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With the widespread use of IoT devices in safety-critical applications, new constraints should be addressed in designing IoT infrastructures. Reliability is one of the most important characteristics of an IoT system which should be satisfied with high consideration. The way how IoT devices communicate with each other in different layers of architec...
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This journal present Real time bus location using WSN and Internet of things. Bus user appealed to get bus arrival at the right time, but until now the shuttle bus service in most countries only provide a timetable and most of the time it is unreliable. In this project, the system is designed to provide real-time location, latest bus stop checkpoin...
Citations
... In [51], the authors investigated the side-effects of CoAP and MQTT protocols acting as the two end-nodes of the network, using the Arduino platform. They reported that nodes are located at a distance of 1m from each other and that the experiments occurred in standard room conditions. ...
The Internet of Things (IoT) envisions billions of everyday objects sharing information. As new devices, applications and communication protocols are proposed for the IoT context, their evaluation, comparison, tuning and optimization become crucial and raise the need for a proper benchmark. While edge computing aims to provide network efficiency by distributed computing, this article moves towards sensor nodes in order to explore efficiency in the local processing performed by IoT devices. We present IoTST, a benchmark based on per-processor synchronized stack traces with the isolation and precise determination of the introduced overhead. It produces comparable detailed results and assists in determining the configuration that has the best processing operating point so that energy efficiency can also be considered. On benchmarking applications which involve network communication, the results can be influenced by the constant changes that occur in the state of the network. In order to circumvent such problems, different considerations or assumptions were used in the generalization experiments and the comparison to similar studies. To present IoTST usage on a real problem, we implemented it on a commercial off the-shelf (COTS) device and benchmarked a communication protocol, producing comparable results that are unaffected by the current network state. We evaluated different Transport-Layer Security (TLS) 1.3 handshake cipher suites at different frequencies and with various numbers of cores. Among other results, we could determine that the selection of a specific suite (Curve25519 and RSA) can improve the computation latency by up to four times over the worst suite candidate (P-256 and ECDSA), while both providing the same security level (128 bits).
... By the end of 2025, the number of Internet of Things (IoT) devices will rise to 75 billions worldwide, creating a global market of around 11.1 trillions USD according to some estimates [1]. Functional and robust IoT applications improve our life quality, and provide convenience in many fields, including transportation and logistics (e.g., to support assisted driving or help in the management of goods), healthcare (e.g., to improve workflow in hospitals or facilitate automatic data collection and sensing), agriculture (e.g., to monitor soil and crop parameters), and smart cities [2], [3]. ...
The advent of the Internet of Things (IoT) era, where billions of devices and sensors are becoming more and more connected and ubiquitous, is putting a strain on traditional terrestrial networks, that may no longer be able to fulfill service requirements efficiently. This issue is further complicated in rural and remote areas with scarce and low-quality cellular coverage. To fill this gap, the research community is focusing on non-terrestrial networks (NTNs), where Unmanned Aerial Vehicles (UAVs), High Altitude Platforms (HAPs) and satellites can serve as aerial/space gateways to aggregate, process, and relay the IoT traffic. In this paper we demonstrate this paradigm, and evaluate how common Low-Power Wide Area Network (LPWAN) technologies, designed and developed to operate for IoT systems, work in NTNs. We then formalize an optimization problem to decide whether and how IoT traffic can be offloaded to LEO satellites to reduce the burden on terrestrial gateways.
... Message queuing telemetry transfer (MQTT) gained widespread use in a range of applications, such as in smart homes [2][3][4], agricultural IoT [5,6], and industrial applications. This is mainly due to its capacity to communicate at low bandwidths, the necessity for minimum memory, and reduced packet loss [1,7,8,9]. Figure 1 depicts the architecture of the MQTT protocol for use in the IoT. The IoT and associated technologies evolved at a rapid rate, with 15 billion linked devices in 2015, which is likely to increase to 38 billion devices by 2025, according to Gartner [11]. ...
The Internet of Things (IoT) grew in popularity in recent years, becoming a crucial component of industrial, residential, and telecommunication applications, among others. This innovative idea promotes communication between physical components, such as sensors and actuators, to improve process flexibility and efficiency. Smart gadgets in IoT contexts interact using various message protocols. Message queuing telemetry transfer (MQTT) is a protocol that is used extensively in the IoT context to deliver sensor or event data. The aim of the proposed system is to create an intrusion detection system based on an artificial intelligence algorithm, which is becoming essential in the defense of the IoT networks against cybersecurity threats. This study proposes using a k-nearest neighbors (KNN) algorithm, linear discriminant analysis (LDA), a convolutional neural network (CNN), and a convolutional long short-term memory neural network (CNN-LSTM) to identify MQTT protocol IoT intrusions. A cybersecurity system based on artificial intelligence algorithms was examined and evaluated using a standard dataset retrieved from the Kaggle repository. The dataset was injected by five attacks, namely brute-force, flooding, malformed packet, SlowITe, and normal packets. The deep learning algorithm achieved high performance compared with the developing security system using machine learning algorithms. The performance accuracy of the KNN method was 80.82%, while the accuracy of the LDA algorithm was 76.60%. The CNN-LSTM model attained a high level of precision (98.94%) and is thus very effective at detecting intrusions in IoT settings.
