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Caching has recurrently evolved across a range of animal taxa to withstand fluctuations in food availability and in the context of intraspecific competition. It is widespread in the corvid family, which exhibit considerable interspecific variation in their behavioural and morphological adaptations to caching. However, the evolutionary drivers responsible for this diversity have seldom been explored. The present study systematically reviews the literature on caching behaviour in corvids globally to determine (1) which food caching strategies species have adopted (specialist, generalist or non-cacher) and (2) whether ecological factors affect the occurrence of different strategies, namely (a) climate breadth, (b) trophic niche, (c) habitat breadth, (d) centroid latitude, (e) centroid longitude, (f) breeding system, and (g) body mass. In addition, the ancestral states of caching are reconstructed to assess the evolutionary trajectory of each strategy. Caching strategies were identified in 63 species from 16 genera (out of 128 corvid species and 22 genera). Ancestral state analysis suggested specialist caching as the ancestral state in corvids. Type of caching is associated with distance from equator and by average body mass, with generalist caching concentrated around the equatorial zone and among heavier corvids, while specialist caching occurring more commonly in smaller species found farther from the equator. Although specialist caching most likely was the ancestral state in corvids, both specialist and generalist caching evolved several times independently in the family of corvids. Our results show caching to be widespread in corvids and affected by body size and latitude but ecological factors such as topic niche and habitat breadth and breeding system, not to be strong drivers shaping caching behaviour. [ABSTRACT FROM AUTHOR]

This article proposes an opportunistic online caching strategy (OOCS) capable of updating cached content even when the traffic load of a non-orthogonal multiple access (NOMA)-enabled heterogeneous network (HetNets) is at its peak. The macro base station (MBS) has an opportunity to update cached content when none of the user-requested files are cached. The OOCS selects a file from the historical record based on a newly proposed metric called the Cache Index, which takes into account the popularity of the contents and the backhaul latency. Furthermore, we propose a content-based user pairing strategy for NOMA and analyse the outage performance of the HetNets. [ABSTRACT FROM AUTHOR]

The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable efficient data distribution and support the mobility, scalability, and manageability of wireless networks. Mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), Wireless Sensor Networks (WSNs), and Vehicular ad hoc Networks (VANETs) have adopted caching practices to overcome these hurdles progressively. In this paper, we discuss the problems and issues in the current wireless ad hoc paradigms as well as spotlight versatile cooperative caching as the potential solution to the increasing complications in ad hoc networks. We classify and discuss multiple cooperative caching schemes in distinct wireless communication contexts and highlight the advantages of applicability. Moreover, we identify research directions to further study and enhance caching mechanisms concerning new challenges in wireless networks. This extensive review offers useful findings on the design of sound caching strategies in the pursuit of enhancing next-generation wireless networks. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), where technology has revolutionized our interaction with the world around us and bridged the gap between the physical and digital realms, providing an effective fine-grained access control system is paramount to safeguarding security of the IoT ecosystem. This paper introduces SecShield, a novel Software Defined Network (SDN)-based framework, particularly designed for IoT environments. SecShield operates by evaluating access requests and granting access to IoT services only when the set of defined access policies are satisfied. Utilizing the Attribute-Based Access Control (ABAC) model, SecShield specifies fine-grained access policies for IoT services and employs an algorithm for evaluating access requests. Additionally, the framework incorporates a local cache at the edge of the IoT network, enhanced with a Least Recently Used (LRU) algorithm, to optimize the process of access request evaluation. Experimental results validate the efficiency and feasibility of SecShield, positioning it as a viable solution for improving security of real-world IoT networks. [ABSTRACT FROM AUTHOR]

The Unmanned Aerial Vehicle (UAV) can be used as a good flying base station to cache popular content and follow a user mobility pattern, to help them in suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and improve the cache hit ratio. We examine a network containing of a UAV as a fog node in this paper. Considering that caching the appropriate content in the UAV has a great effect on increasing the hit rate and reducing the response time for the user, we use the content-based recommender system method, and we determine the attribute coefficients considered in the recommender system in a more efficient way with the feature importance method. Also, we study three methods for content-based recommendation to compare results with the common methods of Most Popular Contents (MPC) and Uniform Distributed Caching (UDC). The first and third methods have better results than the common methods of MPC and UDC. Finally, we propose two methods that are a hybrid of the methods described in this paper. The proposed method is better than the previous methods described in this paper, with a value of about 0.01 for songs dataset and about 0.03 for MovieLens dataset. It is possible to customize similarity according to the dataset. Also, since response time is really important in UAV, the run time is short compared to artificial intelligence methods. [ABSTRACT FROM AUTHOR]

It is natural to generalize the online k -Server problem by allowing each request to specify not only a point p, but also a subset S of servers that may serve it. To date, only a few special cases of this problem have been studied. The objective of the work presented in this paper has been to more systematically explore this generalization in the case of uniform and star metrics. For uniform metrics, the problem is equivalent to a generalization of Paging in which each request specifies not only a page p, but also a subset S of cache slots, and is satisfied by having a copy of p in some slot in S. We call this problem Slot-Heterogenous Paging. In realistic settings only certain subsets of cache slots or servers would appear in requests. Therefore we parameterize the problem by specifying a family S ⊆ 2 [ k ] of requestable slot sets, and we establish bounds on the competitive ratio as a function of the cache size k and family S : If all request sets are allowed ( S = 2 [ k ] \ { ∅ } ), the optimal deterministic and randomized competitive ratios are exponentially worse than for standard Paging ( S = { [ k ] } ). As a function of | S | and k, the optimal deterministic ratio is polynomial: at most O (k 2 | S |) and at least Ω (| S |) . For any laminar family S of height h, the optimal ratios are O(hk) (deterministic) and O (h 2 log k) (randomized). The special case of laminar S that we call All-or-One Paging extends standard Paging by allowing each request to specify a specific slot to put the requested page in. The optimal deterministic ratio for weighted All-or-One Paging is Θ (k) . Offline All-or-One Paging is N P -hard. Some results for the laminar case are shown via a reduction to the generalization of Paging in which each request specifies a set P of pages, and is satisfied by fetching any page from P into the cache. The optimal ratios for the latter problem (with laminar family of height h) are at most hk (deterministic) and h H k (randomized). [ABSTRACT FROM AUTHOR]

Database engines and file systems have been using prefetching and caching technologies for decades to enhance the performance of I/O-intensive applications. When future data access needs to be accelerated, prefetching methods often provide gains depending on the latency of the entire system by loading primary memory elements. Its execution time, where the data level prefetching rules are set, has to be much improved, as they are challenging to optimize, comprehend, and manage. This paper aims to introduce a novel distributed file system (DFS) model through dynamic prefetching, that includes four processes such as (1) Identification of popular files, (2) Estimation of support value for a file block, (3) Extraction of frequent block access patterns, and (4) Matching algorithm. At first, the input files are given to the first phase (i.e.), identification of popular sizes, where the popular files are identified. The support value of the file blocks that correspond to popular files is calculated in the second stage. Then, the extraction of frequent block access patterns is done in the third phase. At last, in the matching algorithm, the identification or prediction of frequent access pattern of the query is done by the optimized Neural Network (NN). Here, the weight of NN is optimally tuned by the Harmonic Mean based Grey Wolf Optimization (HMGWO) Algorithm.The proposed NN + HMGWO model produces reduced FPR values with good quality, which are 70.84%, 73.86%, 70.51%, 62.90%, 55.76%, 78.63%, and 73.86%, respectively, in comparison to other standard models like NN + WOA, NN + GWO, NN + PSO, NN + FF, FBAP, NN, and SVM. Lastly, the effectiveness of a chosen scheme is compared to other current methods in terms of delay analysis, latency analysis, hit ratio analysis, and correspondingly. [ABSTRACT FROM AUTHOR]

To address the problem of limited network coverage in remote areas, combining space-air-ground integrated network with mobile edge computing could provide low-latency and high-reliability transmissions for user requests in these areas, as well as timely caching services. Considering the dynamic change of the topology of the space-air-ground integrated network and the content popularity being constantly updated, a network architecture of space-air-ground integrated edge collaborative caching was proposed first Then, the cache replacement problem for edge servers was modeled as a Markov decision process. Finally, a federated discrete soft actor-critic (FDSAC) algorithm was proposed, with the core idea of integrating a weighted attention mechanism into the federated learning framework and incorporating a bidirectional long short-term memory network into the DSAC model. With the reconfigured reward function as the optimization objective, the optimal cache replacement policy was learned by maximizing the expectation of negative long-term rewards. Simulation results show that compared with other algorithm, the proposed algorithm can improve the cache hit rate of user requests by 18% and reduce the access latency of content by 25% while protecting user privacy. [ABSTRACT FROM AUTHOR]

The dual frequency Heterogeneous Network (HetNet), including sub-6 GHz networks together with Millimeter Wave (mmWave), achieves the high data rates of user in the networks with hotspots. The cache-enabled HetNets with hotspots are investigated using an analytical framework in which Macro Base Stations (MBSs) and hotspot centers are treated as two independent homogeneous Poisson Point Processes (PPPs), and locations of Small Base Stations (SBSs) and users are modeled as two Poisson Cluster Processes (PCPs). Under the PCP-based modeling method and the Most Popular Caching (MPC) scheme, we propose a cache-enabled association strategy for HetNets with limited storage capacity. The performance of association probability and coverage probability is explicitly derived, and Monte Carlo simulation is utilized to verify that the results are correct. The outcomes of the simulation present the influence of antenna configuration and cache capacities of MBSs and SBSs on network performance. Numerical optimization of the standard deviation ratio of SBSs and users of association probability is enabled by our analysis.

In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child's fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. [ABSTRACT FROM AUTHOR]

In response to the escalating volume of Internet traffic, scalability challenges have emerged in content delivery. Information-Centric Networking (ICN) has emerged as a solution to accommodate this surge in traffic by leveraging caching. Collaborative caching within ICN is pivotal for enhancing network performance and reducing content distribution costs. However, current pricing strategies on the Internet do not align with ICN interconnection incentives. This paper delves into the economic incentive caching of free content among various types of ICN providers, including advertisers and Internet service providers (ISPs). Specifically, we employ game-theoretic models to analyze the interaction between providers within an ICN framework, where providers are incentivized to cache and share content. Content popularity is modeled using a generalized Zipf distribution. We formulate the interactions among ISPs as a non-cooperative game and, through mathematical analysis, establish the existence and uniqueness of the Nash equilibrium under certain conditions. Additionally, we propose an iterative and distributed algorithm based on best response dynamics to converge towards the equilibrium point. Numerical simulations demonstrate that our proposed game models yield a winwin solution, showcasing the effectiveness of our approach in incentivizing collaborative caching of free content within ICN. [ABSTRACT FROM AUTHOR]

The querying of large XML data over wireless broadcast channels can reduce bandwidth utilization, cause significant latency, and produce inefficient energy usage. This paper proposes a scheme to improve XML querying over wireless broadcast channels in order to address the issues mentioned above. Various techniques, including partitioning, load balancing, and query routing, have been combined into one approach. The proposed scheme partitions the XML data stream into several partitions based on criteria like document size, type, or content. Each partition is routed to a separate channel to balance the load on each wireless broadcast channel. A query routing mechanism that directs queries to the right channel or combination of channels that hold the relevant XML data partition was implemented. This study simulates, evaluates, and compares the proposed scheme's performance. The results from the comparison study with existing schemes demonstrate a considerable reduction in the access time for XML querying via wireless broadcast channels. [ABSTRACT FROM AUTHOR]

The branch-and-bound algorithm based on decision diagrams is a framework for solving discrete optimization problems with a dynamic programming formulation. It works by compiling a series of bounded-width decision diagrams that can provide lower and upper bounds for any given subproblem. Eventually, every part of the search space will be either explored or pruned by the algorithm, thus proving optimality. This paper presents new ingredients to speed up the search by exploiting the structure of dynamic programming models. The key idea is to prevent the repeated expansion of nodes corresponding to the same dynamic programming states by querying expansion thresholds cached throughout the search. These thresholds are based on dominance relations between partial solutions previously found and on pruning inequalities given by rough upper bounds and local bounds — two additional filtering techniques recently introduced. Computational experiments show that the pruning brought by this caching mechanism allows for significantly reducing the number of nodes expanded by the algorithm. This results in more benchmark instances of difficult optimization problems being solved in less time while using narrower decision diagrams. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0340), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0340). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Rapid response, namely, low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on Text Retrieval Conference Conversational Assistant Track datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end. [ABSTRACT FROM AUTHOR]

