Miralda Cuka

A Fuzzy-Based System for Selection of IoT Devices in Opportunistic Networks Considering IoT Device Storage, Waiting Time and Node Centrality Parameters

The opportunistic networks are a subclass of delay-tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The Internet of Things (IoT) present the notion of large networks of connected devices, sharing data about their environments and creating a diverse ecosystem of sensors, actuators, and computing nodes. IoT networks are a departure from tradi- tional enterprise networks in terms of their scale and consist of heterogeneous collections of resource constrained nodes that closely interact with their environment. There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use three input parameters: IoT Device Storage (IDST), IoT Device Waiting Time (IDWT) and IoT Device Node Centrality (IDNC). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that IoT device selection is increased up to 33% and 43% by increasing IDNC and IDSD, respectively.

Effect of Node Density on Actor Selection in WSANs: A Comparison Study for Two Fuzzy-Based Systems

A group of wireless devices with the ability to sense physical events (sensors) or/and to perform relatively complicated actions (actors), is referred to as Wireless Sensor and Actor Network (WSAN). In this work, we propose and implement two Fuzzy Based Actor Selection Systems (FBASS): FBASS1 and FBASS2 for actor selection in WSANs. The systems decide whether the actor will be selected for the required job or not, based on data supplied by sensors and actual actor condition. We evaluated the proposed system by computer simulations. Comparing FBASS1 with FBASS2, the FBASS2 is more complex than FBASS1, because it has more rules in FRB. By increasing node density, the FBASS2 can save better the energy.

Evaluation of an IoT-based e-learning testbed: Performance of OLSR protocol in a NLoS environment and mean-shift clustering approach considering electroencephalogram data

Purpose This paper aims to present the design and implementation of an Internet of Things (IoT)-based e-learning testbed using Raspberry Pi mounted on Raspbian operating system (OS). Design/methodology/approach The testbed is composed of five Raspberry Pi B+ computers. The experiments are carried out in the department floor considering an non line of sight (NLoS) environment. Single constant bit rate (CBR) flows were transmitted over user datagram protocol (UDP), and data were collected for five metrics: throughput, packet delivery ratio (PDR), hop count, delay and jitter using the Iperf. Findings The implemented testbed was evaluated using experiments. The experimental results showed that the nodes in the testbed were communicating smoothly, and by using attention value, the learner concentration is increased. Research limitations/implications The performance of the Optimized Link State Routing (OLSR) protocol was analyzed in a floor environment considering the NLoS scenario. However, this testbed can be implemented to other protocols also. Originality/value Because of the opportunities provided by the internet, people are taking advantage of e-learning courses, and enormous research efforts have been dedicated to the development of e-learning systems. To date, many e-learning systems are proposed and used practically. However, in these systems, the e-learning completion rate is low. To deal with this problem, an IoT-based e-learning system was implemented to increase the e-learning completion ratio by increasing the learner concentration.

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.

A Fuzzy-Based System for Actor Node Selection in WSANs for Improving Network Connectivity and Increasing Number of Covered Sensors

Wireless Sensor and Actor Network (WSAN) is formed by the collaboration of micro-sensor and actor nodes. The sensor nodes have responsibility to sense an event and send information towards an actor node. The actor node is responsible to take prompt decision and react accordingly. In order to provide effective sensing and acting, a distributed local coordination mechanism is necessary among sensors and actors. In this work, we consider the actor node selection problem and propose a fuzzy-based system that based on data provided by sensors and actors selects an appropriate actor node. We use 4 input parameters: Size of Giant Component (SGC), Distance to Event (DE), Remaining Energy (RE) and Number of Covered Sensors (NCS) as new parameter. The output parameter is Actor Selection Decision (ASD). The simulation results show that by increasing SGC to 0.5 and 0.9, the ASD is increased 12% and 68%, respectively.

