Effective Caching for the Secure Content Distribution in Information-Centric Networking

The secure distribution of protected content requires consumer authentication and involves the conventional method of end-to-end encryption. However, in information-centric networking (ICN) the end-to-end encryption makes the content caching ineffective since encrypted content stored in a cache is useless for any consumer except those who know the encryption key. For effective caching of encrypted content in ICN, we propose a novel scheme, called the Secure Distribution of Protected Content (SDPC). SDPC ensures that only authenticated consumers can access the content. The SDPC is a lightweight authentication and key distribution protocol; it allows consumer nodes to verify the originality of the published article by using a symmetric key encryption. The security of the SDPC was proved with BAN logic and Scyther tool verification.Comment: 7 pages, 9 figures, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring

Hierarchical Network Data Analytics Framework for B5G Network Automation: Design and Implementation

5G introduced modularized network functions (NFs) to support emerging services in a more flexible and elastic manner. To mitigate the complexity in such modularized NF management, automated network operation and management are indispensable, and thus the 3rd generation partnership project (3GPP) has introduced a network data analytics function (NWDAF). However, a conventional NWDAF needs to conduct both inference and training tasks, and thus it is difficult to provide the analytics results to NFs in a timely manner for an increased number of analytics requests. In this article, we propose a hierarchical network data analytics framework (H-NDAF) where inference tasks are distributed to multiple leaf NWDAFs and training tasks are conducted at the root NWDAF. Extensive simulation results using open-source software (i.e., free5GC) demonstrate that H-NDAF can provide sufficiently accurate analytics and faster analytics provision time compared to the conventional NWDAF.Comment: 7 page

Secure Distribution of Protected Content in Information-Centric Networking

The benefits of the ubiquitous caching in ICN are profound, such features make ICN promising for content distribution, but it also introduces a challenge to content protection against the unauthorized access. The protection of a content against unauthorized access requires consumer authentication and involves the conventional end-to-end encryption. However, in information-centric networking (ICN), such end-to-end encryption makes the content caching ineffective since encrypted contents stored in a cache are useless for any consumers except those who know the encryption key. For effective caching of encrypted contents in ICN, we propose a secure distribution of protected content (SDPC) scheme, which ensures that only authenticated consumers can access the content. SDPC is lightweight and allows consumers to verify the originality of the published content by using a symmetric key encryption. Moreover, SDPC naming scheme provides protection against privacy leakage. The security of SDPC was proved with the BAN logic and Scyther tool verification, and simulation results show that SDPC can reduce the content download delay.Comment: 15 pages, 8 figures, This article is an enhancement version of journal article published in IEEE Systems Journal, DOI: 10.1109/JSYST.2019.2931813. arXiv admin note: text overlap with arXiv:1808.0328

Robust hierarchical mobile IPv6 (RH-MIPv6): an enhancement for survivability and fault-tolerance in mobile IP systems

Abstract — In wireless networks, system survivability is one of the most important issues in providing quality of service (QoS). However, since failure of home agent (HA) or mobile anchor point (MAP) causes service interruption, the Hierarchical Mobile IPv6 (HMIPv6) has only weak survivability. In this paper, we propose Robust Hierarchical Mobile IPv6 (RH-MIPv6), which provides fault tolerance and robustness in mobile networks. In RH-MIPv6, a mobile node (MN) registers primary (P-RCoA) and secondary (S-RCoA) regional care of addresses to two different MAPs (Primary and Secondary) simultaneously. We develop a mechanism to enable the mobile node or correspondent node (CN) to detect the failure of primary MAP and change their attachment from the primary to secondary MAP. By this recovery procedure, it is possible to reduce the failure recovery time. Analytical evaluation indicates that RH-MIPv6 has faster recovery time than HMIPv6 and we also show through simulation as like analytical result. Consequently, RH-MIPv6 shows about 60 % faster recovery time compared with HMIPv6

Estimating fuel-efficient air plane trajectories using machine learning

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy

Performance Analysis of Fast Handover in Mobile IPv6 Networks

The Fast Handover protocol [1] provides seamless handover in wireless IP networks by minimizing handover latency. To reduce handover latency and to provide faster handover, Fast Handover uses anticipation based on layer 2 (L2) trigger information. Therefore, it incurs higher signaling costs compared with the basic Mobile IP protocol. Furthermore, since the L2 trigger is based on fluctuating wireless channel states, the handover anticipation using the L2 trigger may sometimes be incorrect. In the case of incorrect anticipation, unnecessary buffer space may be used for the purpose of providing a smooth handover. Therefore, it is essential to analyze these overhead costs, in order to evaluate and compare the performance of Fast Handover with that of the basic Mobile IP protocol. In this paper, we analyzed the overhead associated with Fast Handover including the signaling cost and the packet delivery cost. We formulated these costs based on a timing diagram and compared Fast Handover with basic Mobile IPv6 in terms of their packet loss rates and buffer requirements. Also, we studied the impact of the L2 triggering time on the total overhead cost. As a result, we found that the L2 triggering time is an important factor to consider in the optimization of handover performance

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites’ high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites’ downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes