Information-centric Internet Of Things Research Articles

Age of correlated information-optimal dynamic policy scheduling for sustainable Green IoT devices: A multi-agent deep reinforcement learning approach

The rapid progress in communication technologies and the widespread adoption of connected devices have given rise to various information-centric Internet-of-Things (IoT) systems, typically necessitating timely updates of information. Green IoT (G-IoT) strives to enhance the environment by reducing the power consumption of billions of devices engaged in extensive data exchange, addressing the substantial energy demand in the process. The Age of Correlated Information (AoCI) measures the freshness of information shared among two or more devices that contribute to the same decision-making process. Optimizing AoCI using Deep Reinforcement Learning (DRL) in IoT reduces energy consumption, optimizes resource utilization, and promotes environmentally conscious communication, contributing to the development of a sustainable G-IoT system. This paper focuses on scheduling the transmission of status update packets among interconnected G-IoT devices to minimize the application-specific long-term average AoCI. The problem is modeled as an NP-hard episodic Markov Decision Process (MDP), highlighting its computational complexity. To handle correlations and the curse of dimensionality, a multi-agent deep reinforcement learning algorithm, specifically the Multi-Agent Deep Deterministic Policy Grading (MADDPG) algorithm, is developed. The training progress displays episode rewards, with an environment designed to penalize multiple agents transmitting simultaneously or none at all, promoting cooperative behavior and minimizing the average age of correlated information. We provide comprehensive simulation results, including reward convergence, the learning process of actors and critics, and the resulting average AoCI with the number of episodes, demonstrating the effectiveness of the MADDPG algorithm.

SoftCaching: A framework for caching node selection and routing in Software-Defined Information Centric Internet of Things

A considerable demand for Internet of Things (IoT) services is causing exponential growth in Internet traffic. Software-Defined Networking (SDN) and Information-Centric Networking (ICN) are complementary technologies for IoT services provisioning that reduce network traffic and service provisioning delays by caching content at intermediate nodes in the network. Existing research on Software-Defined Information-Centric Networking (SDICN) for IoT service provisioning suffers from two major challenges: (a) optimal caching node selection and (b) optimal path computation from content consumers to caching nodes based on end-to-end delay and link utilization constraints. We propose a SoftCaching framework that optimizes caching node selection for content publishers and computes the optimal routing path to caching nodes for content consumers. It computes optimal locations for caching nodes and finds optimal routing paths for IoT services to access cached content. SoftCaching analyzes network traffic on all nodes in the network and collects traffic metrics. It estimates the best possible caching nodes by using Waypoint Enforcement (WPE) and solving the traffic matrix using Singular Value Decomposition (SVD) and QR Factorization. WPE and SVD-based QR factorization provide optimal locations to cache the content for IoT services. Subsequently, SoftCaching uses a constraint-based shortest path algorithm to compute the optimal routing path from content consumers to caching nodes. We make sure that the caching node and the routing path selection follow delay and link utilization constraints. We perform extensive experiments for evaluation, and the results show that SoftCaching outperforms state-of-the-art caching schemes in terms of delay, link load, hop count, path stretch, and energy consumption metrics.

Blockchain-based Collaborative Caching Mechanism for Information Center IoT

The development of fifth generation mobile communication (5G) technology and Internet of Things (IoT) has enabled more mobile terminals to access the network and generate huge amounts of information content. This will make it difficult for the traditional IP-based host-to-host model to cope with the demand for massive data transmission, making network congestion an increasingly serious problem. To cope with these problems, a new network architecture, the Information-Centric Networking (ICN), a content network with content caching as one of its most core functions, has emerged. In addition, in the era when 5G and future 6G networks gradually realize the interconnection of everything, the Information-Centric Internet of Things (IC-IoT) based on ICN architecture has emerged, and a large number of IoT devices can use ICN nodes as edge devices to realize collaborative caching. The caching capacity of IC-IoT is directly related to the transmission efficiency and capacity of the whole network, and the performance of IC-IoT caching capacity is the top priority of research in this field. To address the above issues, the research in this paper focuses on deploying blockchains in IC-IoT networks and using the consensus mechanism of custom blockchains to motivate ICN nodes and non-ICN nodes in the network to perform caching collaboratively, which in turn improves the caching capacity of the whole network. The main work of this paper has the following points. First, incentivize IC-IoT collaborative caching based on blockchain consensus mechanism: by deploying blockchain in IC-IoT, rewarding nodes that obtain bookkeeping rights to incentivize network-wide collaborative caching, and designing experiments to compare the caching capacity of the network before and after the incentive; second, improve DPoS consensus mechanism to incentivize collaborative caching: Experiments are designed to compare the incentive capacity of PoW consensus mechanism and improved DPoS consensus mechanism for IC-IoT network collaborative caching, and to select the consensus mechanism with better performance; third, the design and implementation of IC-IoT test bed: write ICN program to form ICN network from basic communication to multiple nodes, and then deploying blockchain on the network for subsequent extension studies. This thesis demonstrates the feasibility of using blockchain for IC-IoT network cache collaborative incentive, and proves that the blockchain incentive method in this paper can improve the throughput of IC-IoT network cache by building a test bed.

Reassessing caching performance in information-centric IoT

The performance of in-network caches has traditionally been evaluated using well-understood metrics such as the cache hit rate. This extends to the field of Information-Centric Networking (ICN), where caching strategies are evaluated in the same way. In this paper, we argue that in the subdomain of information-centric Internet of Things (IoT), these traditional metrics are not sufficient to describe caching performance. Using a series of experiments on real hardware as a demonstration, we show the shortcomings of the cache hit rate metric and introduce the two new metrics cache access factor and cache latency factor that provide deeper insights into the effectiveness of in-network caching strategies.

Performance Comparison of Cache Based Routing in Information Centric IoT Networks

In-network caching within the distributed computing is one of the most promising aspects of Information-Centric Networking (ICN) in comparison with traditional centralized server based data retrieval. With ICN, it can ensure that required data is readily available within the intermediate IoT routers, even when the original producer is not reachable. However, in Internet of Things (IoT), where the storage capacity is often severely limited, IoT nodes cannot simply cache any and all the content that will receive to the intermediate caching device(routers). Hence, it is very significant to know which content has to cache and how long the content has to be in the intermediate caching device. Moreover, cache-based routing plays a predominant role in reducing the end-to-end delay with enhanced network throughput by efficiently discovering the shortest-path from the intermediate caching devices in ICN-IoT networks. In this paper, comparison of different cache based routing protocols for ICN-IoT networks is discussed in terms of ICN caching and cache replacement strategies along with the performance metrics suitable for evaluating in an IoT context.

A cloud-fog computing system for classification and scheduling the information-centric IoT applications

Cloud users submit their applications for processing on the provider's infrastructure expecting a diverse set of computational, storage, or communicational resources. Classifying the tasks of these applications based on their needs and scheduling them onto the most suitable resources is a big obstacle. To manage with such issues, a system that supports classification and scheduling them on to the most efficient resource for execution is essential. An information-centric (IC) internet of things (IoT) system for cloud-fog computing environment, which supports these two key functionalities through ensured QoS was proposed in this article. The experimental simulations and performance analysis of the proposed approach was prepared by considering a global cloud with three servers, four fogs, and four types of IoT applications with a sum of 3,000 tasks collectively. Experimental outcomes state that the anticipated cloud-fog computing architecture reduces the average makespan time and service cost up to a maximum of 11.8%, and 12.63% respectively when compared with other existing approaches. Also, the cost-makespan trade-off (CMT) gains with the proposed CSIc_IoTA is up to 28.5% and can guarantee better QoS requirements of real-time IoT applications.

Application-Oriented Scheduling for Optimizing the Age of Correlated Information: A Deep-Reinforcement-Learning-Based Approach

Recent advances in communications technologies and the proliferation of connected devices have spawned a variety of information-centric Internet-of-Things (IoT) systems, where timely information updating is normally required. Age of Information (AoI) has recently been introduced to quantify the freshness of the knowledge the controller has about the remote information sources. With the development of IoT applications, it is becoming increasingly common that the application-level services, e.g., status updates, rely on the timely delivery of fresh information from a number of information sources. In this article, we study the application-oriented scheduling for optimizing information freshness in the presence of correlated information sources. To this end, we adopt the concept of Age of Correlated Information (AoCI) and formulate the scheduling problem as an episodic Markov decision process (MDP) problem with an application-oriented policy. Given the complexity of the above problem, we develop a learning-based approach that not only leverages the emerging deep reinforcement learning (DRL) techniques but also exploits diverse-domain knowledge. The numerical results show that the proposed approach achieves better performance in terms of AoCI, compared to some typical baseline methods.

A congestion-aware decision-driven architecture for information-centric Internet-of-Things applications

This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.

On the impact of QoS management in an Information-centric Internet of Things

The Internet of Things (IoT) comprises a relevant class of applications that require Quality of Service (QoS) assurances. Information Centric Networking (ICN) has shown promising characteristics in constrained wireless networks, but differentiated QoS has not yet fully emerged. In this paper, we design and analyze a QoS scheme that manages the NDN resources forwarding and queuing priorities, as well as the utilization of caches and of forwarding state space. In constrained wireless networks, these resources are scarce with a potentially high impact due to lossy radio transmission. We explore the two basic service qualities (i) prompt and (ii) reliable traffic forwarding. We treat QoS resources not only in isolation, but correlate their use on local nodes and between network members. Network-wide coordination is based on simple QoS code points that can be distributed via a routing protocol. Fairness measures that prevent resource starvation are part of this management scheme. Our findings indicate that our coordinated QoS management in ICN does not only effectively prioritize the privileged data chunks, but also improves regular data communication. We can show that appropriate QoS coordination can enhance the overall network performance by more than the sum of its parts and that it exceeds the impact QoS can have in the IP world.

A Pre-Caching Strategy Based on the Content Relevance of Smart Device’s Request in Information-Centric IoT

In recent years, investigations on various forms of solution for Internet of Things (IoT) paradigm opens the new era of smart devices. The Information-Centric Internet of Things (IC-IoT) can apply the characteristics of content caching which is a typical feature of Information-Centric Networking. Therefore, designing an efficient caching strategy for IC-IoT is able to improve the efficiency of smart devices. In this paper, we propose a Pre-Caching Strategy based on the Relevance of smart device request Content, namely PCSRC. After the first request of content chunk at a smart device side, PCSRC pre-caches the rest of the content chunks. There are two types of cached content chunks, that is, actual requested content chunks and pre-caching content chunks. Actual requested content chunks are pushed forward hop-by-hop according to the trend of the local activity. The popular content will be gradually pushed to the edge of IC-IoT. Furthermore, a sojourn time of a cached content chunk is employed to save storage space. The simulation results show PCSRC can reach 42.7% on the cache hit ratio with the different storage sizes. For the average hop count, PCSRC can increase about 42.2% than that of ProbCache. And PCSRC can improve about 47.9% than that of CEE for the average hop count.

A Reputation Value-Based Early Detection Mechanism Against the Consumer-Provider Collusive Attack in Information-Centric IoT

As the Internet of Things (IoT) has connected large number of devices to the Internet, it is urgently needed to guarantee the low latency, security, scalable content distribution of the IoT network. The benefits of Information-Centric Networking (ICN) in terms of fast and efficient data delivery and improved reliability have raised ICN as a highly promising networking model for IoT environments. However, with the widely spread of the viruses and the explosion of kinds of network devices, the attackers can easily control the devices to form a botnet such as the Mirai. Once the devices are under control, the attackers can launch a consumer-provider collusive attack in the Information-Centric IoT context. In this attack, the malicious clients issue Interest packets that can only be satisfied by the malicious content provider, and the malicious provider replies to the clients just before exceeding the Pending Interest Table entry’s expiration time, to occupy the limited resources. In this paper, we expound the model of the consumer-provider collusive attack and analyze the negative effect of the attack. Then we propose a Reputation Value based Early Detection (RVED) mechanism to relieve the impact of the collusive attack. The method aims to adjust the packet dropping rates of different interfaces based on their reputation value, thus to protect the legitimate packets from being dropped as possible. We implement the consumer-provider collusive model and evaluate our defend mechanism in the simulator, and simulation results verify the feasibility and effectiveness against the collusive attack of the RVED mechanism.

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies.

Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement

Recent literature has demonstrated the improved data discovery and delivery efficiency gained through applying named data networking (NDN) to a variety of information-centric Internet of things (IoT) applications. However, from a data security perspective, the development of NDN-IoT raises several new authentication challenges. In particular, NDN-IoT authentication may require per-packet-level signatures, thus imposing intolerably high computational and time costs on the resource-poor IoT end devices. This paper proposes an effective solution by seamlessly integrating the lightweight and unforgeable physical-layer identity (PHY-ID) into the existing NDN signature scheme for the mobile edge computing (MEC)-enabled NDN-IoT networks. The PHY-ID generation exploits the inherent signal-level device-specific radio-frequency imperfections of IoT devices, including the in-phase/quadrature-phase imbalance, and thereby avoids adding any implementation complexity to the constrained IoT devices. We derive the offline maximum entropy-based quantization rule and propose an online two-step authentication scheme to improve the accuracy of the authentication decision making. Consequently, a cooperative MEC device can securely execute the costly signing task on behalf of the authenticated IoT device in an optimal manner. The evaluation results demonstrate 1) elevated authentication time efficiency, 2) robustness to several impersonation attacks including the replay attack and the computation-based spoofing attack, and 3) increased differentiation rate and correct authentication probability by applying our integration design in MEC-enabled NDN-IoT networks.

Maximizing Sleeping Capacity Based on QoS Provision for Information-Centric Internet of Things

Unlike the IP-address-centric network, heterogeneous devices in the information-centric Internet of Things (IC-IoT) have heterogeneous quality of service (QoS) requirements. Employing sleeping technology in IC-IoT while satisfying the heterogeneous QoS requirements for IoT devices is challenging. In this paper, considering the IoT devices’ heterogeneous QoS requirements, we investigate a sleeping capacity maximizing problem in IC-IoT. First, a content-based heterogeneous network architecture is proposed to empower IC-IoT and decouple the heterogeneous content transmission functions. Specifically, macro base stations (MBSs) are used to guarantee the control coverage and transmit the low QoS requirement services, whereas small base stations (SBSs) are utilized to transmit the high QoS requirement services. The QoS requirement levels are differentiated by the outage constraints which are related to the achievable data rate. Then, an optimization problem is formulated to maximize the sleeping probability and available bandwidth while the outage probability and achievable data rate constraints are guaranteed. In the formulated optimization problem, sleeping probability plays the roles of both an optimization variable and an optimization objective, which makes the optimization problem challenging to be solved. In order to decouple sleeping probability from the optimization values, we first analyze the parameters related to the sleeping probability and the available bandwidth and then propose a bisection-based nonlinear programming algorithm. This algorithm divides the original problem into two subproblems. One is related to the bandwidth reallocation. The other one involves obtaining the optimal sleeping probability. Last but not least, analytical results are concluded due to the simulation results, which indicate the following facts: 1) optimal sleeping probability is linear to the available bandwidth, and inversely proportional to the devices’ density, and; 2) sleeping probability is more sensitive to the changes of channel quality than those of QoS provision.

A Novel Content Popularity Prediction Algorithm Based on Auto Regressive Model in Information-Centric IoT

As information-centric networking (ICN) cache can effectively reduce the requests from customers to producers and improve the efficiency of content acquisition, there are many studies propose to improve system performance of the Internet of Things (IoT) by using the concept of the ICN. In the context of information-centric IoT, the addressing location based on content names and routing transport mechanisms, which presents a high demand for the statistics and prediction of the content popularity. To improve the accuracy of the content popularity prediction, in this paper, we demonstrate a particular analysis of the content popularity and propose a content popularity prediction algorithm based on auto-regressive (AR) model. The algorithm derives regression parameters based on least-squares estimates and predicts future trends of the content popularity through combining various known values in a certain period. The evaluation results show that the proposed algorithm can accurately predict the content popularity of the next time period in information-centric IoT. As a result, the algorithm can increase the cache hit rate in routers, and reduce the network traffic and service access delay effectively to improve the experience of users in various scenarios such as real-time streaming media services.

Single RF-Chain Beam Training for MU-MIMO Energy Efficiency and Information-Centric IoT Millimeter Wave Communications

In a multi-user multiple-input multiple-output (MU-MIMO) communication system, multiple RF (radio frequency) chains are generally connected corresponding to the antenna elements. However, the system complexity, hardware cost, and energy consumption are challenging while using many RF chains. In addition, an information-centric Internet of Things (IoT) will be significant to guarantee fast service demand for radio access in the fifth generation (5G) mmWave wireless systems. In this paper, a new beam training method based on a single RF chain is proposed to overcome these challenges by using the downlink–uplink and downlink–downlink beam training techniques to establish the elite users’ subset group which can be trained in a single training time slot. Additionally, the users’ subset mechanism is implemented by selecting an arbitrary function to guarantee the convergence. Moreover, the proposed techniques are compared with the conventional full search method regarding searching time, cost, and complexity. Different user subset functions are modeled, evaluated, and compared with the existing ones with respect to effective channel gain and average system capacity. The simulation results show that the proposed method can achieve a significant system performance, and the single RF chain is serviceable for a large number of users when connected with a maximum of 64 antenna elements at the BS. The proposed method can also be implemented in a massive MU-MIMO system by using multiple RF chains in which each RF chain is connected to a group of 64 antenna elements. This work is applicable in 5G mmWave communications, which require robust infrastructure to meet the future IoT service demand in 5G hot scenarios.

Active Data Replica Recovery for Quality-Assurance Big Data Analysis in IC-IoT

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time.

An Efficient SCMA Codebook Design Based on Lattice Theory for Information-Centric IoT

Different from traditional communication systems, information-centric Internet of things (IC-IoT) as a novel smart paradigm needs to guarantee end-to-end connectivity for rapidly growing smart devices. How to meet the demands of massive connection has become a key problem for IC-IoT. Sparse code multiple access (SCMA) as a code-domain non-orthogonal multiple access (NOMA) technique has been intensively investigated. With SCMA, each user employs different sub-carrier frequencies for transmission, and different users can share the same sub-carrier frequency via superposition coding. The spectrum efficiency is thus improved. However, designing large-scale codebook sets is still an open problem for SCMA. In this paper, a new lattice-based codebook design method is proposed via mother constellation optimization. First, the optimization problem of the mother constellation is formulated. Through analysis, the problem can be converted into two sub-problems. Accordingly, we first use the lattice theory to find a constellation containing infinite points with large coding gain. After that, we search for a boundary that contains a set of points via spherical packing. Such an approach enables us to obtain a real constellation satisfying a power saving criterion. Secondly, the mother constellation with large power variance is obtained from the real multi-dimensional constellation. Finally, lattice-based codebooks are generated by combining the mother constellation and the mapping matrices with constellation rotation. Simulations demonstrate that the designed codebooks have improved bit error rate (BER) performance with large codebook size, especially at high signal to noise ratio (SNR).

Achieving Semantic Secure Search in Cloud Supported Information-Centric Internet of Things

With the support of cloud computing, the scalability of the information-centric Internet of Things (IC-IoT) can be increased greatly. In the cloud-supported IC-IoT, how to make search more secure and efficient is a key issue to deal with. Searchable encryption is a potential technique to solve the above problem. In the cloud-supported IC-IoT, sensitive data are provided by multiple data users and can be accessed by specific data users. Then, the expression ability, search authorization, dynamic updates, and search efficiency become major concerns of search in the cloud-supported IC-IoT. In this paper, SASDQ, a semantic and authorized search scheme with dynamic updates and quick response, is proposed. In SASDQ, by constructing users’ personal dictionaries, dynamic updates and efficient index generation are achieved, and by leveraging ciphertext policy attribute-based encryption and conceptual graphs, semantic search with file-level fine-grained access authorization can be realized. In addition, quick search response is implemented by the preprocessing of time-consuming authorization tasks. The analysis shows that the proposed scheme can meet the data security requirement of the cloud-supported IC-IoT, and it also has less cost compared with other popular models.

DQN Inspired Joint Computing and Caching Resource Allocation Approach for Software Defined Information-Centric Internet of Things Network

With the rapid development of the Internet of Things (IoT) network, the IoT devices need to perform the artificial intelligence (AI) model to make decisions according to the specific service requirement under a dynamic environment. However, the generation and usage of AI model typically requires a huge amount of communication, computing, and caching resource. Thus, the construction of the network and the scheduling of the limited network resources to realize the rapid generation and propagation of AI models are critical. Therefore, we propose a software-defined Information Centric-Internet of Things (IC-IoT) architecture to bring caching and computing capabilities to the IoT network. Based on the proposed IC-IoT architecture, we design a joint resource scheduling scheme to uniformly manage the computing and caching resources. The objective is to maximize the reward which consists not only short-term reward but also long-term reward brought by caching popular AI models. The resource scheduling problem is formulated into a multi-dimensional optimization problem. A new deep Q-learning method is proposed due to the complexity and high dimension of this problem. The simulation results verify the effectiveness of the software-defined IC-IoT architecture and the joint resource allocation strategy.