Japan encounters an urgent issue of “Carbon Neutrality” as a member of the international world and is required to make the action plans to accomplish this issue, i.e., the zero emission of CO2 by 2050. Our world must change the industries to adapt to the electrification based on the renewable powers. Microwave chemistry is a candidate of electrification of industries for the carbon neutrality on the conditions of usage of renewable energy power generation. This invited paper shows an example of “Microwave Pidgeon process” for smelting magnesium in which heating with burning fossil coals can be replaced with microwave energy for discussing how microwave technology should be developed for that purpose from both the academic and industrial sides.

High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C2vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C2vmMap_heur to decrease the total energy of a data center at run-time. We evaluate C2vmMap_heur under various workload scenarios. The real server experimental results show that C2vmMap_heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.

Because mobile users demand a high quality and energy-friendly video delivery service that efficiently uses wireless resources, we introduce an energy-efficient video delivery system by applying moving route navigation and playout buffer control based on the mobile throughput history data. The proposed system first determines the optimal travel route to achieve high-speed and energy-efficient communications. Then when a user enters a high throughput area, our system temporarily extends the video playout buffer size, and the user aggressively downloads video segments via a high-speed and energy-efficient wireless connection until the extended buffer is filled. After leaving this area, the user consumes video segments from the extended buffer in order to keep smooth video playback without wireless communications. We carry out computer simulations, laboratory and field experiments and confirm that the proposed system can achieve energy-efficient mobile video delivery.

Ethernet passive optical network (EPON) is one of the energy-efficient access networks. Many studies have been done to reach maximum energy saving in the EPON. However, it is a trade-off between achieving maximum energy saving and guaranteeing QoS. In this paper, a predictive doze mode mechanism in an enhanced EPON architecture is proposed to achieve energy saving by using a logistic regression (LR) model. The optical line terminal (OLT) in the EPON employs an enhanced Doze Manager practicing the LR model to predict the doze periods of the optical network units (ONUs). The doze periods are estimated more accurately based on the historical high-priority traffic information, and logistic regression DBA (LR-DBA) performs dynamic bandwidth allocation accordingly. The proposed LR-DBA mechanism is compared with a scheme without energy saving (IPACT) and another scheme with energy saving (GDBA). Simulation results show that LR-DBA effectively improves the power consumption of ONUs in most cases, and the improvement can be up to 45% while it guarantees the QoS metrics, such as the high-priority traffic delay and jitter.

Wireless technologies that offer high data rate are generally energy-consuming ones while low-energy technologies commonly provide low data rate. Both kinds of technologies have been integrated in a single mobile device for different services. Therefore, if the service does not always require high data rate, the low energy technology, i.e., Bluetooth, can be used instead of the energy-consuming one, i.e., Wi-Fi, for saving energy. It is obvious that energy savings are maximized by turning the unused technology off. However, when active sessions of ongoing services migrate between different technologies, the network-layer connectivity must be maintained, or a vertical handover (VHO) between different networks is required. Moreover, when the networks are not interconnected, the VHO must be fully controlled by the device itself. The device typically navigates traffic through the firmware of the wireless network interface cards (WNIC) using their drivers, which are dependent on the vendors. To control the traffic navigation between WNICs without any modification of the WNICs' drivers, Software-Defined Networking (SDN) can be applied locally on the mobile device, the so called local SDN. In the local SDN architecture, a local SDN controller (SDNC) is used to control a virtual OpenFlow switch, which turns WNICs into its switch ports. Although the SDNC can navigate the traffic, it lacks the global view of the network topology. Hence, to correctly navigate traffic in a VHO process, an extended SDN controller (extSDNC) was proposed in a previous work. With the extSDNC, the SDNC can perform VHO based on a link layer trigger but with a significant packet loss rate. Therefore, in this paper, a framework named esVHO is proposed that executes VHO at the network layer to reduce the packet loss rate and reduce energy consumption. Experiments on VHO performance prove that esVHO can reduce the packet loss rate considerably. Moreover, the results of an energy saving experiment show that esVHO performs high energy saving up to 4.89 times compared to the others.

