Services For Internet Of Things Applications Research Articles

On-Demand Centralized Resource Allocation for IoT Applications: AI-Enabled Benchmark

The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.

Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment

The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.

Multi-Agent Deep Reinforcement Learning Based Transmission Latency Minimization for Delay-Sensitive Cognitive Satellite-UAV Networks

With the ubiquitous deployment of a massive number of Internet-of-Things (IoT) devices, the satellite-aerial networks are becoming a promising candidate to provide flexible and seamless service for IoT applications. Concerning about the spectrum scarcity issue, we present a cognitive satellite-aerial network where the multiple unmanned aerial vehicles (UAVs) can share spectrum with the satellite without interfering with satellite communications. To further improve spectral efficiency, non-orthogonal multiple access (NOMA) technique is adopted in this network. Considering the delay-sensitive quality-of-service (QoS) requirement, a joint trajectory and power optimization problem is formulated to minimize the total transmission latency over a long-term task period. In order to reduce the computational complexity and ease the burden of information exchange by using the centralized DRL methods, we propose a multi-agent deep deterministic policy gradient (MADDPG) based algorithm which adopts the framework of centralized training with decentralized execution to solve the sophisticated problem. The simulation results show the proposed algorithm can achieve satisfactory performance through joint trajectory control and power allocation for the UAVs compared with other methods.

Cross-Layer Approach for Self-Organizing and Self-Configuring Communications Within IoT

Internet of Things (IoT) is considered nowadays as the most important and indispensable support to ensure all types of communication, over almost all sectors of activities. The specificity of each area, as well as its own requirements in terms of Quality of Service (QoS), make this communication difficult to ensure and thus, face multiple challenges. One of these challenges is related to the needed autonomy for IoT, not only in terms of available resources (energy and bandwidth for example) but also in terms of self-configuring and self-organizing within the network. It is in this context that we propose a new cross layers approach for better self-configuring and self-organizing of devices and communications within IoT environments. The proposed approach is named 2SAEC-IoT (self-organizing and self-configuring algorithms for efficient communications within IoT) that leads to guarantee an efficient data communication for IoT applications. 2SAEC-IoT is a cross layers solution since it considers important communication parameters related to three levels which are MAC, network, and transport. The proposed approach allows the continuity of services for IoT applications, especially for those with very sensitive data (e-health for example), by tolerating possible communications failures or devices breakdown. The evaluation of the proposed approach shows a clear improvement in terms of QoS, and energy efficiency compared to those obtained by three other IoT networks using different communication algorithms.

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.

Service Placement Optimization Based on Evolutionary Algorithm in Fog Computing

As an emerging distributed computing paradigm, fog computing provides low-latency and real-time interactive services to end-user or Internet of Things(IoT) devices at the edge of the network. One of the main challenges of fog computing is to select the right fog node to deploy and run IoT application services, which is commonly referred to as the fog service placement problem (FSPP). However most schemes model FSPP as a single objective optimization problem. These single- objective optimization schemes usually cannot meet the needs of increasingly complex engineering practice. In this study, we model the fog service placement problem as a constrained multi-objective optimization problem, which aims to improve the resource utilization of the system and reduce network latency and service placement costs. Secondly, the elitist nondominated sorting genetic algorithm II (NSGA-II) is used to optimize the constrained multi-objective service placement problem. Experimental results show that the proposed scheme is superior to the existing schemes in terms of overall performance.

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

RETRACTED ARTICLE: Towards Blockchain-Based Resource Allocation Models for Cloud-Edge Computing in IoT Applications

Today, Blockchain technology is emerging to support high secured performance computing. To improve privacy and security factors of allocating, storage, and manage resources in edge computing, blockchain technology can be used to guarantee safe data transmission and communications in the Internet of Things (IoT). Due to the heterogeneity of resources to cover a range number of IoT applications, resource allocation has a key role in increasing the performance of the IoT systems in edge computing. Also, a blockchain-based resource allocation model can support Quality of Service for IoT applications to ensure a highly secured communication environment in edge computing. Therefore, to detect and analyze technical aspects and new research directions in this issue, this paper reviews different existing blockchain-based resource allocation models in cloud-edge computing. From the review papers, we had a very high rate of relevant publishes 2018 to 2020. The most published papers belong to IEEE with 21 published papers. After IEEE, ACM and Elsevier are the next publishers with 8 papers. The introduced approaches in reviewed papers are compared using some known measures such as time, latency, cost, service spread, storage, and delay. This review aims to classify the principles and technical aspects of blockchain-based resource allocation in cloud-edge computing.

Privacy-Preserving and Distributed Algorithms for Modular Exponentiation in IoT With Edge Computing Assistance

With the development of Internet of Things (IoT) and 5G, edge computing, as a new computing paradigm, has been widely popularized in academia and industry. Due to the distributed architecture and being close to the user, edge computing can faster respond to the IoT device’s request and provide a better quality of service for IoT applications. An important application of edge computing is to outsource the complicated computation task to the nearby edge nodes. Modular exponentiation is widely considered as one of the most common and expensive operations in cryptographic protocols. As far as we know, all secure outsourcing algorithms of modular exponentiation are based on the centralized cloud server, but not based on multiple edge nodes. In this article, we propose the first secure and distributed outsourcing algorithm for modular exponentiation (fixed base and variable exponent) under the multiple noncolluding edge node model. In our algorithm, the exponent is divided into a certain number of parts. In addition, we propose another secure and distributed outsourcing algorithm of modular exponentiation (variable base and variable exponent). The user can protect the privacy in the process of outsourcing and detect the invalid results from edge nodes with high probability. Finally, we provide the experimental evaluation to support that our proposed algorithms are efficient on both the user side and the edge node side.

A robust multicast communication protocol for Low power and Lossy networks

Routing Protocol for low power and Lossy networks (RPL) is a standardized routing protocol for low power and lossy networks (LLNs) such as the Internet of Things (IoT). RPL was designed to be a simple (but efficient) and practical networking protocol to perform routing in IoT networks that consists of resource constrained devices. These tiny intercommunicating devices are currently in use in a large array of IoT application services (e.g., eHealth, smart agriculture, smart grids, and home automation). However, the lack of scalability and the low data communication reliability due to faulty links or malicious nodes, still remains significant challenges in the broader adoption of RPL in LLNs. In this paper, we propose RECOUP, a robust multicast communication routing protocol for Low power and Lossy Networks. RECOUP efficiently uses a low-overhead cluster-based multicast routing technique on top of the RPL protocol. RECOUP increases the probability of message delivery to the intended destination(s), irrespective of the network size and faults (such as broken links or non-responsive nodes), and in the presence of misbehaving nodes. An implementation of RECOUP is realized in Contiki. Our results show the effectiveness of RECOUP over state-of-art protocols concerning packet delivery ratio to 25%, end-to-end delay down to 100 ms, and low radio transmissions required for per packet delivery to 6 mJ. Moreover, it minimizes the impact of various topologies (i.e., rank and sybil) and data communication (i.e., blackhole, wormhole, and jamming) attacks that targets an IoT networking infrastructure.

Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing

AbstractThe requisition for electrical energy, smart grid, and renewable energy paradigm extend a new space for Electrical Energy Data Management and Processing Systems (EEDMS), in such a way that can mitigate the consumption of electrical energy. Similarly, the implementation and maintenance of the EEDMS is a challenging task. Moreover, the heterogeneous energy data generated from residential and commercial sector are the leading challenges for standard Internet of Things (IoT) architecture. This contributes enormous energy data preprocessing and analyzing solutions to IoT landscape. To overcome these challenges, we present a scalable multitasking Internet of Things Gateway (IoTGW) for the modern era of IoT by placing reliance on a new entity called Data Loading and Storing Module (DLSM). The provided DLSM module combine with the Gateway module services like orchestrator, flexibility of bridging front end grid, back end grid and fast formatted data trade between sensing domain and application domain enables a high dynamic distributed framework. Specifically, we add Adaboost‐Multilayer Perceptron hybrid data classifier module to the proposed work to enhance service provision of IoT gateway toward various IoT application services and protocols to facilitate IoT demands such as multitasking, interoperability, classification, and fast data delivery between different modules. IoTGW is implemented and tested using a real‐time IoT data streaming network. The experimental results confirms the superiority of proposed work in terms of scalability to serve novel applications and facilitate broad scope of IoT.

An NFV-Based Service Framework for IoT Applications in Edge Computing Environments

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components ( i.e. , function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship ( i.e. , call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.

Multiple Security Certification System between Blockchain Based Terminal and Internet of Things Device: Implication for Open Innovation

As the number of Internet of Things (IoT) devices increases, services expand and illegal hacking and infringement methods become more sophisticated, an effective solution for blockchain technology is required as a fundamental solution to security threats. In this paper, we develop the security module of an IoT device based on blockchain technology that blocks hacking and information infringement and forms a multi-security blockchain system between the IoT device and the user device and we develop a user application. We contribute to addressing the security threats faced by IoT application services by developing a new method. In particular, we present some schemes for the development of a multi-security certification system based on blockchain for IoT security.

Detection of malicious packet dropping attacks in RPL-based internet of things

The routing protocol for low-power and lossy networks (RPL) is an IPv6-based routing protocol optimised for internet of things (IoT) environments. However, it is susceptible to malicious packet dropping attacks. If a node with a lower rank that is closer to the root node attempts a malicious packet dropping, it may disrupt basic data transmission or even the entire IoT application service. In this paper, we present a novel detection method for malicious packet dropping attacks against RPL-based networks. The proposed method is based on the anomaly intrusion detection system and detects malicious packet dropping in the presence of normal packet losses. We evaluate the performance of the method on Contiki's network simulator, Cooja. The evaluation results show that the method has good performance in detecting malicious packet dropping attacks. In every case, the successful detection rate is greater than 94% and the false alarm rate is less than 3%.

Synergy between Communication, Computing, and Caching for Smart Sensing in Internet of Things

Internet of Things (IoT) is composed of a large number of devices that are connected to the Internet and can exchange information regarding different applications including healthcare, transportation, and agriculture. In IoT, the information and communication technology refers to the involvement of computing to reduce the communication costs for information sharing along with managing the storage capabilities. It becomes more interesting and challenging by involving the concept of caching for optimization that can also be perfectly mapped to improve sensing capabilities. This paper presents the impact of Computing, Communication, and Caching (CCC) over the smart sensing capabilities to highlight the possible opportunities for this convergence in recent enabling technologies including 5G, cloud, fog and edge computing based networks. In this regard, different technologies have been identified that help solving bottlenecks during smart sensing operations by a large number of physical devices in IoT. We have investigated that how the convergence of CCC can improve the smart IoT sensing. A number of requirements for smart IoT based sensing are identified. Moreover, new challenges are identified that cause limited services for IoT applications due to sensing limitation of current and future convergence in IoT enabling technologies.

Deep Learning for Internet of Things Data Analytics

The recent technological innovations and brisk amalgamation of domains such as sensing and actuating technologies, embedded systems, wireless communication, and data analytics are accelerating the growth of Internet of Things (IoT). The massive number of sensors deployed in IoT generate humongous volumes of data for a broad range of applications such as smart home, smart healthcare, smart manufacturing, smart transportation, smart grid, smart agriculture etc. Analyzing such data in order to facilitate enhanced decision making, increase productivity and accuracy, ameliorate revenue is a critical process that makes IoT a precious idea for businesses and a standard of life improving paradigm. Although deriving concealed information and inferences out of IoT data is promising to improve the standard of our lives, it is a complicated task that cannot be accomplished by conventional paradigms. Deep Learning would play a vital role in creating smarter IoT as it has shown remarkable results in different fields including image recognition, information retrieval, speech recognition, natural language processing, indoor localization, physiological and psychological state detection etc. and these form the foundation services for IoT applications. In this regard, investigating the potential of Deep Learning for IoT data analytics becomes indispensable. Motivated to address this concern, this paper explores the flair of Deep Learning for analyzing data generated from IoT environments. A detailed discussion on various Deep Learning architectures, their role in IoT data analytics and potential use cases is also presented. Finally, open research challenges and future research directions are discussed in order to promote future research in this domain.

A framework for enhancing the performance of Internet of Things applications based on RFID and WSNs

Internet of Things (IoT) has emerged as a new paradigm that allows different objects interconnection to create new smart services and applications. In this sense, key features such as traceability, unique identification, energy efficiency, heterogeneity of devices, scalability and ubiquity, are necessary at the network structure for efficient performance of IoT applications. As a single technology, it is not capable to provide all these features, then, the integration of Wireless Sensor Networks (WSNs) and Radio Frequency Identification (RFID) arises as an important approach for these solutions. In this paper, a framework aiming to provide the network features required for IoT applications is proposed, considering the challenges that comes from the integration of these two technologies. In this framework, two components are considered, the Fuzzy Q-Algorithm and the Fuzzy System-Based Route Classifier. The former comprises a fuzzy system to enhance an anti-collision protocol for RFID, and the latter is a fuzzy system that classifies routes and assists routing protocols in applications that use WSNs and RFID. Experiments show that framework provides the following benefits: improves the performance of RFID tags identification, lowers packet loss rates, decreases the nodes energy consumption, and shows improvements in network load balancing. Moreover, these benefits provide greater energy efficiency and quality of service for IoT applications.

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

IoT Localization for Bistatic Passive UHF RFID Systems With 3-D Radiation Pattern

Passive radio-frequency identification (RFID) systems carry critical importance for Internet of Things (IoT) applications due to their energy harvesting capabilities. RFIDbased position estimation, in particular, is expected to facilitate a wide array of location-based services for IoT applications with low-power requirements. In this paper, considering monostatic and bistatic configurations and 3-D antenna radiation pattern, we investigate the accuracy of received signal strength-based wireless localization using passive ultra high frequency RFID systems. The Cramer-Rao lower bound (CRLB) for the localization accuracy is derived, and is compared with the accuracy of maximum likelihood estimators for various RFID antenna configurations. Numerical results show that due to RFID tag/antenna sensitivity, and the directional antenna pattern, the localization accuracy can degrade at blind locations that remain outside of the RFID reader antennas' main beam patterns. In such cases optimizing elevation angle of antennas are shown to improve localization coverage, while using bistatic configuration improves localization accuracy significantly.

QoS-aware Opportunistic Cooperative ARQ Mechanism of Sensors Based on Channel state Sensing for Internet of Things

It is well known that the performance of Internet of Things application services is easy to be affected by internal noise of sensor, external environment interference and constriction of system resource. In order to solve the above problems, we studied the characteristics of bit error rate, outage ratio, signal to noise, and scale of relay sensors with channel state to choose the optimal transmission control scheme between direct mode and cooperative mode, building upon which we proposed the opportunistic cooperative automatic repeat request (ARQ) mechanism based on the rules and features of throughput ratio, average delay, packet error rate and energy efficiency, in consideration of retransmission time and channel quality. More importantly, the proposed opportunistic cooperative ARQ mechanism could set up the max retransmission time and control the progress of cooperative transmission according to the real-time network and sensor state, as well as Quality of Services (QoS) guarantee requirements of Internet of Things applications. The mathematical analyses and experiment results show that the proposed scheme is superior to the QoS provisioning schemes based on cooperative ARQ alone and direct transmission with channel state alone in terms of reliability, real time performance and life cycle of system, as well as energy efficiency.