Internet Of Things Devices In Networks Research Articles

Efficient Key Management System Based Lightweight Devices in IoT

The Internet of Things (IoT) has changed our lives significantly. Although IoT provides new opportunities, security remains a key concern while providing various services. Existing research methodologies try to solve the security and time-consuming problem also exists. To solve those problems, this paper proposed a Hashed Advanced Encryption Standard (HAES) algorithm based efficient key management system for internet-based lightweight devices in IoT networks. The proposed method is mainly divided into two phases namely Data Owner (DO) and Data User (DU) phase. The DO phase consists of two processes namely authentication and secure data uploading. In authentication, the registration process consists of three phases namely KGC, SCC and DVC. In Key Generation Center (KGC), the device ID is converted into a 128-bit hash key using the Point on Curve based Fowler–Noll–Vo (PoC-FNV) algorithm. In DVC, the Data Access Policy (DAP) is created by using the selected attributes which is in turn selected by using the Chen Chaotic Chimp Optimization Algorithm(CCCOA). Thus, the authentication information is stored in the blockchain. If the person is an authorized, then the data is securely stored and uploaded into the cloud server using HAES. In the DU phase, the DU sends the data access request to the blockchain then the blockchain forwards the request to the DO, if the DO accepts the request then the DU obtains the data by using the DAP. Finally, the performance of the proposed method is compared with the existing methods and attains better result than the existing research methodologies.

Detection of DIS Flooding Attacks in IoT Networks Using Machine Learning Methods

In today, Internet of Things (IoT) has a wide usage area and makes easier our lives with smart objects that can communicate with each other without human intervention. However, as with Wireless Sensor Networks, IoT networks bring new risks. These risks reaching worrying levels cause some significant issues such as security, privacy, and energy in the network topology. The IPv6 Routing Protocol for Low-Power and Lossy Network (RPL) is a routing protocol for resource-constrained devices in IoT networks. When it transmits packets between nodes, the nodes can be exposed to a series of attacks. DODAG Information Solicitation (DIS) Flooding attack is one of the most effective types of attacks against this protocol and negatively affects the energy level of the node and its limited processing capacities. Although many intrusion detection methods are used to detect attacks in IoT security, innovative and energy-saving methods are needed. DIS Flooding attacks detection and prevention methods have not been adequately presented in the literature. To address the mentioned need, this study provides high-performance detection of DIS Flooding attacks by applying Logical Regression (LR) and Support Vector Machine machine learning methods. The experiments are implemented by using the Contiki-Cooja simulation environment and the experimental results have been evaluated using various performance metrics. It can be concluded that LR achieves higher attack detection in terms of accuracy.

Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation

In terms of growth, effect, and capability, the 5G-enabled Internet of Things (IoT) is incredible. The volume of data distributed and processed by IoT (Internet of Things) systems that trust connectivity and coverage raises some security problems. As IoT technology is directly used in our daily lives, the threats of present cyberspace may grow more prominent globally. Extended network life, coverage, and connectivity are all required for securing IoT-based 5G network devices. As a result of these failures, there are flaws that lead to security breaches. Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. Securing IoT-based 5G Network Device Connectivity and Coverage for expending Encryption and Authentication Scheme (EAS) framework is proposed in this study, which uses novel security flaws. In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange.

Cloud Based Processing of Vibrational Signal for Oil Pipeline Monitoring System

Petroleum is the economic infrastructure in Iraq because it generates a significant portion of the country's revenue and can be considered as the primary source of financial costs each year. As a consequence, it is critical to protect the sector and continue to develop it. Therefore, it is important to track and maintain pipelines regularly to detect defects on time. In pipeline monitor and control, the advent of the Internet of Things (IoT) technology and the deployment of embedded sensing systems enable successful pipeline maintenance with the simple requirement for real-time precise measurements. In this paper, a wireless network based on an IoT system and integrated with cloud service is proposed for structure monitoring of oil pipelines, to detect the risks on the structure such as tampering and/or wear and tear effects. The method is based on collecting data from a sensor node equipped with an RF module attached to the pipeline structure. These nodes collectively form a network of IoT devices connected to the cloud server. The raw data is collected, stored, and statistically analyzed to be accessible by the user anytime and anywhere through the Internet. The performance of the system is evaluated in different cases, including the distance about the node to detect events on the pipe and to discriminate the distance of event to determine the location the event it was tested by using four different states of the transmitted data. Index Terms— Oil pipeline monitoring, IoT, AWS, ESP32 cam, Lambda function.

SUPnP: Secure Access and Service Registration for UPnP-Enabled Internet of Things

The service-oriented nature of the Universal Plug-and-Play (UPnP) protocol supports the creation of flexible, open, and dynamic systems. As such, it is widely used in Internet-of-Things (IoT) deployments. However, the protocol’s service access mechanism does not consider security from the first principles and is therefore vulnerable to various attacks. In this article, we present an in-depth analysis of the service advertisement, discovery, and access methods of the UPnP protocol stack and identify security issues in an IoT network. Our analysis shows that adversaries can perform resource exhaustion, buffer overflow, reflection, and amplification attacks by exploiting the vulnerabilities of the UPnP protocol. To address these issues, we propose a capability-based security model for UPnP to ensure secure discovery, advertisement, and access of the UPnP services that considers the resource limitations of IoT devices. Our analysis shows the effectiveness of the proposed model against potential attacks, and our experimental evaluation highlights the feasibility of implementing our Secure UPnP (SUPnP) protocol in a network of IoT devices, incurring minimal network and performance overhead.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

Machine Learning Empowered Trust Evaluation Method for IoT Devices

With the rapid development of the Internet of Things (IoT), malicious or affected IoT devices have imposed enormous threats on the IoT environment. To address this issue, trust has been introduced as an important security tool for discovering or identifying abnormal devices in IoT networks. However, evaluating trust for IoT devices is challenging because trust is a degree of belief with regard to various types of trust properties and is difficult to measure. Thus, a machine learning empowered trust evaluation method is proposed in this paper. With this method, the trust properties of network QoS (Quality of Service) are aggregated with a deep learning algorithm to build a behavioral model for a given IoT device, and the time-dependent features of network behaviors are fully considered. Trust is also quantified as continuous numerical values by calculating the similarity between real network behaviors and network behaviors predicted by this behavioral model. Trust values can indicate the trust status of a device and are used for decision making. Finally, the proposed method is verified with experiments, and its effectiveness is described.

Security Framework Connection Assistance for IoT Device Secure Data communication

Today, Internet of Things (IoT) services has been increasing extensively because of their optimum device sizes and their developed network infrastructure that includes devices based on internet embedded with various sensors, actuators, communication, and storage components providing connection and data exchange. Presently number of industries use vast number of IoT devices, there are some challenges like reducing the risks and threats that exposure, accommodating the huge number of IoT devices in network and providing secure vulnerabilities have risen. Supervised learning has recently been gaining popularity to provide device classification. But this supervised learning became unrealistic as producing millions of new IoT devices each year, and insufficient training data. In this paper, security framework connection assistance for IoT device secured data communication is proposed. A multi-level security support architecture which combines clustering technique with deep neural networks for designing the resource oriented IoT devices with high security and these are enabling both the seen and unseen device classification. The datasets dimensions are reduced by considering the technique as auto encoder. Therefore in between accuracy and overhead classification good balancing is established. The comparative results are describes that proposed security system is better than remaining existing systems.

МЕТОД РОЗРАХУНКУ ПРОПУСКНОЇ ЗДАТНОСТІ ПАКЕТНОЇ МЕРЕЖІ ДОСТУПУ ДЛЯ ПРИСТРОЇВ IoT

When creating packet networks of a new generation, the problem of calculating the throughput of broadband multiservice access networks arises. In practice, for this they use mathematical modeling, or without proper justification, the traditional formulas of the theory of information distribution. The well-known analytical solution for ATM technology is extremely cumbersome and practically not used. Today, there is not generally accepted analytical or engineering method for solving the problem. At the same time, telecommunication networks must develop to provide all the necessary conditions for the practical application of the concept of the Internet of Things (IoT). One of these conditions is the maintenance of multiservice traffic with specified quality of service indicators. The paper developed a method for calculating the bandwidth or the number of conditional channels of a packet access network for IoT devices. In this case, the calculation of the bandwidth of the access network of IoT devices is performed at the level of calls and packets separately. At the level of calls from IoT devices, the Engset model is used for traffic due to the small number of groups of the devices themselves, and at the packet level, the model of self-similar flow is applied. Calculation of quality of service characteristics in a packet communication network is reduced to determining the Hurst coefficient of self-similarity of traffic, after which the average number of packets in the system is calculated using the well-known Norros formula. Other characteristics, such as the average number of packets in the queue, the average residence time of packets in the system and the average delay time of packets in a single-channel system, are calculated based on their functional relationship with the previously calculated average number of packets in the system. Based on the approximation of the distribution function of the system states, the probability of waiting for packet servicing and the average delay time of packets in the packet switch queue are calculated.

Energy Harvesting towards Self-Powered IoT Devices

The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system’s life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article.

