We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm . While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [1,2], in this paper, we propose an orthogonal approach to previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Our simulations show the power of our proposed algorithms, revealing their potential to effect significant energy savings (from 10%- 65%) for typical sensor data corresponding to a multitude of sensor modalities.

this paper, we propose using multipath routing to increase resilience to node failure. Multipath routing techniques have been discussed in the literature for several years now (Section V). However, the application of multipath routing to sensor networks and other systems that permit data-centric routing with localized path setup has not yet been explored. We consider two different approaches to constructing multipaths between two nodes. One is the classical node-disjoint multipath adopted by prior work, where the alternate paths do not intersect the original path (or each other). The disjoint property ensures that, when k alternate paths are constructed, no set of k node failures can eliminate all the paths. The other approach abandons the requirement for disjoint paths and instead builds many braided paths. With braided paths, there are typically no completely disjoint paths but rather many partially disjoint alternate paths

Abstract—The paper considers the problem of minimizing the energy used to transmit packets over a wireless link via lazy schedules that judiciously vary packet transmission times. The problem is motivated by the following observation. With many channel coding schemes, the energy required to transmit a packet can be significantly reduced by lowering transmission power and code rate, and therefore transmitting the packet over a longer period of time. However, information is often time-critical or delay-sensitive and transmission times cannot be made arbitrarily long. We therefore consider packet transmission schedules that minimize energy subject to a deadline or a delay constraint. Specifically, we obtain an optimal offline schedule for a node operating under a deadline constraint. An inspection of the form of this schedule naturally leads us to an online schedule which is shown, through simulations, to perform closely to the optimal offline schedule. Taking the deadline to infinity, we provide an exact probabilistic analysis of our offline scheduling algorithm. The results of this analysis enable us to devise a lazy online algorithm that varies transmission times according to backlog. We show that this lazy schedule is significantly more energy-efficient compared to a deterministic (fixed transmission time) schedule that guarantees queue stability for the same range of arrival rates. Index Terms—Minimum energy transmission, optimal schedules, power control, wireless LAN. I.

Despite enormous progress in networking and computing technologies, their application has remained restricted to conventional person-to-person and person-to-computer communication. However, continual reduction in cost and form factor is now making it possible to imbed networking - even wireless networking - and computing capabilities not just in our PCs and laptops but also other objects. Further, a marriage of these ever tinier and cheaper processors and wireless network interfaces with emerging micro-sensors based on MEMS technology is allowing cheap sensing, processing, and communication capabilities to be unobtrusively embedded in familiar physical objects. The result is an emerging paradigm shift where the primary role of information technology would be to enhance or assist in "person to physical world" communication via familiar physical objects with embedded (a) micro-sensors to react to external stimuli, and (b) wireless networking and computing engines for tetherless communication with compute servers and other networked embedded objects. In this paper we present the application of sensor-based wireless networks to a "Smart Kindergarten" that we are developing to target developmental problem-solving environments for early childhood education. This is a natural application as young children learn by exploring and interacting with objects such as toys in their environment. Our envisioned system would enhance the education process by providing a childhood learning environment that is individualized to each child, adapts to the context, coordinates activities of multiple children, and allows continual unobtrusive evaluation of the learning process by the teacher. This would be done by wirelessly-networked, sensor-enhanced toys and other classroom objects with back-...

Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low.

In this paper, we explore a novel avenue of saving power in sensor networks based on predictable mobility of the observer (or data sink). Predictable mobility is a good model for public transportation vehicles (buses, shuttles and trains), which can act as mobile observers in wide area sensor networks. To understand the gains due to predictable mobility, we model the data collection process as a queuing system, where random arrivals model randomness in the spatial distribution of sensors.

Sensornets are large-scale distributed sensing networks comprised of many small sensing devices equipped with memory, processors, and short-range wireless communication. Making effective use of sensornet data will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Recent work has identified data-centric routing as one such method. In this paper we suggest that a companion method, data-centric storage, may also be a useful approach.

Wireless sensor networks are an emerging area of research interest with a number of compelling potential applications. By architecting sensor networks as virtual databases, we can provide a well-understood nonprocedural programming interface suitable to data management, allowing the community to realize sensornet applications rapidly. We argue here that in order to achieve an energy-efficient and useful implementation, query processing operators should be implemented within the sensor network, and that approximate query results will play a key role. We observe that innetwork implementations of database operators require novel data-centric routing mechanisms, as well as a reconsideration of traditional network and database interface layering.

We consider in this paper how to leverage heterogeneous mobile computing capability for efficient processing of real-time range-monitoring queries. In our environment, each mobile object is associated with a resident domain and when an object moves, it monitors its spatial relationship with its resident domain and the monitoring areas inside it. An object reports its location to server whenever its movement affects any query results (i.e., crossing any query boundaries) or it moves out of its resident domain. In the first case, the server updates the affected query results accordingly while in the second case, the server determines a new resident domain for the object. This distributive approach is able to provide accurate query results and real-time monitoring updates with minimal location update and server processing costs. In addition, the new scheme allows a mobile object to negotiate a resident domain based on its computing capability. Thus, a more capable object can have a larger resident domain reducing its chance of having to request a new resident domain because of moving out of it. This feature makes the new approach highly adaptive to the heterogeneity of mobile objects. In our performance study, we compare it with an existing approach using simulation. The study shows that the new technique is many times better in reducing mobile communication and server processing costs.

We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.) I.