Nirwan Ansari - US grants

This award is for the acquisition of a real time disc system with color monitor, camera, printer, and recorder/player. The instruments are to support research projects in motion adaptive video coding with motion compensation and motion measurement by matching approaches. The instrument upgrades current black and white equipment to color. One method of squeezing more use out of limited broadcast channels is to discover new ways of coding color and motion information. These will become particularly important with the advent of high definition television and is critical to some of the proposed HDTV standards. This equipment award is for the acquisition of a real time color video display system for use in research on motion and color coding algorithms.

In addition to the traffic growth in the Internet, new applications and services are imposing a large variety of requirements. At the same time, the emergence of network applications on video, audio, business services, and other data ser-vices is creating an imminent demand for integration of most traffic with de-fined quality of service (QoS) guarantees. Examples of emerging network re-quirements are directly related to reliability, recoverability, and security. Al-though most of these requirements have kindled research interests in recent years, it is still unclear how to integrate them for next generation networks. To manage the increasing variety of QoS requirements, this research project pro-poses a new service model concept, called service vector, as a solution for pro-viding QoS support for a large variety of traffic classes. This concept is expected to help achieving the following objectives: a) robust differentiated service model capable of supporting fine QoS granularity; b) scalability; c) satisfaction of the users' customized end-to-end requirements; d) improved network operator revenue; e) higher utilization of the current Diffserv network model. The com-plexity and feasibility of service vectors for the next generation networks is in-vestigated, and the impact on router architectures and deployment issues are considered for the implementation of service vectors in new-generation and de-ployed routers.

In this grant supplement to encourage collaboration with Japanese researchers in cyber security, Professor Nirwan Ansari from New Jersey Institute of Technology and Professor Nei Kato from Tohoku University will study these topics that are important for securing next generation networks:

(1) Network attack detection technology for next generation networks,
(2) Energy efficient secure sensor networks, and
(3) New security technology combined with QoS control scheme

Next generation networks are defined in this project as consisting not only the traditional fixed networks but also cellular networks, ad-hoc networks, sensor networks, and even satellite networks. Eventually all communications, e.g., data and voice, will be conveyed on next generation networks; yet, these networks and their usage introduce considerable heterogeneity into networking, which makes difficult seamlessly and securely interconnecting these different networks. In particular, security issues encountered in one type of network are intertwined with those of other networks, thus creating many complicated and difficult issues. Conventional methods are not enough cope with these challenges in next generation networks; new approaches and new concepts are needed.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

A storage-centric heterogeneous sensor network (SC-HSN) consists of a large number of resource-poor sensor nodes at the lower tier and relatively fewer powerful master nodes at an upper tier. Sensor nodes produce and submit data to nearby master nodes which then answer the queries from the network owner on behalf of sensor nodes. SC-HSHs are ideal data sensing solutions in remote and extreme environments such as oceans, volcano, animal habitats, and battlefields. In such adverse environments, it is often impossible or prohibitive to maintain a stable always-on communication connection from the sensor network to the external network owner, and thus in-network data storage is a must such that data continuously produced by sensor nodes are stored inside the network and await ad-hoc queries via an on-demand communication connection.

This project studies some fundamental open challenges associated with dependable data management in SC-HSNs. Specifically, there are three main thrusts in this project: (1) Distributed Fault-Tolerant Data Storage; (2) Privacy-Preserving Data Access Control; and (3) Verifiable Queries over Encrypted Data. The outcome of this research will advance the state of the art in data management in WSNs and provide an integrated solution to reliable, secure, efficient data storage and retrieval in SC-HSNs. The results of this project will also provide great insights for dependable data management in other types of emerging wireless networks such as mobile ad hoc networks, wireless mesh networks, and vehicular networks. The results of the project will be disseminated widely through publications and talks, and the proposed research will also be integrated with the education curricula.

This project aims to provision energy-efficient communications by powering wireless access networks with renewable energy. Wireless access networks and the power grid are envisioned to be deployed in a distributed manner in which a distributed base station consumes less amount of power that can be generated by renewable energy. Optimization theory and game theory are applied to study the interaction between the wireless access networks and the power grids, to optimize the operation of wireless access networks by integrating the power distribution of the micro grid into the design and optimization of the wireless access networks, and to design the operation of the micro grid by considering characteristics of both the power demand of the consumers (wireless access networks) and the distributed energy resources (renewable power generators). The research explores the potential of applying renewable energy into powering wireless networks, and stimulates the large scale application of renewable energy in helping green the society and reduce the carbon footprint of the environment. Two major results are anticipated: 1) the algorithms that optimize the operation of renewable energy powered wireless networks (REPWiNet), and 2) the communication protocols that coordinate the wireless access network with the underlying power grids. The insights and results derived from designing REPWiNet will provide guidelines not only for designing wireless access networks but also for designing the power distribution and coordination in micro grids. Research results will be posted on a website as well as submitted for publications in journals and presentations at premium conferences.

This project addresses the reduction of energy consumption in optical and wireless access networks via a series of approaches such as capacity-adaptive network design, and energy-efficient resource allocation and traffic scheduling. The capacity-adaptive access network stays in the 'high-capacity high-power' mode when the network is heavily loaded with bandwidth-hungry applications such as video streaming, and switches into the 'low-capacity low-power' mode when the network is lightly loaded with less bandwidth demanding applications such as voice and future smart grid traffic. The project generalizes the approaches currently being used in data centers to communication links, and focuses on formalizing the problem and obtaining optimal or near-optimal solutions. It will not only improve energy efficiency of current access networks, but will also provide insights and guidelines on how to upgrade the capacity of access networks efficiently.

The concept of designing capacity-adaptive access networks introduces new requirements of 'capacity adaptive' on the design of access networks as opposed to simply provisioning for peak utilization. This project's energy-aware network control, resource allocation, and traffic scheduling schemes consider an additional key performance metric, energy consumption, in addition to network quality of service, while conventional schemes merely consider network performance such as delay and loss. Research results, via theoretical analysis, simulations, and testbed experiments, will not only demonstrate improved energy efficiency of access networks, but will also be potentially tailored for core networks, datacenter networks, and other wireless systems.

Broader Impact:
Reducing the energy consumption of access equipment will not only have positive environment impacts during normal operation but it will provide public benefit during emergencies. Remote access equipment is currently powered by a combination of public power, battery backup, and varying degrees of local generation. Techniques such as those being developed by this project that extend operation when on emergency backup help protect the public health and safety. The project will integrate research and education through participation of both undergraduate and graduate students in the project and by incorporation of research outcomes into undergraduate and graduate course work. The PI will utilize the New Jersey Educational Opportunity Program (EOP), which provides educational opportunities to under-represented populations, as part of the process for recruiting students into the research program.

The project aims to liberate wireless access networks currently constrained by spectral and energy scarcity via a new framework, referred to as FreeNet, by exploiting cognitive networking and green energy. FreeNet will be designed and optimized using a series of novel techniques such as dynamic network architecture optimization, network resource aware traffic scheduling, and spectrum sharing. The network architecture optimization framework will apply advanced probability theory to investigate inherent relationships between the optimal network architecture and the availability of spare spectrum and green energy, and adopt control theory to adapt the network architecture according to the dynamics of the spare spectrum and green energy. The network resource aware traffic scheduling and spectrum sharing algorithms will be designed based on optimization theory. Finally, theoretical analysis will be reduced to practice and translated into communications protocols in enabling and prototyping FreeNet. The theoretical analysis will elicit a series of theorems to direct the utilization of green energy in communication networks.

The communication protocols design and FreeNet prototyping will provide guidelines for designing resource aware communication systems. The research activities will advance the understanding of inherent relationships among the network architecture, traffic scheduling, spectrum utilization, and energy consumption. FreeNet will be deployed for offloading mobile traffic in urban areas, delivering content in rural areas, and enhancing emergency communication capacity during natural disasters. FreeNet will improve the availability and capacity of wireless networks, broaden the benefits of wireless networking, and enhance the living environment, the earth, by reducing the release of carbon footprints. Research outcomes from this project will be disseminated via publications and a website with frequent updates. Other broader impacts include integration of research and education through participation of both undergraduate and graduate students in the project, incorporation of research outcomes into course work, interactions and exchanges with invited speakers, and seeking involvement of REU students and under-represented groups.

Video-based traffic monitoring systems have been widely used for traffic management, incident detection, intersection control, and public safety operations. Current designs pose critical challenges. First, it relies heavily on human operators to monitor and analyze video images. Second, commercially available computer vision technologies cannot satisfactorily handle severe conditions, such as weather and glare, which significantly impair video image quality. Third, the simultaneous transmission of numerous video signals to a central facility creates extreme demands on the communications network, which can lead to jamming. This project presents a novel approach that incorporates wireless sensor networks, hierarchical edge-computing, and advanced computer vision technology. The methods can be expanded to address a wide spectrum of potential applications including wrong-way driving alerts, congestion detection under bad weather conditions, accident scene management support, suspect vehicle tracking, wildfire detection and alert, and emergency evacuation, which could save lives and hundreds of billions of dollars annually. It also aligns with the smart city initiative.

By using bluetooth/WiFi detection technology, the trajectories and speeds of vehicles equipped with such devices will be collected. This information, along with the captured video data, will be analyzed by the proposed computer vision software, installed at the edge of the network on cloudlets, to perform fast detection and prioritization of the video streams from different cameras. The proposed hierarchical edge-computing paradigm will not only enable real-time big data analysis at the edge but will also be demonstrated and actualized to perform timely efficient video analytics. Depending on the weather conditions, different detection and prioritization algorithms will be activated. Video coding will then be implemented to transmit the selected video streams to the central back-end system for further processing. If an incident is detected by the algorithm either at the edge or at the back-end, a necessary feedback action will be taken, such as calling an emergency group, the highway safety dispatch, or the police. Under a technical partnership with New Jersey Department of Transportation, multiple pilot tests of the proposed system will be implemented on selected highway corridors designated by the department.

SoarNet (free Space Optics as bAckhaul and energizeR for drone-assisted NETworking) aims to simultaneously and rapidly transmit data and energy from an access node to a Drone-mounted Base Station (DBS), which can be flexibly deployed to provision wireless broadband access. That is, the DBS can simultaneously receive high-speed data streams and energy via optical beams. The received energy is used to power the DBS to prolong its flight and received data streams are delivered to Mobile Users (MUs) via existing Radio Frequency (RF) channels. SoarNet will be a game changer for mobile access. By leveraging Free Space Optics (FSO) communications and the drone-assisted mobile networking framework, SoarNet will significantly enhance the throughput of the network and Quality of Service (QoS) of MUs and will advance the state of the art of wireless networking and wireless charging. Proposed research activities will advance the understanding of simultaneously charging the DBS and transmitting data at high speed.

SoarNet comprises three major research endeavors. 1) Actualizing the SoarNet architecture to construct a FSO transmitter at an access node and a FSO receiver at a DBS to provision simultaneous energy transfer and data transmission; meanwhile, two mathematical models will be derived to estimate the charging rate by applying FSO as a charger and the transmission rate by applying FSO communications as wireless backhauling. 2) 3D DBS placement in SoarNet, to determine the longitude, latitude, and altitude of the DBS to maximize the throughput of delivering data to MUs in the access network, while guaranteeing the requirement of the DBS's charging rate. 3) Dynamic access node association and MU association, to associate the DBS with an access node (i.e., the DBS receives the data streams and energy from the access node) and adjusts the MU association area of the DBS (i.e., more/less MUs are associated with the DBS to download their data) in order to balance the traffic loads among access nodes, thus further improving the quality of service (QoS) in terms of the average delay of downloading data to MUs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.