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

Peak power management of datacenters has tremendous cost implications. While numerous mechanisms have been proposed to cap power consumption, real datacenter power consumption data is scarce. Prior studies have either used a small set of applications and/or servers, or presented data that is at an aggregate scale from which it is difficult to design and evaluate new and existing optimizations. To address this gap, we collect power measurement data at multiple spatial and fine-grained temporal resolutions from several geo-distributed datacenters of Microsoft corporation over 6 months. We conduct aggregate analysis of this data to study its statistical properties. We find evidence of self-similarity in power demands, statistical multiplexing effects, and correlations with the cooling power that caters to the IT equipment. With workload characterization a key ingredient for systems design and evaluation, we note the importance of better abstractions for capturing power demands, in the form of peaks and valleys. We identify attributes for peaks and valleys, and important correlations across these attributes that can influence the choice and effectiveness of different power capping techniques. We characterize these attributes and their correlations, showing the burstiness of small duration peaks, and the importance of not ignoring the rare but more stringent or long peaks. The correlations between peaks and valleys suggest the need for techniques to aggregate and collectively handle them. With the wide scope of exploitability of such characteristics for power provisioning and optimizations, we illustrate its benefits with two specific case studies. The first shows how peaks can be differentially handled based on our peak and valley characterization using existing approaches, rather than a one-size-fits-all solution. The second illustrates a simple capacity provisioning strategy for energy storage using the peak and valley characteristics.

/pdf/ace-abstracting-characterizing-and-exploiting-datacenter-10mggtpi74.pdf

ACE: Abstracting, characterizing and exploiting datacenter power demands

Chapter 9 Energy Harvesting Aware Power Management 9.1 Introduction The true autonomy of wireless sensor networks depends on their reliable operation for extended times without human intervention. Energy supply is a critical factor in this design. Wireless and ad-hoc deployment, which is essential in some scenarios and cost- effective in others, precludes the use of a wired energy infrastructure. The sensor nodes are thus forced to operate on limited battery reserves and low power design is an important design consideration [1, 2]. Unlike human carried devices such as hand-helds or cell-phones which can be returned to charging docks periodically, sensor node batteries are limited in supply. They cannot be replaced in the large numbers of nodes as the embedded nature of deployment makes it hard to access each individual node. A limited amount of energy supply, however, is not sufﬁcient to ensure uninterrupted operation for the several year long lifetimes typically expected from an embedded deployment. The small node size puts constraints on the

/pdf/energy-harvesting-aware-power-management-hoj6i26pn0.pdf

Energy Harvesting Aware Power Management

The crucial need for long-lived and autonomous operation has elevated power and energy consumption to primary optimization metrics  during wireless sensor network design. While most work in the field of power management and low-power design has focused on optimizing the energy consumer (i.e., the sensor node, including its hardware, software, applications, and network protocols), very little work has targeted the energy supply system itself. A practical approach to alleviating the problem of limited battery resources in sensor nodes is the use of environmental energy harvesting. Solar energy harvesting, in particular, holds significant promise since photovoltaic conversion techniques are now mature enough to permit the development of cheap and small, yet reasonably efficient, solar panels. Our demonstration showcases our recent research in designing solar energy harvesting systems, as well as harvesting aware performance scaling algorithms and network protocols.

/pdf/heliomote-enabling-long-lived-sensor-networks-through-solar-3u1ifnwyif.pdf

Heliomote: enabling long-lived sensor networks through solar energy harvesting (Demo)

The subject disclosure is directed towards a context-aware mobile crowd sourcing service / system. Context information is automatically collected for a mobile device via mobile-device sensors. When a task is received that specifies context-related criteria, a worker is selected for that task based at least in part upon the context information associated with that worker's mobile device. Sensors on the device may be leveraged to capture information related to performing the task. Also described is a cross-platform task configuration that allows a task to be written once and run on different mobile device platforms.

Context-aware mobile crowdsourcing

We consider the network information theoretic problem of finding the rate distortion bound when multiple correlated Gaussian sources are present. One of these is the source of interest but some side information from other sources is also transmitted to help reduce the distortion in the reproduction of the first source. The other sources are treated as helpers and are also coded. Special cases of this problem have been solved before, such as when the reproduction is lossless, when the sources are conditionally independent given one of them, or when the number of helpers is limited to one. We consider a generalized version and show that the previously derived expressions fall out as special cases of our bound. Our results can be directly utilized by designers to choose not only how many of the available sources should actually be communicated but also which sources have the highest potential to reduce the distortion.

/pdf/lossy-source-coding-of-multiple-gaussian-sources-m-helper-3n70oa3pv4.pdf

Aman Kansal

Papers

ACE: Abstracting, characterizing and exploiting datacenter power demands

Energy Harvesting Aware Power Management

Heliomote: enabling long-lived sensor networks through solar energy harvesting (Demo)

Context-aware mobile crowdsourcing

Lossy source coding of multiple Gaussian sources: m-helper problem