An Efficient Signature Scheme Based on Mobile Edge Computing in the NDN-IoT Environment

Named data networking (NDN) supports receiver-driven content retrieval with stateful forwarding (pending interest table (PIT)/forwarding information base) and in-network caching, yet many deployments still rely on classical cryptography that may be undermined by quantum-capable adversaries. Despite NDN’s built-in data authenticity via producer signatures, quantum-enabled attacks become plausible once RSA/ECC-based trust anchors are weakened by Shor-type attacks. Existing quantum-security efforts for NDN largely focus on post-quantum replacements of signatures or key exchange, but do not address the tension between confidentiality-preserving payload protection and NDN’s in-network processing (forwarding/caching decisions) in a quantum setting. This paper presents a quantum-enhanced NDN architecture that preserves NDN’s content-centric semantics—naming, PIT-driven forwarding, and cache-assisted dissemination of verifiable control objects—while delivering privacy-sensitive payloads as quantum homomorphic encryption (QHE)-encrypted quantum states over an underlying quantum network. QHE provides end-to-end confidentiality and enables authorized operations on encrypted quantum payloads without exposing plaintext. To make the design compatible with NDN forwarding, the proposed protocol encrypts the payload while keeping name-based forwarding semantics intact, and supports ciphertext-domain processing for selected policy functions. To protect provenance and integrity of NDN metadata, routing instructions, and session state, we incorporate quantum digital signatures (QDS) bound to NDN names and session identifiers. The main contributions are: (i) a QHE+QDS layered protection model tailored to NDN’s Interest and Data exchange; (ii) a session workflow that couples proactive entanglement distribution with NDN-style stateful signaling for efficient establishment and recovery; and (iii) a security analysis clarifying the achieved guarantees and the remaining trust assumptions. Security analysis indicates robustness against quantum man-in-the-middle and tampering attacks, with modest overhead on NDN control traffic.

Among the distinctive features of the Named Data Networking (NDN) paradigm, in-network caching plays a crucial role in improving data delivery performance. At the network edge, the benefits of in-network caching can be remarkable in terms of reduced network traffic and user-perceived access latency. Multiple strategies have been designed for caching static Internet contents in NDN routers, while less attention has been devoted to transient Internet of Things (IoT) data, which expire after a certain amount of time. In this work, we introduce a novel distributed and autonomous caching strategy where NDN nodes take decisions by considering the popularity of IoT data and their lifetime. The target is to cache the most popular data with the highest residual lifetime in order to maximize the cache hit ratio at the edge. Performance evaluation assessed with the ndnSIM network simulator shows that the proposed solution outperforms existing schemes available in the literature by guaranteeing, among others, the highest cache hit ratio and the shortest content retrieval time.

Named Data Networking (NDN) need routing to build NDN systems. Similar IP protocol, NDN has routing protocols intra-domain and inter-domain routing protocol. Named-data Link State Routing (NLSR) is one of the NDN intra-domain routing protocols in NDN. Deployment NLSR on actual hardware and operating systems give more performance feedback which might not be obtained in a simulation. This paper explain how a NLSR work and performance use Ubuntu installed as NDN router.

Named Data Networking (NDN) has emerged as a promising information-centric networking paradigm that addresses the limitations of the traditional IP-based Internet architecture. The core principle of NDN relies on content naming instead of host addressing, to provide efficient, secure, and scalable content delivery. Routing is a critical component of NDN and is responsible for discovering and maintaining optimal paths to named content. This paper presents a comprehensive review of routing techniques in NDN, focusing on the design principles, algorithms, and performance metrics, especially in wired network architecture. We first summarize the NDN architecture and discuss its key components. We then delve into the fundamental routing concepts in NDN and categorize and examine various routing techniques, including link state, distance vector, and centralized approaches based on Software Defined Network. We also summarize the relevant research efforts proposed to address NDN routing challenges by focusing more on wired network architecture. Finally, we identify open research issues and future directions in NDN routing, emphasizing the need for scalable, efficient, and secure routing techniques that can fulfill the growing demands of the modern Internet. In conclusion, this review serves as a valuable reference for researchers and practitioners in NDN, offering a comprehensive understanding of the current state-of-the-art routing techniques, limitations, and potential future advancements.

