Reliable & Efficient Data Centric Storage for Data Management in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become a mature technology aimed at performing environmental monitoring and data collection. Nonetheless, harnessing the power of a WSN presents a number of research challenges. WSN application developers have to deal both with the business logic of the application and with WSN's issues, such as those related to networking (routing), storage, and transport. A middleware can cope with this emerging complexity, and can provide the necessary abstractions for the definition, creation and maintenance of applications. The final goal of most WSN applications is to gather data from the environment, and to transport such data to the user applications, that usually resides outside the WSN. Techniques for data collection can be based on external storage, local storage and in-network storage. External storage sends data to the sink (a centralized data collector that provides data to the users through other networks) as soon as they are collected. This paradigm implies the continuous presence of a sink in the WSN, and data can hardly be pre-processed before sent to the sink. Moreover, these transport mechanisms create an hotspot on the sensors around the sink. Local storage stores data on a set of sensors that depends on the identity of the sensor collecting them, and implies that requests for data must be broadcast to all the sensors, since the sink can hardly know in advance the identity of the sensors that collected the data the sink is interested in. In-network storage and in particular Data Centric Storage (DCS) stores data on a set of sensors that depend on a meta-datum describing the data. DCS is a paradigm that is promising for Data Management in WSNs, since it addresses the problem of scalability (DCS employs unicast communications to manage WSNs), allows in-network data preprocessing and can mitigate hot-spots insurgence. This thesis studies the use of DCS for Data Management in middleware for WSNs. Since WSNs can feature different paradigms for data routing (geographical routing and more traditional tree routing), this thesis introduces two different DCS protocols for these two different kinds of WNSs. Q-NiGHT is based on geographical routing and it can manage the quantity of resources that are assigned to the storage of different meta-data, and implements a load balance for the data storage over the sensors in the WSN. Z-DaSt is built on top of ZigBee networks, and exploits the standard ZigBee mechanisms to harness the power of ZigBee routing protocol and network formation mechanisms. Dependability is another issue that was subject to research work. Most current approaches employ replication as the mean to ensure data availability. A possible enhancement is the use of erasure coding to improve the persistence of data while saving on memory usage on the sensors. Finally, erasure coding was applied also to gossiping algorithms, to realize an efficient data management. The technique is compared to the state-of-the-art to identify the benefits it can provide to data collection algorithms and to data availability techniques

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Proceedings of the 10th international conference on energy efficiency in motor driven systems (EEMODS' 2017)

The 10th International Conference on Energy Efficiency in Motor Driven Systems (EEMODS'17) was be held in Rome (Italy) on 6-8 September, 2017. The EEMODS conferences have been very successful in attracting distinguished and international presenters and attendees. The wide variety of stakeholders has included professionals involved in manufacturing, marketing, and promotion of energy efficient motors and motor driven systems and representatives from research labs, academia, and public policy. EEMODS’15 provided a forum to discuss and debate the latest developments in the impacts of electrical motor systems (advanced motors and drives, compressors, pumps, and fans) on energy and the environment, the policies and programmes adopted and planned, and the technical and commercial advances made in the dissemination and penetration of energy-efficient motor systems. In addition EEMODS covered also energy management in organizations, international harmonization of test method and financing of energy efficiency in motor systems. The Book of Proceedings contains the peer reviewed paper that have been presented at the conference.JRC.C.2-Energy Efficiency and Renewable