2015 IEEE International Conference on Consumer Electronics (ICCE)
Self-Organized Power Consumption Approximation in the Internet of Things Komal Batool+ and Muaz A. Niazit* +
National University of Science & Technology, Islamabad, Pakistan tCOMSATS Institute of IT, Islamabad, Pakistan Email:
[email protected], *Corresponding author
Abstract-The Internet of Things vision has recently emerged as a result of a proliferation of a large number of networked consumer electronic devices.
There is a growing need to autonomously
monitor power consumption of these devices. We present a self organizing distributed algorithm for the dynamic approximation of power consumption in networked consumer electronic devices. Index Terms-internet of things, consumer network, power
consumption, monitoring
network modeling strategies-random network connectIvIty strategy
(CR )
and lattice network connectivity strategy
(CL)'
This can practically be easily implemented by using any multiagent software toolkit capable of execution on the device
[3]. A. SOPCA Algorithm The basic idea of the algorithm presented in figure 1 is
I. INTRO DUCTION
the use of wireless connectivity between peer devices and
We live in an era of networked consumer electronic devices
server. Devices discover other devices to propagate Energy
from mobile Smartphones and tablets to laptops, from intel
Sniffer Agent (ESA). ESAs locate other devices and keep
ligent thermostats to microwave ovens and smart televisions.
estimating energy consumption till their time out of the Time
The vision of the Internet of Things (loT) by Ashton [1] thus
to-Live (TTL), at which instance they return to sink nodes.
appears to be manifesting itself. For a large number of reasons
We would like to note here that similar ideas of connectivity
such as a high power requirement of wireless connectivity or
of various consumer devices have previously been presented
even co-located power adapters of devices, consumer elec
in [4],
tronic devices can tend to pose as "invisible" heat sources.
proximity by using either signal strength or Global Positioning
[5]. Source nodes can locate other nodes in their
Continuous power usage also results in a requirement for
System (GPS). ESAs update their internal variables based on
air-conditioning bills which is also avoidable. The resultant
observation of local energy consumption and next move on
effects on the planet 's environment predicted by the laws of
to other nodes. To avoid a re-routing through devices which
thermodynamics are quite frightening by themselves [2].
have already been discovered, ESA's can also set a flag on
In this paper, we present first steps towards autonomous
individual devices.
monitoring and approximation of power consumption by net worked devices by using energy sniffer agents. The objective of this research is to present a mechanism to anonymously and autonomously monitor energy power consumption of networked
consumer electronic devices
thereby
giving an
approximation of the current power requirement of a building or room. The structure of the paper is as follows: first, we describe the methods. This is followed by the results and
No
discussion section. Finally, the paper is concluded. Figure 1. SOPCA Mechanism
II. SELF-ORGANIZED POWER CONSUMPTION ApPROXIMATION ALGO RITHM
III. SIMULATION RESULTS AND DISCUSSION
In this section, we present a model of the proposed Self Organized Power Consumption Approximation (SOPCA) al
To better simulate real-world interactions in the loT, we de
gorithm in the loT. Additionally, we discuss the simulation
veloped Agent-based Models (ABM) for two different network
environment.
configurations: Random and Lattice networks. We describe these models next.
Definition 1. Scenario of the loT To realistically model the loT, let us consider a large set S
A. Random Network
of networked consumer electronic devices connected with each
Consider a network formed by connecting different ver
other in different configurations. To model different possibil
tices in a random manner, mathematically referred to as the
ities of connectivity for these devices, we use two different
Erdos-Rnyi random graph model [6]. Formally, a random
978-1-4799-7543-3/15/$31.00 ©2015 IEEE
313
2015 IEEE International Conference on Consumer Electronics (ICCE)
network G R( n, p) is formed with edges connected with a probability,
p
given that
0
