Embedded Internet Of Things Devices Research Articles

Internet-Scale Fingerprinting the Reusing and Rebranding IoT Devices in the Cyberspace

Fingerprinting Internet-of-Things(IoT) devices on types and brands is a necessary work for security analysis in the cyberspace. The existing approaches mainly rely on the dominant features of devices which is response to information in order to identify these online devices. However, the web server components reusing and products rebranding are the common phenomenons of these embedded IoT devices. It caused the existing approaches difficult to identify most devices even errors due to the similar responses. In this paper, we present an approach, IoTXray, which improves the work efficiently of information collection about accelerating the relations between reusing/rebranding devices with the corresponding manufacturers. And these relations can generate more accurate and reliable fingerprints than previous approaches. Using the mixed neural networks, IoTXray comprehensively detects the real manufactures of online IoT devices upon three different kinds of data sources. In the experiment, our approach can identify 7,025,854 IoT devices on HTTP-hosts. The identification rate has reached to several times higher than previous approaches. Our approach has especially detected 3,268,953 reusing and 963,653 rebranding devices with their original manufacturers.

A Novel Hybrid Reconfigurable Architecture for Prediction of Side Channel Attacks with Its Countermeasure Mechanism

The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a very vital role in IoT networks. As a result, the paper proposes a novel reconfigurable architecture as Field Programmable Gate Arrays (FPGA) that integrates deep learning models for effective SCA prediction as well as a countermeasure mechanism based on chaotic maps. Finally, the experimentation results prove the excellence of the proposed framework in terms of prediction accuracy (99%), sensitivity (98.9%), and specificity (98.9%) in comparison with LSTM, ELM SVM, RF, NB, and ANN. The results also demonstrated that the proposed framework requires only 50% of the total energy for network operation.

An Optimized Energy Harvesting Circuit for Low-Power IoT Applications

The concept and development of an independent energy harvesting mechanism functioning intermittently are described in this paper. A power management circuit (PMC) that is self-regulating, an energy scavenging module, a circuit for charging batteries, as well as an electronic load are all a component of the system that has been proposed. This proposed circuit is designed to attain a fixed output power with a diverse input range. In the unavailability of an additional voltage supply, the PMC can react, maintain, and smartly control the electronic load's power supply. The self-powered energy accumulating technique is expected to be used in situations when supplied power is inadequate to drive the load properly, such as Internet of Things (IoT) applications. IoT is a dispersed architecture of reduced-power, limited-storage, lightweight, and nodes that are adaptive. The majority of embedded IoT devices and low-power IoT sensors are driven by short-life batteries that must be replaced every few years. This procedure is expensive and efficient energy regulation could be critical in enabling energy savings for connecting IoT devices. Experiments with the proposed PMC show that the voltage stored in the capacitor remained mostly fixed at 3.3V at widely diverse inputs that vary from 850mV to 4V. At 3.5V input voltage, a peak efficiency of 88.67% is achieved while the load resistance considered is 230Ω.

OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices

This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering level. We propose OTA-TinyML to enable resource-constrained IoT devices to perform end-to-end fetching, storage, and execution of many TinyML models. OTA-TinyML loads the C source file of ML models from a web server into the embedded IoT devices via HTTPS. OTA-TinyML is tested by performing remote fetching of six types of ML models, storing them on four types of memory units, then loading and executing on seven popular MCU boards.

A Novel Workload-Aware and Optimized Write Cycles in NVRAM

With the emergence of the Internet of things (IoT), embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare, home automation and mainly Industry 4.0. These Embedded IoT devices are mostly battery-driven. It has been analyzed that usage of Dynamic Random-Access Memory (DRAM) centered core memory is considered the most significant source of high energy utility in Embedded IoT devices. For achieving the low power consumption in these devices, Non-volatile memory (NVM) devices such as Parameter Random Access Memory (PRAM) and Spin-Transfer Torque Magnetic Random-Access Memory (STT-RAM) are becoming popular among main memory alternatives in embedded IoT devices because of their features such as high thickness, byte addressability, high scalability and low power intake. Additionally, Non-volatile Random-Access Memory (NVRAM) is widely adopted to save the data in the embedded IoT devices. NVM, flash memories have a limited lifetime, so it is mandatory to adopt intelligent optimization in managing the NVRAM-based embedded devices using an intelligent controller while considering the endurance issue. To address this challenge, the paper proposes a powerful, lightweight machine learning-based workload-adaptive write schemes of the NVRAM, which can increase the lifetime and reduce the energy consumption of the processors. The proposed system consists of three phases like Workload Characterization, Intelligent Compression and Memory Allocators. These phases are used for distributing the write-cycles to NVRAM, following the energy-time consumption and number of data bytes. The extensive experimentations are carried out using the IoMT (Internet of Medical things) benchmark in which the different endurance factors such as application delay, energy and write-time factors were evaluated and compared with the different existing algorithms.

