Earticle

In this letter, we propose space?time block coded non-orthogonal multiple access (STBC-NOMA) for underwater acoustic sensor networks (UASNs) to improve reliability by exploiting transmit diversity and spectral efficiency. We derive mathematical expressions for ergodic rate, outage probability and energy efficiency. Results show that STBC-NOMA can combine the advantages of STBCs and NOMA, which significantly enhance the performance of UASNs, without the need for prior CSI status at the transmitter. Finally, we jointly optimize the signal frequency and power allocation coefficient for the STBC-NOMA scheme with respect to the transmission distance using pattern search algorithm, to further enhance the ergodic rate.

Various design schemes for network using wireless sensor nodes have been widely studied on the several application areas ranging from the real world information collection to environmental monitor. Currently, the schemes are focused on the design of sensor network for low power consumption, power-aware routing protocol, micro miniature operating system and sensor network middleware. The indoor localization system that identifies the location of the distributed nodes in a wireless sensor network requires features dealing with mobility, plurality and other environmental constraints of a sensor node. In this paper, we present an efficient location system to cope with mobility of multiple mobile nodes by designing a location handler that processes location information selectively depending on the nodes' density in a specific region. In order to resolve plurality of multiple mobile nodes, a routing method for the location system is also proposed to avoid the occurrence of overlapped location data.

루프 검지기, 영상 검지기 등의 기존 교통 검지기들은 설치와 유지보수에 드는 비용이 크고, 밤과 낮에 따라 상이한 검지 알고리즘이 필요하거나 날씨에 따라 검지율의 편차가 크다는 단점을 가지고 있다. 반면에 밀리미터파 레이더는 악천후에 의한 영향을 받지 않고, 주야간에 관계없이 일정한 검지 성능을 얻을 수 있다. 덧붙여 설치와 유지보수를 위하여 교통 통제의 필요가 없고, 다수의 차량을 동시에 검지 가능하다. 본 연구는 이러한 장점을 가진 레이더 센서를 활용한 다중 물체 검지 알고리즘을 기존의 단일 물체 검지 알고리즘을 응용하여 구현하였으며 이에 대한 평가를 수행하여 의미 있는 결과를 얻었다.

Conventional traffic detection sensors such as loop detectors and image sensors are expensive to install and maintain and require different detection algorithms depending on the night and day and have a disadvantage that the detection rate varies widely depending on the weather. On the other hand, the millimeter-wave radar is not affected by bad weather and can obtain constant detection performance regardless of day or night. In addition, there is no need for blocking trafficl for installation and maintenance, and multiple vehicles can be detected at the same time. In this study, a multi–target detection algorithm for a radar sensor with this advantage was devised / implemented by applying a conventional single target detection algorithm. We performed the evaluation and the meaningful results were obtained.

The recent ubiquitous/pervasive technologies allow general household appliances to be connected within the network at home which is named home network system (HNS, for short). The great advantage of HNS is that it provides more value-added and powerful services by integrating multiple appliances and various sensors. Especially, sensor applications in HNS become much more important technology to build a high-level HNS service. In our earlier study, we have proposed the sensor service framework (SSF, for short) in the home network system for developing context-aware service, which wraps various sensor devices by web services to achieve easy development of context-aware service. In the SSF, a context was defined by a condition over a single sensor, or multiple sensors that derived by logical or arithmetic operations. However, the contexts were limited to the ones that can be defined by current values of the sensors, and can not describe the timing constraint relation in context on using multiple sensor web servers such as “after opening the door for 2 seconds, passed the hall” or “setting on the sofa”, and hindered us from creating high-level timing constraints context. In this paper, we propose a method for deriving the timing constraints context bases on the extended study of SSF. We first divide timing constraint in the context into two types: the sequential timing constraint and the continuous timing constraint. By using two types of timing constraint, the high-level context can be defined as conditions by using multiple sensors. After this, we also present a timer service to implement the timing constraints context within the SSF. We finally demonstrate how the high-level contexts with the timing constraints are registered and detected in a real home network system.

