In a wireless sensor network based on the gradient sinking model, unbalanced energy consumption is an inherent problem and can significantly reduce the network lifetime. In this letter, we propose a subcorona-based scheme to analyze the amount of received data and energy consumption of nodes on gradient sinking model. We then design an algorithm to compute the energy consumption of nodes in different subcoronas. Simulation results indicate the correctness of our proposed algorithm.

Since most of energy consumed by the telecommunication infrastructure is due to the Base Transceiver Station (BTS), switching off BTSs when traffic load is low has been recognized as an effective way of saving energy. In this letter, an energy saving scheme is proposed to minimize the number of active BTSs based on the space-time structure of traffic loads as determined by principal component analysis. Compared to existing methods, our approach models traffic loads more accurately, and has a much smaller input size. As it is implemented in an off-line manner, our scheme also avoids excessive communications and computing overheads. Simulation results show that the proposed method has a comparable performance in energy savings.

Cognitive radio technology, which allows secondary user (SU) to utilize the spectrum holes left by primary user (PU), was proposed to solve spectrum under-utilization problem. However, due to sensing error, SU's transmission will bring negative effects to PU's communication. Recently, cooperative relay technology was introduced to solve this problem. In this paper, a cooperative framework, which allows SU to act as a relay for primary link when needed, is considered and then a cognitive relay scheme is proposed. In order to maximize SU's throughput while keeping the system stable, we study and obtain SU's optimal strategy (i.e., relaying strategy and power allocation) by a constrained optimization problem. Since energy consumption is also an important problem for cognitive radio networks, we also investigate SU's optimal strategy to maximize SU's energy efficiency while keeping the system stable. The numerical results show that the cognitive relay scheme can achieve higher throughput and energy efficiency than reference schemes.

As the fundamental component of dynamic spectrum access, implementing spectrum sensing is one of the most important goals in cognitive radio networks due to its key functions of protecting licensed primary users from harmful interference and identifying spectrum holes for the improvement of spectrum utilization. However, its performance is generally compromised by the interference from adjacent primary channels. To cope with such interference and improve detection performance, this paper proposes a non-coherent power decomposition-based energy detection method for cooperative spectrum sensing. Due to its use of power decomposition, interference cancellation can be applied in energy detection. The proposed power decomposition does not require any prior knowledge of the primary signals. The power detection with its interference cancellation can be implemented indirectly by solving a non-homogeneous linear equation set with a coefficient matrix that involves only the distances between primary transmitters and cognitive secondary users (SUs). The optimal number of SUs for sensing a single channel and the number of channels that can be sensed simultaneously are also derived. The simulation results show that the proposed method is able to cope with the expected interference variation and achieve higher probability of detection and lower probability of false alarm than the conventional method in both hard combining and soft combining scenarios.

Power patterns in the frequency domain are usually used to describe the antenna performance in narrowband communication systems; however, they are not sufficient for ultra wideband (UWB) antennas in wideband communication systems. In this paper, energy pattern and energy gain are introduced to describe the performance of UWB antennas. Numerical simulations and measurements are used to demonstrate the difference between power patterns and energy patterns for narrowband Yagi-Uda antenna, UWB CPW–fed bow-tie slot antenna, and UWB comb taper slot antenna. The results of simulations and measurements are agreement not only in energy pattern, but also in received voltage at various observation angles. For narrowband antennas, the energy patterns are similar to that of the power patterns in the main beam region. However, there are quite differences between power pattern in frequency domain and energy pattern in time domain for UWB antennas.

The electric field mill in our underground observation room detected a co-seismic electromagnetic signal in the vertical electrostatic field ca. 8 s after the origin time of the Niigataken Chuetsu-oki Earthquake in 2007, but ca. 30 s before the arrival time of the P-waves.

Asymptotic expansions of the amplitudes of the direct and scattered waves in a waveguide system with an imperfection core are derived for large core number and the partial cancellation of the direct wave by the scattered wave is shown in detail. The total power of light in the cross section of a waveguide system is analytically derived and it is shown that the total power of the sum of the direct and scattered waves decreases from that of the direct wave because of the cancellation, the difference of the total power transfers to the localized wave and the total power of light is conserved.

This letter proposes a novel TPC scheme that increases the energy efficiency of IEEE 802.11 WLAN users. It can determine whether to access the channel and with what level of transmit power given the current channel condition by comparing the expected energy efficiency to an adaptive threshold.

