• ETRI Journal
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• v.33 no.6
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• pp.857-863
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• 2011

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.450-464
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• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2509-2528
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• 2012

Time division multiple access (TDMA) is a widely used media access control (MAC) technique that can provide collision-free and reliable communications, save energy and bound the delay of packets. In TDMA, energy saving is usually achieved by switching the nodes' radio off when such nodes are not engaged. However, the frequent switching of the radio's state not only wastes energy, but also increases end-to-end delay. To achieve high energy efficiency and low delay, as well as to further minimize the number of time slots, a multi-objective TDMA scheduling problem for industrial wireless mesh networks is presented. A hybrid algorithm that combines genetic algorithm (GA) and simulated annealing (SA) algorithm is then proposed to solve the TDMA scheduling problem effectively. A number of critical techniques are also adopted to reduce energy consumption and to shorten end-to-end delay further. Simulation results with different kinds of networks demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of addressing the problems of energy consumption and end-to-end delay, thus satisfying the demands of industrial wireless mesh networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1074-1086
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• 2015

We Relay-assisted cellular network architecture has been developed to cover cell-edge users and to improve capacity. However, the deployment of relay stations (RSs) in cellular networks may increase the total energy consumption. Though energy efficiency has become a major concern in cellular networks, little work has studied the energy efficiency of relay-assisted cellular networks by sleep scheduling. In this paper, a distributed base stations (BSs) sleep scheduling scheme in relay-assisted cellular networks is proposed. The goal is to maximize the energy efficiency under the spectral efficiency constraint. Firstly, whether the BSs should be sleeping or active is determined by the traffic profile. Then, the transmission powers of the active BSs are optimized within the game-theoretic framework, by using an interior-point method, so as to maximize the energy efficiency. Simulation results demonstrate that the effectiveness of the proposed scheme is superior to that turning on all the BSs without sleep scheduling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.884-891
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• 2006

A wireless sensor node is typically battery operated and energy constrained. Therefore it is critical to design efficient power management technique and scheduling technique. In this paper, we propose an OS-level power management technique for energy saving of wireless sensor node, it is called EA-SENTAS (Energy-Aware Sensor Node TAsk Scheduling). It can decrease the energy consumption of a wireless sensor node to use task scheduling technique that shut down components or use low power mode of each component when not needed. Simulation results show that EA-SENTAS saves energy up to 56 percent to compare with conventional duty cycle.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.78-86
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• 2017

In this paper, we propose an energy-efficient scheduling scheme for real-time periodic tasks on a heterogeneous Grid computing system. The Grid system consists of heterogeneous processors providing the DVFS mechanism with a finite set of discrete clock frequencies. In order to save energy consumption, the proposed scheduling scheme assigns each real-time task to a processor with the least energy increment. Also the scheme activates a part of all available processors with unused processors powered off. Evaluation shows that the proposed scheme saves up to 70% energy consumption of the previous method.

• Journal of the Korea Society of Computer and Information
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• v.19 no.8
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• pp.55-62
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• 2014

A game theoretic approach is applied for studying the energy efficient rate scheduling. The individual utility function is defined first. Then, a non cooperative rate game is modeled in which each user decides the transmission rate to maximize its own utility. The utility function considered here is the consumed energy for the individual user's data transmissions. In particular, using the fact that the utility function is convex, we prove the existence of Nash Equilibrium in the energy efficient rate scheduling problem at hand. Accordingly, a non cooperative scheduling algorithm is provided. For better energy efficiency, the sum of the individual user's utility function is optimized Finally, the convergence analysis and numerical results to show the energy efficiency of the proposed algorithms are provided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5465-5480
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• 2018

Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.327-333
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• 2022

Wireless Sensor Networks (WSNs) plays an important role in our everyday life. WSN is distributed in all the places. Nowadays WSN devices are developing our world as smart and easy to access and user-friendly. The sensor is connected to all the resources based on the uses of devices and the environment [1]. In WSN, Quality of Service is based on time synchronization and scheduling. Scheduling is important in WSN. The schedule is based on time synchronization. Min-Max data size scheduling is used in this proposed work. It is used to reduce the Delay & Energy. In this proposed work, Two-hop neighboring node is used to reduce energy consumption. Data Scheduling is used to identify the shortest path and transmit the data based on weightage. The data size is identified by three size of measurement Min, Max and Medium. The data transmission is based on time, energy, delivery, etc., the data are sent through the first level shortest path, then the data size medium, the second level shortest path is used to send the data, then the data size is small, it should be sent through the third level shortest path.