Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds

Abstract

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data has become easier than ever. However, large scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g. pay-as-you-go infrastructure). Further, not everyone is interested in such large scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called C-MOSDEN (Context-aware Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and is used to highlight the advantages of context-aware selective sensing.

Fog Computing for Sustainable Smart Cities

“…The use cases presented in this section are on 1) Smart Agriculture, 2) Smart Transportation, 3) Smart Healthcare, and 4) Smart Waste Management. These generic use case scenarios were extracted, with minor alterations, from two of our previous publications Perera et al 2015c]. We refer to the scenarios throughout this paper and have used them to extract major functionalities of fog computing platforms.…”

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

“…In order to compute the energy consumption of a sensor node [33], [34], it is necessary to take into consideration the energy consumed by every single operation performed by the node. Generally, the consumed energy relates to four main tasks, namely, sampling, logging, processing, and radio transmission.…”

“…Algorithm 1 -line (29)(30)(31)(32)(33)(34)(35)(36)(37)(38) :Afterward, the CH counts the number of occurrences of each sensor j in the second column of the table and stores them in a list according to their ascending order. Starting from the first sensor j in the ordered list, the CH looks in table 1 for the sensor j in the first column and extract the value of its max correlation from the third column.…”

Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

“…Location, context, and activity-aware selective sensing is used to reduce the consumption of energy, storage, and data processing requirements [16]. The cloud-offloaded global positioning system (CO-GPS) which provides sensing devices to assure duty cycle of the GPS receiver device and logging the millisecond raw data from the GPS signal for processing technique is helpful to conserve energy [17].…”