Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

A Network-Aware Distributed Membership Protocol for Collaborative Defense

To counteract current trends in network malware, distributed solutions have been developed that harness the power of collaborative end-host sensors. While these systems greatly increase the ability to defend against attack, this comes at the cost of complexity due to the coordination of distributed hosts across the dynamic network. Many previous solutions for distributed membership maintenance are agnostic to network conditions and have high overhead, making them less than ideal in the dynamic enterprise environment. In this work, we propose a network-aware, distributed membership protocol, CLUSTER, which improves the performance of the overlay system by biasing neighbor selection towards beneficial nodes based on multiple system metrics and network social patterns (of devices and their users). We provide an extensible method for aggregating and comparing multiple, possibly unrelated metrics. We demonstrate the effectiveness and utility of our protocol through simulation using real-world data and topologies. As part of our results, we highlight our analysis of node churn statistics, offering a new distribution to accurately model enterprise churn

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Chandra Study of the Cepheus B Star Forming Region: Stellar Populations and the Initial Mass Function

Cepheus B (Cep B) molecular cloud and a portion of the nearby Cep OB3b OB association, one of the most active regions of star formation within 1 kpc, has been observed with the ACIS detector on board the Chandra X-ray Observatory. We detect 431 X-ray sources, of which 89% are confidently identified as clustered pre-main sequence stars. Two main results are obtained. First, we provide the best census to date for the stellar population of the region. We identify many members of two rich stellar clusters: the lightly obscured Cep OB3b association, and the deeply embedded cluster in Cep B whose existence was previously traced only by a handful of radio sources and T Tauri stars. Second, we find a discrepancy between the X-ray Luminosity Functions of the Cep OB3b and the Orion Nebula Cluster. This may be due to different Initial Mass Functions of two regions (excess of ~0.3 solar mass stars), or different age distributions. Several other results are obtained. A diffuse X-ray component seen in the field is attributed to the integrated emission of unresolved low mass PMS stars. The X-ray emission from HD 217086 (O7n), the principle ionizing source of the region, follows the standard model involving many small shocks in an unmagnetized radiatively accelerated wind. The X-ray source #294 joins a number of similar superflare PMS stars where long magnetic structures may connect the protoplanetary disk to the stellar surface.Comment: 72 pages, 31 figures, 8 tables. Accepted for publication in Ap

Spatial autocorrelation analysis in plant population: An overview

Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the many local spatial variation and spatial dependency while global measurements provide only one set of values that represent the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are also given