General purpose many-core architecture (MCA) such as GPGPU has recently been used widely to continue the performance scaling when the continuous increase in the working frequency has approached the manufacturing limitation. However, both the general purpose MCA and its building block general purpose processor (GPP) lack a tuning capability to boost energy efficiency for individual applications, especially computation intensive applications. As an alternative to the above MCA platforms, we propose in this paper our LAPP (Linear Array Pipeline) architecture, which takes a special-purpose reconfigurable structure for an optimal MIPS/W. However, we also keep the backward binary compatibility, which is not featured in most special hardware. More specifically, we used a general purpose VLIW processor, interpreting a commercial VLIW ISA, as the baseline frontend part to provide the backward binary compatibility. We also extended the functional unit (FU) stage into an FU array to form the reconfigurable backend for efficient execution of program hotspots to exploit parallelism. The hardware modules in this general purpose reconfigurable architecture have been locally zoned into several groups to apply preferable low-power techniques according to the module hardware features. Our results show that under a comparable performance, the tightly coupled general/special purpose hardware, which is based on a 180nm cell library, can achieve 10.8 times the MIPS/W of MCA architecture of the same technology features. When a 65 technology node is assumed, a similar 9.4x MIPS/W can be achieved by using the LAPP without changing program binaries.

With the shorter time-to-market and the rising cost in SoC development, the demand for post-silicon programmability has been increasing. Recently, programmable accelerators have attracted more attention as an enabling solution for post-silicon engineering change. However, programmable accelerators suffers from 5∼10X less energy efficiency than fixed-function accelerators mainly due to their extensive use of memories. This paper proposes a highly energy-efficient accelerator which enables post-silicon engineering change by a control patching mechanism. Then, we propose a patch compilation method from a given pair of an original design and a modified design. We also propose a design method to add redundant wires in advance to decrease the necessary amount of patch memory for post-silicon engineering change. Experimental results demonstrate that the proposed accelerators offer high energy efficiency competitive to fixed-function accelerators and can achieve about 5X higher efficiency than the existing programmable accelerators. We also show the trade-off between redundant wires and the necessary amount of patch memory.

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.

This paper presents a framework for reducing the energy consumption of embedded real-time systems. We implemented the presented framework as both an optimization toolchain and an energy-aware real-time operating system. The framework consists of the integration of multiple techniques to optimize the energy consumption. The main idea behind our approach is to utilize trade-offs between the energy consumption and the performance of different processor configurations during task checkpoints, and to maintain memory allocation during task context switches. In our framework, a target application is statically analyzed at both intra-task and inter-task levels. Based on these analyzed results, runtime optimization is performed in response to the behavior of the application. A case study shows that our toolchain and real-time operating systems have achieved energy reduction while satisfying the real-time performance. The toolchain has also been successfully applied to a practical application.

While Triple modular Redundancy (TMR) is effective in eliminating soft errors in LSIs, the overhead of the triplicated area as well as the triplicated energy consumption is the problem. In addition to the spatial TMR mode where executions are simply tripricated and the majority is taken, the temporal TMR mode is available where only two copies of an operation are executed and the results are compared, then if the results differ, the third copy is executed to get the correct result. Appropriately selecting the power supply voltage is also an effective technique to reduce the energy consumption. In this paper, a method to derive a TMR design is proposed which selects the TMR mode and supply voltage for each operation to minimize the energy consumption within the time and area constraints.

The energy consumption of the Internet has a huge impact on the world economy and it is likely to increase every year. In present backbone networks, pairs of nodes are connected by “bundles” of multiple physical cables that form one logical link and energy saving can be achieved by shutting down unused network resources. The hose model can support traffic demand variations among node pairs in different time periods because it accommodates multiple traffic matrices unlike the pipe model which supports only one traffic matrix. This paper proposes an OSPF (Open Shortest Path First) link weight optimization scheme to reduce the network resources used for the hose model considering single link failures. The proposed scheme employs a heuristic algorithm based on simulated annealing to determine a suitable set of link weights to reduce the worst-case total network resources used, and considering any single link failure preemptively. It efficiently selects the worst-case performance link-failure topology and searches for a link weight set that reduces the worst-case total network resources used. Numerical results show that the proposed scheme is more effective in the reduction of worst-case total network resources used than the conventional schemes, Start-time Optimization and minimum hop routing.

Wireless sensor networks (WSNs) consist of numerous wireless sensor nodes, each sensor node embedding a tiny communication device enabling the nodes to communicate with each other or the base station. In this paper, we investigate the problem that communication distance must be considered in minimizing the wireless communication energy since the energy consumption is proportional to the 2nd to the 6th power of the distance. Moreover, another problem is that there is a non-uniform energy drain effect in most topologies. Known as the energy hole problem, it can result in premature termination of the entire network. To address these problems, in this paper we first propose a communication routing algorithm that can solve the energy hole problem to the maximum extent possible while minimizing the wireless communication energy by generating an energy efficient spanning tree. This algorithm is beneficial for network lifetimes defined by a high node termination percentage. For the WSNs for which the energy hole problem is critical, we propose two route switching algorithms to solve the energy hole problem; they are beneficial for network lifetimes defined by a low node termination percentage. Simulation results showed that these algorithms avoid the energy hole problem and thereby greatly extend the lifetime of WSNs by more than 3 to 6 times that of ones using direct transmission in a 20-node network and a 50-node network if the lifetime of a WSN is defined by 1% of the number of terminated nodes in the WSN.

As one of the most widely investigated studies in wireless sensor networks (WSNs), multihop networking is increasingly developed and applied for achieving energy efficient communications and enhancing transmission reliability. To accurately and realistically analyze the performance metric (energy efficiency), firstly we provide a measurement of the energy dissipation for each state and establish a practical energy consumption model for a WSN. According to the analytical model of connectivity, Gaussian approximation approaches to experimental connection probability are expressed for optimization problem on energy efficiency. Moreover, for integrating experimental results with theories, we propose the methodology in multihop wireless sensor networks to maximize efficiency by nonlinear programming, considering energy consumptions and the total quantity of sensing data to base station. Furthermore, we present evaluations adapting to various wireless sensor networks quantitatively with respect to energy efficiency and network configuration, in view of connectivity, the length of data, maximum number of hops and total number of nodes. As the consequence, the realistic analysis can be used in practical applications, especially on self-organization sensor networks. The analysis also shows correlations between the efficiency and maximum number of hops, that is the multihop systems with several hops can accommodate enough devices in ordinary applications. In this paper, our contribution distinguished from others is that our model and analysis are extended from experiments. Therefore, the results of analysis and proposal can be conveniently applied to actual networks.

This paper proposes an adaptive band activity ratio control (ABC) with cascaded energy allocation (CEA) scheme to improve end-to-end spectral efficiency for two-hop amplify-and-forward orthogonal frequency division multiplexing relay systems under transmit energy constraint. Subchannel pairing (SP) based spectrum mapping maps spectral components transmitted over high gain subchannels in the source-to-relay link onto high gain subchannels of the relay-to-destination link to improve the spectral efficiency. However, SP suffers from a frame efficiency reduction due to the notification of information of spectral component order. To compensate for the deficiency of SP, the proposed scheme employs dynamic spectrum control with ABC in which spectral components are mapped onto subchannels having high channel gain in each link, while band activity ratio (BAR) is controlled to an optimal value, which is smaller than 1, so that all spectral components are transmitted over relatively high gain subchannels of the two links. To further improve the performance, energy allocation at the source node and the relay node is serially conducted based on convex optimization, and BAR is controlled to improve discrete-input continuous-output memoryless channel capacity at the relay node. In the proposed scheme, since only information of BAR needs to be notified, the notification overhead is drastically reduced compared to that in SP based spectrum mapping. Numerical analysis confirms that the proposed ABC combined with CEA significantly reduces the required notification overhead while achieving almost the same frame error rate performance compared with the SP based scheme.

This paper proposes two energy-efficient standby mode algorithms in short-range one-to-one 60GHz millimeter-wave (mmWave) communications. Among the many usage scenarios for mmWave radio, file downloading from kiosk terminals or peer-to-peer sync service with portable terminals are of great interest. For these portable terminals, reducing power consumption of standby mode as well as keeping connection setup time short is important. Comparing the power consumption between frame transmission and reception in short-range one-to-one 60GHz mmWave, the power consumed for a frame reception may become larger than that for a frame transmission. The proposed two energy-efficient standby mode algorithms for one-to-one communications assure the connection setup time and take each terminal's different requirement for reduction of its power consumption into consideration. In the proposed algorithms, each terminal accesses asynchronously and operates based on an interval consisting of several sub-intervals. In one proposed algorithm (Prop 1), a terminal transmits a connection request frame (CREQ) once every sub-interval and the other terminal waits for the CREQ during one sub-interval per interval. Thus, Prop 1 reduces the power consumption for CREQ transmission. In the other proposed algorithm (Prop 2), a terminal selects one sub-interval randomly for each interval and transmits CREQs repeatedly during that sub-interval. The other terminal waits for a CREQ during this CREQ transmission period at every sub-interval. Prop 2 saves the power consumption for a CREQ reception. We evaluate the power consumption of standby mode and connection setup time for Prop 1 and Prop 2 by both numerical analysis and computer simulations. We show that the power consumption of the CREQ waiting terminal with the proposed algorithms is more than 10mW lower than that with the conventional algorithm. We also show that our numerical analysis of the proposed algorithms derives the optimum parameters and facilitates system design. Next, we implement Prop 2 in a fully-integrated CMOS transceiver chip-set with antenna, RF/analog, PHY, and MAC for 60GHz proximity wireless communication. This experimental result is the same as the analysis result and it is verified that our proposed standby algorithm works as designed.

Bandwidth aggregation (BAG) techniques have been researched for many years in an efforts to enhance throughput for multi-homed streaming service. However, despite of the considerable attention being devoted towards energy-efficient communications, the power efficiency for BAG has not been considered yet. To improve the power efficiency in multi-homed streaming service, this paper proposes Power Minimized Rate Allocation Scheme (PMRAS) with optimal rate allocation at each interface while guaranteeing an allowable packet loss rate. In developing PMRAS, we first formulate a power consumption model based on the network interface state (i.e. active and idle state). We adopt a Lagrangian algorithm to solve the convex optimization problem of power consumption. The performance results gained from a numerical analysis and simulations (NS-2) reveal that the proposed scheme offers superior performance over the existing rate allocation scheme for BAG with guaranteed required quality of service.

The traffic load of cellular networks varies in both time and spatial domains, causing many base stations (BS) to be under-utilized. Assisted by cell zooming, dynamic BS sleep control is considered as an effective way to improve energy efficiency during low traffic hours. Therefore, how densely the BSs should be deployed with cell zooming and BS sleeping is an important issue. In this paper, we explore the energy-optimal cellular network planning problem with dynamic BS sleeping and cell zooming for the cases in which traffic is uniformly distributed in space but time-varying. To guarantee the quality of multi-class services, an approximation method based on Erlang formula is proposed. Extensive simulations under our predefined scenarios show that about half of energy consumption can be saved through dynamic BS sleeping and power control. Surprisingly, the energy-optimal BS density we obtained is larger than the one without considering BS sleeping. In other words, deploying more BSs may help to save energy if dynamic BS sleeping is executed.

The MR image segmentation is always a challenging problem because of the intensity inhomogeneity. Many existing methods don't reach their expected segmentations; besides their implementations are usually complicated. Therefore, we originally interleave the extended Otsu segmentation with bias field estimation in an energy minimization. Via our proposed method, the optimal segmentation and bias field estimation are achieved simultaneously throughout the reciprocal iteration. The results of our method not only satisfy the required classification via its applications in the synthetic and the real images, but also demonstrate that our method is superior to the baseline methods in accordance with the performance analysis of JS metrics.

