IoT Edge Nodes Research Articles

Symmetry-Enabled Resource-Efficient Systolic Array Design for Montgomery Multiplication in Resource-Constrained MIoT Endpoints

In today’s TEST interconnected world, the security of 5G Medical IoT networks is of paramount concern. The increasing number of connected devices and the transmission of vast amounts of data necessitate robust measures to protect information integrity and confidentiality. However, securing Medical IoT edge nodes poses unique challenges due to their limited resources, making the implementation of cryptographic protocols a complex task. Within these protocols, modular multiplication assumes a crucial role. Therefore, careful consideration must be given to its implementation. This study focuses on developing a resource-efficient hardware implementation of the Montgomery modular multiplication algorithm over GF(2l), which is a critical operation in cryptographic algorithms. The proposed solution introduces a bit-serial systolic array layout with a modular structure and local connectivity between processing elements. This design, inspired by the principles of symmetry, allows for efficient utilization of resources and optimization of area and delay management. This makes it well-suited for deployment in compact Medical IoT edge nodes with limited resources. The suggested bit-serial processor structure was evaluated through ASIC implementation, which demonstrated substantial improvements over competing designs. The results showcase an average area reduction of 24.5% and significant savings in the area–time product of 26.2%.

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

Enhancing Field Multiplication in IoT Nodes with Limited Resources: A Low-Complexity Systolic Array Solution

Towards Efficient Resource Allocation for Federated Learning in Virtualized Managed Environments

Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge computing, where IoT devices and edge nodes often possess limited computational power, network bandwidth, and energy resources. While various techniques have been developed to optimize the FL training process, an important question remains unanswered: how should resources be allocated in the training workflow? To address this question, it is crucial to understand the nature of these resources. In physical environments, the allocation is typically performed at the node level, with the entire node dedicated to executing a single workload. In contrast, virtualized environments allow for the dynamic partitioning of a node into containerized units that can adapt to changing workloads. Consequently, the new question that arises is: how can a physical node be partitioned into virtual resources to maximize the efficiency of the FL process? To answer this, we investigate various resource allocation methods that consider factors such as computational and network capabilities, the complexity of datasets, as well as the specific characteristics of the FL workflow and ML backend. We explore two scenarios: (i) running FL over a finite number of testbed nodes and (ii) hosting multiple parallel FL workflows on the same set of testbed nodes. Our findings reveal that the default configurations of state-of-the-art cloud orchestrators are sub-optimal when orchestrating FL workflows. Additionally, we demonstrate that different libraries and ML models exhibit diverse computational footprints. Building upon these insights, we discuss methods to mitigate computational interferences and enhance the overall performance of the FL pipeline execution.

Delay-Aware Container Scheduling in Kubernetes

Kubernetes is a powerful tool to manage containerized applications, which is also regarded as one promising platform to support microservices in edge computing. The scheduler is a key component of Kubernetes. It allocates each pod (i.e., a set of running containers) to one worker node (i.e., a machine). The default scheduler in Kubernetes is designed for the cloud environment containing homogeneous nodes. However, IoT edge nodes usually have various computing power and network bandwidth. The paper proposes a delay-aware container scheduling (DACS) algorithm to address the issue of node heterogeneity in edge computing. To efficiently assign pods to worker nodes, DACS takes account of not only residual resources of worker nodes but also potential delays caused by the pod assignment. We build a Kubernetes cluster by VMware to evaluate system performance. Experimental results reveal that DACS can significantly reduce both processing and network delays, thereby helping Kubernetes perform more efficiently in an edge environment.

