In traditional routing, the routing tables store shortest paths to all other destinations and have size linear in the size of the network, which is not scalable for resource-constrained networks such as wireless sensor networks. In this article we show that by storing selectively a much smaller set of routing paths in the routing tables one can get low-stretch, compact routing schemes. Our routing scheme includes an approximate distance oracle with which one can obtain approximate shortest path length estimates to destinations. This distance oracle can be obtained, for example, by a landmark-based scheme, or in case of sensor networks, from the geographic distance between node locations. With an approximate distance oracle one can attempt greedy routing by forwarding to the neighbor whose estimate is closer to the destination. But there is no guarantee of delivery nor of the routing path length. We augment the distance oracle by storing, for each node u , routing paths to O (log 2 n ) strategically selected nodes that serve as intermediate destinations. These nodes are selected with probability proportional to 1/ r ρ , where r is the distance to u and ρ is a suitable constant for the network. Then we derive a set of sufficient conditions to select the next step at each stage of routing, such that these conditions can be verified locally and guarantee 1+ε stretch routing on any metric. These conditions serve as the “greedy routing” or local decision rule. On graphs of bounded growth, our scheme guarantees 1+ε stretch routing with high probability, with an average routing table size of O (√n log 2 n ). This scheme is favorable for its simplicity, generality, and blindness to any global state. It demonstrates that global routing properties could emerge from purely distributed and uncoordinated routing table design.

In this article we propose a lightweight algorithm for constructing multiresolution data representations for sensor networks. At each sensor node u , we compute O (log n ) aggregates about exponentially enlarging neighborhoods centered at u . The i th aggregate is the aggregated data from nodes approximately within 2 i hops of u . We present a scheme, named the hierarchical spatial gossip algorithm , to extract and construct these aggregates, for all sensors simultaneously, with a total communication cost of O ( n polylog n ). The hierarchical gossip algorithm adopts atomic communication steps with each node choosing to exchange information with a node distance d away with probability ∼ 1/ d 3 . The attractiveness of the algorithm can be attributed to its simplicity, low communication cost, distributed nature, and robustness to node failures and link failures. We show in addition that computing multiresolution aggregates precisely (i.e., each aggregate uses all and only the nodes within 2 i hops) requires a communication cost of Ω( n √ n ), which does not scale well with network size. An approximate range in aggregate computation like that introduced by the gossip mechanism is therefore necessary in a scalable efficient algorithm. Besides the natural applications of multiresolution data summaries in data validation and information mining, we also demonstrate the application of the precomputed multiresolution data summaries in answering range queries efficiently.

We study the localization problem in sensor networks by using local angle measurement. Localization using local angle information was recently proposed as an effective localization technique, which can be used for geographical routing with guaranteed delivery. However, the existing approach is based on linear programming (LP) and can not be implemented distributedly. We propose, design, and evaluate DAL: a purely distributed localization protocol in sensor networks using local angle measurement. Localization with local angle poses unique challenge in sensor networks due to information uncertainties identified in this paper. DAL specifically addresses these challenges. Via extensive simulations using ns2 and our own simulator, we show that the performance of DAL is comparable with that of the centralized LP approach in most cases. Our preliminary results with noisy angle measurement show that DAL keeps the global geometry of the sensor network fairly well.

Developing powerful paradigms for programming sensor networks is critical to realize the full potential of sensor networks as collaborative data processing engines. In this article, we motivate and develop a deductive framework for programming sensor networks, extending the prior vision of viewing sensor network as a distributed database. The deductive programming approach is declarative, very expressive, and amenable to automatic optimizations. Such a framework allows users to program sensor network applications at a high-level without worrying about the low-level tedious details. Our system translates a given deductive program to efficient distributed code that runs on individual nodes. To facilitate the above translation, we develop techniques for distributed and asynchronous evaluation of deductive programs in sensor networks. Our techniques generalize to recursive programs without negations, arbitrary non- recursive programs with negations, and in general to arbitrary "locally non-recursive" programs with function symbols. We present performance results on TOSSIM, a network simulator, and a small network testbed.