Artificial lighting is responsible for a large portion of total energy consumption and has great potential for energy saving. This paper designs an LED light control algorithm based on users' localization using multiple battery-less binary human detection sensors. The proposed lighting control system focuses on reducing office lighting energy consumption and satisfying users' illumination requirement. Most current lighting control systems use infrared human detection sensors, but the poor detection probability, especially for a static user, makes it difficult to realize comfortable and effective lighting control. To improve the detection probability of each sensor, we proposed to locate sensors as close to each user as possible by using a battery-less wireless sensor network, in which all sensors can be placed freely in the space with high energy stability. We also proposed to use a multi-sensor-based user localization algorithm to capture user's position more accurately and realize fine lighting control which works even with static users. The system is actually implemented in an indoor office environment in a pilot project. A verification experiment is conducted by measuring the practical illumination and power consumption. The performance agrees with design expectations. It shows that the proposed LED lighting control system reduces the energy consumption significantly, 57% compared to the batch control scheme, and satisfies user's illumination requirement with 100% probability.

In most existing centralized lighting control systems, the lighting control problem (LCP) is reformulated as a constrained minimization problem and solved by linear programming (LP). However, in real-world applications, LCP is actually discrete and non-linear, which means that more accurate algorithm may be applied to achieve improvements in energy saving. In this paper, particle swarm optimization (PSO) is successfully applied for office lighting control and a linear programming guided particle swarm optimization (LPPSO) algorithm is developed to achieve considerable energy saving while satisfying users' lighting preference. Simulations in DIALux office models (one with small number of lamps and one with large number of lamps) are made and analyzed using the proposed control algorithms. Comparison with other widely used methods including LP shows that LPPSO can always achieve higher energy saving than other lighting control methods.

WLAN infrastructure has been deployed densely and extensively in the past few years. Since APs are always kept online, a dense WLAN will waste energy during idle hours. In this paper, we first state some principles for powering on/off APs in order to save energy. Then we design an energy saving mechanism correspondingly. The energy saving mechanism includes three processes: clustering APs, estimating user location, and powering on/off APs, which tries to choose appropriate APs being online according to user location information. Results of trace-driven simulation show that our mechanism could achieve about 42% energy conservation. More importantly, our mechanism can provide better network service for users than previous mechanisms which usually ignore user location information.

Most research projects with respect to energy saving are trying to improve power efficiency and are using software to manage the power systems in the power on mode; but in our design, we modify the original Suspend to RAM mode-S3 state, which is the 3rd system state as defined by the ACPI specification, in order to reduce power consumption. We've redesigned the control circuit to save power while a PC is in the standby mode. First, we re-examine the entire circuit in the standby mode, and clarify which chip is used both to wake up the system and to turn off all unnecessary standby power previously used by the chips. Secondly, we redesign the power sequence and use an additional chip to control the system power supply, to allow a PC's normal system's operation to turn off the unnecessary power control chips. Third, in order to save power supply in the standby mode, we have simplified the multiple remote wake-up mechanism to control the remote boot device. The improvement shows that our design reduced power consumption to 0.21W from the original 0.56W while all the remote wake-up functions are disabled; and consumes 0.42W when using multiple remote wake-up functions. We implement the above modification from the legacy S3 state, and obtain lower power consumption. In order to distinguish the standby states, we name the modified S3 state as Deep S3 state.

A high energy-efficiency and area-reduction switching scheme for a low-power successive approximation register (SAR) analog-to-digital converter (ADC) is presented. Based on the sequence initialization, monotonic capacitor switching procedure and multiple reference voltages, the average switching energy and total capacitance of the proposed scheme are reduced by 99.4% and 87.5% respectively, compared to the conventional architecture.

