Constrained Internet Of Things Devices Research Articles

Hierarchical Classification for Constrained IoT Devices: A Case Study on Human Activity Recognition

The massive number of Internet-of-Things (IoT) devices generates a hard-to-manage volume of data. Cloud-centric processing approaches for the IoT data suffer from high and unpredictable network latency, which causes poor experience in real-time IoT applications, such as healthcare. To address this issue, in edge computing, the data inference starts from the data source (i.e., the IoT devices). However, the constrained computational capabilities of the IoT device and the power-hungry data transmission demand a tradeoff between onboard processing and computation offloading. Hence, the IoT information inference requires efficient and lightweight techniques that are tailored for this tradeoff and respect the constrained resources on IoT devices, such as wearables. This article presents a hierarchical classification approach that decomposes the problem into three classifiers in two hierarchy layers. In the first layer, a lightweight classifier executes directly on the IoT device and decides whether to offload the computation to the gateway or to perform it onboard. The second layer comprises a lightweight classifier on the IoT device (can only distinguish a subset of classes) and a complex classifier on the gateway (to distinguish the remaining classes). The experimental results (using a real-world data set for human activity recognition and implemented on a wearable IoT device) show higher accuracy (92% on average) than a nonhierarchical classifier (87% on average). The execution time and power measurements on the IoT device show $3\times $ energy saving for the classification.

A novel multi-objective optimizer framework for TDMA-based medium access control in IoT

Recently, cross-layer optimization has been emphasized by researchers for Internet of Things (IoT) where billions of devices communicate intelligently making every day communication highly informative. Medium Access Control (MAC) sub-layer is part of data link layer in IoT protocol stack. The energy consumption of things is greatly affected by MAC protocol which controls the node radio functionalities. In this article, we propose the design of an efficient MAC for IoT by minimizing its energy consumption at various levels. The minimization model is subject to reduction in distortion levels while simultaneously extending network lifetime. This article synthesizes and optimizes MAC layer of IoT based protocols using a novel delta diagram synthesizer and accurately captures both the high heterogeneity of the IoT and the impact of the Internet as part of the MAC layer architecture. Furthermore a novel hybrid Whale-Artificial Bee Colony optimizer framework is proposed to obtain optimal nodes based on energy consumption and the communication parameters among things, by exploiting the interrelations among different layer functionalities in the IoT. This novel system saves considerable computation resources of the resource constrained IoT devices and adapts to various data sources with different distortion levels rapidly.

LRBC: a lightweight block cipher design for resource constrained IoT devices

The internet of things (IoT) is now an in-demand technology that has been adopted in various applications and includes various embedded devices, sensors and other objects connected to the Internet. Due to the rapid development of this technology, it covers a significant portion of the research interests nowadays. IoT devices are typically designed for collecting different types of data from various sources and transmitting them in digitized form. However, data security is the burning issue in the IoT technology, which can broadly impact the privacy of crucial data. In this regard, a new lightweight encryption method called LRBC has been proposed in this work for resource constraint IoT devices which can provide data security at the sensing level. The LRBC has used the structural advantages of both substitution–permutation network (SPN) and Feistel structure together to achieve better security. Furthermore, the proposed method has been tested on NEXYS 4 DDR FPGA (Artix-7) trainer kit and implemented for application specific integrated circuit (ASIC) chip on TSMC 65 nm technology. The proposed algorithm consumes very less power of 11.40 μW and occupies a 258.9 GE (Gate Equivalent) area. Besides, a thorough security analysis shows that the proposed scheme ensures high security against various attacks with robustness. Moreover, the average avalanche effect of LRBC is found to be 58% and 55.75% concerning plaintext and key, respectively.

Integrating LPWAN Technologies in the 5G Ecosystem: A Survey on Security Challenges and Solutions

The convergence of the Internet of Things (IoT) and 5G will open a range of opportunities for the deployment of enhanced sensing, actuating and interactive systems as well as the development of novel services and applications in a plethora of fields. Given the processing and communication limitations of both IoT devices and the most novel IoT transmission technologies, namely, Low Power Wide Area Network (LPWAN), there are notable concerns regarding certain security issues to be overcome in order to achieve a successful integration of LPWAN systems within 5G architectures. In this survey work, we analyze the main security characteristics of LPWANs, specially focusing on network access, and contrast them with 5G security requirements and procedures. Besides, we present a comprehensive review and analysis of research works proposing security solutions for the 5G-LPWAN integration. Finally, we explore open issues and challenges in the field and draw future research directions. From our analysis, it is evident that many efforts are being devoted from the academia, industry and Standards Developing Organizations (SDOs) for achieving the desired confluence of IoT and 5G worlds. We envision a successful integration of both ecosystems by exploiting novel lightweight security schemes addressing the stringent security requirements of 5G while being assumable by constrained IoT devices.

LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications

Certificateless public-key cryptography (CL-PKC) schemes are particularly robust against the leakage of secret information stored on a trusted third party (TTP). These security features are particularly relevant for Internet of Things (IoT) domains, where the devices are typically preconfigured with secret keys, usually stored locally on the TTP for following maintenance tasks. Despite some contributions already proposed for the adoption of CL-PKC schemes in constrained IoT devices, current solutions generally require high message overhead, are computationally demanding, and place a high toll on the energy budget. To close this gap, we propose LiKe, a lightweight pairing-free certificateless key agreement protocol suitable for integration in the latest ZigBee 3.0 protocol stack and constrained IoT devices. LiKe is an authenticated key agreement protocol characterized by: 1) ephemeral cryptographic materials; 2) support for intermittent connectivity with the TTP; 3) lightweight rekeying operations; and 4) robustness against impersonation attacks, even when information stored on the TTP is leaked. LiKe has been thoroughly described, and its security properties have been proved via formal tools. Moreover, we have implemented and tested it on real IoT devices, in networks with up to 11 nodes-the source code has been released as an open source. Results are striking: on the OpenMote-b hardware platform, LiKe requires a total time of 3.259 s to establish session keys on each participating device, and at most 0.258% of the overall battery capacity, emerging as a lightweight and energy-friendly solution. Finally, comparisons with competing solutions do show the superior quality and viability of our proposal.

On the Possibility of Predicting Glycaemia 'On the Fly' with Constrained IoT Devices in Type 1 Diabetes Mellitus Patients.

Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors. However, recent Internet of Things (IoT) devices and novel biosensors have allowed the continuous collection of the value of the glucose level by means of Continuous Glucose Monitoring (CGM) so that, with the proper Machine Learning (ML) algorithms, glucose evolution can be modeled, thus permitting a forecast of this variable. On the other hand, glycaemia dynamics require that such a model be user-centric and should be recalculated continuously in order to reflect the exact status of the patient, i.e., an ‘on-the-fly’ approach. In order to avoid, for example, the risk of being disconnected from the Internet, it would be ideal if this task could be performed locally in constrained devices like smartphones, but this would only be feasible if the execution times were fast enough. Therefore, in order to analyze if such a possibility is viable or not, an extensive, passive, CGM study has been carried out with 25 DM1 patients in order to build a solid dataset. Then, some well-known univariate algorithms have been executed in a desktop computer (as a reference) and two constrained devices: a smartphone and a Raspberry Pi, taking into account only past glycaemia data to forecast glucose levels. The results indicate that it is possible to forecast, in a smartphone, a 15-min horizon with a Root Mean Squared Error (RMSE) of 11.65 mg/dL in just 16.15 s, employing a 10-min sampling of the past 6 h of data and the Random Forest algorithm. With the Raspberry Pi, the computational effort increases to 56.49 s assuming the previously mentioned parameters, but this can be improved to 34.89 s if Support Vector Machines are applied, achieving in this case an RMSE of 19.90 mg/dL. Thus, this paper concludes that local on-the-fly forecasting of glycaemia would be affordable with constrained devices.

Transceiver Design for Energy-Efficiency Maximization in mmWave MIMO IoT Networks

Millimeter wave (mmWave) communications have been regarded as a fundamental enabling technology for future 5G Internet of Things (IoT) networks. This paper studies a hybrid transceiver design problem considering both perfect and imperfect channel state information to maximize the energy-efficiency (EE) of a mmWave multiple-input multiple-output (MIMO) interference channel involving multiple hardware impaired IoT devices. Since the original hybrid transceiver design problem is intractable, we propose a low-complexity two-stage algorithm, both of which are proven to converge. In the first stage, we transform the multivariate problems into their univariate equivalents and exploit the well-known relationship between rate and mean squared error to solve them in an alternating fashion. In the second stage, using the results from stage one, we separate the joint transceiver optimization problems into hybrid precoders and combiners sub-problems and apply orthogonal matching pursuit algorithm to obtain the optimal solution for each sub-problem. Finally, through numerical simulations it is shown that the proposed hybrid algorithms achieve near-optimal EE performance to support power constrained IoT devices with lower complexity than univariate designs.

New Method of Prime Factorisation-Based Attacks on RSA Authentication in IoT

The potential benefits of the Internet of Things (IoT) are hampered by malicious interventions of attackers when the fundamental security requirements such as authentication and authorization are not sufficiently met and existing measures are unable to protect the IoT environment from data breaches. With the spectrum of IoT application domains increasing to include mobile health, smart homes and smart cities in everyday life, the consequences of an attack in the IoT network connecting billions of devices will become critical. Due to the challenges in applying existing cryptographic standards to resource constrained IoT devices, new security solutions being proposed come with a tradeoff between security and performance. While much research has focused on developing lightweight cryptographic solutions that predominantly adopt RSA (Rivest–Shamir–Adleman) authentication methods, there is a need to identify the limitations in the usage of such measures. This research paper discusses the importance of a better understanding of RSA-based lightweight cryptography and the associated vulnerabilities of the cryptographic keys that are generated using semi-primes. In this paper, we employ mathematical operations on the sum of four squares to obtain one of the prime factors of a semi-prime that could lead to the attack of the RSA keys. We consider the even sum of squares and show how a modified binary greatest common divisor (GCD) can be used to quickly recover one of the factors of a semi-prime. The method presented in this paper only uses binary arithmetic shifts that are more suitable for the resource-constrained IoT landscape. This is a further improvement on previous work based on Euler’s method which is demonstrated using an illustration that allows for the faster testing of multiple sums of squares solutions more quickly.

Groundwork for Neural Network-Based Specific Emitter Identification Authentication for IoT

Trust is a prominent concern with the continued expansion of the Internet of Things (IoT). As new devices enter the market, device security must be a design pillar. In order to trust these devices, they must be identifiable and authenticated before they begin transmitting possible sensitive information, and given the vast number of IoT devices in the future, it may prove difficult to properly trust and authenticate these authorized devices on networks with current methods. Machine learning neural networks (NNs) have the ability to uniquely identify transmitters based on their physical waveform characteristics which could be used to identify and authenticate IoT nodes in large networks with little impact to latency, providing an extra layer of security and trust. This paper presents the groundwork for performing NN-based specific emitter identification (SEI) on resource constrained IoT devices using only raw in-phase and quadrature (IQ) streams, with protocols to secure IoT networks. As proof of concept, an existing NN-based SEI algorithm is executed on both a resource-rich and a more resource-constrained device with low latency, demonstrating the feasibility of using such algorithms on IoT devices now and in the future.

Efficient implementation of lightweight block ciphers on volta and pascal architecture

Lightweight block ciphers based on symmetric cipher are tailored to addressing security issues for highly constrained Internet of things devices. In this article, we explore general software implementations of lightweight ciphers on GPU architectures, with a special focus on LED, Piccolo and PRESENT. First, we implement the lightweight block ciphers using lookup table based technique. Then we analyze the effect of different factors on the encryption performance, such as the size of lookup table, the parallelism level, the use of different type of memory and the idle state caused by data transmission. We evaluated the three lightweight block ciphers on Nvidia Tesla V100 and Nvidia GTX1060 GPUs. Finally, the experimental result shows a great improvement on all the investigated ciphers compared to the unoptimized implementations.

End-to-End Delay Enhancement in 6LoWPAN Testbed Using Programmable Network Concepts

This paper introduces a proof-of-concept IPv6 over low power wireless personal area network (6LoWPAN) testbed to study the integration of programmable network technologies in relaxed throughput and low-power Internet of Things (IoT) devices. Open source software and hardware platforms are used in the implemented testbed to increase the possibility of future network extension. The proposed architecture offers end-to-end connectivity via the 6LoWPAN gateway to integrate IPv6 hosts and the low data rate devices directly. Nowadays, software-defined networking (SDN) and network function virtualization (NFV) are the most promising technologies for dealing with the massive increase in machine-to-machine devices and achieving agile traffic. The developed approach in this paper is entitled tailored software defined-NFV (SD-NFV), which is aimed at reducing the end-to-end delay and improving the energy depletion in sensor nodes. Experimental scenarios of the implemented testbed are conducted using a simple sensing application and the obtained results indicate that the introduced approach is appropriate for constrained IoT devices. By utilizing SD-NFV scheme in 6LoWPAN network, the data delivery ratio increased by 5%–14%, the node operational time prolonged by 70%, the end-to-end latency for gathering sensor data minimized by $\approx 160$ %, and the latency for transmitting control messages to a specified node diminished by $\approx 63$ % when compared to a traditional (non SDN-enabled) 6LoWPAN network.

