http://www.eecs.ucf.edu/~dcm/Teaching/COT4810-Spring2011/Literature/SensorNetworksSurvey.pdf

Wireless sensor networks: a survey

https://www.science.smith.edu/~jcardell/Courses/EGR328/Readings/WSNSurvey2.pdf

Wireless sensor network survey

Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

/pdf/cloud-computing-state-of-the-art-and-research-challenges-49ws7tdgj3.pdf

Cloud computing: state-of-the-art and research challenges

The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

/pdf/internet-of-things-vision-applications-and-research-4xfstaiv80.pdf

Internet of things: Vision, applications and research challenges

In the last years, wireless sensor networks (WSNs) have gained increasing attention from both the research community and actual users. As sensor nodes are generally battery-powered devices, the critical aspects to face concern how to reduce the energy consumption of nodes, so that the network lifetime can be extended to reasonable times. In this paper we first break down the energy consumption for the components of a typical sensor node, and discuss the main directions to energy conservation in WSNs. Then, we present a systematic and comprehensive taxonomy of the energy conservation schemes, which are subsequently discussed in depth. Special attention has been devoted to promising solutions which have not yet obtained a wide attention in the literature, such as techniques for energy efficient data acquisition. Finally we conclude the paper with insights for research directions about energy conservation in WSNs.

/pdf/energy-conservation-in-wireless-sensor-networks-a-survey-1bl7d2obnm.pdf

Energy conservation in wireless sensor networks: A survey

Techniques for efficient access to flash databases are described. In one implementation, a method includes performing an operation on a flash database, supplementing at least one portion of a node translation table corresponding to at least one node involved in the operation, and semantically compressing at least one portion of the node translation table. The semantic compression includes discarding at least one log entry that is rendered obsolete by at least one subsequent log entry, and incrementing a version number of the log entries corresponding to the at least one portion of the node translation table. In further embodiments, discarding at least one log entry includes discarding at least one log entry that is at least one of opposed by or overruled by at least one subsequent log entry.

Efficient access of flash databases

We present SenseWeb, an open and scalable infrastructure for sharing and geocentric exploration of sensor data streams. SenseWeb allows sensor owners to share data streams across multiple applications and users, thus amortizing sensor deployment costs effectively. It also provides mechanisms to transparently index and cache data, to process spatio-temporal queries on real-time and historic data, and to aggregate and present results on a geocentric web interface. In this paper, we present the architecture of SenseWeb, its techniques to enable global sharing of heterogeneous sensors, and its mapbased front-end for spatio-temporal data exploration. We enable interactive geocentric data exploration in the mapbased front-end using techniques for rapidly changing map overlaid visualizations of numerous data streams. We also demonstrate flexibility and scalability of the architecture by evaluating a deployed prototype of SenseWeb, which has been publicly available since March 2008.

/pdf/sharing-and-exploring-sensor-streams-over-geocentric-5gtonoa9pl.pdf

Sharing and exploring sensor streams over geocentric interfaces

Recovery is expedited for crashes involving flash memory. Rather than requiring an entire flash memory to be read to reconstruct lost information, only a subset of the memory need be read thereby reducing system down time, among other things. In particular, state information such as a logical to physical mapping is captured via a checkpoint operation periodically. Moreover, a deterministic usage pattern is employed to facilitate recovery of actions performed after a checkpoint.

Rapid crash recovery for flash storage

Disaster recovery is provided for an application that is being hosted on a current data center, thus ensuring the availability of the application. An option for replicating session state data for the application is selected. This selection is made from a set of different session state data replication options each of which has different performance and resource cost trade-offs. The selected option determines how the session state data for the application is to be replicated. The selected option is implemented, where the implementation results in the session state data for the application being replicated outside of the current data center, thus ensuring that this data remains available in the event that the current data center goes offline.

Geo-distributed disaster recovery for interactive cloud applications

Sensor networks collect data at multiple distributed nodes and transfer the acquired information to points of interest. The raw data collected by each individual sensor is typically not of interest. Instead, a reduced representation of the measured phenomenon is to be generated. Multiple readings, however, add to the information about the phenomenon by providing its description at multiple points in space for distributed phenomena and multiple perspectives for a localized phenomenon. We also note that sensor readings have noise, and multiple readings can help mitigate the effect of this noise. Thus, while all the sensor readings need not be communicated, enough data must be exchanged to reliably reproduce the phenomenon. Considering the above effects, it becomes important to determine how much data should be transmitted from multiple sensors such that only useful information is exchanged and energy or bandwidth are not wasted on redundant data. We address this question using information theoretic techniques. The effects of sensor noise and correlation in the sensor readings are explicitly modelled.

/pdf/fidelity-and-resource-sensitive-data-gathering-2acjsjzo25.pdf

Aman Kansal

Papers

Efficient access of flash databases

Sharing and exploring sensor streams over geocentric interfaces

Rapid crash recovery for flash storage

Geo-distributed disaster recovery for interactive cloud applications

Fidelity and Resource Sensitive Data Gathering