Fast and Convergent Method for Large-Scale Sensor Network Localization With Anchor Uncertainty

We study the localization problem in sensor networks by using local angle measurement. Localization using local angle information was recently proposed as an effective localization technique, which can be used for geographical routing with guaranteed delivery. However, the existing approach is based on linear programming (LP) and can not be implemented distributedly. We propose, design, and evaluate DAL: a purely distributed localization protocol in sensor networks using local angle measurement. Localization with local angle poses unique challenge in sensor networks due to information uncertainties identified in this paper. DAL specifically addresses these challenges. Via extensive simulations using ns2 and our own simulator, we show that the performance of DAL is comparable with that of the centralized LP approach in most cases. Our preliminary results with noisy angle measurement show that DAL keeps the global geometry of the sensor network fairly well.

Wireless mesh networks is a new type of broadband access network. It has very typical network architecture of hybrid mesh network architecture. As wireless mesh network with distributed network architecture, it is relatively fragile and vulnerable to attack. In this paper, we first analyze the characters of hybrid mesh network architecture. The security weaknesses of WMNs mainly reflected in the transmission channel, authentication, physical protection and routing protocols. On the basis of analysis of security defects of WMNs, we design a cross-layer security framework to improve the security of WMNs. At last, we interpret some key Technologies in this security Framework.

With rapid increase in the use of technology, the world is now moving towards smart cities which require the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled carefully. Different works have already been proposed to provide a communication in a network for the IoT devices; however, nothing has been founded more effective in terms of resource utilization. Hybrid network architecture is the combination of a centralized and distributed network architectures. The centralized network is used for the communication of IoT devices with edge nodes and distributed network for communicating miner nodes with edge nodes. In this way, the network utilize a lot of resources. In this paper, we are proposing a single network which is the combination of both edge nodes and miner nodes. Blockchain is also implemented in this network to provide secure communication between the devices. The evaluation of the proposed model is done using different performance parameters such as time and cost against the number of devices. Limited number of devices are used to perform this evaluation. Furthermore, the results are obtained by utilizing Proof-of-Work consensus mechanism.

This paper regards the relative localization problem in sensor networks. We study a randomized algorithm, which is based on input-driven consensus dynamics and involves pairwise "gossip" communications and updates. Due to the randomness of the updates, the state of this algorithm ergodically oscillates around a limit value. Exploiting the ergodicity of the dynamics, we show that the time-average of the state almost surely converges to the least-squares solution of the localization problem. Remarkably, the computation of the time-average does not require the sensors to share any common clock. Hence, the proposed algorithm is fully distributed and asynchronous.

The acceptance of electric vehicles, or EVs, is soaring, which offers opportunities and problems for the architecture of current distribution networks. With more people driving electric cars, there will likely be a large increase in the power demand, which might put unprecedented strain on the distribution network. The effects of widespread EV integration on distribution network architecture are thoroughly examined in this paper. Electricity distribution networks face opportunities and obstacles as the number of electric vehicles (EVs) increases. The increased use of EVs is predicted to increase power demand, particularly during peak hours, which could pressure the infrastructure already in place, including feeders, substations, and transformers. This increasing demand may result in equipment overload, voltage drops, and higher system losses, requiring major network capacity and reliability upgrades. On the other hand, developments in demand response tactics and smart charging technology present viable remedies. These include vehicle-to-grid (V2G) technology, which enables EVs to return energy to the grid, and load shifting, which incentivizes charging during off-peak hours. Distribution networks can handle the expansion of electric vehicles (EVs) while reducing risks and guaranteeing system stability using technological innovation and strategic planning.

In this paper, we introduce a rank-one Semi-Definite Programming (SDP) solution method for mobile source localization in sensor networks. The position and velocity of mobile source are jointly estimated using Time Delay (TD) measurements. To obtain the position and velocity of mobile source, a Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward the Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the estimation problem by introducing the penalty terms. By adaptively choosing the penalty coefficient, an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty. We also conduct experiments in both a simulated environment and a real system to demonstrate the effectiveness of the proposed methods. The results have demonstrated that the proposed APF-SDP outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.