With the growth of location-based services (LBS), location-based data is a crucial role for many sensor-network applications. Although the conversion of data formats in heterogeneous sensor networks has been studied extensively, the integration of spatial data models is not well understood, which is important to understand the semantics of spatial data in sensor networks. To determine the spatial data integration in model level, we have investigated the cognitive model of spatial information, the conceptual model of space phenomena, and the logic of spatial data management mode, leading to the integration of various spatial data sets from the view of model integration. Furthermore, we provide a method for the effective storage and management of massive and multi-source heterogeneous spatial data. Ultimately, the experimental result shows that the method offers a better way of model integration that cannot be achieved with existing schemes.

The related theories and technologies on sensor networks are a hot research field. Due to great uncertainties existed in its data processing, multiple target tracking (MTT) has become a difficult problem in distributed sensor networks (DNWs). Hence, this paper mainly studies the uncertainty problems on MTT in the framework of DNW systems by using the fuzzy theory. According to its model structure, a DNW system can be divided into three layers according to its model structure: the data capture layer, data processing layer and data analysis layer. Based on data processing in different layers, the MTT process can be classified as three phrases: data association/fusion of measurement-measurement/sensor track, sensor track-local track, and local track-global track. It presents a complete procedure of data processing on MTT in a real tracking system. Then, the uncertainty problems on MTT are classified. After analyzing data characteristics, one can utilize the fuzzy information processing technology to solve these problems. Finally, the difficulties on MTT in real tracking systems are summarized.

In wireless sensor networks with single sink node, the energy consumption of sensors near the sink or on the critical paths is too fast besides other disadvantages. Therefore, we propose an Energy-efficient Multi-sink Clustering Algorithm (EMCA) for wireless sensor networks. In EMCA, residual energy plays a significant role in the procedure of selecting cluster heads. We also design an inter-cluster and intra-cluster routing algorithm aiming at minimizing the energy consumption. Inside a cluster, a multi-hop way is under consideration for a node to relay data instead of direct transmission. Simulation results show that our proposed algorithm consumes much less energy and owns longer network lifetime than the traditional routing algorithm LEACH.

In the process of coverage for multiple targets, due to the existence of a large number of redundant data make the effective monitoring area coverage decreased and force the network to consume more energy. Therefore, this paper proposes a multi-target k-coverage preservation protocol. First of all, establish the affiliation between the sensor nodes and target nodes through the network model, present a method to compute the coverage expected value of the monitoring area; secondly, in the network energy conversion, using scheduling mechanism in sensor nodes to attain the network energy balance, and achieve different network coverage quality through different nodes energy conversion. Finally, simulation results show that NMCP can effectively reduce the number of active nodes meeting certain coverage requirements and then improve the network lifetime.

넓은 지역에 설치되는 센서 네트워크 시스템은 유지 관리가 문제가 되어 왔으며, 이를 해결하기 위해 로봇을 사용하여 센서 네트워크에 에너지를 공급하려는 연구가 여러 연구자에 의해 수행되었다. 이 연구에서는 전력을 공급 하는 노드들과 그 주변의 센서 노드들로 이루어진 센서 네트워크에 여러 대의 로봇을 사용하여 에너지를 공급하는 문제에서, 로봇이 최소 거리를 이동하도록 수정된 k-means 알고리즘을 사용하여 각 로봇이 작업할 영역을 분할하는 방법을 제안한다. 로봇의 에너지 전달률을 변수로 한 시뮬레이션 실험을 통해 분할된 각 영역의 센서 노드들이 동작 을 유지할 수 있음을 보임으로써 제안한 수정 k-means 알고리즘의 타당성을 검증한다.

The maintenance of sensor networks, installed in a wide area has been an issue for a long time. In order to solve this problem, studies to supply energy to a sensor network using a robot has been carried out by several researchers. In this study, for a sensor network consisting of power nodes supplied with energy by multiple robots and sensor nodes around them, we propose a method of allocating a work area using a modified k-means algorithm so that the robots move the minimum distance. Through the simulation study using the energy transfer rate of the robot as a variable, it is shown that nodes of each allocated area can maintain survival, and the validity of the proposed modified k-means algorithm is verified.

