Popular Internet Of Things Research Articles

Digitalization of the educational process of the transport university

Digital technologies are more present in our lives. Such concepts as blockchain, Big data, Internet of Things (IoT) 5G networks, artifi cial intelligence are already becoming commonplace. The educational process, which should be organized to solve key production problems, is no exception. The training of personnel for the transport industry is carried out taking into account the fact that in the near future every graduate of a transport educational institution should have digital competencies. In this regard, the features and nuances of teaching digital technologies are extremely important and relevant. The article discusses the methodology for teaching students of the popular IoT technology. The possibilities of the hardware and software complex “Industrial Internet of Things” (agro-industrial complex IoT), which has been used in the educational process of PGUPS since 2021, are considered. The questions are discussed how best to conduct IoT training, what practical skills students will acquire when working with elements of the IoT agro-industrial complex. There is no doubt that transformation in technical education should take place with the use of similar IoT agro-industrial complex. In the near future, it is precisely such educational laboratory stands that will allow organizing training in digital competencies at a high level, which is extremely necessary for the preparation of graduates adapted to modern production tasks.

The solution to improve information security for IoT networks by combining lightweight encryption protocols

With the strong development of Internet platforms today, many new technologies are applied in most areas of human social life. The development of the internet of things (IoT) has brought to the networking world many innovations with new protocols. Along with that is the complexity of security issues that greatly affect personal data, and limited resources in information technology development and applications. Many security solutions have been researched but none of them is completely effective for the IoT network because of the characteristics of energy, limited resources, processing capacity as well as operating costs. Therefore, we propose an effective solution for comprehensive protection of IoT systems against attacks to network security with improved security protocols. By combining security solutions on the layers of the IoT and code improvement, algorithmic enhancement positively contributes to this important task. In the article, we propose to improve and combine the DTLS Protocol and the overhearing mechanism and then have some experiments to prove the effectiveness, feasibility, economical and appropriate on popular IoT network models.

A survey on IoT platforms: Communication, security, and privacy perspectives

The Internet of Things (IoT) redefines the way how commodity and industrial tasks are performed every day. The integration of sensors, lightweight computation, and the proliferation of different wireless technologies on IoT platforms enable human beings to easily interact with their surrounding physical world thoroughly. With the recent rise of IoT, several different IoT platforms have been introduced for researchers and developers to ease the management and control of various IoT devices. In general, the IoT platforms act as a bridge between core IoT functionalities and users by providing APIs. Due to their wide variety of applications, IoT platforms are mostly unique in their architectures and designs. Thus, IoT administrators, developers, and researchers (i.e., IoT users) are challenged with substantial configuration differences in the proper configuration, implementation, and protection of the IoT solutions. In this survey, we conduct an in-depth analysis of popular IoT platforms from different application domains. More specifically, we define a comprehensive evaluation framework that considers seven different technical comparison criteria: (1) topology design, (2) programming languages, (3) third-party support, (4) extended protocol support, (5) event handling, (6) security, and (7) privacy. Then, we use the framework to evaluate the different IoT platforms highlighting their distinguishing attributes on communications, security, and privacy. First, we describe the communication protocols supported by the different IoT platforms surveyed. Then, rather than uncovering novel threats affecting IoT, we aim to analyze how the different IoT platforms handle security and privacy vulnerabilities affecting the most common security services of confidentiality, integrity, availability, and access control. Further, we present possible solutions that these platforms could implement to strengthen security and privacy within the IoT solution. Finally, we discuss the advantages and disadvantages of every IoT platform, so IoT administrators, developers, and researchers (i.e., IoT users) can make an informed decision on the use of specific platforms to implement their IoT solutions. To the best of our knowledge, this is the first comprehensive survey to evaluate different IoT platforms using the criteria defined in this work.

A Framework for Multi-Sensor Data fusion in the Context of IoT Smart City Parking Data

Now a day, things are communicating using the Internet, In the context of a smart city all things are interconnected and they are transferring the data to each other. Smart city subapplications like parking, pollution, traffic, transportation are some examples. By using the internet, we can combine with communication, the big challenge in a multi-sensory environment to fetch meaning full data from sensors. There are many of the methods are used to analyse and mine IoT data like classification, clustering, association rule, and regression, etc. The data is collected on the Internet of Things (IoT) from heterogeneous sources in different formats. Data Fusion is a method to operate on fused data that are collected from different sources. In the case of IoT, the concept of distributed data fusion work well. One of the popular IoT applications is Smart City, where we can collect data and analysis them according to need. In this research paper, the researcher introduced the new framework of data fusion where the data is collected from a different type of smart city sub-application. and framework deployment is done by parking data set of smart cities.