Implementation and performance evaluation of two fuzzy-based systems for selection of IoT devices in opportunistic networks

The opportunistic networks are a subclass of delay-tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The internet of things (IoT) present the notion of large networks of connected devices, sharing data about their environments and creating a diverse ecosystem of sensors, actuators, and computing nodes. IoT networks are a departure from traditional enterprise networks in terms of their scale and consist of heterogeneous collections of resource constrained nodes that closely interact with their environment. There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement and compare two fuzzy-based systems (FBS1 and FBS2) for IoT device selection in opportunistic networks. For FBS1, we use three input parameters: IoT device storage (IDST), IoT device waiting time (IDWT) and IoT device remaining energy (IDRE). The output parameter is IoT device selection decision (IDSD). For FBS2, we consider four input parameters adding IoT device security (IDSC) as a new parameter. Comparing complexity of FBS1 and FBS2, the FBS2 is more complex than FBS1. But, the FBS2 is more flexible and makes a better selection of IoT devices than FBS1.

Effect of node centrality for IoT device selection in opportunistic networks: A comparison study: Effect of node centrality for IoT device selection in opportunistic networks: A comparison study

Opportunistic networks (Oppnets) are a subclass of delay‐tolerant networks where communication opportunities (contacts) are intermittent and there is no need to establish an end‐to‐end link between communication nodes. The Internet of Things (IoT) presents the notion of large networks of connected devices, sharing data about their environments and creating a diverse ecosystem of sensors, actuators, and computing nodes. IoT networks consist of heterogeneous collections of resource constrained nodes that closely interact with their environment. There are different issues for these networks. One of them is the selection of IoT devices to carry out a task in Oppnets. In this work, we implement and compare two fuzzy‐based systems (FBSs) for selecting the IoT device that completes the task in Oppnets. We call these systems FBSIDS1 and FBSIDS2. For FBSIDS1, we use three input parameters: IoT device storage, IoT device waiting time (IDWT), and IoT device remaining energy. The output parameter is IoT device selection decision. For FBSIDS2, we consider four input parameters by adding IoT device node centrality (IDNC) as a new parameter. Comparing the complexity of FBSIDS1 and FBSIDS2, FBSIDS2 is more complex than FBSIDS1. However, FBSIDS2 is more flexible and makes a better selection of IoT devices than FBSIDS1. The simulation results show that IoT device selection is increased up to 33% and 71% by increasing IDNC and IDWT, respectively, for FBSIDS2, and up to 46% and 61% by increasing IDWT and IDNC, respectively, for FBSIDS1.

A Fuzzy-Based System for Selection of IoT Devices in Opportunistic Networks Considering IoT Device Speed, Storage and Remaining Energy Parameters

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 work, we consider the IoT device selection problem in opportunistic networks. We propose a fuzzy-based system consisting of three input parameters: IoT Device Speed (IDS), IoT Device Storage (IDST) and IoT Device Remaining Energy (IDRE). The output parameter is IoT Device Selection Decision (IDSD). We evaluate the performance of the proposed system by simulations. The simulation results show that the proposed system makes a proper selection decision of IoT-devices in opportunistic networks.

Performance Evaluation of a Deep Q-Network Based Simulation System for Actor Node Mobility Control in Wireless Sensor and Actor Networks Considering Three-Dimensional Environment

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 actor node mobility control in WSANs. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. We implemented the proposed simulating system by Rust programming language. We evaluated the performance of proposed system for normal distribution of events considering three-dimensional environment. For this scenario, the simulation results show that for normal distribution of events and the best episode all actor nodes are connected but one event is not covered.

Design of a Smart Desk for an IoT Testbed: Improving Learning Efficiency and System Security

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based E-Learning testbed using Raspberry Pi mounted on Raspbian. We carried out some experiments with a student of our laboratory for brain waves and, encryption and decryption time of cipher algorithms. We used MindWave Mobile (MWM) to get the data. For experimental results, when using our system the concentration is 80 [%] from 0 [s] to 400 [s]. But, for 400 [s] to 1000 [s], concentration is decreased. In the case of not using our system, concentration is almost the same (about 40 [%]) from 0 [s] to 1000 [s]. From simulation results, we found that the decryption time is faster than encryption time, however for 3-DES and Raspberry Pi decryption time is higher than encryption time.