Real-time Internet Of Things Applications Research Articles

From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.

LTE/LTE-A Based Advanced Wireless Networks

LTE/LTE-A is a wireless broadband standard that provides quick and reliable connectivity for the Internet of Things (IoT) and other connected devices. It is the de facto standard for 4G wireless, offering high-speed communication, extensive network coverage, and low latency. LTE/LTE-A networks are crucial for real-time IoT applications, offering high-speed data transfer, extensive coverage, low latency, and scalability. However, challenges in evaluating LTE/LTE-A-based wireless networks for IoT include compatibility, cost, power consumption, network coverage, and security. Large-scale deployments are hindered by cost, power usage, real-time data transfer, security vulnerabilities, and interoperability. Network performance may be impacted by high data traffic levels, expensive setup and maintenance, and interference from other wireless devices. LTE/LTE-A networks are complex and require sophisticated network technologies and protocols, making them difficult for some IoT stakeholders to build and administer.

Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.

Towards an automatic deployment model of IoT services in Fog computing using an adaptive differential evolution algorithm

Nowadays, fog computing has joined cloud computing as an emerging computing paradigm to provide resources at the edge of the network, as centralized clouds face challenges such as delay to process the unprecedented volume of data generated by Internet of Things (IoT) devices. Fog computing ensures the processing of real-time IoT applications at the edge of the network with low delay, as there is no need to transfer the entire data to a remote cloud. However, the main challenge is to deploy IoT services as components of IoT applications on fog nodes. Fog nodes are heterogeneous, distributed and resource-constrained, and this motivated us to solve the IoT Fog Service Placement (FSP) problem as a multi-objective optimization problem with evolutionary approaches. Here, we develop an Adaptive Differential Evolution (ADE) algorithm to solve FSP that originates from the MAPE-k (Monitor-Analyze-Plan-Execute over a shared knowledge) autonomous model. ADE considers a reproduction policy based on differential evolution-current-to-best, whose parameters are adjusted adaptively. The proposed method, ADE-FSP, transforms the multi-objective problem into a single-objective optimization problem with the perspective of minimizing deadline violation, resource loss and service cost as well as maximizing resource usage. Meanwhile, ADE-FSP ensures the automatic and efficient deployment of IoT services in the fog environment by considering the priority of services and the distribution of resource consumption. We analyze the proposed ADE-FSP from different perspectives on a simulated fog environment. Experimental results show that compared to state-of-the-art algorithms, ADE-FSP significantly improves delay (up to 5.6%) and resource usage (up to 13.2%).

Post Quantum Lattice-Based Secure Framework using Aggregate Signature for Ambient Intelligence Assisted Blockchain-Based IoT Applications

Many classical cryptographic techniques are breakable due to the quantum computing security threats, and it leads to design public key cryptography based on post-quantum cryptography primitives and security protocols. In recent years, Lattice-Based Cryptography (LBC) becomes a prominent post-quantum cryptographic primitive that can be applied in both traditional and emerging security domains, including encryption, key agreement, digital signature and homomorphic encryption. In this article, we first provide a LBC-based security framework using aggregate signature that can be applied in ambient intelligence-assisted blockchain-based Internet of Things (IoT) applications, called LAS-AIBIoT. In LAS-AIBIoT, the wearable/medical devices deployed in the patients' body securely send the sensing secret data (encrypted messages) with their respective lattice-based signatures to their nearby controller nodes (CN), where the CNs forward these secret messages to the attached aggregator node (Aggr). Each Aggr verifies the individual signature of the devices and constructs the aggregate signature on the received secret messages and signatures, and sends the aggregated secret messages with their aggregate signature to the cloud server(s) for block construction in the blockchain center. Through the consensus protocol, the block is then mined and added into the blokchain. We show the robustness of LAS-AIBIoT against various potential attacks including quantum computing security threats through the threat model discussed in this article. Finally, through the blockchain-based simulation study we show that LAS-AIBIoT can be applied for real-time ambient intelligence-assisted IoT applications.

A dual-mode MAC protocol with service differentiation for industrial IoT networks using wake-up radio

The Internet of Things (IoT) has successfully revolutionized our society and all aspects of our daily lives. Wake-up radio (WuR) technology on its part has contributed to the success of IoT by significantly reducing latency and power consumption. Moreover, emerging mission critical Industrial IoT (IIoT) applications are time sensitive and generate more stringent delay and reliability constraints. Therefore, WuR-based MAC protocols must be carefully designed to meet these requirements. In this paper, a Dual-Mode MAC protocol (D2MAC) is proposed to fulfill QoS requirements of both delay-tolerant and real-time IoT applications by using service differentiation in the context of WuR-based IoT networks. Our protocol adopts two operation modes: normal mode, used in the case of normal traffic conditions, and priority mode for time–critical data. A Markov chain model is used to evaluate the performance of D2MAC. A simulation model based on MiXiM/ OMNet++ framework is also proposed to validate this analytical model. We illustrate the benefits of using the priority mode (PCA-WuR) to fulfill the requirements of IIoT applications. We also demonstrate that the Performance of PCA-WuR strongly depends on the acceptable delay limit for packet transmission.

Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm

The number of Internet of Things (IoT)-based applications is constantly increasing, and transferring all their associated data to a remote centralized cloud requires more latency, energy, bandwidth, and cost. In such cases, the fog layer as a new computing paradigm supports resource-constrained IoT devices. Fog computing equipment at the network edge can allocate its resources to process real-time IoT applications. IoT application placement mechanisms in the fog environment have been developed to address these issues. According to microservice architecture, subordinate services of IoT applications can be independently deployed on fog servers. Hence, optimal utilization of fog resources is of great importance to satisfy the Quality of Service (QoS) and requires a distributed and autonomous mechanism to solve the Service Placement Problem (SPP) in fog. Motivated by the generalizability shortcomings of existing approaches, we use the Asynchronous Advantage Actor-Critic (A3C) algorithm as a new Deep Reinforcement Learning (DRL) approach to solve SPP. The proposed scheme focuses on the placement of IoT services with the objectives of minimizing cost and latency under deadline and resource constraints. According to these objectives, A3C seeks to maximize the long-term cumulative reward for improving QoS. We perform placement on local fog domains and use neighboring fog domains when needed to improve fog utilization. In addition, a resource distribution extraction technique over time is considered to save more resources to handle future requests. The simulation results show that our mechanism significantly improves the cost and latency compared to its counterparts such as DDQL and IMPALA.

Data reduction in fog computing and internet of things: A systematic literature survey

Cloud computing, the most crucial computing and storage tool in IoT (Internet of Things), still meets various challenges. The remoteness of IoT end devices from cloud platforms may lead to significant issues for real-time applications such as disaster handling, healthcare application, etc. In order to address these issues, fog computing is a new platform with specialized features that perform essential IoT data management and real-time application management tasks. Data management in IoT via fog computing is critical for decreasing latency in real-time IoT applications and is needed to produce more professional knowledge and smart decisions. Reduction in the size of data sent to the cloud layer is a fundamental topic in data management on the fog computing platform. In this paper, to select and survey studies about data size reduction in fog computing, we applied the Systematic Literature Review (SLR) process to comprehend and classify the different topics and related approaches in this field. In addition, the studies presented in the edge computing field, which were close to our goal, were also investigated. The primary purpose of this study is to classify and analyze the fog data reduction (FDR) studies published between 2016 and 2022. The topics and related approaches of selected papers are presented in an approach-based taxonomy. The topics of provided taxonomy include data filtering, data compression, data aggregation, data prediction, data pattern recognition, and general facets and related approaches. Finally, open issues for FDR and relevant main challenges are presented in upcoming research.

A Taxonomy of Machine-Learning-Based Intrusion Detection Systems for the Internet of Things: A Survey

The Internet of Things (IoT) is an emerging technology that has earned a lot of research attention and technical revolution in recent years. Significantly, IoT connects and integrates billions of devices and communication networks around the world for several real-time IoT applications. On the other hand, cybersecurity attacks on the IoT are growing at an alarming rate since these devices are vulnerable because of their limited battery life, global connectivity, resource-constrained nature, and mobility. When attacks on IoT networks go undetected within a speculated period, such security attacks may prompt severe threats and disruptive behavior inside the network and make the network unavailable to the end user. Hence, it is quintessential to design an intelligent and robust security approach that promptly detects potential attack surfaces in a dynamic IoT network. This article investigates a comprehensive survey of machine learning, deep learning, and reinforcement learning-based intelligent intrusion detection techniques for securing IoT. Also, this article thoroughly illustrates the implementation of various categories of security threats in IoT with a neat diagram. Significantly, we classify the threats into two broad categories: 1) wireless sensor networks (WSNs) inherited security attacks and 2) routing protocol for low power and lossy networks (RPL) specific security attacks in IoT. Finally, we present potential research opportunities and challenges in intelligent intrusion detection approaches in future IoT security.

An efficient dynamic service provisioning mechanism in fog computing environment: A learning automata approach

Recent advances in the Internet of Things (IoT) technology have contributed to growing the number of IoT applications in various scenarios, e.g., buildings, cities, healthcare, wearable devices, and businesses to change the way work and live. The fog computing model has appeared as an appropriate distributed platform to service at the edge of the network using the resource capacities to support and execute the real-time IoT applications. One of the most challenging issues in IoT application management is the dynamic service provisioning problem to fix changes in IoT application resource usage patterns. In this paper, we propose an efficient dynamic service provisioning mechanism using the learning automata technique to determine the service provisioning decisions to deploy or release the IoT applications over the heterogeneous and dynamic fog infrastructure. Besides, we design an autonomous dynamic service provisioning manager (DSPM) that follows a self-management control loop to provision the IoT applications on the fog infrastructure. The simulation results obtained using synthetic and real-world traffic traces show that our proposed algorithm effectively reduces service delay, cost and service delay violation compared to other algorithms.

Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things

The Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance, these solutions depend heavily on the performance of forecasters. Hence, the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end, in this paper, first, we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second, based on this framework, we develop the Dynamic Automatic Forecaster Selection (DAFS) method, which selects the best-performing forecasting scheme for predictive access, in particular for Joint Forecasting-Scheduling (JFS), in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least 80% of the best performance that can be attained for both throughput and energy consumption. Furthermore, we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices.

RtFog: A Real-Time FPGA-Based Fog Node With Remote Dynamically Reconfigurable Application Plane for Fog Analytics Redeployment

This article presents a real-time FPGA (field-programmable gate array) based fog node architecture with a remote dynamically reconfigurable application plane. The proposed FPGA-based fog node exhibits high architectural flexibility to enable fog analytics dynamic redeployment, which is critical for adapting to application dynamicity and prolonging product lifespan. The proposed fog node architecture is implemented in an FPGA without GPP (general-purpose processor) and tested experimentally in the NetFPGA development board with a case study on anomaly detection analytics that is commonly used in IoT (Internet-of-Things) time-series monitoring applications. The Windowed Gaussian and KNN CAD implementations in FPGA have been benchmarked with the NAB (Numenta Anomaly Benchmark) environment for performance assessment. Significantly, the latency and throughput improvement for the KNN CAD in FPGA implementation over software is approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$61\times $ </tex-math></inline-formula> , while Windowed Gaussian is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42\times $ </tex-math></inline-formula> and thus, both analytics attained <1 ms real-time responsiveness. The implemented FPGA-based Windowed Gaussian and KNN CAD anomaly detection analytics has the respective energy efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.06~\mu \text{J}$ </tex-math></inline-formula> and 0.12 mJ for each detection. In short, the proposed FPGA-based fog node exhibits high energy efficiency, low latency, and high throughput in network packet and data analytics processing for greener fog networks and real-time IoT applications.

Intelligent Resource Allocations for Software-Defined Mission-Critical IoT燬ervices

Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated network environments. To cope with the issues mentioned above, this paper presents an adopted software-defined mobile edge computing (SDMEC) with a lightweight machine learning (ML) algorithm, namely support vector machine (SVM), to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation models. Furthermore, the SVM algorithm plays an essential role in performing QCI classification. Moreover, the software-defined networking (SDN) controller allocates and configures priority resources according to the SVM classification outcomes. Thus, the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint applications. Based on the E2E experimentation metrics, the proposed scheme shows remarkable outperformance in key performance indicator (KPI) QoS, including communication reliability, latency, and communication throughput over the various powerful reference methods.

An enhanced intelligent model: To protect marine IoT sensor environment using ensemble machine learning approach

The research in marine sensors and the Internet of Things (IoT) has grown exponentially with the ample warehouse of natural materials in the sea. The growing activities in the marine sensor environment increased the threat of anomalies and cyber-attacks. Many Intrusion Detection Systems (IDS) and classical machine learning-based models have been proposed to secure the sensor-based IoT infrastructure. Still, these mechanisms have failed to achieve significant results for securing the marine sensor environment due to the discriminant requirements of the IoT appliances in deep oceans, such as distribution, information complexity, scalability, higher network bandwidth requirements, and low computational capacity. Hence, we propose a lightweight and robust ensemble model to secure the marine IoT environment from cyber-attacks and malicious activities. This paper established an optimized Light Gradient Boosting Machine (Light-GBM) algorithm for ocean IoT attack detection. The experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) dataset. The proposed methodology includes a label encoding technique for best feature selection, hyper-parameter tuning, ensemble function, and a novel algorithm to develop an ocean IoT attack detection model. As an extension of traditional methods, the optimized Light-GBM model can handle the distributed IoT attacks in the deeper marine environments with low computational cost and with 98.52% detection accuracy. The comparative analysis confirms the effectiveness of the proposed model for marine sensor safety. Conclusively, the proposed model mitigates the threat of cyber-attacks in the marine sensor environment and presenting a promising future in real-time ocean-based IoT applications.

An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study

Abstract Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.

An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

Internet of Things (IoT) has posed new requirements to the underlying processing architecture, specially for real-time applications, such as event-detection services. Complex Event Processing (CEP) engines provide a powerful tool to implement these services. Fog computing has raised as a solution to support IoT real-time applications, in contrast to the Cloud-based approach. This work is aimed at analysing a CEP-based Fog architecture for real-time IoT applications that uses a publish-subscribe protocol. A testbed has been developed with low-cost and local resources to verify the suitability of CEP-engines to low-cost computing resources. To assess performance we have analysed the effectiveness and cost of the proposal in terms of latency and resource usage, respectively. Results show that the fog computing architecture reduces event-detection latencies up to 35%, while the available computing resources are being used more efficiently, when compared to a Cloud deployment. Performance evaluation also identifies the communication between the CEP-engine and the final users as the most time consuming component of latency. Moreover, the latency analysis concludes that the time required by CEP-engine is related to the compute resources, but is nonlinear dependent of the number of things connected.

Implementation of Blockchain Consensus Algorithm on Embedded Architecture

The adoption of Internet of Things (IoT) technology across many applications, such as autonomous systems, communication, and healthcare, is driving the market’s growth at a positive rate. The emergence of advanced data analytics techniques such as blockchain for connected IoT devices has the potential to reduce the cost and increase in cloud platform adoption. Blockchain is a key technology for real-time IoT applications providing trust in distributed robotic systems running on embedded hardware without the need for certification authorities. There are many challenges in blockchain IoT applications such as the power consumption and the execution time. These specific constraints have to be carefully considered besides other constraints such as number of nodes and data security. In this paper, a novel approach is discussed based on hybrid HW/SW architecture and designed for Proof of Work (PoW) consensus which is the most used consensus mechanism in blockchain. The proposed architecture is validated using the Ethereum blockchain with the Keccak 256 and the field-programmable gate array (FPGA) ZedBoard development kit. This implementation shows improvement in execution time of 338% and minimizing power consumption of 255% compared to the use of Nvidia Maxwell GPUs.

Lightweight Cryptographic Algorithms for Guessing Attack Protection in Complex Internet of Things Applications

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning-based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real-time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password-sized text and paragraph-sized text—that achieves target fair results compared to the existing algorithms in real-time deep learning networks for IoT applications.

New fog computing enabled lossless EEG data compression scheme in IoT networks

The rapid evolution of the Internet of Things (IoT) leads to emerging several applications such as smart home, ehealth, smart agriculture and farming, etc. The edge of the IoT networks will receive a huge amount of data generated by these smart applications which regularly require to be delivered to the servers of the remote data centers for additional real-time treatment. However, transmitting the whole of these IoT data across the network toward the data center of the cloud will impose a high overhead on the IoT network. The data exchange and processing long delay have a significant impact on the response speed of the real-time IoT applications. It can reduce the time responsiveness of these applications. The fog computing has provided for the IoT applications as an intermediate layer between the smart end devices and the cloud to reduce IoT data and improve the response time for IoT applications. In this paper, new fog computing enabled lossless EEG data compression scheme is proposed to minimize the amount of IoT EEG data uploaded to the cloud. The EEG data compression scheme consists of two phases: clustering and compression. First, the received data is clustered into clusters using agglomerative hierarchical clustering. The Huffman encoding is applied to each resulted cluster in the second phase. Finally, the compressed files of smaller clusters are combined and uploaded from fog to cloud. Several experiments have been implemented and the comparison results show that the suggested combined Hierarchical Clustering and Huffman Encoding (HCHE) method significantly decreases the volume of EEG data uploaded to the cloud platform. The proposed HCHE method achieves 4.33 of average compression power that is more than twice as much the compression power of some existing methods.