In the Coordinated Multi-Point (CoMP) system under the condition of limited feedback, a reasonable coordinated set relies heavily on the splitting factor that is used to divide the total feedback bits into channel direction information (CDI) feedback bits and channel quality information (CQI) feedback bits. The relation of splitting factor and coordinated set is examined in this paper. After defining a penalty factor, we derive the net ergodic capacity optimization problem, whose variables to be optimized are the number of coordinated BSs, the divided area's radius and the splitting factor. According to an existing codebook and the quantized channel error model, the downlink received signal model is updated after adding the splitting factor. Through random matrix knowledge, the stochastic property of this model is obtained. A close approximate expression including the splitting factor to be optimized related to coordinated set is given. In addition, a revised adaptive feedback scheme is proposed to split the feedback bits. Simulation results show that the proposed scheme provides a significant performance gain, especially as the user velocity is high.

In this paper, we propose an adaptive voltage huddle-based distributed-register architecture (AVHDR architecture), which integrates dynamic multiple supply voltages and interconnection delay into high-level synthesis. In AVHDR architecture, voltages can be dynamically assigned for energy reduction. In other words, low supply voltages are assigned to non-critical operations, and leakage power is cut off by turning off the power supply to the sleeping functional units. Next, an AVHDR-based high-level synthesis algorithm is proposed. Our algorithm is based on iterative improvement of scheduling/binding and floorplanning. In the iteration process, the modules in each huddle can be placed close to each other and the corresponding AVHDR architecture can be generated and optimized with floorplanning information. Experimental results show that on average our algorithm achieves 43.9% energy-saving compared with conventional algorithms.

Coarse-grained Reconfigurable Architecture (CGRA) is a parallel computing platform that provides both high performance of hardware and high flexibility of software. It is becoming a promising platform for embedded and mobile applications. Since the embedded and mobile devices are usually battery-powered, improving battery lifetime becomes one of the primary design issues in using CGRAs. In this paper, we propose a battery-aware task-mapping method to optimize energy consumption and improve battery lifetime. The proposed method mainly addresses two problems: task partitioning and task scheduling when mapping applications onto CGRA. The task partitioning and scheduling are formulated as a joint optimization problem of minimizing the energy consumption. The nonlinear effects of real battery are taken into account in problem formulation. Using the insights from the problem formulation, we design the task-mapping algorithm. We have used several real-world benchmarks to test the effectiveness of the proposed method. Experiment results show that our method can dramatically lower the energy consumption and prolong the battery-life.

Energy-harvesting devices are materials that allow ambient energy sources to be converters into usable electrical power. While a battery powers the modern embedded systems, these energy-harvesting devices power the energy-harvesting embedded systems. This claims a new energy efficient management techniques for the energy-harvesting systems dislike the previous management techniques. The higher entire system efficiency in an energy-harvesting system can be obtained by a higher generating efficiency, a higher consuming efficiency, or a higher transferring efficiency. This paper presents a generalized technique for a dynamic reconfiguration and a task scheduling considering the power loss in DC-DC converters in the system. The proposed technique minimizes the power loss in the DC-DC converter and charger of the system. The proposed technique minimizes the power loss in the DC-DC converters and charger of the system. Experiments with actual application demonstrate that our approach reduces the total energy consumption by 22% in average over the conventional approach.

Nowadays, with spreads of inexpensive small communication devices, a number of wireless local area networks (WLANs) have been deployed even in the same building for the Internet access services. Their wireless access-points (APs) are often independently installed and managed by different groups such as departments or laboratories in a university or a company. Then, a user host can access to multiple WLANs by detecting signals from their APs, which increases the energy consumption and the operational cost. It may also degrade the communication performance by increasing interferences. In this paper, we present an AP aggregation approach to solve these problems in multiple WLAN environments by aggregating deployed APs of different groups into limited ones using virtual APs. First, we formulate the AP aggregation problem as a combinatorial optimization problem and prove the NP-completeness of its decision problem. Then, we propose its heuristic algorithm composed of five phases. We verify the effectiveness through extensive simulations using the WIMNET simulator.

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

This paper presents a DC output voltage-boosting antenna with high input impedance in wide frequency band for RF (radio frequency) energy harvesting of FM broadcasting signals. Target input power level of -20dBm is used to design a loop antenna for DC output voltage-boosting. The RF energy harvesting on YNU campus provides 924mV DC output for a single rectenna and 1.72V DC output for twin rectennas by receiving several FM broadcasting wave simultaneously.

To raise the energy efficiency of wireless clients, it is important to sleep in idle periods. When multiple network applications are running concurrently on a single wireless client, packets of each application are sent and received independently, but multiplexed at MAC-level. This uncoordinated behavior makes it difficult to control of sleep timing. In addition, frequent state transitions between active and sleep modes consume non-negligible energy. In this paper, we propose a transport-layer approach that resolves this problem and so reduces energy consumed by multiple TCP flows on a wireless LAN (WLAN) client. The proposed method, called SCTP tunneling, has two key features: flow aggregation and burst transmission. It aggregates multiple TCP flows into a single SCTP association between a wireless client and an access point to control packet transmission and reception timing. Furthermore, to improve the sleep efficiency, SCTP tunneling reduces the number of state transitions by handling multiple packets in a bursty fashion. In this study, we construct a mathematical model of the energy consumed by SCTP tunneling to assess its energy efficiency. Through numerical examples, we show that the proposed method can reduce energy consumption by up to 69%.

In this paper, penetration phenomenon of an early-time (E1) high altitude electromagnetic pulse (HEMP) into dispersive underground multilayer structures is analyzed using electromagnetic modeling of wave propagation in frequency dependent lossy media. The electromagnetic pulse is dealt with in the power spectrum ranging from 100kHz to the 100MHz band, considering the fact that the power spectrum of the E1 HEMP rapidly decreases 30dB below its maximum value beyond the 100MHz band. In addition, the propagation channel consisting of several dielectric materials is modeled with the dispersive relative permittivity of each medium. Based on source and channel models, the propagation phenomenon is analyzed in the frequency and time domains. The attenuation levels at a 100m underground point are observed to be about 15 and 20dB at 100kHz and 1MHz, respectively, and the peak level of the penetrating electric field is found 5.6kV/m. To ensure the causality of the result, we utilize the Hilbert transform.

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.

We consider uplink multi-carrier code-division multiple access (MC-CDMA) systems in a multi-cell environment. It is assumed that all intra-cell users employ Alamouti's simple space-time block coding (STBC), which is known to the base station receiver, but the receiver has no information on whether inter-cell users employ STBC or not. In this case we propose a blind adaptive minimum output energy (MOE) receiver for uplink STBC MC-CDMA, which is designed to perfectly remove the interference from intra-cell users by using the spreading sequence information on all intra-cell users and to reduce the interference from inter-cell users by minimizing the constrained output energy. Analysis and simulation results show that the proposed adaptive receiver has a faster convergence rate and higher steady-state signal-to-interference plus noise ratio (SINR) than a conventional scheme in which only the spreading code information of the desired user is utilized.

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.

In classical routing protocols, geographical distances/locations are typically used as the metric to select the best route, under the assumption that shorter distances exhibit lower energy consumption and nodes within the communication range of the sender can receive packets with a certain success probability. However, in underwater acoustic sensor networks (UASNs), sound propagation in the ocean medium is more complex than that in the air due to many factors, including sound speed variations and the interaction of sound waves with the sea surface and floor, causing the sound rays to bend. Therefore, propagation of sound is anisotropic in water, and may cause a phenomenon called shadow zone where nodes in the communication range of the sender cannot hear any signal. This renders conventional routing protocols no longer energy-efficient. In this paper, we make use of the ray-model to account for the environment-dependent behavior of the underwater channel, re-define nodes' one-hop neighbors based on signal attenuation rather than geographical distance, and design a distributed energy-efficient routing protocol for UASNs. Results show that our ray-model-based routing policy consistently outperforms the shortest path policy, and performs very close to the optimal one in several scenarios.

To provide robust wireless data transmission over fading channels, various schemes which involve the use of relays have been proposed. In some of those schemes, the relay chooses not to forward the received message if its reliability is deemed as too low. Some researchers refer to such schemes as selective decode-and-forward. Our work in this paper falls into such a category. More specifically speaking, the relay in our system is a censorial relay (a relay that performs censorial task). It evaluates the reliability, in terms of log likelihood ratio (LLR), of a received data bit (from the source). If its LLR magnitude is below some preset threshold, then it is censored (i.e. not sent to the destination). When the channel is Rayleigh faded, closed-form bit error rate (BER) expressions for the proposed system are derived for several scenarios. Those scenarios are differentiated by the availability of an energy detector (ED) and the various degrees of knowledge regarding the channel state information (CSI). Aided by those closed-form BER expressions, the system parameters can be efficiently optimized to achieve the minimum BER. Simulation results are observed to closely match theoretical values, as computed by the afore-mentioned closed-form BER expressions. As compared to some existing relay-assisted systems in which censoring is incorporated, the performance of our system is better in terms of BER when the same amount of CSI is exploited.

