Showing papers on "Distributed algorithm published in 2004"

Distributed optimization in sensor networks

Abstract: Wireless sensor networks are capable of collecting an enormous amount of data over space and time. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. The distributed algorithms are based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data. Applying results from the theory of incremental subgradient optimization, we show that for a broad class of estimation problems the distributed algorithms converge to within an /spl epsi/-ball around the globally optimal value. Furthermore, bounds on the number incremental steps required for a particular level of accuracy provide insight into the trade-off between estimation performance and communication overhead. In many realistic scenarios, the distributed algorithms are much more efficient, in terms of energy and communications, than centralized estimation schemes. The theory is verified through simulated applications in robust estimation, source localization, cluster analysis and density estimation.

Stability of continuous-time distributed consensus algorithms

Abstract: We study the stability properties of linear time-varying systems in continuous time whose system matrix is Metzler with zero row sums. This class of systems arises naturally in the context of distributed decision problems, coordination and rendezvous tasks and synchronization problems. The equilibrium set contains all states with identical state components. We present sufficient conditions guaranteeing uniform exponential stability of this equilibrium set, implying that all state components converge to a common value as time grows unbounded. Furthermore it is shown that this convergence result is robust with respect to an arbitrary delay, provided that the delay affects only the off-diagonal terms in the differential equation.

Distributed construction of connected dominating set in wireless ad hoc networks

Abstract: Connected dominating set (CDS) has been proposed as virtual backbone or spine of wireless ad hoc networks. Three distributed approximation algorithms have been proposed in the literature for minimum CDS. In this paper, we first reinvestigate their performances. None of these algorithms have constant approximation factors. Thus these algorithms cannot guarantee to generate a CDS of small size. Their message complexities can be as high as O(n2), and their time complexities may also be as large as O(n2) and O(n3). We then present our own distributed algorithm that outperforms the existing algorithms. This algorithm has an approximation factor of at most 8, O(n) time complexity and O(n log n) message complexity. By establishing the Ω(n log n) lower bound on the message complexity of any distributed algorithm for nontrivial CDS, our algorithm is thus message-optimal.

Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks

Abstract: We propose a distributed solution for a canonical task in wireless sensor networks - the binary detection of interesting environmental events. We explicitly take into account the possibility of sensor measurement faults and develop a distributed Bayesian algorithm for detecting and correcting such faults. Theoretical analysis and simulation results show that 85-95 percent of faults can be corrected using this algorithm, even when as many as 10 percent of the nodes are faulty.

Epidemic information dissemination in distributed systems

Abstract: Easy to deploy, robust, and highly resilient to failures, epidemic algorithms are a potentially effective mechanism for propagating information in large peer-to-peer systems deployed on Internet or ad hoc networks. It is possible to adjust the parameters of epidemic algorithm to achieve high reliability despite process crashes and disconnections, packet losses, and a dynamic network topology. Although researchers have used epidemic algorithms in applications such as failure detection, data aggregation, resource discovery and monitoring, and database replication, their general applicability to practical, Internet-wide systems remains open to question. We describe four key problems: membership maintenance, network awareness, buffer management, and message filtering, and suggest some preliminary approaches to address them.

Medians and beyond: new aggregation techniques for sensor networks

Abstract: Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.

SeRLoc: secure range-independent localization for wireless sensor networks

Abstract: In many applications of wireless sensor networks (WSN), sensors are deployed un-tethered in hostile environments. For location-aware WSN applications, it is essential to ensure that sensors can determine their location, even in the presence of malicious adversaries. In this paper we address the problem of enabling sensors of WSN to determine their location in an un-trusted environment. Since localization schemes based on distance estimation are expensive for the resource constrained sensors, we propose a range-independent localization algorithm called SeRLoc. SeRLoc is distributed algorithm and does not require any communication among sensors. In addition, we show that SeRLoc is robust against severe WSN attacks, such as the wormhole attack, the sybil attack and compromised sensors. To the best of our knowledge, ours is the first work that provides a security-aware range-independent localization scheme for WSN. We present a threat analysis and comparison of the performance of SeRLoc with state-of-the-art range-independent localization schemes.

Distributed regression: an efficient framework for modeling sensor network data

Abstract: We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.

