Data collection, storage and retrieval with an underwater sensor network

In this paper we present a novel platform for underwater sensor networks to be used for long-term monitoring of coral reefs and �sheries. The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate point-to-point using a novel high-speed optical communication system integrated into the TinyOS stack, and they broadcast using an acoustic protocol integrated in the TinyOS stack. The nodes have a variety of sensing capabilities, including cameras, water temperature, and pressure. The mobile nodes can locate and hover above the static nodes for data muling, and they can perform network maintenance functions such as deployment, relocation, and recovery. In this paper we describe the hardware and software architecture of this underwater sensor network. We then describe the optical and acoustic networking protocols and present experimental networking and data collected in a pool, in rivers, and in the ocean. Finally, we describe our experiments with mobility for data muling in this network.

Kires: a data-centric telerehabilitation system based on kinect

185 p.

Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks

Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.

Active data-centric framework for data protection in cloud environment

Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users’ concern that their confidential data or privacy would leak out in the cloud’s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users’ data in cloud. Our data-centric framework emphasizes “active” feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request’s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.

On data-centric misbehavior detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is a very important problem with wide range of implications, including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. It is therefore more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alerts with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. After misbehavior is detected, we do not revoke all the secret credentials of misbehaving nodes, as done in most schemes. Instead, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes. © 2011 IEEE.

Intrusion-Resilient Integrity in Data-Centric Unattended WSNs

Unattended Wireless Sensor Networks (UWSNs) operate in autonomous or disconnected mode: sensed data is collected periodically by an itinerant sink. Between successive sink visits, sensor-collected data is subject to some unique vulnerabilities. In particular, while the network is unattended, a mobile adversary (capable of subverting up to a fraction of sensors at a time) can migrate between compromised sets of sensors and inject fraudulent data. In this paper, we provide two collaborative authentication techniques that allow an UWSN to maintain integrity and authenticity of sensor data-in the presence of a mobile adversary-until the next sink visit. Proposed schemes use simple, standard, and inexpensive symmetric cryptographic primitives, coupled with key evolution and few message exchanges. We study their security and effectiveness, both analytically and via simulations. We also assess their robustness and show how to achieve the desired trade-off between performance and security.

Climatography of the United States, no. 70-26 [etc.] Precipitation data from storage-gage stations.

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A highly practical approach toward achieving minimum data sets storage cost in the cloud

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

Real-Time Spatio-Temporal Query Processing in Mobile Ad-Hoc Sensor Networks

Personal communication devices are increasingly equipped with sensors that are able to collect and locally store information from their environs. The mobility of users carrying such devices, and hence the mobility of sensor readings in space and time, opens new horizons for interesting applications. In particular, we envision a system in which the collective sensing, storage and communication resources, and mobility of these devices could be leveraged to query the state of (possibly remote) neighborhoods. Such queries would have spatio-temporal constraints which must be met for the query answers to be useful. Using a simplified mobility model, we analytically quantify the benefits from cooperation (in terms of the system's ability to satisfy spatio-temporal constraints), which we show to go beyond simple space-time tradeoffs. In managing the limited storage resources of such cooperative systems, the goal should be to minimize the number of unsatisfiable spatio-temporal constraints. We show that Data Centric Storage (DCS), or "directed placement", is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, "amorphous placement", in which sensory samples are cached locally, and shuffling of cached samples is used to diffuse the sensory data throughout the whole network. We evaluate conditions under which directed versus amorphous placement strategies would be more efficient. These results lead us to propose a hybrid placement strategy, in which the spatio-temporal constraints associated with a sensory data type determine the most appropriate placement strategy for that data type. We perform an extensive simulation study to evaluate the performance of directed, amorphous, and hybrid placement protocols when applied to queries that are subject to timing constraints. Our results show that, directed placement is better for queries with moderately tight deadlines, whereas amorphous placement is better for queries with looser deadlines, and that under most operational conditions, the hybrid technique gives the best compromise.

Novel approach for information discovery in autonomous wireless sensor networks

Autonomous Wireless sensor networks(WSNs) have sensors that are usually deployed randomly to monitor one or more phenomena. They are attractive for information discovery in large-scale data rich environments and can add value to mission–critical applications such as battlefield surveillance and emergency response systems. However, in order to fully exploit these networks for such applications, energy efficient, load balanced and scalable solutions for information discovery are essential. Multi-dimensional autonomous WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage of in-network information discovery. In this paper, we propose a novel method for information discovery for multi-dimensional autonomous WSNs which sensors are deployed randomly that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service (QoS) improvements that are of immense benefit to mission–critical applications. We present simulation results to show that the proposed approach to information discovery offers significant improvements on query resolution latency compared with current approaches.

Collaborating to develop research data management services and collections

The Queensland University of Technology (QUT) Library, like many other academic and research institution libraries in Australia, has been collaborating with a range of academic and service provider partners to develop a range of research data management services and collections. Three main strategies are being employed and an overview of process, infrastructure, usage and benefits is provided of each of these service aspects. The development of processes and infrastructure to facilitate the strategic identification and management of QUT developed datasets has been a major focus. A number of Australian National Data Service (ANDS) sponsored projects - including Seeding the Commons; Metadata Hub / Store; Data Capture and Gold Standard Record Exemplars have / will provide QUT with a data registry system, linkages to storage, processes for identifying and describing datasets, and a degree of academic awareness. QUT supports open access and has established a culture for making its research outputs available via the QUT ePrints institutional repository. Incorporating open access research datasets into the library collections is an equally important aspect of facilitating the adoption of data-centric eresearch methods. Some datasets are available commercially, and the library has collaborated with QUT researchers, in the QUT Business School especially strongly, to identify and procure a rapidly growing range of financial datasets to support research. The library undertakes licensing and uses the Library Resource Allocation to pay for the subscriptions. It is a new area of collection development for with much to be learned. The final strategy discussed is the library acting as “data broker”. QUT Library has been working with researchers to identify these datasets and undertake the licensing, payment and access as a centrally supported service on behalf of researchers.

Data storage system for wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015