Rule-based approaches for energy savings in an ambient intelligence environment

This paper presents a novel real-world application for energy savings in a Smart Building environment. The proposed system unifies heterogeneous wireless sensor networks under a Semantic Web Service middleware. Two complementary and mutually exclusive rule-based approaches for enforcing energy-saving policies are proposed: a reactive agent based on production rules and a deliberative agent based on defeasible logic. The system was deployed at a Greek University, showing promising experimental results (at least 4% daily savings). Although the percentage of energy savings may seem low, the greatest merit of the method is ensuring that no energy is wasted by constantly enforcing the policies.     

Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers

Job scheduling in data centers can be considered from a cyber–physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber–physical, spatio-temporal (i.e. start time and servers assigned), thermal-aware job scheduling algorithms that minimize the energy consumption of the data center under performance constraints (i.e. deadlines). Savings are possible by being able to temporally “spread” the workload, assign it to energy-efficient computing equipment, and further reduce the heat recirculation and therefore the load on the cooling systems. This paper provides three categories of thermal-aware energy-saving scheduling techniques: (a) FCFS-Backfill-XInt and FCFS-Backfill-LRH, thermal-aware job placement enhancements to the popular first-come first-serve with back-filling (FCFS-backfill) scheduling policy; (b) EDF-LRH, an online earliest deadline first scheduling algorithm with thermal-aware placement; and (c) an offline genetic algorithm for SCheduling to minimize thermal cross-INTerference (SCINT), which is suited for batch scheduling of backlogs. Simulation results, based on real job logs from the ASU Fulton HPC data center, show that the thermal-aware enhancements to FCFS-backfill achieve up to 25% savings compared to FCFS-backfill with first-fit placement, depending on the intensity of the incoming workload, while SCINT achieves up to 60% savings. The performance of EDF-LRH nears that of the offline SCINT for low loads, and it degrades to the performance of FCFS-backfill for high loads. However, EDF-LRH requires milliseconds of operation, which is significantly faster than SCINT, the latter requiring up to hours of runtime depending upon the number and size of submitted jobs. Similarly, FCFS-Backfill-LRH is much faster than FCFS-Backfill-XInt, but it achieves only part of FCFS-Backfill-XInt’s savings.     

Effect of a Traffic Speed Based Cruise Control on an Electric Vehicle’s Performance and an Energy Consumption Model of an Electric Vehicle

This paper proposes a cruise control system (CCS) to improve an electric vehicle’s range, which is a significant hurdle in market penetration of electric vehicles. A typical driver or a conventional adaptive cruise control (ACC) controls an electric vehicle (EV) such that it follows a lead vehicle or drives close to the speed limit. This driving behaviour may cause the EV to cruise significantly above the average traffic speed. It may later require the EV to slow down due to the traffic ripples, wasting a part of the EV’s kinetic energy. In addition, the EV will also waste higher speed dependent dissipative energies, which are spent to overcome the aerodynamic drag force and rolling resistance. This paper proposes a CCS to address this issue. The proposed CCS controls an EV’s speed such that it prevents the vehicle from speeding significantly above the average traffic speed. In addition, it maintains a safe inter-vehicular distance from the lead vehicle. The design and simulation analysis of the proposed CCS were in a MATLAB simulation environment. The simulation environment includes an energy consumption model of an EV, which was developed using data collected from an electric bus operation in London. In the simulation analysis, the proposed system reduced the EV’s energy consumption by approximately 36.6% in urban drive cycles and 15.4% in motorway drive cycles. Finally, the experimental analysis using a Nissan e-NV200 on two urban routes showed approximately 30.8% energy savings.      

Adaptive energy-efficient registration and online scheduling for asymmetric wireless sensor networks

Increasing onboard processing capabilities of sensors enable self-organization in wireless sensor networks to dynamically adapt to ad hoc topologies and to react to task or network changes. Such self-organization, however. comes at a cost of additional energy consumption for the sensor nodes with already limited power resources. As energy limitations in unattended environments raise a major concern, such organizations need to trade-off between power consumption and topology maintenance. In this paper we present our adaptive energy-efficient registration and online scheduling (AEROS) protocol that exploits application based data flow characteristics to reduce power consumption during self-organization. Asymmetric data flow characteristics is used to govern route selection, and to formulate an organized transmission schedule with risk-free sleeping time. Our simulation results suggest that AEROS’s transmission schedule allows the minimum number of data message exchanges and guarantees a collision-free communication. AEROS provides significant energy savings in steady state using a low number of control messages.     

