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

Consider an example: covering a shopping mall using surveillance cameras. Suppose a volume V1 is to be covered. Given a camera that can effectively cover a volume V2, we need approximately V1/V2 cameras. However, in typical applications, the entire volume V1 may not be of interest but only small portions of it (such as spaces containing human faces, for face recognition) may be required to be imaged. This volume is a very small fraction, p, of V1, reducing the number of cameras required to pV1/V2. This reduction is possible only if the cameras can be adaptively moved to orient towards the interesting regions. In many applications, detecting regions of interest, such as humans, is possible using a low resolution image (such as detecting motion for detecting potentially interesting regions where humans may be present) or alternative forms of sensors. We develop methods which use this low resolution side information to adaptively control the motion of deployed cameras such that a smaller number of cameras, pV1/v2 can achieve the effective coverage of V1/V2 cameras.

https://www.microsoft.com/en-us/research/wp-content/uploads/2005/11/kansal_sensys05.pdf

Coordinating camera motion for sensing uncertainty reduction

A power supply is described herein which provides power to a load, such as a load including one or more computing devices. The power supply uses a slow-response power source (such as a fuel-driven mechanism) to handle a slow-moving component of the demand level presented by the load, and uses a fast-response power source (such as a battery or a capacitor, etc.) to handle a fast-moving component of the demand level. By virtue of this approach, the power supply can manage the load level as it appears to the slow-response power source, allowing, in turn, the slow-response power source to service even fast-changing loads—a task which it could not otherwise perform due to its native limitations.

Power supply for use with a slow-response power source

Wireless Sensor network is a network of sensor nodes. A sensor node has, in addition to computation, sensing and wireless communication capability. A network of these nodes is deployed to measure the ecological properties like temperature etc. There is a base station which collects the data from these nodes. The data reaches the base station wirelessly by the multihop communication path established by the sensor nodes. The sensor nodes are resource constrained, and communication is the major consumer of battery resource. The nodes near the base station relay other further nodes data, and as a result die earlier. Once they are gone, the network is effectively disconnected. We explore the option of a chargeable mobile base station traversing the network, and the nodes transfer the data when it is near. This improves the lifetime of the network. The work involves designing network algorithms for the system, and adaptive speed control strategies for the mobile.

/pdf/controlled-mobility-for-increased-lifetime-in-wireless-58shmxccmc.pdf

Controlled Mobility for Increased Lifetime in Wireless Sensor Networks

Many context aware applications can benefit from using high-level sensing results with semantic meanings (e.g, busy/idle). This paper proposes a platform design that provides high-level "virtual sensor" abstractions and enables new virtual sensors to be bootstrapped from existing ones.

Poster: supporting collaborative sensing applications

1. Summary Recent studies estimate that server cost contributes to as much as 57% of the total cost of ownership (TCO) of a datacenter [1]. One key contributor to this high server cost is the procurement of memory devices such as DRAMs, especially for data-intensive datacenter cloud applications that need low latency (such as web search, in-memory caching, and graph traversal). Such memory devices, however, may be prone to hardware errors that occur due to unintended bit flips during device operation [40, 33, 41, 20]. To protect against such errors, traditional systems uniformly employ devices with highquality chips and error correction techniques, both of which increase device cost. At the same time, we make the observations that 1) data-intensive applications exhibit a diverse spectrum of tolerance to memory errors, and 2) traditional one-size-fits-all memory reliability techniques are inefficient in terms of cost. Our DSN-44 paper [30] is the first to 1) understand how tolerant different data-intensive applications are to memory errors and 2) design a new memory system organization that matches hardware reliability to application tolerance in order to reduce system cost. The main idea of our approach is to classify applications based on their memory error tolerance, and map applications to heterogeneous-reliability memory system designs managed cooperatively between hardware and software to reduce system cost. Our DSN-44 paper provides the following contributions: 1. A new methodology to quantify the tolerance of applications to memory errors. Our approach measures the effect of memory errors on application correctness and quantifies an application’s ability to mask or recover from memory errors. 2. A comprehensive characterization of the memory error tolerance of three data-intensive workloads: an interactive web search application [30, 39], an in-memory key‐value store [30, 3], and a graph mining framework [30, 29]. We find that there exists an order of magnitude difference in memory error tolerance across these three applications. 3. An exploration of the design space of new memory system organizations, called heterogeneous-reliability memory, which combines a heterogeneous mix of reliability techniques that leverage application error tolerance to reduce system cost. We show that our techniques can reduce server hardware cost by 4.7%, while achieving 99.90% single server availability.

/pdf/heterogeneous-reliability-memory-exploiting-application-1gct6l9nov.pdf

Aman Kansal

Papers

Coordinating camera motion for sensing uncertainty reduction

Power supply for use with a slow-response power source

Controlled Mobility for Increased Lifetime in Wireless Sensor Networks

Poster: supporting collaborative sensing applications

Heterogeneous-Reliability Memory: Exploiting Application-Level Memory Error Tolerance