Intenet of Things

Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic sim...

Globalization, variation in customer expectations, and the developments connected to Information
& Communication Technology are usually considered as the main drivers of changes in Operations
Management (OM) strategies by firms in both manufacturing and service industries. Emerging digital
technologies, such as Internet of Things or Cloud Computing, represents a novel paradigm that is rapidly
affecting on several aspects of everyday-life of private and business users. This paper contributes to extant
literature about the relationship between emerging digital technologies and OM, by stressing the value
co-creation issue. Specifically, we investigate how emerging digital technologies affect OM through value
co-creation in the maritime industry. Our study reports the case of FairWind project, an open technology
marine service and graphic user interface enabling sailors to personalize their experience on board through
3rd party boat applications. The case of Fair Wind points out how emerging digital technologies contribute
to OM within both organizations and, more in general, maritime industry, as well as the critical role of user’s
co-creation in defining and managing on-board operations

Most of the present technologies gives the door locking system in home in different methods. To increase the security of the home we are using the extra security as a biometric finger print module and the IoT. By using these two security elements we can efficiently access the door by using the fingerprint and IoT. These fingerprint module and the IoT are controlled by the Raspberry pi board. Along with the hardware modules viz., Wi-Fi router, Gas Sensor, Fire Sensor, Door Fringe motor sensor and evaluate our proposed methodology. By using this technique we can establish the home security system very well. The Raspberry pi performs very well to control both the Fingerprint Module and the IoT.

The proliferation of heterogeneous devices connected through large-scale networks is a clear sign that the vision of the
Internet of Things (IoT) is getting closer to becoming a reality. Many researchers and experts in the field share the opinion
that the next-to-come fifth generation (5G) cellular systems will be a strong boost for the IoT deployment. Device-to-
Device (D2D) appears as a key communication paradigm to support heterogeneous objects interconnection and to
guarantee important benefits. In this paper, we thoroughly discuss the added-value features introduced by cellular/noncellular
D2D communications and its potential in efficiently fulfilling IoT requirements in 5G networks. State-of-the-art
solutions, enabling radio technologies, and current standardization activities for D2D communications are surveyed and
their pros and cons with reference to manifold IoT use cases pointed out. Future research directions are then presented towards a fully converged 5G IoT ecosystem.

The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems
lack the support for multiple new data sources and real-time big data processing tools that can assist decision-makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA- and IoT-based disaster management environments. The reference model with
its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research.

Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed. INDEX TERMS Big data analytics, Internet of Things, smart city, disaster management, Hadoop, spark, smart data analytics, geo-social media analytics, disaster resilient smart city.

The IoT is innovative and important phenomenon prone to several services ad applications, but it should consider the legal issues related to the data protection law.
However, should be taken into account the legal issues related to the data protection and privacy law. Technological solutions are welcome, but it is necessary, before developing applications, to consider the risks which we cannot dismiss. Personal data is a value. In this context is fundamental to evaluate the legal issues and prevent them, adopting in each project the privacy by design approach. Regarding the privacy and security risks, there are some issues with potential consequences for data security and liability. The IoT system allows us to transfer data on the Internet, including personal data. In this context, it is important to consider the new European General Data Protection Regulation (GDPR) - already in force from 24 May 2016 - that will be applicable on 25 May 2018. The GDPR introduces Data Protection Impact Assessment (DPIA), data breach notification and very hard administrative fines in respect of infringements of the Regulation. A correct law analysis allows evaluating risks preventing the wrong use of personal data.
The IoT ecosystem is evolving quickly, developing several ap- plications in different sectors. The main topics for the last time are Big Data and the blockchain. People are paying attention to the latest one because of its potential concrete use for services and applications, increasing the security measures to guarantee a secure system. However, it is equally important to analyse the legal issues related to them. Everyone has the right to the protection of personal data concerning him or her. In this context, we cannot dismiss to guarantee an adequate protection of personal data designing any application. The contribution describes the main legal issues related to privacy and data protection especially regarding the blockchain, focusing on the Privacy by Design approach, according to the GDPR. Furthermore, I resolutely believe that is possible to develop a worldwide privacy standard framework that organisations can use for their data protection activities.

Socially critical applications such as wireless and satellite communications, terrestrial and flying radars, cable TV and intelligent transport infrastructure have a significant impact on our everyday lives. The culmination of all modern technological applications is nowadays the Internet of Things (IoT). The LNA design that is presented is intended to be part of a transceiver, which will be placed in a hardware, offering better efficiency to new generation applications.

In this paper, a prototype DC electric system was practically designed. The idea of the proposed system was derived from the microgrid concept. The system contained two houses each have a DC generator and load that consists of four 12 V DC lamps. Each house is controlled fully by Arduino UNO microcontroller to work in Island mode or connected it with the second house or main electric network. House operating mode depends on the power generated by its source and the availability of the main network. Under all operating cases, the minimum price of electricity consumption should satisfy as possible. Information between the houses about the operating mode and the main network state was exchanging wirelessly with the help of the RF-HC12. This information uploaded to the Ubidots platform by the Wi-Fi-ESP8266 included in the node MCU microcontroller. This platform has several advantages such as capture, visualization, analysis, and management of data. The system was examined for different cases to verify its working by varying the load in each building. All tested states showed that the houses transfer from one mode to another automatically with high reliability and minimum energy cost. The information about the main grid states and the sources of the houses were monitored and stored at the Ubidots platform.

Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in Big Data Analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a Disaster Resilient Smart City (DRSC) through the integration of IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed.

Internet of Multimedia Things (IoMT) is becoming attractive day by day and provides more services to the Internet users. The ever-increasing multimedia applications and services led to an outburst in IoMT. The multimedia things connected to IoMT are also increasing; millions of devices and high volume of traffic. This large traffic is directed toward the servers of the service provider in the IoMT cloud through the network switches. As a result, the IoMT infrastructure is facing the crisis of the resource management of switches and servers from two aspects: load imbalance and energy loss. This paper proves that the problem of optimal resource management of IoMT networks with the energy and load constraints simultaneously is an NP-hard problem that has a high time complexity. The problem has decomposed. Then, we propose a modular system of energy and load control in IoMT using the concepts of network softwarization and virtual resources. The proposed controller first dynamically adjusts the resources through accurate determination of the IoMT network size. It then distributes the load between the IoMT servers as well as routes the traffic between switches to the desired server. The Open vSwitch, Floodlight Controller, and Kaa Servers are respectively used for implementing the switches, controller, and servers of IoMT in the test platform. The results show that the proposed system both minimizes the number of IoMT active servers and switches and distributes the load between them. As a result, the parameters for evaluating the quality of service and quality of experience, such as throughput, multimedia delay, R factor, and MOS improved.

Internet of Things (IoT)'emerged as an intelligent collaborative computation and communication between a set of objects capable of providing on-demand services to other objects anytime anywhere. A large-scale deployment of data-driven cloud applications as well as automated physical things such as embed electronics, software, sensors and network connectivity enables a joint ubiquitous and pervasive internet-based computing systems well capable of interacting with each other in an IoT. IoT, a well-known term and a growing trend in IT arena certainly bring a highly connected global network structure providing a lot of beneficial aspects to a user regarding business productivity, lifestyle improvement, government efficiency, etc. It also generates enormous heterogeneous and homogeneous data needed to be analyzed properly to get insight into valuable information. However, adoption of this new reality (i.e., IoT) by integrating it with the internet invites a certain challenges from security and privacy perspective. At present, a much effort has been put towards strengthening the security system in IoT still not yet found optimal solutions towards current security flaws. Therefore, the prime aim of this study is to investigate the qualitative aspects of the conventional security solution approaches in IoT. It also extracts some open research problems that could affect the future research track of IoT arena.

Artificial intelligence using machine leaning algorithms are modern trends in global industrialization. For agriculture to meet the global demand, the need to automate it processes are crucial. The objective of this study was to develop an artificial neural network model; that will be used to detect and identify variety of cowpea seeds in large storage facilities, using its electrical properties. Electrical properties of three variety of cowpea were generated; at five different moisture content, with five different current frequencies. A three-layer model was developed using multi-layer Perceptron method. It was trained and optimized using batch and scaled conjugate gradient methods respectively. Activation functions used were hyperbolic tangent and Softmax for the hidden and output layers; covariates in the input layer were standardized. The developed network model identifies 96, 97 and 93% varieties correctly during training, testing and validation respectively. Receiver Operating Characteristics (ROC) curve plotted for the model performance shows areas under the curve to be above 0.9 for all variety identified. This shows that the model performance was over 90% for predicting all varieties. The cumulative gain and lift charts were plotted to evaluate the model. Inductance was diagnosed to be the most important predictor to the model, while current frequency was the least. Pair t-test analysis at p<0.01, was done to further validate the model. This developed artificial neural network model can be used to program electrical sensors to identify cowpea seeds varieties during bulk storage, handling and processing. Such device can be used for quality control.

In this paper, a prototype DC electric system was practically designed. The idea of the proposed system was derived from the microgrid concept. The system contained two houses each have a DC generator and load that consists of four 12 V DC lamps. Each house is controlled fully by Arduino UNO microcontroller to work in Island mode or connected it with the second house or main electric network. House operating mode depends on the power generated by its source and the availability of the main network. Under all operating cases, the minimum price of electricity consumption should satisfy as possible. Information between the houses about the operating mode and the main network state was exchanging wirelessly with the help of the RF-HC12. This information uploaded to the Ubidots platform by the Wi-Fi-ESP8266 included in the node MCU microcontroller. This platform has several advantages such as capture, visualization, analysis, and management of data. The system was examined for different ...

One of the most prominent challenges commonly acknowledged by modern manufacturing industries is “how to produce more with fewer resources?” Nowhere is this more true than in the food sector due to the recent concerns regarding the long-term availability and security of food products. The unique attributes of food products such as the need for fresh perishable ingredients, health risks associated with inappropriate production environment, stringent storage and distributions requirements together with relatively short post-production shelf-life makes their preparation, production and supply considerably different to other manufactured goods. Furthermore, the impacts of climate change on our ability to produce food, the rapidly increasing global population, as well as changes in demand and dietary behaviours both within developed and developing countries urgently demands a need to change the way we grow, manufacture and consume our food products. This paper discusses a number of key r...

Abstract Globalization, variation in customer expectations, and the developments connected to Information &amp; Communication Technology are usually considered as the main drivers of changes in Operations Management (OM) strategies by firms in both manufacturing and service industries. Emerging digital technologies, such as Internet of Things or Cloud Computing, represents a novel paradigm that is rapidly affecting on several aspects of everyday-life of private and business users. This paper contributes to extant literature about the relationship between emerging digital technologies and OM, by stressing the value co-creation issue. Specifically, we investigate how emerging digital technologies affect OM through value co-creation in the maritime industry. Our study reports the case of FairWind project, an open technology marine service and graphic user interface enabling sailors to personalize their experience on board through 3rd party boat applications. The case of Fair Wind points out how emerging digital technologies contribute to OM within both organizations and, more in general, maritime industry, as well as the critical role of user’s co-creation in defining and managing on-board operations.

'Internet of Things (IoT)'emerged as an intelligent collaborative computation and communication between a set of objects capable of providing on-demand services to other objects anytime anywhere. A large-scale deployment of data-driven cloud applications as well as automated physical things such as embed electronics, software, sensors and network connectivity enables a joint ubiquitous and pervasive internet-based computing systems well capable of interacting with each other in an IoT. IoT, a well-known term and a growing trend in IT arena certainly bring a highly connected global network structure providing a lot of beneficial aspects to a user regarding business productivity, lifestyle improvement, government efficiency, etc. It also generates enormous heterogeneous and homogeneous data needed to be analyzed properly to get insight into valuable information. However, adoption of this new reality (i.e., IoT) by integrating it with the internet invites a certain challenges from security and privacy perspective. At present, a much effort has been put towards strengthening the security system in IoT still not yet found optimal solutions towards current security flaws. Therefore, the prime aim of this study is to investigate the qualitative aspects of the conventional security solution approaches in IoT. It also extracts some open research problems that could affect the future research track of IoT arena.

In Wireless Sensor Network (WSN), energy optimization is an important factor to increase the lifetime of the network. Existing approaches mainly discuss on routing data towards the sink and also do concentrate on static wireless sensor network. As these approaches consume more energy, this paper introduces Mobile Adaptive Distributed Clustering Algorithm (MADCA) that can minimize the energy consumption and also support mobile nodes. This algorithm achieves energy optimization by clustering the nodes, based on similarity of data. Also the nodes which have low data sending rate are allotted a sleep duty cycle for some period. In order to support mobile nodes, the clusters are rebuilt according to the clustering period. Thus it reduces the burden of sink and improves the lifetime of the network. This scenario is simulated using Network Simulator NS2 and performance is analyzed. Simulation results show that MADCA is efficient in terms of control overhead, average end-to-end delay, average packet delivery ratio and energy consumption when compared to a recently proposed approach based on clustering. 

General Terms
Clustering, Energy consumption, Mobility, Wireless sensor network.

Keywords
Data sending rate, Distributed clustering, Similarity measure.