Distributed control design for systems interconnected over an arbitrary graph

Abstract: We consider the problem of synthesizing a distributed dynamic output feedback controller achieving H/sub /spl infin// performance for a system composed of different interconnected sub-units, when the topology of the underlying graph is arbitrary. First, using tools inspired by dissipativity theory, we derive sufficient conditions in the form of finite-dimensional linear matrix inequalities when the interconnections are assumed to be ideal. These inequalities are coupled in a way that reflects the spatial structure of the problem and can be exploited to design distributed synthesis algorithms. We then investigate the case of lossy interconnection links and derive similar results for systems whose interconnection relations can be captured by a class of integral quadratic constraints that includes constant delays.

Load balancing in dynamic structured P2P systems

Abstract: Most P2P systems that provide a DHT abstraction distribute objects randomly among "peer nodes" in a way that results in some nodes having /spl theta/(log N) times as many objects as the average node. Further imbalance may result due to non-uniform distribution of objects in the identifier space and a high degree of heterogeneity in object loads and node capacities. Additionally, a node's load may vary greatly over time since the system can be expected to experience continuous insertions and deletions of objects, skewed object arrival patterns, and continuous arrival and departure of nodes. We propose an algorithm for load balancing in such heterogeneous, dynamic P2P systems. Our simulation results show that in the face of rapid arrivals and departures of objects of widely varying load, our algorithm achieves load balancing for system utilizations as high as 90% while moving only about 8% of the load that arrives into the system. Similarly, in a dynamic system where nodes arrive and depart, our algorithm moves less than 60% of the load the underlying DHT moves due to node arrivals and departures. Finally, we show that our distributed algorithm performs only negligibly worse than a similar centralized algorithm, and that node heterogeneity helps, not hurts, the scalability of our algorithm.

Solving Distributed Constraint Optimization Problems Using Cooperative Mediation

Abstract: Distributed Constraint Optimization Problems (DCDP) have, for a long time, been considered an important research area for multi-agent systems because a vast number of real-world situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCDP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques.

Hood: a neighborhood abstraction for sensor networks

Abstract: This paper proposes a neighborhood programming abstraction for sensor networks, wherein a node can identify a subset of nodes around it by a variety of criteria and share state with those nodes. This abstraction allows developers to design distributed algorithms in terms of the neighborhood abstraction itself, instead of decomposing them into component parts such as messaging protocols, data caches, and neighbor lists. In those applications that are already neighborhood-based, this abstraction is shown to facilitate good application design and to reduce algorithmic complexity, inter-component coupling, and total lines of code. The abstraction as defined here has been successfully used to implement several complex applications and is shown to capture the essence of many more existing distributed sensor network algorithms.

On network correlated data gathering

Abstract: We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms.

Connected K-coverage problem in sensor networks

Abstract: In overdeployed sensor networks, one approach to conserve energy is to keep only a small subset of sensors active at any instant. In this article, we consider the problem of selecting a minimum size connected K-cover, which is defined as a set of sensors M such that each point in the sensor network is "covered" by at least K different sensors in M, and the communication graph induced by M is connected. For the above optimization problem, we design a centralized approximation algorithm that delivers a near-optimal (within a factor of O(lg n)) solution, and present a distributed version of the algorithm. We also present a communication-efficient localized distributed algorithm which is empirically shown to perform well

Particle swarm based Data Mining Algorithms for classification tasks

Abstract: Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.

Maximum lifetime routing in wireless ad-hoc networks

Abstract: Routing problems in mobile ad-hoc networks (MANET) have been receiving increasing attention in the last few years. Most of the proposed routing protocols concentrate on finding and maintaining routes in the face of changing topology caused by mobility or other environmental changes. More recently, power-aware routing protocols and topology control algorithms have been developed to address the issue of limited energy reserve of the nodes in ad-hoc networks. We consider the routing problem in MANET with the goal of maximizing the life time of the network. We propose a distributed routing algorithm that reaches the optimal (centralized) solution to within an asymptotically small relative error. Our approach is based on the formulation of multicommodity flow, and it allows to consider different power consumption models and bandwidth constraints. It works for both static and slowly changing dynamic networks.

Distributed online localization in sensor networks using a moving target

Abstract: We describe a novel method for node localization in a sensor network where there are a fraction of reference nodes with known locations. For application-specific sensor networks, we argue that it makes sense to treat localization through online distributed learning and integrate it with an application task such as target tracking. We propose distributed online algorithm in which sensor nodes use geometric constraints induced by both radio connectivity and sensing to decrease the uncertainty of their position. The sensing constraints, which are caused by a commonly sensed moving target, are usually tighter than connectivity based constraints and lead to a decrease in average localization error over time. Different sensing models, such as radial binary detection and distance-bound estimation, are considered. First, we demonstrate our approach by studying a simple scenario in which a moving beacon broadcasts its own coordinates to the nodes in its vicinity. We then generalize this to the case when instead of a beacon, there is a moving target with a-priori unknown coordinates. The algorithms presented are fully distributed and assume only local information exchange between neighboring nodes. Our results indicate that the proposed method can be used to significantly enhance the accuracy in position estimation, even when the fraction of reference nodes is small. We compare the efficiency of the distributed algorithms to the case when node positions are estimated using centralized (convex) programming. Finally, simulations using the TinyOS-Nido platform are used to study the performance in more realistic scenarios.

Power efficient monitoring management in sensor networks

Abstract: Optimizing the energy consumption in wireless sensor networks has recently become the most important performance objective. We assume the sensor network model in which sensors can interchange idle and active modes. Given monitoring regions, battery life and energy consumption rate for each sensor, we formulate the problem of maximizing sensor network lifetime, i.e., time during which the monitored area is (partially or fully) covered. Our contributions include (1) an efficient data structure to represent the monitored area with at most n/sup 2/ points guaranteeing the full coverage which is superior to the previously used approach based on grid points, (2) efficient provably good centralized algorithms for sensor monitoring schedule maximizing the total lifetime including (1+ln(1-q)/sup -1/)-approximation algorithm for the case when a q-portion of the monitored area is required to cover, e.g., for the 90% area coverage our schedule guarantees to be at most 3.3 times shorter than the optimum, (4) a family of efficient distributed protocols with trade-off between communication and monitoring power consumption, (5) extensive experimental study of the proposed algorithms showing significant advantage in quality, scalability and flexibility.

Distributed Algorithms for DCOP: A Graphical-Game-Based Approach.

Abstract: This paper addresses the application of distributed constraint optimization problems (DCOPs) to large-scale dynamic environments. We introduce a decomposition of DCOP into a graphical game and investigate the evolution of various stochastic and deterministic algorithms. We also develop techniques that allow for coordinated negotiation while maintaining distributed control of variables. We prove monotonicity properties of certain approaches and detail arguments about equilibrium sets that offer insight into the tradeoffs involved in leveraging efficiency and solution quality. The algorithms and ideas were tested and illustrated on several graph coloring domains.

Initializing newly deployed ad hoc and sensor networks

Abstract: A newly deployed multi-hop radio network is unstructured and lacks a reliable and efficient communication scheme. In this paper, we take a step towards analyzing the problems existing during the initialization phase of ad hoc and sensor networks. Particularly, we model the network as a multi-hop quasi unit disk graph and allow nodes to wake up asynchronously at any time. Further, nodes do not feature a reliable collision detection mechanism, and they have only limited knowledge about the network topology. We show that even for this restricted model, a good clustering can be computed efficiently. Our algorithm efficiently computes an asymptotically optimal clustering. Based on this algorithm, we describe a protocol for quickly establishing synchronized sleep and listen schedule between nodes within a cluster. Additionally, we provide simulation results in a variety of settings.

Cheap Paxos

Abstract: Asynchronous algorithms for implementing a fault-tolerant distributed system, which can make progress despite the failure of any F processors, require 2F + 1 processors. Cheap Paxos, a variant of the Paxos algorithm, guarantees liveness under the additional assumption that the set of nonfaulty processors does not "jump around" too fast, but uses only F + 1 main processors that actually execute the system and F auxiliary processors that are used only to handle the failure of a main processor. The auxiliary processors take part in reconfiguring the system to remove the failed processor, after which they can remain idle until another main processor fails.

Decentralizing execution of composite web services

Abstract: Distributed enterprise applications today are increasingly being built from services available over the web. A unit of functionality in this framework is a web service, a software application that exposes a set of "typed'' connections that can be accessed over the web using standard protocols. These units can then be composed into a composite web service. BPEL (Business Process Execution Language) is a high-level distributed programming language for creating composite web services. Although a BPEL program invokes services distributed over several servers, the orchestration of these services is typically under centralized control. Because performance and throughput are major concerns in enterprise applications, it is important to remove the inefficiencies introduced by the centralized control. In a distributed, or decentralized orchestration, the BPEL program is partitioned into independent sub-programs that interact with each other without any centralized control. Decentralization can increase parallelism and reduce the amount of network traffic required for an application. This paper presents a technique to partition a composite web service written as a single BPEL program into an equivalent set of decentralized processes. It gives a new code partitioning algorithm to partition a BPEL program represented as a program dependence graph, with the goal of minimizing communication costs and maximizing the throughput of multiple concurrent instances of the input program. In contrast, much of the past work on dependence-based partitioning and scheduling seeks to minimize the completion time of a single instance of a program running in isolation. The paper also gives a cost model to estimate the throughput of a given code partition.

Resource Allocation in Federated Distributed Computing Infrastructures

Abstract: We consider the problem of allocating combinations of heterogeneous, distributed resources among selfinterested parties. In particular, we consider this problem in the context of distributed computing infrastructures, where resources are shared among users from different administrative domains. Examples of such infrastructures include PlanetLab [15] and computational grids [7]. End-users derive utility from receiving a share of resources. When there is an excess demand for resources, it isn’t possible to completely satisfy all resource requests. Therefore, we argue that it is important for these infrastructures to allocate resources in a way that maximizes aggregate end-user utility. Such an allocation system is known as economically efficient. Because a user’s utility function for resources isn’t typically known a priori, determining an allocation policy to maximize utility is difficult in the presence of excess demand. As use of these infrastructures becomes more widespread, contention for resources will increase, and allocating resources in an economically efficient manner becomes more difficult. Figure 1 shows a snapshot of resource demand in PlanetLab. Due to the way resources are distributed in PlanetLab, the rise in contention decreases the portion of resources received by any individual user, thereby reducing the amount of useful work that can be completed in the system. We argue that given the appropriate mechanisms, end-users can cooperate to arrive at an optimal resource allocation in spite of excess demand. More specifically, by allowing users to express preferences for when and what resources are received, the system will be able to spread out some excess demand over time, and increase the efficiency of the system. To this end, we present the design and early implementation of Bellagio, a distributed resource discov-

Power-conserving computation of order-statistics over sensor networks

Abstract: We study the problem of power-conserving computation of order statistics in sensor networks. Significant power-reducing optimizations have been devised for computing simple aggregate queries such as COUNT, AVERAGE, or MAX over sensor networks. In contrast, aggregate queries such as MEDIAN have seen little progress over the brute force approach of forwarding all data to a central server. Moreover, battery life of current sensors seems largely determined by communication costs --- therefore we aim to minimize the number of bytes transmitted. Unoptimized aggregate queries typically impose extremely high power consumption on a subset of sensors located near the server. Metrics such as total communication cost underestimate the penalty of such imbalance: network lifetime may be dominated by the worst-case replacement time for depleted batteries.In this paper, we design the first algorithms for computing order-statistics such that power consumption is balanced across the entire network. Our first main result is a distributed algorithm to compute an e-approximate quantile summary of the sensor data such that each sensor transmits only O(log2n/e) data values, irrespective of the network topology, an improvement over the current worst-case behavior of Ω(n). Second, we show an improved result when the height, h, of the network is significantly smaller than n. Our third result is that we can exactly compute any order statistic (e.g., median) in a distributed manner such that each sensor needs to transmit O(log3n) values.Further, we design the aggregates used by our algorithms to be decomposable. An aggregate Q over a set S is decomposable if there exists a function, f, such that for all S = S1 ∪ S2, Q(S) = f(Q(S1), Q(S2)). We can thus directly apply existing optimizations to decomposable aggregates that increase error-resilience and reduce communication cost.Finally, we validate our results empirically, through simulation. When we compute the median exactly, we show that, even for moderate size networks, the worst communication cost for any single node is several times smaller than the corresponding cost in prior median algorithms. We show similar cost reductions when computing approximate order-statistic summaries with guaranteed precision. In all cases, our total communication cost over the entire network is smaller than or equal to the total cost of prior algorithms.

Epidemic-style proactive aggregation in large overlay networks

Abstract: Aggregation - that is, the computation of global properties like average or maximal load, or the number of nodes - is an important basic functionality in fully distributed environments. In many cases - which include protocols responsible for self-organization in large-scale systems and collaborative environments - it is useful if all nodes know the value of some aggregates continuously. We present and analyze novel protocols capable of providing this service. The proposed antientropy aggregation protocols compute different aggregates of component properties like extremal values, average and counting. Our protocols are inspired by the antientropy epidemic protocol where random pairs of databases periodically resolve their differences. In the case of aggregation, resolving difference is generalized to an arbitrary (numeric) computation based on the states of the two communicating peers. The advantage of this approach is that it is proactive and "democratic", which means it has no performance bottlenecks, and the approximation of the aggregates is present continuously at all nodes. These properties make our protocol suitable for implementing e.g. collective decision making or automatic system maintenance based on global information in a fully distributed fashion. As our main contribution we provide fundamental theoretical results on the proposed averaging protocol.

Cooperative multihop broadcast for wireless networks

Abstract: We address the minimum-energy broadcast problem under the assumption that nodes beyond the nominal range of a transmitter can collect the energy of unreliably received overheard signals. As a message is forwarded through the network, a node will have multiple opportunities to reliably receive the message by collecting energy during each retransmission. We refer to this cooperative strategy as accumulative broadcast. We seek to employ accumulative broadcast in a large scale loosely synchronized, low-power network. Therefore, we focus on distributed network layer approaches for accumulative broadcast in which loosely synchronized nodes use only local information. To further simplify the system architecture, we assume that nodes forward only reliably decoded messages. Under these assumptions, we formulate the minimum-energy accumulative broadcast problem. We present a solution employing two subproblems. First, we identify the ordering in which nodes should transmit. Second, we determine the optimum power levels for that ordering. While the second subproblem can be solved by means of linear programming, the ordering subproblem is found to be NP-complete. We devise a heuristic algorithm to find a good ordering. Simulation results show the performance of the algorithm to be close to optimum and a significant improvement over the well known BIP algorithm for constructing energy-efficient broadcast trees. We then formulate a distributed version of the accumulative broadcast algorithm that uses only local information at the nodes and has performance close to its centralized counterpart.

Distributed algorithms for maximum lifetime routing in wireless sensor networks

Abstract: A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node in the network drains out of energy. The problem is formulated as a linear programming problem and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal routing scheme that maximizes the network lifetime. The algorithms are illustrated by an example in which an optimal flow is computed for a network of randomly distributed nodes.

A generic distributed broadcast scheme in ad hoc wireless networks

Abstract: We propose a generic framework for distributed broadcasting in ad hoc wireless networks. The approach is based on selecting a small subset of nodes to form a forward node set to carry out a broadcast process. The status of each node, forward or nonforward, is determined either by the node itself or by other nodes. Node status can be determined at different snapshots of network state along time (called views) without causing problems in broadcast coverage. Therefore, the forward node set can be constructed and maintained through either a proactive process (i.e., "up-to-date") before the broadcast process or a reactive process (i.e., "on-the-fly") during the broadcast process. A sufficient condition, called coverage condition, is given for a node to take the nonforward status. Such a condition can be easily checked locally around the node. Several existing broadcast algorithms can be viewed as special cases of the generic framework with k-hop neighborhood information. A comprehensive comparison among existing algorithms is conducted. Simulation results show that new algorithms, which are more efficient than existing ones, can be derived from the generic framework.

A bit-map-assisted energy-efficient MAC scheme for wireless sensor networks

Abstract: The low-energy characteristics of wireless sensor networks (WSNs) pose a great design challenge for MAC protocol design. Recent studies have proposed different cluster-based MAC protocols. In this paper, we propose an intra-cluster communication bit-map-assisted (BMA) MAC protocol for large-scale cluster-based WSNs. BMA is intended for event-driven applications, where sensor nodes transmit data to the cluster head only if significant events are observed. In addition, we provide the energy and packet latency analytical models for BMA, conventional TDMA, and energy efficient TDMA (E-TDMA) when used as intra-cluster MAC schemes. Our results show that BMA is superior for the cases of low and medium traffic loads, relatively few sensor nodes per cluster, and relatively large data packet sizes. In addition, BMA performs better than the TDMA-based MAC schemes in terms of average packet latency.

A decentralized algorithm for spectral analysis

Abstract: In many large network settings, such as computer networks, social networks, or hyperlinked text documents, much information can be obtained from the network's spectral properties. However, traditional centralized approaches for computing eigenvectors struggle with at least two obstacles: the data may be difficult to obtain (both due to technical reasons and because of privacy concerns), and the sheer size of the networks makes the computation expensive. A decentralized, distributed algorithm addresses both of these obstacles: it utilizes the computational power of all nodes in the network and their ability to communicate, thus speeding up the computation with the network size. And as each node knows its incident edges, the data collection problem is avoided as well.Our main result is a simple decentralized algorithm for computing the top k eigenvectors of a symmetric weighted adjacency matrix, and a proof that it converges essentially in O(τMIXlog2n) rounds of communication and computation, where τMIX is the mixing time of a random walk on the network. An additional contribution of our work is a decentralized way of actually detecting convergence, and diagnosing the current error. Our protocol scales well, in that the amount of computation performed at any node in any one round, and the sizes of messages sent, depend polynomially on k, but not on the (typically much larger) number n of nodes.