Optimizing lifetime of a wireless sensor network has received considerable attention in recent years. In this paper, using the feasibility and simplicity of grid-based clustering and routing schemes, we investigate optimizing lifetime of a two-dimensional wireless sensor network. Thus how to determine the optimal grid sizes in order to prolong network lifetime becomes an important problem. At first, we propose a model for lifetime of a grid in equal-grid model. We also consider that nodes can transfer packets to a grid which is two or more grids away in order to investigate the trade-off between traffic and transmission energy consumption. After developing the model for an adjustable-grid scenario, in order to optimize lifetime of the network, we derive the optimal values for dimensions of the grids. The results show that if radio ranges are adjusted appropriately, the network lifetime in adjustable-grid model is prolonged compared with the best case where an equal-grid model is used.

Recently, environmental monitoring applications or home automation systems often deal with wireless sensor data. These applications deal with multi-dimensional sensing data and most processing operations involves skyline queries. In this paper, we focus on skyline queries in WSNs. However, as the number of data dimensions increases, the results of skyline queries become unmanageably large which reduces the lifetime of the sensor network. To solve these problems, we propose a novel k-dominant skyline query processing method using filtering mechanisms. The filter is designed by considering the data property and the data transmission cost. Extensive experiments show that our proposed method results in significant performance improvements over the existing method.

Practical, low complexity time of arrival (TOA) estimation method with high accuracy are attractive in ultra wideband (UWB) ranging and localization. In this paper, a generalized maximum likelihood energy detection (GML-ED) ranging method is proposed and implemented. It offers low complexity and can be applied in various environments. An error model is first introduced for TOA accuracy evaluation, by which the optimal integration interval can be determined. Aiming to suppress the significant error created by the false alarm events, multiple pulses are utilized for accuracy promotion at the cost of extra energy consumption. For this reason, an energy efficiency model is also proposed based on the transmitted pulse number. The performance of the analytical research is evaluated and verified through practical experiments in a typical indoor environment.

In the sensor networks for surveillance, the requirements of providing energy efficiency and service differentiation, which is to deliver high-priority packets preferentially, while maintaining high goodput, which is to deliver many packets within their deadline are increasing. However, previous works have difficulties in satisfying the requirements simultaneously. Thus, we propose GES-MAC, which satisfies the requirements simultaneously. GES-MAC reduces idle listening energy consumption by using a duty cycle, periodic listen (i.e., turn on radio module) and sleep (i.e. turn off radio module) of sensor nodes. Cluster-based multi-hop scheduling provides high goodput in a duty-cycled environment by scheduling clusters of nodes in the listen period and opportunistically forwarding data packets in the sleep period. Priority-aware schedule switching makes more high-priority packets reach the sink node by letting high-priority packets preempt the schedules of low-priority packets. In experiments with MICA2 based sensor nodes and in simulations, the energy consumption of the radio module is reduced by 70% compared to the approaches without a duty cycle, while providing 80% 100% goodput of the approaches that provide high goodput. Service differentiation is also supported with little overhead.

Dynamic spectrum leasing (DSL) is regarded as a promising dynamic spectrum sharing (DSS) scheme both to improve the spectrum revenue of primary users (PUs) and to guarantee the QoS of secondary users (SUs). A pricing-based DSL termed PBDSL is formulated as a Stackelberg DSL game model, where PUs as players entering the interacting game with multiple SUs. The strategic design contains both optimal spectrum pricing schemes (including unit spectrum/interference price and interference sensitivity distributed adjustments) of PUs for the specific shared/leased spectrum and optimal transmission strategies (e.g., transmit power and bandwidth) of SUs. To capture two types of competition relationships among multiple SUs and between SUs and PUs, we investigate two intra-game models of multiple PUs and SUs, respectively, which interact with each other to constitute the final Stackelberg DSL game. The existence and uniqueness of Stackelberg equilibrium solution (SES) are analyzed and proved for presented games, based on which a joint multi-stage PBDSL algorithm is presented to approximate the optimal equilibrium strategies. Numerical results demonstrate the convergence property of the interactive decision-making process, and verify the effectiveness of the proposed algorithm, in a comparison with the Nash equilibrium solution (NES)-based approach.