Low-Complexity One-Dimensional Parallel Semi-Systolic Structure for Field Montgomery Multiplication Algorithm Perfect for Small IoT Edge Nodes

The use of IoT technology in several applications is hampered by security and privacy concerns with IoT edge nodes. Security flaws can only be resolved by implementing cryptographic protocols on these nodes. The resource constraints of the edge nodes make it extremely difficult to implement these protocols. The majority of cryptographic protocols’ fundamental operation is finite-field multiplication, and their performance is significantly impacted by their effective implementation. Therefore, this work mainly focuses on implementing low-area with low-energy and high-speed one-dimensional bit-parallel semi-systolic multiplier for the Montgomery multiplication algorithm. The space and delay complexity analysis of the proposed multiplier structure reveals that the proposed design has a significant reduction in delay and a marginal reduction in the area when compared to the competitive one-dimensional multipliers. The obtained ASIC synthesis report demonstrates that the suggested multiplier architecture saves a marginal amount of space as well as a significant amount of time, area–delay product (ADP), and power–delay product (PDP) when compared to the competitive ones. The obtained results indicate that the proposed multiplier layout is very appropriate for use in devices with limited resources such as IoT edge nodes and tiny embedded devices.

Federated Deep Reinforcement Learning for Traffic Monitoring in SDN-Based IoT Networks

This paper proposes a novel traffic monitoring framework, namely, DeepMonitor, for SDN-based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic types at the network edges. Specifically, we first develop an intelligent flow rule match-field control system, called DeepMonitor agent, for SDN-based IoT edge nodes, taking different granularity-level requirements and their maximum flow-table capacity into consideration. We then formulate the control optimization problem for each edge node employing the Markov decision process (MDP). Next, we develop a double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -network (DDQN) algorithm to quickly achieve the optimal flow rule match-field policy. Moreover, we propose a federated DDQN-based traffic monitoring mechanism to significantly improve the learning performance of the edge nodes. The results obtained through extensive emulations show that by applying the DeepMonitor, the flow-table overflow problem at the edge nodes can be completely bypassed. The average number of match-fields in a flow rule achieved by DeepMonitor is increased by approximately 37% (for medium and diverse granularity-level requirements) and 41.9% (for high granularity-level requirement) compared to that of an existing solution, i.e., FlowStat. Finally, by adopting DeepMonitor, the DDoS attack detection performance of an intrusion detection system can be enhanced by up to 22.83% compared with that of FlowStat.

High-Speed RLWE-Oriented Polynomial Multiplier Utilizing Karatsuba Algorithm

Lattice-based cryptography (LBC) is one of the promising post-quantum candidates which offers good security and performance. The most time consuming operations in LBC is the polynomial multiplication, which can be performed through widely explored algorithms like schoolbook polynomial multiplication algorithm (SPMA) and Number Theoretic Transform (NTT). However, Karatsuba algorithm with better complexity compared to SPMA, is not widely studied for FPGA implementation of LBC. In this brief, we proposed an optimized SPMA-Karatsuba (SK) architecture with novel technique to implement the negacyclic convolution. The proposed architecture is more than 2.09× faster in expense of 96.06% additional hardware resources compared to the state-of-the-art SPMA architecture. This shows that the combination of SPMA and Karatsuba algorithm can produce hardware architecture with higher speed yet maintain balanced area-time efficiency compared to SPMA-only architecture. This is especially useful for developing IoT edge nodes or gateway devices that require high speed but able to tolerate some additional hardware area.

Resource Scheduling Based on Reinforcement Learning Based on Federated Learning

The emergence of edge computing makes up for the limited capacity of devices. By migrating intensive computing tasks from them to edge nodes (EN), we can save more energy while still maintaining the quality of service.Computing offload decision involves collaboration and complex resource management. It should be determined in real time according to dynamic workload and network environment. The simulation experiment method is used to maximize the long-term utility by deploying deep reinforcement learning agents on IOT devices and edge nodes, and the alliance learning is introduced to distribute the deep reinforcement learning agents. First, build the Internet of things system supporting edge computing, download the existing model from the edge node for training, and unload the intensive computing task to the edge node for training; upload the updated parameters to the edge node, and the edge node aggregates the parameters with the The model at the edge nodecan get a new model; the cloud can get a new model at the edge node and aggregate, and can also get updated parameters from the edge node to apply to the device.