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.

Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities

The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.

Fuzzing the Internet of Things: A Review on the Techniques and Challenges for Efficient Vulnerability Discovery in Embedded Systems

With a growing number of embedded devices that create, transform, and send data autonomously at its core, the Internet of Things (IoT) is a reality in different sectors, such as manufacturing, healthcare, or transportation. With this expansion, the IoT is becoming more present in critical environments, where security is paramount. Infamous attacks, such as Mirai, have shown the insecurity of the devices that power the IoT, as well as the potential of such large-scale attacks. Therefore, it is important to secure these embedded systems that form the backbone of the IoT. However, the particular nature of these devices and their resource constraints mean that the most cost-effective manner of securing these devices is to secure them before they are deployed, by minimizing the number of vulnerabilities they ship. To this end, fuzzing has proved itself as a valuable technique for automated vulnerability finding, where specially crafted inputs are fed to programs in order to trigger vulnerabilities and crash the system. In this survey, we link the world of embedded IoT devices and fuzzing. For this end, we list the particularities of the embedded world as far as security is concerned, we perform a literature review on fuzzing techniques and proposals, studying their applicability to embedded IoT devices and, finally, we present future research directions by pointing out the gaps identified in the review.

UAP: A unified authentication platform for IoT environment

The Internet of Things (IoT) is a network that encompasses every physical object being connected to the Internet. The IoT objects face several issues and challenges due to the large number and the heterogeneity of the interconnected devices. Many researchers have shown that security is among the most important challenges that the IoT faces. Security requirements such as confidentiality, integrity and authentication are fundamental, but the last one is considered as the cornerstone security service.This paper considers authentication as its core and presents an extensive review of the already proposed authentication solutions for the IoT. Subsequently, it investigates new authentication requirements and challenges introduced in the context of the embedded IoT devices. Furthermore, it unveils the proposed authentication platform. The platform is based on a reduced signature generated on a distributed way from the devices’ personal keys. Also, it allows a receiving entity to verify the identity of the sender entity and obliges the sender not to deny the emission action. The analysis and simulation comparison between the literature and the proposed solution is presented.The analysis and simulation study provide that the proposed platform respects the requirements of any IoT device and is recommended for low power IoT devices. In the sequel, simulation results validate the analytical study and shows that the proposal is recommended for any IoT embedded device. For instance, the average energy and communication costs are no more than 0.03 microjoules and 2.73 message exchange, respectively.

Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

Lightweight rogue access point detection algorithm for WiFi-enabled Internet of Things(IoT) devices

The Internet of Things (IoT) is a new paradigm that enables the convergence of smart objects and the internet. This convergence has led to the creation of an intelligent network that connects all things to the internet to exchange information. The direct connection of IoT devices to the internet makes them susceptible to several security threats. Several techniques have been proposed by researchers in enhancing the security of IoT devices. Currently, most IoT products use WiFi as their medium of communication. This makes them prone to conventional WiFi attacks of which one is rogue access points. The lightweight nature of most IoT devices makes it difficult to implement conventional security solutions. In this study, we present a real-time and lightweight algorithm, based on information-theoretic approach, that enables rogue access point detection for embedded IoT devices. This is to ensure that WiFi-enabled IoT devices can intelligently distinguish between legitimate and rogue access points. We evaluate the performance of the proposed algorithm on the CPU utilization efficiency and the time taken in identifying the rogue access point.

Fast Operation Mode Selection for Highly Efficient IoT Edge Devices

In the emerging paradigm of edge computing (EC) for Internet of Things (IoT), data processing is pushed to the edge of the IoT network (e.g., gateways and embedded IoT devices). IoT devices must support multiple operation modes in order to adapt to varying runtime situations, like preserving energy at low battery, while still maintaining some crucial functionality, etc. Adapting the optimal operation mode is a challenge for edge devices given the limited resources at the edge of the network (both bandwidth and processing power of the shared gateway), various constraints (e.g., battery lifetime), etc. This paper proposes a fast and low-overhead scheme to determine and adapt the operation mode of edge devices at runtime and orchestrate devices in a way that the efficiency of IoT devices is optimized with respect to the gateway’s resource constraints. The proposed scheme breaks the optimization problem into several smaller ones (i.e., subproblems) whose solutions are aggregated to find the final solution. We present a novel memoization technique that determines the solution to a range of subproblems based on subproblems that are already solved. In addition, we present a novel pruning technique that reduces the search space and consequently reduces both memory and execution time overhead. The experimental results show up to 50% reduction in memory overhead and $14 \times $ reduction in execution time overhead compared to the state-of-the-art solution which is a major step toward efficient EC for IoT.

Analysis of IoT mechanisms for media streaming

One critical component of many Internet of Things (IoT) applications that rely on audio, speech and video for decision making is Real Time Communication (RTC). One known problem of this situation, however, is the fact that RTC imposes computational and networking complexities that are not always compatible with the limitations of the simple embedded IoT devices used for sensing and actuation. Moreover, this problem is further aggravated when using traditional RTC based Real Time Protocol (RTP) mechanisms that are not typically intended for use in Low Power Lossy Networks (LLNs). In this paper we explore well-known IoT session protocols, like the Constrained Application Protocol (CoAP) and the Message Queuing Telemetry Transport Sensor Network Protocol (MQTT-SN), in order to accomplish efficient media transport over LLNs. Specifically, we analyze, model and compare CoAP and MQTT-SN against traditional RTP to define and specify a solution that provides efficient RTC and limits the effects of the computational and networking restrictions associated to embedded IoT devices.

Privacy, Security and Usability for IoT-enabled Weight Loss Apps

Obesity is considered as the main health issue worldwide. The obesity rate within Saudi’s citizens is rising alarmingly. The Internet of Things (IoT)-enabled mobile apps can assist obese Saudi users in losing weight via collecting sensitive personal information and then providing accurate and personalized weight loss advice. These data can be collected using embedded IoT devices in a smartphone. However, these IoT-enabled apps should be usable and able to provide data security and user privacy protection. This paper aims to continue our usability study for two Arabic weight loss IoT-enabled apps by performing a qualitative analysis for them. It discusses users’ and health professionals’ feedbacks, concerns and suggestions. Based on the analysis, a comprehensive usability guideline for developing a new Arabic weight loss IoT-enabled app for obese Saudi users is provided.

SGF-MD: Behavior Rule Specification-Based Distributed Misbehavior Detection of Embedded IoT Devices in a Closed-Loop Smart Greenhouse Farming System

Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

Guest Editorial Special Issue on Secure Embedded IoT Devices for Resilient Critical Infrastructures

The Internet of Things (IoT) creates new technological opportunities for a wide range of systems, such as industrial control systems, smart power grids, vehicular networks (VNs) and intelligent transportation systems, body area networks and healthcare monitoring and control systems and smart homes. At the same time, IoT also increases the threat surface for potential adversaries targeting critical interconnected systems that may depend on embedded IoT systems, such as supervisory control and data acquisition (SCADA) systems. At this point, attackers could take advantage from the incorporation of the paradigm to exploit new security gaps, probably caused by unforeseen interoperability and adaptability problems. Indeed, the deployment of Internet-enabled embedded devices that are distributed over major critical domains may create indirect and nonobvious interconnections with the underlying critical infrastructures (CIs). There is a need to further explore the security issues related to IoT technologies and to assure the resilience of CIs against advanced IoT-enabled attacks. The focus of this special issue is therefore to provide readers with the latest advances in securing the interaction between embedded IoT devices and CIs in order to increase their resilience to advanced IoT-enabled threats.

A Lightweight Rogue Access Point Detection Algorithm for Embedded Internet of Things (IoT) Devices

The Internet of Things (IoT) is a new paradigm that enables the convergence of smart objects and the internet. This convergence has led to the creation of an intelligent network that connects all things to the internet for the purpose of exchanging information. The direct connection of IoT devices to the internet makes them susceptible to several security threats. Researchers have developed techniques aimed at enhancing security of IoT devices at both network and application layers. In this paper, we present a real-time and lightweight algorithm, based on information theoretic approach, that enables rogue access point detection for embedded IoT devices. This is to ensure that WiFi-enabled IoT devices can intelligently distinguish between legitimate and rogue access points. We evaluated the performance of the algorithm with respect to the detection rate and also CPU utilization efficiency.

A Secure and Efficient Lightweight Symmetric Encryption Scheme for Transfer of Text Files between Embedded IoT Devices

Recent advancements in wireless technology have created an exponential rise in the number of connected devices leading to the internet of things (IoT) revolution. Large amounts of data are captured, processed and transmitted through the network by these embedded devices. Security of the transmitted data is a major area of concern in IoT networks. Numerous encryption algorithms have been proposed in these years to ensure security of transmitted data through the IoT network. Tiny encryption algorithm (TEA) is the most attractive among all, with its lower memory utilization and ease of implementation on both hardware and software scales. But one of the major issues of TEA and its numerous developed versions is the usage of the same key through all rounds of encryption, which yields a reduced security evident from the avalanche effect of the algorithm. Also, the encryption and decryption time for text is high, leading to lower efficiency in IoT networks with embedded devices. This paper proposes a novel tiny symmetric encryption algorithm (NTSA) which provides enhanced security for the transfer of text files through the IoT network by introducing additional key confusions dynamically for each round of encryption. Experiments are carried out to analyze the avalanche effect, encryption and decryption time of NTSA in an IoT network including embedded devices. The results show that the proposed NTSA algorithm is much more secure and efficient compared to state-of-the-art existing encryption algorithms.