One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

No abstract

No abstract

We have performed experiments to characterize permanent laser-induced darkening in CdS..,Se1-.2: semiconductor-doped glasses with picosecond pulses as a function of fluence, repetition rate, and pulse width. We find that the darkening occurs by means of a nonlinear process that exhibits an anomalous dependence on pulse width. Transmission spectra show that the induced darkening is uniform over the spectral range from the absorption edge out to 820 J.Lm. Darkening in a number of different glasses is compared. On the basis of our results we propose a mechanism that involves photoassisted trapping of electrons from the semiconductor microcrystallites into states within the glass host material.

For efficient running of wireless sensor network applications, energy conservation of the sensors becomes a prime paradigm for prolonging lifetime of the network. Taking this aspect into consideration, a cluster head weight selection method called Cluster Chain Weight Metrics approach (CCWM) has been discussed that takes service parameters for enhancing performance of the overall network. In a clustering based approach one of the main concerns is selection of appropriate cluster heads in the network and the formation of balanced clusters. Cluster heads are selected first in a network based on weight metric and then cluster formation takes place. This approach not only aims to conserve energy of sensors but also balances load. A local clustering mechanism is adopted within the cluster to reduce computation and communication cost. Also, a new technique for data transmission is explored. The results of the proposed approach are compared through simulation with LEACH, WCA and IWCA. The proposed approach shows an improvement on an average over rounds by 51% over LEACH, 27% from WCA and 18.8% from IWCA in terms of lifetime and energy consumption. Ó 2014 Production and hosting by Elsevier B.V. on behalf

Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

No abstract

No abstract