Aging analysis in large-scale wireless sensor networks

Most research on the lifetime of wireless sensor networks has focused primarily on the energy depletion of the very first node. In this study, we analyze the entire aging process of the sensor network in a periodic data gathering application. In sparse node deployments, it is observed that the existence of multiple alternate paths to a sink leads to a power law relation between connectivity to a sink and hop levels, where the probability of connection to a sink decreases in proportion to the hop level with an exponent, when device failures occur over time. Then, we provide distance-level analysis for the dense deployment case by taking into account the re-construction of a data gathering tree and workload shift caused by the energy depletion of nodes with larger workload. Extensive simulation results obtained with a realistic wireless link model are compared to our analytical results. Finally, we show through an analysis of the aging of first-hop nodes that increasing node density with a fixed radio range does not affect the network disconnection time.     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

Node distribution-based localization for large-scale wireless sensor networks

Distributed localization algorithms are required for large-scale wireless sensor network applications. In this paper, we introduce an efficient algorithm, termed node distribution-based localization (NDBL), which emphasizes simple refinement and low system-load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighboring nodes, updates its position estimate by minimizing a local cost-function, and then passes this updated position to neighboring nodes. This update process uses a node distribution that has the same density per unit area as large-scale networks. Neighbor nodes are selected from the range in which the strength of received signals is greater than an experimentally based threshold. Based on results of a MATLAB simulation, the proposed algorithm was more accurate than trilateration and less complex than multi-dimensional scaling. Numerically, the mean distance error of the NDBL algorithm is 1.08–5.51 less than that of distributed weighted multi-dimensional scaling (dwMDS). Implementation of the algorithm using MicaZ with TinyOS-2.x confirmed the practicality of the proposed algorithm.     

Group key management scheme for large-scale sensor networks

Wireless sensor networks are inherently collaborative environments in which sensor nodes self-organize and operate in groups that typically are dynamic and mission-driven. Secure communications in wireless sensor networks under this collaborative model calls for efficient group key management. However, providing key management services in wireless sensor networks is complicated by their ad-hoc nature, intermittent connectivity, large scale, and resource limitations. To address these issues, this paper proposes a new energy-efficient key management scheme for networks consisting of a large number of commodity sensor nodes that are randomly deployed. All sensor nodes in the network are anonymous and are preloaded with identical state information. The proposed scheme leverages a location-based virtual network infrastructure and is built upon a combinatorial formulation of the group key management problem. Secure and efficient group key initialization is achieved in the proposed scheme by nodes autonomously computing, without any communications, their respective initial group keys. The key server, in turn, uses a simple location-based hash function to autonomously deduce the mapping of the nodes to their group keys. The scheme enables dynamic setup and management of arbitrary secure group structures with dynamic group membership.     

水下光无线传感器网络的连接性分析与定位技术

针对水下光无线传感器网络(UOWSN)节点的传输范围受限和间歇性连接的问题,利用多跳通信扩大传输范围来增强网络连接性,提出一种网络节点定位算法.首先,将UOWSN建模为三维(3D)随机缩放模型图,并根据网络节点数、通信范围以及光发散角推导了该模型下网络节点的连接性概率表达式;然后,利用接收信号强度(RSS)定位算法修正...     

Nonparametric change detection and estimation in large-scale sensor networks

The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.     

Three-dimensional greedy routing in large-scale random wireless sensor networks

In this paper, we investigate how to design greedy routing to achieve sustainable and scalable in a large-scale three-dimensional (3D) sensor network. Several 3D position-based routing protocols were proposed to seek either delivery guarantee or energy-efficiency in 3D wireless networks. However, recent results [1], [2] showed that there is no deterministic localized routing algorithm that guarantees either delivery of packets or energy-efficiency of its routes in 3D networks. In this paper, we focus on design of 3D greedy routing protocols which can guarantee delivery of packets and/or energy-efficiency of their paths with high probability in a randomly deployed 3D sensor network. In particular, we first study the asymptotic critical transmission radius for 3D greedy routing to ensure the packet delivery in large-scale random 3D sensor networks, then propose a refined 3D greedy routing protocol to achieve energy-efficiency of its paths with high probability. We also conduct extensive simulations to confirm our theoretical results.     

Graphical properties of easily localizable sensor networks

The sensor network localization problem is one of determining the Euclidean positions of all sensors in a network given knowledge of the Euclidean positions of some, and knowledge of a number of inter-sensor distances. This paper identifies graphical properties which can ensure unique localizability, and further sets of properties which can ensure not only unique localizability but also provide guarantees on the associated computational complexity, which can even be linear in the number of sensors on occasions. Sensor networks with minimal connectedness properties in which sensor transmit powers can be increased to increase the sensing radius lend themselves to the acquiring of the needed graphical properties. Results are presented for networks in both two and three dimensions. B. D. O. Anderson supported by National ICT Australia, which is funded by the Australian Government’s Department of Communications, Information Technology and the Arts and the Australian Research Council through the Backing Australia’s Ability initiative and the ICT Centre of Excellence Program. A. S. Morse supported by US Army Research Office and US National Science Foundation. W. Whiteley supported in part by grants from NSERC (Canada) and NIH (USA). Y. R. Yang supported in part by US National Science Foundation. Brian Anderson is a Distinguished Professor at the Research School of Information Sciences and Engineering, The Australian National University, Australia. Professor Anderson took his undergraduate degrees in Mathematics and Electrical Engineering at Sydney University, and his doctoral degree in Electrical Engineering at Stanford University. He worked in industry in the United States and at Stanford University before serving as Professor of Electrical Engineering at the University of Newcastle, Australia from 1967 through 1981. At that time, he took up a post as Professor and Head of the Department of Systems Engineering at the Australian National University in Canberra, where he was Director of the Research School of Information Sciences and Engineering from 1994 to 2002. For approximately one year to May 2003, he was the inaugural CEO of the newly formed National ICT Australia, established by the Australian Government through the Department of Communications, Information Technology and the Arts and the Australian Research Council under the Information and Communication Technologies Centre of Excellence program. Professor Anderson has served as a member of a number of government bodies, including the Australian Science and Technology Council and the Prime Minister’s Science, Engineering and Innovation Council. He was a member of the Board of Cochlear Limited, the world’s major supplier of cochlear implants from its listing until 2005. He is a Fellow of the Australian Academy of Science and Academy of Technological Sciences and Engineering, the Institute of Electrical and Electronic Engineers, and an Honorary Fellow of the Institution of Engineers, Australia. In 1989, he became a Fellow of the Royal Society, London, and in 2002 a Foreign Associate of the US National Academy of Engineering. He holds honorary doctorates of the Catholic University of Louvain in Belgium, the Swiss Federal Institute of Technology, and the Universities of Sydney, Melbourne and New South Wales. He was appointed an Officer of the Order of Australia in 1993. He was President of the International Federation of Automatic Control for the triennium 1990 to 1993, and served as President of the Australian Academy of Science for four years from 1998 to 2002. Professor Anderson became the Chief Scientist of National ICT Australia in May 2003 and served in that role till September 2006. Tolga Eren received the B.S. degree in electrical engineering from Bilkent University, Ankara, Turkey, the M.S.E.E. degree in electrical engineering from the University of Massachusetts, the M.S. and the Ph.D. degrees in engineering and applied science from Yale University, New Haven, Connecticut, in 1994, 1998, 1999, and 2003, respectively. From October 2003 to July 2005, he was a postdoctoral research scientist at the Computer Science Department at Columbia University in the City of New York. Since September 2005, he has been at the department of Electrical Engineering at Kirikkale University, Turkey. His research interests are multi-agent (multi-robot, multi-vehicle) systems, sensor networks, computer vision, graph theory, and computational geometry. A. Stephen Morse was born in Mt. Vernon, New York. He received a BSEE degree from Cornell University, MS degree from the University of Arizona, and a Ph.D. degree from Purdue University. From 1967 to 1970 he was associated with the Office of Control Theory and Application OCTA at the NASA Electronics Research Center in Cambridge, Mass. Since 1970 he has been with Yale University where he is presently the Dudley Professor of Engineering and a Professor of Computer Science. His main interest is in system theory and he has done research in network synthesis, optimal control, multivariable control, adaptive control, urban transportation, vision-based control, hybrid and nonlinear systems, sensor networks, and coordination and control of large grouping of mobile autonomous agents. He is a Fellow of the IEEE, a Distinguished Lecturer of the IEEE Control System Society, and a co-recipient of the Society’s 1993 and 2005 George S. Axelby Outstanding Paper Awards. He has twice received the American Automatic Control Council’s Best Paper Award and is a co-recipient of the Automatica Theory/Methodology Prize . He is the 1999 recipient of the IEEE Technical Field Award for Control Systems. He is a member of the National Academy of Engineering and the Connecticut Academy of Science and Engineering. Walter Whiteley (B.Sc. 66, Queen’s University at Kingston, Canada) received his Ph.D. in Mathematics from MIT, Cambridge Mass in 1971. He is currently the Director of Applied Mathematics at York University, and a member of the graduate programs in Mathematics, in Computer Science, and in Education. His research focuses on the rigidity and flexibility of systems of geometric constraints (distances, angles, directions, projections, …). Recent work has included applications of this theory to location in networks, control of formations of autonomous agents, built structures in structural engineering, linkages in mechanical engineering, geometric constraints in computational geometry and CAD, and algorithms for protein flexibility in biochemistry. He is also active in geometry education and development of visual reasoning at all levels of mathematics education and in applications of mathematics. Yang Richard Yang received the B.E. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 1993, and the M.S. and Ph.D. degrees in Computer Science from the University of Texas at Austin in 1998 and 2001, respectively. Since 2001, he has been with the Department of Computer Science, Yale University, New Haven, CT, where currently he is an Associate Professor of Computer Science and Electrical Engineering. His current research interests are in computer networks, mobile computing, and sensor networks. He leads the Laboratory of Networked Systems (LANS) at Yale University.     

An improved key distribution mechanism for large-scale hierarchical wireless sensor networks

Wireless sensor networks are often deployed in hostile environments and operated on an unattended mode. In order to protect the sensitive data and the sensor readings, secret keys should be used to encrypt the exchanged messages between communicating nodes. Due to their expensive energy consumption and hardware requirements, asymmetric key based cryptographies are not suitable for resource-constrained wireless sensors. Several symmetric-key pre-distribution protocols have been investigated recently to establish secure links between sensor nodes, but most of them are not scalable due to their linearly increased communication and key storage overheads. Furthermore, existing protocols cannot provide sufficient security when the number of compromised nodes exceeds a critical value. To address these limitations, we propose an improved key distribution mechanism for large-scale wireless sensor networks. Based on a hierarchical network model and bivariate polynomial-key generation mechanism, our scheme guarantees that two communicating parties can establish a unique pairwise key between them. Compared with existing protocols, our scheme can provide sufficient security no matter how many sensors are compromised. Fixed key storage overhead, full network connectivity, and low communication overhead can also be achieved by the proposed scheme.     

Modulated relay based stable election protocol for large-scale wireless sensor networks

Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.     

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

Telecommunication Systems - The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the...     

Extremal properties of three-dimensional sensor networks with applications

We analyze various critical transmitting/sensing ranges for connectivity and coverage in three-dimensional sensor networks. As in other large-scale complex systems, many global parameters of sensor networks undergo phase transitions. For a given property of the network, there is a critical threshold, corresponding to the minimum amount of the communication effort or power expenditure by individual nodes, above (respectively, below) which the property exists with high (respectively, a low) probability. For sensor networks, properties of interest include simple and multiple degrees of connectivity/coverage. First, we investigate the network topology according to the region of deployment, the number of deployed sensors, and their transmitting/sensing ranges. More specifically, we consider the following problems: assume that n nodes, each capable of sensing events within a radius of r, are randomly and uniformly distributed in a 3-dimensional region R of volume V, how large must the sensing range R/sub SENSE/ be to ensure a given degree of coverage of the region to monitor? For a given transmission range R/sub TRANS/, what is the minimum (respectively, maximum) degree of the network? What is then the typical hop diameter of the underlying network? Next, we show how these results affect algorithmic aspects of the network by designing specific distributed protocols for sensor networks.     

Asymptotic locally optimal detector for large-scale sensor networks under the Poisson regime

We consider distributed detection with a large number of identical binary sensors deployed over a region where the phenomenon of interest (POI) has spatially varying signal strength. Each sensor makes a binary decision based on its own measurement, and the local decision of each sensor is sent to a fusion center using a random access protocol. The fusion center decides whether the event has occurred under a global size constraint in the Neyman-Pearson formulation. Assuming homogeneous Poisson distributed sensors, we show that the distribution of "alarmed" sensors satisfies the local asymptotic normality (LAN). We then derive an asymptotically locally most powerful (ALMP) detector optimized jointly over the fusion form and the local sensor threshold under the Poisson regime. We establish conditions on the spatial signal shape that ensure the existence of the ALMP detector. We show that the ALMP test statistic is a weighted sum of local decisions, the optimal weights being the shape of the spatial signal; the exact value of the signal strength is not required. We also derive the optimal threshold for each sensor. For the case of independent, identically distributed (iid) sensor observations, we show that the counting-based detector is also ALMP under the Poisson regime. The performance of the proposed detector is evaluated through analytic results and Monte Carlo simulations and compared with that of the counting-based detector. The effect of mismatched signal shapes is also investigated.     