We propose a generic routing table design principle for scalable routing on networks with bounded geometric growth. Given an inaccurate distance oracle that estimates the graph distance of any two nodes with constant factor upper and lower bounds, we augment it by storing the routing paths of pairs of nodes, selected in a spatial distribution, and show that the routing table enables 1 + epsiv stretch routing. In the wireless ad hoc and sensor network scenario, the geographic locations of the nodes serve as such an inaccurate distance oracle. Each node p selects O (log n loglog n) other nodes from a distribution proportional to 1/r2where r is the distance to p and the routing paths to these nodes are stored on the nodes along these paths in the network. The routing algorithm selects links conforming to a set of sufficient conditions and guarantees with high probability 1 + epsiv stretch routing with routing table size O(radicn log n loglog n) on average for each node. This scheme is favorable for its simplicity, generality and blindness to any global state. It is a good example that global routing properties emerge from purely distributed and uncoordinated routing table design.

We are interested in topological analysis and processing of the large-scale distributed data generated by sensor networks. Naturally, a large-scale sensor network is deployed in a geometric region with possibly holes and complex shape, and is used to sample some smooth physical signal field. We are interested in both the topology of the discrete sensor field in terms of the sensing holes (voids without sufficient sensors deployed), as well as the topology of the signal field in terms of its critical points (local maxima, minima and saddles). Towards this end, we develop distributed algorithms to construct the Morse-Smale decomposition, and study the performance benefits obtained by this approach. The sensor field is decomposed into simply-connected pieces, inside each of which the sensor signal is homogeneous, i.e., the data flows uniformly from a local maximum to a local minimum. The Morse-Smale decomposition can be efficiently constructed in the network locally, after which applications such as iso-contour queries, data-guided navigation and routing, data aggregation, and topologically faithful signal reconstructions benefit tremendously from it.

Sensor networks are multihop wireless networks of resource-constrained sensor nodes used to realize high-level collaborative sensing tasks. To query or access data generated by the sensor nodes, the sensor network can be viewed as a distributed database. In this paper, we develop algorithms for communication-efficient implementation of join of multiple (two or more) data streams in a sensor network. The distributed implementation of join in sensor networks is particularly challenging due to unique characteristics of the sensor networks such as limited memory and battery energy on individual nodes, arbitrary and dynamic network topology, multihop communication, and unreliable infrastructure. One of our proposed approaches, viz., the perpendicular approach (PA), is load balanced, and in fact, incurs near-optimal communication cost for the special case of binary joins in grid networks under the assumption of uniform generation of tuples across the network. We compare the performance of our designed approaches through extensive simulations on the ns2 simulator, and show that PA results in substantially prolonging the network lifetime compared to other approaches, especially for joins involving spatial constraints.

The diversity of the deployment settings of sensor networks is naturally inherited from the diversity of geographical features of the embedded environment, and greatly influences network design. Many sensor network protocols in the literature implicitly assume that sensor nodes are deployed inside a simple geometric region, without considering possible obstacles and holes in the deployment environment. When the real deployment setting deviates from that, we often observe degraded performance. Thus, it is highly desirable to have a generic approach to handle sensor fields with complex shapes. In this article, we propose a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that algorithms and protocols that assume a nice sensor field can be applied inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. Our segmentation algorithm does not require any extra knowledge (e.g., sensor locations) and only uses network connectivity information. This unified spatial-partitioning approach makes the protocol design become flexible and independent of deployment specifics. Existing protocols are still reusable with segmentation, and the development of new topology-adaptive protocols becomes much easier. We verified the correctness of the algorithm on various topologies and evaluated the performance improvements by integrating shape segmentation with several fundamental problems in network design.

Abstract—We study the problem of data-driven routing and navigation in a distributed sensor network over a continuous scalar field. Specifically, we address the problem of searching for the collection of sensors with readings within a specified range. This is named the iso-contour query problem. We develop a gradient based routing scheme such that from any query node, the query message follows the signal field gradient or derived quantities and successfully discovers all iso-contours of interest. Due to the existence of local maxima and minima, the guaranteed delivery requires preprocessing of the signal field and the construction of a contour tree in a distributed fashion. Our approach has the following properties: (i) the gradient routing uses only local node information and its message complexity is close to optimal, as shown by simulations; (ii) the preprocessing message complexity is linear in the number of nodes and the storage requirement for each node is a small constant. The same preprocessing also facilitates route computation between any pair of nodes where the the route lies within any user supplied range of values. I.