Group-IKEv2 for multicast IPsec in the internet of things

This paper presents Group-IKEv2, a group key management protocol supporting secure group communication based on multicast IPsec. Group-IKEv2 is an adaptation of the IKEv2 protocol for the IPsec suite, and is especially designed to address internet of things (IoT) scenarios composed of resource-constrained devices. Compared to static approaches, Group-IKEv2 enables dynamic and flexible establishment of IPsec group security associations as well as group key material. Also, it integrates the management and renewal of group key material, both on a periodical fashion and upon group membership changes. We have implemented Group-IKEv2 for the Contiki OS and tested it on the OpenMote resource-constrained platform. Our experimental performance evaluation confirms that Group-IKEv2 is affordable and deployable also on constrained IoT devices.

Architecture to Embed Software Agents in Resource Constrained Internet of Things Devices.

Sensing systems in combination with treatment tools and intelligent information management are the basis on which the cities and urban environments of the future will be built. Progress in the research and development of these new and intelligent scenarios is essential to achieve the objectives of efficiency, integration, sustainability, and quality of life for people who live in cities. To achieve these objectives, it is essential to investigate the development of cheaper, more accurate, and smarter hardware devices, which will form the basis of the intelligent environments of the future. This article focuses on an approach based on intelligent multi-agent systems that are integrated into basic hardware devices for the Internet of Things (IoT). A multi-agent architecture is proposed for the fast, efficient, and intelligent management of the generated data. A layer of services adapted to the needs of the new intelligent environments is built on this architecture. With the aim of validating this architecture, a case study based on electric vehicles of the e-bike type is proposed.

Spectral and Energy-Efficient Wireless Powered IoT Networks: NOMA or TDMA?

Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, non-orthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and/or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., "worst user bottleneck problem", in multiuser NOMA systems.

A survey on routing protocols supported by the Contiki Internet of things operating system

Standardization and technology advancements have helped the realization of the Internet of things (IoT). The availability of low-cost IoT devices has also played a key role in furthering IoT research, development, and deployment. IoT operating systems (OSs) provide integration of software and hardware components. The availability of standard protocols, heterogeneous hardware support, ease of development, and simulation or emulation support are desirable features of IoT OSs. Contiki OS is one of the contenders for future IoT OSs. It was proposed in 2003, and since then, it has been continually under development and upgraded by professionals, academia, and researchers. Contiki OS supports open source, Internet standards, power awareness, dynamic module loading, and many hardware platforms. The diverse applications of IoT, including smart homes, smart health, smart cities, require efficient network connectivity and demand intelligent routing protocols that can handle heterogeneous, mobile, and diverse networks. Subsequently, designing routing protocols for memory- and central processing unit (CPU)-constrained IoT devices is a very challenging task. Therefore, this paper surveys the state-of-the-art routing protocols of Contiki OS. To the best of the authors’ knowledge, this is the first study to classify the Contiki OS routing protocol literature and list the potential challenges and future work.

Approaches to Front-End IoT Application Development for the Ethereum Blockchain

There are several distributed ledger protocols potentially suitable for the Internet of things (IoT), including the Ethereum, Hyperledger Fabric and IOTA. This paper briefly presents and compares them from the IoT application development perspective. The IoT applications based on blockchain (BC) can incorporate the on-chain logic –the smart contracts– and Web, mobile or embedded client front-end application parts. We present three possible architectures for the IoT front-end BC applications. They differ in positioning of Ethereum blockchain clients (local device, remote server) and in positioning of key store needed for the management of outgoing transactions. The practical constraints of these architectures, which utilize the Ethereum network for trusted transaction exchange, are the data volumes, the location and synchronization of the full blockchain node and the location and the access to the Ethereum key store. Results of these experiments indicate that a full Ethereum node is not likely to reliably run on a constrained IoT devices. Therefore the architecture with remote Ethereum clients seems to be a viable approach, where two sub-options exist and differ in key store location/management. In addition, we proposed the use of architectures with a proprietary communication between the IoT device and remote blockchain client to further reduce the network traffic and enhance security. We expect it to be able to operate over low-power, low-bitrate mobile technologies, too. Our research clarifies differences in architectural approaches, but final decision for a particular ledger protocol and front-end application architecture is at strongly based on the particular intended use case.

BlinkComm: Initialization of IoT Devices Using Visible Light Communication

Many applications from the Internet of Things (IoT) domain used in healthcare, smart homes, and cities involve a large number of interconnected wireless devices. To ensure privacy, confidentiality, and integrity of the information, devices should be initialized prior to any communication. In this paper, we present a secure initialization method for constrained IoT devices such as wireless sensors devices and/or actuators. The solution uses visible light communication (VLC) for the initial configuration of the IoT devices. The VLC system consists of a modulated light source such as a smartphone screen and a very simple photodetector. We analyze known coding and modulation techniques used for the VLC and propose BlinkComm, a differential coding technique that achieves threefold increase in transmission speed compared to existing solutions. We showed through experiments with 32 participants that the proposed solution achieves fast completion times and low error rates as well as high user satisfaction levels.

CUTE Mote, A Customizable and Trustable End-Device for the Internet of Things

The ubiquitous connectivity of the low-end devices in the Internet of Things (IoT) brings new challenges over the traditional wireless sensor networks’ architectures. Such challenges require not only security and privacy-related features, but also solutions to handle the ever-growing amount of data transferred over the network. However, performing such tasks on resource constrained devices is not straightforward. The need for energy-efficient devices, while preserving their performance and security capabilities, requires new solutions at the architectural level of the wireless device. This paper proposes a heterogeneous architecture that targets low-end and resource constrained IoT devices, combining a hardcore microcontroller unit (MCU) and a reconfigurable computing unit (RCU) with an IEEE 802.15.4 radio transceiver. The MCU hosts an embedded operating system with an IoT-enabled network stack, and exploits the available field-programmable gate array technology to implement the RCU and to deploy customized sensing- and network-related accelerators, offloading heavy, and/or complex software tasks to dedicated hardware blocks. The customizable and trustable end-device mote was implemented using the proposed architecture and the achieved results demonstrates the benefits, both in terms of performance and energy, of accelerating network-related tasks in always-connected resource constrained IoT devices.

Fast and efficient probing of heterogeneous IoT networks

SummaryThe Internet of Things (IoT) leads to the interconnectivity of a wide range of device types running an equally wide range of operating systems and applications. This heterogeneity of hardware and software poses significant challenges to security. Constrained IoT devices often do not have enough resources to carry the overhead of an intrusion protection system or complex security protocols. Furthermore, they are often not properly managed and updated.Network scans are a valuable tool to discover vulnerable devices. In the context of IoT, the initiator of the scan can be particularly interested in finding constrained devices, assuming that they are easier targets for attacks. However, in IoT networks hosting devices of various types, performing a scan with a high discovery rate can be a challenging task, since a scan working well for, eg, a WiFi network might easily overload a low‐power network such as IEEE 802.15.4.In this paper, we propose an approach to increase the efficiency of network scans in heterogeneous environments by combining them with active round‐trip time measurements. The measurements allow the scanner to differentiate IoT nodes by the used network technology. Using the knowledge gained from this differentiation, our approach adapts the scan strategy to reduce probe losses, and hence the speed and efficiency of the scan. We validate our approach through simulations of a mixed IoT infrastructure consisting of WiFi and multihop IEEE 802.15.4 subnetworks.

An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks.

Many Internet of Things (IoT) services utilize an IoT access network to connect small devices with remote servers. They can share an access network with standard communication technology, such as IEEE 802.11ah. However, an authentication and key management (AKM) mechanism for resource constrained IoT devices using IEEE 802.11ah has not been proposed as yet. We therefore propose a new AKM mechanism for an IoT access network, which is based on IEEE 802.11 key management with the IEEE 802.1X authentication mechanism. The proposed AKM mechanism does not require any pre-configured security information between the access network domain and the IoT service domain. It considers the resource constraints of IoT devices, allowing IoT devices to delegate the burden of AKM processes to a powerful agent. The agent has sufficient power to support various authentication methods for the access point, and it performs cryptographic functions for the IoT devices. Performance analysis shows that the proposed mechanism greatly reduces computation costs, network costs, and memory usage of the resource-constrained IoT device as compared to the existing IEEE 802.11 Key Management with the IEEE 802.1X authentication mechanism.