Showing papers in "ACM Transactions on Sensor Networks in 2006"

LEAP+: Efficient security mechanisms for large-scale distributed sensor networks

Abstract: We describe LEAPp (Localized Encryption and Authentication Protocol), a key management protocol for sensor networks that is designed to support in-network processing, while at the same time restricting the security impact of a node compromise to the immediate network neighborhood of the compromised node. The design of the protocol is motivated by the observation that different types of messages exchanged between sensor nodes have different security requirements, and that a single keying mechanism is not suitable for meeting these different security requirements. LEAPp supports the establishment of four types of keys for each sensor node: an individual key shared with the base station, a pairwise key shared with another sensor node, a cluster key shared with multiple neighboring nodes, and a global key shared by all the nodes in the network. LEAPp also supports (weak) local source authentication without precluding in-network processing. Our performance analysis shows that LEAPp is very efficient in terms of computational, communication, and storage costs. We analyze the security of LEAPp under various attack models and show that LEAPp is very effective in defending against many sophisticated attacks, such as HELLO flood attacks, node cloning attacks, and wormhole attacks. A prototype implementation of LEAPp on a sensor network testbed is also described.

Semidefinite programming based algorithms for sensor network localization

Abstract: An SDP relaxation based method is developed to solve the localization problem in sensor networks using incomplete and inaccurate distance information. The problem is set up to find a set of sensor positions such that given distance constraints are satisfied. The nonconvex constraints in the formulation are then relaxed in order to yield a semidefinite program that can be solved efficiently.The basic model is extended in order to account for noisy distance information. In particular, a maximum likelihood based formulation and an interval based formulation are discussed. The SDP solution can then also be used as a starting point for steepest descent based local optimization techniques that can further refine the SDP solution.We also describe the extension of the basic method to develop an iterative distributed SDP method for solving very large scale semidefinite programs that arise out of localization problems for large dense networks and are intractable using centralized methods.The performance evaluation of the technique with regard to estimation accuracy and computation time is also presented by the means of extensive simulations.Our SDP scheme also seems to be applicable to solving other Euclidean geometry problems where points are locally connected.

Distributed weighted-multidimensional scaling for node localization in sensor networks

Abstract: Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cramer-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.

VigilNet: An integrated sensor network system for energy-efficient surveillance

Abstract: This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energy-efficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such an integrated sensor system.

Models and solutions for radio irregularity in wireless sensor networks

Abstract: In this article, we investigate the impact of radio irregularity on wireless sensor networks. Radio irregularity is a common phenomenon that arises from multiple factors, such as variance in RF sending power and different path losses, depending on the direction of propagation. From our experiments, we discover that the variance in received signal strength is largely random; however, it exhibits a continuous change with incremental changes in direction. With empirical data obtained from the MICA2 and MICAZ platforms, we establish a radio model for simulation, called the Radio Irregularity Model (RIM). This model is the first to bridge the discrepancy between the spherical radio models used by simulators and the physical reality of radio signals. With this model, we investigate the impact of radio irregularity on several upper layer protocols, including MAC, routing, localization and topology control. Our results show that radio irregularity has a relatively larger impact on the routing layer than the MAC layer. It also shows that radio irregularity leads to larger localization errors and makes it harder to maintain communication connectivity in topology control. To deal with these issues, we present eight solutions to deal with radio irregularity. We evaluate three of them in detail. The results obtained from both the simulations and a running testbed demonstrate that our solutions greatly improve system performance in the presence of radio irregularity.

Survey and benchmark of block ciphers for wireless sensor networks

Abstract: Cryptographic algorithms play an important role in the security architecture of wireless sensor networks (WSNs). Choosing the most storage- and energy-efficient block cipher is essential, due to the facts that these networks are meant to operate without human intervention for a long period of time with little energy supply, and that available storage is scarce on these sensor nodes. However, to our knowledge, no systematic work has been done in this area so far. We construct an evaluation framework in which we first identify the candidates of block ciphers suitable for WSNs, based on existing literature and authoritative recommendations. For evaluating and assessing these candidates, we not only consider the security properties but also the storage- and energy-efficiency of the candidates. Finally, based on the evaluation results, we select the most suitable ciphers for WSNs, namely Skipjack, MISTY1, and Rijndael, depending on the combination of available memory and required security (energy efficiency being implicit). In terms of operation mode, we recommend Output Feedback Mode for pairwise links but Cipher Block Chaining for group communications.

