Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks

We propose a new strategy for reducing the amount of latency and energy consumption in Blocking Expanding Ring Search (BERS) and enhanced Blocking Expanding Ring Search (BERS*) for mobile ad hoc networks (MANETs). BERS and BERS* are respectively energy and energy–time efficient route discovery protocols for MANETs as compared to conventional Expanding Ring Search (ERS). In this study, we identify unnecessary waiting time caused by a STOP/END instruction in BERS/BERS* and explore the potential of further improvement of their time efficiency. This leads to tBERS and tBERS*, the improved BERS and BERS* respectively. In tBERS/tBERS*, a route node may also issue the STOP/END instruction to terminate flooding. We implement this idea in algorithms, conduct analysis, and achieve further latency reduction in both tBERS and tBERS* as well as the energy saving in tBERS*.     

Analytical Studies of Energy–Time Efficiency of Blocking Expanding Ring Search

This paper investigates the energy–time efficiency of the Blocking Expanding Ring Search algorithms (BERS) using an extended graph model. BERS is an energy efficient alternative that was developed recently based on the Expanding Ring Search (ERS). ERS is widely applied in reactive routing protocols for mobile ad hoc networks. Most studies in this area focus on minimising either energy consumption or search latency, but few look into the strong correlation between the energy saving and the increased latency incurred. We analyse, in this study, the concurrency mechanism of BERS and have developed BERS*, an enhanced scheme based on BERS. Our results show that, among the three schemes (BERS*, BERS and ERS), BERS* incurs the least latency when the hop number of the route nodes is greater than 3, and has achieved the best performance in terms of energy–time efficiency when the hop number of the route nodes is greater than 7. We have also discovered the conditions that allow collective optimisation of BERS* and ERS.     

A mathematical modeling for incorporating energy price hikes into total natural gas consumption forecasting

In some countries that energy prices are low, price elasticity of demand may not be significant. In this case, large increase or hike in energy prices may impact energy consumption in a way which cannot be drawn from historical data. This paper proposes an integrated adaptive fuzzy inference system (FIS) to forecast long-term natural gas (NG) consumption when prices experience large increase. To incorporate the impact of price hike into modeling, a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Linear regressions are used to construct a first order TS-FIS. Furthermore, adaptive network-based FIS (ANFIS) is used to forecast NG consumption in power plants. To cope with random uncertainty in small historical data sets, Monte Carlo simulation is utilized to generate training data for ANFIS. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual NG consumption in Iran where removing energy subsidies has resulted in a hike in NG prices.     

A truck and drones model for last-mile delivery: A mathematical model and heuristic approach

We present a mathematical formulation and a heuristic solution approach for the optimal planning of delivery routes in a multi-modal system combining truck and Unmanned Aerial Vehicle (UAV) operations. In this system, truck and UAV operations are synchronized, i.e., one or more UAVs travel on a truck, which serves as a mobile depot. Deliveries can be made by both UAVs and the truck. While the truck follows a multi-stop route, each UAV delivers a single shipment per dispatch. The presented optimization model minimizes the waiting time of customers in the system. The model determines the optimal allocation of customers to truck and UAVs, the optimal route sequence of the truck, and the optimal launch and reconvene locations of the UAVs along the truck route. We formulate the problem as a Mixed-Integer Linear Programming (MILP) model and conduct a bound analysis to gauge the maximum potential of the proposed system to reduce customer waiting time compared to a traditional truck-only delivery system. To be able to solve real-world problem size instances, we propose an efficient Truck and Drone Routing Algorithm (TDRA). The solution quality and computational performance of the mathematical model and the TDRA are compared together and with the truck-only model based on a variety of problem instances. Further, we apply the TDRA to a real-world case study for e-commerce delivery in São Paulo, Brazil. Our numerical results suggest significant reductions in customer waiting time to be gained from the proposed multi-modal delivery model.     

Modelling the mobile target covering problem using flying drones

This paper addresses the mobile targets covering problem by using unmanned aerial vehicles (UAVs). It is assumed that each UAV has a limited initial energy and the energy consumption is related to the UAV’s altitude. Indeed, the higher the altitude, the larger the monitored area and the higher the energy consumption. When an UAV runs out of battery, it is replaced by a new one. The aim is to locate UAVs in order to cover the piece of plane in which the target moves by using a minimum number of UAVs. Each target has to be monitored for each instant time. The problem under consideration is mathematically represented by defining mixed integer non-linear optimization models. Heuristic procedures are defined and they are based on restricted mixed integer programming (MIP) formulation of the problem. A computational study is carried out to assess the behaviour of the proposed models and MIP-based heuristics. A comparison in terms of efficiency and effectiveness among models and heuristics is carried out.     

Electricity consumption prediction using a neural-network-based grey forecasting approach

Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.     

Modelling and solving optimal placement problems in wireless sensor networks

In this work, the optimal sensor displacement problem in wireless sensor networks is addressed. It is assumed that a network, consisting of independent, collaborative and mobile nodes, is available. Starting from an initial configuration, the aim is to define a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. To mathematically represent the problem under study, different innovative optimization models are proposed and defined, by taking into account different performance objectives. An extensive computational phase is carried out in order to assess the behaviour of the developed models in terms of solution quality and computational effort. A comparison with distributed approaches is also given, by considering different scenarios.     

Performance Analysis for Mobile Ad Hoc Network in Random Graph Models with Spatial Reuse

In this paper,we present a random graph model with spatial reuse for a mobile ad hoc network(MANET) based on the dynamic source routing protocol.Many important performance parameters of theMANET are obtained,such as the average flooding distance (AFD),the probability generating function of theflooding distance,and the probability of a flooding route to be symmetric.Compared with the random graphmodel without spatial reuse,this model is much more effective because it has a smaller value of AFD and alarger probability for finding a symmetric valid route.     

能耗强度与能源结构优化对内生经济增长影响研究

本文在垂直创新模型框架下,将能耗强度和碳减排比例引入生产函数,将人们对气候变化的警惕意识引入效用函数,建立了一个动态的内生低碳经济增长模型。通过求解所建动态优化模型,找到了经济长期均衡增长的最优路径,讨论了能源强度和能源消费结构变化与经济均衡增长的相互关系,分析了各参数对经济最优增长路径的影响,比较了化石能源内部消费结构不变和逐年清洁两种情景下的经济最优增长路径的异同。长期来看,能源强度、二氧化碳减排速率与经济增长率有相互的负向线性关系,缩小反映化石能源内部消费结构的综合碳排放系数对经济增长有促进作用。非化石能源比例越高,低碳技术应用越广泛,研发成果对技术积累的贡献率越大,消费者对当前消费的偏好程度越小,人们对气候变化的警惕意识越强,则越有利于经济的低碳发展。     

Energy aware proactive optimized link state routing in mobile ad-hoc networks

In this paper we develop a novel energy aware routing approach for mobile ad hoc network (MANET) problems. The approach is based on using Optimized Link State Routing Protocol. Our Energy Aware OLSR labeled as OLSR_EA measures and predicts per-interval energy consumptions using the well-known Auto-Regressive Integrated Moving Average time series method. We develop a composite energy cost, by considering transmission power consumption and residual energy of each node, and use this composite energy index as the routing metric. Our extensive ns2 simulation experiments show that OLSR_EA substantially prolongs the network lifetime and saves total energy used in MANET. In our experiments we considered different scenarios considering a variety of traffic loads, node mobilities, homogeneous power consumption, and heterogeneous power consumption. Simulation results also confirm that OLSR_EA improves the traffic balance between nodes, and packet delivery ratio in higher node speed. We further develop characteristics of OLSR_EA in power-wise heterogeneous MANET to achieve efficient energy preserving performance.     

A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem

This paper considers the mobile facility routing and scheduling problem with stochastic demand (MFRSPSD). The MFRSPSD simultaneously determines the route and schedule of a fleet of mobile facilities which serve customers with uncertain demand to minimize the total cost generated during the planning horizon. The problem is formulated as a two-stage stochastic programming model, in which the first stage decision deals with the temporal and spatial movement of MFs and the second stage handles how MFs serve customer demands. An algorithm based on the multicut version of the L-shaped method is proposed in which several lower bound inequalities are developed and incorporated into the master program. The computational results show that the algorithm yields a tighter lower bound and converges faster to the optimal solution. The result of a sensitivity analysis further indicates that in dealing with stochastic demand the two-stage stochastic programming approach has a distinctive advantage over the model considering only the average demand in terms of cost reduction.     

The low power architecture approach towards exascale computing

Energy efficiency is a first-order concern when deploying any computer system. From battery-operated mobile devices, to data centers and supercomputers, energy consumption limits the performance that can be offered.We are exploring an alternative to current supercomputers that builds on low power mobile processors. We present initial results from our prototype system based on ARM Cortex-A9, which achieves 120 MFLOPS/W, and discuss the possibilities to increase its energy efficiency.     

A gray model for increasing sequences with nonhomogeneous index trends based on fractional‐order accumulation

By comparing the class ratio deviation and restoring error of first‐order accumulation with that of fractional‐order accumulation, a gray model for monotonically increasing sequences can obtain optimal simulation accuracy via selecting a proper cumulative order. In this study, a gray model for increasing sequences with nonhomogeneous index trends based on fractional‐order accumulation is proposed. To reduce the modeling error caused by the background value and to improve the prediction accuracy of the model, an optimized model using the 3/8 Simpson formula is constructed. Finally, the 2 proposed models are used to predict the total energy consumption in China and the monthly sales of new products in an enterprise. Compared with the GM(1,1) model based on fractional‐order accumulation, the proposed model exhibits better simulation and prediction accuracy.     

广西能源、环境和经济增长的动态关系研究——基于VAR模型的实证分析

利用广西1990-2011年GDP、能源消费和工业废气排放总量的数据,应用基于VAR模型的动态计量分析方法,对广西经济、能源和环境的关系进行实证研究.结果表明:三者之间存在长期稳定的协整关系,能源消耗是经济增长和环境污染的Granger原因,经济增长是能源消耗和环境污染的Granger原因.能源消耗对经济增长的正向影响效应时间较长且较稳定,环境污染初期对经济增长的抑制作用较弱却呈现缓慢增强的趋势;而经济增长对能源消耗和环境污染的影响持续时间较短.经济增长与能源消耗两者之间的相互影响较大,而环境污染受能源消耗变化影响较明显.     

基于变参数模型的中国能源消费经济增长关系研究

本文利用1953—2002年的统计数据和状态空间模型对中国能源消费与经济增长关系进行了研究。我们的结论是:(1)中国能源消费与经济增长之间存在一种随时间不断变化的长期均衡关系即变参数协整关系;(2)基于状态空间模型的变参数估计很好地揭示了中国能源消费弹性的时变规律。     

中国碳排放与能源消费关系实证研究——基于VEC模型

中国碳减排问题成为世界关注的焦点问题.研究碳排放与能源消费关系有助于实现2020年碳减排目标.选取1953-2008年中国碳排放和能源消费数据,运用协整理论、向量误差修正模型(VEC)和Granger因果关系分析,对中国碳排放量与能源消费之间的相互关系进行实证研究.研究结果表明:碳排放与能源消费之间存在协整关系,能源消费增加1%,碳排放将增加0.9646%,即碳排放对能源消费的长期弹性为0.9646.从短期误差修正模型来看,碳排放与能源消费之间具有动态调整机制.非均衡误差项的存在,能够保证两者之间长期均衡关系的存在.Granger因果关系研究表明:碳排放与能源消费之间互为双向因果关系.通过脉冲函数和方差分解分析了模型的动态特征.根据研究结果,提出降低能源消费和减少碳排放对策.     

一种基于Dijkstra算法的机器人避障问题路径规划

移动机器人的避障问题是移动机器人控制领域的研究热点.针对给定的移动机器人避障问题,探讨了最短路径及最短时间路径的路径规划问题.对于最短路径问题,建立了简化的路径网格模型,将其抽象为由节点及边构成的两维图,再使用经典的Dijkstra算法获得可行的最短路径.对于最短时间路径问题,通过分析移动机器人弯道运行的速度曲线,基于几何方法得出了移动时间与过渡圆弧圆心之间严格的数学关系,此后借助MATLAB优化函数获得最佳的移动路径.算法可为类似机器人避障问题的解决提供借鉴.     

基于遗传算法的固定起讫点危险品配送路线优化

为科学选择危险品配送路线,保障运输安全,将传统TSP(Travelling SalesmanProblem)问题加以推广和延伸,建立以路段交通事故率、路侧人口密度、环境影响因子和路段运输费用为指标的固定起讫点危险品配送路线优化模型.以遗传算法基本框架为基础,引入新的遗传算子,构建了可用于实现模型的多目标遗传算法.实例仿真表明,所建模型和算法在求解固定起讫点危险品配送路线优化问题中有较好的实用性.     

马氏模型下移动自组网随选型路由协议特性分析

移动自组网络（简称MANET）因其移动性及无基础设施支持等特点已经成为无线通信网络中的热门问题．通过将一个MANET网络中每条链边的长度看作一个生灭过程,并且假设在泛洪过程中空间可以复用n次,建立了移动自组网络空间可复用的马氏模型,简记为n-SRBDM．在一个典型的随选型路由协议即动态源路由（DSR）协议的基础上,研究了网络的一些关键性能参数,给出了路由泛洪距离的概率分布和期望,限定泛洪步数时成功寻路的概率、发现τ-时有效路径及对称有效路径的概率,发现一条有效路径的平均时间等,对于路由维护过程,也引入并研究了一些网络性能参数,例如,路由恢复的平均频率,路由有效的平均时间．对于这些网络参数在空间可复用和空间不可复用两种情形下进行了比较．证明了空间可复用模型下的路由选择更为有效．     

Temperature gradient in wood during grinding

Grinding is a commonly used method for producing pulps for papermaking, but its rather poor energy efficiency is a drawback. This paper focuses on developing a model dealing with temperature rise in wood during grinding. The model paves the way for the development of theoretical methods which can be used for reducing the energy consumption of the process. In grinding, wood is loaded by grits, which cause stress waves in the wood matrix. The stress waves fatigue the wood and ultimately separate fibres from the matrix, but because of wood’s viscoelasticity, part of the mechanical energy of waves is converted into heat. In order to understand the wood temperature increase in this process, a mechanistic model is developed here. The model is based on three hypotheses: a flux of mechanical energy occurs through the wood, the magnitude of the flux can be derived from the contact mechanics of the grits, and the rise in wood temperature can be determined from the dissipation of the flux. A temperature distribution in the actual grinding process was simulated with the model. The simulated temperatures were compared with a measured temperature profile obtained from the literature. The modelled and measured temperatures matched quite well. The simulations show that an increase in grit size results higher temperatures, whereas an increase in the distance between grits gives lower temperatures. The main result of the study is that the Hertz theory of contact mechanics can be considered an adequate method for analysing the effect of grits in the grinding process. The result shows that the Hertz theory is applicable when fatigue models are developed; these models can then be used to reduce the energy consumption of the process.