Smart camera networks (SCNs) are increasingly used in applications such as homeland security, border control and traffic monitoring. The cameras are often wireless with an organically growing structure, to reduce the overhead of deployment. We frame a new and important problem in SCNs: how to transmit videos from multiple cameras with overlapping coverage given the limited available wireless bandwidth to maximize the quality of the received videos. We call this problem Coordinated Transport of Correlated Videos (CTCV). CTCV is a more general version of 3D video transport: in that problem, highly correlated videos from two cameras (that provide the 3D perspective) are jointly encoded exploiting their pre-defined and known overlap. In contrast, in CTCV there is an arbitrary number of cameras whose overlap is not known apriori and that require transmission as multiple video streams. To effectively support CTCV, we propose a video delivery protocol that consists of two primary components: (1) Consolidation of correlated videos from multiple cameras which removes spatially redundant fields-of-view; and (2) Network and coverage aware bandwidth allocation to optimize coverage quality cooperatively among the different video streams to match the available bandwidth. We formulate the problem of optimal bandwidth allocation for maximizing coverage. We propose and investigate different heuristic policies for bandwidth allocation. We evaluate CTCV using data from a small camera testbed as well as topologies from realistic deployments. Experiments show that CTCV achieves around 10 dB gains in video quality in the scenarios we consider.

We consider the problem of target coverage in visual sensor networks with Pan-Tilt-Zoom (PTZ) cameras. The finely controllable movement in PTZ dimensions creates a large number of possible Field-of-View (FoV) settings, making it prohibitively expensive to consider them all in coverage algorithms. However, these FoVs are redundant as each group of targets is generally covered by many FoVs. Thus, an important problem is, how to identify FoVs that cover all maximal subsets of targets (MaxFoV) efficiently? We show that MaxFoV is an instance of generating all maximal cliques, which is NP-hard in general but polynomial if the number of cliques is polynomial. We construct an optimal algorithm to solve the problem with a worst case complexity of O(n3). Simulation and testbed experiments show that the algorithm drastically reduces the number of FoVs allowing multi-camera coverage to scale without sacrificing coverage quality.

Network protocol developers typically go through a tedious and error-prone process of testing and debugging their protocol implementation for various settings. They perform a number of tasks manually such as configuration of numerous network settings, controlled reproduction of unexpected protocol behavior, and traffic capture and analysis. We present a Protocol Testing and Evaluation System (PTES) to assist developers in transparently testing their protocol implementations. PTES enables a protocol developer to construct and execute various controlled and repeatable testing scenarios. The developer can use simple iptables- like rules to specify various local and distributed network events and actions. During protocol execution, PTES triggers these events and actions in a coordinated manner and records the protocol responses to these events which can later be examined by the developer. We present the design and implementation of three variants of PTES for native, simulated, and emulated platforms for both wired and wireless networks. We demonstrate the utility of PTES by automating the testing of TCP/IP and Optimized Link State Routing (OLSR) protocols.

Visual sensor networks (VSNs) are becoming increasingly popular in a number of application domains. A distinguishing characteristic of VSNs is to self-configure to minimize the need for operator control and to improve scalability. One of the areas of self-configuration is camera coverage control that is, how should cameras adjust their field-of-views to cover maximum targets? This is an NP-hard problem. We show that the existing heuristics have a number of weaknesses that influence both coverage and overhead. Therefore, we first propose a computationally efficient centralized heuristic that provides near-optimal coverage for small-scale networks. However, it requires significant communication and computation overhead, making it unsuitable for large-scale networks. Thus, we develop a distributed algorithm that outperforms the existing distributed algorithm with lower communication overhead, at the cost of coverage accuracy. We show that the proposed heuristics guarantee to cover at least half of the targets covered by the optimal solution. Finally, to gain benefits of both centralized and distributed algorithms, we propose a hierarchical algorithm where cameras are decomposed into neighborhoods that coordinate their coverage using an elected local coordinator. We observe that the hierarchical algorithm provides scalable near-optimal coverage with networking cost significantly less than that of centralized and distributed solutions.

Visual sensor networks (VSNs) are becoming increasingly popular in a number of application domains. A critical ability of such networks is to self-configure to minimize the need for operator control, improve scalability, and reduce cost. One of the areas of self-configuration is camera coverage control: how cameras adjust their field-of-view to allow automatic tracking of maximum number of targets. The problem is shown to be NP-hard for stationary targets, and efficient centralized, distributed and semi-centralized heuristics exist that perform close to optimal. For stationary targets camera configuration is a one-time activity and can happen offline and before the actual deployment. In contrast, if the targets are mobile, as the targets move away from their recorded positions, the cameras need to configure dynamically and in real-time to ensure coverage accuracy. In this paper, we propose several policies for automatic control of the cameras with a goal of coverage maximization for mobile targets. We study these policies using important performance metrics such as coverage gain, adaptability, scalability, and energy consumption. Our results indicate that factors such as target mobility models, target and camera scales/densities, and target velocities have significant impact on the performance of a given policy. For most of the scenarios, we found that the protocols that take into account non-local information (e.g. neighborhood information) and have self-adapting parameters (e.g. frequency of camera configurations) outperform the protocols that are either purely local or purely global and have non-adaptive parameters.

The proliferation of wireless-enabled portable computing devices has spurred a growing need for efficient and powerful networking protocols. The key challenge in the development of robust wireless networking protocols is an ability to conduct effective and efficient evaluation of the protocol in order to ensure its successful working in real-world settings. We proposed MiNT-2, a fresh re-design of the original MiNT framework developed at Stony Brook University. One of the fundamental requirements of MiNT-2 is to provide location awareness of all the nodes within the network. In this paper, we demonstrate the use of radio-frequency identification (RFID) technology in order to carry out localization of the mobile nodes within the system. We also demonstrate the application of the localization system of constructing different scenarios for wireless protocols evaluation.