Annals of Telecommunications最新文献

Fuzzing is a widely used and effective technique to test software. Unfortunately, certain systems, including network protocols, are more challenging to fuzz than others. An important complication with fuzzing network protocols is that this tends to be a slow process, which is problematic as fuzzing involves many test inputs. This article analyzes the root causes behind the inefficiency of fuzzing network protocols and strategies to avoid them. It extends our earlier work on network protocol fuzzers, which explored some of these strategies, to give a more comprehensive overview of overheads in fuzzing and ways to reduce them.

Low-density parity check (LDPC) codes are employed for data channels due to their capability of achieving high throughput and good performance. However, the belief propagation decoding algorithm for LDPC codes has high computational complexity. The min-sum approach reduces decoding complexity at the expense of performance loss. In this paper, we investigate the performance of LDPC codes using interleaving. The codes are investigated using BPSK modulation for short to moderate message lengths for various numbers of iterations using the min-sum decoding algorithm. The paper aims to improve the block error rate (BLER) and bit error rate (BER) for short to moderate block lengths required for massive machine-type communications (mMTC) supporting numerous IoT devices with short data packets, and ultra-reliable low-latency communications (URLLC) for delay-sensitive services of 5G. By incorporating interleaving alongside min-sum decoding, the performance is not only improved but also reaches a level of comparability with established algorithms such as the belief propagation algorithm (BPA) and the sum-product algorithm (SPA). LDPC coding with interleaving and subsequent min-sum decoding is a promising approach for improving the performance metrics of codes for short to moderate block length without incurring a significant increase in decoding complexity.

With the vigorous development of the Internet of Things (IoT), the demand for user equipment (UE) computing capacity is increasing. Multiaccess edge computing (MEC) provides users with high-performance and low-latency services by offloading computational tasks to the nearest MEC server-configured 5G radio access network (RAN). However, these computationally intensive tasks may lead to a sharp increase in the energy consumption of UE and cause downtime. In this paper, to address this challenge, we design an intelligent scheduling and management system (ISMS) to jointly optimize the allocation of MEC resources and wireless communication resources. The resource allocation problem is a mixed-integer nonlinear programming problem (MINLP), an NP-hard problem. The ISMS models this problem as an MDP with a state, action, reward, and policy and adopts a modified deep deterministic policy gradient (mDDPG) algorithm to ensure the weighted minimization of the energy consumption, latency, and cost of users. The simulation results show that the ISMS can effectively reduce the system’s energy consumption, latency, and cost. The proposed algorithm can provide more stable and efficient performance than other algorithms.

Internet of Things (IoT) applications rely on data collection and centralized processing to assist decision-making. Nevertheless, in multi-hop Low Power and Lossy Networks (LLN) scenarios, data forwarding can be troublesome as it imposes multiple retransmissions, consuming more energy. This paper revisits the concept of mobile agents to collect data from sensors more efficiently. Upon receiving a data request, the IoT gateway performs a cache lookup and promptly dispatches a mobile agent to collect data if this is not available. Data collection then uses closed-loop itineraries computed using a Traveling Salesman Problem (TSP) heuristic starting at the network gateway. The itinerary goes through nodes producing solicited and unsolicited data. We assume that the unsolicited data will be requested soon, and opportunistically collecting it avoids future agent transmissions. We limit the collection capacity of each agent using a knapsack problem approach. Simulation results show that our proposal reduces the network traffic and energy consumption compared with a traditional mobile agent without opportunistic data collection. In addition, we show that data aggregation can further improve the performance of our proposal.

Power line communication systems (PLC) are used for data transmission. Accurate channel state information (CSI) is essential for the receiver design in such systems, however, impulsive noise poses a challenge for the channel estimation task. In this paper, we propose a deep learning based method for PLC channel estimation which is resistant against impulsive noise as well as the additive white Gaussian noise (AWGN). The proposed deep neural network consists of three sub-networks: The first one is a denoising network which aims to remove the noise from the received signal. The second sub-network offers a low-accuracy estimation of the channel using the denoised signal. The third sub-network is designed for high-accuracy channel estimation. The training of the proposed network is done in two stages: Firstly, the denoising sub-network is trained. Secondly, by freezing the trained parameters of the denoising network, the two-channel estimation sub-networks are trained. Moreover, we have derived the Cramer Rao lower bound of the PLC channel estimation problem. The proposed method has been evaluated through various simulation scenarios which confirm the superiority of the proposed method over its counterpart. The suggested algorithm indicates acceptable resistance against impulsive and Gaussian noises.

The increased adoption and development of 5 G-based services have greatly increased the dynamic nature of traffic, including types, sizes, and requirements. Flex-grid elastic optical networks (EONs) have become prolific in supporting these services. However, this transition led to issues such as lower traffic throughput and resource wastage in the form of bandwidth fragmentation. With the continued growth of these services, proper traffic management to reduce this issue has become essential. To overcome this challenge, we propose a Throughput and Latency-First Reinforcement Learning-based spectrum allocation algorithm (TLFRL) in IP-over-fixed/flex-grid optical networks. The main target of TLFRL is to reduce the need to reallocate the spectrum by lowering the fragmentation and blocking probability. We achieve this by leveraging advanced demand organization techniques while using traditional networking infrastructure intelligently to offload compatible services, avoiding latency violations. Extensive simulations evaluated traffic throughput, fragmentation, and average latency. The results show that the proposed solution outperforms contemporary fixed grid-based and heuristic approaches. It also provides comparable results to state-of-the-art flex-grid spectrum allocation techniques.

End-to-end encrypted messaging applications such as Signal became widely popular thanks to their capability to ensure the confidentiality and integrity of online communication. While the highest security guarantees were long reserved for two-party communication, solutions for n-party communication remained either inefficient or less secure until the standardization of the MLS protocol (Messaging Layer Security). This new protocol offers an efficient way to provide end-to-end secure communication with the same guarantees originally offered by the Signal protocol for two-party communication. However, both solutions still rely on a centralized component for message delivery, called the Delivery Service in the MLS protocol. The centralization of the delivery service makes it an ideal target for attackers and threatens the availability of any protocol relying on MLS. In order to overcome this issue, we propose DiSCreet (Distributed delIvery Service with Context-awaRE coopEraTion), a design that allows clients to exchange protocol messages efficiently and without any intermediary. It uses a probabilistic reliable-broadcast mechanism to efficiently deliver messages and the Cascade Consensus protocol to handle messages requiring an agreement. Our solution strengthens the availability of the MLS protocol without compromising its security. We compare the theoretical performance of DiSCreet with another distributed solution, the DCGKA protocol, and detail the implementation of our solution.

Internet of Things (IoT) has revolutionized data manipulation across various applications, particularly in online healthcare paradigm, where medical data are collected and processed for remote monitoring and analysis. To improve the privacy and security of such sensitive healthcare data, the attribute-based encryption (ABE) with non-monotonic access policies has recently provided a fine-grained access control within cloud and IoT-based healthcare ecosystems. Specifically, the adoption of multi-authority ABE with untrusted authorities has eliminated the need for a trusted authority. However, ensuring the privacy of user’s identity and attribute sets from these untrusted authorities remains a significant challenge in this context. To address this challenge, this paper introduces an enhanced multi-authority ABE approach, incorporating a robust attribute revocation mechanism. This enhancement safeguards user’s identity and attribute-set privacy while remaining resilient against collusion attacks and ensuring backward secrecy. Moreover, the proposed approach provides non-monotonic access policies, which supports positive and negative constraints using NOT operation as well as AND and OR operations.