Sudha Krishnamurthy

TinySIP: Providing Seamless Access to Sensor-based Services

Recent technological trends are transforming sensor nodes from passive data-gathering entities to a collaborative network of sensors; capable of providing data-related and event-related information services. Such a sensor-based information service is useful only if it can be seamlessly accessed across traditional networks and through familiar device interfaces. In order to enable such ubiquitous access to sensor-based services, we need a remote messaging protocol that supports versatile messaging options, interoperates over different types of network, and allows messages to be routed based on flexible attribute-based addressing of endpoints. Furthermore, in order to ensure ease of adoption and deployment within an enterprise as well as in consumer environments, we must leverage communication abstractions that are well-known and already supported on traditional networks. In this paper, we propose TinySIP as a communication abstraction for accessing sensor-based services. TinySIP is based on the session initiation protocol (SIP), which is a standard application-level signaling mechanism. Users on traditional networks remotely interact with a sensor network service by sending SIP messages. A gateway maps the SIP abstractions to the corresponding TinySIP abstractions and propagates the messages to the sensor nodes. We are currently planning on deploying the SIP-based solution that we propose on a research testbed to enable users on a wireless mesh to access the in-network storage and event correlation services offered by a sensor network consisting of 20 MicaZ sensor nodes

Netcompress: Coupling network coding and compressed sensing for efficient data communication in wireless sensor networks

Measurements from sensor networks consisting of thousands of nodes are often correlated, since nearby sensors observe the same phenomenon. Using Compressed Sensing, that data can be reconstructed with a high probability from a small collection of random linear combinations of those measurements. This opens a new approach to simultaneously extract, transmit and distribute information in wireless sensor networks. Efficient communication schemes well matched to compressive sensing are, nonetheless, needed to realize the full benefits of this approach. We present a simple, practical scheme, called NetCompress, using a novel form of Network Coding. It preserves the reconstruction conditions required for Compressed Sensing and also overcomes the high link-failure rate in wireless sensor networks. NetCompress simultaneously transmits packets of sensor measurements and encodes them to form random projections for Compressed Sensing recovery. A recent result in Compressed Sensing guarantees that the data at all nodes can be accurately recovered with a high probability from a small number of projections, which is much less than the total number of nodes in the network. NetCompress demonstrates this result on both the TOSSIM simulation platform and a testbed comprising 20 micaz and tmote sensor nodes. Our experimental results show that the number of packets that is needed to reconstruct light intensity measurements with reasonable quality is just half the number of nodes in the network.

CREST: An Opportunistic Forwarding Protocol Based on Conditional Residual Time

Opportunistic forwarding protocols take advantage of contact opportunities to route data in intermittently connected environments. In these environments, a fully connected path between the source and destination may not always exist and the contact schedules of all the nodes are not known in advance. Hence, one of the key challenges for a node is to make effective forwarding decisions using only a limited knowledge of the contact behavior of the nodes in the network. Based on an analysis of human mobility traces that we collected from our office environment, we introduce a new link metric, conditional residual time, that accurately estimates the time remaining for a pair of nodes to meet using only the local knowledge of their past contacts. We then propose a distributed protocol called CREST, that uses the conditional residual time to opportunistically forward messages between pairs of nodes. Experimental results show that CREST has a lower end-to-end delay compared to protocols that depend on future contact schedules and global knowledge of the contact behavior across the network. Furthermore, by disseminating only a few additional copies of the message, the delivery ratio of CREST improves significantly and is comparable to that of the flooding protocol.

Automation of facility management processes using machine-to-machine technologies

The emergence of machine-to-machine (M2M) technologies as a business opportunity is based on the observation that there are many more machines and objects in the world than people and that an everyday object has more value when it is networked. In this paper, we describe an M2M middleware that we have developed for a facility management application. Facility management is a time and labour-intensive service industry, which can greatly benefit from the use of M2M technologies for automating business processes. The need to manage diverse facilities motivates several requirements, such as predictive maintenance, inventory management, access control, location tracking, and remote monitoring, for which an M2M solution would be useful. Our middleware includes software modules for interfacing with intelligent devices that are deployed in customer facilities to sense real-world conditions and control physical devices; communication modules for relaying data from the devices in the customer premises to a centralized data center; and service modules that analyze the data and trigger business events. We also present performance results of our middleware using our testbed and show that our middleware is capable of scalably and reliably handling concurrent events generated by different types of M2M devices, such as RFID tags, Zigbee sensors, and location tracking tags.

Context-Based Adaptation of Mobile Phones Ussing Near-Field Communication

Smart environments that can adapt based on the current context are extremely useful for automating even simple, mundane tasks. The key components of such an environment are sources that can extract the raw context, a synthesizer that can draw inferences by aggregating the context from different sources, and a set of policies that drive the adaptation. In this paper, we describe a context-based adaptation system to alleviate the distraction caused by one of the most ubiquitous devices of modern day-the mobile phone. Our system makes use of sources, such as RFID devices, that employ near-field communication technology to extract raw context. We describe how we adapt the roles of these devices to extract both environmental and personal context. To reduce the distraction caused by mobile phones, we have developed a policy-based mechanism to enable context-based adaptation of these devices. We have implemented a prototype of our system and conducted some usage studies. We describe the details of our implementation and present the lessons learned from the usability experiments

Distributed Interactions with Wireless Sensors Using TinySIP for Hospital Automation

In order to seamlessly interact with everyday objects embedded with wireless sensors, we need diverse messaging abstractions that support convenient application-level interactions with sensor nodes. We have developed TinySIP with the goal of providing a unifying messaging protocol that not only supports distributed interactions among sensor nodes but also enables users on a traditional network to interact with the sensor nodes through familiar devices, such as mobile phones and PDAs. TinySIP leverages the communication abstractions provided by the Session Initiation Protocol (SIP), but uses a more compact and energy- efficient message format for communication with resource- constrained nodes. TinySIP supports interaction with sensor nodes using publish-sub scribe, instant messaging, and session-based semantics. We have implemented the TinySIP messaging library on Zigbee motes running TinyOS. In this paper, we describe a prototype application that we have developed that illustrates the use of TinySIP for interacting with sensor nodes, in order to automate processes in a hospital environment. Despite the diversity of sensor network applications and the heterogeneity of the sensor-generated data, our example shows that typical sensor-based interactions can be supported through a set of common communication abstractions. Hence, using a standard application- level messaging protocol, like TinySIP, that supports a common set of communication abstractions which can be used in a variety of sensor-based interactions will benefit sensor network application developers and users.

Improving information availability in storage-centric sensor networks

We address the issue of improving information availability in a class of delay-tolerant sensor network applications, where the sensor nodes are deployed in disconnected environments. In such environments, since there is no continuous access to a remote base station, there is a need to leverage the collaborative resources of the sensor network to support in-network storage. The stored data can then be retrieved opportunistically by mobile collectors in the proximity. We have developed a data-centric, in-network storage architecture that partitions the network into storage zones. In such a scheme, some of the storage nodes may be unavailable at the time of storage and retrieval, because they are either sleeping to conserve energy or because they have failed.We present two schemes based on random linear network coding for improving information availability within such a storage architecture. In the centralized scheme, the encoding is performed by the managers in each storage zone, whereas in the decentralized scheme, the encoding is done locally by the zone members. We have implemented the network coding schemes in TinyOS and we present results that show the impact of the zone size, duty cycle, and the degree of encoding on the decoding probability, based on our experiments on a testbed of Micaz motes.

Enabling distributed messaging with wireless sensor nodes using tinySIP

Smart sensor nodes have the potential to bridge the gap between the physical and digital world. To realize this potential, we need to integrate a network of sensors within a larger context-aware network, so that we can seamlessly interact with everyday objects embedded with sensor nodes. However, sensor networks currently lack high-level messaging abstractions that support convenient user-level interactions. In this paper, we present TinySIP, which is a messaging library that has been designed with the goal of enabling a client on a traditional network to seamlessly interact with the sensor nodes through diverse communication abstractions and familiar devices. TinySIP leverages the communication abstractions provided by the Session Initiation Protocol (SIP). The clients interact with the sensor nodes by sending SIP messages that provide publish-subscribe, instant messaging, and session-based semantics. A gateway maps the SIP messages to a more energy-efficient TinySIP message format, and forwards them to the sensor nodes, which then parse and react to the incoming TinySIP messages. We present the challenges in adapting SIP for resource constrained nodes and report the performance of our TinySIP implementation on Zigbee motes running TinyOS.