We study the problem of aggregating data from a sparse set of nodes in a wireless sensor network. This is a common situation when a sensor network is deployed to detect relatively rare events. In such situations, each node that should participate in the aggregation knows this fact based on its own sensor readings, but there is no global knowledge in the network of where all these interesting nodes are located. Instead of blindly querying all nodes in the network, we show how the interesting nodes can autonomously discover each other in a distributed fashion and form an ad hoc aggregation structure that can be used to compute cumulants, moments, or other statistical summaries. Key to our approach is the capability for two nodes that wish to communicate at roughly the same time to discover each other at a cost that is proportional to their network distance. We show how to build nearly optimal aggregation structures that can further deal with network volatility and compensate for the loss or duplication of data by exploiting probabilistic techniques.

Abstract — Sensor networks are multi-hop 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 article, 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. 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. I.

A SEA Swarm (Sensor Equipped Aquatic Swarm) moves as a group with water current and enables 4D (space and time) monitoring of local underwater events such as contaminants and intruders. For prompt alert reporting, mobile sensors forward events to mobile sinks (i.e., autonomous underwater vehicles) via geographic routing, thus requiring a location service. In this paper, we analyze various design choices to realize an efficient location service in a SEA Swarm. We find that conventional ad hoc network location service protocols cannot be directly used, because the entire swarm moves along water current. We show that maintaining location information in a 2D plane is an optimal design choice. Given this, we propose a bio-inspired location service called a Phero-Trail location service protocol. In Phero-Trail, location information is stored in a 2D upper hull of a SEA Swarm, and a mobile sink uses its trajectory (à la a pheromone trail of ants) projected to the 2D hull to maintain location information. This enables mobile sensors to efficiently locate a mobile sink via an expanding spiral curve search. Our preliminarily results show that Phero-Trail performs better than existing approaches. 1.

Abstract. The in-network aggregation and processing of information is what sets a sensor network apart from a pure data acquisition device. One way to model the exchange of information between the network nodes is to distinguish between nodes that are producers of information, i.e., those that have collected data, detected events, etc., and nodes that are consumers of information, i.e., nodes that seek data or events of certain types. In this paper we aim to support that exchange of information via a so-called information brokerage scheme. Main features of our proposed scheme are that 1) it works in a location-free setting where nodes are unaware of their geographic locations 2) it is robust to non-regular network topologies and 3) it does not require the information producers and consumers to know of each other. Our proposed scheme employs boundary detection algorithms which only quite recently have been developed to extract geometry and topology information even in location-free network deployments. 1

Abstract—Routing in wireless networks has been heavily studied in the last decade and numerous routing protocols were proposed in literature. The packets usually follow the shortest paths between sources and destinations in routing protocols to achieve smallest traveled distance. However, this leads to the uneven distribution of traffic load in a network. For example, wireless nodes in the center of the network will have heavier traffic since most of the shortest routes go through them. In this paper, we first describe a novel routing method, called Circular Sailing Routing (CSR), which can distribute the traffic more evenly in the network. The proposed method first maps the network onto a sphere via a simple stereographic projection, and then the route decision is made by the distance on the sphere instead of the Euclidean distance in the plane. We theoretically prove that for a network the distance traveled by the packets using CSR is no more than a small constant factor of the minimum (the distance of the shortest path). We then extend CSR to a localized version, Localized CSR, by modifying the greedy routing without any additional communication overhead. Finally, we further propose CSR protocols for 3D networks where nodes are distributed in a 3D space instead of a 2D plane. For all proposed methods, we conduct simulations to study their performances and compare them with global shortest path routing or greedy routing. I.

Abstract—The high mobility of VANET makes information exchange across the network excessively difficult. Traditional approaches designed for stationary networks are not applicable due to the high dynamics among the nodes. Applying the routing techniques tailored for general mobile networks inevitably brings huge traffic burden to the crowded urban VANET and leads to low efficiency. To make the information exchange fluent and efficient, we explore the unique features of the urban VANET. By chasing the invariants in the mobile network topology, we are able to efficiently manage the information on top of the “intersection graph ” transformed from the underlying network of road segments in the urban area. Our approach can thus achieve efficient query dissemination and data retrieval on this information organization. This approach is further extended to provide efficient end-to-end routing among vehicles with much reduced network overhead. We conduct a trace based study to fully adapt our design to the practical conditions in the real circumstance. We intensively investigate and analyze a trace that records the movement of more than 4000 taxies in the urban area of Shanghai City over several months. We grasp the key impact of the fundamental factors that affect the VANET behaviors and accordingly develop tailored techniques to maximize the performance of this design. Experimental results validate the effectiveness and efficiency of our design. Compared with those approaches for general mobile networks, we believe this design best suits the urban VANET environment. Keywords—Urban VANET; information exchange; routing; road map 1

Geographical hash table (GHT) has been widely used to provide energy efficiency for data-centric storage in wireless sensor networks. Such a mechanism, however, suffers from high communication cost when we apply multi-dimensional event search in the network. In this work, we present MDS, a flexible, complete, and efficient multi-dimensional search mechanism atop traditional GHT based data-centric storage architecture. MDS utilizes bloom filters to reduce the communication cost of in-network intersection and union operations for multi-dimensional queries in wireless sensor networks. This scheme can be easily extended to support multi-dimensional range queries. Our mathematical analysis indicates the optimal settings for the bloom filters that maximize the traffic savings according to the information popularities. We conduct comprehensive simulations to evaluate our design. Results show that MDS achieves significant performance improvement in terms of energy consumptions and thus improves the applicability of the multidimensional search over the GHT based data-centric storage in sensor networks.

Abstract—The high mobility of VANET makes information exchange across the network excessively difficult. Traditional approaches designed for stationary networks are not applicable due to the high dynamics among the nodes. Applying the routing techniques tailored for general mobile networks inevitably brings huge traffic burden to the crowded urban VANET and leads to low efficiency. To make the information exchange fluent and efficient, we explore the unique features of the urban VANET. By exploring the invariants in the mobile network topology, we are able to efficiently manage the information on top of the “intersection graph ” transformed from the underlying network of road segments in the urban area. Our approach can thus achieve efficient query dissemination and data retrieval on this information organization. We intensively investigate and analyze a trace that records the movement of more than 4000 taxies in the urban area of Shanghai City over several months. We grasp the key impact of the fundamental factors that affect the VANET behaviors and accordingly develop tailored techniques to maximize the performance of this design. Experimental results validate the effectiveness and efficiency of our design. I.

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Abstract—In this paper we propose an algorithm to construct a “space filling ” curve for a sensor network with holes. Mathematically, for a given multi-hole domain R, we generate a path P that is provably aperiodic (i.e., any point is covered at most a constant number of times) and dense (i.e., any point of R is arbitrarily close to P). In a discrete setting as in a sensor network, the path visits the nodes with progressive density, which can adapt to the budget of the path length. Given a higher budget, the path covers the network with higher density. With a lower budget the path becomes proportional sparser. We show how this density-adaptive space filling curve can be useful for applications such as serial data fusion, motion planning for data mules, sensor node indexing, and double ruling type in-network data storage and retrieval. We show by simulation results the superior performance of using our algorithm vs standard space filling curves and random walks. I.