Constrained Internet Of Things Research Articles

Design and Implementation of Low Power and High Data Rate Edge Coded Signaling Architecture for IoT Devices

Abstract Edge coded signaling (ECS) is a recently introduced communication protocol designed for single-channel signaling among constrained internet of things (IoT) nodes. This study aims to enhance the data rate of ECS, without increasing the clock frequency or power consumption. The goal is to develop an improved protocol that maintains the inherent advantages of ECS while achieving a higher data rate. The proposed solution, double data rate ECS (DDR-ECS), leverages both pulse edges of the ECS pulse stream for information encoding, drawing an analogy to DDR memory systems. The study presents a detailed design of low-power architecture for the DDR-ECS transceiver. This design was synthesized using a 65 nm ASIC process to evaluate its performance in terms of power consumption, gate count, and data rate. The synthesis results demonstrate that the DDR-ECS transceiver preserves the key features of ECS, including tolerance to clock frequency variations and compatibility with lightweight cryptographic algorithms. It effectively doubles the dynamic data rate to the range of 12–73.5 Mb/s while consuming an equivalent power of 13 μW and occupying a compact form factor of only 1,755 gates. The introduction of DDR-ECS represents a significant advancement in the ECS protocol, offering a novel approach to doubling the data rate without increasing the clock frequency or power envelope. This innovation retains the simplicity and efficiency of ECS, providing an enhanced communication solution for constrained IoT nodes.

An Authentication Protocol for Next Generation of Constrained IoT Systems

With the exponential growth of connected Internet of Things (IoT) devices around the world, security protection and privacy preservation have risen to the forefront of design and development of innovative systems and services. For low-value IoT devices that identify and track billion of goods in various industries—such as radio-frequency identification (RFID) tags—this involves multiple challenges in very constrained environments. IoT devices aim to design low-cost, low-complexity infrastructure while enabling robust authentication protocols with reduced latency and energy consumption. Given these challenges, in this article, we present a new lightweight authentication protocol for IoT applications, employing an authenticated-encryption (AE) cryptosystem with associated data (AEAD). Since AEAD algorithms provide data confidentiality and message integrity simultaneously, security analysis [Real-or-Random (RoR) and Scyther] results prove the robustness of the proposed protocol against IoT threats. Furthermore, to measure the computation and communication cost, FPGA and ASIC simulations using four different AEAD candidates of National Institute of Standards and Technology (NIST) lightweight cryptography competition are executed. The implementation results [e.g., 4744 gate equivalent (GE) and 0.87-mw power] clearly show that our novel design can be applied to a wide range of constrained IoT devices complying with low-cost, lightweight, and high-speed requirements.

Analysis of the NTRU Post-Quantum Cryptographic Scheme in Constrained IoT Edge Devices

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.

Piezoelectric Energy Harvesting at Circuit Resonance with Active Inductor (CRAI)

Background: Most of the proposed interface circuits use bulky inductors to enhance the key performance parameter i.e., power transfer efficiency. This sets constraints on the design of power conditioning circuitry for constrained IoT applications. Objective: To replace the bulky physical inductor with area optimized components suitable for integrated circuit realization with reduced silicon footprint for constrained applications like Internet-of-Things (IoT). Method: This paper presents the implementation of Circuit Resonance with Active Inductor (CRAI) technique based interface circuit design to deliver the maximum power generated from the Piezoelectric Energy (PEH) source to the load. Results: Compared to the conventional FWBR technique, the proposed CRAI technique improves ≈2X power delivered to the load. Conclusion: The proposed work presents an inductor-less interface circuit for PEH. An active inductor (gyrator) is used to induce ‘IP’ rejection at the PEH circuit resonant frequency to enhance the performance parameters. Since the proposed technique is based on active inductor, it can be easily fabricated in small integrated circuit (IC) packages, allowing integration with state-of-the-art constrained IoT applications. CRAI technique based on the rejection of ‘IP’ at the resonance using active inductor is first reported here. The proposed concept is non-adiabatic, but it could be used for constrained self-powered autonomous IoT applications and it could be of importance in guiding the design of new interface circuits for PEH.

Performance Evaluation of Group OSCORE for Secure Group Communication in the Internet of Things

The Constrained Application Protocol (CoAP) is a major application-layer protocol for the Internet of Things (IoT). The recently standardized security protocol Object Security for Constrained RESTful Environments (OSCORE) efficiently provides end-to-end security of CoAP messages at the application layer, also in the presence of untrusted intermediaries. At the same time, CoAP supports one-to-many communication, targeting use cases such as smart lighting and building automation, firmware update, or emergency broadcast. Securing group communication for CoAP has additional challenges. It can be done using the novel Group Object Security for Constrained RESTful Environments (Group OSCORE) security protocol, which fulfills the same security requirements of OSCORE in group communication environments. While evaluations of OSCORE are available, no studies exist on the performance of Group OSCORE on resource-constrained IoT devices. This article presents the results of our extensive performance evaluation of Group OSCORE over two popular constrained IoT platforms, namely Zolertia Zoul and TI Simplelink. We have implemented Group OSCORE for the Contiki-NG operating system and made our implementation available as open source software. We compared Group OSCORE against unprotected CoAP as well as OSCORE. To the best of our knowledge, this is the first comprehensive and experimental evaluation of Group OSCORE over real constrained IoT devices.

SmartABAC: Enabling Constrained IoT Devices to Make Complex Policy-Based Access Control Decisions

While Attribute-Based Access Control (ABAC) is a promising technique to govern interactions in the Internet of Things (IoT), most existing ABAC models are designed to run on remote servers or gateway devices. This scenario is misaligned with recent trends towards IoT decentralization, such as the Swarm, which expects devices to autonomously share resources, making their own access decisions for enhanced privacy and reliability. In this paper, we propose SmartABAC: a fast, concise, and expressive ABAC model that can be executed in constrained IoT devices. It combines the performance of policies based on attribute enumeration, with techniques that enhance policy expressiveness, such as typed and hierarchical attributes. We specified SmartABAC using first-order logic, designed a use case, and evaluated it in both constrained and non-constrained IoT environments. Results show that our model can represent a variety of access policies, including nested multi-attribute rules, while using less than 100 bytes per policy, on average, for a smart home use case. Our C-based SmartABAC implementation is at least 255 times faster than existing models and can evaluate 3000 policies under 5 milliseconds on a 32 MHz MCU.

Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways.

Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containerized services deployment on IoT devices and gateways. They fail to account for the limited energy and computing resources of these devices which limit the scalability and concurrent services deployment. This paper aims to establish guidelines and identify critical factors for containerized services deployment on resource constrained IoT devices. For this purpose, two container orchestration tools (i.e., Docker Swarm and Kubernetes) are tested and compared on a baseline IoT gateways testbed. Experiments use Deep Learning driven data analytics and Intrusion Detection System services, and evaluate the time it takes to prepare and deploy a container (creation time), Central Processing Unit (CPU) utilization for concurrent containers deployment, memory usage under different traffic loads, and energy consumption. The results indicate that container creation time and memory usage are decisive factors for containerized micro service architecture.

Implementing a Symmetric Lightweight Cryptosystem in Highly Constrained IoT Devices by Using a Chaotic S-Box

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects.

MUP: Simplifying Secure Over-The-Air Update with MQTT for Constrained IoT Devices

Message Queuing Telemetry Transport (MQTT) is one of the dominating protocols for edge- and cloud-based Internet of Things (IoT) solutions. When a security vulnerability of an IoT device is known, it has to be fixed as soon as possible. This requires a firmware update procedure. In this paper, we propose a secure update protocol for MQTT-connected devices which ensures the freshness of the firmware, authenticates the new firmware and considers constrained devices. We show that the update protocol is easy to integrate in an MQTT-based IoT network using a semantic approach. The feasibility of our approach is demonstrated by a detailed performance analysis of our prototype implementation on a IoT device with 32 kB RAM. Thereby, we identify design issues in MQTT 5 which can help to improve the support of constrained devices.

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.

Improving Congestion Control of TCP for Constrained IoT Networks.

For smooth integration with middleboxes on the Internet, TCP (Transmission Control Protocol) is favorably considered as a transport-layer protocol for IoT (Internet of Things) networks. In constrained networks, TCP tends to perform well with a small window size. For example, the uIP (micro IP) TCP/IP stack sets the TCP window size to one segment by default. In such a case, managing the retransmission timer is a primary approach to congestion control. In this paper, we examine the congestion control mechanism of TCP in the uIP stack using grid topology networks. In the preliminary test using the Cooja network simulator, the results show that the original uIP TCP causes lots of retransmissions when a radio duty cycling mechanism such as ContikiMAC is used. One main reason is that, once retransmission is deemed to be necessary, the original uIP TCP sets the retransmission timer based on the fixed RTO (retransmission timeout) before performing a retransmission. Since ContikiMAC may cause large RTT (round-trip time) variations due to the hidden terminal problem, the retransmission timer based on the fixed RTO value may cause lots of retransmissions. To address the problem, we propose a new scheme for managing the retransmission timer which adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Simulation results show that our proposed scheme reduces retransmissions while enhancing throughput and fairness when an RDC (radio duty cycling) mechanism is used.

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.

Secure Data Provenance in IoT Network using Bloom Filters

Internet of Things (IoT) network are considered the future in monitoring and surveillance. With the advent of autonomous organization, and network and service management, conventional networks are fast integrated with IoT networks. With minimal human interaction, the security of IoT data travelling through the networks becomes more critical. Data Provenance ensures the path through which data was received for providing integrity and confidence. However, the provenance frameworks are also prone to attacks, as the attacker can manipulate the data and the meta-date used by the provenance mechanism. Therefore, a secure provenance mechanism is required for IoT networks, which is efficient and light-weight as the IoT devices have limited computational and storage resources. In this paper, we propose a secure provenance mechanism which utilizes less storage, specifically designed for IoT devices, by using Bloom-filters and outsourcing attribute based cryptographic mechanisms. The proposed scheme helps in reducing the storage requirement and computational time in IoT devices. The performance of the proposed mechanism is evaluated, and the results show that the proposed solution is best suited for resourced constrained IoT network.

Secure Data Provenance in Internet of Things based Networks by Outsourcing Attribute based Signatures and using Bloom Filters

With the dawn of autonomous organization and network and service management, the integration of existing networks with Internet of Things (IoT) based networks is becoming a reality. With minimal human interaction, the security of IoT data moving through the network becomes prone to attacks. IoT networks require a secure provenance mechanism, which is efficient and lightweight because of the scarce computing and storage resources at the IoT nodes. In this paper, we have proposed a secure mechanism to sign and authenticate provenance messages using Ciphertext-Policy Attribute Based Encryption (CP-ABE) based signatures. The proposed technique uses Bloom filters to reduce storage requirements and an outsourced ABE mechanism to use lessen the computational requirements at the IoT devices. The proposed technique helps in reducing the storage requirements and computation time in IoT devices. The performance of the proposed mechanism is evaluated and the results show that the proposed solution is best suited for resourced constrained IoT network.

A Simplified Outpatient Health Monitoring System in Resource Constrained IoT Infrastructure

Health monitoring is an essential task in managing both the daily routine progress of a patient/outpatient during and after the medication period. It plays not only a significant role in the overall well-being of the patient/outpatient but also as the measure of quality and effective healthcare services deliverance. However, achieving this is very challenging in areas with limited infrastructure to support a myriad of current social services, including advanced healthcare services. For instance, instead of relying only on the outpatient’s physical visitations to the healthcare center as a feedback mechanism in case of any complications during medications, continuously remote monitoring of the outpatients' health can improve the deliverance of healthcare services. This paper presents a simplified system model for remote monitoring of the outpatients well-being that has been designed to work in the limited Internet of Things (IoT) infrastructure to address the challenge above. The design is cheap and easily deployable for health remote monitoring purposes as the outpatient’s well-being can be monitored in real-time so that to facilitate detection of the subtle changes and avoid drug intoxication. The system advises and alerts in real-time the doctors/medical assistants about the changing of vital parameters in order to take preventive measures.

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.

Integration of Heterogeneous Devices and Communication Models via the Cloud in the Constrained Internet of Things

As the Internet of Things continues to expand in the coming years, the need for services that span multiple IoT application domains will continue to increase in order to realize the efficiency gains promised by the IoT. Today, however, service developers looking to add value on top of existing IoT systems are faced with very heterogeneous devices and systems. These systems implement a wide variety of network connectivity options, protocols (proprietary or standards-based), and communication methods all of which are unknown to a service developer that is new to the IoT. Even within one IoT standard, a device typically has multiple options for communicating with others. In order to alleviate service developers from these concerns, this paper presents a cloud-based platform for integrating heterogeneous constrained IoT devices and communication models into services. Our evaluation shows that the impact of our approach on the operation of constrained devices is minimal while providing a tangible benefit in service integration of low-resource IoT devices. A proof of concept demonstrates the latter by means of a control and management dashboard for constrained devices that was implemented on top of the presented platform. The results of our work enable service developers to more easily implement and deploy services that span a wide variety of IoT application domains.