Wireless body area networks (WBANs) provide medical and/or consumer electronics (CE) services within the vicinity of a human body. In a WBAN environment, immediate and reliable data transmissions during an emergency situation should be supported for medical services. In this letter, we propose a flexible emergency handling scheme for WBAN MAC protocols. The proposed scheme can be applied to superframe-structured MAC protocols such as IEEE 802.15.4 and its extended versions. In addition, our scheme can be incorporated into the current working draft for IEEE 802.15.6 standards. Extensive simulations were performed and the low latency of emergent traffics was validated.

Recently, photovoltaic power systems and electric vehicles have been commonly used. Therefore, the importance of DC (direct current) switching is expected to increase in the near future. The authors have been examining a method of evaluating the electrode loss of AgNi contacts for an electromagnetic contactor with a medium DC load current at a resistive circuit. In this study, the arc energy and electrode mass changes were investigated in more detail. We carried out experiments of 100,000 operations for an electromagnetic contactor at a load current of 5 A constant with a source voltage change from 100 to 160 V. The arc duration, contact resistance, arc energy, and electrode mass changes were measured. As a result, the arc duration was found out increase with the source voltage. In addition, the stationary cathode mass change (loss) increased proportion only to the total arc energy. However, the stationary cathode loss per unit arc energy decreased at the highest source voltage.

A mobile station (MS) in an IEEE 802.16e network manages its limited energy using the sleep mode operation. An MS can power down its physical operation components during the unavailability interval of the sleep mode. To reduce energy consumption by increasing the unavailability interval, this paper proposes an enhanced power saving mechanism (ePSM) when both activated Type I and Type II power saving classes (PSCs) exist in an MS. A performance evaluation confirms that ePSM results in the improved performance in terms of the unavailability interval as well as the energy consumption than conventional schemes.

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.

The past decade has seen a surge of research activities in the fields of mobile computing and wireless communication. In particular, recent technological advances have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. To effectively operate portable devices, energy efficiency and Quality of Service (QoS) provisioning are two primary concerns. Dynamic Voltage Scaling (DVS) is a common method for energy conservation for portable devices. However, due to the amount of data that needs to be dynamically handled in varying time periods, it is difficult to apply conventional DVS techniques to QoS sensitive multimedia applications. In this paper, a new adaptive DVS algorithm is proposed for QoS assurance and energy efficiency. Based on the repeated learning model, the proposed algorithm dynamically schedules multimedia service requests to strike the appropriate performance balance between contradictory requirements. Experimental results clearly indicate the performance of the proposed algorithm over that of existing schemes.

In this paper, a new power control scheme is proposed to maximize the network throughput with fairness provisioning. Based on the Stackelberg game model, the proposed scheme consists of two control mechanisms; user-level and system-level mechanisms. Control decisions in each mechanism act cooperatively and collaborate with each other to satisfy efficiency and fairness requirements. Simulation results demonstrate that the proposed scheme has excellent network performance, while other schemes cannot offer such an attractive performance balance.

Wireless Sensor Networks (WSNs) are gradually moving toward the adoption of clustered heterogeneous designs, incorporating a mixture of variety kinds of sensor nodes with different radio coverage and battery capacity. Compared with homogeneous networks, heterogeneous networks are able to reduce the initial cost of the network or prolong the network lifetime. The architecture and routing protocol for this type of heterogeneous WSN should be energy aware in order to prolong the lifetime of the network. However, most of the existing clustering methods consider only initial energy of the sensor nodes and ignore the non-uniform energy drainage caused by many-to-one traffic near sink and/or cluster heads in heterogeneous network environment. In this paper, we propose a new clustering method for WSN with heterogeneous node types which selects cluster heads considering not only the transmission power and residual energy of each node but also those of its adjacent nodes. Simulation experiments show that the proposed method increases network lifetime by 80% and 60% more than that of the CC and HEED, respectively.