Performance evaluation of Attribute-Based Encryption on constrained IoT devices

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. This raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e., smartphones or embedded systems, has been demonstrated in recent works. In this paper, we consider IoT devices characterized by strong limitations in terms of computing, storage, and power. Specifically, we assess the performance of ABE in typical IoT constrained devices. We evaluate the performance of three representative ABE schemes configured considering the worst-case scenario on two popular IoT platforms, namely ESP32 and RE-Mote. Our results show that, if we assume to employ up to 10 attributes in ciphertexts and to leverage hardware cryptographic acceleration, then ABE can indeed be adopted on devices with very limited memory and computing power, while obtaining a satisfactory battery lifetime. In our experiments, as also performed in other works in the literature, we consider only the worst-case configuration, which, however, might not be completely representative of the real working conditions of sensors employing ABE. For this reason, we complete our evaluation by proposing a novel benchmark method that we used to complement the experiments by evaluating the average performance. We show that by always considering the worst case, the current literature significantly overestimates the processing time and the energy consumption.

Experimental Evaluation of the Packet Reception Performance of LoRa.

LoRa technology is currently one of the most popular Internet of Things (IoT) technologies. A substantial number of LoRa devices have been applied in a wide variety of real-world scenarios, and developers can adjust the packet reception performance of LoRa through physical layer parameter configuration to meet the requirements. However, since the important details of the relationship between the physical layer parameters and the packet reception performance of LoRa remain unknown, it is a challenge to choose the appropriate parameter configuration to meet the requirements of the scenarios. Moreover, with the increase in application scenarios, the requirements for energy consumption become increasingly high. Therefore, it is also a challenge to know how to configure the parameters to maximize the energy efficiency while maintaining a high data rate. In this work, a complex evaluation experiment on the communication capability under a negative Signal to Noise Ratio is presented, and the specific details of the relationship between physical layer parameters and the packet reception performance of LoRa are clarified. Furthermore, we study the impact of the packet length on the packet reception performance of LoRa, and the experimental results show that when there is a large amount of data to be transmitted, it is better to choose long packets instead of short packets. Finally, considering the influence of physical layer parameters and the packet length on the packet reception performance of LoRa, the optimal parameter combination is explored, so as to propose a transmission scheme with a balanced reliability, delay, and energy consumption. This scheme is the first to consider the physical layer parameters and packet length together to study the communication transmission scheme, which reduces the communication time by 50% compared with the traditional transmission scheme and greatly reduces the energy consumption.

The enhancement of agricultural productivity using the intelligent IoT

ABSTRACT The abnormal climatic changes due to global warming causes damage to crops. Uncontrollable factors make it difficult for farmers to use conventional methods for farming. Furthermore, with the aging population and the reluctance of the younger generation to be devoted to farming jobs that require a large amount of physical work, there is a problem of global food shortage. Therefore, several countries have invested in the development of science and technology in agriculture, expecting to promote the overall development of agricultural production through the Agricultural Internet of Things (AIoT). AIoT is a highly integrated and comprehensive application of a new generation of information technology in the agricultural field. In this study, the currently popular Internet of Things (IoT) is used to build a smart greenhouse. To solve the problem of farmers not understanding the operation of the information interface, this study proposes an IoT-based smart voice control architecture, which details the operation of the voice system architecture. It successfully replaces the conventional unfriendly operation interface and is generally recognized by users, which lowers the barrier of entry with respect to of science and technology for those who are willing to devote themselves into the development of scientific agriculture, inspires a wide range of applications, and effectively improves the production efficiency of agriculture.

Presenting a Reliable Routing Approach in IoT Healthcare Using the Multiobjective‐Based Multiagent Approach

Developments in information and related technologies have led to a wider use of the Internet of things (IoT). By integrating both virtual and physical worlds, IoT creates an integrated communication framework of interrelated things and operating systems. With the advent of IoT systems based on digital remote care, transferring medical data is becoming a daily routine. Healthcare is one of the most popular IoT applications and tries to monitor patients’ vital signs during the day for weeks and to eliminate the need for hospitalization. In a healthcare system, many sensors are installed to collect the patient’s information, including environmental monitoring sensors and vital and unstructured message sensors in order to reduce the patients’ expenses. The IoT network contains flexible sensors in dynamically changing environments where sensors collect environmental information and send it to nursing stations for healthcare applications. Due to the wireless nature of IoT networks, secure data transmission in the healthcare context is very important. Data collected from sensors embedded in healthcare devices may be lost for various reasons along the transmission path. Therefore, establishing a secure communication path in IoT networks in the context of healthcare is of great importance. In this paper, in order to provide a reliable data transfer protocol in the context of healthcare, a reliable routing using multiobjective genetic algorithm (RRMOGA) method is presented. The contribution of this paper can be summarized in two steps: (i) using a multiobjective optimization approach to find near‐optimal paths and (ii) using reliable agents in the network to find backup paths. The simulation outcomes reveal that the proposed approach, based on the use of the multiobjective optimization approach, tries to find optimal paths for information transfer that improve the main parameters of the network. Also, the use of secure agents leads to a secure information transfer in the network in the context of healthcare. Experimental results show that the proposed method has achieved reliability and data delivery rates, 99% and 99.9%, respectively. The proposed method has improved network lifetime, delivery rate, and delay by 14%, 2%, and 5.6%, respectively.

Software-defined wireless urban network

Software-defined wireless urban network (SD-WUN) is a new network subclass for implementation in the “Smart City” project. SD-WUN can be offered as a single technical solution, built based on the most relevant technologies in the world of computer networks and the Internet. The theoretical model of existing technological solutions interaction is one of the best ways to eliminate the problems of the “Smart City” project mentioned in the work and offers one of the possible ways of its development in the future. The analysis is based on assessments of the prospects for the development of existing methods for implementing computer network nodes and the Internet of things (IoT), and their comparison with each other. One of the most pressing problems of the “Smart City” project implementation is the best choice from the point of view of network technologies. An urgent problem in the field of network technologies is the problem of the transition of network addressing from the IPv4 address space to IPv6. It is obvious that the Smart City project is associated with the popular IoT technology (Internet of Things) due to the progression of the number of devices. Another key point is the need to implement a project based on a high-speed wireless network, excluding wired switching. It is obvious that the network should possess such properties as security, distribution of computing resources, high data transfer speed and ease of use and support of the network.

Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

Electromagnetic (EM) side-channel radiation from Internet of Things (IoT) devices are shown to be effective at acquiring forensic insights during digital investigations. These EM radiation patterns can be analysed with the help of machine learning algorithms to detect internal behaviours of IoT devices, which can be relevant to an investigation. However, the real-world application of EM side-channel analysis for digital forensic purposes is obstructed by the lack of suitable tools and the technical expertise among law-enforcement communities. Although certain frameworks, such as EMvidence, exist to cater this requirement, the sheer diversity of the IoT ecosystem makes it difficult to support a sufficiently large collection of devices that are commonly encountered in forensic investigations. The work presented in this paper makes multiple contributions towards addressing this problem. Initially, a detailed discussion on the challenges of applying EM side-channel analysis in practical digital forensic purposes is provided, where the practical difficulties are illustrated. Then, it was shown that the existing EM side-channel analysis frameworks, such as EMvidence, can be used to overcome the diversity of IoT devices in forensics by equipping them with extensible plug-ins targeting the internal system-on-chips (SoC) of each device type. These plug-ins are expected to incorporate trained machine learning models, which are capable of recognising patterns of specific IoT device SoCs. However, the development of such plug-ins requires sufficiently diverse EM datasets from IoT devices. Facilitating this requirement, this work presents a comprehensive EM side-channel dataset representing a diverse collection of popular IoT devices and smartphones. The presented dataset is used to demonstrate the potential usage of machine learning models to recognise device behaviour.

Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments.

LoRaWAN is a popular internet of things (IoT) solution over the unlicensed radio band. It sustains low-cost, durable, and long range IoT wireless communications. Nonetheless, with over 24 billion connected IoT devices being expected by the end of the year, and over 50 billion by 2025, the concurrent and legacy approaches to spreading factor and channel assignment in LoRaWAN networks can no longer keep up. This is exacerbated with the growing densification of IoT device deployments and, with the increasing requirements for better throughput and packet delivery ratios. In this paper, we propose a proportional fair-based joint optimal formulation for spreading factor and channel assignment in multi-operator LoRaWAN deployments. The objective of this problem is to maximize the total sum of the logarithmic normalized throughput. We split the problem into two subproblems, and propose a game theoretic approach to solving them. We prove that our games converge towards a pure Nash equilibrium and, afterwards, solve the optimization problems using both semi-distributed and completely distributed algorithms. Via simulations, we show that our algorithms greatly improve the total normalized throughput for LoRaWAN as well as the packet success rate, in comparison to the legacy approaches.