This paper addresses a noise matching problem for MIMO receiver with mutual coupling in the presence of signal and antenna noise coupling. The matching network in this paper is designed to maximize the system's ergodic capacity by means of minimizing the noise figure matrix. For reducing RF circuit complexity, low noise matching design without crossover elements of the matching circuit is derived for compact symmetrical 2$ imes$2 MIMO receiver system with mutually coupled antenna. Numerical simulation verifies our analytical results and demonstrates the superiority of the proposed matching method among feasible ones. The paper furthermore investigates the lossy matching circuit with the corresponding circuit parameters in a specific condition and the effect of practical matching circuit.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

Energy harvesting technology was introduced into wireless sensor networks (WSNs) to solve the problem of the short lifetimes of sensor nodes. The technology gives sensor nodes the ability to convert environmental energy into electricity. Sufficient electrical energy can lengthen the lifetime and improve the quality of service of a WSN. This paper proposes a novel use of mutual information to evaluate data transmission behavior in the energy harvesting WSNs. Data at a sink for a node deteriorates over time until the next periodic transmission from the node is received. In this paper, we suggest an optimized intermittent transmission method for WSNs that harvest energy. Our method overcomes the problem of information deterioration without increasing energy cost. We show that by using spatial correlation between different sensor nodes, our proposed method can mitigate information deterioration significantly at the sink.

Wireless body area networks (WBANs) have to work with low power and long lifetime to satisfy human biological safety requirements in e-health; therefore extremely low energy consumption is significant for WBANs. IEEE 802.15.6 standard has been published for wearable and implanted applications which provide communication technology requirements in WBANs. In this paper, the cross-layering optimization methodology is used to minimize the network energy consumption. Both the priority strategy and sleep mechanism in IEEE802.15.6 are considered. Macroscopic sleep model based on WBAN traffic priority and microscopic sleep model based on MAC structure are proposed. Then the network energy consumption optimization problem is solved by Lagrange dual method, the master problem are vertically decomposed into two sub problems in MAC and transport layers which are dealt with gradient method. Finally, a solution including self-adaption sleep mechanism and node rate controlling is proposed. The results of this paper indicate that the algorithm converges quickly and reduces the network energy consumption remarkably.

Differential games are considered an extension of optimal control problems, which are used to formulate centralized control problems in smart grids. Optimal control theory is used to study systems consisting of one agent with one objective, whereas differential games are used to formulate systems consisting of multiple agents with multiple objectives. Therefore, a differential-game-theoretic approach is appropriate for formulating decentralized demand-side energy management systems where there are multiple decision-making entities interacting with each other. Moreover, in many smart grid applications, we need to obtain information for control via communication systems. To formulate the influence of communication availability, differential game theory is also promising because the availability of communication is considered as part of an information structure (i.e., feedback or open-loop) in differential games. The feedback information structure is adopted when information for control can be obtained, whereas the open-loop information structure is applied when the information cannot be obtained because of communication failure. This paper proposes a comprehensive framework for evaluating the performance of demand-side actors in a demand-side management system using each control scheme according to both communication availability and sampling frequency. Numerical analysis shows that the proposed comprehensive framework allows for an analysis of trade-off for decentralized and centralized control schemes.

In mobile wireless sensor networks, coverage and energy are two significant factors determining network performance. When taking both factors into account, the challenges include how to select and migrate nodes to keep coverage quality, how to forecast and prevent potential coverage holes and how to use energy control in mobile networks. In this paper, we propose a new Coverage Maintenance and Energy Control (CMEC) algorithm to achieve and keep high coverage quality and energy efficiency. For CMEC, we provide a new cost metric for selecting migration nodes. Our simulation results confirm that our algorithm improves coverage performance and lifetime of network.

This paper presents a method to seamlessly extend the coverage of energy supply field for wireless sensor networks in order to free sensors from wires and batteries, where the multi-point scheme is employed to overcome path-loss attenuation, while the carrier shift diversity is introduced to mitigate the effect of interference between multiple wave sources. As we focus on the energy transmission part, sensor or communication schemes are out of scope of this paper. To verify the effectiveness of the proposed wireless energy transmission, this paper conducts indoor experiments in which we compare the power distribution and the coverage performance of different energy transmission schemes including conventional single-point, simple multi-point and our proposed multi-point scheme. To easily observe the effect of the standing-wave caused by multipath and interference between multiple wave sources, 3D measurements are performed in an empty room. The results of our experiments together with those of a simulation that assumes a similar antenna setting in free space environment show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are limited by path-loss, standing-wave created by multipath and interference between multiple wave sources. On the other hand, the proposed scheme can overcome power attenuation due to the path-loss as well as the effect of standing-wave created by multipath and interference between multiple wave sources.