Joint Energy-Balanced and Full-Coverage Mechanism Using Sensing Range Control for Maximizing Network Lifetime in WSNs

[[abstract]]Coverage is an important issue that has been widely discussed in wireless sensor networks (WSNs). However, it is still a big challenge to achieve both the purposes of full coverage and energy balancing. This paper considers the area coverage problem for a WSN where each sensor has variable sensing radius. A Weighted Voronoi Diagram (WVD) is proposed as a tool for determining the responsible sensing region of each sensor node according to the remaining energy in a distributed manner. To maximize the network lifetime, techniques for balancing energy consumptions of sensors are further presented. Performance evaluation reveals that the proposed joint energy-balanced and full-coverage mechanism, called EBFC, outperforms the existing studies in terms of network lifetime and degree of energy balancing.[[conferencetype]]國際[[conferencedate]]20120704~20120706[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Phuket, Thailan

LAACAD: Load bAlancing k-area coverage through autonomous deployment in wireless sensor networks

Session 6B: Coverage & LocalizationAlthough the problem of k-area coverage has been intensively investigated for dense wireless sensor networks (WSNs), how to arrive at a k-coverage sensor deployment that optimizes certain objectives in relatively sparse WSNs still faces both theoretical and practical difficulties. In this paper, we present a practical algorithm LAACAD (Load bAlancing k-Area Coverage through Autonomous Deployment) to move sensor nodes toward k-area coverage, aiming at minimizing the maximum sensing range required by the nodes. LAACAD enables purely autonomous node deployment as it only entails localized computations. We prove the convergence of the algorithm, as well as the (local) optimality of the output. We also show that our optimization objective is closely related to other frequently considered objectives. Therefore, our practical algorithm design also contributes to the theoretical understanding of the k-area coverage problem. Finally, we use extensive simulation results both to confirm our theoretical claims and to demonstrate the efficacy of LAACAD. © 2012 IEEE.postprin

Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue

In this report, we try to improve the performance of existing approaches for search operations in multi-robot context. We propose three novel algorithms that are using a triangular grid pattern, i.e., robots certainly go through the vertices of a triangular grid during the search procedure. The main advantage of using a triangular grid pattern is that it is asymptotically optimal in terms of the minimum number of robots required for the complete coverage of an arbitrary bounded area. We use a new topological map which is made and shared by robots during the search operation. We consider an area that is unknown to the robots a priori with an arbitrary shape, containing some obstacles. Unlike many current heuristic algorithms, we give mathematically proofs of convergence of the algorithms. The computer simulation results for the proposed algorithms are presented using a simulator of real robots and environment. We evaluate the performance of the algorithms via experiments with real robots. We compare the performance of our own algorithms with three existing algorithms from other researchers. The results demonstrate the merits of our proposed solution. A further study on formation building with obstacle avoidance for a team of mobile robots is presented in this report. We propose a decentralized formation building with obstacle avoidance algorithm for a group of mobile robots to move in a defined geometric configuration. Furthermore, we consider a more complicated formation problem with a group of anonymous robots; these robots are not aware of their position in the final configuration and need to reach a consensus during the formation process. We propose a randomized algorithm for the anonymous robots that achieves the convergence to a desired configuration with probability 1. We also propose a novel obstacle avoidance rule, used in the formation building algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:1402.5188 by other author

A Map-algebra-inspired Approach for Interacting With Wireless Sensor Networks, Cyber-physical Systems or Internet of Things

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a high-level programming model. Several macroprogramming models have been proposed, but none to date has adopted a comprehensive spatial model. This thesis takes the unique approach of adapting the well-known Map Algebra model from Geographic Information Science to extend the functionality of WSN/IoT and the opportunities for user interaction with WSN/IoT. As an inherently spatial model, the Map Algebra-inspired metaphor supports the types of computation desired from a network of geographically dispersed WSN nodes. The AeMA data model aligns with the conceptual model of GIS layers and specific layer operations from Map Algebra. A declarative query and network tasking language, based on Map Algebra operations, provides the basis for operations and interactions. The model adds functionality to calculate and store time series and specific temporal summary-type composite objects as an extension to traditional Map Algebra. The AeMA encodes Map Algebra-inspired operations into an extensible Virtual Machine Runtime system, called MARS (Map Algebra Runtime System) that supports Map Algebra in an efficient and extensible way. Map algebra-like operations are performed in a distributed manner. Data do not leave the network but are analyzed and consumed in place. As a consequence, collected information is available in-situ to drive local actions. The conceptual model and tasking language are designed to direct nodes as active entities, able to perform some actions on their environment. This Map Algebra inspired network macroprogramming model has many potential applications for spatially deployed WSN/IoT networks. In particular the thesis notes its utility for precision agriculture applications

Exact Distributed Voronoi Cell Computation in Sensor Networks

Distributed computation of Voronoi cells in sensor networks, i.e. computing the locus of points in a sensor field closest to a given sensor, is a key building block that supports a number of applications in both the data and control planes. For example, knowledge of Voronoi cells facilitates efficient methods for computing the piece-wise approximation of a field, whereby each sensor acts as a representative for the set of points in its Voronoi cell; awareness of Voronoi boundaries and Voronoi neighbors is also useful in load balancing and energy conservation. The methods currently advocated for distributed Voronoi computation in sensor networks are heuristic approximations that can introduce significant inaccuracies that are difficult to rigorously quantify; we demonstrate that these methods may err by a factor of 5 or more in some circumstances. We present and prove an exact method which eliminates these inaccuracies, at the cost of increased messaging overhead, but without necessitating contact with the entire network. To our knowledge, this is the first distributed algorithm that computes accurate Voronoi cells without requiring all-to-all communication. We implement it as a TinyOS module and quantitatively analyze its performance

Analysis and implementation of distributed algorithms for multi-robot systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 159-166).Distributed algorithms for multi-robot systems rely on network communications to share information. However, the motion of the robots changes the network topology, which affects the information presented to the algorithm. For an algorithm to produce accurate output, robots need to communicate rapidly enough to keep the network topology correlated to their physical configuration. Infrequent communications will cause most multirobot distributed algorithms to produce less accurate results, and cause some algorithms to stop working altogether. The central theme of this work is that algorithm accuracy, communications bandwidth, and physical robot speed are related. This thesis has three main contributions: First, I develop a prototypical multi-robot application and computational model, propose a set of complexity metrics to evaluate distributed algorithm performance on multi-robot systems, and introduce the idea of the robot speed ratio, a dimensionless measure of robot speed relative to message speed in networks that rely on multi-hop communication. The robot speed ratio captures key relationships between communications bandwidth, mobility, and algorithm accuracy, and can be used at design time to trade off between them. I use this speed ratio to evaluate the performance of existing distributed algorithms for multi-hop communication and navigation. Second, I present a definition of boundaries in multi-robot systems, and develop new distributed algorithms to detect and characterize them. Finally, I define the problem of dynamic task assignment, and present four distributed algorithms that solve this problem, each representing a different trade-off between accuracy, running time, and communication resources. All the algorithms presented in this work are provably correct under ideal conditions and produce verifiable real-world performance.(cont.) They are self-stabilizing and robust to communications failures, population changes, and other errors. All the algorithms were tested on a swarm of 112 robots.by James Dwight McLurkin, IV.Ph.D

Distributed algorithms for extending the functional lifetime of wireless sensor networks

The functional lifetime of a wireless sensor network (WSN) is among its most important features and serves as an essential metric in the evaluation of its energy-conserving policies. Approaches for extending the lifetime of a wireless sensor node include using an on/off strategy on the sensor nodes and using a topology control algorithm on each node to regulate its transmission power. However, the need to keep the network functional imposes certain additional constraints on strategies for energy conservation. A sensing constraint imposes that the sensing tasks essential to the functionality of the WSN are not compromised. A communication constraint similarly imposes that communications essential to an application on the network remain possible even as battery resources deplete on the nodes. This dissertation presents new distributed algorithms for energy conservation under these two classes of constraints: sensing constraints and communication constraints. One sensing constraint, called the representation constraint in this dissertation, is the requirement that active (on) sensor nodes are evenly distributed in the region of interest covered by the sensor network. This dissertation develops two essential metrics which together allow a rigorous quantitative assessment of the quality of representation achieved by a WSN and presents analytical results which bound these metrics in the common scenario of a planar region of arbitrary shape covered by a sensor network deployment. The dissertation further proposes a new distributed algorithm for energy conservation under the representation constraint. Simulation results show that the proposed algorithm is able to significantly improve the quality of representation compared to other related distributed algorithms. It also shows that improved spatial uniformity has the welcome side-effect of a significant increase in the functional lifetime of a WSN. One communication constraint, called the connectivity constraint, imposes that the network remains connected during its functional life. The connectivity required may be weak (allowing unidirectional communication between nodes) or strong (requiring bidirectional link layer communication between each pair of communicating nodes). This dissertation develops new distributed topology control algorithms for energy conservation under both the strong and the weak connectivity constraint. The proposed algorithm for the more ideal scenario of the weak connectivity constraint uses a game-theoretic approach. The dissertation proves the existence of a Nash equilibrium for the game and computes the associated price of anarchy. Simulation results show that the algorithms extend the network lifetime beyond those achieved by previously known algorithms.Ph.D., Computer engineering -- Drexel University, 201