본 논문에서는 주거지 내의 재실자의 행동을 분류하기 위한 LSTM 모델을 개발하는 연구에 대해 다룬다. 다 중센서의 구성은 실내 공기질을 측정하는 IAQ(Indoor air quality) 센서, 재실감지 및 위치를 추적하는 UWB 레이 더, 재실자의 생체정보를 측정하기 위한 Piezo 센서로 구성되며 실제 주거환경과 유사한 실험환경을 구축하여 외출, 재실, 요리, 청소, 운동, 수면 등의 재실자 행동 데이터를 수집한다. 수집한 데이터를 이상치와 결측치를 전처리 후 LSTM 모델을 사용하여 재실자 행동 분류 모델의 정확도, 민감도, 특이도, 그리고 T1스코어를 계산 후 평가한다.

In this paper discuss with research developing an LSTM model for classifying the behavior of occupants within a residence. The multi-sensor consists of an IAQ (Indoor Air Quality) sensor that measures indoor air quality, a UWB radar that tracks occupancy detection and location, and a Piezo sensor to measure occupants' biometric information, and collects occupant behavior data such as going out, staying, cooking, cleaning, exercise, and sleep by constructed an experimental environment similar to the actual residential environment. After the data with removed outliers and missing, the LSTM model is used to calculate accuracy, sensitivity, specificity of the occupant behavior classification model, T1 score.

센서 네트워크에서 센서 노드들은 물체의 추적 및 생태 감시와 같은 복잡한 작업을 수행하기 위해 배치되어야 한다. 그동안 주로 비행기를 이용하여 센서 네트워크를 배치하는 것에 대해서는 연구가 되어 왔다. 본 연구에서는 파워 모델을 고려하고 다중 이동 로봇을 이용한 센서 배치 방법을 제시 하였다. 다중 로봇은 비행기보다는 센서 노드들을 정확하고 안전하며 효율적으로 배치할 수 있다. 자동화된 센서 배치는 Pioneer3DX와 같은 상업용 로봇의 실제적인 파워 모델에 기반하고 탐욕적인(greedy) 알고리즘을 사용하였다. 전력 모델은 로봇의 모터, 센서, 제어기 및 통신을 고려하였다. 시뮬레이션은 로봇의 대수, 센서 노드 및 로봇의 속도를 변경하여 모의실험을 하였다. 시뮬레이션 결과는 센서 노드 구축을 위해 전력 모델이 유효함을 보여준다.

Sensor nodes in a sensor network should be deployed to perform complex tasks such as object tracking and habitat monitoring. Autonomous sensor deployment, which is mainly studied using aerial vehicles, is proposed employing multiple mobile robots with their power models. Multi-robots can deploy sensor nodes exactly, safely, and efficiently than aircrafts. The proposed autonomous deployment is a greedy method based on realistic power models of a commercial robot, Pioneer 3DX. Power models are considered for motors, sensors, controllers, and communication devices of the robot. Simulated experiments are presented by changing the number of robots, sensor nodes, and speed of robots. In addition, simulations show communication energy increases as the number of robots increases. These simulations show the importance of power models of mobile robots for sensor network construction.

본 논문에서는 인력이 접근하기 힘든 오염지역 감시를 위하여 다수 무인기 기반 이동센서네트워크를 고려한 다. 개별 무인기의 센싱 범위는 제한되어 있으므로 효과적인 지역 감시를 위해서는 무인기가 서로 협력하여 효과적인 센싱 커버리지를 획득하고 보다 많은 유해물질이 검출되는 지점으로 이동할 수 있어야 한다. 본 논문에서는 센싱 및 통신 거리의 제약을 극복하기 위하여 가상력 기반의 알고리즘을 이용하는 감시 네트워크를 제안하고 테스트베드를 구 축한다. 감시 네트워크에서 각 무인기는 이웃 무인기의 센싱값과 위치 정보를 바탕으로 최적 커버리지를 획득하고 감 시 지역의 센싱 최대치 지점으로 이동하게 된다. 야외 테스트베드를 이용한 시험을 통해 제안하는 유해 물질 감시 센 서 네트워크는 오염 지역에 자발적 접근이 가능하고 높은 가중 커버리지(Weighted Coverage) 획득이 가능함을 보인다.

In this paper, we consider a mobile sensor network for monitoring a polluted area where human beings cannot access. Due to the limited sensing range of individual unmanned vehicles, they need to cooperate to achieve an effective sensing coverage and move to a more polluted region. In order to address the limitations of sensing and communication ranges, we propose a hazardous substance monitoring network based on virtual force algorithms, and develop a testbed. In the considered monitoring network, each unmanned vehicle achieves an optimal coverage and move to the highest interest area based on neighboring nodes sensing values and locations. By using experiments based on the developed testbed, we show that the proposed monitoring network can autonomously move toward a more polluted area and obtain a high weighted coverage.

In acquiring the surface information of a part, two types of measuring machines have been used: contact type and non-contact type. Since each measuring device has the pres and cons, an integrated measuring system is proposed to acquire the optimal point data. In order to implement the integrated measuring system, the relationship of coordinate systems between each measuring device should be established. In this paper, a new datum fixture and a calibration method for the multiple-sensor measuring system are proposed. The datum fixture is designed to interface two machines, a CMM and a laser scanner. The position of the datum fixture is calibrated by the axis information off motorized rotation stage which is used for a part setup.

본 연구에서는 다중 센서를 사용하여 화재 감지를 수행하는 알고리즘을 제안하고 시스템을 구현하였다. 제안하는 알고리즘은 다중 센서의 데이터를 기반으로 규칙을 적용하여 화재를 판정한다. 화재 발생은 약 3~5분의 시간이 걸리며 이 시간은 화재 감지의 최적 시간이다. 이는 잠재적 화재 발생을 적시에 식별하는 것이 화재 관리에 중요하다는 것을 의미한다. 국내의 경우 화재 국가 법령에 따라 대부분 건물에 화재경보기 설비를 장착하고 있다. 그러나 현재 사용하는 화재 감지 장치는 연기나 열을 감지하는 하나의 센서에 의존하기 때문에 허위 경보에 매우 취약하다. 최근에는 IoT의 기술 발달로 화재 감지기에 여러 개의 센서를 통합할 수 있다. 또한, 화재 감지기는 다른 물체와 통신을 할 수 있으며 프로그램된 작업을 수행할 수 있는 스마트 기술이 개발되었다. 제작된 프로토타입은 10건의 실제 실험을 기준으로 90%의 성공률과 10%의 거짓 경보율을 기록했다.

In this paper, we propose a prototype design and implementation of a fire detection algorithm using multiple sensors. The proposed topic detection system determines fire by applying rules based on data from multiple sensors. The fire takes about 3 to 5 minutes, which is the optimal time for fire detection. This means that timely identification of potential fires is important for fire management. However, current fire detection devices are very vulnerable to false alarms because they rely on a single sensor to detect smoke or heat. Recently, with the development of IoT technology, it is possible to integrate multiple sensors into a fire detector. In addition, the fire detector has been developed with a smart technology that can communicate with other objects and perform programmed tasks. The prototype was produced with a success rate of 90% and a false alarm rate of 10% based on 10 actual experiments.

This paper presents an energy-efficient spatial join algorithm for multiple sensor networks employing a spatial semijoin strategy. For optimization of the algorithm, we propose a GR-tree index and a grid-ID-based spatial approximation method, which are unique to sensor networks. The GR-tree is a distributed spatial index over the sensor nodes, which efficiently prunes away the nodes that will not participate in a spatial join result. The grid-ID-based approximation provides great reduction in communication cost by approximating many spatial objects in simpler forms. Our experiments demonstrate that the algorithm outperforms existing methods in reducing energy consumption at the nodes.

Purpose: The objective of this study is to develop a data middleware for u-IT convergence in agricultural environment monitoring, which can support non-standard data interfaces and solve the compatibility problems of heterogenous sensor networks. Methods: Six factors with three different interfaces were chosen as target data among the environmental monitoring factors for crop cultivation. PostgresSQL and PostGIS were used for database and the data middleware was implemented by Python programming language. Based on hierarchical model design and key-value type table design, the data middleware was developed. For evaluation, 2,000 records of each data access interface were prepared. Results: Their execution times of File I/O interface, SQL interface and HTTP interface were 0.00951 s/record, 0.01967 s/record and 0.0401 s/record respectively. And there was no data loss. Conclusions: The data middleware integrated three heterogenous sensor networks with different data access interfaces.