An industrial IoT edge node for buffer level detection in a cardboard production line

Computer Science and Internet have evolved rapidly the last decades and equally impressive is the evolution of the Industrial Internet of Things technology into factories’ shop floors. Among other technologies: modern CPU Architectures, Edge Computing, Deep Learning, Computer Vision, and Low Power Wide Area Networks, are playing a key role in this new competitive environment. In this paper, we present an Industrial IoT Edge Node for level detection on an overhead bridge conveyor (buffer) which is part of a 5-ply corrugated cardboard production line. We focused on the Edge Node and the development of the system was accomplished by using state of the art technologies from disciplines of computer vision and deep learning. We present two implementations using contour detection and CNN techniques. Finally, we implemented a LoRaWAN solution in the IIoT node to send alert messages to the control room. Experimental results are presented for the proposed system implementations.

FSpiNN: An Optimization Framework for Memory-Efficient and Energy-Efficient Spiking Neural Networks

Spiking Neural Networks (SNNs) are gaining interest due to their event-driven processing which potentially consumes low power/energy computations in hardware platforms, while offering unsupervised learning capability due to the spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs require a large memory footprint to achieve high accuracy, thereby making them difficult to be deployed on embedded systems, for instance on battery-powered mobile devices and IoT Edge nodes. Towards this, we propose FSpiNN, an optimization framework for obtaining memory- and energy-efficient SNNs for training and inference processing, with unsupervised learning capability while maintaining accuracy. It is achieved by (1) reducing the computational requirements of neuronal and STDP operations, (2) improving the accuracy of STDP-based learning, (3) compressing the SNN through a fixed-point quantization, and (4) incorporating the memory and energy requirements in the optimization process. FSpiNN reduces the computational requirements by reducing the number of neuronal operations, the STDP-based synaptic weight updates, and the STDP complexity. To improve the accuracy of learning, FSpiNN employs timestep-based synaptic weight updates, and adaptively determines the STDP potentiation factor and the effective inhibition strength. The experimental results show that, as compared to the state-of-the-art work, FSpiNN achieves 7.5x memory saving, and improves the energy-efficiency by 3.5x on average for training and by 1.8x on average for inference, across MNIST and Fashion MNIST datasets, with no accuracy loss for a network with 4900 excitatory neurons, thereby enabling energy-efficient SNNs for edge devices/embedded systems.

Blockchain at the Edge: Performance of Resource-Constrained IoT Networks

The proliferation of IoT in various technological realms has resulted in the massive spurt of unsecured data. The use of complex security mechanisms for securing these data is highly restricted owing to the low-power and low-resource nature of most of the IoT devices, especially at the Edge. In this article, we propose to use blockchains for extending security to such IoT implementations. We deploy a Ethereum blockchain consisting of both regular and constrained devices connecting to the blockchain through wired and wireless heterogeneous networks. We additionally implement a secure and encrypted networked clock mechanism to synchronize the non-real-time IoT Edge nodes within the blockchain. Further, we experimentally study the feasibility of such a deployment and the bottlenecks associated with it by running necessary cryptographic operations for blockchains in IoT devices. We study the effects of network latency, increase in constrained blockchain nodes, data size, Ether, and blockchain node mobility during transaction and mining of data within our deployed blockchain. This study serves as a guideline for designing secured solutions for IoT implementations under various operating conditions such as those encountered for static IoT nodes and mobile IoT devices.

