Seokhoon Kim

A 3-dimensional group management MAC scheme for mobile IoT devices in wireless sensor networks

We are truly entering the era of Internet of Things (IoT) in which all things are connected and can be used in a variety of ways regardless of time and place. Various types of sensor devices using advanced technology will gather data around us and deliver the information wherever we want. In this paper, we propose an energy efficient MAC scheme for IoT ecosystem environments that include mobile IoT sensor devices. The mobile sensor devices in a target IoT ecosystem gather and collect any required data while moving and transmitting the collected data to a sink node. Energy consumption of the sensor devices depends on the distance from the sink node and also affects overall lifetime of the IoT ecosystems. The proposed 3-D group management MAC (3-D GM MAC) scheme groups sensor devices based on the distance (hop) from the sink node and transmits the collected data only to the next higher group level. That is, the data is transmitted only in the direction to the sink node. In addition, the energy efficiency of the entire IoT ecosystem can be improved by transmitting data based on pre-configured buffer threshold values that are set differently for each group and consequently minimizing the energy consumption of sensor devices near the sink node. When any sensor device cannot transmit data to the next higher group level due to movement, it is newly assigned an appropriate group number and transmits data using a new route. We have shown that the proposed 3-D GM MAC scheme shows excellent behavior in the aspect of energy efficiency for the target IoT ecosystem by simulation. Therefore, the proposed scheme might be adaptable for mobile sensor devices used in various kinds of computing and networking environments such as IoT, big data, cloud computing, and fog computing.

An AHP-Based Interface and Channel Selection for Multi-channel MAC Protocol in IoT Ecosystem

Recently, the significance of internet of things (IoT) is rapidly growing in the next generation of information and communication technology due to rapid advances in wireless communication technologies which have triggered the development of low-power, reliable and miniaturized devices. These advances lead to the creation of IoT ecosystem that enables development of various services. Especially, there are a number of attempts to apply communication technology of IoT in the field of healthcare services which deals with medical applications such as a remote medical treatment. In IoT-based healthcare system, in general, a number of medical devices are densely deployed in narrow area. Therefore, they are exposed to signal interference among them which causes significant performance degradation. In this paper, we present an Analytical Hierarchy Process (AHP)-based network interface and channel selection algorithm for multi-channel MAC protocols in IoT ecosystem that take into account a multitude of decision factors, such as expected channel condition, latency and frame reception ratio. The proposed scheme can provide flexibility for various requirements of different medical services through performing AHP. In particular, the proposed scheme considers IoT-based healthcare system because it is the most complex scenario of fundamental IoT applications. To evaluate the performance of the proposed algorithm, we perform extensive simulations, and the simulation study shows that the proposed algorithm provides low latency and high reliability.

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naive Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown.

Dual-channel medium access control of low power wide area networks considering traffic characteristics in IoE

The Internet of Thing (IoT) is evolving into the Internet of Everything (IoE). Combining cloud computing with the IoE has attracted attention for wide area applications as a major service. In addition, low power wide area networks (LPWANs) have become a remarkable communication technology in IoT. Because the LPWAN provides long range communication with low power, it can be widely exploited for IoE applications. To improve quality of service, data traffic should be transmitted by considering its priority. However, this is not easy because the LPWAN has a low data rate and long transmission delay. Therefore, this paper proposes a dual-channel medium access control (MAC) to satisfy these requirements in the LPWAN. Generated data is classified into three categories by considering traffic characteristics and is delivered with different priority in dual channels. The performance evaluation is carried out with computer simulations. The results show that the proposed scheme outperforms existing schemes in the LPWAN.

Efficient data-forwarding method in delay-tolerant P2P networking for IoT services

These days Internet of Things (IoT), which consists of smart objects such as sensor nodes is the most important technology for providing intelligent services. In the IoT ecosystem, wireless sensor networks deliver collected information from IoT devices to a server via sink nodes, and IoT services are provided by peer-to-peer (P2P) networking between the server and the IoT devices. Particularly, IoT applications with wide service area requires the mobile sink nodes to cover the service area. To employ mobile sink nodes, the network adopts delay-tolerant capability by which delay-tolerant nodes try to transmit data when they connect to the mobile sink node in the application service field. However, if the connection status between a IoT device and a mobile sink node is not good, the efficiency of data forwarding will be decreased. In addition, retransmission in bad connection cause high energy consumption for data transmission. Therefore, data forwarding in the delay-tolerant based services needs to take the connection status into account. The proposed method predicts the connection status using naïve Bayesian classifier and determines whether the delay tolerant node transmits data to the mobile sink node or not. Furthermore, the efficiency of the proposed method was validated through extensive computer simulations.

Network Access Control for Location-Based Mobile Services in Heterogeneous Wireless Networks

Recent advances in information communication technology and software have enabled mobile terminals to employ various capabilities as a smartphone. They adopt multiple interfaces for wireless communication and run as a portable computer. Mobile services are also transferred from voice to data. Mobile terminals can access Internet for data services anytime anywhere. By using location-based information, improved mobile services are enabled in heterogeneous networks. In the mobile service environment, it is required that mobile terminals should efficiently use wireless network resources. In addition, because video stream becomes a major service among the data services of mobile terminals in heterogeneous networks, the necessity of the efficient network access control for heterogeneous wireless networks is raised as an important topic. That is, quality of services of the location-based video stream is determined by the network access control. Therefore, this paper proposes a novel network access control in the heterogeneous wireless networks. The proposed method estimates the network status with Naive Bayesian Classifier and performs network access control according to the estimated network status. Thus, it improves data transmission efficiency to satisfy the quality of services. The efficiency of the proposed method is validated through the extensive computer simulation.

Adaptive data rate control in low power wide area networks for long range IoT services

Abstract Internet of Things (IoT) technologies can provide various intelligent services by collecting information from objects. To collect information, Wireless Sensor Networks (WSNs) are exploited. The Low Power Wide Area Network (LPWAN), one type of WSN, has been designed for long-range IoT services. It consumes low power and uses a low data rate for data transmission. The LPWAN includes several communication standards, and Long Range Wide Area Network (LoRaWAN) is the representative standard of the LPWAN. LoRaWAN provides several data rates for transmission and enables adaptive data rate control in order to maintain network connectivity. In the LoRaWAN, the wireless condition is considered by the reception status of the acknowledgement (ACK) message, and adaptive data rate control is performed according to the wireless condition. Because the judgment of the wireless condition by the reception status of ACK messages does not reflect congestion, adaptive data rate control can lead to inefficiency in data transmission. For efficient data transmission in long-range IoT services, this paper proposes a congestion classifier using logistic regression and modified adaptive data rate control. The proposed scheme controls the data rate according to the congestion estimation. Through extensive analysis, we show the proposed scheme’s efficiency in data transmission.

Remote Software Update in Trusted Connection of Long Range IoT Networking Integrated with Mobile Edge Cloud

The Internet of Things (IoT) leads to intelligent services by collecting information from tiny sensor devices. In recent years, storage-less sensing devices have been used to implement IoT services. They depend on delivered software from a network server to operate service functions, and IoT services are based on collected user information. Therefore, it is important to maintain trusted connections during software delivery or data transmission. If a network connection is untrustworthy, stable data transmission cannot be achieved. Untrustworthy data connections cause many problems in IoT services. Therefore, this paper proposes a software update method in trusted connection of IoT networking. The proposed method employs a low-power wide area network (LPWAN) as a long-range IoT networking technology and uses a mobile edge cloud to improve computing efficiency in an access network that consists of IoT devices with insufficient resources. In the proposed method, the mobile edge cloud is integrated into a gateway and processes sensing data and remote software updates of LPWAN. IoT devices can receive software functions from the mobile edge cloud. The proposed method analyzes statistical information about connections in an access network and determines the LPWAN trusted connections. Then, software updates can be performed over the trusted connection. Using trusted connections leads to an increased packet delivery rate and reduced transmission energy consumption. The proposed method is compared with currently available systems through computer simulation and the proposed method’s efficiency is validated.

Traffic management in the mobile edge cloud to improve the quality of experience of mobile video

Abstract The use of mobile network traffic is increasing due to the development of communication devices such as smart phones. Data traffic accounts for more than voice traffic. Video traffic accounts for the largest proportion of mobile data traffic and this proportion is increasing. However, various mobile environments affect the status of the mobile network and limit the provision of video services using the Internet. Therefore, a method for improving the quality of experience (QoE) of a video service in mobile environments is required. This paper presents a traffic management method using the mobile edge cloud. The mobile edge cloud is placed in the mobile edge network and monitors the status of mobile terminals. Through the mobile edge cloud, it becomes possible to effectively manage the traffic of mobile terminals by the network. The proposed method manages video traffic from the content server on the Internet according to the edge network status and mobility of a mobile terminal, and provides video traffic to mobile terminals. This method, using the mobile edge cloud, leads to improvement of the QoE for mobile video users. The superiority of this method compared to currently available systems was validated by computer simulation.

Radio resource management for data transmission in low power wide area networks integrated with large scale cyber physical systems

Recent advances in ICT technologies lead to intelligent services based on monitoring data. A system for the intelligent services is called as a cyber physical system (CPS). The CPS is an IoT-Cloud system which is an integrated system between computing world and a physical sensor field. It makes feasible applications by connecting the monitoring data in the physical world to decision making in the computing world. That is, between the physical world and the computing world, the CPS supports various interactions to objects as IoT-Cloud system. In the physical sensor field of the CPS, a sensor network, which consists of sensors and actuators is constructed. ZigBee is considered as the representative wireless communication technology for the sensor network. However, it has short transmission range and uses shared frequency band with high competition. Therefore, it is not suitable for the CPS applications with long service range such as smart factory or smart environment monitoring. For these CPS applications, it is required to apply low power wide area network (LPWAN) to the sensor network. In this paper, LPWAN requirements for the CPS (i.e., the IoT-Cloud system) applications are analyzed and a radio management method for data transmission of LPWAN in the IoT-Cloud system is designed. For the validation of the efficiency of the data transmission, computer simulations are carried out. Results show that the proposed method outperforms state of the art methods. In this way, the proposal improves the transmission delay over TDMA in between 33 and 51% depending on the number of sensors considered and presents a superior performance in the transmission of data packets.