This research examines the implementation of Time-To-Live (TTL) caching within a Content Delivery Network (CDN) model that incorporates API integration, structured to simulate a hierarchical configuration of CDN edge servers across Indonesia's administrative tiers. The analysis centers on the influence of TTL configurations on critical performance metrics—namely latency, cache hit ratio, throughput, and bandwidth consumption. Special focus is placed on scenarios in which a 1 MB data object originating from the Central Government (Level 1) is primarily accessed through edge servers positioned at the village level (Level 5). The simulation envisions a CDN architecture where in the Central Government functions as the Main Server/Origin Server, with edge servers extending across 38 provinces (Level 2), 514 regencies (Level 3), 7,277 districts (Level 4), and 83,763 villages (Level 5). [ABSTRACT FROM AUTHOR]

Eurasian Jays (Garrulus glandarius) typically store seeds on the ground in shallow caches, promoting tree recruitment. However, speculation exists that Eurasian Jays occasionally store a portion of seeds in microhabitats unsuitable for proper germination. Here, we report that unexpected caching sites in Eurasian Jays can be much more widespread than previously considered and despite their accidental character it seems to be a durable aspect of Eurasian Jay's hoarding behavior. Out of 259 removed acorns of Pedunculate Oak (Quercus robur), we localized 31 consumed and 222 stored acorns. Six experimental acorns (3% of stored acorns) were found stored by jays in unexpected caching sites: (i) above the ground on individuals of Scots Pine (Pinus sylvestris), (ii) inside the woody stems of Reynoutria sp. individuals, (iii) in a rotten trunk, and (iv) among ruin debris. Our findings suggest the need to revise our understanding of so-called unexpected caching in Eurasian Jays. This highlights a previously overlooked aspect of oak-jay interactions, offering a valuable piece to the puzzle. [ABSTRACT FROM AUTHOR]

The caching mechanism of federated learning in smart cities is vital for improving data handling and communication in IoT environments. Because it facilitates learning among separately connected devices, federated learning makes it possible to quickly update caching strategies in response to data usage without invading users' privacy. Federated learning caching promotes improved dynamism, effectiveness, and data reachability for smart city services to function properly. In this paper, a new caching strategy for Named Data Networking (NDN) based on federated learning in smart cities' IoT contexts is proposed and described. The proposed strategy seeks to apply a federated learning technique to improve content caching more effectively based on its popularity, thereby improving its performance on the network. The proposed strategy was compared to the benchmark in terms of the cache hit ratio, delay in content retrieval, and energy utilization. These benchmarks evidence that the suggested caching strategy performs far better than its counterparts in terms of cache hit rates, the time taken to fetch the content, and energy consumption. These enhancements result in smarter and more efficient smart city networks, a clear indication of how federated learning can revolutionize content caching in NDN-based IoT. [ABSTRACT FROM AUTHOR]

Modern cloud-native OLAP databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Computation pushdown is a promising solution to tackle this issue, which offloads some computation tasks to the storage layer to reduce network traffic. This paper presents FlexPushdownDB (FPDB), where we revisit the design of computation pushdown in a storage-disaggregation architecture, and then introduce several optimizations to further accelerate query processing. First, FPDB supports hybrid query execution, which combines local computation on cached data and computation pushdown to cloud storage at a fine granularity. Within the cache, FPDB uses a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Second, we design adaptive pushdown as a new mechanism to avoid throttling the storage-layer computation during pushdown, which pushes the request back to the computation layer at runtime if the storage-layer computational resource is insufficient. Finally, we derive a general principle to identify pushdown-amenable computational tasks, by summarizing common patterns of pushdown capabilities in existing systems, and further propose two new pushdown operators, namely, selection bitmap and distributed data shuffle. Evaluation on SSB and TPC-H shows each optimization can improve the performance by 2.2 × , 1.9 × , and 3 × respectively. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN. [ABSTRACT FROM AUTHOR]

Unlike conventional CPU caches, non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements. While every cache miss results in a cache update in a conventional cache, non-datapath caches allow for the flexibility of selective caching, i.e., the option of not having to update the cache on each miss. We propose a new, generalized, bimodal caching algorithm, Fear Of Missing Out (FOMO), for managing non-datapath caches. Being generalized has the benefit of allowing any datapath cache replacement policy, such as LRU, ARC, or LIRS, to be augmented by FOMO to make these datapath caching algorithms better suited for non-datapath caches. Operating in two states, FOMO is selective—it selectively disables cache insertion and replacement depending on the learned behavior of the workload. FOMO is lightweight and tracks inexpensive metrics in order to identify these workload behaviors effectively. FOMO is evaluated using three different cache replacement policies against the current state-of-the-art non-datapath caching algorithms, using five different storage system workload repositories (totaling 176 workloads) for six different cache size configurations, each sized as a percentage of each workload's footprint. Our extensive experimental analysis reveals that FOMO can improve upon other non-datapath caching algorithms across a range of production storage workloads, while also reducing the write rate. [ABSTRACT FROM AUTHOR]

This study investigated upscaling current data caching and prefetching strategies for online databases in Nigeria, with a focus on practical implications. The research design adopted for this study was the descriptive survey. The population comprised of all undergraduate's library students in public tertiary institutions in Ekiti State. A simple random sampling technique was adopted to select 200 library students from Ekiti State University in the study area. The instrument used for data collection was a structured 4 Likert type questionnaire. The questionnaire was distributed to the respondents to find out the effectiveness of caching and prefetching techniques on online databases. The instrument was both face and content validated by two experts from the department of Library studies in Ekiti State University, Ado-Ekiti State. The reliability of the instrument was ensured using Pearson Product Moment Correlation formula which yielded a coefficient of 0.99. The data collected were analyzed using descriptive statistics such as mean and standard deviation. The result showed that the current caching and prefetching techniques employed in online databases are highly effective; the different access patterns have effect on the effectiveness of caching and prefetching techniques in online databases and there are impacts of cache coherence mechanisms on the efficiency of caching and prefetching techniques in online databases. It was therefore recommended that the inclusion of caching and prefetching in curriculum is important across all educational level in Nigeria, with a clear emphasis on the practical implications. In addition, caching and perfecting have come under fire for focusing mostly on computer science. [ABSTRACT FROM AUTHOR]

The rapid development of 5G/6G and AI enables an environment of Internet of Everything (IoE) which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay. However, these massive devices will lead to explosive traffic growth, which in turn cause great burden for the data transmission and content delivery. This challenge can be eased by sinking some critical content from cloud to edge. In this case, how to determine the critical content, where to sink and how to access the content correctly and efficiently become new challenges. This work focuses on establishing a highly efficient content delivery framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative system, in which the concept of digital twin is applied to promote the collaboration. Based on the digital asset obtained by digital twin from end users, a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention (TPA) enabled Long Short-Term Memory (LSTM) model. Then, the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning (RL) technology. Finally, a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead. The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

A revolutionary approach for creating and executing scalable, adaptable, and robust software systems is microservices architecture. Microservices present a strong answer to the problems with traditional monolithic designs in the context of e-learning platforms, where performance, scalability, and adaptability are critical requirements. This paper provides a thorough analysis of how microservices are transforming e-learning platforms, emphasizing how they can improve performance, scalability, and flexibility. First, we give a summary of microservices architecture, emphasizing its main ideas and advantages. Next, we explore the particular uses of microservices in e-learning platforms, talking about how they facilitate fault isolation, modularization, and independent scaling. Through dissecting e-learning platform architectures into discrete microservices and analyzing their essential elements, we demonstrate how microservices enable effective user administration, content distribution, analytics, and more vital features. We also discuss scalability and performance issues, showing how the microservices design allows for horizontal scaling and providing guidance on how to maximize performance using asynchronous processing, load balancing, and caching. We discuss frequent issues that arise while introducing microservices in e-learning platforms and offer solutions throughout the article. Lastly, we highlight new technologies and areas for more study and invention as we go over future trends and directions in microservices for e-learning. [ABSTRACT FROM AUTHOR]

Solving constraint satisfaction problems often involves a large amount of redundant exploration stemming from the existence of subproblems whose information can be reused for other subproblems. Subproblem dominance is a general notion of reusability that arises when one subproblem imposes more constraints on the remaining part of the search than another subproblem and allows the search to reuse the solutions of the dominating subproblem for the dominated subproblem. The search can exploit subproblem dominance by storing the subproblems that have been explored in a cache and abandoning the current subproblem whenever the cache contains a subproblem that dominates it. While using caching makes it possible to solve problems where subproblem dominance arises orders of magnitude faster, storing all of these subproblems can require a substantial amount of memory, making it impractical in many cases. This paper analyses the dominance between different subproblems for various constraint problems, revealing that only a relatively small number of subproblems dominate other subproblems. Based on these findings, two types of strategies are proposed for reducing the number of subproblems stored in the cache: limiting the number of subproblems that can be stored in the cache and periodically cleaning up the cache. An experimental evaluation demonstrates that these strategies provide an effective instrument for reducing the memory consumption of caching, allowing it to be used on a larger scale. However, there is a trade-off between saving memory and reducing redundant exploration, as removing subproblems from the cache may prevent dominance from being detected for certain subproblems. [ABSTRACT FROM AUTHOR]

Caching food for later retrieval is vital for many animals' survival, but little is known about how this behaviour varies among individuals in the wild. If individuals consistently differ in where, when and how they store food, it could indicate that caching is subject to selection. In this study, we experimentally quantified the repeatability and relationship between different aspects of caching behaviour over two winter seasons in a wild population of toutouwai, Petroica longipes. Individuals were repeatable both within and between years in the number of cache sites they created, the distance they travelled to cache and food item handling time prior to caching. All three of these caching behavioural measures were positively correlated with one another, suggesting that toutouwai exhibit a caching syndrome analogous to a behavioural syndrome, with individuals ranging between 'clump caching' and 'scatter caching'. The number of food items that birds ate prior to caching and latency to begin cache retrieval were also consistently correlated, indicating a second 'fast - slow' syndrome linking cache retrieval to satiation level. In both syndromes most individuals were normally distributed between the behavioural extremes, indicating that birds tended to partake in intermediate caching rather than clustering at either end of the continuum. We posit that this distribution may be the result of stabilizing selection that balances the costs and benefits of each extreme. • Toutouwai are repeatable in numerous aspects of food-storing behaviour. • Different measures of caching behaviour consistently correlate with one another. • Individuals fall along an axis between 'scattering' and 'clumping' caching types. • An intermediate caching type may be maintained by stabilizing selection. [ABSTRACT FROM AUTHOR]

Content-Centric Networking (CCN) has emerged as the most convenient architecture for efficient traffic management in contrast to the IP-based Internet. The in-network caching characteristic of CCN reduces server load and traffic in the network. Furthermore, it enhances end-user Quality-of-Service (QoS) by reducing content retrieval delay. Towards this, the proposed research focuses on the in-network caching capability of CCN-enabled IoT networks to improve content distribution and reduction of load from servers. In CCN-enabled IoT networks, content caching can be performed in any node of the fog layer that exists between the cloud server and IoT devices. To effectively utilize the available caching resources, it is crucial to determine the suitable fog node during content placement decisions. In this direction, a novel fuzzy hyper-edge clustering and content popularity-based caching scheme is proposed for CCN-based IoT networks. The proposed fuzzy clustering scheme dynamically partitions the network into overlapping clusters based on node connectivity. The proposed scheme overcomes the limitations of existing techniques where the number of clusters needs to be fixed initially. The proposed scheme considers the cluster information and content access frequency parameters for content placement decisions. Using the proposed heuristics, the scheme cooperatively caches the popular contents in the fog nodes near IoT devices. The performance of the proposed strategy is examined using extensive simulations on a realistic network configuration. Experiments are performed on the standard Abilene topology, and performance is measured using metrics such as cache hit ratio, average network hop count, and average network delay on cache sizes 50 and 100. The simulation results are recorded at 1, 250, 500, 750, 1000, 1250, 1500, 1750, and 2000 Simulation Time Units (STU). The results show that the proposed caching solution outperforms recent state-of-the-art techniques such as LCE, PDC, CPNDD, HPHC, and CSDD, making it suitable for CCN-enabled IoT networks. [ABSTRACT FROM AUTHOR]

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm. [ABSTRACT FROM AUTHOR]

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called "MFO-SDN-EC", Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm's superiority over several current approaches in terms of assessment measures. [ABSTRACT FROM AUTHOR]

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM's efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM's proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM's substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions. [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing (VEC) technology holds great promise, but also poses significant challenges to the limited computing power of in-vehicle devices and the capacity of Roadside Units (RSUs). At the same time, the highly mobile nature of vehicles and the frequent changes in the content of requests from vehicles make it critical to offload applications to edge servers and to effectively predict and cache the most popular content, so that the most popular content can be cached in advance in the RSU. And also considering protecting the privacy of vehicle user vehicular users (VUs), traditional data-sharing methods may not be suitable for this work, so we use an asynchronous Federated learning (FL) approach to update the global model in time and at the same time can protect the personal privacy of VUs. Unlike the traditional synchronous FL, asynchronous FL no longer needs to wait for all vehicles to finish training and uploading local models before updating the global model, which avoids the problem of long training time. In this paper, we propose an in-vehicle edge computing caching scheme based on asynchronous federated learning and deep reinforcement learning(AFLR), which prefetches possible popular contents in advance and caches them in the edge nodes or vehicle nodes according to the vehicle’s location and moving direction while reducing the latency of the content requests. After extensive experimental comparisons, the AFLR scheme outperforms other benchmark caching schemes.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

Caching technology is essential for enhancing content transmission rates and reducing data transmission delays in heterogeneous networks, making it a crucial component of the Internet of Things (IoT). However, during data transmission and caching model training, the security of information is destroyed by untrusted third parties. In addition, the flexibility of storage locations presents another bottleneck in heterogeneous network caching technology. Deep learning (DL) is an important method for improving caching performance due to its powerful learning capabilities. Nonetheless, the DL process is vulnerable to various attacks, including white-box and black-box attacks, disclosing private information. Therefore, this study proposes a DL-based caching framework aimed to enhance security in heterogeneous networks based on differential privacy-preserving technology. Moreover, we utilize a boosting integrated method to improve caching accuracy. Simulated experiments demonstrate that the proposed framework ensures security and accuracy in the heterogeneous network caching process, outperforming existing solutions.

Caching plays an important role in reducing latency and increasing the overall performance of fog computing systems. With the rise of the Internet of Things (IoT) and edge computing, fog computing has become an essential paradigm for addressing the challenges related to latency-sensitive and bandwidth-intensive applications. The integration of Information-Centric Networking (ICN) and Fog Computing (ICN-Fog) has emerged as a promising solution for meeting the demands of low-latency and high-throughput applications in rapidly growing IoT devices. A step was taken by ICN-Fog to reduce latency and achieve higher data communication and information gathering for fog computing. Using Artificial Intelligence (AI) methods, the Firefly Optimization Technique was introduced as an effective optimization algorithm to enhance the caching technique. In this study, we aim to improve several performance metrics, such as the cache hit ratio, internal link load, and average query duration. To achieve these enhancements, we propose a unique solution inspired by the Firefly Optimization Technique. This technique applies to the ICN-Fog caching model, which was utilized to intelligently determine cache placement and network topology adaptations. CloudSim was employed to execute a simulation of the proposed strategy. The results of the experiments suggest that the Multi-Objective Firefly Algorithm (MOFA) outperforms the compared algorithms in terms of both efficiency and effectiveness in identifying the optimal caching technique.

The task of gathering data from nodes within mobile ad hoc wireless sensor networks presents an enduring challenge. Conventional strategies employ customized routing protocols tailored to these environments, with research focused on refining them for improved efficiency in terms of throughput and energy utilization. However, these elements are interconnected, and enhancements in one often come at the expense of the other. An alternative data collection approach involves the use of unmanned aerial vehicles (UAVs). In contrast to traditional methods, UAVs directly collect data from mobile nodes, bypassing the need for routing. While existing research predominantly addresses static nodes, this paper proposes an evolutionary based, innovative path selection approach based on future position prediction of caching enabled mobile ad hoc wireless sensor network nodes for UAV data collection, aimed at maximizing node encounters and gathering the most valuable information in a single trip. The proposed technique is evaluated for different movement models and parameter configurations.