To support network programming, we present Deluge, a reliable data dissemination protocol for propagating large data objects from one or more source nodes to many other nodes over a multihop, wireless sensor network. Deluge builds from prior work in density-aware, epidemic maintenance protocols. Using both a real-world deployment and simulation, we show that Deluge can reliably disseminate data to all nodes and characterize its overall performance. On Mica2dot nodes, Deluge can push nearly 90 bytes/second, oneninth the maximum transmission rate of the radio supported under TinyOS. Control messages are limited to 18 % of all transmissions. At scale, the protocol exposes interesting propagation dynamics only hinted at by previous dissemination work. A simple model is also derived which describes the limits of data propagation in wireless networks. Finally, we argue that the rates obtained for dissemination are inherently lower than that for single path propagation. It appears very hard to significantly improve upon the rate obtained by Deluge and we identify establishing a tight lower bound as an open problem.

of tiny networked devices that communicate untethered. For large scale networks it is important to be able to dynamically download code into the network. In this paper we present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.

Recent technological advances and the continuing quest for greater efficiency have led to an explosion of link and network protocols for wireless sensor networks. These protocols embody very different assumptions about network stack composition and, as such, have limited interoperability. It has been suggested [3] that, in principle, wireless sensor networks would benefit from a unifying abstraction (or “narrow waist ” in architectural terms), and that this abstraction should be closer to the link level than the network level. This paper takes that vague principle and turns it into practice, by proposing a specific unifying sensornet protocol (SP) that provides shared neighbor management and a message pool. The two goals of a unifying abstraction are generality and efficiency: it should be capable of running over a broad range of link-layer technologies and supporting a wide variety of network protocols, and doing so should not lead to a significant loss of efficiency. To investigate the extent to which SP meets these goals, we implemented SP (in TinyOS) on top of two very different radio technologies: B-MAC on mica2 and IEEE 802.15.4 on Telos. We also built a variety of network protocols on SP, including examples of collection routing [53], dissemination [26], and aggregation [33]. Measurements show that these protocols do not sacrifice performance through the use of our SP abstraction.

Augmenting heavy and power-hungry data collection equipment with lighter, smaller wireless sensor network nodes leads to faster,larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible,allowing scientific studies that aren’t feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors ’ sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands. Wireless sensor networks — in which numerous resource-limited nodes are linked via low-bandwidth wireless radios — have been the focus of intense research during the past few years. Since their conception, they’ve excited a range of scientific communities because of their potential to facilitate data acquisition and scientific studies. Collaborations between computer scientists and other domain scientists have produced networks that can record data at a scale and resolution not previously possible. Taking this progress one step further, wireless sensor networks can potentially advance the pursuit of geophysical studies of volcanic activity. Two years ago, our team of computer scientists at Harvard University began collaborating with volcanologists at the University of North Carolina, the University of New Hampshire, and the Instituto

The literature on programming sensor networks has, by and large, focused on providing higher-level abstractions for expressing local node behavior. Kairos is a natural next step in sensor network programming in that it allows the programmer to express, in a centralized fashion, the desired global behavior of a distributed computation on the entire sensor network. Kairos’ compile-time and runtime subsystems expose a small set of programming primitives, while hiding from the programmer the details of distributed code generation and instantiation, remote data access and management, and inter-node program flow coordination. Kairos ’ runtime is greatly simplified by assuming eventual consistency in node state; this assumption underlies many practical distributed computations proposed for sensor networks. In this paper, we describe Kairos ’ programming model, and the flexibility and robustness it affords programmers. We demonstrate its suitability, through actual implementation, for a variety of distributed programs—both infrastructure services and signal processing tasks—typically encountered in sensor network literature: routing tree construction, localization, and object tracking. Our experimental results suggest that Kairos does not adversely affect the performance or accuracy of distributed programs, while our implementation experiences suggest that it greatly raises the level of abstraction presented to the programmer.

Reprogramming of sensor networks is an important and challenging problem as it is often necessary to reprogram the sensors in place. In this paper, we propose a multihop reprogramming service designed for Mica-2/XSM motes. One of the problems in reprogramming is the issue of message collision. To reduce the problem of collision and hidden terminal problem, we propose a sender selection algorithm that attempts to guarantee that in a neighborhood there is at most one source transmitting the program at a time. Further, our sender selection is greedy in that it tries to select the sender that is expected to have the most impact. We also use pipelining to enable fast data propagation. MNP is energy efficient because it reduces the active radio time of a sensor node by putting the node into “sleep ” state when its neighbors are transmitting a segment that is not of interest. Finally, we argue that it is possible to tune our service according to the remaining battery level of a sensor, i.e., it can be tuned so that the probability that a sensor is given the responsibility of transmitting the code is proportional to its remaining battery life.

From experience with wireless sensor networks it has become apparent that dynamic reprogramming of the sensor nodes is a useful feature. The resource constraints in terms of energy, memory, and processing power make sensor network reprogramming a challenging task. Many different mechanisms for reprogramming sensor nodes have been developed ranging from full image replacement to virtual machines. We have implemented an in-situ run-time dynamic linker and loader that use the standard ELF object file format. We show that run-time dynamic linking is an effective method for reprogramming even resource constrained wireless sensor nodes. To evaluate our dynamic linking mechanism we have implemented an application-specific virtual machine and a Java virtual machine and compare the energy cost of the different linking and execution models. We measure the energy consumption and execution time overhead on real hardware to quantify the energy costs for dynamic linking. Our results suggest that while in general the overhead of a virtual machine is high, a combination of native code and virtual machine code provide good energy efficiency. Dynamic run-time linking can be used to update the native code, even in heterogeneous networks.

Sensor networks are notoriously difficult to program, given that they encompass the complexities of both distributed and embedded systems. To address this problem, we present the design and implementation of a declarative sensor network platform, DSN: a declarative language, compiler and runtime suitable for programming a broad range of sensornet applications. We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively – these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization. To our knowledge, this is the first time these disparate sensornet tasks have been addressed by a single high-level programming environment. Moreover, the declarative approach accommodates the desire for architectural flexibility and simple management of limited resources. Our results suggest that the declarative approach is well-suited to sensor networks, and that it can produce concise and flexible code by focusing on what the code is doing, and not on how it is doing it.

We present the philosophy, design, and initial evaluation of the Trio Testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

Abstract — We present an incremental network programming mechanism which reprograms wireless sensors quickly by transmitting the incremental changes for the new program version. Using the Rsync algorithm we generate the difference of the two program images, which allows us to distribute just the key changes of the program. Unlike previous approaches, our design does not assume any prior knowledge of the program code structure and can be applied to any hardware platform. To meet the resource constraints of wireless sensors we tuned the Rsync algorithm which was originally made for updating binary files among compuationally powerful machines. In our design, the sensor node processes the delivery and the decoding of the difference script in separate steps. This makes it easy to extend for multi-hop network programming. We are able to achieve the speedup of 9.1 for changing a constant and 2.1 to 2.5 for changing a few lines in the source code over the non-incremental delivery. I.