The next wave of applications for Unmanned Aerial Vehicles (UAVs) or drones range from delivery of consumer goods or Internet connectivity during natural disasters to defense scenarios such as autonomous combat or search and rescue, all of which require coordination of multiple entities across various altitudes from in-flight to ground-based stations. However, there are two important challenges to realizing such applications. First, positioning many antennas to communicate in three dimensions is non-trivial since the load capacity in terms of power and weight is highly restricted, and the drone body may block reception on the opposite side of the antenna. Second, large-scale antenna arrays are increasingly being used to increase channel quality in a given direction. However, there is limited antenna scale on a single UAV, and the challenge of distributing the antenna array across a drone swarm is extremely complex due to constant mobility, varying relative positions, and the inability to update the channel state of all transmitting nodes. In this project, the goal is to build MuDDI, a Multi-Dimensional Drone Communication Infrastructure, which will enable indoor and outdoor experimentation with UAVs to address research issues related to 3-D connectivity, distributed antennas across a drone swarm, and 3-D swarm formations that optimize the transmission to intended receivers.
Wirelessly-connected users encounter a vast array of environmental settings from factors including diverse terrain, buildings, vegetation, weather conditions, and velocities. Today, when traversing across these diverse conditions, each environmental change triggers a new wireless channel characterization so that links can have optimal performance for transmission rate and frequency band decisions, both of which depend on spatial and environmental characteristics. This characterization process can induce a high overhead on the network, greatly reducing the overall performance of the wireless links which were seeking to be optimized. In this project, diverse wireless scenarios will be classified into a finite set to recognize wireless channel types and optimize per-link and network-wide decisions. The project will significantly reduce the amount of characterization that needs to be performed per environment, especially when revisiting a location or when a new location shares many similarities as those previously visited.
The FCC seeks to connect 19 million unserved Americans to broadband by 2020. These rural areas have yet to be fully connected to the Internet due to wireline infrastructure costs which exceed potential revenue opportunities. Even in heavily-populated environments with sufficient wireline infrastructure, capacity issues remain in congested stadiums, disaster recovery zones, and public transportation. Each aforementioned access challenge seemingly is well-suited for wireless mesh networks, but have yet to be fully solved. However, recently, there has been a sizable growth in radios operating in diverse frequency bands (e.g., TV white spaces) with emerging multi-antenna schemes. In this project, multi-user beamforming and diverse frequency bands are leveraged to significantly build upon the flexibility originally sought by mesh networks. In doing so, frequency-agile beamforming mesh (FabMesh) networks seek to truly scale in complexity and cost according to the user population and traffic demand.
Due to the culmination of the recent reapportionment of FCC frequency bands and the acceleration of wireless device complexity, simultaneous access to multiple diverse wireless frequency bands is becoming more of a reality. Naturally, the question arises: How can simultaneous access to a wide range of frequency bands with distinct propagation and communication capabilities improve current wireless network performance? In this CAREER research, a foundation is laid for exploiting multiple frequency bands for multihop wireless networks from per-link to network-wide decisions. To do so, this project uses experimental design, measurement analysis, analytical modeling, and embedded programming on an array of devices and testbeds from mobile phones globally reachable via application downloads to in-lab and in-the-field wireless infrastructures.
Wireless system performance is known to be highly dependent upon the characteristics of the environment. Despite the increasing ability of wireless devices to sense their surroundings, wireless systems have yet to fully leverage contextual data to improve performance. To this end, DART-CARs is an NSF-funded infrastructure (NSF CISE CRI) that allows the study of wireless performance in a broad class of mobile and static environments in and around the Dallas area from indoor labs to outdoor high-way speeds within a single testbed. Our hardware platform functions across many different wireless bands to enable real-time, multi-band operation. Such a first-of-its-kind infrastructure is critical for designing context-aware and cognitive algorithms that utilize multiple frequency bands to adapt to dynamic environmental settings.
Development of Wireless Networking Testbed and Emulator (WiNeTestEr)
PIs: Dinesh Rajan (SMU) and Ravi Prakash (UT-Dallas) Co-PIs: Jinghong Chen (Arizona), Ping Gui (SMU), and Joseph Camp (SMU)
In the evaluation of wireless networks, there is a fundamental tradeoff between the scalability of the experimental environment and the realism of the wireless channel. The greatest degree of scalability can be achieved by simulators which use course channel models, and emulators with the highest degree of fidelity emulate a single link. This NSF-funded project (NSF MRI) aims to bridge the gap between the two extremes to build a versatile wireless networking testbed called Wireless Networking Testbed and Emulator (WiNeTestEr). The main objectives and the novelty of this testbed is in its capability to (i) emulate the large-scale wireless networks in multiple licensed and unlicensed bands, (ii) allow access to local and remote users to configure and control the same emulator, and provide repeatability, (iii) support experiments related to node mobility, multi-antenna (MIMO) operation, and cognitive radios, and (iv) provide an easy-to-use interface for remotely running wireless experiments remotely.
The WARP platform is a clean-slate design of media access (MAC) and physical (PHY) layers to prototype advanced wireless networks. Exchange of novel physical and network layers is available via the open-access WARP repository. Joseph Camp was a core member of the initial WARP team and has since designed novel rate selection protocols and experimentally evaluated WARP designs in diverse scenarios, including residential and downtown urban areas. He continues work on WARP with embedded applications in urban contexts.
The TFA Network is a large-scale wireless mesh deployment which provides Internet access to over 4,000 users in an under-resourced community in Houston, Texas. There is an open repository for measurement studies that have been performed on the network. Joseph Camp was the Chief Network Architect and lead graduate student for the TFA Network where he architected, deployed, operated, and managed the network, designed a number of measurement studies, and modeled network performance.
The driving vision of the TAPs Project was to form a high-performance wireless backbone using multiple-input multiple-output (MIMO) wireless links. TAPs was an FPGA-based platform that preceded the WARP platform. Joseph Camp designed a CSMA MAC protocol that ran on the embedded processor on the TAPs FPGA and directly interfaced with ns-2 to abstract the other network layers (physical and routing) which were yet to be built.
The 100x100 Project was a collaborative research effort to take a clean-slate approach to fixing problems with today's Internet. Involved with numerous project retreats and teleconferences, Joseph Camp represented students working on the access piece of the Internet infrastructure via a wireless mesh network architecture that served thousands of real users yet allowed programmability and observability for research.