information fusion in a cloud computing era: a systems-level perspective

16 IEEEA&ESYSTEMSMAGAZINE OCTOBER2014

Information Fusion in a Cloud Computing Era: A Systems-Level PerspectiveBingwei Liu, Yu Chen, Ari HadiksBinghamton University, New York

Erik Blasch, Alex AvedAir Force Research Lab, Rome, New York

Dan Shen, Genshe ChenIntelligent Fusion Technology, Inc., Germantown, Maryland

INTRODUCTION

Information fusion utilizes a collection of data sources for un-certainty reduction, coverage extension, and situation aware-ness. Future information fusion solutions require systems de-sign [1], coordination with users [2], metrics of performance [3], and methods of multilevel security [4]. A current trend that can enable all of these services is cloud computing. Cloud com-puting as defined by NIST is:

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, serv-ers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. [5]

Cloud computing provides capabilities (on-demand self ser-vice, broad network access, resource pooling, rapid elasticity, and measured service) over different types of clouds (private, community, public, and hybrid).

An area of growing popularity of cloud-based applications is robotic systems [6], [7]. These applications of cloud servic-es have implications for avionics systems in telerobotics [8], space communications [9], multisensor fusion [10], wide-area motion imagery [11], and information management [12]; not to mention numerous other emerging applications. Based on the services provided over a distributed network, cloud computing

supports large scale data processing and analytics which is es-sential for future avionics systems designs.

In this paper, we present a comprehensive view on a system level information fusion design using cloud computing technol-ogy. A systematic comparison among four different distributed computing paradigms is provided, which illustrates the advan-tages and constraints of cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. A holistic dis-tributed cloud-enabled robotics framework for information fu-sion is proposed using robotic systems. The system-level design principles of service-based architectures are highlighted in this framework where we considered the implementation of both the cloud and robot networks with additional security features. In ad-dition, on the cloud side, we include a virtual machine (VM) and a physical machine into our framework as dynamic computing clusters. A preliminary performance evaluation through a case study based on a video tracking application is demonstrated to highlight the advantages of cloud computing.

CLASSIFICATION OF DISTRIBUTED AND CLOUD

COMPUTING

Figure 1 compares the hardware and software stacks of four major distributed computing paradigms in order of develop-ment: cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. The solid arrows indicate the layers on which users run their application directly and dashed arrows indicate layers controlled by the middleware layer. It is important to note that cloud computing has emerged as a meth-od for large data management [13] that is useful for distributed avionics systems.

The lowest level of the stack is the networking infrastruc-ture, which connects physical computers. Scalable clusters can be built with loosely coupled node computers which are hierar-chically connected via a local (LAN), storage (SAN), or wide-area network (WAN). Each node computer has its own operating system which manages its local resources. The cluster is located on the Internet and is addressable via the internet protocol (IP)

Authors’ current addresses: B. Liu, Y. Chen, A. Hadiks, Depart-ment of Electrical and Computer Engineering, Binghamton University, SUNY, Binghamton, NY. E. Blasch, A. Aved, Air Force Research Lab, Information Directorate, 525 Brooks Road Rome, NY 13441-4505, E-mail: [email protected]. D. Shen, G. Chen, Intelligent Fusion Technology, Inc. German-town, MD. SYSAES-2013-0115 was received July 22, 2013, ready for pub-lication April 23, 2014. DOI. No. 10.1109/MAES.2014.130115. Review handled by M. Jah. 0885/8985/14/ $26.00 © 2014 IEEE

OCTOBER2014 IEEEA&ESYSTEMSMAGAZINE 17

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address of it gateway (i.e., the IP address of the cluster). In P2P net-works, all computed nodes act au-tonomously and may join or leave the system freely, where each node functions as both a client and as a server. Peers are connected to each other through the Internet without any central coordination. In ac-cordance with communication or application requirements, a logical overlay network may be formed by mapping each physical node with its identification (ID). When a new user joins, a node with their corresponding ID is added in the overlay network. When an exist-ing user leaves, the node—with its ID—is removed from the overlay network. Host computers utilized in grid computing are generally powerful workstations, servers, clusters, or even supercomputers, and are owned by the respective organizations which join the grid. To guarantee user jobs are serviced in a secure and ef-ficient manner, these organizations are connected through high-speed virtual private networks (VPNs). In cloud computing, the service providers manage huge server farms in which hundreds of thousands of servers are connected on top of cloud enabled networks (CBNs).

The host operating system (OSs) run atop the physical com-puting hardware are generally similar, regardless of the para-digm they are organized within, with the exception of P2P. P2P networks may consist of heterogeneous mobile devices based upon operating systems such as the Android operating system.

In the middleware layer, the four paradigms are very dif-ferent. Cluster operating systems are designed to provide an il-lusion of single system image (SSI), which presents the user with a single powerful machine. In contrast, the middleware

of P2P computing is more like a resource pool that is open, heterogeneous, and dynamically changing. While there is not a central manager of controller, users cannot control when and what types of machines and software will be available. These characteristics bring about challenging compatibility, packet routing, and scalability issues as workloads and the number of P2P nodes grow.

The grid computing middleware organizes the computing resources owned by different organizations into virtual plat-forms to support virtual organizations. User jobs are scheduled and monitored by the central grid operating system. While the participating organizations have the right to decide when and how many resources they are willing to contribute, they cannot randomly revoke a resource while a guest job is running on it.

Taking advantage of virtual machine (VM) technology, the middleware layer of the cloud computing enables hardware,

Figure 1. Comparison of hardware/software layered architectures of distributed computing paradigms: cluster comput-ing, peer-to-peer (P2P) computing, grid computing, cloud computing.


Information Fusion in a Cloud Computing Era

software, and data sets be provided on demand, elastically cor-responding to a user’s requirements. Cloud computing provides a flexible solution to cope with user requests under different contexts. “Everything as a Service” (XaaS) has entered our vo-cabulary [14]. XaaS can refer to a number of services that are delivered through the Internet. For example, communication as a service (CaaS), database as a service (DBaaS), security as a service (SECaaS), identity management as a service (IMaaS), and desktop as a service (DaaS). The most well-known and widely accepted service models are infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).

C Infrastructure as a service (IaaS): users are allowed to provision processing, storage, networks, and other funda-mental computing resources. Multiple VMs running guest OSs and specific application software can be deployed.

C Platform as a service (PaaS): users are enabled to deploy user-created or acquired applications created using pro-gramming languages and tools onto the cloud infrastruc-ture supported by the cloud service provider.

C Software as a service (SaaS): users exercise the service provider’s already-deployed applications, which are host-ed on a cloud infrastructure. The applications are acces-sible from client devices through a thin client interface, such as a Web browser (e.g., Web-based email).

Figure 2 presents a brief comparison of the four distributed computing paradigms in terms of parallelism, scalability, elas-ticity, availability, power efficiency, and security. The green, yellow, and red bars are symbols corresponding to the high, medium, and low applicability of the respective topics in their row. While each paradigm has inherent advantages and con-straints, the main weakness of cluster and grid computing lies in their lack of elasticity, since computing resources are assigned to accommodate the peak resource requirement when user jobs are scheduled. P2P computing suffers from lack of trust among peers, complexity in failure management and load balancing, and high heterogeneity in hardware, software, and network con-nections. Grid computing like cloud computing provides paral-lelism and power efficiency by utilizing the appropriate nodes

to service workloads. Cloud computing outperforms the other three with its high elasticity, availability, and security.

A HOLISTIC CLOUD ENABLED ROBOTICS SYSTEM

The system-level design for information fusion in the cloud computing environments needs to address the issues at both lo-cal and data collector layers and the distributed cloud layer. The key question is how to take full advantage of the properties of high elasticity and scalability. In this paper, we illustrate some design rationales through a holistic framework for cloud-en-abled robotic systems (CERS), in which robot services register with the cloud as robotic service providers.

CLOUDROBOTICS

Cloud robotic systems take advantage of all the benefits of the cloud, while at the same time providing economy to users who want focus on the functionalities of robots. There are at least the following benefits when we integrate multiple robotic systems within cloud architecture:

1. Faster robotic applications development: developers in ro-botics can cooperate in the platform provided by the cloud to develop robotic applications in a more efficient way. Once the application developers deploy their applications into the cloud, they can take advantage of the fast provi-sion technology of the cloud to serve an almost unlimited amount of users.

2. Easier to get started: a user doesn’t need to configure the development environment in order to control robots, or a specific type of robot. All they need is a web browser to ac-cess these services in cloud robotics.

3. More efficient robot resources usage: robot owners can also reduce their cost in maintenance by charging a small fee to each user.

Figure 3 shows a typical scenario of a CERS. Robotic sys-tems that are able to provide robot services register with the cloud as robotic service providers. The cloud provides a uni-form web interface for all customers. Developers communicate and cooperate with each other through the web portal. Admin-istrators also manage robotic systems through a specific secure website hosted in the cloud. Robotic systems and the cloud ex-

Figure 3. A typical scenario of a cloud-enabled robotic system.

Figure 2. Comparison of cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing in terms of parallelism, scalability, elasticity, availability, power efficiency, and security.


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change messages through robot operating system (ROS) [15] messaging mechanism.

ANABSTRACTARCHITECTURE

As a general discussion of CERS, we first consider the abstract architecture in Figure 4. At the top of this system is an appli-cation layer, including three types of applications (APPs) for different purposes. The Management APPs consist of authenti-cating and access control functionalities, as well as the manage-ment of computing, storage, network, auditing, and QoS, etc. The Services APPs include customized applications for differ-ent robot systems, resource databases, and system monitor, etc. The User APPs provide applications directly interacting with end users, such as a webpage that can see the video captured by a robot in real time. The application programming interface (API) layer is the middleware between applications and under-lying layers where developers use them for the application de-velopment.

Under the API layer is the computing, storage, and databas-es platform layer. Basic databases such as user registration and robot states will be created for system management. The cloud provides elastic computing and storage resources on demand and schedules jobs or tasks according to its load balance poli-cies. High availability can also be provided to desired clients.

In order to provide generic information fusion services in a cloud, we suggest that heterogeneous robotic systems use the widely adopted robot operating system (ROS) as the robot plat-form. The cloud communicates with ROS directly to acquire data and sends commands to robots. Each robot team must have at least one ROS master to take care of message exchange, ro-bot services registration, and robot control. The residence of ROS masters is flexible. An ROS master can run on local com-puter which is located in the same area as managed robots. It can also run on a virtual machine in the cloud.

ROBOTNETWORK

In the ROS system, the term “nodes” are processes running on the robot system. Multiple nodes can run on the same ROS sys-tem. The ROS master serves as a name server for all other nodes so that they can find each other. Once the existence of other nodes is discovered, each node (robot) can communicate with any other node directly in an ad-hoc wireless fashion or through

a centralized access point. A good choice to host a ROS master is a computer that is in the same local network with all other distributed robots. An access point is also needed in order to communicate with the cloud. Another option is to run the ROS master on a virtual machine (VM) in the cloud. In this case, a robot must have a public IP address of a local manager node is added to route the message traffic.

There are two running modes under our proposed CERS:

1. Local mode: when the network connection is not satisfied, the local robotic network operates under local mode. A man-ager node will be chosen and host the ROS master. This manager node can be a laptop or simply a robot that has the most powerful processing ability.

2. Cloud mode: in order to take advantage of the cloud, the round-trip delay time (RTT) of the communications to the cloud must be suitable for the services provided by the local robot system. If it is, the ROS master will switch to cloud mode.

Robot states are stored in a local ROS master as well as a cloud ROS master. A clone of the local ROS master is also reg-istered in the dedicated cloud database.

CLOUDSIDECONSIDERATION

Figure 5 illustrates a possible implementation on the cloud side in order to provide robot services to the public. The underlying cloud infrastructure employs a dynamic VM computing cluster and a storage system. The following aspects are worth noting when designing a cloud robotic system:

1. Dynamic computing cluster: The dynamic cluster is a com-puting cluster consisting of a VM cluster and physical clus-ter. According to the system load, the cloud will allocate appropriate computing resources to different tasks. The dashed lines in Figure 5 represent the appropriate isolation between every two VMs. One of the VMs in the cluster is chosen as a computing scheduler. The scheduler monitors the performance and load of all other VMs. We assume that the scheduler VM has a strong and secure environment that is difficult to compromise. This is reasonable since we can always use a private IP for this VM, as well as further protect it behind a secure firewall. This elastic computing structure is able to process sophisticated computing intensive tasks.

Figure 4. Abstract architecture of a cloud-enabled robotics system.

Figure 5. Dynamic computing cluster and storage system.



2. Separated storage: every VM can have one or more associ-ated storage volumes. These volumes are assigned by the hypervisor and cannot be modified without root privileges in the hypervisor. User data security and integrity can also be included in the system by adopting a storage integrity auditing service. A distributed storage system like Hadoop Distributed File System (HDFS) can be employed as the un-derlying storage infrastructure.

3. Distributing functionalities into VMs: every individual function in our model can be implemented in a virtual machine with proper computing and storage resources to handle user requests, message exchanges, or performance monitoring. This is an efficient and economic way to im-plement a complex system. The elasticity of the cloud will also assure high availability of services. If any functioning VM goes wrong, a new VM can be rapidly deployed to replace the bad VM.

WEBINTERFACE

Providing computing services through the web has proved to be a successful service delivery mechanism by public cloud-service providers like Amazon. We also recommend using RESTful web services [16] to implement cloud applications. The cloud provides a uniform web interface for administrators, developers, and regular user to access data and to perform tele-operation of robots. Administrators monitor the health of VMs, robot states, and user behavior. Developers store program code on the revision control and source code management system provided by the cloud and can easily cooperate with each other on the hosted repository. Regular users visit the web interface to access history data, design tele-operated robots, request data processing, and monitor VMs with owner privileges.

SECURITY

Security is usually the first concern to both service providers and users. Users want to ensure that their data and software are safe in the cloud. Important and sensitive data in the cloud should be stored securely. The cloud should also be able to ap-propriately defend against attacks to web servers and user data. The study of security in CERS is still an open area; hence it needs to be considered carefully when putting robotic resources online. A compromised server can send malicious commands to robots, causing tremendous loss for robot owners.

To protect user data and ensure security of the system, sev-eral policies need to be enforced:

1. VM isolation: VMs must be isolated by the hyper visor even when they are running on the same physical machine;

2. Secure storage: the cloud must implement secure storage of user data before deploying robot services. The cloud can provide an auditing service for users to guarantee data in-tegrity. A third party auditor (TPA) can be employed to audit the cloud’s data integrity. The auditing procedure is usually a challenge-response style: a user or the TPA challenges the

cloud with the integrity of his data, and the cloud then re-sponds with a message to prove that it actually possesses the user’s data and that all data blocks are intact in the cloud storage infrastructure; and

3. Network management: each virtual local area network (vLAN) has strict rules to prevent unauthorized access even from within the same vLAN. Usually, the hypervisor will take care of the packet routing and virtual network optimi-zation. If necessary a trusted network manager can be de-ployed to monitor the network with proper permissions.

WORKFLOWEXAMPLE

As a proof of concept example, we show the workflow of a user requesting a service through the cloud robotic system in Figure 6.

Step 1: the user first visits the web interface and requests for the service (1);

Step 2: the web server then calls the user authentication and access control module (2);

Step 3: the web server queries the user database (3) and grants access if the user has the correct permissions (4);

Step 4: the robot manager then checks the robot database to make sure a robot exists that can provide this service (5), (6);

Step 5: the API issues a message to the ROS manager (7);

Step 6: the ROS manager then sends a command to the ap-propriate robot (8) and retrieves the requested data (9);

Step 7: the data is then passed all the way to the user. The computing module also performs necessary computations and returns to other modules and the user (10).

Figure 6. Work flow of a user requesting a service through a web portal.


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PERFORMANCE EVALUATION

To evaluate the performance of off-loading image processing tasks in video tracking application to the cloud, we designed a cloud-enabled distributed robotics prototype, consisting of a re-mote robot network and a cloud test bed in our datacenter. The robot network includes three SRV1 robots and one AR.Drone 2.0 flying robot, shown in Figure 7. The flying robot collects image sequences with its on-board camera and transfers them to a local server of the cloud in real time. Building on previous work in mobile robot applications [17] and image processing [18], our cloud runs an image segmentation algorithm [19] and the L1 tracker [20], [21] to follow the motion of ground robots. We compare the performance in executing the video tracking task between three virtual machines and a physical machine by implementing twenty simultaneous requests of video signal processing and collecting the performance data.

EXPERIMENTALSETUP

1. Cloud Testbed: the Cloud test bed consists of 16 servers in our data center, all using Xen Cloud Platform (XCP) 1.6 [22]. We choose Citrix XenCenter as our management soft-ware, which provides convenient management features for our experiment purpose. We can easily create, clone, and move a virtual machine within the XenCenter. Live migrate, within the same pool or access different pools, is also an attractive feature. Each cloud server is equipped with two Intel Xeon E5405 quad-core processors at 2.0 GHz, 32 GB memory, and 3 TB storage. A pool of four servers are adopt-ed in this experiment, Figure 8 shows the time and resource utilities, including CPU and memory of each server. Figure 9 shows the real-time monitor of the central processing unit (CPU), memory, and network performance of the large in-stance in the experiment.

2. Authentication and access control: we have implemented the functionalities of the web interface in our framework. The administrator uses a web interface to monitor all robots’ camera images. All users, including the administrator, are authenticated by a username password scheme before they can access any resource.

3. Machines under comparison: to evaluate the performance differences among a local machine and virtual machines in the cloud, we compared the local machine with three in-stances in the cloud (see Table 1). We denote the VMs as small, medium, and large instances according to their com-puting capacities. The small instance has comparative con-figuration with the local machine. The medium and large instances double the number of virtual CPUs and memory each time. All machines, physical or virtual, use an Ubuntu 12.04 LTS operating system.

4. Task analysis: the task of this CERS prototype is to track the motion of ground robots using computer vision and informa-tion fusion technologies. The image processing procedure is computationally expensive on a workstation or a laptop. We want to investigate the feasibility of offloading computing tasks to the cloud and assess system scalability. To assure the quality of the robot tracking application, the frame-per-sec-

Figure 7. A UAV drone tracking three mobile robots.

Figure 8. Resource utility of the pool of cloud servers.

Figure 9. VM performance monitoring.



ond (FPS) rate must not fall under a certain threshold so that video quality will not cause too much delay. The time to pro-cess a frame after captured by a camera consists of the time to transfer back and forth between a robot and the server as well as the time for the server to process this frame. Since the transfer time could vary under different network conditions, in this experiment, we only consider the FPS that a server can process as the indicator of robust performance.

5. Algorithm: we tested our target motion detection and tracking algorithm in this experiment. For each frame sent by the robot, first the tracker finds the edge of every object using a threshold algorithm. Then we find the contours in the edge frame and find an approximate polygon for each contour and calculate a bounding box for it. Next, we merge bounding boxes that are close to each other and finally get bounding boxes for all objects. Figure 10 shows a frame of the result. The image pro-cessing algorithm for video tracking was written in C++ using openCV 2.4.4 libraries [23]. The same algorithm was imple-mented in all physical and virtual machines. The FPS data was collected as every machine runs the processing algorithm. All FPS values were an average of 20 trials under the same condi-tions for a number of simultaneous requests.

RESULTS

The results of our experiment are illustrated in Figure 11. Over-all, the medium and large instances had higher performance since they have more CPUs to process the images. All machines have almost the same FPS for one to three requests, which means that handling three requests will not affect the CPU per-

formance in any cases in our experiment. The medium instance surprisingly had a higher FPS than the large instance which has eight virtual CPUs. One reason could be that the tracking algo-rithm was not optimized for parallel processing, which implies that a few requests at the same time will not take full advantage of all the available processors.

Started at eight requests, the large instance had higher FPS (2.27) than the medium instance (24-58). Considering the gen-eral FPS of the image processing algorithm, the large instance had fair performance in this experiment with over 10 FPS for up to 20 requests. When there are more than 20 requests, the cloud can easily assign more VMs to serve the users. An interesting phenomenon we observed in our experiments is that he amount of memory has no direct impact on the FPS after 2 GB.

Because of the time consuming transfer of video back to the robot, we suggest that he server only return the coordinates of tar-gets in each frame. In this way, the transfer delay from the cloud to the robot is negligible, requiring only the transfer of image sequences or video streams through the cloud. In our case, only 24 B of data is needed for each frame. The cloud architecture also enables the display of the results on a web page to the user.

RELATED WORK

Chen et al. [24] defined the concept of Robot as a Service (RaaS) based on service-oriented architecture (SOA). The au-thors presented the idea of combining robot services with a cloud, using Microsoft Robotics Developer Studio (MRDS)

Table 1.

Specification of Compared Machine

Item Local Workstation VM1 VM2 VM3

CPU type Core2DueE8400 XeonE5-2609

CPU frequency (GHz) 3.0 2.4

Number of cores 2 2 4 8

Cache (MB) 6 10

Memory (GB) 4 4 8 16

Figure 11. Comparison of four frames-per-second (FPS) image processing environ-ments: local machine (local), small instance (small), medium instance (medium), and large instance (large).

Figure 10. Image frame with tagged targets using the video tracking algorithm.


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and Visual Programming Language (VPL). RaaS was imple-mented and tested on two processors, Intel Core 2 Due and Atom. Agostinho et al. [25] proposed a cloud computing envi-ronment for networked robotics applications. VMs are assigned different roles in the cloud environment. A layered workflow management system was used for scheduling purposes. Kehoe et al. [26] illustrated a system architecture for cloud-based ro-bot grasping using a Google object recognition engine. A pro-totype and initial experiments for a cloud-based robot grasp-ing system were implemented in their work. Arumugam et al. [27] proposed a distributed agent with collective intelligence framework, in which heterogeneous robots can work together in large environments. A ROS platform [15] was used for sen-sor data collection and communication. In their implementa-tion, Hadoop [28] was used as a high performance computing and storage platform. A grid based FastSLAM algorithm was implemented as a Hadoop Map/Reduce task [27]. Hu et al. [29] suggested using gossip protocols for communication between robots within a highly dynamic mobile robotic network. No route discoveries and maintenance are needed in this system. It is simple to implement and has low computation and memory requirements. However the latency of message exchange could be expensive in this ad hoc wireless network. The authors also considered energy consumption in the decision of whether or not to offload computation to the cloud.

Compared to previous works in cloud robotics, we highlight the following contributions of this paper.

1. Relatively complete framework: the architecture of both the cloud side and the local robot network side are considered in our framework. Previous work either built the cloud en-vironment on a single machine using virtualization software instead of using a cloud platform or didn’t provide enough specifications of the cloud architecture.

2. Dynamic computing cluster: we combine a virtual machine cluster with a physical cluster in our dynamic computing cluster infrastructure. This architecture has potential for flexible scalability, efficient resource allocation, and net-work security.

3. Work flow prototype: we investigated the workflow of a user requesting a robot service from the web interface provided by the cloud as a proof of concept example.

4. Performance comparison between a physical workstation and multiple virtual machine instances: we evaluated the performance between a workstation and different sizes of virtual machine instances in a video tracking algorithm pro-cessing simultaneous requests. The distributed tracking ex-ample is exemplar of information fusion tasks from multiple sources.

DISCUSSION AND CONCLUSION

In this paper, we provide some insights on system-level cloud enhanced information fusion technology. Some design prin-ciples have been illustrated through a case study of a general

architecture for a distributed cloud-enabled robotic information fusion system. A performance evaluation test was investigated on a video tracking task for a robot network consisting of a UAV drone tracking multiple mobile robots. Our results show that offloading computation to the cloud is feasible and is espe-cially beneficial when there are a lot of robot networks request-ing image processing tasks.

We conducted all the experiments in our cloud test bed con-sisting of 16 servers running on a Xen Cloud platform. A web portal with authentication module has been implemented and allows the user to communicate with the cloud. Our current work is just a beginning to explore the possibilities in combin-ing robotic systems with cloud computing to accomplish more computationally intensive information fusion tasks such as ten-sor learning [30].

There still exist interesting performance and implementa-tion questions to be investigated for future avionics information fusion systems designs, such as seamless integration with the ROS system to control multiple UAVs in real time, efficient scheduling of computing resources, and dynamic bandwidth al-location according to the system load.

ACKNOWLEDGMENTS

The authors thank Dr. Haibin Ling for the L1 tracker compiled at Temple University and the guidance from Dr. Khanh Pham. Yu Chen and Ari Hadiks appreciate the AFRL/RI Summer Fac-ulty/Student Program support. Research supported in part by an AFOSR grant in Dynamic Data-Driven Applications Systems and Contract No. FA9453-12-C-0228. The views and conclu-sions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Air Force Research Labora-tory, or the U.S. Government.

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