Autonomous interception of unmanned aerial vehicles (UAVs) is a critical capability in modern defense, requiring agents to navigate adversarial maneuvers under environmental uncertainty. While conventional path planning often assumes full observability and static behaviors, this study addresses the complex challenge of intercepting an evasive target within a partially observable Markov decision process (POMDP) framework. We propose a deep Q-network (DQN) framework enhanced by a novel sampling strategy: direction prediction error–prioritized experience replay (DPE-PER). Unlike standard replay mechanisms, DPE-PER utilizes an auxiliary prediction task to prioritize transitions based on direction prediction (DP) error and directional uncertainty. By emphasizing experiences where the agent’s internal model fails to anticipate target maneuvers, DPE-PER focuses learning on the most informative adversarial interactions. Extensive simulations demonstrate that our approach achieves a 48.2% improvement in target interception rates and a 25.4% increase in average return compared to standard experience replay (ER) and prioritized experience replay (PER), highlighting the effectiveness of integrating predictive signals into experience selection for robust UAV autonomy in adversarial environments.

In this paper, we investigate the problem of completing data collection tasks for IoTDs (Internet of Things devices) using Unmanned Aerial Vehicles (UAVs) under the constraints of UAVbattery capacity and predefined data age. Our objective was to minimize UAV’s flight energy consumption by optimizing their trajectory allocation and achieve the research goal with the minimum number of UAVs. We transformed the multi-objective optimization problem (MOP) into a Traveling Salesman Problem with Neighborhoods (TSPN) by considering UAVs’ ability to collect data within the communication range of IoT devices. We studied two modes of data collection for UAVs: one in which they can only collect data during hovering and another in which they can collect data while hovering and moving simultaneously. We proposed two algorithms: UAV hovering data collection algorithm with neighborhood (UHDCN) and UAV moving data collection algorithm with neighborhood (UMDCN). We evaluate the performance of the proposed algorithms through extensive simulation experiments. The results demonstrate that UHDCN algorithm requires at least 4.8% fewer number of UAVs compared to existing algorithms, while UMDCN algorithm requires at least 22.2% fewer number of UAVs. Additionally, UHDCN algorithm consumes at least 4.9% less total energy compared to existing algorithms, while UMDCN algorithm consumes at least 22.8 less energy.

Technological, social and economic forces are changing the notion of mobility as culture is exerting strong pressure on demand for a “shared,” “reliable” and “on-demand” economy. In an era of the next mobility revolution, autonomous vehicles (AV) still fail to overcome edge cases that can stop an AV in their tracks. Remote driving has, however, emerged as a practical and promising solution to help AV in edge cases, such as AVs passing through bad weather or near a construction site. Remote driving relies on communication network infrastructure, which presents many challenges, both in terms of latency and performance, in connectivity between road vehicles and the remote driver. In addition, governments are passing laws to test AV technology without a safety driver, which has sparked the need for a remote driver who can take vehicle control if it encounters an edge case. In this paper, we enhance our previous work and present algorithms to select multiple remote drivers under user-defined objectives, to minimize the distance between the vehicle and remoter drivers. To evaluate the performance of the proposed algorithm, we perform extensive simulations both in a simulated environment using synthetic data and in a real environment with real-world data. We compare the performance of our proposed algorithms with baseline algorithms. The results show that the proposed heuristic algorithms perform similar to the optimal baseline line algorithms with much shorter computation time.

Due to the privacy concerns on the data generated by the users, Peer to Peer Social Network Services are getting popular these days because the data is kept in a distributed manner. In some cases, the list of neighbors of a node should be kept private, too. However, for some applications, we may need to compute the list of common neighbors between two nodes without revealing the whole list of neighbors. In this paper, we propose a Bloom filter based approach to compute the intersection of neighbors between two nodes in SNSes. We exploit the cross-checking property enabled by the neighbor relationships to simplify the computation while getting more accurate results. Our proposed method can get a near perfect intersection with mostly zero or one false common neighbors. Furthermore, the Bloom filter can successfully hide the neighbor information from attackers. We show the performance through numerical analysis and extensive simulations.

A significant portion of a typical Enterprise IT services budget is spent on steady state management operations known as ”operational expenses”. Recently ”Standard Services Offerings” based approaches are proposed to reduce such cost where only a few set of versions and configurations are used to build an Enterprise IT services platform. A key challenge in these approaches is the choice of appropriate base set of configurations that can be used as seed configurations. Our paper addresses the above problem by studying several methods towards choosing the right set of application stack with configurations that minimizes the cost of building the target services environment from this seed set of offerings. We present four different methods and use model driven simulation studies to compare the performance of these approaches. We validate our key findings by analyzing our approaches for a large Enterprise customer IT environment.

Due to the advancement of virtual machine technologies, large server farms or data centers consolidate their servers into a set of more powerful machines to increase the flexibility, agility, and adaptability to the fluctuating resource demands. Furthermore, managing a small number of powerful machines rather than a large number of low-performing machines decreases the management costs. In such a consolidation procedure, the network traffic as well as the usage of CPU and memory should be taken into account because the network bandwidth is also a critical resource. Since the network traffic of two servers or applications can be eliminated when they are consolidated into the same machine, traditional binpacking algorithms for the consolidation may not work efficiently. In this paper, we propose a cluster based consolidation algorithm, which reduces the maximum resource usage ratio of the machines. Through extensive simulation analysis, we show that the network traffic based clustering approach achieves a near optimal maximum resource usage, that is, the usage is close to the simple lower bound that only considers CPU and memory usage.

The performance (such as throughput) of wireless mesh networks (WMNs) are affected by many factors such as routing protocols and interferences. To improve the throughput, several studies have proposed efficient routing protocols to find better paths and new link state metrics to accurately represent the wireless link qualities. Most of these works assume that the locations of the mesh routers are pre-defined. In this paper, we propose a mesh router placement scheme based on genetic algorithm accounting for dynamic location for all the mesh routers, and indoor obstacles that can minimize the interferences among the mesh routers. Through extensive simulations, we find that the existence of obstacles such as walls can be exploited to decrease the interferences to improve the performance of the indoor wireless mesh networks. We show that the proposed scheme improves the performance by 30-40% compared to the random selection scheme. Furthermore, the existence of walls improves the performance by as much as 50% compared to the cases without walls.

Peer to peer based live multimedia streaming systems over the Internet have been gaining popularity these days because they can be easily deployed without router based IP multicast capabilities. However, the peers frequently join and leave the system so that some peers may experience service disruptions, which degrade the overall quality and reputation of the services. In this situation, a hybrid approach is considered as a viable alternative. In the hybrid approach, the service providers deploy multiple proxies all over the Internet and make them constitute a core tree in the peer to peer system so that the proxies behave as stable nodes that are always on. Thus the system quality can increase. In this paper, we propose a centralized core tree construction scheme based on various optimization algorithms. The scheme focuses on constructing a core tree that maximizes the overall quality of the system. Because the real time multimedia streaming systems have higher QoS requirements than the content distribution such as Akamai, we take into account not only the locations of the proxies but also the topology formed by the proxies. The general framework can obtain a tree for arbitrary performance metrics not only for a specific metric such as minimum delay. Through simulations over diverse scenarios, we show that our optimization based approaches can provide a better core tree than some greedy heuristic approaches.

Live multimedia streaming over the Internet has steadily gained popularity over the past decade primarily fueled by the growth in the available network bandwidth and rich multimedia applications. Various approaches to support live multimedia streaming can be broadly categorized into two alternatives, namely, IP multicast based and overlay based. While IP multicast based solution is dependent on the availability of IP multicast enabled network, peer-to-peer approach does not require any changes at the the network layer. However in a peer-to-peer based approach, peers frequently join and leave the system which often results in service disruptions, hence degradation of the overall quality and reputation of the services. In this situation, a hybrid approach is considered as a viable alternative where the service providers deploy multiple proxies all over the Internet and make them constitute a core tree in the peer-to-peer system. The proxies behave as stable nodes that are always in the system in spite of the frequent join and leave of the ordinary peers. In the hybrid approach, the main issue is the construction of the core tree that maximizes the overall quality of the system. The locations of the proxies as well as the topology formed by the proxies affect the performance due to the real time requirements in a live streaming. In this paper, we propose a centralized core tree construction scheme based on various optimization algorithms. Our proposed method provides a general framework to obtain a tree for arbitrary performance metrics. Furthermore, we show that our optimization based approaches can provide a better core tree than some greedy heuristic approaches such that the average delay from the source to each proxy decreases by 20-40%.

