Ryoichiro Obukata

Performance evaluation of a QoS-aware fuzzy-based CAC for LAN access

With the popularisation of mobile devices such as smart phones, many device communicate over wireless local area networks (WLANs). The IEEE 802.11e standard is an important extension of the IEEE 802.11 standard focusing on QoS that works with any PHY implementation. The IEEE 802.11e standard introduces EDCA and HCCA. Both these schemes are useful for QoS provisioning to support delay-sensitive voice and video applications. The EDCA uses the contention window to differentiate between high priority and low priority services. However, it does not consider the priority of users. In this paper, in order to deal with this problem, we propose and implement a fuzzy-based admission control system (FACS). We carried out many simulations to evaluate the performance of the proposed system. The simulation results show that our proposed system has a good performance.

Design of an Ambient Intelligence Testbed for Improving Quality of Life

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of Optimized Link State Routing (OLSR) and Wired Equivalent Privacy (WEP) protocol in an indoor scenario. For evaluation we considered throughput, delay and jitter metrics. The experimental results show that the nodes in the testbed were communicating smoothly.

Design and Implementation of a Simulation System Based on Deep Q-Network for Mobile Actor Node Control in Wireless Sensor and Actor Networks

A Wireless Sensor and Actor Network (WSAN) is a group of wireless devices with the ability to sense physical events (sensors) or/and to perform relatively complicated actions (actors), based on the sensed data shared by sensors. This paper presents design and implementation of a simulation system based on Deep Q-Network (DQN) for mobile actor node control in WSANs. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. In this work, we implement the proposed simulating system by Rust programming language. We describe the design and implementation of the simulation system, and show some simulation results to evaluate its performance.

Experimental Results of a Raspberry Pi Based WMN Testbed in Indoor Environment: A Comparison Study of LoS and NLoS Scenarios

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers, because of their potential use in several fields such as collaborative computing and communications. In this paper, we present the implementation of a testbed for WMNs using OpenWRT OS. We analyze the performance of Optimized Link State Routing (OLSR) protocol in an indoor environment considering Line-of-Sight (LoS) and Non Line-of-Sight (NLoS) scenarios. For evaluation we considered throughput, Packet Delivery Ratio (PDR), hop count, delay and jitter metrics. The experimental results show that the performance of the testbed was better for LoS scenario. Also, the nodes in the testbed were communicating smoothly.

Experimental results of a Raspberry Pi and OLSR based wireless content centric network testbed: comparison of different platforms

The Content Centric Networks (CCN) replaces the IP narrow waist of the internet architecture by a content layer. When a node wants to access a piece of content, it sends an Interest packet to the network. Then, the network by using the name of the content for routing forwards the Interest to one or more copies of the content object. In this paper, we present the implementation of a testbed for CCN. We analyse the performance of Optimised Link State Routing (OLSR) protocol in an indoor scenario. For evaluation, we considered delay and jitter metrics. The experimental results show that the nodes in the testbed were communicating smoothly and we got low values of delay and jitter.

A User Prediction and Identification System for Tor Networks Using ARIMA Model

Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor networks. In this paper, we present the application of Autoregression Integrated Moving Average (ARIMA) for prediction of user behavior in Tor networks. We constructed a Tor server and a Deep Web browser (Tor client) in our laboratory. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and then used the ARIMA model to make the prediction. The simulation results show that proposed system has a good prediction of user behavior in Tor networks.

Performance Evaluation of an Ambient Intelligence Testbed for Improving Quality of Life: Evaluation Using Clustering Approach

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm. For evaluation we considered respiratory rate and heart rate metrics. The simulation results show that the k-means clustering algorithm has a good performance.

Performance Evaluation of an AmI Testbed for Improving QoL: Evaluation Using Clustering Approach Considering Parallel Processing

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm considering sensing data. For evaluation we considered respiratory rate and heart rate metrics. We speeded up the k-means clustering algorithm by using parallel processing.

A Fuzzy-Based System for Actor Node in an Ambient Intelligence Testbed: Effects of Different Parameters on Human Sleeping Conditions

Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In our previous work, we presented the implementation and evaluation of actor node for AmI testbed. The actor node is equipped with a Reidan Shiki PAD and a fluorescent lamp. For evaluation, we considered respiratory rate and heart rate metrics. We carried out an experiments and clustered sensed data by k-means clustering algorithm. From experimental results, we found that the actor node of the implemented AmI testbed gives a good effect to human during sleeping. In this paper, we use fuzzy based model to check the effect of three parameters on human during sleeping.

An Integrated Intelligent System for IoT Device Selection and Placement in Opportunistic Networks Using Fuzzy Logic and Genetic Algorithm

The Internet is dramatically evolving and creating various connectivity methodologies. The Internet of Things (IoT) is one of these methodologies which transform current Internet communication to Machine-to-Machine (M2M) basis. The IoT can seamlessly connect the real world and cyberspace via physical objects that are embeded with various types of intelligent sensors. The opportunistic networks are the variants of Delay Tolerant Networks (DTNs). These networks can be useful for routing in places where there are few base stations and connected routes for long distances. In an opportunistic network, when nodes move away or turn off their power to conserve energy, links may be disrupted or shut down periodically. These events result in intermittent connectivity. When there is no path existing between the source and the destination, the network partition occurs. Therefore, nodes need to communicate with each other via opportunistic contacts through store-carry-forward operation. In this paper, we present the design of an integrated intelligent system for IoT device selection and placement in opportunistic networks using Fuzzy Logic and Genetic Algorithm. We introduce the system structure and present in details the design and implementation issues.