Search results

This paper aims to provide a roadmap for organizations to build big data projects and reap the most rewards out of their data. It covers all aspects of big data project implementation, from data collection to final project evaluation.

In each stage of the proposed roadmap, we introduce different sets of information and communications technology platforms and tools to assist IT professionals and managers in gaining a comprehensive understanding of the methods and technologies involved and in making the best use of them. The authors also complete the picture by illustrating the process through different real-world big data projects implementations.

By adopting the proposed roadmap, companies and organizations willing to establish an effective and rewarding big data solution can tackle all implementation challenges in each stage of their big data project setup: from strategy elaboration to final project evaluation. Their expectations of privacy and security are also baked, in advance, into the big data project design.

While technologies to build and run big data projects have started to mature and proliferate over the last couple of years, exploiting all potentials of big data is still at a relatively early stage. The value of this paper consists in providing a clear and systematic methodology to move businesses and organizations from an opinion-operated era where humans’ skills are a necessity to a data-driven and smart era where big data analytics plays a major role in discovering unexpected insights in the oceans of data routinely generated or collected.

Energy consumption has always been the most serious issue to consider while deploying wireless sensor networks (WSNs). Sensor nodes are limited in power, computational capacities and memory so reporting the occurrence of specific events, such as fire or flooding, as quickly as possible using minimal energy resources is definitely a challenging issue. The purpose of this paper is to propose a new, reactive and energy‐efficient scheme for reporting events. In this scheme, nodes that detect a certain event will organize themselves into a cluster, elect a clusterhead that will collect data from the cluster members, aggregate it and forward it to the mobile sink.

In order to evaluate the scheme, a new sensor node model was designed, where the network layer is implemented from scratch. This layer contains the state process model of the algorithm which was made available through a high‐fidelity process modeling methodology.

Simulation results show that a high‐event notification delivery ratio and a significant energy saving is achieved by deploying the proposed sensor node model; comparisons with existing methods show the efficiency of using the new scheme.

The new contribution in this paper is a novel, reactive and energy‐efficient scheme for reporting events over WSNs. The concept introduced in this paper will decrease energy consumption inside the network and, thus, improve its lifetime.