Distributed Data-Gathering and -Processing in Smart Cities: An Information-Centric Approach

The technological advancements along with the proliferation of smart and connected devices (things) motivated the exploration of the creation of smart cities aimed at improving the quality of life, economic growth, and efficient resource utilization. Some recent initiatives defined a smart city network as the interconnection of the existing independent and heterogeneous networks and the infrastructure. However, considering the heterogeneity of the devices, communication technologies, network protocols, and platforms the interoperability of these networks is a challenge requiring more attention. In this paper, we propose the design of a novel Information-Centric Smart City architecture (iSmart), focusing on the demand of the future applications, such as efficient machineto-machine communication, low latency computation offloading, large data communication requirements, and advanced security. In designing iSmart, we use the Named-Data Networking (NDN) architecture as the underlying communication substrate to promote semantics-based communication and achieve seamless compute/data sharing

ICedge: When Edge Computing Meets Information-Centric Networking

In today’s era of explosion of Internet of Things (IoT) and end-user devices and their data volume, emanating at the network’s edge, the network should be more in-tune with meeting the needs of these demanding edge computing applications. To this end, we design and prototype Information-Centric edge (ICedge), a general-purpose networking framework that streamlines service invocation and improves reuse of redundant computation at the edge. ICedge runs on top of Named-Data Networking, a realization of the Information-Centric Networking vision, and handles the “low-level” network communication on behalf of applications. ICedge features a fully distributed design that: (i) enables users to get seamlessly on-boarded onto an edge network, (ii) delivers application invoked tasks to edge nodes for execution in a timely manner, and (iii) offers naming abstractions and network-based mechanisms to enable (partial or full) reuse of the results of already executed tasks among users, which we call “compute reuse”, resulting in lower task completion times and efficient use of edge computing resources. Our simulation and testbed deployment results demonstrate that ICedge can achieve up to 50× lower task completion times leveraging its networkbased compute reuse mechanism compared to cases, where reuse is not available

Social forwarding in opportunistic ad-hoc networks

Dans les réseaux ad hoc opportunistes, le transfert multi-sauts de données est inapproprié puisque les appareils mobile sont souvent déconnectées les unes des autres. Des travaux antérieurs ont étudié l'utilisation de différents types d'informations sur le réseau pour guider et améliorer les décisions de transfert. En général, il est difficile de concevoir un algorithme de transfert opportuniste, puisque ses performances dépendent des caractéristiques de la mobilité des nœuds dans le réseau. Cette thèse contribue à une meilleure compréhension des performances de tous les algorithmes de transfert opportunistes en termes de nombre de sauts et de délais. Nous prouvons analytiquement et validons empiriquement qu'il existe des chemins qui sont courts aussi bien en termes de délais et nombre de sauts dans ces réseaux opportunistes. Ce résultat a des répercussions importantes sur la façon de concevoir des algorithmes de transfert dans les réseaux opportunistes. En particulier, il indique que les messages peuvent être éliminés après un petit nombre de sauts sans impacter les performances. Des similitudes entre les interactions sociales entre les individus et leurs mobilités confirment qu'une classification des nœuds en fonction de leurs propriétés sociales pourrait être pertinente pour le réseau opportuniste. Nous développons PeopleRank une approche systématique pour l'utilisation des interactions sociales pour guider les décisions de transfert dans ce type de réseau.PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

[Accepted Article Manuscript Version (Postprint)] Distributed Data-Gathering and -Processing in Smart Cities: An Information-Centric Approach

The technological advancements along with the proliferation of smart and connected devices (things) motivated the exploration of the creation of smart cities aimed at improving the quality of life, economic growth, and efficient resource utilization. Some recent initiatives defined a smart city network as the interconnection of the existing independent and heterogeneous networks and the infrastructure. However, considering the heterogeneity of the devices, communication technologies, network protocols, and platforms the interoperability of these networks is a challenge requiring more attention. In this paper, we propose the design of a novel Information-Centric Smart City architecture (iSmart), focusing on the demand of the future applications, such as efficient machineto-machine communication, low latency computation offloading, large data communication requirements, and advanced security. In designing iSmart, we use the Named-Data Networking (NDN) architecture as the underlying communication substrate to promote semantics-based communication and achieve seamless compute/data sharing

[Accepted Article Manuscript Version (Postprint)] Track Me to Track Us: Leveraging Short Range Wireless Technologies for Enabling Energy Efficient WI-Fi-Based Localization

Given the success of outdoor tracking via GPS and the rise of real-time context-aware services, users will soon rely on applications that require higher granularity indoor localization. This need is further manifested in countries like Qatar, where various social and business activities occur indoors. Wi-Fi-based indoor localization is one of the most researched techniques due to its ubiquitous deployment and acceptable accuracy for a wide range of applications. However, we do not witness such techniques widely deployed mainly due to their high demand on energy, which is a precious commodity in mobile devices. We propose an energy-efficient indoor localization system that leverages peoples\u27 typical group mobility patterns and short-range wireless technologies available on their devices. Our system architecture, shown in the figure, is designed to be easily integrated with existing Wi-Fi localization engines. We first utilize low-energy wireless technologies, such as Bluetooth, to detect and cluster individuals moving together. Our system then assigns a group representative to act as a designated cluster head that would be constantly tracked. The location of other group members can be inferred so long as they remain within proximity of the cluster heads. Afterwards, cluster heads continue to send the periodic received signal strength indicator (RSSI) updates, while the remaining members turn off their Wi-Fi interface relying on the cluster head to be localized. Our system is responsible for dynamically handling the merger or splitting of clusters as a result of mobility. We implement a prototype of the system, and evaluate it at scale using the QualNet simulator. Our results show that we can achieve up to 55% energy reduction with a relatively small degradation in localization accuracy averaging 2 meters. This accuracy reduction is non-impactful given the typical applications expected to leverage our system

A power-of-two choices based algorithm for fog computing

The fog computing paradigm brings together storage, com-munication, and computation resources closer to users’ end-devices.Therefore, fog servers are deployed at the edge of the network, offeringlow latency access to users. With the expansion of such fog computingservices, different providers will be able to deploy multiple resourceswithin a restricted geographical proximity.In this paper, we investigate an incentive-based cooperation schemeacross fog providers. We propose a distributed cooperative algorithmamongst fog computing providers where fully collaborative fog nodesare subject to different loads. The proposed algorithm leverages thepower-of-two result and exploits a cooperation probability, namely theprobability that a given provider collaborates by accepting a computationrequest from another provider, as a mean to achieve a fair cooperation.We adopt an analytical approach based on exploiting a simplifiedperformance model to demonstrate numerically that a set of optimalaccepting probabilities exits when the number of server nodes goes toinfinity. This result then drives the design of our distributed algorithm.Second, in our experimental approach, we perform a set of simulationanalysis to verify the validity of the proposed solution when the numberof servers is limited