A Large-Scale Empirical Study on the Vulnerability of Deployed IoT Devices

The Internet of Things (IoT) has become ubiquitous and greatly affected peoples’ daily lives. With the increasing development of IoT devices, the corresponding security issues are becoming more and more challenging. Such a severe security situation raises the following questions that need urgent attention: What are the primary security threats that IoT devices face currently? How do vendors and users deal with these threats? In this article, we aim to answer these critical questions through a large-scale systematic study. Specifically, we perform a ten-month-long empirical study on the vulnerability of 1,362,906 IoT devices varying from six types. The results show sufficient evidence that N-days vulnerability is seriously endangering the IoT devices: 385,060 (28.25 percent) devices suffer from at least one N-days vulnerability. Moreover, 2669 of these vulnerable devices may have been compromised by botnets. We further reveal the massive differences among five popular IoT search engines: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shodan</i> [1], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Censys</i> [2], [3], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zoomeye</i> [4], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fofa</i> [5], and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NTI</i> [6]. To study whether vendors and users adopt defenses against the threats, we measure the security of MQTT [7] servers, and identify that 12740 (88 percent) MQTT servers have no password protection. Our analysis can serve as an important guideline for investigating the security of IoT devices, as well as advancing the development of a more secure environment for IoT systems.

Memoryless Techniques and Wireless Technologies for Indoor Localization With the Internet of Things

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). 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 are necessary for efficient workspaces to be developed. In this paper, 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: Zigbee, Bluetooth Low Energy (BLE), and 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 RSSI dataset of all the experiments is available online.

Cross Technology Distributed MIMO for Low Power IoT

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands &#x2013; LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4&#x00D7; and battery life of up to 3.5 years.

A Routing Framework With Protocol Conversions Across Multiradio IoT Platforms

The proliferation of sensors as part of the Internet-of-Things (IoT) paradigm envisions a world of interconnected, interdependent deployments, involving one or multiple radio technologies. Multiradio mesh deployments facilitated using a network of low-cost IoT platforms, such as Raspberry Pis (RPis), ASUS Tinker Board S, Banana Pi M64, etc., seem an ideal fit as wireless backbones for such deployments, with many of these platforms having built-in support for multiple wireless technologies. However, the state-of-the-art routing solutions need to be revisited to suit the capabilities of low-power multiradio solutions, especially while performing best effort transmissions. This is because a majority of the existing routing solutions favor channel diversity while routing, ignoring the possible overheads associated with protocol conversions. We argue that in reality, these overheads can become quite significant in a multiradio network where the radios are quite different in terms of packet formats, sizes, data rates, etc. To prove this, we estimate the impact of these overheads using an experimental study performed across three popular IoT technologies—WiFi, Bluetooth, and ZigBee, using a real-time RPi-based testbed. We stress on how severe this impact could be, by estimating how often these conversions happen in a randomly chosen multihop path, using a probabilistic mathematical framework. Based on this, we develop Chorus, a first-of-its-kind routing algorithm, which estimates the least overhead path, inclusive of conversion overheads. Chorus achieves this by developing a comprehensive layered graph modeling of the given multiradio mesh network. The modeling is capable of handling all conversion overheads, including the factors associated with the possible fragmentation and aggregation. We implement Chorus in an indoor testbed comprising of RPis and demonstrate the performance improvement while routing across the network of RPis. Our results estimate that Chorus can provide an improvement close to 33% in battery usage per RPi with an average reduction of 25% in packet drops.

The DNS in IoT: Opportunities, Risks, and Challenges

The Internet of Things (IoT) is widely expected to make our society safer, smarter, and more sustainable. However, a key challenge remains, which is how to protect users and Internet infrastructure operators from attacks on or launched through vast numbers of autonomously operating sensors and actuators. In this article, we discuss how the security extensions of the domain name system (DNS) offer an opportunity to help tackle that challenge, while also outlining the risks that the IoT poses to the DNS in terms of complex and quickly growing IoT-powered distributed denial of service (DDoS) attacks. We identify three challenges for the DNS and IoT industries to seize these opportunities and address the risks, for example, by making DNS security functions (e.g., response verification and encryption) available on popular IoT operating systems.

Caching in Dynamic IoT Networks by Deep Reinforcement Learning

The sensing service of Internet-of-Things (IoT) networks enables IoT sensors to sense the environment information (e.g., temperature and traffic conditions) and send them through the IoT gateway to the users who request those information. The explosive growth of IoT users and sensors injects massive traffic into IoT networks and easily depletes the battery of IoT sensors. Caching at the IoT gateway is hence a promising solution to mitigate this problem by storing popular IoT data at the IoT gateway and sending them directly to the users instead of activating IoT sensors to transmit the data. In our work, we investigate the content placement problem, which determines data to be cached at each time epoch in dynamic IoT networks with the objective to minimize the average data transmission delay constrained by the cache storage capacity and IoT data freshness. We formulate our problem as an integer linear programming (ILP) problem and then model it as a Markov decision process (MDP). A deep reinforcement learning algorithm is proposed to solve this problem and its performances are demonstrated via extensive simulations.

Modelling and Analysis of IoT Technology Using Neural Networks In Agriculture Environment

The rapid development of internet, cloud computing and sensor networks lead to develop and deploy the Internet of Things (IoT) which is a hot topic for the researchers. It has started to be used in various areas. Thus, agriculture is one of the most popular IoT research area. In agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air temperature, solar radiations, and relative humidity etc. These extreme weather conditions effect crops and farms very significantly. But with the benefits of Internet of Things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. This paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. This paper proposed a time-delay radial basis function (TDRBF) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environment. The performance of the acquired network model was analysed statistically and presented in this paper. As a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years.

Internet of things in the archives: novel tools for environmental monitoring of archival collections

Purpose Cultural heritage archives rely on environmental monitoring devices, such as dataloggers or more complex networked systems, to ensure collection preservation through collecting temperature, humidity, light and/or air quality measures. Existing systems are often costly, inflexible and do not use a modern, internet of things (IoT) approach. This paper aims to determine the suitability of currently popular general-purpose IoT devices, standards and technologies to the environmental monitoring needs of archivists, as well as identify any challenges. Design/methodology/approach This paper describes an exploratory study detailing the design, construction and usability testing of a do-it-yourself datalogger and data dashboard system, which seeks to manage previously identified trade-offs in cost, required technical skill and maintainability. Findings The environmental monitoring system presented met archivists’ needs well and was generally noted to be easy-to-use, efficient and an improvement on existing systems. This suggests that an IoT approach can support archivists’ needs in this area. Research limitations/implications Potential limitations of this study include lack of archival staff with sufficient technical training to maintain such a system and the rapid pace of IoT evolution yielding unstable and constantly changing technologies. Practical implications The system design presented in this work provides a blueprint for cultural heritage organizations desiring a fuller-featured, lower cost environmental monitoring system for archival collections. Originality/value This research takes a novel user-centered, open-source, IoT approach to construct an environmental monitoring system that is designed directly from archivists’ requirements and is extensible for future needs.

PGFit: Static permission analysis of health and fitness apps in IoT programming frameworks

Popular Internet of Things (IoT) programming frameworks, such as Google Fit, enable third-party developers to build apps that store and retrieve user data from a variety of data sources such as wearable devices. Most of these apps, particularly those that are health and fitness-related, collect potentially sensitive personal data and send it to cloud servers. Analogous to Android OS, IoT programming frameworks often follow similar permission model; third-party apps on IoT platforms prompt users to grant the apps the access to their private data stored on cloud servers of IoT programming frameworks. Most users have a poor understanding of why these permissions are being asked. This can often lead to unnecessary permissions being granted, which in turn grant these apps with excessive privileges. Over-privileged apps might not be harmful to users when they are used as designed, however, they can potentially be exploited by a malicious actor in a cyber security attack. This is of particular concern with health and fitness apps, which may be exploited to leak highly sensitive personal information. This paper presents PGFit, a static permission analysis tool that precisely and efficiently identifies privilege escalation in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFit extracts the set of requested permission scopes and the set of used data types in Google Fit-enabled apps to determine whether the requested permission scopes are actually necessary. PGFit performs graph reachability analysis on inter-procedural control flow graph. PGFit serves as a quality assurance tool for developers and a privacy checker for app users. We evaluated PGFit using a set of 20 popular Google Fit-enabled apps downloaded from Google Play. Our tool successfully identified the unnecessary permission scopes granted in our data set apps and found that 6 (30%) of the 20 apps are over-privileged.