Redundancy and coverage detection in sensor networks

Abstract: We study the problem of detecting and eliminating redundancy in a sensor network with a view to improving energy efficiency, while preserving the network's coverage. We also examine the impact of redundancy elimination on the related problem of coverage-boundary detection. We reduce both problems to the computation of Voronoi diagrams, prove and achieve lower bounds on the solution of these problems, and present efficient distributed algorithms for computing and maintaining solutions in cases of sensor failures or insertion of new sensors. We prove the correctness and termination properties of our distributed algorithms, and analytically characterize the time complexity and traffic generated by our algorithms. Using detailed simulations, we also quantify the impact of system parameters such as sensor density, transmission range, and failure rates on network traffic.

Stochastic coverage in heterogeneous sensor networks

Abstract: We study the problem of coverage in planar heterogeneous sensor networks. Coverage is a performance metric that quantifies how well a field of interest is monitored by the sensor deployment. To derive analytical expressions of coverage for heterogeneous sensor networks, we formulate the coverage problem as a set intersection problem, a problem studied in integral geometry. Compared to previous analytical results, our formulation allows us to consider a network model where sensors are deployed according to an arbitrary stochastic distribution; sensing areas of sensors need not follow the unit disk model but can have any arbitrary shape; sensors need not have an identical sensing capability. Furthermore, our formulation does not assume deployment of sensors over an infinite plane and, hence, our derivations do not suffer from the border effect problem arising in a bounded field of interest. We compare our theoretical results with the spatial Poisson approximation that is widely used in modeling coverage. By computing the Kullback-Leibler and total variation distance between the probability density functions derived via our theoretical results, the Poisson approximation, and the simulation, we show that our formulas provide a more accurate representation of the coverage in sensor networks. Finally, we provide examples of calculating network parameters such as the network size and sensing range in order to achieve a desired degree of coverage.

A simple improved distributed algorithm for minimum CDS in unit disk graphs

Abstract: Several routing schemes in ad hoc networks first establish a virtual backbone and then route messages via backbone nodes. One common way of constructing such a backbone is based on the construction of a connected dominating set (CDS). In this article we present a very simple distributed algorithm for computing a small CDS. Our algorithm has an approximation factor of at most 6.91, improving upon the previous best-known approximation factor of 8 due to Wan et al. [2002]. The improvement relies on a refined analysis of the relationship between the size of a maximal independent set and a minimum CDS in a unit disk graph. This subresult also implies improved approximation factors for many existing algorithm.

Modeling spatially correlated data in sensor networks

Abstract: The physical phenomena monitored by sensor networks, for example, forest temperature or water contamination, usually yield sensed data that are strongly correlated in space. With this in mind, researchers have designed a large number of sensor network protocols and algorithms that attempt to exploit such correlations.There is an increasing need to synthetically generate large traces of spatially correlated data representing a wide range of conditions to carefully study the performance of these algorithms. Further, a mathematical model for generating synthetic traces would provide guidelines for designing more efficient algorithms. These reasons motivate us to obtain a simple and accurate model of spatially correlated sensor network data.The proposed model is Markovian in nature and can capture correlation in data irrespective of the node density, the number of source nodes, or the topology. We describe a rigorous mathematical procedure and a simple practical method to extract the model parameters from real traces. We also show how to efficiently generate synthetic traces on a given topology using these parameters. The correctness of the model is verified by statistically comparing synthetic and real data. Further, the model is validated by comparing the performance of algorithms whose behavior depends on the degree of spatial correlation in data, under real and synthetic traces. The real traces are obtained from remote sensing data, publicly available sensor data, and sensor networks that we deploy. We show that the proposed model is more general and accurate than the commonly used jointly Gaussian model. Finally, we create tools that can be easily used by researchers to synthetically generate traces of any size and degree of correlation.

Scalable logical coordinates framework for routing in wireless sensor networks

Abstract: In this article, we present logical coordinates based routing (LCR), a novel framework for scalable and location-independent routing in wireless sensor networks. LCR assigns each node a logical coordinate vector, and routes packets following these vectors. We demonstrate that LCR (i) guarantees packet delivery with a high probability, (ii) finds good paths, and (iii) exhibits robust performance in the presence of network voids and node failures. We systematically evaluate the performance of LCR through simulations and compare it with other state-of-the-art protocols. We also propose two extensions of LCR, one for three-dimensional node deployments and the other for unreliable wireless links.

Distributed metric calibration of ad hoc camera networks

Abstract: We discuss how to automatically obtain the metric calibration of an ad hoc network of cameras with no centralized processor. We model the set of uncalibrated cameras as nodes in a communication network, and propose a distributed algorithm in which each camera performs a local, robust bundle adjustment over the camera parameters and scene points of its neighbors in an overlay “vision graph.” We analyze the performance of the algorithm on both simulated and real data, and show that the distributed algorithm results in a fairer allocation of messages per node while achieving comparable calibration accuracy to centralized bundle adjustment.

Communication power optimization in a sensor network with a path-constrained mobile observer

Abstract: We present a procedure for communication power optimization in a network of randomly distributed sensors with an observer (data collector) moving on a fixed path. The key challenge in using a mobile observer is that it remains within communication range of any sensor for a brief duration, and inability to transfer data in this duration leads to data loss. We establish that the process of data collection can be modeled by a queue with deadlines, where arrivals correspond to the observer entering the range of a sensor and a missed deadline means data loss. The queuing model is then used to identify the combination of system parameters that ensures adequate data collection with minimum power. The results obtained from the queuing analogy take a simple form in the asymptotic regime of dense sensor networks. Additionally, for sensor networks that cannot tolerate data loss, we derive a tight bound on minimum sensor separation that ensures that no data will be lost on account of mobility. We present two examples to illustrate our results, from which it is seen that power reduction by two orders of magnitude or more is typical relative to a static sensor network. The scenarios chosen for power comparisons also provide guidelines on the choice of path, if such a choice is available.

Power-efficient sensor placement and transmission structure for data gathering under distortion constraints

Abstract: We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use either joint entropy coding based on explicit communication between sensor nodes, where coding is done when side information is available, or Slepian-Wolf coding where nodes have knowledge of network correlation statistics. We consider both maximum and average distortion bounds. We prove that this optimization is NP-complete since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds.We address this problem by first looking at the simplified problem of optimal placement in the one-dimensional case. An analytical solution is derived for the case when there is a simple aggregation scheme, and numerical results are provided for the cases when joint entropy encoding is used. We use the insight from our 1-D analysis to extend our results to the 2-D case and compare it to typical uniform random placement and shortest-path tree. Our algorithm for two-dimensional placement and transmission structure provides two to three fold reduction in total power consumption and between one to two orders of magnitude reduction in bottleneck power consumption. We perform an exhaustive performance analysis of our scheme under varying correlation models and model parameters and demonstrate that the performance improvement is typical over a range of data correlation models and parameters. We also study the impact of performing computationally-efficient data conditioning over a local scope rather than the entire network. Finally, we extend our explicit placement results to a randomized placement scheme and show that such a scheme can be effective when deployment does not permit exact node placement.

Dynamic data fusion for future sensor networks

Abstract: DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.

