Using a Multiobjective Approach to Balance Mission and Network Goals within a Delay Tolerant Network Topology

This thesis investigates how to incorporate aspects of an Air Tasking Order (ATO), a Communications Tasking Order (CTO), and a Network Tasking Order (NTO) within a cognitive network framework. This was done in an effort to aid the commander and or network operator by providing automation for battlespace management to improve response time and potential inconsistent problem resolution. In particular, autonomous weapon systems such as unmanned aerial vehicles (UAVs) were the focus of this research This work implemented a simple cognitive process by incorporating aspects of behavior based robotic control principles to solve the multi-objective optimization problem of balancing both network and mission goals. The cognitive process consisted of both a multi-move look ahead component, in which the future outcomes of decisions were estimated, and a subsumption decision making architecture in which these decision-outcome pairs were selected so they co-optimized the dual goals. This was tested within a novel Air force mission scenario consisting of a UAV surveillance mission within a delay tolerant network (DTN) topology. This scenario used a team of small scale UAVs (operating as a team but each running the cognitive process independently) to balance the mission goal of maintaining maximum overall UAV time-on-target and the network goal of minimizing the packet end-to-end delays experienced within the DTN. The testing was accomplished within a MATLAB discrete event simulation. The results indicated that this proposed approach could successfully simultaneously improve both goals as the network goal improved 52% and the mission goal improved by approximately 6%

TCP over geo-routing for high mobility: vehicle grids and airborne swarms

Ad hoc wireless networks have become the architecture of choice for peer to peer communications in areas where the telecommunications infrastructure is inadequate or has failed. A major challenge is the reliable delivery of data when nodes move. The reliable Internet protocol is TCP. However, TCP performs poorly in mobile ad hoc networks, mainly because of route breakage. To overcome this problem, a robust routing protocol must be used. To this effect, Geo-routing has recently received attention in large scale, mobile systems as it does not require end- to-end path establishment and pre-computed packet forwarding routing structure at nodes. These properties make Geo-routing robust to highly dynamic route changes. For best performance, however, several parameters must be carefully tuned. In this paper we study the joint optimization of TCP and Geo-routing parame- ters to handle high speeds. We first introduce two highly mobile ad hoc scenarios that require reliable delivery, namely the vehicle urban grid and the airborne swarms. Then, we study the impact of critical system parameters (e.g., hello message ex- change rate, delay timer in TCP for out-of-order delivery, etc) on the performance of both UDP and TCP. We improve hello message effciency in Geo-routing by using an adaptive hello exchange scheme. Then, we fix the out-of-order problem in TCP by using a receiver-side out-of-order detection and delayed ack strategy. We show that these parameter adjustments are critical for effcient TCP over Geo-routing in highly mobile applications. With these enhancements our TCP with Geo-routing solution easily outperforms TCP over traditional ad hoc routing schemes, such as AODV.1st IFIP International Conference on Ad-Hoc NetWorkingRed de Universidades con Carreras en Informática (RedUNCI

LS-AODV: A ROUTING PROTOCOL BASED ON LIGHTWEIGHT CRYPTOGRAPHIC TECHNIQUES FOR A FANET OF NANO DRONES

With the battlespace rapidly shifting to the cyber domain, it is vital to have secure, robust routing protocols for unmanned systems. Furthermore, the development of nano drones is gaining traction, providing new covert capabilities for operators at sea or on land. Deploying a flying ad hoc network (FANET) of nano drones on the battlefield comes with specific performance and security issues. This thesis provides a novel approach to address the performance and security concerns faced by FANET routing protocols, and, in our case, is specifically tailored to improve the Ad Hoc On-Demand Distance Vector (AODV) routing protocol. The proposed routing protocol, Lightweight Secure Ad Hoc On-Demand Distance Vector (LS-AODV), uses a lightweight stream cipher, Trivium, to encrypt routing control packets, providing confidentiality. The scheme also uses Chaskey-12-based message authentication codes (MACs) to guarantee the authenticity and integrity of control packets. We use a network simulator, NS-3, to compare LS-AODV against two benchmark routing protocols, AODV and the Optimized Link State Routing (OLSR) protocol, in order to gauge network performance and security benefits. The simulation results indicate that when the FANET is not under attack from black-hole nodes, LS-AODV generally outperforms OLSR but performs slightly worse than AODV. On the other hand, LS-AODV emerges as the protocol of choice when a FANET is subject to a black-hole attack.ONROutstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods