Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

On robust estimation of the location parameter

Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category. Moreover, protocols using contemporary methodologies such as network flow and quality of service modeling are also discussed. The paper concludes with open research issues. � 2003 Elsevier B.V. All rights reserved.

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A survey on routing protocols for wireless sensor networks

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Locating the nodes: cooperative localization in wireless sensor networks

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Resource reservation - both just in time and advance in time - has been advocated as a means of guaranteeing quality of service (QoS) to mobile users. Often, advance bandwidth reservation results in low utilization due to bandwidth fragmentation wherein holes of unused bandwidth in time may be too small to allocate to any user yet the sum of several holes may be sufficient to satisfy the user. In this paper, we introduce and investigate the problem of bandwidth fragmentation and explore different methods of minimizing bandwidth fragmentation. One is a selective call rejection scheme and the other a multi-class bandwidth sharing scheme. Through an extensive set of simulation results, we show that our proposed schemes reduces bandwidth fragmentation.

Bandwidth fragmentation in multimedia cellular network

A system and method for automatically altering device functionality based on the occurrence of certain predetermined conditions. A link may be established between a device and a trusted server to provide an association between various conditions that ma be detected at the device and actions that are to be taken on the device. In particular, software traps can be set up and linked to device functionality such that execution of the trap may automatically disable or enable certain device capabilities. Some aspects of the invention are directed to a system and method for remotely setting software traps for detecting software viruses and, upon execution of the traps, several methods for establishin a uarantine on infected devices

System and method for remotely controlling device functionality

To avoid congestion before it happens, the traffic from the sources should be "reduced" (traffic control) or the available resources should be "increased" (resource control) in advance. While the traffic control in the context of congestion avoidance has been extensively studied mostly through the active queue management (AQM), resource control has received little attention for congestion avoidance. In this paper, we present a congestion avoidance scheme called congestion-aware packet generalized processor sharing (CPGPS) that differentiates between transient congestion and persistent congestion and takes different actions based on the type of congestion when congestion is anticipated. Unlike persistent congestion that is avoided normally by controlling the incoming traffic using the AQM algorithm, we show that transient congestion can be better handled by temporarily increasing the bandwidth available to the sources of transient congestion before packets belonging to these sources start to be dropped. To allocate bandwidth fairly among the sources of transient congestion, CPGPS is implemented on top of the packet fair queueing (PFQ) algorithm.

CPGPS: congestion-aware packet generalized processor sharing

The current pollution measurement methodology is coarse-grained where the pollution measurements are spatiotemporally few and far in-between. Our vision is to provide broadly accessible, fine-grained pollution information to a variety of end-users, and in turn, allow them to make better informed decisions using a new, more accurate information stream. To this end, this study proposes a new neural network model to estimate Carbon Monoxide (CO) concentrations outside vehicle from crowd-sourced CO measurements inside vehicles measured using mobile devices (dosimeters). End-users can benefit from the fine-grained pollution information generated by this prediction model along with data from direct measurements. A neural network is used to model the dynamic relationship between the CO measurements inside and outside a moving vehicle. The resulting neural network model is then used to predict outside CO concentrations from CO measurements inside vehicles. Mobile CO dosimeters were used inside and outside vehicles to collect measurements used in training a neural network based regression model. For this regression task, a new neural network architecture was designed using Convolutional layers and Gated Recurrent Unit (GRU) layers. The results show that outside CO concentrations can be estimated from inside vehicle CO measurements with high accuracy. The proposed neural network model provides a promising new and novel source of fine-grained pollution information along with direct measurement streams.

InsideOut: Model to Predict Outside CO Concentrations from Mobile CO Dosimeter Measurements Inside Vehicles

Systems and methods are described for establishing credentials at a device and at a device management server for the purpose of exchanging secure credentials in order to mutually authenticate the device and the server. A credential generation algorithm is described which uses a plurality of seeds, including the hardware identity of the device, the server identity, and a shared private key, to generate two sets of credentials, one to be used by the device and the other to be used by the device management server. The credentials are exchanged between the device and the server during any session, thereby assuring mutual authentication.

Badri Nath

Papers

Bandwidth fragmentation in multimedia cellular network

System and method for remotely controlling device functionality

CPGPS: congestion-aware packet generalized processor sharing

InsideOut: Model to Predict Outside CO Concentrations from Mobile CO Dosimeter Measurements Inside Vehicles

Credential generation management servers and method for communications devices and device management servers