Memory accesses are a major cause of energy consumption for embedded systems. This paper presents the implementation of a fully software technique which places stack and static data into a scratch-pad memory (SPM) in order to reduce the energy consumed by the processor while accessing them. Since an SPM is usually too small to include all these data, some of them must be left into the external main memory (MM). Therefore, further energy reduction is achieved by moving some stack data between both memories at run time. The technique employs integer linear programming in order to find at compile time the optimal placement of static data and management of the stack and implements it by inserting stack operations inside the code. Experimental results show that with an SPM of only 1 KB, our technique is able to exploit it for reducing the energy consumption related to the static and stack data accesses by more than 90% for several applications and on an average by 57% compared to the case where these data are fully placed into the main memory.

The proposed scheduling scheme minimizes the energy consumption of a real-time task on the multi-core processor with the dynamic voltage and frequency scaling capability. The scheme allocates a pertinent number of cores to the task execution, inactivates unused cores, and assigns the lowest frequency meeting the deadline. For a periodic real-time task with consecutive real-time instances, the scheme prepares the minimum-energy solutions for all input cases at off-line time, and applies one of the prepared solutions to each real-time instance at runtime.

Let (X,Y) be a Rd R-valued random vector. In regression analysis one wants to estimate the regression function m(x):=E(Y|X=x) from a data set. In this paper we consider the convergence rate of the error for the k nearest neighbor estimators in case that m is (p,C)-smooth. It is known that the minimax rate is unachievable by any k nearest neighbor estimator for p > 1.5 and d=1. We generalize this result to any d ≥ 1. Throughout this paper, we assume that the data is independent and identically distributed and as an error criterion we use the expected L2 error.

The description and analysis of emergence in complex adaptive system has recently become a topic of great interest in the field of systems, and lots of ideas and methods have been proposed. A Sign-based model of Stigmergy is proposed in this paper. Stigmergy is widely used in complex systems. We pick up “Sign” as a key notion to understand it. A definition of “Sign” is given, which reveals the Sign's nature and exploit the significations and relationships carried by the “Sign”. Then, a Sign-based model of Stigmergy is consequently developed, which captures the essential characteristics of Stigmergy. The basic architecture of Stigmergy as well as its constituents are presented and then discussed. The syntax and operational semantics of Stigmergy configurations are given. We illustrate the methodology of analyzing emergence in CAS by using our model.

In this letter, we propose a power allocation scheme to optimize the ergodic secrecy rate of multiple-input single-output (MISO) fading wiretap channels with a probabilistic constraint, using the statistical channel state information (CSI) of the eavesdropper (CSI-E). The analytical expressions of the false secrecy probability are derived and used as constraints in the rate maximization problem. Moreover, we obtain a suboptimal solution by formulating the power allocation problem as a Rayleigh quotient problem.

In MANET (Mobile Ad-hoc NETworks), there are two kinds of routing methods: proactive and reactive. Each has different characteristics and advantages. The latter generally employs the flooding technique to finding a routing path to the destination. However, flooding has big overheads caused by broadcasting RREQ packets to the entire network. Therefore, reducing this overhead is really needed to enable several network efficiencies. Previous studies introduced many approaches which are mainly concerned with the restriction of flooding. However, they usually configure the detailed routing path in the forward flooding procedure and ignore the factors causing the flooding overheads. In this paper, we propose the FSRS (First Search and Reverse Setting) routing protocol which is a new approach in flooding techniques and a new paradigm shift. FSRS is based on cluster topology and is composed of two main mechanisms: inter-cluster and intra-cluster flooding. Inter-cluster routing floods RREQ packets between cluster units and sets a cluster path. When the destination node receives the RREQ packet, it floods RREP packets to an intra-cluster destination which is a gateway to relay the RREP packet to a previous cluster. This is called intra-cluster routing. So to speak, a specific routing path configuration progresses in the RREP process through the reverse cluster path. Consequently, FSRS is a new kind of hybrid protocol well adapted to wireless ad-hoc networks. This suggests a basic wireless networking architecture to make a dynamic cluster topology in future work. In the simulation using NS-2, we compare it to several other protocols and verify that FSRS is a powerful protocol. In the result of the simulation, FSRS conserves energy by a maximum of 12% compared to HCR.

The many-to-one communication nature of wireless sensor networks (WSNs) leads to an unbalanced traffic distribution, and, accordingly, sensor nodes closer to the base station have to transmit more packets than those at the periphery of the network. This problem causes the nodes closer to the base station to deplete their energy prematurely, forming a hole surrounding the base station. This phenomenon is called the energy hole problem, and it severely reduces the network lifetime. In this paper, we present a cooperative power-aware routing algorithm for uniformly deployed WSNs. The proposed algorithm is based on the idea of replacing the constant transmission range of relaying sensor nodes with an adjusted transmission range, in such a way that each individual node consumes its energy smoothly. We formulate the dynamic transmission range adjustment optimization (DTA) problem as a 0-1 Multiple Choice Knapsack Problem (0-1 MCKP) and present a dynamic programming method to solve the optimization problem. Simulations confirm that the proposed method helps to balance the energy consumption of sensor nodes, avoiding the energy hole problem and extending the network lifetime.

To reduce power consumption of wireless terminals, we have developed ultra-low leakage regulator circuits that control the intermittent terminal operation with very small activity ratio. The regulator circuits supply about 100 mA in the active mode and cut the leakage current to a nanoampere level in the standby mode. The operation of the ultralow-leakage regulator circuits with CMOS/SOI and bulk technologies is described. The leakage-current reduction mechanism in a proposed power switch with bulk technology is explained. Measurement shows that the power switch using reversely biased bulk transistors has a leakage current that is almost as small as that of conventional CMOS/SOI transistor switches.

In cellular networks, maximizing the energy efficiency (EE) while satisfying certain QoS requirements is challenging. In this article, we utilize effective capacity (EC) theory as an effective means of meeting these challenges. Based on EC and taking a realistic base station (BS) power consumption model into account, we develop a novel energy efficiency (EE) metric: effective energy efficiency (EEE), to represent the delivered service bit per energy consumption at the upper layer with QoS constraints. Maximizing the EEE problem with EC constraints is addressed and then an optimal power control scheme is proposed to solve it. After that, the EEE and EC tradeoff is discussed and the effects of diverse QoS parameters on EEE are investigated through simulations, which provides insights into the quality of service (QoS) provision, and helps the system power consumption optimization.

This paper discusses recent developments for pattern recognition focusing on boosting approach in machine learning. The statistical properties such as Bayes risk consistency for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functions associates with the boosting learning algorithms for the loss minimization, which includes AdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROC curve. We expand boosting to unsupervised learning, typically density estimation employing U-loss function. Finally, a future perspective in machine learning is discussed.

With Wireless Sensor Networks (WSNs) involving in diverse applications, the realistic analysis of energy consumption of a sensor node in an error-prone network environment is emerging as an elementary research issue. In this paper, we introduce a Distributed Communication Model (DCM) that can accurately determine the energy consumption through data communication from source to destination in error-prone network environments. The energy consumption is affected with the quality of link, which is characterized by symmetry, directivity, instability, and irregularity of the communication range of a sensor node. Due to weak communication links, significant packet loss occurs that affects the overall energy consumption. While other models unable to determine energy consumption due to lossy links in error-prone and unstable network environments, DCM can accurately estimate the energy consumption in such situations. We also perform comprehensive analysis of overheads caused by data propagation through multi-hop distributed networks. We validate DCM through both simulations and experiments using MICAz motes. Similarity of the results from energy consumption analysis with both simulations and experimentations shows that DCM is realistic, compared to other models in terms of accuracy and diversity of the environments.

Energy minimization has become one of the primary goals in the embedded real-time domains. Consequently, scratch-pad memory has been employed as partial or entire replacement for cache memory due to its better energy efficiency. However, most previous approaches were not applicable to a preemptive multi-task environment. We propose three methods of partitioning and allocation of scratch-pad memory for fixed-priority-based preemptive multi-task systems. The three methods, i.e., spatial, temporal, and hybrid methods, achieve energy reduction in the instruction memory subsystems. With the spatial method, each task occupies its exclusive space in scratch-pad memory. With the temporal method, the running task uses entire scratch-pad space. The content of scratch-pad memory is swapped out as a task executes or gets preempted. The hybrid method is based on the spatial one but a higher priority task can temporarily use the space of lower priority task. The amount of space is prioritized for higher priority tasks. We formulate each method as an integer programming problem that simultaneously determines (1) partitioning of scratch-pad memory space for the tasks, and (2) allocation of program code to scratch-pad memory space for each task. Our methods not only support the real-time task scheduling but also consider aggressively the periods and priorities of tasks for the energy minimization. Additionally, we implement an RTOS-hardware cooperative support mechanism for runtime code allocation to the scratch-pad memory space. We have made the experiments with the fully functional real-time operating system. The experimental results have demonstrated the effectiveness of our techniques. Up to 73% energy reduction compared to a conventional method was achieved.