• ETRI Journal
• /
• v.44 no.3
• /
• pp.491-503
• /
• 2022

Metal-oxide-semiconductor field-effect transistors (MOSFETs) are continuously scaling down in the nanoscale region to improve the functionality of integrated circuits. The scaling down of MOSFET devices causes short-channel effects in the nanoscale region. In nanoscale region, leakage current components are increasing, resulting in substantial power dissipation. Very large-scale integration designers are constantly exploring different effective methods of mitigating the power dissipation. In this study, a transistor-level input-controlled stacking (ICS) approach is proposed for minimizing significant power dissipation. A low-power ICS approach is extensively discussed to verify its importance in low-power applications. Circuit reliability is monitored for process and voltage and temperature variations. The ICS approach is designed and simulated using Cadence's tools and compared with existing low-power and high-speed techniques at a 22-nm technology node. The ICS approach decreases power dissipation by 84.95% at a cost of 5.89 times increase in propagation delay, and improves energy dissipation reliability by 82.54% compared with conventional circuit for a ring oscillator comprising 5-inverters.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.32-40
• /
• 2016

IEEE 802.15.4 is the one of the protocols for radio communication in a personal area network. Because of low cost and low power communication for IoT communication, it requires the highest optimization level in the implementation. Recently, the studies of location aware algorithm based on IEEE802.15.4 standard has been achieved. Location estimation is performed basically in equal consideration of reference node information and blind node information. However, an error is not calculated in this algorithm despite the fact that the coordinates of the estimated location of the blind node include an error. In this paper, we enhanced a conventual maximum likelihood estimation using weighted coefficient and implement the hardwired location aware engine for small code size and low power consumption. On the field test using test-beds, the suggested hardware based location awareness method results better accuracy by 10 percents and reduces both calculation and memory access by 30 percents, which improves the systems power consumption.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.5
• /
• pp.1063-1070
• /
• 2015

In this paper, we propose a distributed user scheduling with transmit power control based on the amount of generating interference to other base stations (BSs) in multi-cell multi-input multi-output (MIMO) networks. Assuming that the time-division duplexing (TDD) system is used, the interference channel from users to other cell BSs is obtained at each user. In the proposed scheduling, each user first generates a transmit beamforming vector by using singular value decompositon (SVD) over MIMO channels and reduces the transmit power if its generating interference to other BSs is larger than a predetermined threshold. Each BS selects the user with the largest effective channel gains among users, which reflects the adjusted power of users. Simulation results show that the proposed technique significantly outperforms the existing user scheduling algorithms.

• Proceedings of the Korean Information Science Society Conference
• /
• 2008.06a
• /
• pp.307-308
• /
• 2008

• Journal of information and communication convergence engineering
• /
• v.5 no.2
• /
• pp.104-108
• /
• 2007

Ad hoc networks are a type of mobile network that function without any fixed infrastructure. One of the weaknesses of ad hoc network is that a route used between a source and a destination is to break during communication. To solve this problem, one approach consists of selecting routes whose nodes have the most stable link cost. This paper proposes a method for improving the low power distributed MAC. This method is based on the context awareness of the each nodes energy in clustering. We propose to select a new scheme to optimize energy conserving between the clustering nodes in MANET. And this architecture scheme would use context-aware considering the energy related information such as energy, RF strength, relative distances between each node in mobile ad hoc networks. The proposed networks scheme could get better improve the awareness for data to achieve and performance on their clustering establishment and messages transmission. Also, by using the context aware computing, according to the condition and the rules defined, the sensor nodes could adjust their behaviors correspondingly to improve the network routing.

• Journal of Internet Computing and Services
• /
• v.6 no.1
• /
• pp.65-72
• /
• 2005

Flash memories are one of best media to support portable computers in mobile computing environment. The features of non-volatility, low power consumption. and fast access time for read operations are sufficient grounds to support flash memory as major database storage components of portable computers. However. we need to Improve traditional transaction management scheme due to the relatively slow characteristics of flash operation as compared to RAM memory. In order to achieve this goal. we devise a new scheme called flash-aware transaction management (FATM). FATM improves transaction performance by exploiting SRAM and W-Cache, We also propose a simulation model to show the performance of FATM. Based on the results of the performance evaluation, we conclude that FATM scheme outperforms the traditional scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.527-530
• /
• 2010

WSN is composed of a lot of small sensors with the limited hardware resources. In WSN, at the initial stage, sensor nodes are randomly deployed over the region of interest, and self-configure the clustered networks by grouping a bunch of sensor nodes and selecting a cluster header among them. Specially, in WSN environment, in which the administrator's intervention is restricted, the self-configuration capability is essential to establish a power-conservative WSN which provides broad sensing coverage and communication coverage. In this paper, we propose a communication coverage-aware cluster head election algorithm for Herearchical WSNs which consists of communication coverage-aware of the Base station is the cluster head node is elected and a clustering.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.7B
• /
• pp.510-517
• /
• 2008

In wireless sensor networks, a number of nodes deployed in dense manner should be self-configured to establish a topology that provides communication and sensing coverage under stringent energy constraints. To establish an efficient topology, we propose the TRaffic-Aware Topology control (TRAT) algorithm that reduces energy dissipation by considering total amount of data flows in the network. Our algorithm controls the number of active nodes with traffic information and adjusts nodal transmission power by estimating amount of data flows. According to the result, the proposed algorithm shows about 30% better performance than the other methods in terms of energy efficiency.

• Korea Information Processing Society Review
• /
• v.11 no.6
• /
• pp.56-75
• /
• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.6
• /
• pp.185-196
• /
• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.