Minimum interference channel assignment for multicast in multi-channel multi-radio wireless mesh networks

Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow hosts to access the Internet via the gateway nodes. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications such as distributed games, distance education, and video conferencing. In this work, we address the channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, when two nearby nodes transmit on the same channel, they will interfere with each other and cause throughput decrease. Thus, an important goal for multicast channel assignment is to reduce the interference among the tree nodes. We have developed a Minimum Interference Channel Assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughout and lower end-to-end packet delay compared with an existing channel assignment algorithm named MCM. In addition, MICA achieves much lower throughput variation among the destination nodes than MCM

Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy

Connectivity among straits of the northwest Pacific marginal seas

The connectivity among straits of the northwest Pacific marginal seas is investigated with a primitive-equation ocean circulation model simulated for 10 years from 1994 to 2003. Over the simulation interval the temporal and spatial means and variations of the model sea surface temperature are comparable to those of the satellite sea surface temperature. The model transport through the straits shows good agreement with the available observations and a high seasonality in the Taiwan Strait, the Korea Strait, and the Soya Strait but relatively low seasonality in the Tsugaru Strait. The Kuroshio and Taiwan Warm Current (TWC) are two sources of water flowing through the Korea Strait. The volume transport in the Korea Strait is dominated by the Kuroshio in winter (83%) and by the TWC in summer (66%). Relative to the transport through the Korea Strait, the transport percentages of the Tsugaru Strait connecting to the northwest Pacific Ocean are 79% in winter and 65% in summer. The seasonality of the Korea Strait transport is positively correlated with the cross-strait wind stress. The drifter experiments show that it takes about 4 months for most of the drifters deployed in the Taiwan Strait to enter the Korea Strait and more than 2 months to travel from the Korea Strait to the Tsugaru and Soya straits

On-board Processing of Acceleration Data for Real-time Activity Classification

The assessment of a person’s ability to consistently perform the fundamental activities of daily living is essential in monitoring the patient’s progress and measuring the success of treatment. Therefore, many researchers have been interested in this issue and have proposed various monitoring systems based on accelerometer sensors. However, few systems focus on energy consumption of sensor devices. In this paper, we introduce an energy-efficient physical activity monitoring system using a wearable wireless sensor. The proposed system is capable of monitoring most daily activities of the human body: standing, sitting, walking, lying, running, and so on. To reduce energy consumption and prolong the lifetime of the system, we have focused on minimizing the total energy spent for wireless data exchange by manipulating real-time acceleration data on the sensor platform. Furthermore, one of our key contributions is that all functionalities including data processing, activity classification, wireless communication, and storing classified activities were achieved in a single sensor node without compromising the accuracy of activity classification. Our experimental results show that the accuracy of our classification system is over 95%

Minimum interference channel assignment for multicast in multi-channel multi-radio wireless mesh networks

Wireless mesh networks (WMNs) have emerged as a key technology for next-generation wireless networking. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow hosts to access the Internet via the gateway nodes. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications such as distributed games, distance education, and video conferencing. In this work, we address the channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, when two nearby nodes transmit on the same channel, they will interfere with each other and cause throughput decrease. Thus, an important goal for multicast channel assignment is to reduce the interference among the tree nodes. We have developed a Minimum Interference Channel Assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughout and lower end-to-end packet delay compared with an existing channel assignment algorithm named MCM. In addition, MICA achieves much lower throughput variation among the destination nodes than MCM.</p

Neighbor Discovery Optimization for Big Data Analysis in Low-Power, Low-Cost Communication Networks

Big data analysis generally consists of the gathering and processing of raw data and producing meaningful information from this data. These days, large collections of sensors, smart phones, and electronic devices are all connected in the network. One of the primary features of these devices is low-power consumption and low cost. Power consumption is one of the important research concerns in low-power, low-cost communication networks such as sensor networks. A primary feature of sensor networks is a distributed and autonomous system. Therefore, all network devices in this type of network maintain the network connectivity by themselves using limited energy resources. When they are deployed in the area of interest, the first step for neighbor discovery involves the identification of neighboring nodes for connection and communication. Most wireless sensors utilize a power-saving mechanism by powering on the system if it is off, and vice versa. The neighbor discovery process becomes a power-consuming task if two neighboring nodes do not know when their partner wakes up and sleeps. In this paper, we consider the optimization of the neighbor discovery to reduce the power consumption in wireless sensor networks and propose an energy-efficient neighbor discovery scheme by adapting symmetric block designs, combining block designs, and utilizing the concept of activating nodes based on the multiples of a specific number. The performance evaluation demonstrates that the proposed neighbor discovery algorithm outperforms other competitive approaches by analyzing the wasted awakening slots numerically

Asymmetric Block Design-Based Neighbor Discovery Protocol in Sensor Networks

Neighbor discovery is one of the emerging research areas in a wireless sensor network. After sensors are distributed, neighbor discovery is the first process to set up a communication channel with neighboring sensors. This paper proposes a new block design–based asymmetric neighbor discovery protocol for sensor networks. We borrow the concept of combinatorial block designs for our block combination scheme for neighbor discovery. First, we introduce an asymmetric neighbor discovery problem and define a target research question. Second, we propose a new asymmetric block design–based neighbor discovery protocol and explain how it works. Third, we analyze the worst-case neighbor discovery latency numerically between our protocol and some well-known protocols in the literature, and compare and evaluate the performance between the proposed protocol and others. Our protocol reveals that the worst-case latency is much lower than that of Disco and U-Connect. Finally, we conclude that the minimum number of slots per a neighbor schedule shows the lowest discovery time in terms of discovery latency and energy consumption

Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network

Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM

Organizational capacity, community asset mobilization, and performance of Korean social enterprises

This paper develops an integrative analysis framework for assessing the performance of social enterprises in Korea in the context of combined organizational and environmental factors that provide positive feedback. We surveyed 120 social enterprises in Korea and analyzed the relationships between organizational capacity, community asset mobilization, and performance of those social enterprises. The analysis showed that organizational capacity and community asset mobilization influenced performance in different ways. In addition, management capacity emerged as the most important mediating variable of the organizational capacities, and the mobilization of the community assets of social enterprises contributed to improving their social performance. Finally, strategic leadership contributed to mobilizing the community assets of social enterprises. However, community asset mobilization had negative effects on economic performance. Important lessons for policy makers and future research directions are drawn from these results