究利用2004—2015年的面板数据,以劳动力、资本、能源和技术为投入变量,以GDP、生态产品和非期望产出为产出变量,运用SBM-Undesirable模型和泰尔指数对我国生态产品有效供给的规模效率及其差异性进行测算,并运用Tobit模型进一步对其影响因素进行实证分析.结果表明: 我国生态产品供给规模效率在2011年以前呈下降趋势,以2012年为转折点呈现上升态势,但地区间仍有较大差距.全国及各地区生态产品有效供给效率的影响因素不尽相同,从全国总体情况看,经济发展水平、产业结构(第二产业增加值占GDP比重)、科技投入、金融资本投入(环境污染治理占GDP比重)、能源消费结构(煤炭消费量占能源消费总量比重)和农业面源污染的影响为负,而生态空间及外贸结构的影响为正;在东部地区,产业结构、科技投入、金融资本、能源消费结构和农业面源污染的影响为负,经济发展水平、生态空间及外贸结构的影响为正;在东北地区,经济发展水平、农业面源污染和外贸结构的影响为负,产业结构、科技投入、能源消费结构和生态空间的影响为正;在中部地区,经济发展水平、科技投入、金融资本、能源消费结构、农业面源污染和生态空间的影响为负,产业结构和外贸结构的影响为正;在西部地区,产业结构、能源消费结构和农业面源污染的影响为负,经济发展水平、科技投入、金融资本、生态空间及外贸结构的影响为正.在进一步对以上实证测算进行理论分析的基础上,从供给侧改革的角度提出有效提高生态产品供给规模效率的政策启示,包括注重转变经济发展方式、优化生态系统结构和遵循生态法则进行整体治理等.     

The Application of Gaussian Mixture Models for Signal Quantification in MALDI-ToF Mass Spectrometry of Peptides

Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) coupled with stable isotope standards (SIS) has been used to quantify native peptides. This peptide quantification by MALDI-TOF approach has difficulties quantifying samples containing peptides with ion currents in overlapping spectra. In these overlapping spectra the currents sum together, which modify the peak heights and make normal SIS estimation problematic. An approach using Gaussian mixtures based on known physical constants to model the isotopic cluster of a known compound is proposed here. The characteristics of this approach are examined for single and overlapping compounds. The approach is compared to two commonly used SIS quantification methods for single compound, namely Peak Intensity method and Riemann sum area under the curve (AUC) method. For studying the characteristics of the Gaussian mixture method, Angiotensin II, Angiotensin-2-10, and Angiotenisn-1-9 and their associated SIS peptides were used. The findings suggest, Gaussian mixture method has similar characteristics as the two methods compared for estimating the quantity of isolated isotopic clusters for single compounds. All three methods were tested using MALDI-TOF mass spectra collected for peptides of the renin-angiotensin system. The Gaussian mixture method accurately estimated the native to labeled ratio of several isolated angiotensin peptides (5.2% error in ratio estimation) with similar estimation errors to those calculated using peak intensity and Riemann sum AUC methods (5.9% and 7.7%, respectively). For overlapping angiotensin peptides, (where the other two methods are not applicable) the estimation error of the Gaussian mixture was 6.8%, which is within the acceptable range. In summary, for single compounds the Gaussian mixture method is equivalent or marginally superior compared to the existing methods of peptide quantification and is capable of quantifying overlapping (convolved) peptides within the acceptable margin of error.     

A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids

The sensitivity analysis of a Cellular Genetic Algorithm (CGA) with local search is used to design a new and faster heuristic for the problem of mapping independent tasks to a distributed system (such as a computer cluster or grid) in order to minimize makespan (the time when the last task finishes). The proposed heuristic improves the previously known Min-Min heuristic. Moreover, the heuristic finds mappings of similar quality to the original CGA but in a significantly reduced runtime (1,000 faster). The proposed heuristic is evaluated across twelve different classes of scheduling instances. In addition, a proof of the energy-efficiency of the algorithm is provided. This convergence study suggests how additional energy reduction can be achieved by inserting low power computing nodes to the distributed computer system. Simulation results show that this approach reduces both energy consumption and makespan.     

Dipole Source Localization of Mouse Electroencephalogram Using the Fieldtrip Toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.     

Using ant-based agents for congestion control in ad-hoc wireless sensor networks

Ad-hoc wireless sensor networks suffer from problems of congestion, which lead to packet loss and excessive energy consumption. In this paper, we address the issue of congestion in these networks. We propose a new routing protocol for wireless sensor networks namely Ant-based Routing with Congestion Control (ARCC), which takes into account the congestion of the network at a given instant and proposes to reduce it and then finds the optimum paths between the source and the sink nodes. Simulation results show that ARCC performs better with respect to the throughput, the number of packets lost and the priority performance.     

Energy aware DAG scheduling on heterogeneous systems

We address the problem of scheduling directed a-cyclic task graph (DAG) on a heterogeneous distributed processor system with the twin objectives of minimizing finish time and energy consumption. Previous scheduling heuristics have assigned DAGs to processors to minimize overall run-time of the application. But applications on embedded systems, such as high performance DSP in image processing, multimedia, and wireless security, need schedules which use low energy too.     

Decolourization of paprika dye effluent with hydrogen peroxide produced by glucose oxidase

Abstract

Hydrogen peroxide was produced from bran by a two-step process using cellulase/xylanase and glucose oxidase, sequentially. The decolourization efficiency of the produced reagent was tested using paprika oil dye (effluent from industrial source) and high levels of colour removal (96%) were achieved after saponification pre-treatment and hydrogen peroxide application. The method is economically and environmentally advantageous since lower energy and chemical input are needed and wastewater pollution is considerably reduced. At the same time, the utilization of waste materials was successfully achieved.     

Distance estimation by mining characteristics in anisotropic sensor networks

Localization is useful for many position-dependent applications in wireless sensor networks, where distance estimation from sensor nodes to beacon nodes plays a fundamental role. Most current ranging methods rely on an assumption that deployed WSNs are isotropic. Hence, adjustments on measured distances are the same in all directions. Unfortunately, this assumption does not hold in practice. Present methods introduce such great ranging errors that they are not feasible for real applications. In order to obtain better distance estimation in anisotropic WSNs, we propose a new metric, Dominating Degree, to describe the local deployment characteristics of sensor nodes, and to identify turning nodes along paths. We further propose a method to scale deployment irregularities of WSNs as global characteristics. Finally, appropriate adjustments to distance measurements are performed by synthesizing both local and global characteristics. Simulation results show that the proposed method outperforms PDM and DV-distance especially when beacon nodes are not deployed uniformly.     

Imbalance of CPU temperatures in a blade system and its impact for power consumption of fans

We are now developing a new metric of data center power efficiency to fairly evaluate the contribution of each improvement for power efficiency. In order to develop it, we built a testbed of a data center and measured power consumption of each components and environmental variables in some detail, including the power consumption and temperature of each node, rack and air conditioning unit, as well as load on the CPU, Disk I/O and the network. In these measurements we found that there was a significant imbalance of CPU temperatures that caused an imbalance in the power consumption of fans. We clarified the relationship between CPU load and fan speed, and showed that scheduling or rearrangement of nodes could reduce the power consumption of fans. We reduced fan power consumption by a maximum of 62% and total power consumption by a maximum of 12% by changing the scheduling of five nodes, changing the nodes used from hot nodes to cool nodes.     

An improved localization algorithm based on genetic algorithm in wireless sensor networks

Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.     

A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increase the provider’s profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to make HPC applications consuming less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO₂ emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson’s PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO₂ emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications.