U.S. patent application number 16/794638 was filed with the patent office on 2020-06-11 for method for leak detection in a fluid carrying pipeline.
This patent application is currently assigned to KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS. The applicant listed for this patent is KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS. Invention is credited to Abdullatif ALBASEER, Uthman BAROUDI.
Application Number | 20200187122 16/794638 |
Document ID | / |
Family ID | 65806962 |
Filed Date | 2020-06-11 |
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United States Patent
Application |
20200187122 |
Kind Code |
A1 |
BAROUDI; Uthman ; et
al. |
June 11, 2020 |
METHOD FOR LEAK DETECTION IN A FLUID CARRYING PIPELINE
Abstract
A wireless sensor network (WSN) includes a sensor node cluster
having a plurality of sensor nodes positioned along a section of a
pipeline; a base station; a designated cluster head for the sensor
node cluster, the designated cluster head configured to forward
sensor data packets towards the base station; and a server having
circuitry. The circuitry is configured to determine when the
designated cluster head has a battery energy level below a
predetermined level, elect a replacement cluster head for the
designated cluster head when the battery energy level is below the
predetermined level, and forward an energy status of the designated
cluster head to the replacement cluster head.
Inventors: |
BAROUDI; Uthman; (Dhahran,
SA) ; ALBASEER; Abdullatif; (Dhahran, SA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS |
Dhahran |
|
SA |
|
|
Assignee: |
KING FAHD UNIVERSITY OF PETROLEUM
AND MINERALS
Dhahran
SA
|
Family ID: |
65806962 |
Appl. No.: |
16/794638 |
Filed: |
February 19, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15716100 |
Sep 26, 2017 |
10631245 |
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16794638 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/12 20130101;
H04W 4/38 20180201; H04W 4/70 20180201; H04W 52/0261 20130101; H04W
84/18 20130101 |
International
Class: |
H04W 52/02 20060101
H04W052/02; H04W 4/70 20060101 H04W004/70; H04L 29/08 20060101
H04L029/08; H04W 4/38 20060101 H04W004/38; H04W 84/18 20060101
H04W084/18 |
Claims
1: A method of sensor node data transfer using leakage sensors
attached to a plurality of connected segments of a section of a
fluid carrying pipeline, wherein the section of the fluid carrying
pipeline has a length of 950-9,500 meters, the method comprising:
assigning a sensor node from a first cluster of sensor nodes as a
first head sensor node of the first cluster, wherein the first
cluster is positioned along a first segment of the section of the
fluid carrying pipeline, wherein 30-300 sensor nodes are attached
to the section of the fluid carrying pipeline; generating sensor
data packets representing fluid leakage in the section of the fluid
carrying pipeline at each sensor node in the first cluster;
forwarding one or more sensor data packets from each remaining
sensor node of the first cluster to the first head sensor node;
forwarding the one or more sensor data packets from the first head
sensor node to a second head sensor node of a second cluster of
sensor nodes positioned along a second segment of the section of
the fluid carrying pipeline in a direction of a controlling base
station, wherein the second segment is physically connected to the
first segment; determining an energy level of the first sensor head
node; electing a replacement first head sensor node from the
remaining sensor nodes of the first cluster when the energy level
of the first head sensor node reaches a predetermined level; and
forwarding an energy status of the first head sensor node to the
replacement first head sensor node.
2: The method of claim 1, wherein a length of the first segment and
a length of the second segment does not exceed a maximum
transmission range of each sensor node within the respective first
segment and the second segment.
3: The method of claim 1, wherein a distance between two adjacent
sensor nodes of a segment is within a predetermined fidelity range
of detecting a fluid leakage signal from one of the adjacent sensor
nodes.
4: The method of claim 1, wherein a total number of clusters of
sensor nodes is determined by a total length of the section of the
fluid carrying pipeline divided by a maximum transmission range of
each of the sensor nodes.
5: The method of claim 1, wherein a total power consumption of the
first cluster includes an intra power consumed by the sensor nodes
within the first cluster and an inter power consumed by the first
head sensor node of the first cluster.
6: The method of claim 1, wherein the predetermined level comprises
a minimum battery charge level needed to forward the one or more
sensor data packets towards the second sensor head node.
7: The method of claim 1, wherein a number of sensor nodes within
the first cluster is equal to a number of sensor nodes within the
second cluster.
8: The method of claim 1, wherein each one of the sensor nodes
within the first cluster is equally spaced from each other sensor
node within the first segment of the section of the fluid carrying
pipeline, and each one of the sensor nodes within the second
cluster is equally spaced from each other sensor node within the
second segment of the section of the fluid carrying pipeline.
9-17. (canceled)
Description
TECHNICAL FIELD
[0001] The present invention relates to placement of sensor nodes
of a wireless sensor network (WSN) along pipelines for pipeline
monitoring. The pipelines can carry water, gas, oil, or other
fluids.
BACKGROUND
[0002] The "background" description provided herein is for the
purpose of generally presenting the context of the disclosure. Work
of the presently named inventors, to the extent it is described in
this background section, as well as aspects of the description
which may not otherwise qualify as prior art at the time of filing,
are neither expressly or impliedly admitted as prior art against
the present invention.
[0003] Node clustering is a beneficial technique for applications
that require a high scalability of tens to hundreds of sensor
nodes. This may be due to heavier loads placed upon sensor nodes
near a monitoring unit.
[0004] Typically, a long-distance pipeline distribution system is
used to transport water from a water reservoir to a metropolis. For
example, in Saudi Arabia, long pipelines are used to transfer water
from the Shoaiba Desalination Plant in Al-Jubail, a city in eastern
province of Saudi Arabia, to Riyadh. See I. Jawhar and N. Mohamed,
"A hierarchical and topological classification of linear sensor
networks," 2009 Wirel. Telecommun. Symp. WTS 2009, 2009.
[0005] Online pipeline monitoring helps maintain proper operation
and also has environmental and safety advantages. Long distance
pipelines and pipelines carrying critical supplies need to be
continuously monitored to avoid any potential damage. However, the
expected topology for pipeline placement may be linear which poses
many challenges in placing sensors optimally.
[0006] In recent years, attention has been devoted on node
placement in linear topology (i.e. pipeline monitoring systems). In
Xue, the authors disclosed an algorithm that supports the proper
selection for the relay sensor node placement and accordingly
selects the transmission power levels of sensor nodes that provides
the maximum lifetime. See G. Xue, "Relay node placement in wireless
sensor networks for pipeline inspection," Am. Control Conf., vol.
13, no. 7, pp. 5905-5910, 2013. However, these studies have not
taken into consideration sudden damage scenarios. The monitoring in
this case is only temporary.
[0007] Similarly, Guo et al. have studied equal-power and
equal-distance node placement schemes. Two heuristics were
disclosed for sensor node placement with a view towards improving
the lifetime of the network by proposing an evenly consumed power
model, which increases the number of sensor nodes closer to the
base station and configures these sensor nodes to carry the data at
lower power. See Y. Guo, F. Kong, D. Zhu, A. S. Tosun, and Q. Deng,
"Sensor Placement for Lifetime Maximization in Monitoring Oil
Pipelines," Proc. 1st ACM/IEEE Int. Conf. Cyber-Physical
Syst.--ICCPS '10, pp. 61-68, 2010.
[0008] Djame et al. took advantage of energy harvesting
capabilities. See D. Djenouri and M. Bagaa, "Energy harvesting
aware relay node addition for power-efficient coverage in wireless
sensor networks," in IEEE International Conference on
Communications, 2015, vol. 2015-September, pp. 86-91. Generally, it
was proposed to use harvesting-enabled sensor nodes for only
relaying the packets and non-harvesting sensor nodes for sensing
and transmitting their readings to relay sensor nodes.
[0009] In relation to node placement on pipeline monitoring, a
non-uniform scheme called linearly decreasing distance (LDD) has
been presented by Alnuem. See M. Alnuem, "Performance Analysis of
Node Placement in Linear Wireless Sensor Networks," vol. 5, no. 1,
pp. 1-8, 2014. LDD gradually reduces the distance among sensor
nodes, wherein the sensor nodes are placed near the gateway.
[0010] In Cheng et al., a constrained multivariable nonlinear
programming problem has been formulated. See P. Cheng and C. Chuah,
"Energy-aware Node Placement in Wireless Sensor Networks," pp.
3210-3214, 2004. The results show that the performance of the
optimal node placement strategies is better than uniform node
placement strategies.
[0011] Alduraibi et al have studied the coverage problem when the
event detectability varies with proximity to the sensor node and
when some desired level of sensing fidelity is to be maintained.
See F. Alduraibi, N. Lasla, and M. Younis, "Coverage-based Node
Placement Optimization in Wireless Sensor Network with Linear
Topology," 2016. Three optimization models have been presented to
determine the node density.
[0012] Node placement in WSNs has been widely investigated.
However, only a few studies have been devoted to pipeline
applications where the sensor nodes are deployed linearly.
Moreover, few of these studies have adopted a realistic power model
without considering all-discrete power levels. In addition, most of
the studies use greedy heuristic approaches which increase the
density of sensor nodes with lower power levels nearest to the base
station (BS). Also, all sensor nodes are responsible for forwarding
the data packets towards the BS all the time. Moreover, these
solutions do not introduce the reliable communication in a
practical manner because the access can only be one way. In
addition, most of the previous studies did not consider the
required fidelity of the sensor nodes.
SUMMARY
[0013] A clustering approach called Equal Distance Equal Members
(EDEM) clusters the sensor nodes of a wireless sensor network (WSN)
based on their power levels and their required fidelity. See Uthman
Baroudi, "Performance Evaluation of Node Placement Schemes for
Water Pipeline Monitoring," chapter 1, 2017, incorporated herein by
reference in its entirety. This approach balances the loads among
the sensor nodes.
[0014] In one embodiment, a method of sensor node data transfer
includes assigning a sensor node from a first cluster of sensor
nodes as a first head sensor node of the first cluster, wherein the
first cluster is positioned along a first segment of a pipeline;
forwarding one or more sensor data packets from each remaining
sensor node of the first cluster to the first head sensor node;
forwarding the one or more sensor data packets from the first head
sensor node to a second head sensor node of a second cluster of
sensor nodes positioned along a second segment of the pipeline in a
direction of a controlling base station; electing a replacement
first head sensor node from the remaining sensor nodes of the first
cluster when an energy level of the first head sensor node reaches
a predetermined level; and forwarding an energy status of the first
head sensor node to the replacement first head sensor node.
[0015] The foregoing paragraphs have been provided by way of
general introduction, and are not intended to limit the scope of
the following claims. The described embodiments, together with
further advantages, will be best understood by reference to the
following detailed description taken in conjunction with the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] A more complete appreciation of the invention and many of
the attendant advantages thereof will be readily obtained as the
same becomes better understood by reference to the following
detailed description when considered in connection with the
accompanying drawings, wherein:
[0017] FIG. 1A illustrates a wireless sensor network (WSN)
architecture of multiple sensor nodes (SNs) placed on pipelines
according to one embodiment;
[0018] FIG. 1B illustrates an exemplary WSN architecture according
to one embodiment;
[0019] FIG. 2 illustrates limitations of a greedy heuristic
approach and how the receiving SN cannot acknowledge the
transmitting SN according to one embodiment;
[0020] FIG. 3 illustrates a level of fidelity needed to capture
leak signals by more than one SN according to one embodiment;
[0021] FIG. 4 is a graph illustrating a greedy (HTL) approach and
an Equal Distance Equal Members (EDEM) fixed approach according to
one embodiment;
[0022] FIG. 5 illustrates a data forwarding process when the last
SN in each cluster is the cluster head according to one
embodiment;
[0023] FIG. 6 illustrates a data forwarding process when the middle
SN in each cluster is the cluster head according to one
embodiment;
[0024] FIG. 7 illustrates a data forwarding process when the first
SN in each cluster is the cluster head according to one
embodiment;
[0025] FIG. 8 is a flow chart illustrating the EDEM algorithm
according to one embodiment;
[0026] FIG. 9 illustrates the intra-power consumption and the
inter-cluster head power consumption model according to one
embodiment;
[0027] FIG. 10 is a graph illustrating the network lifetime of SN
batteries according to one embodiment;
[0028] FIG. 11 is a bar graph illustrating the lifetime of the
network using the greedy HTL approach and the EDEM clustering
approach according to one embodiment;
[0029] FIG. 12 is a graph illustrating the power consumption in all
tested scenarios according to one embodiment;
[0030] FIG. 13 is a graph illustrating the total number of packets
transmitted and forwarded according to one embodiment;
[0031] FIG. 14 is a bar graph illustrating the battery lifetime of
the last three SNs and the BS under the greedy HTL approach
according to one embodiment;
[0032] FIG. 15 is a bar graph illustrating the battery lifetime
comparison between the greedy HTL approach and the EDEM clustering
approach according to one embodiment;
[0033] FIG. 16 is a graph illustrating the power consumption of the
last three nodes for the greedy HTL approach according to one
embodiment;
[0034] FIG. 17 is a graph illustrating the cumulative power
consumption of the last three nodes using the greedy HTL approach
according to one embodiment;
[0035] FIG. 18 is a graph illustrating the power consumption of the
last three cluster nodes using the EDEM clustering approach
according to one embodiment;
[0036] FIG. 19 is a graph illustrating the power consumption of the
last three cluster nodes using the EDEM clustering approach
according to one embodiment;
[0037] FIG. 20 is a graph illustrating the power consumption of
each stage of the last sensor node in the greedy HTL approach
according to one embodiment;
[0038] FIG. 21 is a graph illustrating the power consumption of
each stage of the last sensor node in the EDEM clustering approach
according to one embodiment;
[0039] FIG. 22 is a graph illustrating confidence intervals of
power consumption of the five experiments using the EDEM clustering
approach according to one embodiment;
[0040] FIG. 23 is a block diagram illustrating an exemplary
electronic device according to one embodiment;
[0041] FIG. 24 is a block diagram of a hardware description of a
computer according to one embodiment;
[0042] FIG. 25 is a schematic diagram of an exemplary data
processing system according to one embodiment;
[0043] FIG. 26 illustrates an implementation of a CPU according to
one embodiment;
[0044] FIG. 27 illustrates an exemplary cloud computing system
according to one embodiment; and
[0045] FIG. 28 is a flowchart for a method of sensor node data
transfer according to one embodiment.
DETAILED DESCRIPTION
[0046] The following descriptions are meant to further clarify the
present disclosure by giving specific examples and embodiments of
the disclosure. These embodiments are meant to be illustrative
rather than exhaustive. The full scope of the disclosure is not
limited to any particular embodiment disclosed in this
specification, but rather is defined by the claims.
[0047] It will be appreciated that in the development of any such
actual implementation, numerous implementation-specific decisions
need to be made in order to achieve the developer's specific goals,
such as compliance with application- and business-related
constraints, and that these specific goals will vary from one
implementation to another and from one developer to another.
[0048] As used herein, scalability refers to the need for load
balancing, efficient resource usage, and reliable data
aggregation.
[0049] FIG. 1A illustrates a Wireless Sensor Network (WSN)
architecture of multiple sensor nodes (SNs) placed on pipelines,
which are directed to a base station (BS). The SNs are deployed
along the pipelines in preselected sites. The SNs are in charge of
data acquisition and report periodically to the BS. All SNs play an
important role for forwarding the data between the reported SN and
the BS using a multi-hop forwarding scheme. The data to be
forwarded to the BS is carried by the SNs located between the
sending SN and the BS, via multi-hop routes. This tends to waste
the energy of the SNs placed nearest to the BS due to highly
asymmetric loads on those SNs. After processing the received data,
the BS determines whether a problem has already occurred or
not.
[0050] FIG. 1B illustrates an exemplary WSN architecture 100 as
used with embodiments described herein. WSN architecture 100
includes a plurality of spatially distributed autonomous sensor
nodes 110, each configured to monitor physical and/or environmental
conditions, such as temperature, sound, and pressure and to
cooperatively pass their data to an associated sensor node 110 and
subsequently through the WSN architecture 100 to a main location,
such as a gateway sensor node 120. A WSN architecture 100 can be
bi-directional, which enables it to control sensor activity, as
well as receive data from the sensor nodes 110.
[0051] Each sensor node 110 includes structural features for
retrieving sensor data and passing the data to an adjacent sensor
node 110, and eventually to the gateway sensor node 120. Structural
node features include a radio transceiver with an internal antenna
or connection to an external antenna, a microcontroller, and an
electronic circuit for interfacing with other sensor nodes 110 and
to an energy source. The energy source can be a battery and/or an
embedded form of energy harvesting. A sensor node 110 can vary in
size depending upon purpose, cost, and energy requirements.
[0052] FIG. 1B also illustrates one or more servers 130 in which
the WSN architecture 100 is controlled, and various client devices
140 in which a user 150 has access to the WSN architecture 100.
Connections between the user 150 and the client devices 140 and/or
the server 130 can be wired connections and/or wireless
connections. Likewise, the connection between the gateway sensor
node 120 and the client devices 140 and/or the server 130 can be
wired connections and/or wireless connections.
[0053] The topology of WSN architecture 100 can vary from a simple
star network to an advanced multi-hop wireless mesh network. FIG.
1B illustrates just a few sensor nodes 110 for simplicity. However,
embodiments described herein are not limited to a particular size,
topology, or function of the WSN architecture 100.
[0054] Certain system model assumptions can be made for a WSN.
[0055] 1. Each SN is responsible for performing a periodic
inspection based on its sensing range. [0056] 2. All SNs are
homogeneous, i.e., have the same power model, communication
capabilities, energy supply, etc. [0057] 3. Each SN delivers its
packet to its adjacent neighbor towards the BS. [0058] 4. The
distances between adjacent SNs are equal, due to the need for a
reliable communication. FIG. 2 illustrates limitations of a greedy
heuristic approach and how the receiving SN cannot acknowledge the
transmitting SN. [0059] 5. The BS receives the data from all SNs
and performs the required actions.
[0060] In FIG. 1A, L denotes the length of a pipeline cluster. The
monitoring sink node unit (i.e. BS) aggregates and summarizes the
data. Let n denote the SNs along the pipeline cluster and let i
denote a specific SN where 1.ltoreq.i.ltoreq.n. Let m denote the
number of power levels (e.g. m=31 for TelosB, MicaZ) and each SN
has a transmission power P.sub.j with a communication range R.sub.j
where j=1, 2, 3, . . . , m. For example, to transmit the data at
power level j, the required transmission power is P.sub.j. Any SN
can be set to a different power level and therefore, any SN can
communicate within different transmission ranges.
[0061] An objective of embodiments described herein is to determine
SNs that will serve as cluster heads, such that they reduce the
total power consumption within the entire WSN. Each SN is assigned
to only one cluster c.sub.r, where 1.ltoreq.r.ltoreq.NCH, where NCH
is the number of clusters (NCH.ltoreq.n). Each SN can completely
communicate with its cluster head, via a single or multiple hops.
The objective is to balance the energy consumed by all SNs as much
as possible.
[0062] To avoid shortcomings of greedy heuristic approaches, the
length of the pipeline is divided into equal small segments and
each segment should not exceed the maximum transmission range (e.g.
95 m if TelosB mote is used).
[0063] In one example, each segment represents a cluster and each
cluster has three SNs. The distance between adjacent SNs is less
than or equal to 32 m to acquire the necessary fidelity. The signal
is acoustic and it is necessary to place more than one SN to detect
the leak signals in order to obtain the required fidelity. Any leak
signals should be heard by more than one SN because if the failure
in detecting the problem occurs in one side, another SN can detect
and report to the BS. FIG. 3 illustrates a level of fidelity needed
to capture leak signals by more than one SN.
d fid .ltoreq. R max n min ##EQU00001##
where d.sub.fid is the optimal distance to assure the fidelity
R.sub.max is the maximum transmission range and n.sub.min is the
minimum number of SNs to achieve this fidelity.
[0064] In the given example, all clusters have the same number of
SNs and the cluster head is responsible for sending the loads from
its members to the next cluster head to reach the BS. In each
cluster, the SNs other than the cluster head transmit and forward
the packets only among the same cluster leading to reduced energy
consumption. A small pipeline section of 950 m and 30 SNs are given
to illustrate the effect of clustering in power consumption. This
example is given without using dynamic clustering.
[0065] FIG. 4 is a graph illustrating a greedy (HTL) approach and
an Equal Distance Equal Members (EDEM) fixed approach when the
pipeline length is 950 m and the number of sensor nodes is 30. In
the greedy (HTL) approach, the last SNs in each cluster work as a
cluster head all the time, which leads to more power consumed by
the cluster head while the sensor nodes other than the cluster head
still retain a large amount of energy. In contrast, for the EDEM
fixed approach, the leader (i.e. cluster head) is elected
periodically to balance the energy consumption among the same
cluster so every SN serves as a cluster head.
[0066] FIG. 5 illustrates a data forwarding process when the last
SN in each cluster is the cluster head. The cluster head sends the
packet with maximum transmission power to deliver the data to the
forwarding cluster head towards the BS. Similarly, FIG. 6
illustrates a data forwarding process when the middle SN in each
cluster is the cluster head. Similarly, FIG. 7 illustrates a data
forwarding process when the first SN in each cluster is the cluster
head.
[0067] The next two algorithms describe the mechanism of this
approach. It is assumed that all sensor nodes are synchronized.
TABLE-US-00001 TABLE 1 EDEM Algorithm 1 Input: n, L, and (P.sub.j,
R.sub.j) with j = 1 ... m; // m = 31 2 If (n, R.sub.m < L) then
3 Exit: the input parameters are inadequate to cover the pipeline
length 4 End if 5 Calculate number of clusters, NC = L/R.sub.m //
the number of clusters 6 If (NC = n) 7 Exit: all nodes are
transmitting at maximum power and clustering is not possible 8 End
if 9 NMs = round (R.sub.m/R.sub.8) // number of members 10 If (n
<NMs * NC) to obtain required accuracy (range of PL8 is 32) 11
Exit: NMs does not achieve the required fidelity 12 End if 13 Start
communicating based on a dynamic EDEM Algorithm
[0068] Table 1 is an algorithm illustrating the EDEM approach. In
steps 2-4, if the number of sensors n is not enough to cover the
intended pipeline length L, the algorithm will fail and it will
exit. Otherwise, the SNs are grouped into clusters based on the
maximum transmission range R.sub.m in step 5. Therefore, the length
of each cluster is equal to R.sub.m (e.g. R.sub.m=95 in a CC2420
power model). In addition, each cluster selects one SN to be a
cluster head in charge of forwarding the internal and incoming
packets. The cluster head transmits at its maximum transmission
power.
[0069] To start the clustering, the number of SNs should be
adequate (line 6-8). In order to select the member SNs, they should
be selected to obtain an optimal number of members that achieve the
minimum required fidelity (steps 9-12). Line 13 starts the dynamic
EDEM algorithm, as illustrated in Table 2 herein.
[0070] FIG. 8 is a flowchart illustrating the EDEM algorithm 800 of
Table 1. In step S810, the EDEM algorithm starts.
[0071] In step S815, variables for n, L, and (P.sub.j, R.sub.j)
with j=1 . . . m are input (line 1).
[0072] In step S820, it is determined whether the number of SNs
covers the length of the pipeline. If the number of SNs does not
cover the length of the pipeline (a "NO" decision at step S820),
the process ends at step S825. Input parameters are not enough to
cover the length of the pipeline (lines 2-3). If the number of SNs
does cover the length of the pipeline (a "YES" decision at step
S820), the process proceeds to step S830.
[0073] In step S830, the number of clusters is calculated as
NC=L/R.sub.m (line 5).
[0074] In step S835, it is determined whether NC=n. If NC is equal
to n (a "YES" decision at step S835), the process ends at step
S840. All nodes are transmitting at maximum power and therefore,
clustering is not possible. If NC is not equal to n (a "NO"
decision at step S835), the process proceeds to step S845.
[0075] At step S845, the number of members (NMs) is calculated,
wherein NMs=R.sub.m/R.sub.8.
[0076] At step S850, it is determined whether n<NMs*NC. If n is
less than NMs*NC (a "YES" decision at step S850), the process ends
at step S855. The number of members does not achieve a required or
established fidelity. If n is not less than NMs*NC (a "NO" decision
at step S850), the process proceeds to step S860.
[0077] At step S860, communication begins based on a dynamic EDEM
Algorithm. The dynamic EDEM Algorithm is illustrated in Table 2
herein.
TABLE-US-00002 TABLE 2 EDEM Mechanism Algorithm 1 Start: assign
last SN in each cluster as a cluster head (CH) and start announcing
2 Set i = 1 3 For all SNs along the pipeline: SN.sub.i .ltoreq. n 4
Compute the Threshold.sub.index = .alpha. E.sub.budget where
.alpha. is a predefined constant or variable that determines how
much of the energy budget to be utilized while a node is serving as
a cluster head node. 5 If SN.sub.i is CH 6 Set the transmission
power to P.sup.max 7 End if 8 If SN, is normal node // Normal
sensor node 9 Set the transmission power to P.sup.8 10 End if 11
Check the energy status of all CHs 12 If (E.sub.budget .ltoreq.
Threshold.sub.index) 13 Send advertisement "I am NOT a CH" 14 In
all clusters: select other SN to be a CH 15 End if 16 End for
[0078] In Table 2, the last SN in each cluster is selected as a
cluster head (step 1). The other SNs are set to a transmission
power of level 8. In steps 3 to 16, the clustering process of the
dynamic EDEM is based on the energy budget (steps 11 to 12), which
should be periodically checked to know the time for changing the
cluster heads (steps 13 and 14).
[0079] The total power consumption of each cluster is computed by
calculating the inner power consumption consumed by cluster members
and the power consumption consumed by the cluster head itself. It
can be modeled as
P.sub.total=intra-power consumption+inter-cluster head power
consumption.
[0080] The intra-power consumption is the energy consumed by the
SNs inside the same cluster, while the inter-cluster head power
consumption is the energy consumed by the cluster head of the
cluster. FIG. 9 illustrates the intra-power consumption and the
inter-cluster head power consumption model when the cluster head
(CH) sends signal packets from its cluster, C and from other
clusters.
[0081] The intra-power consumption EC.sub.i can be calculated
as
EC i = j = 1 k - 1 j P T t + ( j - 1 ) P R t ( 1.1 )
##EQU00002##
where EC.sub.i is the energy consumption of SNs in cluster i and k
is the number of the SNs in each cluster, which is evenly adopted
in this approach. Also, P.sub.T is the required transmission power
for one signal packet, P.sub.R is the required receiving power for
one signal packet, and t is the required time for transmitting or
receiving a signal packet.
[0082] The inter-cluster head power consumption of the cluster head
CH.sub.i can be calculated as
ECH.sub.i=(i.k).P.sub.C.t+(i.k-1).P.sub.R.t (1.2)
[0083] From Equation 1.2, the total power consumption of each
cluster can be modeled as
P.sub.total.sub.i=EC.sub.i+ECH.sub.i (1.3)
[0084] Based on Equation 1.3, the lifetime of each cluster can be
calculated as
LT c i = k E budget P total i ( 1.4 ) ##EQU00003##
[0085] Simulation experiments were conducted to examine the
effectiveness of the EDEM technique. MATLAB was used to simulate
the EDEM technique with different pipeline lengths of 950 meters to
9500 meters. Experimental parameters are outlined in Table 3.
TABLE-US-00003 TABLE 3 Experimental Parameters Parameter Value
Simulation tool MATLAB Pipeline lengths (meters) 950, 1900, 3800,
4750, 9500 Number of sensor nodes 30, 60, 120, 150, 300 Battery
capacity 2600 mAh Battery voltage 3 V Time, t to transmit/receive
one packet 1 second Receiving power, R.sub.x 0.0564 Watts
Transmission power, P.sub.T (member node) 0.0297 Watts Transmission
power, P.sub.C (cluster head) 0.0510 Watts
[0086] The following performance metrics were used. [0087] 1. The
total power consumption measures the total energy of each SN and
for EDEM clustering technique as in Equations 1.1 and 1.2. This
metric shows the effectiveness of the EDEM clustering technique in
terms of the energy conserved. [0088] 2. The network lifetime
measures the estimated lifetime of each SN based on the lifetime
equation for the greedy HTL technique and based on equations 1.4
for the EDEM clustering technique. This metric also determines the
lifetime of the entire network and shows the ability of the EDEM
clustering technique to expand the network lifetime. [0089] 3. The
total number of packets counts the number of packets that are
forwarded in each round throughout the network. This metric
illustrates the ability of the EDEM clustering technique to forward
the signal packets. The performance metrics can be used to
determine the node placement approach to be used and why.
[0090] The EDEM clustering technique was compared to the greedy HTL
approach used in previous studies. In a greedy HTL approach, the
density of the deployed SNs increases as the distance gets closer
to the BS. Also, the SNs most distant from the BS transmit at a
maximum transmission power, and the SNs closest to the BS transmit
at a minimum transmission power.
[0091] The performance evaluation of the node placement approach
was considered under different scenarios. In a first approach for
sensor node distribution, the SNs in a greedy HTL approach were
deployed based on the output vector V, as used in greedy HTL
algorithms. In the EDEM clustering approach, a power level of 31
for all cluster heads was used, and a distance between all adjacent
SNs of 32 m with a transmission range power level of 8 was used.
For both approaches, the same number of SNs was used, but the
distances between SNs was based on the transmission ranges of the
assigned power levels.
[0092] For the EDEM clustering approach, the cluster heads were
changed periodically, based on the .alpha. value. FIG. 10 is a
graph illustrating the network lifetime of SN batteries for a
number of lifetime transmission cycles based on the associated
.alpha. value when the pipeline length is 950 m and the number of
sensors is 30. The testing parameters were 950 m for a from 0.01 to
0.25. As illustrated in FIG. 10, the lifetime increased as the
.alpha. value decreased. Therefore, the minimum .alpha. value was
adopted for all testing scenarios.
[0093] FIG. 11 is a bar graph illustrating the lifetime of the
network using the greedy HTL approach and the EDEM clustering
approach when the pipeline length is 950, 1900, 3800, 4750, and
9500 m. It can be observed that in all scenarios, the lifetime of
the EDEM clustering approach outperforms the greedy HTL approach.
In the EDEM clustering approach, the loads decrease along the
network and only specific SNs cooperatively carry out the signal
packets towards the BS. The increasing ratio ranges from 56% when
the length of the pipeline is 950 m up to 62% when the length of
the pipeline is 9500 m. In both approaches, increasing the length
of pipelines significantly shortened the network lifetime.
[0094] FIG. 12 is a graph illustrating the power consumption in all
tested scenarios when the pipeline length is 950, 1900, 3800, 4750,
and 9500 m. The EDEM clustering approach conserves the energy along
the entire pipeline length because the loads are shared among all
cluster SNs and the power consumption is balanced. In contrast, for
the greedy HTL approach, the last SN is in charge all the time to
deliver all forwarded signal packets to the BS. The amount of
energy savings can reach up to 300% when L=950 m and up to more
than 500% when the pipeline is 9500 m. This big difference is a
result of reducing the number of forwarded signal packets, which in
turn significantly reduce the required transmission and reception
power.
[0095] FIG. 13 is a graph illustrating the total number of packets
transmitted and forwarded when the pipeline length is 950, 1900,
3800, 4750, and 9500 m. The number of total packets sent and
forwarded in the EDEM clustering approach dramatically decreased
because the number of hops ensued by the packets decreased
significantly. For greedy HTL approaches, the number of hops of the
packet sent by SN.sub.i is n-i while in EDEM approach, the number
of hops is equal to the number of clusters, NC.
Num of hops node ( i , j ) = { NC - j if the sender is CH ( NC - j
) + ( k - i ) if the sender is a normal node . ##EQU00004##
where j is the cluster id and k is the number of member SNs.
[0096] Simulation results were validated, in which the two
approaches were implemented with hardware devices in an outdoor
environment using a greedy HTL algorithm and the EDEM clustering
algorithm.
[0097] An experiment for each approach was conducted using motes
hardware. Each experiment was repeated five times to acquire more
reliable results. The objective was to determine the impact of node
placement approaches on the SN battery lifetime.
[0098] Experimental studies were implemented using TelosB motes.
The TelosB motes have been supplied by AA batteries.
[0099] Experimental setup include the following parameters. [0100]
30 TelosB motes were deployed along 950 meters of pipeline length.
[0101] 01 mote was connected to the gateway as a sink node to
receive data from the other motes. It forwards the data to the PC.
[0102] Gateway uses a serial dump tool to get data from the sink
node's serial port and a terminal client running to capture the
data. [0103] ContikiOS programs the motes.
[0104] The motes were deployed based on the greedy output vector V,
which identifies the transmission power level of each mote, as
illustrated in Table 4. The distances between the motes were
adopted based on the transmission range of each power level.
TABLE-US-00004 TABLE 4 Power level assignment of greedy HTL
approach experiment Power level 31 24 20 15 11 8 5 4 3 2 1 Number
of motes 6 1 1 1 1 1 1 2 7 5 4
[0105] In the EDEM approach experiment, the same components were
used, but the deployment of the motes was achieved based on the
EDEM algorithm. The distance between the adjacent SNs was equal to
32 m. The length of each cluster was 95 m.
[0106] Contiki's internal power profiling was used for power
consumption. Contiki has a built-in power profiling module that
measures the up-time of various components. For example, it can be
used to estimate the radio duty cycle.
[0107] For every sensor node in the network, the energest module
has been combined with the uploaded code to track the power
consumption and append the readings to the messages sent to the BS.
The energest module is used to track the power consumption, and the
uploaded code is the code running on the sensor nodes. Specific
readings, such as temperature, light, and/or voltage can be
considered, which are retrieved by a sensor node, such as a TelosB
mote.
[0108] The following steps of the EDEM clustering approach were
used to estimate the energy consumption. [0109] 1. Each SN collects
its readings and reports to the BS every two minutes. [0110] 2. The
time of sending and forwarding all signal packets in one round is
called a cycle. [0111] 3. For every reading of the Tx, Rx, LPM, and
CPU, the energy consumption of each mode is computed based on its
current consumption (e.g. the current of Tx at level 31 is 17.4
mA), where Tx is the Transmission Power, Rx is the Receiving Power,
and LPM is the low power mode. [0112] 4. The total energy
consumption of each cycle is calculated as follows.
[0112] Energest.sub.valuepercycle=current
Energest.sub.valve-previous Energest.sub.value.
[0113] where Energest.sub.value is the times the mote spends in the
state [0114] 5. To calculate the overall energy consumption,
P.sub.total for all cycles is calculated.
[0115] Table 5 illustrates experimental parameters used in the EDEM
clustering approach experiment.
TABLE-US-00005 TABLE 5 EDEM clustering approach experiment
parameters Parameter Value ContikiOs Ver 2.7 Number of sensor nodes
(SNs) 30 Pipeline length 950 meters Tx current consumption 8.5-17.4
mA Rx current consumption 18.8 mA CPU current consumption 1.8 mA
LPM current consumption 5.1 .mu.A Voltage 3 V Nominal capacity 2600
mAh
[0116] As illustrated in Table 5, TelosB mote was used with current
in an active mode of 1.8 mA, and a sleep mode of 5.1 .mu.A. The Tx
varied from 8.5 to 17.4, based on an adopted power level of the Rx
at 18.8 mA and the voltage at 3 V.
[0117] ContikiOs enables tracking the how much time each mote is in
an active state. For example, ALL_Tx is the total (high) Tx time
from the beginning of sensor operation, in the form of a number of
ticks. In order to estimate the energy consumption in a duration of
time, the power incurred during that time is calculated by
subtracting the current of ALL_Tx from the previous ALL_Tx since
the Energest.sub.value is incremented and is not reset to zero.
[0118] The performance of the two approaches was analyzed using
different setups to explore the effect of using real sensor nodes
in outdoor environments. In a first analysis, the effect on the
sensor node lifetime and total energy consumption was analyzed for
the greedy HTL algorithm.
[0119] FIG. 14 is a bar graph illustrating the battery lifetime of
the last three SNs and the BS under the greedy HTL approach when
the pipeline length is 950 m and the number of deployed sensors is
30. The lifetime of the network is dictated by the lifetime of node
2, which is nearest to the BS. The node 2 responsibility is to
forward all packets to the BS all the time, which leads to
depletion of its energy quickly. FIG. 14 also illustrates the
lifetime of nodes 3 and 4 at the same power level as node 2. As
illustrated, the lifetime of the nodes decreases based on their
distance from the BS.
[0120] FIG. 15 is a bar graph illustrating the battery lifetime
comparison between the greedy HTL approach and the EDEM clustering
approach when the pipeline length is 950 m and the number of
deployed sensors is 30. It can be concluded from FIG. 15 that the
EDEM clustering approach can increase the lifetime of a sensor node
battery by 50%. This enhancement in the battery lifetime is a
result of the dynamic clustering and sharing of the loads. Other
sensor nodes simply pass the signal packets within the same cluster
towards their respective cluster head.
[0121] The power consumption analysis primarily focused on the
power consumed for each transaction. FIG. 16 is a graph
illustrating the power consumption of the last three nodes for the
greedy HTL approach when the pipeline length is 950 m and the
number of deployed sensor nodes is 30. The last node (node 2)
consumes the highest power because it forwards all incoming packets
for the entire network. The power consumption is gradually
decreased as the sensor nodes become farther away from the BS.
[0122] FIG. 17 is a graph illustrating the cumulative power
consumption of the last three nodes using the greedy HTL approach
when the pipeline length is 950 m and the number of deployed sensor
nodes is 30. The power consumption increases steadily as the rounds
increase. In this approach, each sensor node keeps consuming
approximately the same power for all rounds during the operational
time.
[0123] In contrast, FIGS. 18 and 19 are graphs illustrating the
power consumption of the last cluster using the EDEM clustering
approach when the pipeline length is 950 m and the number of
deployed sensor nodes is 30. The power consumption of each sensor
node within the cluster is varied over time. This occurs because
the cluster head sensor node is in charge for a period of time,
then it works as a normal sensor node. It is also observed that the
cumulative power consumption at the end of the experiment
approximately reached steady values for all cluster nodes. However,
in the greedy HTL approach, the last sensor node still consumed the
highest power over all time.
[0124] An analysis of the power consumption of each stage on sensor
nodes was made. FIG. 20 is a graph illustrating the power
consumption of each stage of the last sensor node in the greedy HTL
approach. The graph of FIG. 20 is divided into four stages for the
transmitting stage (TX), the receiving stage (RX), the LPM stage,
and CPU stage. The first peak of the TX and CPU stages indicates
the wake up of micro-controller unit, and the chip starts to do
some pre-processing work, including message packaging and some
hardware initiation. In the greedy HTL approach of FIG. 20, each
stage works at the same power level and continues to consume more
energy over time.
[0125] In contrast, FIG. 21 is a graph illustrating the power
consumption of each stage of the last sensor node in the EDEM
clustering approach. The graph of FIG. 21 is divided into four
stages for the transmitting stage (TX), the receiving stage (RX),
the LPM stage, and CPU stage. In FIG. 21, the power consumption is
varied from one stage to another over time. A cluster head sensor
node consumes a large amount of power. However, other non-cluster
head sensor nodes consume less power. This behavior results in a
conservation of energy.
[0126] The experiments were replicated five times to verify
results. FIG. 22 is a graph illustrating confidence intervals of
power consumption of the five experiments using the EDEM clustering
approach when the pipeline length was 950 m and the number of
deployed sensor nodes was 30. The average power consumption was
used in the experiments. The confidence intervals were calculated
with a 95% degree of confidence.
[0127] Sensor node placement for in-line pipeline monitoring
applications is a critical issue and has a major influence in the
entire network performance, due to its effect on its scalability
and lifetime. The advantages of the EDEM clustering technique has
been used to investigate the lifetime and energy consumption of a
WSN with the objective of maximizing the lifetime and reducing the
energy consumption.
[0128] The EDEM clustering approach prominently clusters the sensor
nodes based on their power levels to balance the loads for all of
the sensor nodes within the same cluster. The simulation
experiments conducted under several scenarios showed results of a
62% increase in the lifetime of a sensor node battery, compared
with heuristic techniques. Real experiments were conducted to
validate the simulation results. Results showed that the
performance of the EDEM clustering approach outperformed the greedy
HTL approach by 50%. The results also showed that the power
consumption for the EDEM clustering approach was very
power-efficient and more suitable for linear topology networks.
[0129] FIG. 23 is a block diagram illustrating an exemplary
electronic device used in accordance with embodiments of the
present disclosure. In the embodiments, electronic device 2300 can
be a smartphone, a laptop, a tablet, a server, an e-reader, a
camera, a navigation device, etc. Electronic device 2300 could be
used as one or more of the client devices 140 illustrated in FIG.
1B.
[0130] The exemplary electronic device 2300 of FIG. 23 includes a
controller 2310 and a wireless communication processor 2302
connected to an antenna 2301. A speaker 2304 and a microphone 2305
are connected to a voice processor 2303. The controller 2310 can
include one or more Central Processing Units (CPUs), and can
control each element in the electronic device 2300 to perform
functions related to communication control, audio signal
processing, control for the audio signal processing, still and
moving image processing and control, and other kinds of signal
processing. The controller 2310 can perform these functions by
executing instructions stored in a memory 2350. Alternatively or in
addition to the local storage of the memory 2350, the functions can
be executed using instructions stored on an external device
accessed on a network or on a non-transitory computer readable
medium.
[0131] The memory 2350 includes but is not limited to Read Only
Memory (ROM), Random Access Memory (RAM), or a memory array
including a combination of volatile and non-volatile memory units.
The memory 2350 can be utilized as working memory by the controller
2310 while executing the processes and algorithms of the present
disclosure. Additionally, the memory 2350 can be used for long-term
storage, e.g., of image data and information related thereto.
[0132] The electronic device 2300 includes a control line CL and
data line DL as internal communication bus lines. Control data
to/from the controller 2310 can be transmitted through the control
line CL. The data line DL can be used for transmission of voice
data, display data, etc.
[0133] The antenna 2301 transmits/receives electromagnetic wave
signals between base stations for performing radio-based
communication, such as the various forms of cellular telephone
communication. The wireless communication processor 2302 controls
the communication performed between the electronic device 2300 and
other external devices via the antenna 2301. For example, the
wireless communication processor 2302 can control communication
between base stations for cellular phone communication.
[0134] The speaker 2304 emits an audio signal corresponding to
audio data supplied from the voice processor 2303. The microphone
2305 detects surrounding audio and converts the detected audio into
an audio signal. The audio signal can then be output to the voice
processor 2303 for further processing. The voice processor 2303
demodulates and/or decodes the audio data read from the memory 2350
or audio data received by the wireless communication processor 2302
and/or a short-distance wireless communication processor 2307.
Additionally, the voice processor 2303 can decode audio signals
obtained by the microphone 2305.
[0135] The exemplary electronic device 2300 can also include a
display 2320, a touch panel 2330, an operations key 2340, and an
antenna 2306 connected to the short-distance communication
processor 2307. The display 2320 can be a Liquid Crystal Display
(LCD), an organic electroluminescence display panel, or another
display screen technology. In addition to displaying still and
moving image data, the display 2320 can display operational inputs,
such as numbers or icons which can be used for control of the
electronic device 2300. The display 2320 can additionally display a
GUI for a user to control aspects of the electronic device 2300
and/or other devices. Further, the display 2320 can display
characters and images received by the electronic device 2300 and/or
stored in the memory 2350 or accessed from an external device on a
network. For example, the electronic device 2300 can access a
network such as the Internet and display text and/or images
transmitted from a Web server.
[0136] The touch panel 2330 can include a physical touch panel
display screen and a touch panel driver. The touch panel 2330 can
include one or more touch sensors for detecting an input operation
on an operation surface of the touch panel display screen. The
touch panel 2330 also detects a touch shape and a touch area. Used
herein, the phrase "touch operation" refers to an input operation
performed by touching an operation surface of the touch panel
display with an instruction object, such as a finger, thumb, or
stylus-type instrument. In the case where a stylus or the like is
used in a touch operation, the stylus can include a conductive
material at least at the tip of the stylus such that the sensors
included in the touch panel 2330 can detect when the stylus
approaches/contacts the operation surface of the touch panel
display (similar to the case in which a finger is used for the
touch operation).
[0137] According to aspects of the present disclosure, the touch
panel 2330 can be disposed adjacent to the display 2320 (e.g.,
laminated) or can be formed integrally with the display 2320. For
simplicity, the present disclosure assumes the touch panel 2330 is
formed integrally with the display 2320 and therefore, examples
discussed herein can describe touch operations being performed on
the surface of the display 2320 rather than the touch panel 2330.
However, the skilled artisan will appreciate that this is not
limiting.
[0138] For simplicity, the present disclosure assumes the touch
panel 2330 is a capacitance-type touch panel technology. However,
it should be appreciated that aspects of the present disclosure can
easily be applied to other touch panel types (e.g., resistance-type
touch panels) with alternate structures. According to aspects of
the present disclosure, the touch panel 2330 can include
transparent electrode touch sensors arranged in the X-Y direction
on the surface of transparent sensor glass.
[0139] The touch panel driver can be included in the touch panel
2330 for control processing related to the touch panel 2330, such
as scanning control. For example, the touch panel driver can scan
each sensor in an electrostatic capacitance transparent electrode
pattern in the X-direction and Y-direction and detect the
electrostatic capacitance value of each sensor to determine when a
touch operation is performed. The touch panel driver can output a
coordinate and corresponding electrostatic capacitance value for
each sensor. The touch panel driver can also output a sensor
identifier that can be mapped to a coordinate on the touch panel
display screen. Additionally, the touch panel driver and touch
panel sensors can detect when an instruction object, such as a
finger is within a predetermined distance from an operation surface
of the touch panel display screen. That is, the instruction object
does not necessarily need to directly contact the operation surface
of the touch panel display screen for touch sensors to detect the
instruction object and perform processing described herein. Signals
can be transmitted by the touch panel driver, e.g. in response to a
detection of a touch operation, in response to a query from another
element based on timed data exchange, etc.
[0140] The touch panel 2330 and the display 2320 can be surrounded
by a protective casing, which can also enclose the other elements
included in the electronic device 2300. According to aspects of the
disclosure, a position of the user's fingers on the protective
casing (but not directly on the surface of the display 2320) can be
detected by the touch panel 2330 sensors. Accordingly, the
controller 2310 can perform display control processing described
herein based on the detected position of the user's fingers
gripping the casing. For example, an element in an interface can be
moved to a new location within the interface (e.g., closer to one
or more of the fingers) based on the detected finger position.
[0141] Further, according to aspects of the disclosure, the
controller 2310 can be configured to detect which hand is holding
the electronic device 2300, based on the detected finger position.
For example, the touch panel 2330 sensors can detect a plurality of
fingers on the left side of the electronic device 2300 (e.g., on an
edge of the display 2320 or on the protective casing), and detect a
single finger on the right side of the electronic device 2300. In
this exemplary scenario, the controller 2310 can determine that the
user is holding the electronic device 2300 with his/her right hand
because the detected grip pattern corresponds to an expected
pattern when the electronic device 2300 is held only with the right
hand.
[0142] The operation key 2340 can include one or more buttons or
similar external control elements, which can generate an operation
signal based on a detected input by the user. In addition to
outputs from the touch panel 2330, these operation signals can be
supplied to the controller 2310 for performing related processing
and control. According to aspects of the disclosure, the processing
and/or functions associated with external buttons and the like can
be performed by the controller 2310 in response to an input
operation on the touch panel 2330 display screen rather than the
external button, key, etc. In this way, external buttons on the
electronic device 2300 can be eliminated in lieu of performing
inputs via touch operations, thereby improving water-tightness.
[0143] The antenna 2306 can transmit/receive electromagnetic wave
signals to/from other external apparatuses, and the short-distance
wireless communication processor 2307 can control the wireless
communication performed between the other external apparatuses.
Bluetooth, IEEE 802.11, and near-field communication (NFC) are
non-limiting examples of wireless communication protocols that can
be used for inter-device communication via the short-distance
wireless communication processor 2307.
[0144] The electronic device 2300 can include a motion sensor 2308.
The motion sensor 2308 can detect features of motion (i.e., one or
more movements) of the electronic device 2300. For example, the
motion sensor 2308 can include an accelerometer to detect
acceleration, a gyroscope to detect angular velocity, a geomagnetic
sensor to detect direction, a geo-location sensor to detect
location, etc., or a combination thereof to detect motion of the
electronic device 2300. According to aspects of the disclosure, the
motion sensor 2308 can generate a detection signal that includes
data representing the detected motion. For example, the motion
sensor 2308 can determine a number of distinct movements in a
motion (e.g., from start of the series of movements to the stop,
within a predetermined time interval, etc.), a number of physical
shocks on the electronic device 2300 (e.g., a jarring, hitting,
etc., of the electronic device 2300), a speed and/or acceleration
of the motion (instantaneous and/or temporal), or other motion
features. The detected motion features can be included in the
generated detection signal. The detection signal can be
transmitted, e.g., to the controller 2310, whereby further
processing can be performed based on data included in the detection
signal. The motion sensor 2308 can work in conjunction with a
Global Positioning System (GPS) 2360. The GPS 2360 detects the
present position of the electronic device 2300. The information of
the present position detected by the GPS 2360 is transmitted to the
controller 2310. An antenna 2361 is connected to the GPS 2360 for
receiving and transmitting signals to and from a GPS satellite.
[0145] Electronic device 2300 can include a camera 2309, which
includes a lens and shutter for capturing photographs of the
surroundings around the electronic device 2300. In an embodiment,
the camera 2309 captures surroundings of an opposite side of the
electronic device 2300 from the user. The images of the captured
photographs can be displayed on the display panel 2320. A memory
saves the captured photographs. The memory can reside within the
camera 2309 or it can be part of the memory 2350. The camera 2309
can be a separate feature attached to the electronic device 2300 or
it can be a built-in camera feature.
[0146] FIG. 24 is a block diagram of a hardware description of a
computer 2400 used in exemplary embodiments. In the embodiments,
computer 2400 can be a desk top, laptop, or server. Computer 2400
could be used as the server 130 or one or more of the client
devices 140 illustrated in FIG. 1B.
[0147] In FIG. 24, the computer 2400 includes a CPU 2401 which
performs the processes described herein. The process data and
instructions may be stored in memory 2402. These processes and
instructions may also be stored on a storage medium disk 2404 such
as a hard drive (HDD) or portable storage medium or may be stored
remotely. Further, the claimed advancements are not limited by the
form of the computer-readable media on which the instructions of
the inventive process are stored. For example, the instructions may
be stored on CDs, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM,
EEPROM, hard disk or any other information processing device with
which the computer 2400 communicates, such as a server or
computer.
[0148] Further, the claimed advancements may be provided as a
utility application, background daemon, or component of an
operating system, or combination thereof, executing in conjunction
with CPU 2401 and an operating system such as Microsoft.RTM.
Windows.RTM., UNIX.RTM., Oracle.RTM. Solaris, LINUX.RTM., Apple
macOS.RTM. and other systems known to those skilled in the art.
[0149] In order to achieve the computer 2400, the hardware elements
may be realized by various circuitry elements, known to those
skilled in the art. For example, CPU 2401 may be a Xenon.RTM. or
Core.RTM. processor from Intel Corporation of America or an
Opteron.RTM. processor from AMD of America, or may be other
processor types that would be recognized by one of ordinary skill
in the art. Alternatively, the CPU 2401 may be implemented on an
FPGA, ASIC, PLD or using discrete logic circuits, as one of
ordinary skill in the art would recognize. Further, CPU 2401 may be
implemented as multiple processors cooperatively working in
parallel to perform the instructions of the inventive processes
described above.
[0150] The computer 2400 in FIG. 24 also includes a network
controller 2406, such as an Intel Ethernet PRO network interface
card from Intel Corporation of America, for interfacing with
network 2424. As can be appreciated, the network 2424 can be a
public network, such as the Internet, or a private network such as
LAN or WAN network, or any combination thereof and can also include
PSTN or ISDN sub-networks. The network 2424 can also be wired, such
as an Ethernet network, or can be wireless such as a cellular
network including EDGE, 3G and 4G wireless cellular systems. The
wireless network can also be WiFi.RTM., Bluetooth.RTM., or any
other wireless form of communication that is known.
[0151] The computer 2400 further includes a display controller
2408, such as a NVIDIA.RTM. GeForce.RTM. GTX or Quadro.RTM.
graphics adaptor from NVIDIA Corporation of America for interfacing
with display 2410, such as a Hewlett Packard.RTM. HPL2445w LCD
monitor. A general purpose I/O interface 2412 interfaces with a
keyboard and/or mouse 2414 as well as an optional touch screen
panel 2416 on or separate from display 2410. General purpose I/O
interface 2412 also connects to a variety of peripherals 2418
including printers and scanners, such as an OfficeJet.RTM. or
DeskJet.RTM. from Hewlett Packard.
[0152] The general purpose storage controller 2420 connects the
storage medium disk 2404 with communication bus 2422, which may be
an ISA, EISA, VESA, PCI, or similar, for interconnecting all of the
components of the computer 2400. A description of the general
features and functionality of the display 2410, keyboard and/or
mouse 2414, as well as the display controller 2408, storage
controller 2420, network controller 2406, and general purpose I/O
interface 2412 is omitted herein for brevity as these features are
known.
[0153] FIG. 25 is a schematic diagram of an exemplary data
processing system. The data processing system is an example of a
computer in which code or instructions implementing the processes
of the illustrative embodiments can be located.
[0154] In FIG. 25, data processing system 2500 employs an
application architecture including a north bridge and memory
controller hub (NB/MCH) 2525 and a south bridge and input/output
(1/O) controller hub (SB/ICH) 2520. The central processing unit
(CPU) 2530 is connected to NB/MCH 2525. The NB/MCH 2525 also
connects to the memory 2545 via a memory bus, and connects to the
graphics processor 2550 via an accelerated graphics port (AGP). The
NB/MCH 2525 also connects to the SB/ICH 2520 via an internal bus
(e.g., a unified media interface or a direct media interface). The
CPU 2530 can contain one or more processors and even can be
implemented using one or more heterogeneous processor systems.
[0155] FIG. 26 illustrates an implementation of CPU 2530. In one
implementation, an instruction register 2638 retrieves instructions
from a fast memory 2639. At least part of these instructions are
fetched from an instruction register 2638 by a control logic 2636
and interpreted according to the instruction set architecture of
the CPU 2530. Part of the instructions can also be directed to a
register 2632. In one implementation the instructions are decoded
according to a hardwired method, and in another implementation the
instructions are decoded according to a microprogram that
translates instructions into sets of CPU configuration signals that
are applied sequentially over multiple clock pulses. After fetching
and decoding the instructions, the instructions are executed using
an arithmetic logic unit (ALU) 2634 that loads values from the
register 2632 and performs logical and mathematical operations on
the loaded values according to the instructions. The results from
these operations can be fed back into the register 2632 and/or
stored in a fast memory 2639. According to aspects of the
disclosure, the instruction set architecture of the CPU 2530 can
use a reduced instruction set computer (RISC), a complex
instruction set computer (CISC), a vector processor architecture,
or a very long instruction word (VLIW) architecture. Furthermore,
the CPU 2530 can be based on the Von Neuman model or the Harvard
model. The CPU 2530 can be a digital signal processor, an FPGA, an
ASIC, a PLA, a PLD, or a CPLD. Further, the CPU 2530 can be an x86
processor by Intel or by AMD; an ARM processor; a Power
architecture processor by, e.g., IBM; a SPARC architecture
processor by Sun Microsystems or by Oracle; or other known CPU
architectures.
[0156] Referring again to FIG. 25, the data processing system 2500
can include the SB/ICH 2520 being coupled through a system bus to
an I/O Bus, a read only memory (ROM) 2556, universal serial bus
(USB) port 2564, a flash binary input/output system (BIOS) 2568,
and a graphics controller 2558. PCI/PCIe devices can also be
coupled to SB/ICH 2520 through a PCI bus 2562.
[0157] The PCI devices can include, for example, Ethernet adapters,
add-in cards, and PC cards for notebook computers. The Hard disk
drive 2560 and CD-ROM 2566 can use, for example, an integrated
drive electronics (IDE) or serial advanced technology attachment
(SATA) interface. In one implementation the I/O bus can include a
super I/O (SIO) device.
[0158] Further, the hard disk drive (HDD) 2560 and optical drive
2566 can also be coupled to the SB/ICH 2520 through a system bus.
In one implementation, a keyboard 2570, a mouse 2572, a parallel
port 2578, and a serial port 2576 can be connected to the system
bus through the I/O bus. Other peripherals and devices can be
connected to the SB/ICH 2520 using a mass storage controller such
as SATA or PATA, an Ethernet port, an ISA bus, a LPC bridge, SMBus,
a DMA controller, and an Audio Codec.
[0159] FIG. 27 illustrates an exemplary cloud computing system,
wherein users access the cloud through mobile device terminals or
fixed terminals that are connected to the Internet. One or more of
the devices illustrated in the WSN architecture 100 of FIG. 1B
could be used in the cloud computing system illustrated in FIG.
27.
[0160] The mobile device terminals can include a cell phone 2710, a
tablet computer 2712, and a smartphone 2714, for example. The
mobile device terminals can connect to a mobile network service
2720 through a wireless channel such as a base station 2756 (e.g.,
an Edge, 3G, 4G, or LTE Network), an access point 2754 (e.g., a
femto cell or WiFi network), or a satellite connection 2752. In one
implementation, signals from the wireless interface to the mobile
device terminals (e.g., the base station 2756, the access point
2754, and the satellite connection 2752) are transmitted to a
mobile network service 2720, such as an EnodeB and radio network
controller, UMTS, or HSDPA/HSUPA. Mobile users' requests and
information are transmitted to central processors 2722 that are
connected to servers 2724 to provide mobile network services, for
example. Further, mobile network operators can provide service to
mobile users for authentication, authorization, and accounting
based on home agent and subscribers' data stored in databases 2726,
for example. The subscribers' requests are subsequently delivered
to a cloud 2730 through the Internet.
[0161] A user can also access the cloud through a fixed terminal
2716, such as a desktop or laptop computer or workstation that is
connected to the Internet via a wired network connection or a
wireless network connection. The mobile network service 2720 can be
a public or a private network such as an LAN or WAN network. The
mobile network service 2720 can be wireless such as a cellular
network including EDGE, 3G and 4G wireless cellular systems. The
wireless mobile network service 2720 can also be Wi-Fi, Bluetooth,
or any other wireless form of communication that is known.
[0162] The user's terminal, such as a mobile user terminal and a
fixed user terminal, provides a mechanism to connect via the
Internet to the cloud 2730 and to receive output from the cloud
2730, which is communicated and displayed at the user's terminal.
In the cloud 2730, a cloud controller 2736 processes the request to
provide users with the corresponding cloud services. These services
are provided using the concepts of utility computing,
virtualization, and service-oriented architecture.
[0163] In one implementation, the cloud 2730 is accessed via a user
interface such as a secure gateway 2732. The secure gateway 2732
can for example, provide security policy enforcement points placed
between cloud service consumers and cloud service providers to
interject enterprise security policies as the cloud-based resources
are accessed. Further, the secure gateway 2732 can consolidate
multiple types of security policy enforcement, including for
example, authentication, single sign-on, authorization, security
token mapping, encryption, tokenization, logging, alerting, and API
control. The cloud 2730 can provide to users, computational
resources using a system of virtualization, wherein processing and
memory requirements can be dynamically allocated and dispersed
among a combination of processors and memories to create a virtual
machine that is more efficient at utilizing available resources.
