Ramesh Govindan

Directed diffusion: a scalable and robust communication paradigm for sensor networks

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

Next century challenges: scalable coordination in sensor networks

Networked sensors-those that coordinate amongst themselves to achieve a larger sensing task-will revolutionize information gathering and processing both in urban environments and in inhospitable terrain. The sheer numbers of these sensors and the expected dynamics in these environments present unique challenges in the design of unattended autonomous sensor networks. These challenges lead us to hypothesize that sensor network coordination applications may need to be structured differently from traditional network applications. In particular, we believe that localized algorithms (in which simple local node behavior achieves a desired global objective) may be necessary for sensor network coordination. In this paper, we describe localized algorithms, and then discuss directed diffusion, a simple communication model for describing localized algorithms.

Directed diffusion for wireless sensor networking

Advances in processor, memory, and radio technology will enable small and cheap nodes capable of sensing, communication, and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed-diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed-diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.

Understanding packet delivery performance in dense wireless sensor networks

Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.

A wireless sensor network For structural monitoring

Structural monitoring---the collection and analysis of structural response to ambient or forced excitation--is an important application of networked embedded sensing with significant commercial potential. The first generation of sensor networks for structural monitoring are likely to be data acquisition systems that collect data at a single node for centralized processing. In this paper, we discuss the design and evaluation of a wireless sensor network system (called Wisden for structural data acquisition. Wisden incorporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchronization. We also study the applicability of wavelet-based compression techniques to overcome the bandwidth limitations imposed by low-power wireless radios. We describe our implementation of these mechanisms on the Mica-2 motes and evaluate the performance of our implementation. We also report experiences from deploying Wisden on a large structure.

GHT: a geographic hash table for data-centric storage

Making effective use of the vast amounts of data gathered by large-scale sensor networks will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Previous work has identified data-centric routing as one such method. In an asso-ciated position paper [23], we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordi-nates, and stores a key-value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data lo-cally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key-value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads predicted in [23], offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.

Impact of network density on data aggregation in wireless sensor networks

In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greedy aggregation can achieve up to 45% energy savings over opportunistic aggregation in high-density networks without adversely impacting latency or robustness.

Diversity in smartphone usage

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

Data-centric storage in sensornets with GHT, a geographic hash table

Making effective use of the vast amounts of data gathered by large-scale sensor networks (sensornets) will require scalable, self-organizing, and energy-efficient data dissemination algorithms. For sensornets, where the content of the data is more important than the identity of the node that gathers them, researchers have found it useful to move away from the Internets point-to-point communication abstraction and instead adopt abstractions that are more data-centric. This approach entails naming the data and using communication abstractions that refer to those names rather than to node network addresses [1,11]. Previous work on data-centric routing has shown it to be an energy-efficient data dissemination method for sensornets [12]. Herein, we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we first define DCS and predict analytically where it outperforms other data dissemination approaches. We then describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordinates, and stores a key–value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data locally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key–value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads we analytically predict, offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.

Data-centric storage in sensornets

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. While there are many ways to achieve data-centric storage, this paper proposes a mechanism that builds upon two recent advances; (1) the GPSR geographic routing algorithm and (2) a new generation of efficient peer-to-peer lookup systems (such as Chord, CAN, Pastry, Tapestry, etc.). We evaluate the performance of data-centric storage and two other dissemination approaches in several sensornet scenarios and identify the conditions under which the various approaches are preferable.