Computer Communications最新文献

Modern portable devices can execute increasingly sophisticated AI models on sensed data. The complexity of such processing tasks is data-dependent and has relevant energy cost. This work develops an Age of Information Markovian model for a system where multiple battery-operated devices perform data processing and energy harvesting in parallel. Part of their computational burden is offloaded to an edge server which polls devices at given rate. The structural properties of an optimal policy for a single device-server system are derived. They permit to define a new model-free reinforcement learning method specialized for monotone policies, namely Ordered Q-Learning, providing a fast procedure to learn the optimal policy. The method is oblivious to the devices’ battery capacities, the cost and the value of data batch processing and to the dynamics of the energy harvesting process. Finally, the polling strategy of the server is optimized by combining this policy improvement technique with stochastic approximation methods. Extensive numerical results provide insight into the system properties and demonstrate that the proposed learning algorithms outperform existing baselines.

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the $\beta$ parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Over the recent years unmanned aerial vehicles (UAVs) have been utilized extensively in mission-critical operations, especially as it relates to disaster management scenarios. Clearly, during these missions, the UAVs should be able to communicate effectively (amongst themselves and with the ground control station (GCS)) in order to transmit and receive commands and other related information. Moreover, accurate positioning is paramount during this type of operations. This has motivated the exploration of a number of alternative navigation methods to address robustness issues that arise when the global positioning system (GPS) becomes unavailable, due to GNSS disruption or sensor malfunction. This work addresses these issues by initially developing and implementing an integrated LoRa-ROS-based (long range communication - robot operating system) system for UAV-to-X communications. Subsequently, it presents a novel cooperative positioning approach, where a group of autonomous UAVs employ various algorithms (detection, tracking, communication, and localization) for cooperative positioning in order to counter any GPS/sensor malfunction. For evaluation purposes, a prototype multi-agent system is designed and implemented, utilizing the proposed integrated ROS-LoRa-based communication architecture, as well as sensor (inertial measurement unit - IMU) fusion. Specifically, LoRA mesh networking (using a custom printed circuit board - BALORA), is incorporated to maintain communication and distribute the sensor information between the UAVs. The prototype of the proposed communications architecture and cooperative relative positioning system (CRPS) is subsequently tested in a real-world environment, demonstrating the feasibility and effectiveness of the proposed communications solution, as well as the robust and accurate localization that is analogous to the ground truth (GPS+IMU).

Small satellites enable many important applications for both economic and scientific purposes. Many of these applications are inherently data-centric and rely on large amounts of high-resolution satellite imagery to be delivered in a timely manner. However, communicating this data to Earth is challenging due to intermittent connectivity, high packet losses, low data rates, and similar issues. Therefore, efficient onboard prioritization and data processing are essential for future satellite missions. Machine learning methods, such as deep neural networks, are very suitable for such prioritization, as they are already used extensively for satellite imagery processing and they can be deployed onboard of satellites. However, updating them to support new classification requirements when the satellite is already in orbit is difficult, as often multiple passes are required to complete model transmission due to the communication challenges. To cope with this issue, we propose a progressive transmission mechanism for model updates, which leverages vector quantization and arithmetic coding. Our mechanism allows to achieve high accuracies even with partially updated models. Evaluation results show that our mechanism significantly outperforms other less optimized transmission schemes.

Reliable communications play a pivotal role in ensuring an efficient response and the coordination of recovery and rescue efforts. However, conventional communication methods may not always be accessible or dependable in such situations. In such circumstances, constellations of Low Earth Orbit (LEO) satellites can provide high bandwidth capabilities with relatively low latency, making them well-suited for supporting on-the-ground disaster management teams. Satellites can either complement or replace terrestrial telecommunication infrastructures. In this context, reliance on the recently defined QUIC protocol allows for a seamless transition from terrestrial to satellite communication as needed. Therefore, we investigate the possible use of a dual-stack node architecture along with the employment of the QUIC transport protocol for emergency communications, assuming that the backhaul link used to transfer users’ applications data may need to be changed (seamlessly). We conduct an extensive emulation study, evaluating the performance of QUIC under varying queuing policies and Congestion Control Algorithm (CCA) behaviour, providing practical insights and recommendations to enhance the protocol’s efficiency and robustness. The key aspects and configurations of QUIC protocol stack are identified, presenting optimal communication configurations leveraging CoDel and BBR CCA.

With the development of smart healthcare, eliminating information silos through trusted data sharing has become a social consensus, but there are still many problems that need to be solved. The multi-party sharing process of medical data usually occurs in an untrusted network environment, and the separation of data ownership and usage rights can result in the leakage of patients’ private information. Meanwhile, the communication and computation overheads of existing medical data sharing schemes are too large, resulting in inefficient data sharing. To address the above problems, we propose a blockchain-based trusted medical data sharing scheme (BTMDS) with privacy protection and access control. In it, we subdivided patient privacy into identity and data privacy, and designed a privacy protection mechanism for blockchain medical data sharing using local differential privacy technology and searchable encryption technology. The cloud server acts as a proxy server, and the on-chain-off-chain storage structure of the blockchain and the cloud server implements fine-grained access control to prevent conspiracy attacks. Security analysis proves the security of BTMDS and prioritizes it over other schemes. In terms of performance, BTMDS saves 30% and 48% in the decryption phase compared to Feng and Chen schemes, which is more suitable for digital healthcare data sharing services.

A Non-Orthogonal Multiple Access (NOMA) network, in which the base station (BS) incorporates massive Multiple-Input Multiple-Output (MIMO) technology, is considered in this paper. This research study focuses on investigating physical layer security in this network when a jammer is present, leveraging intelligent Reflecting Surface (IRS) technology. The IRS is an innovative approach strategically implemented to enhance communication quality by assisting distant users in establishing a reliable connection with the BS. Two key metrics in physical layer security are evaluated: the secrecy rate (SR) for pairs of NOMA users and the secrecy outage probability (SOP). Additionally, the impact of using a jammer is assessed by comparing the network’s performance with and without a jammer. The results indicate that by increasing in the antenna numbers, the rate of secrecy is improved, and the SOP is decreased. Moreover, as the transmit signal-to-noise ratio (SNR) increases, the SR is enhanced, but the SOP is degraded. However, the increase in the IRS element numbers results in a tendency for the SOP to rise. Furthermore, it is evident that incorporating a jammer improves the network’s performance.

Coexistence of enhanced mobile broadband and ultra-reliable low latency communication in 5G networks is a challenging problem due to the conflicting requirements. In this paper, we decompose the problem into eMBB and URLLC resource allocation phases. For the first phase we propose a heuristic algorithm with $O\left(n\right)$ runtime and prove its efficiency and optimality under min–max fairness paradigm. For the URLLC resource allocation, the puncturing framework is adopted and a novel approach using the Graph Neural Networks is proposed to maximize eMBB data rates and fairness while minimizing URLLC outage probability. We show that the runtime of this GNN-based algorithm is also $O\left(n\right)$. To train the GNN, an application-specific loss function is designed and empirically shown to be convergent. Our simulation results show that our proposed approach performs very well in terms of eMBB data rates, fairness, and URLLC outage probability in comparison to a number of thoughtfully chosen baselines. We also demonstrate that the proposed GNN is robust to changes in network topology and traffic volume. As we show our algorithm has $O\left(n\right)$ runtime, it is fully practical for solving the resource allocation problem in the very short time spans that are required by 5G and future generation networks.

Rumors in social media platforms and the identification of their sources is a challenging issue in modern-day computer communication. Existing approaches mostly fail to localize the source node accurately due to the lack of complete network information or timestamps. Besides, most of the techniques focused on single-source identification only, while sometimes multiple sources exist in the network. In this paper, we designed a new algorithm called Multi Snowballing with Partial Timestamps (MSPT) to find multiple sources utilizing partial timestamps available to monitors. We have explored the snowballing technique to determine the vulnerable radius that may contain the rumor source based on the partial timestamps of a few nodes. The overall complexity of the algorithm is $O(N_S\ast (N_S+E_S))$, where $N_S$ is the set of snowball nodes and $E_S$ represents edges in between snowball nodes. Extensive empirical analysis is performed on a variety of networks, which include small-scale, large-scale, and artificial networks. Empirical outcomes demonstrate that the presented algorithm is efficient in terms of error distance and execution time compared to baseline algorithms.

Innovation in medical technology and communication has rapidly empowered the development of smart healthcare devices. This has led to privacy breaches, threats and vulnerabilities to sensitive patient data that result in unwanted or targeted advertising. Previous research has focused on protecting access to sensitive patient data from unauthorized entities, especially by defining roles of healthcare entities in the overall system with their access privileges. However, such efforts need to be further robust due to the involvement of a single key authority that may lead to a critical point of failure. In this paper, this vulnerability has been addressed by developing a novel approach to crucially increase the number of key authorities using homomorphic encryption. The proposed approach ensures genuine access to the verified entity by forming a subsystem of t key authorities from a total of n authorities $(t<n)$. This creates rigorous challenge to a malicious attacker, obfuscating the selection and functioning of key access packets in a multi-key authority setup. The results of the proposed approach achieve medical data confidentiality, entity authentication, and strategic data sharing. The security of the proposed approach is assessed for different vulnerabilities of the overall system using a challenge–response game model. Moreover, the proposed approach is found to be better and secure as compared to existing schemes.