Big Data Collection in Large-Scale Wireless Sensor Networks

Abstract

Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

A Novel Security Protocol for Wireless Sensor Networks with Cooperative Communication

“…For instance, mote-type sensor has 8-bit 4 MHz microcontroller, 40 KB of memory, and a radio with a bit rate of about 10 kbps. However, midrange nodes, such as "UCLA/ROCKWELL'S WINS", have a strong ARM 1100 processor, 128 KB of RAM, and a 100 Kbps radio [60]. For our analysis in this section, we will assume that the sensors utilize a memory up to 10 kilobytes (1 kilobyte = 1024 bytes).…”

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

“…The literature defines big data according to a formalism called V; three to five Vs allow the characterization of this mass of data [18]. For Doug Laney [1], big data is characterized by the volume, speed, and variety of data, giving rise to the 3 Vs principle. Volume describes the size of the data, velocity refers to the speed at which the data is produced while variety describes the range of data types and sources.…”

“…The last decade has undeniably been the decade of the rapid growth of wireless communication technologies [1,2]. However, the many application perspectives of wireless communication-based applications, including increasingly common Wireless Sensor Networks (WSNs), continue to pose major technical and scientific challenges [3,4].…”

“…Furthermore, the number of application perspectives of WSNs, including precision agriculture, forest monitoring for fire detection, patient monitoring, natural disaster management, etc., makes it possible to consider the use of these networks for collecting big data generated by smart cities [7][8][9]. However, in such contexts, these WSNs are consisting of an almost large number of sensors to be deployed over large areas [1]. Deploying such a large number of sensors is a common practice to overcome frequent sensor failures and avoid human intervention to replace them or recharge their batteries.…”

“…However, few studies in the literature have considered the choice of a good sensor deployment architecture as a solution for both connectivity and energy optimization in Large-Scale Wireless Sensor Networks (LS-WSNs) [1,15,16]. The different deployment architectures, whether deterministic or random, generally consider a single objective to be achieved, that of coverage or connectivity.…”