There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

As the term “smart city” gains wider and wider currency, there is still confusion about what a smart city is, especially since several similar terms are often used interchangeably. This paper aims to clarify the meaning of the word “smart” in the context of cities through an approach based on an in-depth literature review of relevant studies as well as official documents of international institutions. It also identifies the main dimensions and elements characterizing a smart city. The different metrics of urban smartness are reviewed to show the need for a shared definition of what constitutes a smart city, what are its features, and how it performs in comparison to traditional cities. Furthermore, performance measures and initiatives in a few smart cities are identified.

Smart Cities: Definitions, Dimensions, Performance, and Initiatives

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

In the midst of Internet of Things development, a first requirement was tracking and identification of those mentioned "things" which could be done thanks to Radio Frequency Identification. However, since then, the development of RFID allowed a new range of applications among which is remote sensing of environmental values. While RFID can be seen as a more efficient solution than traditional Wireless Sensor Networks, two main issues remain: first reading collisions and second proficient data gathering solution. In this paper, we examine the implementation of two applications: for industrial IoT and for smart cities, respectively. Both applications, in regards to their requirements and configuration, challenge the operation of a RFID sensing solution combined with a dynamic wireless data gathering over multihops. They require the use of both mobile and fixed readers to cover the extent of deployment area and a quick retrieval of tag information. We propose a distributed crosslayer solution for improving the efficiency of the RFID system in terms of collision and throughput but also its proficiency in terms of tag information routing towards one or multiple sinks. Simulation results show that we can achieve high level of throughput while maintaining a low level of collision and a fairness of reader medium access above 95% in situations where readers can be fix and mobile, while tag information is routed with a data rate of 97% at worst and reliable delays for considered applications.

/pdf/dacar-distributed-adaptable-crosslayer-anticollision-and-brlrf00wq8.pdf

DACAR: Distributed & Adaptable Crosslayer Anticollision and Routing Protocol for RFID

Etude de convergence dans les réseaux sans fil

La construction repartie d’un graphe oriente acyclique (ou DAG) est une technique bien connue pour briser la symetrie dans les reseaux uniformes ou semi-uniformes. En effet, l’abscence de cycles permet les constructions inductives a partir des sources du DAG. Pour plusieurs algorithmes recents proposes pour les reseaux sans fil (par exemple [HT04] ou les articles references par [MFGL+05]), ce DAG permet l’execution d’algorithmes deterministes localises (dont le temps de convergence depend de la hauteur du DAG construit). Un tel DAG peut etre construit grâce a un coloriage distribue des nœuds dans le reseau (par exemple en orientant les aretes dans le sens des couleurs decroissantes). Dans cet article, nous analysons analytiquement et par simulation le temps de construction du DAG induit par un algorithme probabiliste de coloriage. En outre, nous mesurons par simulation l’influence de la taille du domaine des couleurs Ω sur ce temps de stabilisation et sur la hauteur du DAG resultant. Il apparait qu’un compromis est a faire pour determiner le parametre Ω : plus la valeur de |Ω| est grande, plus le temps de convergence de la coloration est faible mais plus la hauteur du DAG est importante. La hauteur moyenne est cependant de plusieurs ordres de grandeur plus faible que la hauteur maximum theorique. Etant donnee la limitation sur le nombre de pages, il n’est pas possible de decrire notre etude dans son integralite. Le lecteur interesse par ce sujet peut se reporter a [MFGL05]. Nous donnons dans un premier temps les notations utilisees par la suite et nous decrivons brievement l’algorithme de coloriage et et les methodes d’ordonnancement distribue consideres.

Convergence dans les réseaux sans fil

Geographic routing has gained much attention as a basic routing primitive in wireless sensor networks (WSNs) due to its memory-less, scalability, efficiency and low overhead features. However, it suffers from communication holes, where no next hop candidate is closer to the destination than the node currently holding the packet. This paper proposes greedy with ACO-based recovery routing protocol (GRACO), an efficient geographic routing protocol with a novel void recovery strategy based on ant colony optimisation (ACO). GRACO is able to adaptively adjust the forwarding mechanism to avoid the blocking situation and effectively deliver data packets. Compared to greedy-face-greedy (GFG), one of the best performing geographic routing protocols, simulation results demonstrate that GRACO can successfully find shorter routing paths with higher delivery rate, less control packet overhead and shorter end-to-end delay.

/pdf/graco-a-geographic-greedy-routing-with-an-aco-based-void-3xhgkdf2lw.pdf

Nathalie Mitton

Papers

DACAR: Distributed & Adaptable Crosslayer Anticollision and Routing Protocol for RFID

Etude de convergence dans les réseaux sans fil

Convergence dans les réseaux sans fil

GRACO: a geographic greedy routing with an ACO based void handling technique

Energy Harvesting in Wireless Sensor Networks