We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G.

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Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.

NOMA-Based Resource Allocation for Cluster-Based Cognitive Industrial Internet of Things

Internet of things: A survey on enabling technologies, application and standardization

IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

The “not yet officially defined” 5G standard leads to the consequence that its relevant technological innovation is still widely open. For identifying innovations opportunity, we should discover the specific technical area to contribute to the technological development of 5G. This paper dicusses a framework answering the main question: In which technological area one may contribute to the innovation? The answer shall benefit countries, firms, universities and research institute which intends to contribute to the formulation of official 5G standard. First, we reviewed the key technologies of 5th generation mobile communication technology (5G). Ubiquitous and interoperability of the network are main technical focus. A flat IP-based network concept was reviewed, as well as cognitive radio technology to reach the terminal which have artificial intelligence. BDMA technology was proposed to help achieving system efficiency in terms of multiple access system. Second, we identified technological challenges, focusing on the issues related to security and problems to deal with limited frequency spectrum resources. Subsequently, we mapped the innovation opportunity based on technical area which is recently published in research article. We concluded that innovation opportunities lies on the research regarding security, network, technological implementation and applications issues.

5G key technologies: Identifying innovation opportunity

This paper aims to propose a system design, working on the basis of the Internet of Things (IoT) LoRa, for tracking and monitoring the patient with mental disorder. The system consists of a LoRa client, which is a tracking device on end devices installed on the patient, and LoRa gateways, installed in hospitals and other public locations. The LoRa gateways are connected to local servers and cloud servers by utilizing both mobile cellular and Wi-Fi networks as the communications media. The feasibility of the system design is developed by employing the results of our previous work on LoRa performance in the Line of Sight (LoS) and Non-Line of Sight (Non-LoS) environments. Discussions are presented concerning the LoRa network performance, battery power and scalability. The future work is to build the proposed the design in a real system scenarios.

The IoT LoRa system design for tracking and monitoring patient with mental disorder

Heart disease is the primary cause of death nowadays. Treatments of heart disease patients have been advanced, for example with machine-to-machine (M2M) technology to enable remote patient monitoring. To use M2M to take care remote heart disease patient, his/her medical condition should be measured periodically at home. Thus, it is difficult to perform complex tests which need physicians to help. Meanwhile, heart disease can be predicted by analysing some of patient's health parameters. With help of data mining techniques, heart disease prediction can be improved. There are some algorithms that have been used for this purpose like Naive Bayes, Decision Tree, and k-Nearest Neighbor (KNN). This study aims to use data mining techniques in heart disease prediction, with simplifying parameters to be used, so they can be used in M2M remote patient monitoring purpose. KNN is used with parameter weighting method to improve accuracy. Only 8 parameters are used (out of 13 parameters recommended), since they are simple and instant parameters that can be measured at home. The result shows that the accuracy of these 8 parameters using KNN algorithm are good enough, comparing to 13 parameters with KNN, or even other algorithms like Naive Bayes and Decision Tree.

Heart disease prediction system using k-Nearest neighbor algorithm with simplified patient's health parameters

The development of the Internet of Things (IoT) technology has a positive impact on human life. A smart home is one of the IoT technology applications that facilitate human activities. The problem of physical abnormalities is a matter of limited activities for disabled people. This paper proposes a design of IoT-based smart home application with a remote control device that developed using voice commands for disabled people. Smart home control systems help disabled people to control their home electrical devices such as television (TV), lights, and fans using only voice commands without moving to turn on or turn off electrical equipment. The voice recognition on electrical equipment is using the Google Assistant's application on smartphones. The Google Assistant application will accept voice commands when the pronunciation is correct. Voice commands on IoT-based Smart Home are more simple to apply, without typing text messages. Users get convenience compared to using text. The signal strength of an Internet connection will create a useful performance device at the Response Time of Google Assistant, Response Time of System Processing, activating and deactivating Electrical Equipment. We expect that this device could be more useful to help disabled people interact with their environment by utilizing IoT technology facilities.

Design and Implementation of IoT-Based Smart Home Voice Commands for disabled people using Google Assistant

Our work has developed the Internet of Things (IoT) LoRa prototype to support the remote measurement of temperature at the frequency of 915 MHz. The device is structured by a temperature sensor, memory storage, data processing and data visualization using Cloud Server on the Web Browser. The performance is evaluated by both LoS and Non-LoS field trial of 14 dBm transmit power. By using the IoT platform of LoRa, end devices with low energy consumption that connected to gateways, shall perform a more efficient data transmission to other devices and network servers. We conduct field trial on Line of Sight (LoS) and Non-LoS condition in the surrounding area of University Indonesia campus. The results have indicated that prototype is working well on both condition, while RSSI may vary from −50 dB to −105 dB. For LoS condition, RSSI is at intervals of −55 dB to −90 dB for distance of 100 meters to 700 meters, while for Non-LoS condition is at intervals of −62 dB to −99 dB for distance of 100 meters to 400 meters. Accordingly, the LoRa is proven to be efficiently used for the IoT applications of both LoS and Non-LoS condition, while a specific design should be adjusted.

Muhammad Suryanegara

Papers

5G key technologies: Identifying innovation opportunity

The IoT LoRa system design for tracking and monitoring patient with mental disorder

Heart disease prediction system using k-Nearest neighbor algorithm with simplified patient's health parameters

Design and Implementation of IoT-Based Smart Home Voice Commands for disabled people using Google Assistant

The development of IoT LoRa: A performance evaluation on LoS and Non-LoS environment at 915 MHz ISM frequency