Sensor networks introduce a new application domain and set of challenges in distributed computing including new network-level programming languages, global system abstractions, and general-purpose communication protocols. These challenges are brought about by the tight integration of computation, communication, and distributed real-time interaction with the physical world. With the growing interest in interconnecting different sensor networks across a wide-area communication infrastructure, an overarching challenge becomes one of arriving at an agreed-upon global sensor network architecture that ensures interoperability. Unlike the Internet, where a layered communication stack (namely, the TCP/IP stack) defines the network architecture, a sensor network architecture must unify not only communication interfaces but also programming interfaces, since network communication and computation functions are tightly intertwined. In that sense, the sensor network architecture refers to a layered stack of distributed computing abstractions. This paper presents an architecture and key considerations in designing and interconnecting local and global sensor networks. Candidate protocols and middleware instantiations are described from the authors' ongoing work that meet the discussed considerations

Graph invariants for unique localizability in cooperative localization of wireless sensor networks: Rigidity index and redundancy index

Sensor network usually refers to a wireless distributed self-configuring network that consists of sensors and execution units and is designed for machine-to-machine communication, automation, machine diagnostics, telemetry task solving. Sensor networks differ from other networks by the ease of use and deployment, low maintenance requirements, fault tolerance, and reliability. Every year, sensor networks, which are usually used in industrial systems, are increasingly exposed to malicious attacks. Although some sensor network communication protocols support encrypted connections, the nature of node data exchange allows attackers to identify key nodes and possible attack vectors. In this paper we present a method of hiding the architecture and configuration of the sensor network, which allows to protect against compromise not only the nodes involved in data exchange, but also the transmitted data. This method does not interfere with the attacker’s actions, but significantly complicates network traffic analysis, so that the intruder cannot carry out an attack because of the lack of data on network structure and configuration.

This chapter presents some considerations related to the applications in environment monitoring of some concepts as: estimation, fault detection and diagnosis, theory of distributed parameter systems and artificial intelligence based on the modern technology of wireless sensor networks. All these concepts allow treatment of large, complex, non-linear and multivariable system of the environment by learning and extrapolation. The environment may be seen as a complex ensemble of different distributed parameter systems, described with partial differential equations. Sensor networks (Akyildiz & all, 2002) have large and successful applications in monitoring the environment, they been capable to measure, as a distributed sensor, the physical variables, on a large area, which are characterizing the environment, and also to communicate at long distance the measured values, from the distributed parameter environmental processes. A lot of papers and books have been published in the fields of using sensor networks in environment monitoring in the last years. Some related work is surveyed as follows. The paper (Cuiyun & all., 2006) presents some research consideration related the changes of urban spatial thermal environment, for sustainable urban development, to improve the quality of human habitation environment. The urban thermal phenomenon is revealed using thermal remote sensing imagery, based on the instantaneous radiant temperature of the land surfaces. An architecture of sensor network for environment is presented in (Lan & all, 2008). Environmental pollution and meteorological processes may be studied using various kinds of environmental sensor networks. The modern intelligent sensor networks comprise automatic sensor nodes and communication systems which communicate their data to a sensor network server, where these data are integrated with other environmental information. The paper (Giannopoulos & all, 2009) presents the design and implementaion of a wireless sensor network for monitoring environmental variables and evaluates its effectiveness. It has application in environment variable monitoring such as: temperature, humidity, barometric pressure, soil moisture and ambient light, for research in agriculture, habitat monitoring, weather monitoring and so on. In order to improve the capacity of the environmental sensor networks different techniques may be used. The paper (Talukder & all, 2008) is using a model predictive control for optimal resource management in environment sensor networks, for with application at spatiotemporal events of a coastal monitoring and forecast system. The paper (Dardari & all, 2007)

This paper proposes two new non-reference image quality metrics that can be adopted by the state-of-the-art image/video denoising algorithms for auto-denoising. The first metric is proposed based on the assumption that the noise should be independent of the original image. A direct measurement of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus tackles the homogeneous regions and highly-structured regions separately. Nevertheless, this metric is only stable when the noise level is relatively low. Most denoising algorithms reduce noise by (weighted) averaging repeated noisy measurements. As a result, another metric is proposed for high-level noise based on the fact that more noisy measurements will be required when the noise level increases. The number of measurements before converging is thus related to the quality of noisy images. Our patch-matching based metric proposes to iteratively find and add noisy image measurements for averaging until there is no visible difference between two successively averaged images. Both metrics are evaluated on LIVE2 (Sheikh et al. in LIVE image quality assessment database release 2: 2013) and TID2013 (Ponomarenko et al. in Color image database tid2013: Peculiarities and preliminary results: 2005) data sets using standard Spearman and Kendall rank-order correlation coefficients (ROCC), showing that they subjectively outperforms current state-of-the-art no-reference metrics. Quantitative evaluation w.r.t. different level of synthetic noisy images also demonstrates consistently higher performance over state-of-the-art non-reference metrics when used for image denoising.

Understanding Packet Delivery Performance In Dense Wireless Sensor Networks ∗ Computer Science Department University of Southern California Los Angeles, CA 90089-0781 Jerry Zhao Computer Science Department University of Southern California Los Angeles, CA 90089-0781 Ramesh Govindan zhaoy@usc.edu ABSTRACT Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environ- ments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspec- tive, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal charac- teristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks. In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks. ramesh@usc.edu spectrum under use, the particular modulation schemes un- der use, and possibly on the communicating devices them- selves. Communication quality can vary dramatically over time, and has been reputed to change with slight spatial displacements. All of these are true to a greater degree for ad-hoc (or infrastructure-less) communication than for wire- less communication to a base station. Given this, and the paucity of large-scale deployments, it is perhaps not surpris- ing that there have been no medium to large-scale measure- ments of ad-hoc wireless systems; one expects measurement studies to reveal high variability in performance, and one suspects that such studies will be non-representative. Wireless sensor networks [5, 7] are predicted on ad-hoc wireless communications. Perhaps more than other ad-hoc wireless systems, these networks can expect highly variable wireless communication. They will be deployed in harsh, inaccessible, environments which, almost by definition will exhibit significant multi-path communication. Many of the current sensor platforms use low-power radios which do not have enough frequency diversity to reject multi-path prop- agation. Finally, these networks will be fairly densely de- ployed (on the order of tens of nodes within communica- tion range). Given the potential impact of these networks, and despite the anecdotal evidence of variability in wireless communication, we argue that it is imperative that we get a quantitative understanding of wireless communication in sensor networks, however imperfect. Our paper is a first attempt at this. Using up to 60 Mica motes, we systematically evaluate the most basic aspect of wireless communication in a sensor network: packet delivery. Particularly for energy-constrained networks, packet de- livery performance is important, since that translates to net- work lifetime. Sensor networks are predicated using low- power RF transceivers in a multi-hop fashion. Multiple short hops can be more energy-efficient than one single hop over a long range link. Poor cumulative packet delivery per- formance across multiple hops may degrade performance of data transport and expend significant energy. Depending on the kind of application, it might significantly undermine application-level performance. Finally, understanding the dynamic range of packet delivery performance (and the ex- tent, and time-varying nature of this performance) is impor- tant for evaluating almost all sensor network communication protocols. We study packet delivery performance at two layers of the communication stack (Section 3). At the physical-layer and in the absence of interfering transmissions, packet de- Categories and Subject Descriptors C.2.1 [Network Architecture and Design]: Wireless communication; C.4 [Performance of Systems]: Perfor- mance attributes, Measurement techniques General Terms Measurement, Experimentation Keywords Low power radio, Packet loss, Performance measurement 1. INTRODUCTION Wireless communication has the reputation of being no- toriously unpredictable. The quality of wireless communica- tion depends on the environment, the part of the frequency ∗ This work is supported in part by NSF grant CCR-0121778 for the Center for Embedded Systems. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SenSys’03, November 5–7, 2003, Los Angeles, California, USA. Copyright 2003 ACM 1-58113-707-9/03/0011 ... $ 5.00.

We present in this paper an investigation on a special class of wireless sensor networks for monitoring critical infrastructures that may extend for hundreds of miles in distances. Such networks are fundamentally different from traditional sensor networks in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Based on careful analysis of existing sensor network architectures, we first demonstrate the needs to develop new architecture and networking protocols to match the unique topology of chain-type sensor networks. We then propose hierarchical network architecture that consists of clusters of sensor nodes to enable the chain-type sensor networks to be scalable to cover typically long range of infrastructure with tolerable delay in network-wide data collection. To maintain energy efficient operations and maximize the lifetime for such a chain-type sensor network, we devise a smart strategy for the deployment of cluster heads. Protocols for network initialization and seamless operations of the chain-type sensor networks are also developed to match the proposed hierarchical architecture and cluster head deployment strategy. Simulations have been carried out to verify the performance of the hierarchical architecture, the smart node deployment strategy, and the corresponding network initialization and operation protocols.

The sensor network localization (SNL) problem in embedding dimension r consists of locating the positions of wireless sensors, given only the distances between sensors that are within radio range and the positions of a subset of the sensors (called anchors). Current solution techniques relax this problem to a weighted, nearest, (positive) semidefinite programming (SDP) completion problem by using the linear mapping between Euclidean distance matrices (EDM) and semidefinite matrices. The resulting SDP is solved using primal-dual interior point solvers, yielding an expensive and inexact solution. This relaxation is highly degenerate in the sense that the feasible set is restricted to a low dimensional face of the SDP cone, implying that the Slater constraint qualification fails. Cliques in the graph of the SNL problem give rise to this degeneracy in the SDP relaxation. In this paper, we take advantage of the absence of the Slater constraint qualification and derive a technique for the SNL problem, with exact data, that explicitly solves the corresponding rank restricted SDP problem. No SDP solvers are used. For randomly generated instances, we are able to efficiently solve many huge instances of this NP-hard problem to high accuracy by finding a representation of the minimal face of the SDP cone that contains the SDP matrix representation of the EDM. The main work of our algorithm consists in repeatedly finding the intersection of subspaces that represent the faces of the SDP cone that correspond to cliques of the SNL problem.

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction