Abstract—Data redundancy caused by correlation has motivated the application of collaborative multimedia in-network processing for data filtering and compression in wireless multimedia sensor networks (WMSNs). This paper proposes an information theoretic data compression framework with an objective to maximize the overall compression of the visual information gathered in a WMSN. To achieve this, an entropy-based divergence measure (EDM) scheme is proposed to predict the compression efficiency of performing joint coding on the images collected by spatially correlated cameras. The novelty of EDM relies on its independence of the specific image types and coding algorithms, thereby providing a generic mechanism for prior evaluation of compression under different coding solutions. Utilizing the predicted results from EDM, a distributed multi-cluster coding protocol (DMCP) is proposed to construct a compression-oriented coding hierarchy. The DMCP aims to partition the entire network into a set of coding clusters such that the global coding gain is maximized. Moreover, in order to enhance decoding reliability at data sink, the DMCP also guarantees that each sensor camera is covered by at least two different coding clusters. Experiments on H.264 standards show that the proposed EDM can effectively predict the joint coding efficiency from multiple sources. Further simulations demonstrate that the proposed compression framework can reduce 10 %- 23 % total coding rate compared with the individual coding scheme, i.e., each camera sensor compresses its own image independently. I.

Abstract—Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a spatial correlation function is derived to describe the correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed correlation function and entropy-based framework, a correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial correlation function can model the correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm. Index Terms—Camera selection, spatial correlation, visual information, wireless multimedia sensor networks. I.

are networks of wirelessly interconnected devices that allow retrieving video and audio streams, still images, and scalar sensor data. WMSN require the sensor network paradigm to be rethought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). In this paper, a new cross-layer communication architecture based on the time-hopping impulse radio ultra wide band technology is described, designed to reliably and flexibly deliver QoS to heterogeneous applications in WMSNs, by leveraging and controlling interactions among different layers of the protocol stack according to applications requirements. Simulations show that the proposed system achieves the performance objectives of WMSNs without sacrificing on design modularity. I.

The recent advances in miniaturization and the creation of low-power circuits, combined with small-sized batteries have made the development of wireless sensor networks a working reality. Lately, the production of cheap complementary metal-oxide semiconductor cameras and microphones, which are able to capture rich multimedia content, gave birth to what is called Wireless Multimedia Sensor Networks (WMSNs). WMSNs will boost the capabilities of current wireless sensor networks, and will fuel several novel applications, like multimedia surveillance sensor networks. WMSNs introduce several new research challenges, mainly related to mechanisms to deliver applicationlevel Quality-of-Service (e.g., latency minimization). To address this goal in an environment with extreme resource constraints, with variable channel capacity and with requirements for multimedia in-network processing, the caching of multimedia data, exploiting the cooperation among sensor nodes is vital. This article

Abstract—In directional sensor networks, sensors can steer around to serve multiple target points. Most previous works assume there are always enough deployed sensors so that all target points can be served simultaneously. However, this assumption may not hold when the mission requirement changes or when more target points need to be served. Since it is not always practical to deploy new sensors, we propose to reconfigure the network by letting existing sensors steer and serve the targets periodically. As a result, targets may not be served continuously, and the service delay affects the quality of service. One important problem is how to choose the optimal set of targets to serve by each sensor such that the maximum service delay is minimized. We first show that this problem is NP-complete, and then we propose a centralized protocol whose performance is bounded by a logarithm factor of the optimal solution. We also propose a distributed protocol which achieves the same performance as the centralized protocol. Finally, we extend the optimization model and the protocols by considering the rotation delay, which is critical for some applications but ignored by previous work. I.

Abstract. The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observations at multiple scales. This scale dependency is not handled well by today’s rather technical sensor definitions. Geosensor networks are normally defined as distributed ad-hoc wireless networks of computing platforms serving to monitor phenomena in geographic space. Such definitions also do not admit animals as sensors. Consequently, they exclude human sensors, which are the key to volunteered geographic information, and they fail to support connections between phenomena observed at multiple scales. We propose definitions of sensors as information sources at multiple aggregation levels, relating physical stimuli to observations. An algebraic formalization shows their behavior as well as their aggregations and generalizations. It is intended as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different types of sensors. 1

Abstract—In this work we consider an event-driven wireless visual sensor network (WVSN) comprised of untethered camera nodes and scalar sensors deployed in a hostile environment. In the event-driven paradigm, each camera node transmits a surveillance frame to the cluster-head only if an event of interest was captured in the frame, for energy and bandwidth conservation. We thus examine a simple image processing algorithm at the camera nodes based on difference frames and the chi-squared detector. We show that the test statistic of the chi-squared detector is equivalent to that of a robust (non-parametric) detector and that this simple algorithm performs well on indoor surveillance sequences and some, but not all, outdoor sequences. In outdoor sequences containing significant changes in background and lighting, this simple detector may produce a high probability of error and benefits from the inclusion of scalar sensor decisions. The scalar sensor decisions are, however, prone to attack and may exhibit errors that are arbitrarily frequent, pervasive throughout the network and difficult to predict. To achieve attack prediction and mitigation given an attacker whose actions are not known a priori, we employ game-theoretic analysis. We show that the scalar sensor error can be controlled through cluster-head checking and appropriate selection of cluster size. Given this attack mitigation, we employ real-life sequences to determine the total probability of error when individual and combined decisions are utilized and we discuss the ensuing ramifications and performance issues. Index Terms—Actuation, event-detection, game theory, scalarsensors, sensor network security, wireless visual sensor networks

Abstract—Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called full-view coverage. An object is considered to be full-view covered if for any direction from 0 to 2π (object’s facing direction), there is always a sensor such that the object is within the sensor’s range and more importantly the sensor’s viewing direction is sufficiently close to the object’s facing direction. With this model, we propose an efficient method for full-view coverage detection in any given camera sensor networks. We also derive a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment. Finally, we show a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice based deployment. I.

Networking for challenged environments, or Delay- and Disruption-Tolerant Networking as it is now most commonly referred to, has attracted great attention in the past few years by the networking research community. Connectivity disruptions, limited network capacity, energy and storage constraints of the participating, mobile devices and the arbitrary movement of nodes are only a few of the challenges that the protocol stack has to deal with. Clearly, current Internet protocols (i.e., the TCP/IP protocol stack) suffer and can fail under such conditions. In this paper, we initially give the DTN Problem Statement; we contend that not all applications have the same requirements from the system and hence, equal (blind) treatment of all data packets will result in reduced network efficiency. Based on that we propose a Design Position for DTN protocols, which states that protocol design has to be done proactively, on the basis of the application’s requirements. We then survey the most recent contributions on the whole spectrum of Delay- and Disruption-Tolerant Networking, from the architectural and the application point of view down to the transport- and the network-layer of the emerging DTN protocol stack. We find that although not explicitly mentioned

Wearable sensing systems are becoming widely used for a variety of applications, including sports, entertainment, and military. These systems have recently enabled a variety of medical monitoring and diagnostic applications in Wireless Health. The need for multiple sensors and constant monitoring lead these systems to be power hungry and expensive, with short operating lifetimes. In this paper, we introduce a novel methodology that takes advantage of the influence of human behavior on signal properties and reduces those three metrics from the data size point of view. This, in turn, directly influences the wireless communication and local processing power consumption. We exploit intrinsic space and temporal correlations between sensor data while considering both user and system behavior. Our goal is to select a small subset of sensors to accurately capture and/or predict all possible signals of a fully instrumented wearable sensing system. Our approach leverages novel modeling, partitioning, and behavioral optimization, which consists of signal characterization, segmentation and time shifting, mutual signal prediction, and subset sensor selection. We demonstrate the effectiveness of the technique on an insole instrumented with 99 pressure sensors placed in each shoe, which cover the bottom of the entire foot, resulting in energy reduction of 56 % to 96 % for error rates of 5 % to 17.5%.