Optimization of Energy-Efficient Cluster Head Selection Algorithm for Internet of Things in Wireless Sensor Networks

The Internet of Things (IoT) now uses the Wireless Sensor Network (WSN) as a platform to sense and communicate data. The increase in the number of embedded and interconnected devices on the Internet has resulted in a need for software solutions to manage them proficiently in an elegant and scalable manner. Also, these devices can generate massive amounts of data, resulting in a classic Big Data problem that must be stored and processed. Large volumes of information have to be produced by using IoT applications, thus raising two major issues in big data analytics. To ensure an efficient form of mining of both spatial and temporal data, a sensed sample has to be collected. So for this work, a new strategy to remove redundancy has been proposed. This classifies all forms of collected data to be either relevant or irrelevant in choosing suitable information even before they are forwarded to the base station or the cluster head. A Low-Energy Adaptive Clustering Hierarchy (LEACH) is a cluster-based routing protocol that uses cluster formation. The LEACH chooses one head from the network sensor nodes, such as the Cluster Head (CH), to rotate the role to a new distributed energy load. The CHs were chosen randomly with the possibility of all CHs being concentrated in one locality. The primary idea behind such dynamic clustering was them resulted in more overheads due to changes in the CH and advertisements. Therefore, the LEACH was not suitable for large networks. Here, Particle Swarm Optimization (PSO) and River Formation Dynamics are used to optimize the CH selection (RFD). The results proved that the proposed method to have performed better compared to other methods

Average Load Distance (ALD) radio communication model for wireless sensor networks

The lifetime of network is one of the most critical issues that have to be considered in the application of wireless sensor networks. The network nodes are battery powered and remain operational as long as they can transmit the sensed data to the processing (sink) node. The main energy consumption of sensor node can be attributed to the task of data transmission to sink node or cluster head. Hence, conserving energy in transmitting data shall maximize functional life of the wireless networks. In this paper we proposed a computationally efficient Average Load Distance (ALD) communication model for forwarding data from sensor to the cluster head. Experiment results indicate that the proposed model can be up to 88% more efficient over direct mode of communication, in respect of per-round maximum energy consumption. An application study shows that ALD can save up to 89% of wireless sensor networks operational cost when compared to direct mode transmission

