Pengaruh Push, Pull, dan Mooring terhadap Switching Intention pada Konsumen Pengguna Wifi di Era Pandemi Covid-19

Internet use during the current pandemic is increasing, and most are done online. Work From Home (WFH) and School From Home (SFH) require stable and smooth internet. In addition to the internet, smartphones also play an essential role in people's daily lives during restrictions due to the pandemic. This study aims to analyze and discuss the effect of push, pull and mooring on switching intention. This study uses a push-pull-mooring model with an approach through switching intention. Respondents in this study were at least 20 years old. Due to this age, respondents have entered the adult stage in switching intentions from internet data packages to wifi. The sample used is 110 respondents—the sampling method used non-probability sampling with the judgmental sampling method. The analysis technique uses multiple linear regression with SPSS26 software. Based on the results obtained, it is known that the push variable on switching intention to use wifi has a positive effect. The pull variable has a positive impact on switching intention to use wifi. When the push and pull are getting stronger, consumers will switch. Mooring variable does not affect internet data packet switching intention to private wifi. This result shows that the switching intention of data packets to use wifi privately at home is relatively high. Due to using private wifi, you can access or surf the internet at a stable speed compared to data packet internet, in contrast to using data packet internet whose network is unstable.

IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where P=0.012<α=0.05, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments.

Formation of service information in telemetry systems with data compression

In telemetry systems, using irreversible data compression, several message generation methods can be used. In the channel output packet, there may be several code words defining its composition. They can be combined and arranged in a strictly defined sequence. Such a data packet is a constant or variable length code combination, wherein the constant length of the packet is generated in the case of a predetermined and unchanged amount of information at the data output interval, and the variable is otherwise generated. The channel data packet can then be treated as a single whole: provide it with address information about the source of the message, information about the time interval at which the packet was formed, to bind significant samples to time, additional check symbols and codes to increase interference immunity of transmission, or to form a packet structure in the same way. Address, time and synchronization information in the literature is called overhead. The need to transmit overhead information reduces the efficiency of the transceiver systems. Therefore, the problem of reducing the volume of service information is extremely urgent.

An Improved Backoff Scheme and Its Performance Analysis for Full Duplex MAC Protocols in VLC Networks

IEEE 802.15.7 Visible Light Communication (VLC) networks suffer from performance degradation caused by the hidden device collisions due to the directional transmission with narrow beamwidth. One of the solutions for mitigating the hidden device collisions is to employ a full-duplex transmission technique. As a side effect of the full-duplex transmission in the VLC networks, however, the data-packet discard due to the retransmission limitation occurs frequently in the networks. This paper proposes an improved backoff scheme and its performance analysis to suppress the packet discard. The proposed backoff scheme increases the Backoff Exponent (BE) and the Number of Backoff stage (NB) in IEEE 802.15.7 only when the data packet transmission fails. To evaluate the system performance theoretically, this paper also provides the Markov-chain model for channel access with the proposed scheme. The performance evaluations through simulation and theoretical analysis show the effectiveness of the proposed scheme.

Load Balanced Multipath Routing using Seagull Optimizer Based Garson’s Pruned Recurrent Neural Network for Multipath Routing in MANET

In recent years, routing is considered one of the most challenging issues in MANET. The location of the stable node and the routing is based on the predicted locations that assists in establishing a routing path in MANET. The major intention of this paper is to detect the stable neighbor node in the MANET and also to establish stable multi-path routing for the various mobility patterns. Also, this paper deals with the data packet scheduling over multi-paths for balancing the load and forward the entire packets in the least broadcast time. The proposed approach elucidates four significant phases: stable node prediction, determination of stability measure, route exploration and packet dissemination. Initially, the stable node is predicted using the RMSG approach. Here, the stable neighbors are selected via Garson’s pruning based Recurrent neural network with a Modified seagull optimization algorithm (RMSG). In the route exploration phase, the path is created among the source and destination by the stable node. If any of the links fails, the route recovery process is established. Finally, the structure is formed for data packet distribution across the multipath. The proposed approach is evaluated by few performance measures such as throughput, packet delivery ratio, end-to-end delay, routing overhead energy consumption, and optimal path. This result describes that the proposed approach outperforms other state-of-art approaches.

Mitigation of Data Packet Loss in Bluetooth Low Energy-Based Wearable Healthcare Ecosystem

Bluetooth Low Energy (BLE) plays a critical role in wireless data transmission in wearable technologies. The previous work in this field has mostly focused on optimizing the transmission throughput and power consumption. However, not much work has been reported on a systematic evaluation of the data packet loss of BLE in the wearable healthcare ecosystem, which is essential for reliable and secure data transmission. Considering that diverse wearable devices are used as peripherals and off-the-shelf smartphones (Android, iPhone) or Raspberry Pi with various chipsets and operating systems (OS) as hubs in the wearable ecosystem, there is an urgent need to understand the factors that influence data loss in BLE and develop a mitigation solution to address the data loss issue. In this work, we have systematically evaluated packet losses in Android and iOS based wearable ecosystems and proposed a reduced transmission frequency and data bundling strategy along with queue-based packet transmission protocol to mitigate data packet loss in BLE. The proposed protocol provides flexibility to the peripheral device to work with the host either in real-time mode for timely data transmission or offline mode for accumulated data transmission when there is a request from the host. The test results show that lowered transmission frequency and data bundling reduce the packet losses to less than 1%. The queue-based packet transmission protocol eliminates any remaining packet loss by using re-request routines. The data loss mitigation protocol developed in this research can be widely applied to the BLE-based wearable ecosystem for various applications, such as body sensor networks (BSN), the Internet of Things (IoT), and smart homes.