In the era of smart cities, there are a plethora of applications where localization of indoor environments is important; from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a costefficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, Global Positioning System (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: WiFi (IEEE 802.11n-2009 at the 2.4GHz band), Bluetooth Low Energy (BLE), Zigbee, and Long Range Wide-Area Network (LoRaWAN). These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The Received Signal Strength Indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI dataset is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application

The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this article, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, a received signal strength-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the cloud, to improve localization, precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of indoor positioning systems (IPSs) and location-based services (LBSs). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS is necessary for efficient workspaces to be developed. In this article, we examine two memoryless positioning techniques, K -nearest neighbor (KNN) and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: 1) ZigBee; 2) Bluetooth low energy (BLE); and 3) WiFi (2.4-GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The received signal strength indicator data set of all the experiments is available online.

Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network’s distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform’s ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient’s privacy and a predictive service’s integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.

Artificial intelligence (AI) and 5G system have been two hot technical areas that are changing the world. On the deep convergence of computing and communication, networking systems of AI (NSAI) is presenting a paradigm shift, where distributed AI becomes immersive in all elements of the network, i.e., cloud, edge, and terminal devices, which make AI virtually operating as a networking system. On the other hand, by the evolution of the communication systems, a network is becoming a service-specific system interweaved with AI, i.e., the network operates as an AI system, enabling real-time smart services. With the developing technology trends of “AI as a network and network as an AI,” the ecosystem of NSAI can be presenting the next-generation waves of both AI systems and B5G-6G communication networks. In this article, we mainly aim to provide a comprehensive survey on the system architecture, key technologies, application scenarios, challenges, and opportunities of NSAI, which can shed light on the future developments of both telecommunications and AI computing. The contributions of this article also include: 1) providing a unified framework for the deep convergence of computing and communications, where the network and application/service can be jointly optimized as a single integrated system and 2) suggesting the roadmap and open research problems in realizing the online-evolutive integration of cyberspace, physical world, and human society, toward the ubiquitous brain networks (UBNs), which are requiring the joint efforts from both research communities of computing and communication.