Rule based auto-scalability of IoT services for efficient edge device resource utilization

Conveying the workload of IoT systems from the cloud to edge nodes have been widely adopted by industrial and academic sectors. This tendency is generally promoted to meet the requirements of some time-sensitive use cases such as IoT healthcare applications. However, IoT devices at the edge network are likely to be resource-limited, as well as, they perform under an extremely heterogeneous environment in terms of the connected devices and the deployed software modules. Thus, both of the aforementioned concerns have considerably led to hindering the deployment process of services on IoT edge devices. In this paper, we propose an approach to facilitate a scalable and lightweight solution for service deployment for efficient resource utilization on IoT edge nodes. Our solution is based on the container concept, and we adopt the cluster concept to define a group of IoT edge devices. Containers are lightweight virtualization technique that enables services to be packaged and deployed with their dependencies regardless of the hosts infrastructure, as well as, they facilitate the service communication and the update process. Furthermore, containers are supported by some means of orchestration such as swarm. These orchestration tools can be configured to enable services deployment and resources sharing among IoT edge devices falling within the same cluster. However, they lack elasticity in terms of auto-scaling up/down of services instances in corresponding to the resource utilization of all cluster elements, as well as, service performance metrics. Our approach overcomes these limitations by following an auto-scaling process based on MAPE-K loop, which is based on our proposed rule model to generate a scaling plan by analyzing collected performance metrics of a cluster. Our evaluation shows the efficiency of the proposed approach in adapting the system performance to meet service performance requirements and the availability of system resources.

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

Offloading-Assisted Energy-Balanced IoT Edge Node Relocation for Confident Information Coverage

The promising Industrial Internet of Things (IIoT) consisting of heterogeneous resource-restricted IoT nodes recently has attracted great attention from both academia and industry communities. However, the battery-powered, computing and communication resource-constrained, and randomly uneven distributed features of the IoT nodes pose several great tough hurdles, including the quality of services of real-time processing, energy efficiency, network lifetime, and coverage holes to the IIoT-based industrial applications. To deal with these challenges, based on the emerging edge computing paradigm and the novel confident information coverage (CIC) model, this paper investigates how to relocate redundant IoT edge nodes to provide timely CIC service in an offloading-assisted energy-balanced manner while extending the network lifetime, which is called as the CIC-based IoT edge node relocation (CICENR) problem. To effectively handle the CICENR problem, we propose an offloading-assisted energy-balanced IoT edge node relocation approach CIC-based offloading-assisted energy-balanced approach (CIC-OAEBA) and the other CIC-based direct replacement approach. Specially, the CIC-OAEBA adopts the Grid-Quorum strategy to quickly detect the redundant IoT edge nodes by offloading the communication-intensive and computing-intensive tasks from grid header nodes to peer IoT edge nodes, and make full use of the cascaded movement strategy to move the nearest redundant IoT edge nodes to the requesting CIC hole locations. Experimental results indicate the proposed approaches remarkably outperform other peer methods in terms of response time, energy efficiency, and especially the network lifetime and coverage performance.

Protean Authentication Scheme – A Time-Bound Dynamic KeyGen Authentication Technique for IoT Edge Nodes in Outdoor Deployments

The IoT edge/sensor nodes are exposed to large attack surface and could easily succumb to several well-known attacks in the wireless sensor network (WSN) domain. Authenticating edge nodes before they join a network, especially after a sleep state, is a critical step to maintain the overall trust of any given IoT local area network (IoT LAN). The low resources and computational constraints of such IoT nodes make this a challenging and non-trivial problem. As many IoT deployments are in uncontrolled environments, the IoT devices are often placed in the open so that physical access to them is inevitable. Due to easy physical access of the devices, common attacks, including cloning of devices or stealing secret keys stored in an edge node, are some of the most common attacks on the IoT deployments. This paper focuses on developing an extremely lightweight authentication scheme for constrained end-devices that are part of a given IoT LAN. Authentication occurs between the end-device and the gateway that acts as an edge computing device. The proposed authentication scheme is put through both formal and informal security verification. Voltage drop, current, and power are measured to gauge the overall impact of the security scheme. All the three parameters were measured while configuring the edge node as an end-device or as a router. Our testing results show that the impact on the resources was minimal.

An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid.