The recent academic research surrounding indoor positioning systems (IPS) and indoor location-based services (ILBS) are reviewed to establish the current state-of-the-art for IPS and ILBS. This review is focused on the use of IPS / ILBS for cyber-physical systems to support secure and safe asset management (including people as assets), exploring the potential applications of IPS for industry as suggested in the literature. Current application areas in industry are presented, separated into physical item and human traceability applications for context. The literature are reviewed to identify gaps in the ILBS development for industrial applications, future research needs to focus a development framework to enable scalable solutions for industry. The key gaps identified in the literature are: (i) a lack of pathways to extend IPS research into an ILBS, (ii) no end-to-end ILBS have been developed and (iii) no framework has been reported that outlines the information pathways from sensor data collection and location information to an established ILBS. The technologies reviewed are presented in a comparison table (Table 1) intended as a reference for selecting technologies for future systems based on requirements. The techniques used to extract location information from each of the technologies identified are also explored stating current accuracy and aligning the techniques with their suitable technologies. 

A Survey of Indoor Location Technologies, Techniques and Applications in Industry

Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests. Although Kubernetes’ horizontal pod autoscaler provides the resource autoscaling feature by monitoring the resource status of nodes and then making pod adjustments if necessary, it evenly allocates pods to worker nodes without considering the imbalance of resource demand between nodes in an edge computing environment. This paper proposes the traffic-aware horizontal pod autoscaler (THPA), which operates on top of Kubernetes to enable real-time traffic-aware resource autoscaling for IoT applications in an edge computing environment. THPA performs upscaling and downscaling actions based on network traffic information from nodes to improve the quality of IoT services in the edge computing infrastructure. Experimental results show that Kubernetes with THPA improves the average response time and throughput of IoT applications by approximately 150% compared to Kubernetes with the horizontal pod autoscaler. This indicates that it is important to provide proper resource scaling according to the network traffic distribution to maximize IoT applications performance in an edge computing environment.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09709810.pdf

Traffic-Aware Horizontal Pod Autoscaler in Kubernetes-Based Edge Computing Infrastructure

Container-based Internet of Things (IoT) applications in an edge computing environment require autoscaling to dynamically adapt to fluctuations in IoT device requests. Although Kubernetes’ horizontal pod autoscaler provides the resource autoscaling feature by monitoring the resource status of nodes and then making pod adjustments if necessary, it evenly allocates pods to worker nodes without considering the imbalance of resource demand between nodes in an edge computing environment. This paper proposes the traffic-aware horizontal pod autoscaler (THPA), which operates on top of Kubernetes to enable real-time traffic-aware resource autoscaling for IoT applications in an edge computing environment. THPA performs upscaling and downscaling actions based on network traffic information from nodes to improve the quality of IoT services in the edge computing infrastructure. Experimental results show that Kubernetes with THPA improves the average response time and throughput of IoT applications by approximately 150% compared to Kubernetes with the horizontal pod autoscaler. This indicates that it is important to provide proper resource scaling according to the network traffic distribution to maximize IoT applications performance in an edge computing environment. 

/pdf/traffic-aware-horizontal-pod-autoscaler-in-kubernetes-based-2g55vorl.pdf

The rapid development of the Bluetooth technology offers a possible solution for indoor localization scenarios. Compared with other indoor localization technologies, such as vision, light detection and ranging, ultrawide band, etc., Bluetooth has been characterized by low cost, easy deployment, low energy consumption, and potentially high localization accuracy, which enable itself to be a competitive technology in indoor location-based services, the Internet of Things, and many other fields. In this article, we first present a comprehensive survey of Bluetooth localization technology, including the measurements for localization, working principles, and method comparison. We highlight the learning-based methods and integrated localization methods. Then, we review the applications and existing commercial solutions, revealing the possible directions for the industrialization of Bluetooth localization. Finally, this article proposes several open issues of Bluetooth localization (e.g., multichannel difference, multipath, co-channel interference, and device heterogeneity) and projects several future trends.

