Sameer Tilak

A taxonomy of wireless micro-sensor network models

In future smart environments, wireless sensor networks will play a key role in sensing, collecting, and disseminating information about environmental phenomena. Sensing applications represent a new paradigm for network operation, one that has different goals from more traditional wireless networks. This paper examines this emerging field to classify wireless micro-sensor networks according to different communication functions, data delivery models, and network dynamics. This taxonomy will aid in defining appropriate communication infrastructures for different sensor network application sub-spaces, allowing network designers to choose the protocol architecture that best matches the goals of their application. In addition, this taxonomy will enable new sensor network models to be defined for use in further research in this area.

Infrastructure tradeoffs for sensor networks

In a sensor network, the infrastructure (in terms of the sensor capabilities, number of sensors, and deployment strategy) plays a significant role in determining the performance of the network. In this paper, we study the effect of infrastructure decisions on the performance of a sensor network. We study the effect of the infrastructure for two types of network delivery models (phenomenon driven and continuous) and different network protocols (DSR, DSDV and AODV). We show the performance both in terms of network efficiency as well as meeting the application accuracy and latency demands. By exploring the criteria for effective infrastructure configurations, we open the door for network optimizations that control the effective topology to better achieve the application requirements.

Dynamic localization control for mobile sensor networks

Localization is a fundamental operation in mobile and self-configuring networks such as sensor networks and mobile ad hoc networks. For example, sensor location is often critical for data interpretation. Existing research focuses on localization mechanisms: algorithms and infrastructure designed to allow the sensors to determine their location. In a mobile environment, the underlying localization mechanism must be invoked repeatedly to maintain accurate location information. We propose and investigate adaptive and predictive protocols that control the frequency of localization based on sensor mobility behavior to reduce the energy requirements for localization while bounding the localization error. In addition, we evaluate the energy-accuracy tradeoffs. Our results indicate that the proposed protocols reduce the localization energy significantly without sacrificing accuracy.

The Ring Buffer Network Bus (RBNB) DataTurbine Streaming Data Middleware for Environmental Observing Systems

The environmental science and engineering communities are actively engaged in planning and developing the next generation of large-scale sensor-based observing systems. These systems face two significant challenges: heterogeneity of instrumentation and complexity of data stream processing. Environmental observing systems incorporate instruments across the spectrum of complexity, from temperature sensors to acoustic Doppler current profilers, to streaming video cameras. Managing these instruments and their data streams is a serious challenge. Critical infrastructure requirements common to all of these sensor-based observing systems are reliable data transport, the promotion of sensors and sensor streams to first-class objects, a framework for the integration of heterogeneous instruments, and a comprehensive suite of services for data management, routing, synchronization, monitoring, and visualization. In this paper we present the RBNB DataTurbine, an open-source streaming data middleware system, and discuss how the RBNB DataTurbine satisfies the critical cyberinfrastructure requirements core to these sensor-based observing systems. The discussion includes the results from real-world deployments.

Non-uniform information dissemination for sensor networks

Future smart environments are characterized by multiple nodes that sense, collect, and disseminate information about environmental phenomena through a wireless network. In this paper, we define a set of applications that require a new form of distributed knowledge about the environment, referred to as non-uniform information granularity. By non-uniform information granularity we mean that the required accuracy or precision of information is proportional to the distance between a source node (information producer) and current sink node (information consumer). That is, as the distance between the source node and sink node increases, loss in information precision is acceptable. Applications that can benefit from this type of knowledge range from battlefield scenarios to rescue operations. The main objectives of this paper are two-fold: first, we precisely define non-uniform information granularity, and second we describe the different protocols that achieve non-uniform information dissemination and analyze these protocols based on complexity, energy consumption, and accuracy of information.

Avoiding head of line blocking in directional antenna [MAC protocol]

In existing directional MAC protocols a single queue is used at the MAC layer; this is inherited from omnidirectional implementations. However, while there is a single channel state in omnidirectional transmission (either the channel is busy or not), the state of the channel varies with the desired direction of transmission in directional antennas. Thus, existing implementations which use a single FIFO queue potentially lead to head of line blocking if the medium is busy in the direction of the packet at the top of the queue but is available in other directions. We propose a new queuing organization which could take advantage of the channel more effectively using the underlying antenna system by eliminating head of line blocking. We also identify a problem with the directional virtual carrier sense implementation due to side-lobes and provide a solution to it. Our results indicate that by using a greedy approach, to schedule the packet which has the least wait time, increases the overall throughput and reduces end-to-end delay considerably, especially under heavy loads.

Real-World Deployments of Participatory Sensing Applications: Current Trends and Future Directions

With the advent of participatory sensing (sensors integrated with consumer electronics such as cell phones and carried by people), exciting new opportunities arise. Mobile sensors (e.g., those mounted on cars or carried by people) can provide spatial sampling diversity not possible with traditional static sensor networks. Recently, participatory sensing has attracted considerable attention of research community. In this paper, we survey existing participatory sensing deployments and discuss current trends and few possible future directions.

Collaborative storage management in sensor networks

In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example: a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource; the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage techniques to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. In addition, local coordination can be used to adjust the sampling rate to match the required application fidelity. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering. These savings allow the network to operate for a longer time without exhausting storage space. Furthermore, the savings reduce the amount of data that will eventually be relayed in response to queries or upon eventual collection of the data. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.

Dynamic resource discovery for sensor networks

As sensor networks mature the current generation of sensor networks that are application-specific and exposed only to a limited set of users will give way to heterogeneous sensor networks that are used dynamically by users that need them. The available sensors are likely to be dynamic (e.g., due to mobility) and heterogeneous in terms of their capabilities and software elements. They may provide different types of services and allow different configurability and access. A critical component in realizing such a vision is dynamic resource discovery. In this paper, we develop a resource discovery protocol for sensor networks, outline some of the challenges involved, and explore solutions to some of the most important ones. Specifically, we first discuss the problem of what resources to track and at what granularity: in addition to the individual sensor capabilities, some resources and services are associated with sensor networks as a whole, or with regions within the network. We also consider the design of the resource discovery protocol, and the inherent tradeoff between interoperability and energy efficiency.

Protocols for local data delivery in wireless microsensor networks

Sensor networks are becoming increasingly important as tools for monitoring remote environments. As sensors are typically battery-operated, it is important to efficiently use the limited energy of the nodes to extend the lifetime of the sensor network. Two factors can greatly influence the performance of protocols for these networks: the data delivery model, which describes how the end user wants to access the data; and the network dynamics, which include sensor mobility as well as changes in sensor data rates throughout the lifetime of the network. In this paper, we look at several media access control protocols for sending data from sensors to a local data collector. Comparing these protocols shows that there is an inherent tradeoff in energy efficiency with adaptability of the protocol.