A scalable joint routing and scheduling scheme for large-scale wireless sensor networks

The multihop configuration of a large-scale wireless sensor network enables multiple simultaneous transmissions without interference within the network. Existing time division multiple access (TDMA) scheduling schemes exploit gain based on the assumption that the path is optimally determined by a routing protocol. In contrast, our scheme jointly considers routing and scheduling and introduces several new concepts. We model a large-scale wireless sensor network as a tiered graph relative to its distance from the sink, and introduce the notion of relay graph and relay factor to direct the next-hop candidates toward the sink fairly and efficiently. The sink develops a transmission and reception schedule for the sensor nodes based on the tiered graph search for a set of nodes that can simultaneously transmit and receive. The resulting schedule eventually allows data from each sensor node to be delivered to the sink. We analyze our scheduling algorithm both numerically and by simulation, and we discuss the impact of protocol parameters. Further, we prove that our scheme is scalable to the number of nodes, from the perspectives of mean channel capacity and maximum number of concurrent transmission nodes. Compared with the existing TDMA scheduling schemes, our scheme shows better performance in network throughput, path length, end-to-end delay, and fairness index.     

CE2RV: Commissioned energy-efficient virtualization for large-scale heterogeneous wireless sensor networks

The Internet of Things (IoT) comprises sensor networks, intelligent things, devices, and humans for heterogeneous services and applications. Energy constraints in conventional wireless networks impact IoT performance resulting in service failures. For reducing the adverse impact of energy, this article introduces a commissioned energy-efficient resource virtualization (CE2RV) scheme. This proposed scheme classifies the sensor nodes as fast and slow-depleting for identifying service failures. The fast-depleting nodes are discontinued from the service replications, and the remaining energy high-node-connected resources are identified. Such resources are virtualized for thwarting the existing energy failures over various services. The node classifications are performed using a tree-learning algorithm. The classifications are performed for node replacement and service virtualization under different energy depletion rates. This is required for preventing sensor network disconnections between the users and service providers. The classification is required for overcoming multiple virtualizations between common nodes across different service providers. The proposed scheme's performance is analyzed using the metrics of service disconnections, energy utilization, energy efficiency, and service delay.     

Estimation of the number of operating sensors in large-scale sensor networks with mobile access

This paper investigates the estimation of the number of operating sensors in a sensor network in which the data collection is made by a mobile access point. In this paper, an estimator based on the Good-Turing estimator of the missing mass is proposed and it is generalized to other related problems such as the estimation of the distribution of energy available at sensors. The estimator is analyzed using the theory of large deviations. Closed-form bounds on the large deviation exponent are presented and confidence intervals for the estimator are characterized.     

Connectivity analysis of one-dimensional ad-hoc networks

Application and communication protocols in dynamic ad-hoc networks are exposed to physical limitations imposed by the connectivity relations that result from mobility. Motivated by vehicular freeway scenarios, this paper analyzes a number of important connectivity metrics for instantaneous snapshots of stochastic geographic movement patterns: (1) The single-hop connectivity number, corresponding to the number of single-hop neighbors of a mobile node; (2) the multi-hop connectivity number, expressing the number of nodes reachable via multi-hop paths of arbitrary hop-count; (3) the connectivity distance, expressing the geographic distance that a message can be propagated in the network on multi-hop paths; (4) the connectivity hops, which corresponds to the number of hops that are necessary to reach all nodes in the connected network. The paper develops analytic expressions for the distributions and moments of these random variables for general stationary MAP processes on a one dimensional space. The numerical results compare bursty vehicular traffic with independent movement scenarios described by a Poisson process, illustrate in examples the impact of a random communication range, and demonstrate the usefulness of MAP processes via comparison with vehicular simulation traces.     

Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-typically related to proximity-and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio.