Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.

Abstract. The widespread dissemination of small-scale sensor nodes has sparked interest in a powerful new database abstraction for sensor networks: Clients “program” the sensors through queries in a high-level declarative language permitting the system to perform the low-level optimizations necessary for energy-efficient query processing. In this paper we consider multi-query optimization for aggregate queries on sensor networks. We develop a set of distributed algorithms for processing multiple queries that incur minimum communication while observing the computational limitations of the sensor nodes. Our algorithms support incremental changes to the set of active queries and allow for local repairs to routes in response to node failures. A thorough experimental analysis shows that our approach results in significant energy savings, compared to previous work. 1

Abstract — Existing solutions to achieve load balancing in DHTs incur a high overhead either in terms of routing state or in terms of load movement generated by nodes arriving or departing the system. In this paper, we propose a set of general techniques and use them to develop a protocol based on Chord, called Y0, that achieves load balancing with minimal overhead under the typical assumption that the load is uniformly distributed in the identifier space. In particular, we prove that Y0 can achieve near-optimal load balancing, while moving little load to maintain the balance, and increasing the size of the routing tables by at most a constant factor. Using extensive simulations based on real-world and synthetic capacity distributions, we show that Y0 reduces the load imbalance of Chord from O(log n) to a less than 4 without increasing the number of links that a node needs to maintain. In addition, we study the effect of heterogeneity on both DHTs, demonstrating significantly reduced average route length as node capacities become increasingly heterogeneous. For a real-word distribution of node capacities, the route length in Y0 is asymptotically less than half the route length in the case of a homogeneous system. Index Terms — System design, Simulations I.

An overall sensornet architecture would help tame the increasingly complex structure of wireless sensornet software and help foster greater interoperability between different codebases. A previous step in this direction is the Sensornet Protocol (SP), a unifying link-abstraction layer. This paper takes the natural next step by proposing a modular network-layer for sensornets that sits atop SP. This modularity eases implementation of new protocols by increasing code reuse, and enables co-existing protocols to share and reduce code and resources consumed at run-time. We demonstrate how current protocols can be decomposed into this modular structure and show that the costs, in performance and code footprint, are minimal relative to their monolithic counterparts. 1

Recent work in sensor databases has focused extensively on distributed query problems, notably distributed computation of aggregates. Existing methods for computing aggregates broadcast queries to all sensors and use in-network aggregation of responses to minimize messaging costs. In this work, we focus on uniform random sampling across nodes, which can serve both as an alternative building block for aggregation and as an integral component of many other useful randomized algorithms. Prior to our work, the best existing proposals for uniform random sampling of sensors involve contacting all nodes in the network. We propose a practical method which is only approximately uniform, but contacts a number of sensors proportional to the diameter of the network instead of its size. The approximation achieved is tunably close to exact uniform sampling, and only relies on well-known existing primitives, namely geographic routing, distributed computation of Voronoi regions and von Neumann’s rejection method. Ultimately, our sampling algorithm has the same worst-case asymptotic cost as routing a point-to-point message, and thus it is asymptotically optimal among request/reply-based sampling methods. We provide experimental results demonstrating the effectiveness of our algorithm on both synthetic and real sensor topologies. 1

This paper proposes a new sensornet protocol design goal: visibility. Visibility into behaviors at the network level will simplify debugging and ease the development process. We argue that increasing visibility is the responsibility of the network protocols themselves, and not solely the responsibility of existing debugging tools. We describe a quantitative visibility metric to evaluate and compare protocols, where visibility is defined as the energy cost of diagnosing the cause of a behavior in a protocol. The design and evaluation of Pull Collection Protocol, a novel multi-hop collection protocol, is an example of how to design for visibility without sacrificing throughput or node-level fairness. We also describe our optimizations for an existing protocol, Deluge, to increase its visibility and efficiency. 1

Abstract—The problem of computing functions of values at the nodes in a network in a fully distributed manner, where nodes do not have unique identities and make decisions based only on local information, has applications in sensor, peer-to-peer, and adhoc networks. The task of computing separable functions, which can be written as linear combinations of functions of individual variables, is studied in this context. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend in general to the computation of the actual values of separable functions. The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions. The running time of the algorithm is shown to depend on the running time of a minimum computation algorithm used as a subroutine. Using a randomized gossip mechanism for minimum computation as the subroutine yields a complete fully distributed algorithm for computing separable functions. For a class of graphs with small spectral gap, such as grid graphs, the time used by the algorithm to compute averages is of a smaller order than the time required by a known iterative averaging scheme. Index Terms—Data aggregation, distributed algorithms, gossip algorithms, randomized algorithms. I.

Recently, there has been a growing interest in gossip-based protocols that employ randomized communication to ensure robust information dissemination. In this paper, we present a novel gossip-based scheme using which all the nodes in an n-node overlay network can compute the common aggregates of MIN, MAX, SUM, AVERAGE, and RANK of their values using O(n log log n) messages within O(log n log log n) rounds of communication. To the best of our knowledge, ours is the first result that shows how to compute these aggregates with high probability using only O(n log log n) messages. In contrast, the best known gossip-based algorithm for computing these aggregates requires O(n log n) messages and O(log n) rounds. Thus, our algorithm allows system designers to trade off a small increase in round complexity with a significant reduction in message complexity. This can lead to dramatically lower network congestion and longer node lifetimes in wireless and sensor networks, where channel bandwidth and battery life are severely constrained. 1.

This paper introduces a new consistency metric, Network Imprecision (NI), to address a central challenge in largescale monitoring systems: safeguarding correctness despite node and network failures. To implement NI, an overlay that monitors a set of attributes also monitors its own state so that queries return not only attribute values but also information about the stability of the overlay—the number of nodes whose recent updates may be missing and the number of nodes whose inputs may be double counted due to overlay reconfigurations. When NI indicates that the network is stable, query results reflect the true state of the system, but when the network is unstable, NI puts applications on notice that query results should not be trusted, allowing them to take corrective action such as filtering out inconsistent results. To implement NI’s introspection scalably, our prototype introduces a key optimization, dual-tree prefix aggregation, which exploits overlay symmetry to reduce overheads by more than an order of magnitude. Evaluation of three monitoring applications demonstrates that NI flags inaccurate results while incurring low overheads, and monitoring applications that use NI to select good information can reduce their inaccuracy by nearly a factor of five.

Many emerging sensor network applications involve mobile nodes with communication patterns requiring any-to-any routing topologies. We should be able to build upon the MANET work to implement these systems. However, translating these protocols into real implementations on resource-constrained sensor nodes raises a number of challenges. In this paper, we present the lessons learned from implementing one such protocol, Adaptive Demand-driven Multicast Routing (ADMR), on CC2420-based motes using the TinyOS operating system. ADMR was chosen because it supports multicast communication, a critical requirement for many pervasive and mobile applications. To our knowledge, ours is the first non-simulated implementation of ADMR. Through extensive measurement on Motelab, we present the performance of the implementation, TinyADMR, under a wide range of conditions. We highlight the real-world impact of path selection metrics, radio link asymmetry, protocol overhead, and limited routing table size. Categories and Subject Descriptors: