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

Progress in the area of MHD stability and disruptions, since the publication of the 1999 ITER Physics Basis document (1999 Nucl. Fusion 39 2137-2664), is reviewed. Recent theoretical and experimental research has made important advances in both understanding and control of MHD stability in tokamak plasmas. Sawteeth are anticipated in the ITER baseline ELMy H-mode scenario, but the tools exist to avoid or control them through localized current drive or fast ion generation. Active control of other MHD instabilities will most likely be also required in ITER. Extrapolation from existing experiments indicates that stabilization of neoclassical tearing modes by highly localized feedback-controlled current drive should be possible in ITER. Resistive wall modes are a key issue for advanced scenarios, but again, existing experiments indicate that these modes can be stabilized by a combination of plasma rotation and direct feedback control with non-axisymmetric coils. Reduction of error fields is a requirement for avoiding non-rotating magnetic island formation and for maintaining plasma rotation to help stabilize resistive wall modes. Recent experiments have shown the feasibility of reducing error fields to an acceptable level by means of non-axisymmetric coils, possibly controlled by feedback. The MHD stability limits associated with advanced scenarios are becoming well understood theoretically, and can be extended by tailoring of the pressure and current density profiles as well as by other techniques mentioned here. There have been significant advances also in the control of disruptions, most notably by injection of massive quantities of gas, leading to reduced halo current fractions and a larger fraction of the total thermal and magnetic energy dissipated by radiation. These advances in disruption control are supported by the development of means to predict impending disruption, most notably using neural networks. In addition to these advances in means to control or ameliorate the consequences of MHD instabilities, there has been significant progress in improving physics understanding and modelling. This progress has been in areas including the mechanisms governing NTM growth and seeding, in understanding the damping controlling RWM stability and in modelling RWM feedback schemes. For disruptions there has been continued progress on the instability mechanisms that underlie various classes of disruption, on the detailed modelling of halo currents and forces and in refining predictions of quench rates and disruption power loads. Overall the studies reviewed in this chapter demonstrate that MHD instabilities can be controlled, avoided or ameliorated to the extent that they should not compromise ITER operation, though they will necessarily impose a range of constraints.

https://iopscience.iop.org/article/10.1088/0029-5515/47/6/S03/pdf

Chapter 3: MHD stability, operational limits and disruptions

Review of IoT applications in agro-industrial and environmental fields

Recent technological advances have led to an increase in the carbon footprint. Energy efficiency in the Internet of Things (IoT) has been attracting a lot of attention from researchers and designers over the last couple of years, paving the way for an emerging area called green IoT. There are various aspects (such as key enablers, communications, services, and applications) of IoT, where efficient utilization of energy is needed to enable a green IoT environment. We explore and discuss how the various enabling technologies (such as the Internet, smart objects, sensors, etc.) can be efficiently deployed to achieve a green IoT. Furthermore, we also review various IoT applications, projects and standardization efforts that are currently under way. Finally, we identify some of the emerging challenges that need to be addressed in the future to enable a green IoT.

Enabling Technologies for Green Internet of Things

In recent years there has been several technological advances in Wireless Sensor Networks (WSN), but energy still remains a paramount resource. The amount of available energy has a direct effect on the performance, functionality and lifetime of WSN. Being bound by cost and size, sensor nodes are usually equipped with limited amount of energy and therefore requires a replacement of batteries occasionally. But replacement might not always be feasible option and in some scenarios might even be prohibitive. This indicates the need for more viable solutions, these involve generating energy at the sensor nodes or have it delivered to them i.e., energy harvesting or wireless energy transfer. The objective of this paper is threefold: first we present a survey on potential renewable energy resources along with their characteristics and applications in WSN. Second, this study also describes various battery recharging techniques and their applications with respect to WSN. Finally, we discuss formidable issues, challenges and future research directions.

Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review

wildCENSE is a Wireless Sensor Network (WSN) system which attempts to monitor the behaviour and migration patterns of Barasingha (Swamp Deer). The system would collect the micro-climatic as well as positional information of the animal and communicate it to a base station through flooding of data using peer-to-peer network. The base station, using a gateway, will upload all the collected data to a database server on Internet and portray the information using browser based visualization software. Each node would monitor five parameters namely position (using GPS), temperature, humidity, head orientation and ambient light. Also, the node will have a real time clock for the synchronization of the network and to keep timing information. An external data flash memory would be used to record the data collected from sensors and other peer nodes. A radio transceiver would transmit the data to the base station by using a peer to peer communication protocol. A solar energy harvesting system for recharging nodepsilas power supply batteries is being added to prolong the lifetime of nodes. The system would be integrated in the form of a collar that can be easily fitted on the neck of animal.

wildCENSE: GPS based animal tracking system

A neural network technique has been used to predict disruptions in the ADITYA tokamak. A time series prediction method is employed whereby a series of past values of some time dependent quantity is used to predict its value in the future. The time varying observables used in the present work are the different diagnostic signals from four Mirnov probes, one soft X ray monitor and one Hα monitor. The predicted quantities are the same observables at some future time. The neural network is trained with the past values of the different diagnostic signals as inputs and the future values of the same quantities as targets. The trained neural network is used to forecast in a multistep sequence. This amounts to a prediction several time steps earlier. Very good prediction results have been obtained up to 8 ms earlier with little distortion of the signals and no appreciable time lag, a capability which is believed to be well suited to the task of on-line predictions of disruptions in ADITYA. As actual experimental signals are used, confidence regarding the performance of the neural network on hardware implementation is automatically ensured.

http://iopscience.iop.org/article/10.1088/0029-5515/40/12/304/pdf

Forecasting disruptions in the ADITYA tokamak using neural networks

Wireless sensor network is a densely deployed resource stress network where each small chunk of resource adds the high value to over all network functionality. Topology control is a technique through which network controls it's dense deployment for efficient resource utilization. This article examines the classification of topology control of emerging wireless sensor network field, based on different Network models, graph models and node groupings. Further article covers the effect of mobility on topology control and various topology control measurement parameters. This article will aid in defining appropriate topology control mechanism, allowing network designers to choose the protocol architecture that best matches the goals of their application. In addition, this article will enable new sensor network topology control method to be defined for use in further research in this area.

A Survey: Topology Control For Wireless Sensor Networks

Wireless Sensor Network (WSN) in combination with image sensors opens plethora of opportunities in the wildlife tracking. It provides a glimpse into previously unseen, remote and inaccessible world of some of the most endangered species on earth. tigerCENSE is such an attempt to put sensor network technology in conserving one of the rarest and most elusive big cat species. The node, triggered by the Passive Infrared (PIR) sensor, captures the image of tiger using a CMOS image sensor and stores it in an external memory chip. To avoid any disturbance to animal, the node uses an Infrared (IR) flash, instead of white flash, to illuminate the target at night. The stored images get transferred to the base station via radio transceiver. This is transferred to the database server through Internet links for analysis by wildlife researchers. A solar energy harvesting system for recharging node's batteries is being added to avoid frequent human visit to change the batteries, making it highly non-intrusive system.

TigerCENSE: wireless image sensor network to monitor tiger movement

mobile target tracking in sensor network field is an important area of wireless sensor network (WSN) application. The speed of target and constraints on WSN nodes vary from one application to another. In this paper, we report our work on application of WSN to track movements of small turtles in Wildlife Institute of India (WII) campus. This would allow wildlife researchers to understand behaviour of these turtles. These turtles are very small in size and Global Positioning System (GPS) based monitoring is not very useful. So we have developed a GPS-less system to track movement of these turtles. In addition, we monitor the micro-climate around the animal as well as monitor period of hibernation during winter time. We have carried out trial of this system in our campus with a simulated mobile target carrying one node with a grid of four fixed sensor nodes. This system uses incremental grid based approach to localize moving target. The data obtained is passed on to a Web based data server through a gateway and movement is shown on a digital map using a browser based visualization software utilizing Google Map APIs (application programming interface).

Prabhat Ranjan

Papers

wildCENSE: GPS based animal tracking system

Forecasting disruptions in the ADITYA tokamak using neural networks

A Survey: Topology Control For Wireless Sensor Networks

TigerCENSE: wireless image sensor network to monitor tiger movement

GPS-less animal tracking system