The Internet of Things (IoT) has already emerged as one of the most popular directions in today’s information and communication technology (ICT) domain. With its advancement over different application areas, such as smart home, smart healthcare, industry 4.0, etc., a huge amount of data has been generated by billions of IoT devices, which aggravates the shortcomings of IP-based networks, such as limited expressiveness of IP addressing, inefficient support for mobility and in-network caching. Building IoT on top of information-centric networking (ICN) is believed to be a promising solution to tackle the above challenge, especially the in-network caching of ICN can significantly benefit IoT in terms of reducing data and saving IoT devices’ energy. However, caching IoT data is more challenging than caching traditional Internet content, e.g., video, because IoT data are usually valid within a certain period of time, and IoT devices are typically constrained with battery. Hence, in this survey, we first review the current implementation proposals of ICN-IoT. Next, we present the conventional caching decision policies and replacement policies which could be adopted to mitigate the aforementioned challenges, e.g., reducing IoT traffic, saving energy, reducing data retrieval latency. Further, since leveraging machine learning (ML) techniques have the potential to further improve the caching efficiency by dealing with uncertainties, e.g., predicting unknown information, adaptively interacting with the environment, we also demonstrate the recently proposed ML-based caching schemes for ICN-IoT. In addition, we outline the open research issues and point out the future opportunities of caching in ICN-IoT. 

In-network Caching for ICN-based IoT (ICN-IoT): A Comprehensive Survey

After a large improvement in health care services, Wireless Body Area Sensor Network (WBASN) is used in different fields where health care monitoring can be done continuously and from a distance. Many selected protocols are given in literature work which can improve the performance of WBASN by focusing on delay, routing and energy efficiency. In our research, we will focus on improving throughput, network life time, and decrease end-to-delay. In given research, the two sink nodes utilized and AnyCasting concept will be used. Dual Sinks based Scheme (DSS) for WBASN will be compared with existing DARE, SIMPLE, LAEEBA and M-ATTEMPT protocols. DSS performance found more efficient than DARE, SIMPLE, LAEEBA and M-ATTEMPT respectively in throughput. From results, DSS network life time is greater than DARE, SIMPLE, LAEEBA and M-ATTEMPT with minimum delay. In DSS, energy parameter is in tradeoff with the improved parameters, because of a computation of RSSI which do more process and utilize more energy.

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Design and Performance Analysis of DSS (Dual Sink Based Scheme) Protocol for WBASNs

Data traffic increased in recent years due to the expansion of IoT (Internet of Things) applications, and most IoT applications follow a content-oriented paradigm. The host-to-host nature of the existing internet architecture makes it inefficient for handling content distribution and information sharing. ICN (Information Centric Network) is a content-centric network that offers guaranteed solutions for data transfer between consumers and producers. ICN caching enables the caching technique in available resources, which can enhance the overall network performance by enabling users to access data within the network. This study aims to review the literature on caching approaches and strategies in ICN-IoT to identify areas that need more attention in current and future research directions. A systematic mapping study was conducted to identify the relevant literature, and 88 publications that are categorized as caching approaches and caching strategies were chosen as the most relevant studies. Based on caching approaches and caching strategies, the articles are grouped. Edge caching (50%), in-network caching (25%), and hybrid caching (25%) represent the majority of the articles on caching approaches. Popularity-based caching (26%), cooperative caching (17%), collaborative caching (17%), machine learning-based caching (19%), and probabilistic caching (7%) represent the majority of the articles in caching strategies. This study further presents the simulators, performance metrics, parameters and performance evaluation of the existing literature. The results confirm that ICN is a futuristic internet architecture that assigns a content name to each content in the network and enables in-network caching; it is a superior technique for resolving IoT challenges and increases the overall network efficiency. 

A systematic survey on content caching in ICN and ICN-IoT: Challenges, approaches and strategies

Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, our study introduces two innovative algorithms crafted for FL within a peer-to-peer framework. These algorithms aim to enhance performance, especially in decentralized and resource-limited settings. Furthermore, we propose a client-balancing Dirichlet sampling algorithm with probabilistic guarantees to mitigate oversampling issues, optimizing data distribution among clients to achieve more accurate and reliable model training. Within the specifics of our study, we employed 10, 20, and 40 Raspberry Pi devices as clients in a practical FL scenario, simulating real-world conditions. The well-known FedAvg algorithm was implemented, enabling multi-epoch client training before weight integration. Additionally, we examined the influence of real-world dataset noise, culminating in a performance analysis that underscores how our novel methods and research significantly advance robust and efficient FL techniques, thereby enhancing the overall effectiveness of decentralized machine learning applications, including edge intelligence and edge caching.

Federated Edge Intelligence and Edge Caching Mechanisms

The rapid advancement in robotics technology has catalyzed the emergence of Internet of the Robotic Things crowdsourcing, a novel paradigm in the digital economy era. However, the escalating complexity of crowdsourcing tasks, coupled with the constrained resources of robot nodes and the diverse demands of stakeholders, has resulted in inefficiency, which poses a significant challenge to the burgeoning robot crowdsourcing market in IoT. To address these challenges, we develop a comprehensive analytical model that encapsulates the interests of both task scheduler nodes and robotic nodes within Internet of Things, which integrates various factors such as communication, computation, mobility, latency, and energy, thereby tailoring utility functions for different stakeholders. Secondly, we propose a Taskdriven Robotic Crowdsourcing strategy based on Hierarchical Game (TRC-HG), which conceptualizes TNs and RNs as rational entities with sequential actions. At the first layer, the interaction between participants is transformed into a Stackelberg game. This aids TNs in determining optimal pricing strategies while guiding RNs toward the most efficient task-completion strategies. Furthermore, we delve into the collaborative dynamics within the RNs, a cooperative coalition formation strategy at the second layer is established, which iteratively determines node responsibilities and coalition members. The Nash stability and optimality of coalition are ensured, thereby maximizing the utility of RNs. Finally, we validate the performance through a series of high-fidelity simulation experiments. These experiment results, benchmarked against classical methods, highlight significant improvements in terms of latency, energy consumption, and cooperative efficiency. 

Task-Driven Cooperative Internet of the Robotic Things Crowdsourcing: From the Perspective of Hierarchical Game-Theoretic

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep $Q$ -networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).

iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks

According to the requirements of structure and communications of Body Area Network, a study is made on the node energy control strategy of Body Area Network. On the basis of dynamic routing protocol DSR, energy saving self-adaptive route E-DSR of Body Area Network has been designed, and the simulation analysis has also been made. The results show that effectively utilizing the node energy can improve the communication efficiency and lifecycle of Body Area Network by adopting E-DSR route.

Routing Design and Simulation of Body Area Network Based on Node Energy Consumption Control Strategy

This paper investigates the resource allocation problem for a wireless-powered backscatter communication network where the sensors can utilize the radio frequency (RF) signals from a hybrid access point (H-AP) to transmit data and store energy. We assume that the sensors receive RF signals from the H-AP and transmit data to the sensors’ gateway by backscattering techniques. The sensors harvest energy from the RF signals, where a portion of the energy is harvested to transmit data to satisfy the gateway data demand, and the rest of the energy is stored in their batteries. In order to allocate the overall storage energy among sensors fairly, we propose a resource allocation problem based on the Nash bargaining solution (NBS) to maximize the product of the storage energy of each sensor. We prove that the energy allocation problem can be equivalent to a convex problem which can be solved by the interior-point method. Simulation results show that the proposed algorithm achieves a good balance between fairness and efficiency with other benchmark algorithms.

Resource Allocation for Wireless-Powered Backscatter Communication Network Based on Nash Bargaining Solution

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In-network Caching for ICN-based IoT (ICN-IoT): A Comprehensive Survey

iCache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks

Routing Design and Simulation of Body Area Network Based on Node Energy Consumption Control Strategy

Resource Allocation for Wireless-Powered Backscatter Communication Network Based on Nash Bargaining Solution