Hierarchical distributed fog-to-cloud data management in smart cities

There is a vast amount of data being generated every day in the world with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of science and big data environments. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields for efficient usage and exploitation during all data life cycles. Although several specific Data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields. In particular scenario, smart cities are the current technological solutions to handle the challenges and complexity of the growing urban density. Traditionally, Smart City resources management rely on cloud based solutions where sensors data are collected to provide a centralized and rich set of open data. The advantages of cloud-based frameworks are their ubiquity, as well as an (almost) unlimited resources capacity. However, accessing data from the cloud implies large network traffic, high latencies usually not appropriate for real-time or critical solutions, as well as higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. It proposes the use of devices at the edge to provide closer computing facilities and, therefore, reducing network traffic, reducing latencies drastically while improving security. We have defined a new framework for data management in the context of a Smart City through a global fog to cloud resources management architecture. This model has the advantages of both, fog and cloud technologies, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. In this thesis, we propose many novel ideas in the design of a novel F2C Data Management architecture for smart cities as following. First, we draw and describe a comprehensive scenario agnostic Data LifeCycle model successfully addressing all challenges included in the 6Vs not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. Then, we introduce the Smart City Comprehensive Data LifeCycle model, a data management architecture generated from a comprehensive scenario agnostic model, tailored for the particular scenario of Smart Cities. We define the management of each data life phase, and explain its implementation on a Smart City with Fog-to-Cloud (F2C) resources management. And then, we illustrate a novel architecture for data management in the context of a Smart City through a global fog to cloud resources management architecture. We show this model has the advantages of both, fog and cloud, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. As a first experiment for the F2C data management architecture, a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. And, we provide some solutions to both reduce the data transmission and improve the data management. Then, we used some data filtering techniques (including data aggregation and data compression) to estimate the network traffic in this model during data collection and compare it with a traditional real system. Indeed, we estimate the total data storage sizes through F2C scenario for Barcelona smart citiesAl món es generen diàriament una gran quantitat de dades, amb diferents formats, nivells de qualitat, etc. Aquestes noves dades, juntament amb les dades històriques arxivades, constitueixen la llavor per al descobriment de coneixement i la generació de valor en diversos camps de la ciència i grans entorns de dades (big data). Descobrir el valor de les dades és un procés complex de càlcul on les dades són el recurs clau, no només durant el seu processament, sinó també durant tot el seu cicle de vida. Tanmateix, encara hi ha una gran preocupació per com organitzar i gestionar aquestes dades en tots els camps per a un ús i explotació eficients durant tots els cicles de vida de les dades. Encara que recentment s'han definit diversos models específics de Data LifeCycle (DLC) per a escenaris particulars, argumentem que no hi ha un marc global i complet de DLC que s'utilitzi àmpliament en diferents camps. En particular, les ciutats intel·ligents són les solucions tecnològiques actuals per fer front als reptes i la complexitat de la creixent densitat urbana. Tradicionalment, la gestió de recursos de Smart City es basa en solucions basades en núvol (cloud computing) on es recopilen dades de sensors per proporcionar un conjunt de dades obert i centralitzat. Les avantatges dels entorns basats en núvol són la seva ubiqüitat, així com una capacitat (gairebé) il·limitada de recursos. Tanmateix, l'accés a dades del núvol implica un gran trànsit de xarxa i, en general, les latències elevades no són apropiades per a solucions crítiques o en temps real, així com també per a riscos de seguretat més elevats. Alternativament, el processament de boira (fog computing) sorgeix com una tecnologia prometedora per absorbir aquests inconvenients. Proposa l'ús de dispositius a la vora per proporcionar recuirsos informàtics més propers i, per tant, reduir el trànsit de la xarxa, reduint les latències dràsticament mentre es millora la seguretat. Hem definit un nou marc per a la gestió de dades en el context d'una ciutat intel·ligent a través d'una arquitectura de gestió de recursos des de la boira fins al núvol (Fog-to-Cloud computing, o F2C). Aquest model té els avantatges combinats de les tecnologies de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es poden utilitzar les grans capacitats informàtiques de la tecnologia en núvol. En aquesta tesi, proposem algunes idees noves en el disseny d'una arquitectura F2C de gestió de dades per a ciutats intel·ligents. En primer lloc, dibuixem i descrivim un model de Data LifeCycle global agnòstic que aborda amb èxit tots els reptes inclosos en els 6V i no adaptats a un entorn específic, però fàcil d'adaptar-se als requisits de qualsevol camp en concret. A continuació, presentem el model de Data LifeCycle complet per a una ciutat intel·ligent, una arquitectura de gestió de dades generada a partir d'un model agnòstic d'escenari global, adaptat a l'escenari particular de ciutat intel·ligent. Definim la gestió de cada fase de la vida de les dades i expliquem la seva implementació en una ciutat intel·ligent amb gestió de recursos F2C. I, a continuació, il·lustrem la nova arquitectura per a la gestió de dades en el context d'una Smart City a través d'una arquitectura de gestió de recursos F2C. Mostrem que aquest model té els avantatges d'ambdues, la tecnologia de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es pot utilitzar la gran capacitat de processament de la tecnologia en núvol. Com a primer experiment per a l'arquitectura de gestió de dades F2C, s'analitza una ciutat intel·ligent real, corresponent a la ciutat de Barcelona, amb especial èmfasi en les capes responsables de recollir les dades generades pels sensors desplegats. S'ha estimat la quantitat de dades de sensors diàries que es transmet a través de la xarxa i s'ha realitzat una projecció aproximada assumint un desplegament exhaustiu que cobreix tota la ciutat