This letter introduces an Energy-Aware LED Light System (EA-LLS) that provides adequate illumination to users according to the analysis of the sun's position, the user's movement, and various environmental factors, without sun illumination detection sensors. This letter presents research using algorithms and scenarios. We propose an EA-LLS that offers not only On/Off and dimming control, but dimming control through daylight, space, and user behavior analysis.

In emerging wearable sensor systems, it is crucial to save energy because these systems are severely energy-constrained. For making the sensors in these systems energy efficient, transmission power control (TPC) is widely used, and thus far, many TPC algorithms have been proposed in the literature. However, these TPC algorithms do not always work well in all wireless body channel conditions, which are capriciously varied due to diverse sensor environments such as sensor placements, body movements, and body locations. In this paper, we propose a simple TPC algorithm that quickly and stably approaches the optimal transmission power level and works well in all wearable sensor environments. We experimentally evaluated the proposed TPC algorithm and proved that it works well under all wireless body channel conditions.

This paper studies the energy-saving problem in cognitive multicast orthogonal frequency-division multiplexing (OFDM) systems, for which a time-frequency two-dimensional model is established to enable the system energy conservation through joint temporal and spectral adaptations. The formulated two-dimensional problem, minimizing the total power consumption whilst guaranteeing the minimal-rate requirement for each multicast session and constraining the maximal perceived interference in each timeslot for the active primary user, is categorized as mixed integer non-convex programming, whose optimal solution is intractable in general. However, based on the time-sharing property, an asymptotically optimal algorithm is proposed by jointly iterating spectrum element (SE) assignment and power allocation. Moreover, a suboptimal algorithm, which carries out SE assignment and power allocation sequentially, is presented as well to reduce the computation complexity. Simulation results show the proposed joint algorithm can achieve the near-optimal solution, and the proposed sequential algorithm approximates to the joint one very well with a gap of less than 3%. Compared with the existing slot-by-slot energy-saving algorithms, the total power consumption is considerably decreased due to the combined exploitation of time and frequency dimensions.

This paper represents an illumination modeling method for lighting control which can model the illumination distribution inside office buildings. The algorithm uses data from the illumination sensors to train Radial Basis Function Neural Networks (RBFNN) which can be used to calculate 1) the illuminance contribution from each luminaire to different positions in the office 2) the natural illuminance distribution inside the office. This method can be used to provide detailed illumination contribution from both artificial and natural light sources for lighting control algorithms by using small amount of sensors. Simulations with DIALux are made to prove the feasibility and accuracy of the modeling method.

In wireless sensor networks, energy depletion of bottleneck nodes which have more data packets to relay than others, dominates the network lifetime referred to as the funnel effect problem. To overcome this problem, multiple sink methods have been proposed where sensor nodes send observed data packets toward several sinks to distribute traffic load of bottleneck nodes. If both of the topology and the traffic pattern are symmetric, bottleneck nodes are located near sinks. However, in a general sensor network with an asymmetric topology and/or an asymmetric traffic pattern, bottleneck nodes may exist any place in the network. In this paper, we propose DCAM (DispersiveCast of packets to Avoid bottleneck nodes for Multiple sink sensor network), which is a load balancing method to improve lifetime of a sensor network with an asymmetric topology and an asymmetric traffic pattern. DCAM first finds bottleneck nodes, and then balances the load on the bottleneck nodes. Selected nodes send data packets to several sinks dispersively according to some criteria. The criteria classify DCAM into three variations: DCAM with probability (DCAM-P), DCAM with moving boarder (DCAM-MB), and DCAM with round-robin (DCAM-RR). This paper gives details of the DCAM methods, and thereafter evaluates them with asymmetric topologies and asymmetric traffic patterns. To deal with these dynamic asymmetry, the topology is modeled by a grid network with virtual holes that are defined as vacant places of nodes in the network. Asymmetry of traffic pattern is modeled by defining a hot area where nodes have heavier data traffic than the others. The evaluations are conducted as changing hot-area traffic patterns as well as fixing hot-area patterns. The results show that DCAM improves network lifetime up to 1.87 times longer than the conventional schemes, (i.e., nearest sink transmissions and optimal dispersive cast of packet). We also discuss DCAM on several aspects such as overhead, energy consumption, and applications.