Localized topology control for heterogeneous wireless sensor networks

Abstract: This article studies topology control in heterogeneous wireless sensor networks, where different wireless sensors may have different maximum transmission ranges and two nodes can communicate directly with each other if and only if they are within the maximum transmission range of each other. We present several localized topology control strategies in which every wireless sensor maintains logical communication links to only a selected small subset of its physical neighbors using information of sensors within its local neighborhood in a heterogeneous network environment. We prove that the global logical network topologies formed by these locally selected links are sparse and/or power efficient and our methods are communication efficient. Here a structure is power efficient if the total power consumption of the least cost path connecting any two nodes in it is no more than a small constant factor of that in the original heterogeneous communication network. By utilizing the wireless broadcast channel capability, and assuming that a message sent by a sensor node will be received by all sensors within its transmission region with at most a constant number of transmissions, we prove that all our methods use at most O(n) total messages, where each message has O(log n) bits. We also conduct extensive simulations to study the practical performance of our methods.

Decentralized energy-conserving and coverage-preserving protocols for wireless sensor networks

Abstract: In this article, we propose several decentralized protocols that schedule sensors' active and sleeping periods to prolong the network lifetime while maintain the sensing field sufficiently covered. The proposed protocols are based on a model similar to that of Yan et al. [2003], but improve its results in several senses. First, our approach can significantly reduce the computational complexity incurred, and at the same time achieve better accuracy in determining the coverage of the sensing area. Second, we extend the result such that it can support multilayer coverage of the sensing field. Third, we further enhance it [Yan et al. 2003] by proposing several optimization mechanisms to balance or reduce sensors' energy expenditure.

Optimal real-time sampling rate assignment for wireless sensor networks

Abstract: How to allocate computing and communication resources in a way that maximizes the effectiveness of control and signal processing, has been an important area of research. The characteristic of a multi-hop Real-Time Wireless Sensor Network raises new challenges. First, the constraints are more complicated and a new solution method is needed. Second, a distributed solution is needed to achieve scalability. This article presents solutions to both of the new challenges. The first solution to the optimal rate allocation is a centralized solution that can handle the more general form of constraints as compared with prior research. The second solution is a distributed version for large sensor networks using a pricing scheme. It is capable of incremental adjustment when utility functions change. This article also presents a new sensor device/network backbone architecture---Real-time Independent CHannels (RICH), which can easily realize multi-hop real-time wireless sensor networking.

VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network

Abstract: We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-based vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the “bumpiness” of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is fully functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.

A prediction error-based hypothesis testing method for sensor data acquisition

Abstract: We present a statistical method that uses prediction modeling to decrease the temporally redundant data transmitted back to the sink. The major novelties are fourfold: First, a prediction model is fit to the sensor data. Second, prediction error is utilized to adaptively update the model parameters using hypothesis testing. Third, a data transformation is proposed to bring the sensor sample series closer to weak stationarity. Finally, an efficient implementation is presented. We show that our proposed preDiction eRror bASed hypoThesis testInG (DRASTIG) method achieves low energy dissipation while keeping the prediction errors at user-defined tolerable magnitudes based on real data experiments.

Segmenting object space by geometric reference structures

Abstract: A model for segmentation of an object space by an array of binary, radiation-field sensors and geometric reference structures is described. Given a family of binary, radiation-field sensors and a geometric reference structure, we refer to the set of sensor states induced by a source at point p as the signature of p. We study the segmentation of an object space into signature cells and prove near optimal bounds on the number of distinct signatures induced by a point source, as a function of sensor and reference structure complexity. We also show that almost any family of signatures can be implemented under this model.

On conditions for constant throughput in wireless networks

Abstract: In this article we propose a set of necessary and sufficient conditions under which the long-term averaged throughput in an ad hoc network can remain constant as the number of nodes n increases. Throughput refers to the minimum achievable rate between a source-destination pair for a given routing mechanism and physical model, when the network is shared by Θ(n) randomly chosen source-destination pairs. The main idea is to use a connectivity graph, which does not represent the actual physical network but rather the available communication resources. This graph also allows one to translate the problem of maximizing the throughput in ad hoc networks to the multicommodity flow problem and directly apply related results.