Network distance estimation through Euclidean embedding of Internet hosts has been extensively studied as a scalable approach. In this method, each Internet host is assigned a computed co-ordinate where the distance between any two arbitrary hosts is approximated by their Euclidean distance. In this paper, we investigate whether such co-ordinate based method can be leveraged in designing efficient overlay based routing path for peer-to-peer streaming. Specifically we explore the usage of co-ordinate based estimation with well known Delaunay Triangulation (DT) based routing path design for peer-to-peer streaming. We devise algorithms for this combined approach that we refer as e-DT. Using real measurement data sets, we show that e-DT improves the overlay based routing significantly over purely DT based approach. The comparison with other well known overlay based routing approaches indicates that e-DT is able to construct similar overlay paths with smaller average forwarding degree requirements at each peering node. In addition, e-DT can support both point-to-multi point and multi point to multi point streaming requirements.

In this paper, we design and evaluate the prototype of a network planning service utilizing the coordinate based embedding of network hosts. The kernel of our prototype consists of a scalable network distance embedding method, a core set of services built on top of this embedding, and a generic set of APIs exposed to the applications for utilizing these services. The implemented service core consists of four generic services that we argue to be common to a wide range of applications requiring management of services and monitoring of network distance among Internet hosts. The proposed service does not require any support from the end-hosts. We evaluate the implemented service core using real Internet data and demonstrate its efficacy. Our experience provides several key insights for the design and management of a suitable embedding scheme.

With the emergence of voice over IP and other real-time business applications, there is a growing demand for an IP network with high service availability. Unfortunately, in today's Internet, transient failures occur frequently due to faulty interfaces, router crashes, etc., and current IP networks lack the resiliency needed to provide high availability. To enhance availability, we proposed failure inferencing based fast rerouting (FIFR) approach that exploits the existence of a forwarding table per line-card, for lookup efficiency in current routers, to provide fast rerouting similar to MPLS, while adhering to the destination-based forwarding paradigm. In our previous work, we have shown that the FIFR approach can deal with single link failures. In this paper, we extend the FIFR approach to ensure loop-free packet delivery in case of single router failures also, thus mitigating the impact of many scenarios of failures. We demonstrate that the proposed approach not only provides high service availability but also incurs minimal routing overhead.

Abstract — Static broadband wireless networks, due to their ease of deployment, are likely to proliferate in the near future. The major stumbling block, however, is that wireless links are prone to external interference, channel fading, inclement weather, etc. Therefore scalable and reliable routing despite frequent link quality fluctuations is needed for accelerating the growth of these networks. Most of the wireless routing schemes proposed in the literature are less suitable for these networks, as they are designed primarily for mobile ad hoc networks with dynamic and unpredictable topologies. In this paper, we propose a novel link-state-based blacklist-aided forwarding (BAF) approach, that takes advantage of the fact that the nodes and therefore their adjacencies are relatively static, for scalable packet delivery in static wireless networks. Under BAF, each packet carries a blacklist, a minimal set of degraded-quality links encountered along its path, and the next hop is determined based on both its destination and blacklist. BAF provides loop-free delivery of packets to reachable destinations regardless of the number of degraded links in the network. We evaluate the performance of BAF and show that it is not only reliable but also scalable. I.

Multi-homing is a common practice among many (especially large) customer (or stub) networks. Although the purpose of multi-homing is primarily for enhanced reliability, it has also increasingly been used for load balancing and latency reduction. In this paper, we address the problem of how to perform scalable route selection in a multi-homed stub network to optimize network latency to various destinations as measured by round-trip-time (RTT). A straight forward method is to simply perform RTT measurements (e.g., using ping) to each destination via each provider and select the one with the minimum RTT as the "best" next-hop to the destination. Is there a more.

Dealing with network failures effectively is a major operational challenge for Internet service providers. Commonly deployed link state routing protocols such as OSPF react to link failures through global (i.e., network-wide) link state advertisements and routing table recomputations, causing significant forwarding discontinuity after a failure. The drawback with these protocols is that they need to trade off routing stability and forwarding continuity. To improve failure resiliency without jeopardizing routing stability, we propose a proactive local rerouting based approach called failure insensitive routing (FIR). The proposed approach prepares for failures using interface-specific forwarding, and upon a failure, suppresses the link state advertisement and instead triggers local rerouting using a backwarding table. In this paper, we prove that when no more than one link failure notification is suppressed, FIR always finds a loop-free path to a destination if one such path exists. We also formally analyze routing stability and network availability under both proactive and reactive approaches, and show that FIR provides better stability and availability than OSPF.