Current network elements consume 10-20% of the total power in data centers. Today's network elements are not energy-proportional and consume a constant amount of energy regardless of the amount of traffic. Thus, turning off unused network switches is the most efficient way of reducing the energy consumption of data center networks. This paper presents Honeyguide, an energy optimizer for data center networks that not only turns off inactive switches but also increases the number of inactive switches for better energy-efficiency. To this end, Honeyguide combines two techniques: 1) virtual machine (VM) and traffic consolidation, and 2) a slight extension to the existing tree-based topologies. Honeyguide has the following advantages. The VM consolidation, which is gracefully combined with traffic consolidation, can handle severe requirements on fault tolerance. It can be introduced into existing data centers without replacing the already-deployed tree-based topologies. Our simulation results demonstrate that Honeyguide can reduce the energy consumption of network elements better than the conventional VM migration schemes, and the savings are up to 7.8% in a fat tree with k=12.

We observe that the state-of-the-art power-saving mechanisms (PSM) for IEEE 802.16e is neither optimal in terms of delay nor in terms of energy consumption. We propose a new PSM which achieves the optimality in terms of the average buffering delay without increasing energy consumption. In order to do so, we derive a formula which relates the average buffering delay to sleep intervals. Simulation results show that our scheme surpasses the BTE algorithm (used by the current IEEE 802.16e Mobile Stations) by 56.75–76% and the PSID algorithm by 8.52–24.39% in terms of the delay-energy consumption product.

When computing resources are consolidated in a few huge data centers, a massive amount of data is transferred to each data center over a wide area network (WAN). This results in increased power consumption in the WAN. A distributed computing network (DCN), such as a content delivery network, can reduce the traffic from/to the data center, thereby decreasing the power consumed in the WAN. In this paper, we focus on the energy-saving aspect of the DCN and evaluate its effectiveness, especially considering traffic locality, i.e., the amount of traffic related to the geographical vicinity. We first formulate the problem of optimizing the DCN power consumption and describe the DCN in detail. Then, numerical evaluations show that, when there is strong traffic locality and the router has ideal energy proportionality, the system's power consumption is reduced to about 50% of the power consumed in the case where a DCN is not used; moreover, this advantage becomes even larger (up to about 30%) when the data center is located farthest from the center of the network topology.

In this letter, we introduce two different resource allocation and Tx power management schemes, called resource control and fixed power (RCFP) and fixed resource and power control (FRPC), in an LTE-Advanced femtocell network. We analyze and compare the two schemes in terms of the system throughput for downlink and energy consumption of home evolved NodeB (HeNB) Tx power according to the number of HeNBs and home user equipment (HUE)'s user traffic density (C). The simulation results show that the FRPC scheme has better performance in terms of system throughput for macro user equipments (MUEs) and energy consumption in low C.

With energy shortages and global climate change leading our concerns these days, the energy consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduler for reducing energy consumption of datacenters in Cloud computing platforms. It is composed of four algorithms: prediction, ON/OFF, task scheduling, and evaluation algorithms. The prediction algorithm employs a neural predictor to predict future load demand based on historical demand. According to the prediction, the ON/OFF algorithm dynamically adjusts server allocations to minimize the number of servers running, thus minimizing the energy use at the points of consumption to benefit all other levels. The task scheduling algorithm is responsible for directing request traffic away from powered-down servers and toward active servers. The performance is monitored by the evaluation algorithm to balance the system's adaptability against stability. For evaluation, we perform simulations with two load traces. The results show that the prediction mode, with a combination of dynamic training and dynamic provisioning of 20% additional servers, can reduce energy consumption by 49.8% with a drop rate of 0.02% on one load trace, and a drop rate of 0.16% with an energy consumption reduction of 55.4% on the other. Our method is also proven to have a distinct advantage over its counterparts.