Hybrid Clouds brokering: Business opportunities,QoS and energy-saving issues

Hybrid Clouds couple the scalability offered by public Clouds with the greater control supplied by private ones. A (hybrid) Cloud broker acting as an intermediary between users and providers of public Cloud services, may support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality.The paper presents a Cloud brokering algorithm delivering services with different level of non-functional requirements, to the private or public resources, on the basis of different scheduling criteria. With the objective of maximize user satisfaction and broker’s revenues, the algorithm pursues profit increases by reducing energy costs, through the adoption of energy saving mechanisms. A simulation model is used to evaluate performance in terms of broker’s revenue, user satisfaction and energy behavior of various allocation policies. Simulation results show that differences among policies depend on system loads and that the use of turn on and off techniques greatly improves energy savings at low and medium load rates.     

WSN中一种基于身份的短签名广播认证协议

广播认证是传感器网络中很重要的安全服务,它允许发送者通过安全的方式广播信息给多个节点。无线传感器网络中的μTESLA、M-μTESLA等基于消息认证码的广播认证协议存在一些不足,加上最近的研究显示,基于双线性对的加密算法可应用于资源有限的传感器节点。本文介绍一种高效的基于身份的无证书短签名协议,它拥有目前最短的签名长度160bits,计算量相比其他公钥签名低得多,还能提供认证多个基站的广播信息的功能。基于MICA2DOT平台对其通信和计算能量消耗进行分析,以及对该协议的其他性能的分析,得出该协议引入的能量消耗小,满足广播认证的一些重要性质,适合无线传感器网络环境。     

Data center demand response: Avoiding the coincident peak via workload shifting and local generation

Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers’ participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center’s peak loads and energy expenditure: workload shifting and the use of local power generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with local generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.     

An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping

In this paper a vendor managed inventory (VMI) service in tramp shipping is considered. VMI takes advantage of introducing flexibility in delivery time and cargo quantities by transferring inventory management and ordering responsibilities to the vendor which in this case is a shipping company. A two-phase heuristic is proposed to determine routes and schedules for the shipping company. The heuristic first converts inventories into cargoes, thus turning the problem into a classic ship routing and scheduling problem. It then uses adaptive large neighborhood search to solve the resulting cargo routing and scheduling problem. The heuristic iteratively changes the cargoes generated to handle the customer’s inventories, based on the information obtained from an initial solution. Computational results are presented, discussed and compared with exact solutions on large realistic instances. The results reveal the potential savings from converting traditional contracts of affreightment to an integrated VMI service. The factors that influence the benefits obtainable through VMI are also analyzed.     

多核系统中基于Global EDF 的在线节能实时调度算法

随着多核系统能耗问题日益突出,在满足时间约束条件下降低系统能耗成为多核实时节能调度研究中亟待解决的问题之一.现有研究成果基于事先已知实时任务属性的假设,而实际应用中,只有当任务到达之后才能够获得其属性.为此,针对一般任务模型,不基于任何先验知识提出一种多核系统中基于Global EDF在线节能硬实时任务调度算法,通过引入速度调节因子,利用松弛时间,结合动态功耗管理和动态电压/频率调节技术,降低多核系统中任务的执行速度,达到实时约束与能耗节余之间的合理折衷.所提出的算法仅在上下文切换和任务完成时进行动态电压/频率调节,计算复杂度小,易于在实时操作系统中实现.实验结果表明,该算法适用于不同类型的片上动态电压/频率调节技术,节能效果始终优于Global EDF算法,最多可节能15％～20％,最少可节能5％～10％.     

A distributed multi-agent meeting scheduler

Electronic calendars are important tools that are used by consumers on a daily basis. However, scheduling a meeting that involves persons with different commitments and preferences remains a difficult task. Meeting scheduling is difficult because current calendaring applications cannot handle the responsibility of automatically and autonomously managing time slots. This paper presents a distributed multi-agent system architecture in which each person is represented by an agent. These agents automatically and autonomously work together to assist different users to book meetings on their behalf. Each agent has the capability to manage, negotiate and schedule tasks, meetings, events, appointments for its assigned user. In this multi-agent system, the agents coordinate their activities and negotiate on behalf of their associated users to find a solution that satisfies the users' meeting requirements and preferences. A prototype of this system is implemented to demonstrate how the agents can automatically book meetings.     

装备保障动态任务柔性调度策略研究

高技术战争条件下,装备保障任务的动态性特点更加显著。如何柔性调度装备保障动态任务成为研究装备保障指挥控制系统柔性问题的关键。在对装备保障任务Agent和动态任务柔性调度框架进行描述的基础上,提出了一种基于Agent的装备保障动态任务的柔性调度策略。     

Dynamic schedule execution in an agent based holonic manufacturing system

The present paper deals with the negotiation based task allocation to the resources for preparing dynamic scheduling in an agent based holonic control framework. The scheduling priority is developed by Multi Objective Optimization on the basis of Ratio Analysis (MOORA) technique under Fuzzy Multi Criteria Decision Making (FMCDM) environment considering several attributes. The well-known Contract Net Protocol (CNP) is followed for the purpose of task allocation by negotiation and cooperation, where message based communication is accomplished by eXtensible markup language (XML) using J2EE. Different Document Type Definitions (DTDs) are developed for intended applications. Necessary modifications in the scheduling arising out of changes in the volume-mix are made by a distributed cooperative problem-solving algorithm to meet the demand without violating the deadline. The algorithm is implemented using HTML code in front-end with Java Server Page (JSP) through Apache Tomcat 6.02 server. It is advocated that the cooperation based teamwork coupled with higher flexibility and agility is the key to success to remain unperturbed and provide reasonably good solution in the face of disturbances and stands superior to its hierarchical counterpart.     

Grid Scheduling with Makespan and Energy-Based Goals

The need for better energy efficiency in grid computing is significant given the massive amount of energy dissipated by large grids. We approximate the optimal allocation of compute nodes to a job stream, with each job consisting of multiple tasks, and while considering both the computing requirements and a desired balance of shorter makespans and lower energy consumption. The approach is widely applicable to many grid scenarios and does not require the scheduler to have administrative rights to change the workers’ DVFS or hibernation state. A discrete particle swarm optimisation (PSO) determines the worker assignments based on estimations of the tasks’ service times and energy consumption using an online learning process, and taking into account pending task executions from prior jobs. The performance of the proposed system is then evaluated through extensive Monte Carlo simulations using traces of real multi-threaded program executions on representative computer hardware. The results demonstrate the latent energy savings that are possible in grid computing through an energy-aware task scheduling.     

Web3D graphics enabled through sensor networks for cost-effective assessment and management of energy efficiency in buildings

The past decade has seen the advent of numerous building energy efficiency visualization and simulation systems; however, most of them rely on theoretical thermal models to suggest building structural design for new constructions and modifications for existing ones. Sustainable methods of construction have made tremendous progress. The example of the German Energy-Plus-House technology uses a combination of (almost) zero-carbon passive heating technologies. A web-enabled X3D visualization and simulation system coupled with a cost-effective set of temperature/humidity sensors can provide valuable insights into building design, materials and construction that can lead to significant energy savings and an improved thermal comfort for residents, resulting in superior building energy efficiency. A cost-effective hardware-software prototype system is proposed in this paper that can provide real-time data driven visualization or offline simulation of 3D thermal maps for residential and/or commercial buildings on the Web.     

Rolling-horizon scheduling for energy constrained distributed real-time embedded systems

Energy-efficient scheduling approaches are critical to battery driven real-time embedded systems. Traditional energy-aware scheduling schemes are mainly based on the individual task scheduling. Consequently, the scheduling space for each task is small, and the schedulability and energy saving are very limited, especially when the system is heavily loaded. To remedy this problem, we propose a novel rolling-horizon (RH) strategy that can be applied to any scheduling algorithm to improve schedulability. In addition, we develop a new energy-efficient adaptive scheduling algorithm (EASA) that can adaptively adjust supply voltages according to the system workload for energy efficiency. Both the RH strategy and EASA algorithm are combined to form our scheduling approach, RH-EASA. Experimental results show that in comparison with some typical traditional scheduling schemes, RH-EASA can achieve significant energy savings while meeting most task deadlines (namely, high schedulability) for distributed real-time embedded systems with dynamic workloads.     

Reducing delay and enhancing DoS resistance in multicast authentication through multigrade security

Many techniques for multicast authentication employ the principle of delayed key disclosure. These methods introduce delay in authentication, employ receiver-side buffers, and are susceptible to denial-of-service (DoS) attacks. Delayed key disclosure schemes have a binary concept of authentication and do not incorporate any notion of partial trust. This paper introduces staggered timed efficient stream loss-tolerant authentication (TESLA), a method for achieving multigrade authentication in multicast scenarios that reduces the delay needed to filter forged multicast packets and, consequently, mitigates the effects of DoS attacks. Staggered TESLA involves modifications to the popular multicast authentication scheme, TESLA, by incorporating the notion of multilevel trust through the use of multiple, staggered authentication keys in creating message authentication codes (MACs) for a multicast packet. We provide guidelines for determining the appropriate buffer size, and show that the use of multiple MACs and, hence, multiple grades of authentication, allows the receiver to flush forged packets quicker than in conventional TESLA. As a result, staggered TESLA provides an advantage against DoS attacks compared to conventional TESLA. We then examine two new strategies for reducing the time needed for complete authentication. In the first strategy, the multicast source uses assurance of the trustworthiness of entities in a neighborhood of the source, in conjunction with the multigrade authentication provided by staggered TESLA. The second strategy achieves reduced delay by introducing additional key distributors in the network.     

不确定环境下智能大厦空调系统调度策略评估

近年来,智能大厦的概念在国内外受到了高度的关注.相比于传统的建筑,智能大厦更加节能、舒适、易维护,已成为未来建筑的发展趋势.作为智能大厦空调通风系统的关键部分,空调系统及其调度策略决定了大厦整体的节能效果以及大厦中用户的舒适度.然而由于智能大厦所处的环境具有许多不确定因素,这极大增加了空调系统调度策略设计与评估的复杂程度.因此如何设计与评估不确定环境下空调系统的调度策略成为了智能大厦设计者面临的一大挑战.已有的方法主要针对智能大厦空调系统进行能耗与性能等方面的分析,但尚未有方法针对调度策略本身进行分析与评估.提出了一种基于价格时间自动机的调度策略评估框架,支持对不确定环境下的智能大厦进行精确建模与定量评估.该框架使用UPPAAL-SMC作为属性查询引擎对模型进行随机模拟运行,根据模拟结果对不同调度策略下大厦的能耗及用户的舒适度进行定量分析.实验结果表明,该方法能有效地帮助设计者进行策略的制定和选取.     

普适计算广播安全协议的研究

广播是普适计算环境中最基本的一种通信方式。对广播通信安全协议的研究显得十分重要。点对点的鉴别模式并不适用于广播安全;数字签名方法则对普适计算硬件要求太高。给出一种基于对称密钥算法的安全鉴别方案,通过对密钥发布方式的改进,满足普适计算硬件环境的广播安全鉴别,对μTESLA进行改进,避免了其由于时间同步错误导致的节点攻击。改进的方案获得更高的安全性能。     

Meetings scheduling solver enhancement with local consistency reinforcement

Meeting scheduling (MS) represents an important real-world group decision application that denotes one of the actual combinatorial problems. Solving this problem consists of scheduling all the meetings while satisfying all the constraints related to both the users and the meetings. However, given human nature, the solution is usually delineated by the encountering of conflicting preferences. Most of existing research efforts allow the relaxation of the users' preferences in order to reach an agreement between all the participants, which is not always possible. In addition, they do not deal with the achievement of any level of local consistency to enhance the efficiency of the solving process, and finally, they do not address the real difficulty of distributed systems, which is the complexity of message passing operations. Here we propose a new approach to facilitate and streamline the scheduling meetings process in any organization. This approach is based on the distributed reinforcement of arc consistency model, which takes into account the difficulties mentioned above. The present work focuses mainly on satisfying meetings hosts' preferences as much as possible, while taking into consideration all users' availability. The underlying selfish protocol is able to efficiently reach the best solution for the host of the meeting (according to the predefined criteria) whenever possible. This process is achieved with the minimal number of exchanged messages and while retaining as much of the privacy of the involved users as possible. An experimental comparative analysis divulges that our approach is scalable and worthwhile especially for strong constraints. Ahlem Ben Hassine received a B.S and M.S. degrees from the High Institute of Management of Tunis (ISG), in 1996 and 1999. She received her PhD degree in 2005 from the School of Knowledge Science at the Japan Advanced Institute of Science and Technology (JAIST). Her main research interests include artificial intelligence mainly for solving complex problems, constraint satisfaction problems, multi-agent systems, meeting scheduling problems. Takayuki Ito received a B.Eng., M.S., and PhD degrees from Nagoya Institute of Technology (NIT) in 1995, 1997 and 2000. He is an Associate Professor at the Graduate School of Engineering, NIT. His research interests include computational mechanism design, auction, agent-mediated electronic commerce, multi-agent negotiation, agent-based Tu Bao Ho received a B. Eng. degree from Hanoi University of Technology in 1978, M.S. and PhD. Degrees from University Paris 6, in 1984 and 1987, a Habilitation diploma in 1998 from University Paris Dauphine. He is currently a professor at School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST). His research interests include artificial intelligence, machine learning, knowledge based systems, knowledge discovery and data mining.     

Energy load superposition and spatial optimization in urban design: A case study

Building energy consumption accounts for a large portion of total energy-use in a city or a regional district. However, energy load spatial distribution has seldom been considered during urban design phase. And energy conservation and energy efficiency measures pay more attention to individual building than buildings in a district or regional space as a whole. If buildings with different functions are mixed together and share same energy system, the savings on system capacity and peak electricity load can be significant. In this paper, a load superposition concept is proposed. The term ‘superposition’ refers to overlapping of energy demand load curves from different buildings and so that the total peak is smaller than the sum of individual peaks. Three spatial optimization methods of demand side load management and three different schemes of energy systems are proposed in this paper. And economic analysis is recommended to evaluate the different energy systems. The applicability of different approaches and the significance of load superposition was analyzed and elaborated through a case study to offer planners a feasible way for evaluating the potential of load spatial optimization.