International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5714-5718
© Research India Publications. http://www.ripublication.com
RF Based Node Location and Mobility Tracking in IoT
J. Ann Roseela
Research Scholar, ECE Department,
Dr. M. G. R. Educational and Research Institute, Chennai, India
Dr. S. Ravi
Professor & Head, ECE Department,
Dr. M. G. R. Educational and Research Institute, Chennai, India
Dr. M. Anand
Professor, ECE Department,
Dr. M. G. R. Educational and Research Institute, Chennai, India
network based methodology for node localization. The
Received Signal Strength Indicator (RSSI) values of the
anchor node beacons are used. The multi-layer Perceptron
(MLP) neural network model was trained using Matlab. In
order to evaluate the performance of the proposed method in
real time, the model obtained was then implemented on the
Arduino microcontroller. For obtaining the best neural
network for localization, the BR training algorithm is
evaluated to give the best result. However, it is recommended
that the RSSI value of the localization beacon request signals
received by the anchor nodes also be utilized that can result in
a further reduction of the localization error (Shiu Kumar et al.,
(2016)).
This paper presents an algorithm that uses connectivity
information which node is within communications range of
whom to derive the locations of the nodes in the network. The
method can take advantage of additional information, such as
estimated distances between neighbors or known positions for
certain anchor nodes, if it is available. The algorithm is based
on multidimensional scaling, a data analysis technique that
takes O(n3) time for a network of n nodes (Yi Shang et al.,
(2003).
This paper reviews different approaches of node localization
discovery in wireless sensor networks. The overview of the
schemes proposed by different scholars for the improvement
of localization in wireless sensor networks is also presented
Amitangshu Pal (2010).
In this paper a fuzzy set-based localization method as an
enhancement of the ring-overlapping scheme proposed to
tackle the problem of RSS uncertainty. In the proposed
method, first fuzzy membership function based on RSS
measurements to generate fuzzysets of rings that constrain
sensor node position with respect to each anchor is used. Then
fuzzy set of regions by intersecting rings from different ring
sets is generated Andrija S. Velimirovic et al., (2010).
This paper proposes an APS that a distributed, hop by hop
positioning algorithm, that works as an extension of both
distance vector routing and GPS positioning in order to
provide approximate position for all nodes in a network where
only a limited fraction of nodes have self-positioning
capability DragosNiculescu and BadriNath (2002).
Abstract
In this paper, a new method RF based IoT node localization
and status is proposed. In this paper a robust mapping is done
between the measured RSSI vector and the existing RSSI
signature to minimize location estimation errors encouraged
by the instability problem of RSSI node evaluations. The
proposed system extends study on multicast networks in
which the communication IoT nodes should be with in the RF
server region of the network. In proposed method, the routing
is performed peer to peer instead of a static network
infrastructure to provide network connectivity. Objectives of
this paper are reliable communication, cooperative network,
expanding of network and shrinking of network. The study
includes RSSI technique for locating the position of the Wi-Fi
enabled nodes and determining the variation in the strength of
the signal at individual nodes connected to an access point, as
the number of nodes increases or decreases.
Keywords: IoT nodes, RF server, RSSI, Node Loaction and
Mobility.
Introduction
A IoT nodes are expected to provide mobility, flexibility,
composed of active nodes, ease to distribute, low cost, less
electric power consumption and also achieve the demanded
throughput etc. In IoT nodes, localization identification is
done by distance-dependent (DD) and distance independent
(DI) algorithms. In DD, the position of the unknown IoT
nodes is determined by using the distances between adjacent
nodes. The distance is calculated from the average round trip
time and measures the direction of arrival of signals. The
distances for position estimation have the information to
provide connectivity to other nodes and identification of
neighboring nodes. The methods of localization using
received signal strength indicator (RSSI) meets the coverage
needs (without extra hardware requirement) by measuring the
distances between nodes. Shiu Kumar et al., (2016).
Related works
In this paper, 2D localization algorithm for WSN by utilizing
ANN is proposed. This paper adopted feed-forward neural
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5714-5718
© Research India Publications. http://www.ripublication.com
In this paper a radio frequency based system for locating and
tracking users inside buildings is proposed ParamvirBahl and
VenkataN. Padmanabhan (2000).
This paper provides an overview of the measurement
techniques in sensor network localization and the one-hop
localization algorithms based on these measurements. A
detailed investigation on multi-hop connectivity-based and
distance-based localization algorithms are presented Guoqiang
Mao et al., (2007).
iii.
Workflow for measuring signal strength is shown in
figure 2.
Proposed system
In proposed system, ‘n’ numbers of nodes with RF transmitter
are considered. RF server is connected with Laptop. Block
diagram of proposed system is shown in figure 1.
Figure 2: Work flow of proposed system
OPTIMAL RESOURCE ALLOCATION
Resource allotted to the IoT node at a specific time slot after
tracking the IoT node state i. e., resources being allocated to
the IoT node which has strongest signal strength. Portion of
total resources across time and frequency (degree of the
freedom of the channel) and sharing of resources across the
users is possible through optimal resource allocation.
Memory Allocation and Deallocation
Place an IoT node on any point of the server region and move
it along a block line to different point. Once the node is
moved, it must remain on the point to which it is moved. Now
place a second IoT node on any unoccupied point of server
region, and move it in similar fashion to any other unoccupied
point. Continue in this way until all the IoT nodes have been
placed on points. This is shown in Figure 3.
Figure 1: Proposed system
MEASURING SIGNAL STRENGTH
Signal strength measured by transmitted signal strength by
each RF transmitter during mobilization of IoT nodes. RSSI
based localization technique is attractive for a wide variety of
applications, where the significant estimation error has
negative effects related to signal propagation. RSSI is a
common way to represent the signal energy in the local
environment. The attenuation of radio signals during
propagation is charecterized by RSSI. The distances between
IoT nodes are calculated by measuring the signal attenuation
in RSSI. Thus, RSSI is highly sensitive to the distances. The
IoT nodes packet contain the RSSI information which if lost
can affect the overall localization accuracy. The rapid increase
of IoT node devices with Wi-Fi as preferred method of
network access, predominantly indoor demands the location
analysis method to have minimum mean square error (MSE)
so as to realize unprecedented benefits from location-based
services. This includes;
i.
Location analytics: The Number of IoT nodes enters
to the network is estimated along with the amount of
time spent and the frequency of their IoT node
ii.
Advanced analytics: Provides signature of
movement patterns of the IoT nodes while available
in the RF server area.
A
D
F
G
C
B
H
E
Figure 3: Optimal memory allocations
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5714-5718
© Research India Publications. http://www.ripublication.com
Distance from RF Server Vs IoT node
Generally the problem arises when IoT node going far away
from the RF server, i. e., and signal strength become weak not
able to achieve good communication due to interference. This
typical situation is overcome by using successive interference
cancellation method, which is shown in Table 1.
The IoT nodes service with respect to the radio range of a
RF server wireless link is limited with single access point.
The solution to this issue is to add multiple RF server to
the network and extend the range of mobility of a IoT node
in the network.
Table 1: Comparison of SIC Receiver with Conventional
Receiver
COMMUNICATION AMONG NODES
Given a set V of IoT nodes, the authenticated IoT nodes are
just a string of symbols from V. The communication is
modeled as a graph
, , where E is the set of edges
between unreliable pairs of IoT nodes, i. e. IoT nodes which
cause confusion during transmission. For analogy, in speech
transmission, the pronunciation B and P are connected by an
edge (since upon reception they are indistinguishable). G is
called the confusion graph.
Distance from IoT node signal Performance
RF server
strength
Conventional
Closer
Stronger
Accepted
Far
Weaker
Unaccepted
SIC
Closer
Stronger
Accepted
Far
Stronger
Accepted
Type
MAXIMIZING THE PROBABILITY OF LOCALIZING
IoT NODE
Let the number of localization with available probability
points less than belong to elements of group2, while the
others are elements of group1. Then, the objective is to
maximize the probability of selecting elements of group1 in a
mixture of points. For Ex: If the size of the elements of
group1 and group2 are equal to N/2, then to maximize the
probability of accessing a higher priority task the IoT nodes
are grouped as shown in Figure. In this, one element of
group1 is placed in access1 queue and other remaining
elements of both group1& group2 are given to access 2 queue.
Due to this, the probability of accessing a higher priority node
is increased.
Figure 5: IoT nodes arranged as a graph, with adjacent IoT
nodes representing confusion IoT node
If ‘ ’ represents the symbol and ‘ ’ represents the vertexes,
then possible combinations include:
and can be confused with
•
•
and can be confused with , or
•
Can be confused and
can be
confused.
The IoT nodes are represented by the adjacency matrix ‘A’
shown below; with value 1 indicates the adjacent nodes.
0 1 0 0 1
1 0 1 0 0
0 1 0 1 0
0 0 1 0 1
1 0 0 1 0
The communication among IoT nodes uses the minimum
eigenvalue approach to distinguish the elements of the three
sets v1, v2 and v3 where
Figure 4: Allocating tasks to maximize access probability
P (Choosing high priority task in direction-1) =1
P (Choosing high priority task from direction-2) =
P (Choosing among direction-1 or direction-2) =1/2
1
1
2
1
2
1
2
1
/
As an example, for N=20; equation (4. 1) = 9/38
14/19
The possible arrangement of IoT nodes includes (i)
i. e. null and (ii)
at any time can become malicious
i. e. any of the nodes in
and join the set v3.
3/4
In this work selection of optimal access points that identifies
potential bandwidth in which aIoT node is likely to receive
after affiliating with a particular access point’s area is done.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5714-5718
© Research India Publications. http://www.ripublication.com
ALGORITHM FOR DATA TRANSFER AMONG
FRIENDLY IoT NODES
Data multicast from source and is open for reception to all IoT
nodes.
then forward packet to next nearest IoT
l1: if (IoT node
node)
then stop forwarding) else goto l1;
if (forwarded IoT node
All the IoT nodes are movable with variable signal strength as
it need not be connected to a single access point always, and
this makes authentication between two ends challenging.
NUMBER OF NODES PER (AP) ACCESS POINT
The maximum number of IoT nodes an AP can support
with uniform traffic load against different transmission
speed in MAC protocol is determined. The throughput is
measured against the number of IoTnodes and with the
MAC protocol, it is observed that all generated data traffic
reaches its destination and the medium is not saturated at
the current load level.
Figure 7
Figure 8 shows RF server state with IoT node is in the RF
server region and it is in the active state.
Hardware implementation
Number of IoT nodes =8
Server = centralized
Monitoring stateÆ C, U, A.
CÆ Connected: IoT node is in the RF server, but it is not
active
UÆ Unconnected: IoT node is not in the RF server, it may be
active or not active.
AÆActive: IoT node is in the RF server, but it is active
Connection details
Initially RF Server connect with the PC by serial port
communication. Connect key is used to enable the RF server
to measure the receiving RF signal strength. Get status key is
used to get monitoring state of various IoT nodes.
Figure 8
Results
Figure 6 shows the initial stage of RF server with zero
IoTnode in the server region.
Conclusion
In this paper, proximity method and triangulation method used
to estimate IoT node location. The performance of a mobile
device under varying channel fading scenarios analyzed. RF
Signal, power variations, outage probability for different
channel fading scenarios are determined.
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Figure 6
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Figure 7 shows the state of RF server with IoT node 4 in the
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