• International Journal of Internet, Broadcasting and Communication
• /
• v.10 no.2
• /
• pp.68-73
• /
• 2018

In recent years, the utilization of the WSN have been rapid. Energy consumption of these networks must be as low as possible. LEACH algorithm is one of the clustering technique. We modify the traditional LEACH algorithm in such way that it will be capable to self-organize large number of nodes and for saving communication resources such as processing time and initiation time. The efficiency of the network highly depends on how the algorithm divides cluster area and selects cluster head. The proposed algorithm can be evaluated through the extensive simulation the result we obtained shows that the life time of a network is increased when energy load is distributed equally among the sensor.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5382-5403
• /
• 2018

In this paper, for a spectrum sensing purpose, we heuristically established a test statistic (TS) from a sample covariance matrix (SCM) for multiple antennas based cognitive radio. The TS is formulated as a scaled-energy which is calculated as a sum of scaled diagonal entries of a SCM; each of the diagonal entries of a SCM scaled by corresponding row's Euclidean norm. On the top of that, by combining theoretical results together with simulation observations, we have approximated a decision threshold of the TS which does not need prior knowledge of noise power and primary user signal. Furthermore, simulation results - which are obtained in a fading environment and in a spatially correlating channel model - show that the proposed method stands effect of noise power mismatch (non-uniform noise power) and has significant performance improvement compared with state-of-the-art test statistics.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.3
• /
• pp.1321-1332
• /
• 2016

Wireless sensor networks (WSN) are self-organized networks that typically consist of thousands of low-cost, low-powered sensor nodes. The reliability and availability of WSNs can be affected by faults, including those from radio interference, battery exhaustion, hardware and software failures, communication link errors, malicious attacks, and so on. Thus, we propose a novel multi-agent fault tolerant system for wireless sensor networks. Since a major requirement of WSNs is to reduce energy consumption, we use multi-agent and mobile agent configurations to manage WSNs that provide energy-efficient services. Mobile agent architecture have inherent advantages in that they provide energy awareness, scalability, reliability, and extensibility. Our multi-agent system consists of a resource manager, a fault tolerance manager and a load balancing manager, and we also propose fault-tolerant protocols that use multi-agent and mobile agent setups.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4781-4803
• /
• 2017

Environmental monitoring systems using Wireless Sensor Networks (WSNs) face the challenge of high power consumption, due to the high levels of multi-hop data communication involved. In order to overcome the issue of fast energy depletion, a proof-of-concept implementation proves that adopting a clustering algorithm in environmental monitoring applications will significantly reduce the total power consumption for environment sensor nodes. In this paper, an energy-efficient WSN-based environmental monitoring system is proposed and implemented, using eight sensor nodes deployed over an area of $1km^2$, which took place in the city of Tabuk in Saudi Arabia. The effectiveness of the proposed environmental monitoring system has been demonstrated through adopting a number of real experimental studies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.413-435
• /
• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.4
• /
• pp.19-24
• /
• 2017

In this paper, we propose an automatic segmentation method of left and right heart in computed tomography angiography (CTA) using separating energy function. First, we smooth the images by applying anisotropic diffusion filter to remove noise. Then, the volume of interest (VOI) is detected by using k-means clustering. Finally, we extract the left and right heart with separating energy function which we proposed to split the heart. We tested our method in ten CT images and they were obtained from a different patient. For the evaluation of the computational performance of the proposed method, we measured the total processing time. The average of total processing time, from first step to third step, was $14.39{\pm}1.17s$. We expect for our method to be used in cardiac diagnosis for cardiologist.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.5
• /
• pp.354-365
• /
• 2015

This work presents an Internet of Things (IoT) system for home energy management based on a custom-designed Impulse Radio Ultra-Wideband (IR-UWB) transceiver that targets a generic and multi-standard control system. This control system enables the interoperability of heterogeneous devices: it integrates various sensor nodes based on ZigBee, EnOcean and UWB in the same middleware by utilizing an ad-hoc layer as an interface between the hardware and software. The paper presents as a first the design of the IR-UWB transceiver for a portable sensor node integrated with the middleware layer, and also describes the receiver connected to the control system. The custom-designed low-power transmitter on the sensor node is fabricated with 130 nm CMOS technology. It generates a signal with a 1.1 ns pulse width while consuming $39{\mu}W$ at 1 Mbps. The UWB sensor node with a temperature measurement capability consumes 5.31 mW, which is lower than the power level of state-of-the-art solutions for smart-home applications. The UWB hardware and software layers necessary to interface with the control system are verified in over-the-air measurements in an actual office environment. With the implementation of the presented sensor node and its integration in the energy management system, we demonstrate achievement of the broad flexibility demanded for IoT.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5203-5217
• /
• 2018

In order to increase the capacity and improve the spectrum efficiency of wireless communication systems, this paper proposes a rate-based two-sided many-to-one matching game algorithm for energy-harvesting small cells with non-orthogonal multiple access (NOMA) in heterogeneous cellular networks (HCN). First, we use a heuristic clustering based channel allocation algorithm to assign channels to small cells and manage the interference. Then, aiming at addressing the user access problem, this issue is modeled as a many-to-one matching game with the rate as its utility. Finally, considering externality in the matching game, we propose an algorithm that involves swap-matchings to find the optimal matching and to prove its stability. Simulation results show that this algorithm outperforms the comparing algorithm in efficiency and rate, in addition to improving the spectrum efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.2821-2839
• /
• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.5
• /
• pp.461-474
• /
• 2009

The cognitive radio (CR) technique is a useful tool for improving spectrum utilization by detecting and using the vacant spectrum bands in which cooperative spectrum sensing is a key element, while avoiding interfering with the primary user. In this paper, we propose a novel cluster-based cooperative spectrum sensing scheme in cognitive radio with two solutions for the purpose of improving in sensing performance. First, for the cluster header, we use the double adaptive energy thresholds and a multi-bit quantization with different quantization interval for improving the cluster performance. Second, in the common receiver, the weighed HALF-voting rule will be applied to achieve a better combination of all cluster decisions into a global decision.