We study the problem of contour tracking with binary sensors, an important problem for monitoring spatial signals and tracking group targets. In particular, we track the boundaries of the blobs of interest and capture the topological changes as the blobs merge or split. Only the nodes on the boundaries of these deformable blobs stay active and the repair cost is proportional to the size of the contour changes. Our algorithm is completely distributed, requires only local information, and yet captures the global topological properties. The algorithm performs a fundamental monitoring function and is a foundation for further information processing of spatial sensor data.

Manycast is a group communication primitive wherein the source is required to send data packets to a certain number of a given set of destinations. In this article, we design fault-tolerant protocols for manycast operations in sensor networks with mobile destinations. To develop efficient protocols, we propose a location management scheme, which manages information about the locations of mobile destinations in a distributed manner. Based upon that, we develop rectangle-based and GridTree-based fault-tolerant manycast routing protocols. Simulation results show that the GridTree approach achieves sufficiently high success ratio, while using minimal transmission cost.

Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close-by object by reading a tag device attached to the object. To improve coverage, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of slotted scheduled access of RFID tags in a multiple reader environment. In particular, we develop centralized algorithms in a slotted time model to read all the tags using near-optimal number of time slots. We consider two scenarios -one wherein the tag distribution in the physical space is unknown, and the other where tag distribution is known or can be estimated a priori. For each of these scenarios, we consider two cases depending on whether a single channel or multiple channels are available. All the above version of the problem are NP-hard. We design approximation algorithms for the single channel and heuristic algorithms for the multiple channel cases. Through extensive simulations, we show that for the single channel case, our heuristics perform close to the approximation algorithms. In general, our simulations show that our algorithms significantly outperform colorwave, an existing algorithm for similar problems.

Many sensor network protocols in the literature implicitly assume that sensor nodes are deployed uniformly inside a simple geometric region. When the real deployment deviates from that, we often observe degraded performance. It is desirable to have a generic approach to handle a sensor field with complex shape. In this paper, we propose a segmentation algorithm that partitions an irregular sensor field into nicely shaped pieces such that algorithms and protocols that assume a nice sensor field can be applied inside each piece. Across the segments, problem dependent structures specify how the segments and data collected in these segments are integrated. This unified topology-adaptive spatial partitioning would benefit many settings that currently assume a nicely shaped sensor field. Our segmentation algorithm does not require sensor locations and only uses network connectivity information. Each node is given a 'flow direction' that directs away from the network boundary. A node with no flow direction becomes a sink, and attracts other nodes in the same segment. We evaluate the performance improvements by integrating shape segmentation with applications such as distributed indices and random sampling.

In this paper we propose a lightweight algorithm for constructing multi-resolution data representations for sensor networks. We compute, at each sensor node u, O(log n) aggregates about exponentially enlarging neighborhoods centered at u. The ith aggregate is the aggregated data among nodes approximately within 2i hops of u. We present a scheme, named the hierarchical spatial gossip algorithm, to extract and construct these aggregates, for all sensors simultaneously, with a total communication cost of O(n polylog n). The hierarchical gossip algorithm adopts atomic communication steps with each node choosing to exchange information with a node distance d away with probability 1 /d3. The attractiveness of the algorithm attributes to its simplicity, low communication cost, distributed nature and robustness to node failures and link failures. Besides the natural applications of multi-resolution data summaries in data validation and information mining, we also demonstrate the application of the pre-computed spatial multi-resolution data summaries in answering range queries efficiently.

We study the problem of information brokerage in sensor networks, where information consumers (sinks,users)search for data acquired by information producers (sources). In-network storage such as geographical hash table (GHTs) has been proposed to store data at rendezvous nodes for consumers to retrieve. In this paper, we propose a double rulings scheme which stores data replica at a curve instead of one or multiple isolated sensors. The consumer travels along another curve which guarantees to intersect with the producer curve. The double rulings is a natural extension of the flat hashing scheme such as GHTs with improved query locality, i.e., consumers close to producers find the data quickly, and structured aggregate queries, i.e., a consumer following a curve is able to retrieve all the data. Further, by the flexibility of retrieval mechanisms we have better routing robustness and data robustness. We show by simulation that the double rulings scheme provide reduced communication costs and more balanced traffic load on the sensors.