Virtualization creates an appearance of using a single seamless
computer, even though multiple computational resources and memories
can be utilized according to increases or decreases in demand. In
one implementation, virtualization is achieved using a provisioning
tool 2740 that prepares and equips the cloud resources, such as the
processing center 2734 and data storage 2738 to provide services to
the users of the cloud 2730. The processing center 2734 can be a
computer cluster, a data center, a main frame computer, or a server
farm. In one implementation, the processing center 2734 and data
storage 2738 are collocated.
[0164] Embodiments described herein can be implemented in
conjunction with one or more of the devices described above with
reference to FIGS. 23-27. Embodiments are a combination of hardware
and software, and circuitry by which the software is
implemented.
[0165] FIG. 28 is a flowchart for a method 2800 of sensor node data
transfer. Method 2800 is executed using the WSN 100 described with
reference to FIG. 1B. WSN 100 includes the server 130, the gateway
sensor node 120, and the plurality of sensor nodes 110. WSN 100 has
circuitry configured to execute method 2800 according to
embodiments described herein.
[0166] In step S2810, a sensor node from a first cluster of sensor
nodes is assigned as a first head sensor node of the first cluster.
This can be determined by a battery charge level of the first head
sensor node. The first cluster is positioned along a first segment
of a pipeline. The pipeline can be a water pipeline, a
petrochemical pipeline such as an oil or gas pipeline, or other
fluid-carrying pipeline.
[0167] In step S2820, one or more sensor data packets are forwarded
from each remaining sensor node of the first cluster to the first
head sensor node. In one embodiment, the sensor nodes are equally
spaced apart from their adjacent sensor nodes within the first
segment.
[0168] In step S2830, the one or more sensor data packets are
forwarded from the first head sensor node to a second head sensor
node of a second cluster of sensor nodes in a direction of a
controlling base station. The second cluster of sensor nodes is
positioned along a second segment of the pipeline.
[0169] In step S2840, a replacement first head sensor node is
elected from remaining sensor nodes of the first cluster when an
energy level of the first head sensor node reaches a predetermined
level. The replacement first head sensor node can be elected based
upon its available battery charge level.
[0170] In step S2850, an energy status of the first head sensor
node is forwarded to the replacement first head sensor node.
[0171] Embodiments described herein determine where to place SNs
along a pipeline efficiently and to determine the number of members
in each cluster. The power efficient deployment provides maximum
lifetime of the sensor node battery, it minimizes the power
consumption, and it obtains a needed fidelity.
[0172] A cluster head is elected based upon either the energy
budget of the cluster head or a predefined time period, such as one
day. If the energy of the cluster head reaches a threshold, such as
a certain percentage of its energy budget, it sends an announcement
to adjacent SNs to elect one of them as the cluster head. This
process continues until all member SNs have acted as a cluster
head, then the process is repeated.
[0173] Embodiments described herein include the following
aspects.
[0174] (1) A method of sensor node data transfer includes assigning
a sensor node from a first cluster of sensor nodes as a first head
sensor node of the first cluster, wherein the first cluster is
positioned along a first segment of a pipeline; forwarding one or
more sensor data packets from each remaining sensor node of the
first cluster to the first head sensor node; forwarding the one or
more sensor data packets from the first head sensor node to a
second head sensor node of a second cluster of sensor nodes
positioned along a second segment of the pipeline in a direction of
a controlling base station; electing a replacement first head
sensor node from the remaining sensor nodes of the first cluster
when an energy level of the first head sensor node reaches a
predetermined level; and forwarding an energy status of the first
head sensor node to the replacement first head sensor node.
[0175] (2) The method of sensor node data transfer of (1), wherein
a length of the first segment and a length of the second segment
does not exceed a maximum transmission range of each sensor node
within the respective first segment and the second segment.
[0176] (3) The method of sensor node data transfer of either one of
(1) or (2), wherein a distance between two adjacent sensor nodes is
within a predetermined fidelity range of detecting a leakage signal
from one of the adjacent sensor nodes.
[0177] (4) The method of sensor node data transfer of any one of
(1) through (3), wherein a total number of clusters of sensor nodes
is determined by a total length of the pipeline divided by a
maximum transmission range of each of the sensor nodes.
[0178] (5) The method of sensor node data transfer of any one of
(1) through (4), wherein a total power consumption of the first
cluster includes an intra power consumed by the sensor nodes within
the first cluster and an inter power consumed by the first head
sensor node of the first cluster.
[0179] (6) The method of sensor node data transfer of any one of
(1) through (5), wherein the predetermined level comprises a
minimum battery charge level to forward the one or more sensor data
packets towards the controlling base station.
[0180] (7) The method of sensor node data transfer of any one of
(1) through (6), wherein a number of sensor nodes within the first
cluster is equal to a number of sensor nodes within the second
cluster.
[0181] (8) The method of sensor node data transfer of any one of
(1) through (7), wherein the sensor nodes within the first cluster
are equally spaced within the first segment of the pipeline, and
the sensor nodes within the second cluster are equally spaced
within the second segment of the pipeline.
[0182] (9) A wireless sensor network (WSN) includes a sensor node
cluster having a plurality of sensor nodes positioned along a
section of a pipeline; a base station; a designated cluster head
for the sensor node cluster, the designated cluster head configured
to forward sensor data packets towards the base station; and a
server having circuitry. The circuitry is configured to determine
when the designated cluster head has a battery energy level below a
predetermined level, elect a replacement cluster head for the
designated cluster head when the battery energy level is below the
predetermined level, and forward an energy status of the designated
cluster head to the replacement cluster head.
[0183] (10) The WSN of (9), wherein a length of the section of the
pipeline does not exceed a maximum transmission range of any of the
plurality of sensor nodes.
[0184] (11) The WSN of either one of (9) or (10), wherein a
distance between two adjacent sensor nodes of the plurality of
sensor nodes is within a predetermined fidelity range of detecting
a leakage signal from one of the adjacent sensor nodes.
[0185] (12) The WSN of any one of (9) through (11), wherein a total
number of sensor node clusters is determined by a total length of
the pipeline divided by a maximum transmission range of each sensor
node of the plurality of sensor nodes.
[0186] (13) The WSN of any one of (9) through (12), wherein a total
power consumption of the first cluster includes an intra power
consumed by each sensor node of the plurality of sensor nodes
within the sensor node cluster and an inter power consumed by the
designated cluster head of the sensor node cluster.
[0187] (14) The WSN of any one of (9) through (13), wherein the
predetermined level comprises a minimum battery charge level to
forward the sensor data packets towards the base station.
[0188] (15) The WSN of any one of (9) through (14), further
includes a plurality of sensor node clusters positioned along
respective sections of the pipeline; and a designated cluster head
for each of the plurality of sensor node clusters.
[0189] (16) The WSN of any one of (9) through (15), wherein a
number of sensor nodes within a first sensor node cluster
positioned along a first segment of the pipeline is equal to a
number of sensor nodes within a second sensor node cluster
positioned along a second segment of the pipeline.
[0190] (17) The WSN of any one of (9) through (16), wherein the
sensor nodes within the first sensor node cluster are equally
spaced within the first segment of the pipeline, and the sensor
nodes within the second sensor node cluster are equally spaced
within the second segment of the pipeline.
[0191] A number of implementations have been described.
Nevertheless, it will be understood that various modifications may
be made without departing from the spirit and scope of this
disclosure. For example, preferable results may be achieved if the
steps of the disclosed techniques were performed in a different
sequence, if components in the disclosed systems were combined in a
different manner, or if the components were replaced or
supplemented by other components. The functions, processes, and
algorithms described herein may be performed in hardware or
software executed by hardware, including computer processors and/or
programmable circuits configured to execute program code and/or
computer instructions to execute the functions, processes, and
algorithms described herein. Additionally, an implementation may be
performed on modules or hardware not identical to those described.
Accordingly, other implementations are within the scope that may be
claimed.
[0192] The foregoing discussion describes merely exemplary
embodiments of the present disclosure. As will be understood by
those skilled in the art, the present disclosure may be embodied in
other specific forms without departing from the spirit or essential
characteristics thereof. Accordingly, the disclosure is intended to
be illustrative, but not limiting of the scope of the disclosure,
as well as the claims. The disclosure, including any readily
discernible variants of the teachings herein, defines in part, the
scope of the foregoing claim terminology such that no inventive
subject matter is dedicated to the public.
* * * * *