[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining Practical Machine Learning Tools and Techniques

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.

Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing

Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

Real-time surveillance systems with green wireless sensor networks (WSNs) are vital for maintaining high energy efficiency in many situations. This paper considers a scenario utilizing green WSNs to monitor the situation of Internet of Things (IoT), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ICT). More specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green WSNs. We first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. We then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. Hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. Simulation results indicate that the proposed Algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.

Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

The deployment of unmanned aerial vehicle (UAV) for surveillance and monitoring gives rise to the confidential information leakage challenge in both civilian and military environments. The security and covert communication problems for a pair of terrestrial nodes against UAV surveillance are considered in this paper. To overcome the information leakage and increase the transmission reliability, a multi-hop relaying strategy is deployed. We aim to optimize the throughput by carefully designing the parameters of the multi-hop network, including the coding rates, transmit power, and required number of hops. In the secure transmission scenario, the expressions of the connection probability and secrecy outage probability of an end-to-end path are derived and the closed-form expressions of the optimal transmit power, transmission and secrecy rates under a fixed number of hops are obtained. In the covert communication problem, under the constraints of the detection error rate and aggregate power, the sub-problem of transmit power allocation is a convex problem and can be solved numerically. Simulation shows the impact of network settings on the transmission performance. The trade-off between secrecy/covertness and efficiency of the multi-hop transmission is discussed which leads to the existence of the optimal number of hops.

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

For wireless networks in the Internet of Things (IoT), cognitive radio (CR) is a promising way to obtain the available spectrum for objects Wide-band spectrum sensing plays an important role in building such CR networks of IoT In this paper, we propose a novel dynamic compressive wide-band spectrum sensing method based on channel energy reconstruction After a bank of wide-band random filters is employed to measure the channel energy, rather than to recover all the channel energy in the whole spectrum, only the channel energy with a changing occupancy status in consecutive time slots is recovered Furthermore, it is unnecessary to use reconstruction algorithm unless there are two or more channels changing their occupancy status Compared to the existing methods, our proposed schemes bear significant improvements in the probability of detection and reduction of probability of false alarms Simulation results also show its fast speed and robustness to noise

Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things

Due to overshadow and channel fading, many mobile users are unable to receive the signal transmitted from satellite directly. Hence, some relay stations should be set to help this type of users to receive signals reliably. In this paper, we present a novel cognitive hybrid satellite-terrestrial model, where two cognitive relays forward their received signal for a mobile user successively. Furthermore, we address its achievable rate maximization. We first convert the co-channel interference threshold into transmit power constraints, and then formulate the maximization of the achievable rate as an optimization problem. Based on Karush–Kuhn–Tucker conditions, the optimization problem is decomposed into four cases, each of which is solved in closed form. Simulation study with different system settings is presented, and the efficiency of the proposed power allocation scheme is shown.

Achievable Rate Maximization for Cognitive Hybrid Satellite-Terrestrial Networks With AF-Relays

Dynamically adjusting the time duration of a node to stay active in one data collection period, i.e., duty cycle, is an efficient strategy to save energy and prolong the lifetime of network. In this paper, we propose a novel Adaptation Duty Cycle Control (ADCC) scheme based on feedback signals for wireless sensor networks (WSNs) which can reduce to-sink data transmission delay while lifetime is also improved. In ADCC, every node adaptively adjusts its duty cycle by comparing its own energy consumption with the largest energy consumption of the entire packet delivery flow, which is stored in a feedback ACK packet generated by sink node. Since in WSNs, a huge number of sensor nodes in the area far from the sink node have much remaining energy when network dies, even up to 90 %, these nodes have much larger duty cycles in ADCC compared with previous schemes, therefore the data transmission delay can be reduced to a great extent. Additionally, ADCC provides a largest-energy notification mechanism in order to determine the appropriate duty cycle of nodes in each data collection flow according to the application-dependent requirements. Comparing with the Wake on Idle, Dual-QCon and the Same Duty Cycle (SDC) schemes, ADCC can reduce the delay by more than 59.5 % under the same network lifetime, or increase the lifetime by 32.8 %–63.4 % under the same delay requirements, while also increase energy efficiency as much as 43.1 %.

Zhetao Li

Papers

Minimizing Convergecast Time and Energy Consumption in Green Internet of Things

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

Dynamic Compressive Wide-Band Spectrum Sensing Based on Channel Energy Reconstruction in Cognitive Internet of Things

Achievable Rate Maximization for Cognitive Hybrid Satellite-Terrestrial Networks With AF-Relays

Distributed duty cycle control for delay improvement in wireless sensor networks