Bluetooth Localization Technology: Principles, Applications, and Future Trends

The rapid development of the Bluetooth technology offers a possible solution for indoor localization scenarios. Compared with other indoor localization technologies, such as vision, light detection and ranging, ultrawide band, etc., Bluetooth has been characterized by low cost, easy deployment, low energy consumption, and potentially high localization accuracy, which enable itself to be a competitive technology in indoor location-based services, the Internet of Things, and many other fields. In this article, we first present a comprehensive survey of Bluetooth localization technology, including the measurements for localization, working principles, and method comparison. We highlight the learning-based methods and integrated localization methods. Then, we review the applications and existing commercial solutions, revealing the possible directions for the industrialization of Bluetooth localization. Finally, this article proposes several open issues of Bluetooth localization (e.g., multichannel difference, multipath, co-channel interference, and device heterogeneity) and projects several future trends. 

Containerization is a cutting-edge DevOps technology which unifies the IT operations and Development domains. In recent times, virtualization using Virtual Machines has become an overkill for its large overhead on systems. As a lightweight alternative, containerization offers containers that constitute a package of an application along with all its dependencies that is required for it to execute. Containerization platforms help in building containers from images. Docker is a widely popular containerization platform. Containerization Orchestration tools manage these containers. Kubernetes is the front-runner of the emerging market of container orchestration tools. These software work together seamlessly in order to successfully implement containerization both locally and on the cloud. In this paper, we aim to deploy the container orchestration tool Kubernetes on a local system with a Docker sample container. The purpose of this is to ensure that all the configurations and management needed for a Docker container is set successfully on the local system before it is deployed onto the cloud or on the premise. The on-premise deployment use case is very important in domains such as finance and healthcare where organizations hesitate to upload confidential information on to the cloud for security reasons but still require scaling of their applications.

Self-Hosted Kubernetes: Deploying Docker Containers Locally With Minikube

Proximity marketing is a technique which involves marketing at the particular location using various wireless media. Technically, it can be used to display useful information to potential users at particular places. In this paper, we discuss the implementation of proximity marketing using Bluetooth Low Energy at a retail store. Bluetooth low energy is one of the forms of Bluetooth which is designed for IoT based applications. Bluetooth low energy is a standard of Bluetooth that does not provide a connection functionality(pairing)but instead, transmits signals continuously. These signals can be detected by clients that are on the lookout for that particular beacon. These clients usually involve mobile applications on smartphones which support Bluetooth Smart. In our paper, the implementation of the same for an automated shopping experience has been explored in detail. Many features are incorporated into the client application in order to implement the system, which include: an indoor positioning system, a recommendation engine, Web APIs for handling web requests and more. This paper is based on a real-life project that has been implemented and can be marketed as a product. The extensive literature survey and research that has gone into the project is documented in this paper. This approach helps to ease the shopping experience and it will save the time of users by using automation.

Implementation of Proximity Marketing Leveraging Bluetooth Low Energy

Proximity marketing is a type of marketing that involves a local approach to advertising using wireless media. Technically, it can be used for use cases which involve advertising to potential users at a particular location. In this paper, we propose to use the technology of Bluetooth Low Energy (Bluetooth 4.0) for proximity marketing at a retail store. Bluetooth Low Energy, a wireless PAN technology, is an eco-friendly form of Bluetooth specifically designed for IoT applications. With the advent of the practice of using the IoT framework for automating processes, an approach to use this technology at a retail store could potentially eradicate the need to hire marketing staff and invest in traditional marketing strategies. This process of automation makes use of devices called beacons for transmitting BLE signals, a mobile application on a smartphone and centralised data storage on a server. Beacons can be detected by a mobile application on a smartphone and beacon specific data regarding particular offers and products can be collected from the centralized storage hosted on a server, which can be used for marketing the retail store's products and offers. This application basically acts as a shopping guide for the customers of that particular retail store. In addition to the automation of the marketing task, machine learning can be used for personalizing offers based on each customer's purchase history. Some other features that can be incorporated into the product include an indoor positioning system, a click and collect facility, payment wallet integration, etc. This approach automates the entire shopping experience and is a novel application of IoT in automating trivial tasks.

Ruchika Muddinagiri

Papers

Self-Hosted Kubernetes: Deploying Docker Containers Locally With Minikube

Implementation of Proximity Marketing Leveraging Bluetooth Low Energy

Proximity Marketing Using Bluetooth Low Energy