... Common IoT system architectures are usually divided into three layers (see Figure 1) [36]: the Object (Perception) Layer, Network Layer and Application Layer [11]. There are dedicated protocols for each layer of the architecture. ...
The exponential growth of internet connected devices in this past year has led to a significant increase in IoT targeted attacks. The lack of proper integration of security in IoT development life cycle along with a plethora of different protocols (e.g., Zigbee, LoRa, MQTT, etc.) have greatly impacted the resilience of such devices against cyber-attacks, a fact also exacerbated by the size and physical hardware structure of these devices. Thus, it is imperative to develop effective and efficient countermeasures that can also be applied post-production to help build resilience in modern IoT systems. Honeypots are prime example of this notion. Being designed to act as vulnerable computer components or systems, they provide useful intelligence regarding potential attackers. Nevertheless, honeypots have seen little use in protection IoT systems and their underlying protocols, especially in cases where honeypots can leverage the decentralized nature of IoT. In this research, we enhance the HosTaGe honeypot to build an IoT protocol honeypot that runs over mobile devices. The purpose of this paper is to introduce a honeypot specifically for IoT communication protocols over public networks that is easy-to-use and utilizes Android devices. The protocol honeypot utilizes the cellular network to establish decentralized, simulated infrastructures of IoT systems over different types of IoT network protocols. We test four IoT network implementations, one for each of the newly implemented MQTT, CoAP and AMQP protocols. Additionally, we upgrade existing Telnet and SSH protocols used in IoT systems to work over the simulated mobile honeypot. We use the virtualized honeypot networks to capture log, and analyze real-world public attacks on these protocols from the internet and provide an interface for interaction with the implemented honeypot.
... Ever since the advent of smartphones, a variety of embedded sensors is constantly surrounding us -whether we are at work, in transit, or even within our own four walls. With visions of "smart homes", "smart cities", "connected healthcare", and all sorts of new consumer electronics, the emerging Internet of Things (IoT) is predicted to further increase the number of sensors in our everyday environment by several orders of magnitude [1,2]. ...
... While many other types of sensors, such as magnetometers, air quality sensors, and smart electric meters, can be exploited for inference attacks as well [14,43], the focus on accelerometers, eye-tracking sensors, and microphones was chosen based on their expected privacy-intruding potential, their presence in experimental literature, and their role in current technology trends. The accelerometer, for example, which measures acceleration forces, is one of the sensors most frequently accessed by mobile apps [8] and most widely built into wearable devices, such as smartphones, tablets, smartwatches, digital cameras, wearable fitness trackers, game 1 The Art. 29 WP was an independent EU advisory body made up of the European Data Protection Supervisor and representatives from the European Commission and the data protection authorities of all EU member states, established according to Art. 29 of the Data Protection Directive 95/46/EC. In 2018, with the EU's General Data Protection Regulation (GDPR) coming into e ect, the Art. ...
Embedded in mobile devices, private homes, workplaces, and public spaces, sensors have become ubiquitous in modern society. While improving our lives in many ways, the widespread use of sensors also raises serious privacy concerns. Data from everyday sensors often contains intimate personal information and is accessible to various possibly untrusted parties. In addition, advances in data analytics have made it possible to draw detailed inferences even from seemingly innocuous sensor data. This chapter explains why it is important for research to investigate and expose the wealth of personal data that can be extracted from certain types of sensor data. Corresponding publications are needed as a basis for consumer education, risk assessments, privacy safeguards, critical discourse on data protection law, and for understanding the societal impact of sensors. After highlighting the relevance, the scope and limitations of such research are also reflected.
... These applications include smart cities, industry, smart agriculture, health monitoring systems, unmanned vehicles, and intelligent transportation systems. Therefore, due to the increasing trend in the number of IoT applications, it has been predicted that there will be more than 9 devices per person at the end of 2025 [1]. On the other hand, according to reports published by the McKinsey Global Institute, IoT could have a financial impact of 3.9 to 11.1 trillion dollars on the global economy by the end of 2025 [2]. ...
... These policies enables RPL to organize the nodes in form of a tree, which is called Destination Oriented Directed Acyclic Graph (DODAG). RPL uses its OF along with four control messages including DODAG Information Object (DIO), Destination Information Solicitation (DIS), Destination Advertisement Object (DAO), and DAO-ACK to create the DODAG [4], [1]. ...
... A DAG consisting of a single sink node is called DODAG. In order to create, update, and maintain the DODAG, RPL utilizes four ICMPv6 control messages: 1) DODAG Information Object (DIO), 2) DODAG Information Solicitation (DIS), 3) Destination Advertisement Object (DAO), and DAO Acknowledgement (DAO-ACK) [1]. ...
Routing between the IoT nodes has been considered an important challenge, due to its impact on different link/node metrics, including power consumption, reliability, and latency. Due to the low-power and lossy nature of IoT environments, the amount of consumed power, and the ratio of delivered packets plays an important role in the overall performance of the system. Meanwhile, in some IoT applications, e.g., remote health-care monitoring systems, other factors such as End-to-End (E2E) latency is significantly crucial. The standardized routing mechanism for IoT networks (RPL) tries to optimize these parameters via specified routing policies in its Objective Function (OF). The original version of this protocol, and many of its existing extensions are not well-suited for dynamic IoT networks. In the past few years, reinforcement learning methods have significantly involved in dynamic systems, where agents have no acknowledgment about their surrounding environment. These techniques provide a predictive model based on the interaction between an agent and its environment to reach a semi-optimized solution; For instance, the matter of packet transmission, and their delivery in unstable IoT networks. Accordingly, this paper introduces PEARL; a machine-learning based routing policy for IoT networks, which is both, delay-aware, and power-efficient. PEARL employs a novel routing policy based on the q-learning algorithm, which uses the one-hop E2E delay as its main path selection metric to determine the rewards of the algorithm, and to improve the E2E delay, and consumed power simultaneously in terms of Power-Delay-Product (PDP). According to an extensive set of experiments conducted in the Cooja simulator, in addition to improving reliability in the network in terms of Packet Delivery Ratio (PDR), PEARL has improved the amount of E2E delay, and PDP metrics in the network by up to 61% and 72%, against the state-of-the-art, respectively.
... global economy will rise to more than 75 billion connected "things" by the end of 2025. There will be more than 9 smart devices per person which would raise many challenges [12]. ...
The rise of online devices, online shopping, online gaming, online users, and online teaching has ultimately given rise to online attacks and online crimes. As the cases of COVID-19 seem to increase day by day, so do the online crimes and attacks (as many sectors and organizations went 100% online now). The current technological advancements and the cyber war already coined the ethical issue as well as the rise of internet users and the sudden need of ethical cyber defense. This was the problem on one end, and on the other nation states (some secretly, some openly), are investing in Robot Weapons and Autonomous Weapons Systems. New technologies have combined with countries’ security worries to give rise to a new arms race. Because a country / nation can enter the automated weapons space in a way that is impractical for nuclear weapons, nations are trying to make their presence known in both the offline and online battlefields. My thesis is that it is possible to frame the ethical security model based on the increasing online crimes, robot weapons and online attacks.
The main contribution of this dissertation will be to show that there are multiple cyber defense principles, counter measures, and ethical actions to slow down these ongoing threats (which is the first and foremost need in this current online era). Most importantly, the countermeasures and security strategies developed (based on increasing online attacks and rise of AWS) can save billions of dollars (invested in developing autonomous weapons, firewalls & robotics industries for arms race between nation states) and work towards global peace and security.
... Machines are an increasing part of every domain of our daily lives. In 2015, there was approximately two connected devices per human on earth (Safaei et al., 2017). Safaei et al. estimated that the number of connected devices would increase to nine per human on earth by 2030. ...
Researchers and practitioners recognize four domains of behavior analysis: radical behaviorism, the experimental analysis of behavior, applied behavior analysis, and the practice of behavior analysis. Given the omnipresence of technology in every sphere of our lives, the purpose of this conceptual article is to describe and argue in favor of a fifth domain: machine behavior analysis. Machine behavior analysis is a science that examines how machines interact with and produce relevant changes in their external environment by relying on replicability, behavioral terminology, and the philosophical assumptions of behavior analysis (e.g., selectionism, determinism, parsimony) to study artificial behavior. Arguments in favor of a science of machine behavior include the omnipresence and impact of machines on human behavior, the inability of engineering alone to explain and control machine behavior, and the need to organize a verbal community of scientists around this common issue. Regardless of whether behavior analysts agree or disagree with this proposal, I argue that the field needs a debate on the topic. As such, the current article aims to encourage and contribute to this debate.
... However, there is nowadays a trend of people simultaneously using multiple data-producing devices such as smartphones, smartwatches and smart earbuds to collect data about their physical activities, health, or context. It is even predicted that by 2025, each person will own 9.3 connected devices on average [61]. From a sensing perspective, multi-device environments offer exciting opportunities to develop accurate and generalizable models by leveraging the similarities and differences across devices. ...
Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human-activity recognition, FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by high non-IID-ness across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.8-33.8% higher F-1 score, 1.02-2.86x greater energy efficiency, and up to 2.02x speedup in convergence to target accuracy through fair distribution of the FL workload.
... If users register browsers to their account, as described in Section 6.1, is compared to the fingerprint of these browsers that are already identified. Users are expected to register fewer than ten devices[82], hence is expected to be smaller than (e.g., a website having a thousand accounts registered). It would also be possible to find the right fingerprint among the possibilities by leveraging the UserAgent to recognize which browser the user is using. ...
Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this article, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly six months. About their performance, we show that our fingerprints weigh a dozen of kilobytes and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes and their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.
