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INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6118
WEIGHT BASED AUTO UPDATING INVENTORY MANAGEMENT SYSTEM
Ranjan Simha H S1, Rajath S2, Rohini B R3, Srivathsa M A4
1,2,4Dept. of ISE, DBIT, Visweshvaraya Technological University, Kumbalagodu, Bengaluru, Karnataka, India
3Asst. Professor, Dept. of ISE, DBIT, Visweshvaraya Technological University, Kumbalagodu, Bengaluru,
Karnataka, India
-------------------------------------------------------------------------***------------------------------------------------------------------------
Abstract — As the emerging information network, more
and more attention is paid to the Internet of Things
technology. For the application in the field of logistics,
tracking of goods and information traceability provides
advanced technical support. In recent years, China's
manufacturing industry rise rapidly, but the level of
automation of inventory management, safety stock, the
collection of information is not accurate. Things
technology has the advantage of better information
traceability, data processing and other aspects. The
data acquisition in the manufacturing process of
inventory management can help to solve the above
risks. Articles from the status of Manufacturing
Inventory Management, its existence doesn’t update
timely. The page builds a manufacturing inventory
management model based on Internet of Things
technology, and also explains the design and
implementation of the model. Finally, it proposed
measures to promote the manufacturing inventory
management model based on Internet of Things
technology.
Keywords—Internet of Things, inventory,
manufacturing, management (key words)
I. INTRODUCTION
The traditional inventory management method, such as
data update is not timely. This can be summarized in two
aspects:first,inventoryconfusion,thegoodsoutofstorage or
the shift library is not recorded in a timely manner. Due to
the low level of business automation, many production
operations cannot be mechanized, mostly done by hand.
Error-prone manual work, production data flow between
employees lead to data loss, defacement, data distortion, etc
easily. These risks are ultimately resulting in denied access
to the real basis of reliable data and information.
Information update speed is slow. It does not allow
managers real-time tracking of warehouse operations, to
grasp the production process of its decision-making. Smart
systems are playing a major role in industries, home,
colleges, and other native environments. In the smart
systems, there is a linear growth in the localization concept,
because localization is playing a crucial role in
contemporary life. It is really challenging to locate any
particular object accurately. Localization can be done in
two ways Type-1 and Type-2 IoT is a vision that permits
individuals and things to be associated in a perfect world
utilizing any path or any service. The need or urge for this
warehouse inventory management system, it is very
challenging to track, identify products or objects in big
industries. To track any product in a precise span of time it
is very difficult.
The section where goods or products are stored is called
the Warehouse. The prime goal of the Warehouse is to
control the flow of products or items. The products must be
managed cautiously otherwise it may have an effect on
time, cost. In the globalization of industries, the inventory
management.
Inventory management is a part of the supply chain. The
efficient operation of a certain degree of Things is related to
supply chain fluency. Internet of Things technology
industry throughout the logistics supply chain, real-time
visualization monitoring and management. In turn, efficient
inventory management can help supply chain more quickly
axis move, so closely related to the needs of both the
Internet of things such binders and lubricants. There is a
very wide field of application of things, and a strong cross-
cutting nature of many industries, effective links in various
industries, strengthen coordination and interaction
between the industry. The formation of a unified Internet of
Things technology-based inventory management, this mode
is more universal, and can also get considerable economies
of scale. Such as the textile industry and clothing industry,
there is a big common. The two co-feasibility of the
implementation of this management model is relatively
large. Applied to the higher costs of manufacturing
inventory management, networking, co-operation between
the industries can integrate.
II. SCOPE
In recent years, some shoes and apparel companies have
tested the water of Things, IOT technology into production,
logistics management. This electronic chip can help
enterprises in the pipeline between stores, companies and
manufacturers to set up a fast-running, the store sales
information and inventory together effectively. Same time,
through the electronic tags, the staff spends more time on
customerservice, whichled to the growth in sales volume.
As the simple operation of RFID and a heavy workload,
logistics management has been a growing body of research
and application. Inventory management ensures accurate
inventory information. Internet of Things is driven by many
of the traditional industrial structure adjustment and
industrial upgrading; the final will also promote the
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6119
development of the entire economic structure of the mode
from extensive tointensive.
MRP inventory management methods, for example, the
combination of the Internet of Things are a common
technique in the field of logistics, especially its core
technology of RFID and network technology. Inventory
management model is based on Internet of things
technology to build the basic flow to improve inventory
management. The perception layer is related to the Internet
of Things technology anytime, anywhere and access to
items fromthestorage.Thelibrary playsanimportantpart of
the real data. All data and information eventually focused
on inventory management centre database.
Internet of Things is a multi-device, multi-network,
multi-application, interoperability, and integration between
network, including a variety of sensors, computers and
communication networks. In combination with the
inventory management system to these technologies,
devices and systems connected a unified standard for the
development of interface design. Communication protocol is
particularly important. Standard interface will not be able
to achieve the sharing of resources. In April 2012, the
International Telecommunication Union examined the draft
standard submitted by the country "Overview of Things.
This standard covers the concepts, terminology, and
technical view, characteristic of Things the basic content of
the demand, the reference model, business model, reflecting
the interest demands of our country in the networking
industry. The Fig. 1 shows the IoT overview over time.
.
Fig. 1
According to market forecasts and customer contracts
production planning, and then automatically calculated
based on customer demand for finished parts, components
and raw materials demand projections, according to the
date of delivery of the finished product all parts of the
production schedule and materials procurement schedule.
First, the master production schedule, product structure
and bill of materials, existing inventory input MRP system.
MRP system according to the master production schedule
under the final demand and product structure information,
the demand for products are based on the specific
operating procedures broken down into the production
planning. Finally, demand time is limit for placing orders to
suppliers.
III. IMPLEMENTATION
The load cell sends the analog data to the ADC HX711
which plays a major role in converting that analog data
of load cell to digital data. This digital data is sent to the
Raspberry Pi through the GPIO ports which are General
Purpose Input Output (GPIO) ports used for sending and
receiving data from external sensors. This data is
converted to weight according to the program present at
the Raspberry Pi. Then this data is used for computation
of quantity of items present on the load cell at any given
point of time and this data is also used to calculate the
quantity removed or added. The data from the Raspberry
Pi is sent to the server through RESTful API in JSON
format. This data being received at the server is
processed by using some JSON libraries and is fed to the
database present in the server. This process is done after
every change in the reading and as son as a new reading
is received to maintain the integrity of the data and real
time computation and availability.The data from the
database is used to reflect the changes and the present
quantity present at the load cell using a Python front-end
where the data is shown in a grid along with time. There
is a feature where even when one entry is edited the next
cant me made the ID f last entry so any kind of cheating
will be known by seeing these IDs. The RESTful APIs are
defined and configured at the server using Java and JS.
The python program at the Raspberry Pi and the client
front end consumes this using HTTP Get and Post
requests. Post is used to hide the data from being visible
to users when sent making it more secure. The Fig. 2
shows the circuit diagram of the system.
Fig. 2
IV. PERFORMANCE TESTING CHALENGES IN IOT
 IoT does not have a standard protocol set to
establish connection between IoT application and
devices. IoT protocols used range from HTTP,
MQTT, AMQP and more. These protocols are still in
early phases of development and different IoT
vendors come up specific protocol standards.
Since these are new protocols, current
performance testing tools may or may not support
INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056
VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072
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them. The Table 1 shows the testing details of the
inventory system.
Table 1.
 IoT devices or sensors spread across different
places and use different network to connect to
servers to send and receive data. As a part of PT,
we can simulate devices from different locations
using different networks such as 2G, 3G, 4G,
Bluetooth, WIFI etc.
 Sometimes IoT implementations require the data
from device that needs to be processed at runtime
and based on data received, corresponding
decision to be made. These decisions are generally
notifications or alerts. As a part of PT, these
notifications are to be monitored i.e. time taken to
generate the notification.
 IoT PT Simulation On devices or sensors Scale
Few devices to thousands of devices Protocols IoT
uses non-standard and new protocols to
communicate Requests/Response IoT devices
create the requests and receive response as well
as request and provide response. Amount of data
sent and received minimal data per request but
data is shared continuously with time interval.
Table 2 shows the test cases of the rack contents.
Table 2.
V. CONCLUSION
The system is capable of detecting the changes in the
load cell and according to the unit value it has the
intelligence to compute the changes and it can feed this
in real time to the server thus helping in monitoring the
stock
in real time and with very less human error. This is
because in the existing system there are humans to
maintain the stock and there is a considerable amount of
error during the stock arrival and even when there is
utilization of the stock there is a chance of that not being
entered into the stock and even he can use the power to
his advantage and enter wrong quantities. The sensors
are always active and very precise thus any changes is
immediately reflected to the stock owner on what the
change was and if there is any discrepancies then this
data can be produced as an evidence to claim the reality
and thus making the owner secure and he doesn’t
become a prey for the people who want to cheat him.
Since there is a use of HTTP and networks the owner
need not be in the same location to access the stock and
the current amount of items present there. He can access
that data from his system from any other place. He can
cross check the stock from the customer to know if they
match. He can immediately take necessary actions if the
stock data that he has doesn’t match with what the
customer had given him.
VI. FUTURE WORK
 Using RFID for identifying a particular item in
inventory. Fig. 3 shows the connection of RFID
sensor to Raspberry PI.
Working:
Fig. 3
The RFID RC522 is a very low-cost RFID (Radio-
frequency identification) reader and writer that is
based on the MFRC522 microcontroller.
This microcontroller provides its data through the
SPI protocol and works by creating a 13.56MHz
electromagnetic field that it uses to communicate
with the RFID tags. Make sure that the tags you
purchase for your RFID RC522 operate on the
13.56MHz frequency otherwise we will fail to read
them.
 Using CCTV camera to improve the security
prospects.
 Improving the user interface to involve with the
main system so that direct monitoring is made.
TEST
CONDITION
INPUT
SPECIFICATION
OUTPUT
SPECIFICATION
PASS
/FAIL
System
sends
Data
to Database
Data through
RESTfulAPI
Data at Database PASS
RACK ID REMOVED ADDED CURRENT
CONTENT
STATUS
1 0 1 1 MATCHING
1 0 1 2 MATCHING
1 1 0 1 MATCHING
1 1 0 0 MATCHING
1 1 0 -1 ERROR
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VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072
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 Using a more complex sub system for interfacing
the PI and client side system.
 GSM modules can be used to find the precise
location of the items in the rack.
 Using cloud to store and fetch the data.
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IRJET- Weight based Auto Updating Inventory Management System

  • 1. INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056 VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6118 WEIGHT BASED AUTO UPDATING INVENTORY MANAGEMENT SYSTEM Ranjan Simha H S1, Rajath S2, Rohini B R3, Srivathsa M A4 1,2,4Dept. of ISE, DBIT, Visweshvaraya Technological University, Kumbalagodu, Bengaluru, Karnataka, India 3Asst. Professor, Dept. of ISE, DBIT, Visweshvaraya Technological University, Kumbalagodu, Bengaluru, Karnataka, India -------------------------------------------------------------------------***------------------------------------------------------------------------ Abstract — As the emerging information network, more and more attention is paid to the Internet of Things technology. For the application in the field of logistics, tracking of goods and information traceability provides advanced technical support. In recent years, China's manufacturing industry rise rapidly, but the level of automation of inventory management, safety stock, the collection of information is not accurate. Things technology has the advantage of better information traceability, data processing and other aspects. The data acquisition in the manufacturing process of inventory management can help to solve the above risks. Articles from the status of Manufacturing Inventory Management, its existence doesn’t update timely. The page builds a manufacturing inventory management model based on Internet of Things technology, and also explains the design and implementation of the model. Finally, it proposed measures to promote the manufacturing inventory management model based on Internet of Things technology. Keywords—Internet of Things, inventory, manufacturing, management (key words) I. INTRODUCTION The traditional inventory management method, such as data update is not timely. This can be summarized in two aspects:first,inventoryconfusion,thegoodsoutofstorage or the shift library is not recorded in a timely manner. Due to the low level of business automation, many production operations cannot be mechanized, mostly done by hand. Error-prone manual work, production data flow between employees lead to data loss, defacement, data distortion, etc easily. These risks are ultimately resulting in denied access to the real basis of reliable data and information. Information update speed is slow. It does not allow managers real-time tracking of warehouse operations, to grasp the production process of its decision-making. Smart systems are playing a major role in industries, home, colleges, and other native environments. In the smart systems, there is a linear growth in the localization concept, because localization is playing a crucial role in contemporary life. It is really challenging to locate any particular object accurately. Localization can be done in two ways Type-1 and Type-2 IoT is a vision that permits individuals and things to be associated in a perfect world utilizing any path or any service. The need or urge for this warehouse inventory management system, it is very challenging to track, identify products or objects in big industries. To track any product in a precise span of time it is very difficult. The section where goods or products are stored is called the Warehouse. The prime goal of the Warehouse is to control the flow of products or items. The products must be managed cautiously otherwise it may have an effect on time, cost. In the globalization of industries, the inventory management. Inventory management is a part of the supply chain. The efficient operation of a certain degree of Things is related to supply chain fluency. Internet of Things technology industry throughout the logistics supply chain, real-time visualization monitoring and management. In turn, efficient inventory management can help supply chain more quickly axis move, so closely related to the needs of both the Internet of things such binders and lubricants. There is a very wide field of application of things, and a strong cross- cutting nature of many industries, effective links in various industries, strengthen coordination and interaction between the industry. The formation of a unified Internet of Things technology-based inventory management, this mode is more universal, and can also get considerable economies of scale. Such as the textile industry and clothing industry, there is a big common. The two co-feasibility of the implementation of this management model is relatively large. Applied to the higher costs of manufacturing inventory management, networking, co-operation between the industries can integrate. II. SCOPE In recent years, some shoes and apparel companies have tested the water of Things, IOT technology into production, logistics management. This electronic chip can help enterprises in the pipeline between stores, companies and manufacturers to set up a fast-running, the store sales information and inventory together effectively. Same time, through the electronic tags, the staff spends more time on customerservice, whichled to the growth in sales volume. As the simple operation of RFID and a heavy workload, logistics management has been a growing body of research and application. Inventory management ensures accurate inventory information. Internet of Things is driven by many of the traditional industrial structure adjustment and industrial upgrading; the final will also promote the
  • 2. INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056 VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6119 development of the entire economic structure of the mode from extensive tointensive. MRP inventory management methods, for example, the combination of the Internet of Things are a common technique in the field of logistics, especially its core technology of RFID and network technology. Inventory management model is based on Internet of things technology to build the basic flow to improve inventory management. The perception layer is related to the Internet of Things technology anytime, anywhere and access to items fromthestorage.Thelibrary playsanimportantpart of the real data. All data and information eventually focused on inventory management centre database. Internet of Things is a multi-device, multi-network, multi-application, interoperability, and integration between network, including a variety of sensors, computers and communication networks. In combination with the inventory management system to these technologies, devices and systems connected a unified standard for the development of interface design. Communication protocol is particularly important. Standard interface will not be able to achieve the sharing of resources. In April 2012, the International Telecommunication Union examined the draft standard submitted by the country "Overview of Things. This standard covers the concepts, terminology, and technical view, characteristic of Things the basic content of the demand, the reference model, business model, reflecting the interest demands of our country in the networking industry. The Fig. 1 shows the IoT overview over time. . Fig. 1 According to market forecasts and customer contracts production planning, and then automatically calculated based on customer demand for finished parts, components and raw materials demand projections, according to the date of delivery of the finished product all parts of the production schedule and materials procurement schedule. First, the master production schedule, product structure and bill of materials, existing inventory input MRP system. MRP system according to the master production schedule under the final demand and product structure information, the demand for products are based on the specific operating procedures broken down into the production planning. Finally, demand time is limit for placing orders to suppliers. III. IMPLEMENTATION The load cell sends the analog data to the ADC HX711 which plays a major role in converting that analog data of load cell to digital data. This digital data is sent to the Raspberry Pi through the GPIO ports which are General Purpose Input Output (GPIO) ports used for sending and receiving data from external sensors. This data is converted to weight according to the program present at the Raspberry Pi. Then this data is used for computation of quantity of items present on the load cell at any given point of time and this data is also used to calculate the quantity removed or added. The data from the Raspberry Pi is sent to the server through RESTful API in JSON format. This data being received at the server is processed by using some JSON libraries and is fed to the database present in the server. This process is done after every change in the reading and as son as a new reading is received to maintain the integrity of the data and real time computation and availability.The data from the database is used to reflect the changes and the present quantity present at the load cell using a Python front-end where the data is shown in a grid along with time. There is a feature where even when one entry is edited the next cant me made the ID f last entry so any kind of cheating will be known by seeing these IDs. The RESTful APIs are defined and configured at the server using Java and JS. The python program at the Raspberry Pi and the client front end consumes this using HTTP Get and Post requests. Post is used to hide the data from being visible to users when sent making it more secure. The Fig. 2 shows the circuit diagram of the system. Fig. 2 IV. PERFORMANCE TESTING CHALENGES IN IOT  IoT does not have a standard protocol set to establish connection between IoT application and devices. IoT protocols used range from HTTP, MQTT, AMQP and more. These protocols are still in early phases of development and different IoT vendors come up specific protocol standards. Since these are new protocols, current performance testing tools may or may not support
  • 3. INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056 VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6120 them. The Table 1 shows the testing details of the inventory system. Table 1.  IoT devices or sensors spread across different places and use different network to connect to servers to send and receive data. As a part of PT, we can simulate devices from different locations using different networks such as 2G, 3G, 4G, Bluetooth, WIFI etc.  Sometimes IoT implementations require the data from device that needs to be processed at runtime and based on data received, corresponding decision to be made. These decisions are generally notifications or alerts. As a part of PT, these notifications are to be monitored i.e. time taken to generate the notification.  IoT PT Simulation On devices or sensors Scale Few devices to thousands of devices Protocols IoT uses non-standard and new protocols to communicate Requests/Response IoT devices create the requests and receive response as well as request and provide response. Amount of data sent and received minimal data per request but data is shared continuously with time interval. Table 2 shows the test cases of the rack contents. Table 2. V. CONCLUSION The system is capable of detecting the changes in the load cell and according to the unit value it has the intelligence to compute the changes and it can feed this in real time to the server thus helping in monitoring the stock in real time and with very less human error. This is because in the existing system there are humans to maintain the stock and there is a considerable amount of error during the stock arrival and even when there is utilization of the stock there is a chance of that not being entered into the stock and even he can use the power to his advantage and enter wrong quantities. The sensors are always active and very precise thus any changes is immediately reflected to the stock owner on what the change was and if there is any discrepancies then this data can be produced as an evidence to claim the reality and thus making the owner secure and he doesn’t become a prey for the people who want to cheat him. Since there is a use of HTTP and networks the owner need not be in the same location to access the stock and the current amount of items present there. He can access that data from his system from any other place. He can cross check the stock from the customer to know if they match. He can immediately take necessary actions if the stock data that he has doesn’t match with what the customer had given him. VI. FUTURE WORK  Using RFID for identifying a particular item in inventory. Fig. 3 shows the connection of RFID sensor to Raspberry PI. Working: Fig. 3 The RFID RC522 is a very low-cost RFID (Radio- frequency identification) reader and writer that is based on the MFRC522 microcontroller. This microcontroller provides its data through the SPI protocol and works by creating a 13.56MHz electromagnetic field that it uses to communicate with the RFID tags. Make sure that the tags you purchase for your RFID RC522 operate on the 13.56MHz frequency otherwise we will fail to read them.  Using CCTV camera to improve the security prospects.  Improving the user interface to involve with the main system so that direct monitoring is made. TEST CONDITION INPUT SPECIFICATION OUTPUT SPECIFICATION PASS /FAIL System sends Data to Database Data through RESTfulAPI Data at Database PASS RACK ID REMOVED ADDED CURRENT CONTENT STATUS 1 0 1 1 MATCHING 1 0 1 2 MATCHING 1 1 0 1 MATCHING 1 1 0 0 MATCHING 1 1 0 -1 ERROR
  • 4. INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056 VOLUME: 06 ISSUE: 05 | MAY 2019 WWW.IRJET.NET P-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6121  Using a more complex sub system for interfacing the PI and client side system.  GSM modules can be used to find the precise location of the items in the rack.  Using cloud to store and fetch the data. REFERENCES 1) S.M. Huynh, D. Parry, A. Fong, J. Tang, Home localization system for misplaced objects, in: Proc. IEEE International Conference on Consumer Electronics, 2014, pp. 462–463 2) Son Minh Huynh, David Parry, A.C.M. Fong, Novel RFID and ontology based home localization system for misplaced objects, IEEE Trans. Consum. Electron. 60 (3) (2014). 3) Y.X. Lu, T.B. Chen, Y. Meng, Evolution guideline system and intelligent evaluation process on the Internet of things, Am. J. Eng. Technol. Res. 11 (9) (2011) 537–541. 4) A. Ramaa, K.N. Subramanya, T.M. Rangaswamy, Impact of warehouse inventory management system in a supply chain, Int. J. Comput. Appl. 54 (6) (2012) (0975-8887). 5) M. Bruccoleri, S. Cannella, G. La Porta, Inventory record inaccuracy in supply chains: the role of workers’ behavior, Int. J. Phys. Distribution Logistics Manage. 44 (10) (2014) pp. 6) N. Wartha, V. Londhe, Context-aware approach for enhancing security and privacy of RFID, Int. J. Eng. Comput. Sci. 4(2015), pp. 10,078-88. 7) Samer S. Saab, Zahi S. Nakad, A standalone RFID indoor positioning system using passive tags, IEEE Trans. Ind. Electron. 58 (5) (2011). 8) J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of things (IoT): a vision architectural elements and future directions, Future Gener. Comput. Syst. 29 (7) (Sep. 2013) 1645–1660. 9) K. Stravoskoufos et al, IoT-A and FIWARE: bridging the barriers between the cloud and IoT systems design and implementation, in: Proc. 6th Int’l Conf. Cloud Computing and Services Science (CLOSER 2016), 2016, pp.146–153. 10) D. Bandyopadhyay, J. Sen, Internet of things: applications and challenges in technology and standardization, Wireless Pers. Commun. 58 (1) (2011) 49–69. 11) P. Yang, PRLS-INVES: a general experimental investigation strategy for high accuracy and precision in passive RFID location systems, IEEE Internet Things J. 2 (2) (2015) 159–167. 12) A. Ibrahim, D. Ibrahim, Real-time GPS based outdoor WiFi localization system with map display, Adv. Eng. Softw. 41 (2010) 1080–1086 13) Ashton, K. (2009). That ‘internet of things’ thing. RFID journal, 22(7), 97–114 14) Atmojo, U. D., Salcic, Z., Wang, K. I.-K., & Park, H. (2015). System-level approach to the design of ambient intelligence systems based on wireless sensor and actuator networks. Journal of Ambient Intelligence and Humanized Computing,6(2),153– 169. https://doi.org/10.1007/s12652-014- 0221-3 15) Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15),2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010 16) Azanha, A., Vivaldini, M., Pires, S. R.I., & Camargo Junior, J. B. d. (2016). Voice picking: analysis of critical factors through a case study in Brazil and the United States. International Journal of Productivity and Performance Management, 65(5), 723–739. https://doi.org/10.1108/IJPPM- 11-2015-0163 17) Ballestín, F., Pérez, Á., Lino, P., Quintanilla, S., & Valls, V. (2013). Static and dynamic policies with RFID for the scheduling of retrieval and storage warehouse operations. Computers & Industrial Engineering, 66(4), 696–709. https://doi.org/10.1016/j.cie.2013.09.020 18) Ballou, R. H., Gilbert, S. M., & Mukherjee, A. (2000). New Managerial Challenges from Supply Chain Opportunities. Industrial Marketing Management, 29(1), 7–18. https://doi.org/10.1016/S00198501(99)00107-8 19) Bell, E., & Bryman, A. (2007). The Ethics of Management Research: An Exploratory Content Analysis. British Journal of Management, 18(1), 63–77. https://doi.org/10.1111/j.14678551.2006.00487. x 20) Bizcommunity. (2017). Industry 4.0 and IoT central to manufacturing transformation. Retrieved from http://www.bizcommunity.com/PDF/PDF.aspx?l= 196&c=399&ct=1&ci=162851
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