Service-Oriented Energy-Latency Tradeoff for IoT Task Partial Offloading in MEC-Enhanced Multi-RAT Networks

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

Rethinking Blockchains in the Internet of Things Era from a Wireless Communication Perspective

Due to the rapid development of Internet of Things (IoT), a massive number of devices are connected to the Internet. For these distributed devices in IoT networks, how to ensure their security and privacy becomes a significant challenge. The blockchain technology provides a promising solution to protect the data integrity, provenance, privacy, and consistency for IoT networks. In blockchains, communication is a prerequisite for participants, which are distributed in the system, to reach consensus. However, in IoT networks, most of the devices communicate through wireless links, which are not always reliable. Hence, the communication reliability of IoT devices influences the system security. In this article, we rethink the roles of communication and computing in blockchains by accounting for communication reliability. We analyze the tradeoff between communication reliability and computing power in blockchain security, and present a lower bound to the computing power that is needed to conduct an attack with a given communication reliability. Simulation results show that adversarial nodes can succeed in tampering a block with less computing power by hindering the propagation of blocks from other nodes.

Validation of IoT Infrastructure for the Construction of Smart Cities Solutions on Living Lab Platform

The use of Internet-of-things (IoT) applications (solutions) in the real world has increased exponentially. In smart cities, networked IoT devices are collecting data from the physical medium to optimize the decisions to improve city services to citizens. One way to evaluate this services solution is the use of the living labs, shown as a good option to evaluate previous real applications. However, when this is implemented in the real-world cases, most of them are no scalar to the complexity of the city. One of the factors is that it is assumed an IoT infrastructure designed to meet the properties of the scalability for a smart city. This article proposes a validation methodology for the scalability compliant infrastructure: modularity, interoperability, and resiliency properties. The proposed methodology is based on the best practices achieved during a living lab of the Smart Cities Innovation Center in the Universidad de Guadalajara implementation.

An architecture for adaptive task planning in support of IoT-based machine learning applications for disaster scenarios

The proliferation of the Internet of Things (IoT) in conjunction with edge computing has recently opened up several possibilities for several new applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for rapid disaster response, photogrammetry, surveillance, and environmental monitoring. To support the flourishing development of Machine Learning assisted applications across all these networked applications, a common challenge is the provision of a persistent service, i.e., a service capable of consistently maintaining a high level of performance, facing possible failures. To address these service resilient challenges, we propose APRON, an edge solution for distributed and adaptive task planning management in a network of IoT devices, e.g., drones. Exploiting Jackson’s network model, our architecture applies a novel planning strategy to better support control and monitoring operations while the states of the network evolve.To demonstrate the functionalities of our architecture, we also implemented a deep-learning based audio-recognition application using the APRON NorthBound interface, to detect human voices in challenged networks. The application’s logic uses Transfer Learning to improve the audio classification accuracy and the runtime of the UAV-based rescue operations.

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

BTNC: A blockchain based trusted network connection protocol in IoT

Along with the rapid growth of the size and complexity of Internet of Things (IoT), the security of terminal devices has increasingly become a focus. In order to ensure the security of terminals, the trusted network connect (TNC) could realize not only the user authentication but also the platform attestation during the network access process. However, the existing TNC infrastructure is based on a centralized architecture, which is not suitable for distributed services. To address this problem, we present a blockchain-based TNC protocol named BTNC to ensure the reliability of terminals in IoT. Due to the decentralization, trustlessness, trackability, and immutability features of blockchain, BTNC is able to verify the security of terminal devices in IoT networks. First, we come up with some threats, including unauthorized user, illegal platform and platform replacement attack, then correspondingly define the security goals of our scheme. Second, combining key exchange protocol based on blockchain and D–H PN protocol included in TNC specification, we propose a blockchain-based trusted network connection protocol, which realizes mutual user authentication, platform attestation and trust network access by cryptography among terminals in IoT. Third, we make a security analysis in the PCL mode and conclude that our protocol can resist the attacks above. Finally, the performance overheads caused by our scheme are evaluated and the experiments show that it is efficient and feasible for different kinds of terminals in IoT.

Thinly sliced

A collaboration of US and Swedish researchers presents, for the first time, radio frequency (RF) reliability studies of fully integrated complimentary metal-oxide-semiconductor (CMOS) radio frequency integrated circuits (RFICs), important for designing next generation wireless communication applications, especially those that are based on ultra-thin flexible packages. Mitra et al. have a wide spread of research areas, including such topics as communications radar, hand-held medical scanners and portable weapon scanning technology. It is, however, their research into CMOS RFICs that lead to the present paper. Mastery of CMOS technology, with its small chip size, is thought to be crucial for the continued miniaturisation of circuits, with the world of nanoelectronics providing a potential gateway to the next generation of highly integrated multifunctional devices, circuits and systems. The Radio Frequency Integrated Circuits (RFIC) Lab members at North Dakota State University. (From Left) Arka Biswas, Chitralekha Biswas, Dipankar Mitra (in the back), and Babak Hamidi. RFIC Characterisation Set-up at NDSU. The ‘thinning-down’ of integrated circuits is one step towards mastering CMOS technology, with thinner and more lightweight circuitry providing more efficient and cost-effective solutions for various applications, including wireless communications, wearable and flexible electronics and Internet of Things (IoT) devices. One of the biggest challenges for realisation of flexible and ultra-thin ICs is the effect die-substrate thinning has on the RF performance of the IC. It was this problem that triggered Mitra et al. into producing the RF reliability studies presented in Electronics Letters. Thinning of the die-substrate, the small block of semiconducting material upon which a circuit is built, is one of the main contributors to performance degradation for RFICs, whilst also being the most important for producing thinner and more flexible circuitry. In their work, Mitra et al. use a voltage-controlled oscillator (VCO) to measure the effects of die-substrate thickness on the RF performance of the IC, including frequency range, RF output power variation, phase noise variation and DC parameters. The usual thickness of die-substrate for CMOS circuits as between 250–300 micrometres. Mitra et al. used a 250 µm thickness VCO as a test case, and then thinned down the die-substrate to 50 µm, 35 µm and 25 µm. They found that deviations of frequency and output power are within 1% and 1 dB respectively, with DC parameters also shown to be fairly similar for the different substrate thicknesses. The sensitivity to phase noise when thinning down was found to be significant, which confirms the well-known fact that phase noise sensitivity increases due to leakage. Flexible electronics is changing the way we make and use electronics. Many existing applications, such as wireless communication, wearable electronics, and healthcare monitoring require flexibility to promote more efficient and cost-effective solutions. Development of these flexible electronics is driving progress in the field, which enables futuristic applications such as a network of IoT devices and connected, smart cities. Several initiatives from governments and industry have also contributed to the progress and it is now estimated that the market for flexible electronics will reach $300 billion by 2028, with expected growth from $29 billion in 2017 to over $63 billion in 2023 for printed, flexible and organic electronics alone. The high-performance of CMOS electronics will be critical to this growth in flexible electronics, as a great number of current and future electronics rely on the fast communication and computation they can provide.

Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.

Labeled Network Stack: A High-Concurrency and Low-Tail Latency Cloud Server Framework for Massive IoT Devices

Internet of Things (IoT) applications have massive client connections to cloud servers, and the number of networked IoT devices is remarkably increasing. IoT services require both low-tail latency and high concurrency in datacenters. This study aims to determine whether an order of magnitude improvement is possible in tail latency and concurrency in mainstream systems by proposing a hardware–software codesigned labeled network stack (LNS) for future datacenters. The key innovation is a cross-layered payload labeling mechanism that distinguishes different requests by payload across the full network stack, including application, TCP/IP, and Ethernet layers. This type of design enables prioritized data packet processing and forwarding along the full datapath, such that latency-insensitive requests cannot significantly interfere with high-priority requests. We build a prototype datacenter server to evaluate the LNS design against a commercial X86 server and the mTCP research, using a cloud-supported IoT application scenario. Experimental results show that the LNS design can provide an order of magnitude improvement in tail latency and concurrency. A single datacenter server node can support over 2 million concurrent long-living connections for IoT devices as a 99-percentile tail latency of 50 ms is maintained. In addition, the hardware–software codesign approach remarkably reduces the labeling and prioritization overhead and constrains the interference of high-priority requests to low-priority requests.

Internet of Things in Smart City Environments

With the advent of smart devices, a huge paradigm shift is observed in the way the users define service quality. Further, these devices or Internet of Things (IoT) devices as they are generally addressed, have acted as catalyst for comfort and connectivity and are building blocks of Smart City environment. With limited thought related to security is involved during the deployment of such devices, they offer a dangerous environment of opportunity to the attackers from the internet; which not only jeopardize network security, bus also the privacy of the users. Hence, it is of utmost importance to address the security concerns in smart city environment. This paper attempts to study the current IoT technologies deployed in a smart-city environment along with its vulnerabilities and possible solutions to improve IoT security. An approach is made to study the various vulnerabilities available with the IoT devices deployed in the smart city setup, various motivation of an attacker and the analyse some of the recent attacks witnessed by IoT devices. A few possible solutions for mitigation are suggested in this paper. The findings of the paper can be implemented in any network of IoT devices.