In wireless sensor networks (WSNs), the base station (BS) is a critical sensor node whose failure causes severe data losses. Deploying multiple fixed BSs improves the robustness, yet requires all BSs to be installed with large batteries and large energy-harvesting devices due to the high energy consumption of BSs. In this paper, we propose a scheme to coordinate the multiple deployed BSs such that the energy supplies required by individual BSs can be substantially reduced. In this scheme, only one BS is selected to be active at a time and the other BSs act as regular sensor nodes. We first present the basic architecture of our system, including how we keep the network running with only one active BS and how we manage the handover of the role of the active BS. Then, we propose an algorithm for adaptively selecting the active BS under the spatial and temporal variations of energy resources. This algorithm is simple to implement but is also asymptotically optimal under mild conditions. Finally, by running simulations and real experiments on an outdoor testbed, we verify that the proposed scheme is energy-efficient, has low communication overhead and reacts rapidly to network changes.

Reducing communication cost is crucial for outdoor wireless monitoring cameras which are constrained by limited energy budgets. From event detection point of view, traditional video coding schemes such as H.264 are inefficient as they ignore the “meaning” of video content and thus waste many bits to convey irrelevant information. To take advantage of the powerful computing resource on cameras, we propose a novel event-driven video coding scheme. Unlike previous approach that attempts to find anomalous image frame with potential events, we propose to detect salient regions in each image and transmit the image fragments marked with saliency to the receiver. This scheme rarely drops an event as it transmits all image fragments with potential events, and also requires no training procedure. The experimental results show that it performs substantially better than conventional video coding schemes for outdoor monitoring task.

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.

In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multi-camera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm. Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%-50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower.

Reducing energy cost is crucial for energy-constrained smart wireless cameras. Existing platforms impose two main challenges: First, most commercial smart phones have a closed platform, which makes it impossible to manage low-level circuits. Since the sampling frequency is moderate in environmental monitoring context, any improper power management in idle period will incur significant energy leak. Secondly, low-end cameras tailored for wireless sensor networks usually have limited processing power or communication range, and thus are not capable of outdoor monitoring task under low data rate. To tackle these issues, we develop Sensorcam, a long-range, smart wireless camera running a Linux-base open system. Through better power management in idle period and the "intelligence" of the camera itself, we demonstrate an energy-efficient wireless monitoring system in a real deployment.

In environmental monitoring applications, having multiple cameras focus on common scenery increases robustness of the system. To save energy based on user demand, successive refinement image coding is important, as it allows us to progressively request better image quality. By exploiting the broadcast nature and correlation between multiview images, we investigate a two-camera setup and propose a novel two-encoder successive refinement scheme which imitates a ping-pong game. For the bivariate Gaussian case, we prove that this scheme is successively refinable on the theoretical rate-distortion limit of distributed coding (Wagner surface) under arbitrary settings. For stereo-view images, we develop a practical successive refinement coding algorithm using the same idea. The simulation results show that this scheme operates close to the distributed coding bound.

SensorScope is a turnkey solution for environmental monitoring systems, based on a wireless sensor network and resulting from a collaboration between environmental and network researchers. Given the interest in climate change, environmental monitoring is a domain where sensor networks will have great impact by providing high resolution spatio-temporal data for long periods of time. SensorScope is such a system, which has already been successfully deployed multiple times in various environments (e.g., mountainous, urban). Here, we describe the overall hardware and software architectures and especially focus on the sensor network itself. We also describe one of our most prominent deployments, on top of a rock glacier in Switzerland, which resulted in the description of a micro-climate phenomenon leading to cold air release from a rock-covered glacier in a region of high alpine risks. Another focus of this paper is the description of what happened behind the scenes to turn SensorScope from a laboratory experiment into successful outdoor deployments in harsh environments. Illustrated by various examples, we point out many lessons learned while working on the project. We indicate the importance of simple code, well suited to the application, as well as the value of close interaction with end-users in planning and running the network and finally exploiting the data.

A sensor network data gathering and visualization infrastructure is demonstrated, comprising of global sensor networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications.

SensorScope is a collaborative project between network, signal processing, and environmental researchers that aims at providing a cheap and out-of-the-box environmental monitoring system based on a wireless sensor network. It has been successfully used in a number of deployments to gather hundreds of megabytes of environmental data. With data gathering techniques well mastered, the efficient processing of the huge amounts of the acquired information to allow for useful exploitation has become an increasingly important issue. In this paper, we present a number of challenging and relevant signal processing tasks that arise from the SensorScope project. We believe the resolution of these problems will benefit from a better understanding of the underlying physical processes. We show an example to demonstrate how physical correlations between different sensing modalities can help reduce the sampling rate.

Environmental monitoring constitutes an important field of application for wireless sensor networks. Given the severity of potential climate changes, environmental impact on cities, and pollution, it is a domain where sensor networks can have great impact and as such, is getting more and more attention. Current data collection techniques are indeed rather limited and make use of very expensive sensing stations, leading to a lack of appropriate observations. In this paper, we present SensorScope, a collaborative project between environmental and network researchers, that aims at providing an efficient and inexpensive out-of-the-box environmental monitoring system, based on a wireless sensor network. We especially focus on data gathering and present the hardware and network architecture of SensorScope. We also describe a real-world deployment, which took place on a rock glacier in Switzerland, as well as the results we obtained.

The successful deployment of a wireless sensor network is a difficult task, littered with traps and pitfalls. Even a functional network does not guarantee gathering meaningful data. In SensorScope, with its multiple campaigns in various environments (e.g., urban, high-mountain), we have acquired much knowledge in planning, conducting, and managing real-world sensor network deployments. In this paper, we share our experience by stepping through the entire process, from the preparatory hard- and software development to the actual field deployment. Illustrated by numerous real-life examples, excerpted from our own experience, we point out many potential problems along this way and their possible solutions. We also indicate the importance of a close interaction with the end-user community in planning and running the network, and finally exploiting the data.

How often have you been able to implement an algorithm as it is described in a paper? And when you did, were you confident that you had exactly the same parameter values and results as the authors of the paper? All too often, articles do not describe all the details of an algorithm and thus prohibit an implementation by someone else. In this paper, we describe our experience with reproducible research, a paradigm to allow other people to reproduce with minimal effort the results we have obtained. We discuss both the reproducibility of data and algorithms, and give examples for each of them. The effort required to make research reproducible is compensated by a higher visibility and impact of the results.

Sensor networks are usually dense networks where the network diversity can be exploited in order to overcome failures. In this paper, we study the use of multiple description techniques in the context of sensor networks where the cause of failures is due to the usual practical constraint of having finite buffers in the sensors, instead of the more traditional case of link failures considered in previous research. Although from a theoretical point of view we observe that the use of more descriptions provides usually better performance, we show experimentally that this is not the case in practice, when real constraints are introduced, such as finite buffers and the presence of header information, necessary for any real application. Our main result is that the optimal number of descriptions, in terms of average distortion, decreases as the fraction of header information increases for a given buffer size

The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases. Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.

We study network capacity limits and optimal routing algorithms for regular sensor networks, namely, square and torus grid sensor networks, in both, the static case (no node failures) and the dynamic case (node failures). For static networks, we derive upper bounds on the network capacity and then we characterize and provide optimal routing algorithms whose rate per node is equal to this upper bound, thus, obtaining the exact analytical expression for the network capacity. For dynamic networks, the unreliability of the network is modeled in two ways: a Markovian node failure and an energy based node failure. Depending on the probability of node failure that is present in the network, we propose to use a particular combination of two routing algorithms, the first one being optimal when there are no node failures at all and the second one being appropriate when the probability of node failure is high. The combination of these two routing algorithms defines a family of randomized routing algorithms, each of them being suitable for a given probability of node failure.