In this letter, a novel adaptive total variation (ATV) model is proposed for image inpainting. The classical TV model is a partial differential equation (PDE)-based technique. While the TV model can preserve the image edges well, it has some drawbacks, such as staircase effect in the inpainted image and slow convergence rate. By analyzing the diffusion mechanism of TV model and introducing a new edge detection operator named difference curvature, we propose a novel ATV inpainting model. The proposed ATV model can diffuse the image information smoothly and quickly, namely, this model not only eliminates the staircase effect but also accelerates the convergence rate. Experimental results demonstrate the effectiveness of the proposed scheme.

The design of an energy-efficient wavelength division multiplexing (WDM) transponder is proposed and effectiveness of the proposed WDM transponder is experimentally studied. The proposed WDM transponder interworking with the link-aggregation technique possessed by a layer 2 switch can achieve power saving depending on traffic volume variations by utilizing an adaptive interface control. Monitoring methods for the link connectivity of a sleep link are also discussed.

The design and performance of a sub-nanoampere two-stage power management circuit that uses off-chip capacitors for energy accumulation are presented. Focusing on the leakage current and the transition time of the power switch transistor, we estimated the minimum current for accumulating. On the basis of the results, we devised a two-stage power management architecture for sub-nanoampere operation. The simulated and experimental results for the power management circuit describe the accumulating operation with a 1-nA current source.

The detection of object categories with large variations in appearance is a fundamental problem in computer vision. The appearance of object categories can change due to intra-class variations, background clutter, and changes in viewpoint and illumination. For object categories with large appearance changes, some kind of sub-categorization based approach is necessary. This paper proposes a sub-category optimization approach that automatically divides an object category into an appropriate number of sub-categories based on appearance variations. Instead of using predefined intra-category sub-categorization based on domain knowledge or validation datasets, we divide the sample space by unsupervised clustering using discriminative image features. We then use a cluster performance analysis (CPA) algorithm to verify the performance of the unsupervised approach. The CPA algorithm uses two performance metrics to determine the optimal number of sub-categories per object category. Furthermore, we employ the optimal sub-category representation as the basis and a supervised multi-category detection system with χ2 merging kernel function to efficiently detect and localize object categories within an image. Extensive experimental results are shown using a standard and the authors' own databases. The comparison results reveal that our approach outperforms the state-of-the-art methods.

This paper proposed a novel platform for sensor nodes to resolve the energy and latency challenges. It consists of a processor, an adaptive compressing module and several compression accelerators. We completed the proposed chip in a 0.18µm HJTC CMOS technology. Compared to the software-based solution, the hardware-assisted compression reduces over 98% energy and 212% latency. Besides, we balanced the energy and latency metric using an adaptive module. According to the scheduling algorithm, the module tunes the state of the compression accelerator, as well as the sampling frequency of the online sensor. For example, given a 9µs constraint for a 1-byte operation, it reduces 34% latency while the energy overheads are less than 5%.

P. Kirchberger proved that, for a finite subset X of Rd such that each point in X is painted with one of two colors, if every d+2 or fewer points in X can be separated along the colors, then all the points in X can be separated along the colors. In this paper, we show a more colorful theorem.

Wireless sensor networks (WSN) is composed of so many small sensor nodes which have limited resources. So the technique that raises energy efficiency is the key to prolong the network life time. In the paper, we propose an agent based framework which takes the biological characteristics of gene. The gene represents an operation policy to control agent behavior. Agents are aggregated to reduce duplicate transmissions in active period. And it selects next hop based on the information of neighbor agents. Among neighbors, the node which has enough energy is given higher priority. The base station processes genetic evolution to refine the behavior policy of agent. Each agent is taken latest gene and spread recursively to find the optimal gene. Our proposed framework yields sensor nodes that have the properties of self-healing, self-configuration, and self-optimization. Simulation results show that our proposed framework increases the lifetime of each node.

In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.

Network on Chip (NoC) is proposed as a new intra-chip communication infrastructure. In current NoC design, one related problem is mapping IP cores onto NoC architectures. In this paper, we propose a performance-aware hybrid algorithm (PHA) for mesh-based NoC to optimize performance indexes such as latency, energy consumption and maximal link bandwidth. The PHA is a hybrid algorithm, which integrates the advantages of Greedy Algorithm, Genetic Algorithm and Simulated Annealing Algorithm. In the PHA, there are three features. First, it generates a fine initial population efficiently in a greedy swap way. Second, effective global parallel search is implemented by genetic operations such as crossover and mutation, which are implemented with adaptive probabilities according to the diversity of population. Third, probabilistic acceptance of a worse solution using simulated annealing method greatly improves the performance of local search. Compared with several previous mapping algorithms such as MOGA and TGA, simulation results show that our algorithm enhances the performance by 30.7%, 23.1% and 25.2% in energy consumption, latency and maximal link bandwidth respectively. Moreover, simulation results demonstrate that our PHA approach has the highest convergence speed among the three algorithms. These results show that our proposed mapping algorithm is more effective and efficient.

Due to the lack of end-to-end paths between the communication source and destination, the routing of Delay Tolerant Networks (DTN) exploits the store-carry-and-forward mechanism. This mechanism requires nodes with sufficient energy to relay and forward messages in a cooperative and selfless way. However, in the real world, the energy is constrained and most of the nodes exhibit selfish behaviors. In this paper, we investigate the performance of DTN routing schemes considering both the energy constraint and selfish behaviors of nodes. First, we model the two-hop relay and epidemic routing based on a two-dimensional continuous time Markov chain. Then, we obtain the system performance of message delivery delay and delivery cost by explicit expressions. Numerical results show that both the energy constraint and node selfishness reduce the message delivery cost at the expense of increasing the message delivery delay. Furthermore, we demonstrate that the energy constraint plays a more important role in the performance of epidemic routing than that of two-hop relay.

In the vehicular ad hoc network (VANET), broadcast is used to disseminate emergency warning messages (EWM) in public safety applications and delay is one of the most stringent requirements. Although flooding is the simplest broadcast scheme, it introduces a broadcast storm problem, and numerous broadcast protocols have been proposed for VANET in order to improve the performance of broadcast. In this letter, we analyze the tradeoff between two location-based broadcast protocols, i.e., distance-based relay selection (DBRS) and range-based relay selection (RBRS) schemes under various vehicular densities. Then, we propose a dynamic broadcast scheme, which selects an appropriate scheme from DBRS and RBRS, based on the estimation of vehicle density. The results of a performance analysis show that the proposed scheme dynamically selects the better scheme for varying vehicle density.

Energy conservation is an important issue in mobile ad hoc networks (MANET), where the terminals are always supplied with limited energy. A new routing protocol is presented according to the study on the influence of low-energy nodes in ad hoc networks. The novel routing protocol (energy sensing routing protocol, ESRP) is based on the energy sensing strategy. Multiple strategy routing and substitute routing are both adopted in this paper. Referring to the level of the residual energy and the situation of energy consumption, different routes are chosen for packets transmission. The local maintenance is adopted, which can reduce packets retransmission effectively when the link breaks. We focus on the network lifetime most in all performances. The evaluation is done in comparison with other routing protocols on NS2 platform, and the simulation results show that this routing protocol can prolong the network lifetime and balance energy consumption effectively.

Recently, IEEE 802.15.4 has been standardized for WSNs (Wireless Sensor Networks). This paper proposes an enhanced CCA scheme which involves the data transmission device sending a notifyBusyChannel (nBC) signal in the backoff period when the Channel Using Quotient (CUQ) exceeds 0.5. The CUQ stands for the rate of channel utilization in the previous slot duration. In a single CCA operation, the device nodes are made aware of the busy status of the channel by the nBC signal. We implement the ECCA scheme in a hardware chip for a performance evaluation. The results show that the proposed scheme has short queuing times and less energy consumption than IEEE 802.15.4 CCA. And the scheme is compatible with conventional IEEE 802.15.4 devices.