Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds I E E E T R A N SA C T I O N S O N CO M P U TAT I O N A L S O CI A L SYST E M S , D EC . 2 0 1 5

C H A R I T H P E R E R A , M E M B E R , I E E E , D U M I D U S . TA L AG A L A , M E M B E R , I E E E , C H I H A RO L D L I U, S E N I O R M E M B E R , I E E E , A N D J U L I O C . EST R E L L A , M E M B E R , I E E E

Introduction 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.

The purpose of this paper is to collect information from a large group of people in order to analyze and use that information for the benefit of the group as a whole.

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Nonselective Sensing The authors define nonselective sensing as the process of collecting sensor data from all possible sensors available, all the time without any filtering.

Issues:1. Cost : computational resource requirements (e.g., CPU, memory, and

storage space).

2. Energy consumption

3. Network communication

The authors propose a scalable energy-efficient data analytics platform for on-demand distributed mobile crowd sensing called C-MOSDEN

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IoT Middleware Large-scale data management tasks are economically costly. For example, Microsoft Azure cloud computing platform charges 541

USD/month for 8 cores and 14 GB RAM.

Need a mobile crowd sensing platform that is capable of capturing sensor data on-demand based on user requests and the conditions imposed by the data consumers.

Sensing as a service model : Global sensor network (GSN)

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Global sensor network (GSN) An IoT cloud platform aimed at providing flexible middleware to address the challenges of sensor data integration and distributed query processing.

GSN provides the capability to integrate, discover, combine, query, and filter sensor data through a declarative XML-based language.

“Virtual Sensor”

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http://lsir.epfl.ch/research/current/gsn/https://github.com/LSIR/gsn

C-MOSDEN Context aware mobile sensor data engine (C-MOSDEN) Client-side tool

Collect and process sensor data without programming efforts

plug-in-based IoT middleware

Activity-aware module. Recognizing six different activities: moving

in a vehicle, cycling, walking, running, still (not moving), tilting(falling).

Location-aware module Latitude, longitude, radius in meters.

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Scenario1. Environmental Monitoring

Air pollution sensor on bus

2. Rehabilitation Wearable sensors

3. Health and Well-Being

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Query in C-MOSDEN

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System Work Flow

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Cost ModelThe authors compare context-aware selective sensing and nonselective noncontext-aware sensing.

1. Energy

2. Storage

3. Network communication

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Implementation Android platform

Google Nexus 4 device (Android 4.4)

Experiments consists of both real-world experiments and simulated lab-based experiments.

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Experiments1. Overheads created by activity-aware and location-aware capabilities.

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Experiments2. Test the capabilities of C-MOSDEN in the real world.

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Experiments3. Simulate the three scenarios

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Compare to other works1. DAM4GSN

Based on GSN

No processing capabilities

2. Dynamix Plug-and play context framework for Android

3. NORS Sharing data among mobile phone is not supported.

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Conclusion Although the context-aware functionalities have generated a small amount of overhead, it was revealed that the cost savings and benefits far outweighed the increased complexity.

Future works : privacy-preserving data analytics capabilities.

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MOSDEN: https://bitbucket.org/ngcharithperera/mosden