With information becoming a first-class citizen on the Internet, information-centric networking (ICN) is considered as a promising direction for the future Internet. Named data networking (NDN) is a prominent example of emerging ICN architectures. Unfortunately, NDN is vulnerable to various attacks targeting its in-network caching mechanism. In this paper, we focus on the false-locality pollution attack, in which an adversary repeatedly requests a number of unpopular data objects to waste the precious cache space on the NDN router and to reduce normal users’ hit ratios. With simulation experiments, we show that such an attack can cause considerable damage to the NDN network. To detect and mitigate such an attack, we introduce an algorithm that exploits the diversity of the Interest traversing paths within an Internet service provider’s point-of-presence network. We also propose inexpensive methodologies based on the probabilistic counting and Bloom filter techniques to implement the algorithm on an NDN router. The experimental results indicate that our proposed algorithm is effective in thwarting false-locality pollution. We also experiment with strategies that the adversary may utilize against our antipollution algorithm and demonstrate that such strategies are either ineffective or impractical in the real world.

At this time forwarding strategy became the center of attention of researchers in area named data networking (NDN). This is because the forwarding strategy is responsible for managing every packet of interest received by the NDN router, then the characteristics of forwarding strategies will have a significant impact on NDN performance. Therefore this study aims to perform analysis using a large approach in forwarding strategy that is broadcast approach. It is expected that the results of this study can be utilized by researchers in the field of NDN to determine the direction of research. Parameter of study in this research is delay in observation time interval. A forwarding strategy using a broadcast approach is simulated in order to understand the NDN delay characteristics within the observation interval. The simulation of the research was conducted on the NDNSim simulator with several scenarios. From the simulation results obtained that cumulatively delay broadcast forwarding strategy in multi producer scenario there is no difference between fulldelay and lastdelay. However, in the single producer scenario there is a difference between fulldelay and lastdelay this can be influenced by the large number of consumers who send interest to the producer and also based on the flooding method of the interest package in order to find the nearest NDN router containing the expected content as soon as possible, So that it can overload the network performance and effects overhead.

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

A SM2 based efficient and lightweight batch verification approach for IC cards

A Lightweight Forwarding Strategy for Named Data Networking in Low-end IoT

The vision of the Internet of Things (IoT) is that it connects all kinds of things by leveraging the creation of increasingly affordable and small devices that can be embedded for sensing, processing, wireless communication, and actuation. Named data networking (NDN) is a newly emerging Internet paradigm that may replace the current Internet architecture and that fulfills most of the expectations of the IoT. Software-defined networking (SDN) is an emerging paradigm of technology that is highly capable of managing overall networks efficiently and transforming complex network architectures into manageable, simple ones. The combination of the SDN controller, NDN, and IoT can be lethal in the overall performance of the network. Broadcast storms, due to the flooding nature of NDN’s wireless channel, are a serious issue when it comes to forwarding interest and data packets. Energy consumption of sensor nodes in dense IoT scenarios causes problems in forwarding as well as unnecessary delays, decreases network performance, and increases the cost and packet delay for important packets. We took these problems as our baseline and proposed an energy-efficient, priority-based forwarding (EPF) in SDN-enabled NDN–IoT. Our scheme EPF used the efficient flow management of the SDN controller to control the broadcast storm and efficiently forward the priority-based packets. A defer timer mechanism was used to prioritized the packet upon its arrival to the node. An energy threshold mechanism was used to control energy consumption and improve overall energy efficiency. We compared our scheme with the traditional flooding mechanism and geographic interest forwarding; EPF outclassed the other schemes and produced the best results in terms of total number of interests and retransmissions, content retrieval time, total number of priority interests, energy consumption, and network lifetime.

Internet of Things (IoT) is changing our life for the better. IoT covers various heterogeneous devices which are sensing the environment and/or monitoring a special event in the network. These devices are mostly mobile and are wirelessly connected either to each other or to the Internet. Additionally, IoT devices run many different applications and services which provide the consumers with data and information. This paper focuses on the design and implementation of an Application Programming Interface (API) for IoT services using the Named Data Networking (NDN). NDN provides an efficient and intelligent communication paradigm for constrained devices like in IoT and uses named content to transmit data in the network. We extend the IoT-NDN architecture and add a new layer for IoT services which will effectively improve the performance of communication between the heterogeneous devices and the services integrated in IoT-NDN. Furthermore, we show the usage of our API and give a small example of network time synchronisation on IoT devices. This illustrates how flexible and lightweight is our approach for several services implemented on heterogeneous devices in IoT.

Mobile Edge Computing (MEC) is considered an essential future service for the implementation of 5G networks and the Internet of Things, as it is the best method of delivering computation and communication resources to mobile devices. It is based on the connection of the users to servers located on the edge of the network, which is especially relevant for real-time applications that demand minimal latency. In order to guarantee a resource-efficient MEC (which, for example, could mean improved Quality of Service for users or lower costs for service providers), it is important to consider certain aspects of the service model, such as where to offload the tasks generated by the devices, how many resources to allocate to each user (specially in the wired or wireless device-server communication) and how to handle inter-server communication. However, in the MEC scenarios with many and varied users, servers and applications, these problems are characterized by parameters with exceedingly high levels of dimensionality, resulting in too much data to be processed and complicating the task of finding efficient configurations. This will be particularly troublesome when 5G networks and Internet of Things roll out, with their massive amounts of devices. To address this concern, the best solution is to utilize Machine Learning (ML) algorithms, which enable the computer to draw conclusions and make predictions based on existing data without human supervision, leading to quick near-optimal solutions even in problems with high dimensionality. Indeed, in scenarios with too much data and too many parameters, ML algorithms are often the only feasible alternative. In this paper, a comprehensive survey on the use of ML in MEC systems is provided, offering an insight into the current progress of this research area. Furthermore, helpful guidance is supplied by pointing out which MEC challenges can be solved by ML solutions, what are the current trending algorithms in frontier ML research and how they could be used in MEC. These pieces of information should prove fundamental in encouraging future research that combines ML and MEC.

The blockchain-enabled internet of medical things (IoMT) is an emerging paradigm that could provide strong trust establishment and ensure the traceability of data sharing in the IoMT networks. One of the fundamental building blocks for Blockchain is Elliptic Curve Digital Signature Algorithm (ECDSA). Nevertheless, when processing a large number of transactions, the verification of multiple signatures will incur cumbersome overhead to the nodes in Blockchain. Although batch verification is able to provide a promising approach that verifies multiple signatures simultaneously and efficiently, the upper bound of batch size is limited to small-scale and the efficiency will drop rapidly as the batch size grows in the state-of-the-art ECDSA batch schemes. Meanwhile, most of the existing researches only focus on improving the efficiency of batch verification algorithms in various cryptosystem while ignoring the identification of invalid signatures, which could cause severe performance degradation when the batch verification fails. Motivated by these observations, this paper proposes an efficient and large-scale batch verification scheme with group testing technology based on ECDSA. The application of the presented protocols in Bitcoin and Hyperledger Fabric has been analyzed as supportive and effective. When the batch verification returns a false result, we utilize group testing technology to improve the efficiency of identifying invalid signatures. Comprehensive simulation results demonstrate that our protocol outperforms the related ECDSA batch verification schemes.

Named data networking (NDN) is a future Internet architecture that directly emphasizes accessible content by assigning each piece of content a unique name. Data transmission in NDN is realized via name-based routing and forwarding. Name-based forwarding information base (FIB) usually has much more and longer prefixes than IP-based ones, and therefore, name-based forwarding brings more challenges on the NDN router in terms of high forwarding throughput, low memory consumption, and fast FIB update. In this paper, we present an index data structure called BFAST for the name-based FIB. BFAST is designed based on a basic hash table, it employs a counting Bloom filter to balance the load among hash table slots, so that the number of items in each non-empty slot is close to 1, leading to low searching time in each slot. Meanwhile, the first-rank-indexed scheme is proposed to effectively reduce the massive memory consumption required by the pointers in all the hash table slots. Evaluation results show that, for the longest prefix match FIB lookup, BFAST achieves a speed of 2.14 MS/S using one thread, and meanwhile, the memory consumption is reasonably low. By leveraging the parallelism of today’s multi-core CPU, BFAST arrives at an FIB lookup speed of 33.64 MS/S using 24 threads, and the latency is around $0.71~\mu s$ .

For mobile devices (MDs) and Internet of Things (IoT) devices with limited computing capacity and battery, offloading part of tasks to the mobile edge computing (MEC) server is attractive. In this paper, we propose a joint partial computation offloading and pricing scheme in a multi-user MEC system. Firstly, we establish MD’s cost model and MEC server’s revenue model in terms of money. Secondly, we investigate MD’s cost minimization partial offloading strategy to jointly control MD’s task allocation, local CPU frequency and the amount of computational resource blocks (CRBs) requested. Finally, we formulate the revenue maximization problem for MEC server with limited computing capacity, a heuristic algorithm is proposed for MEC server to find the optimal service price. Numerical results verify the effectiveness of our proposed scheme in cost saving and pricing.