Efficient Security Algorithm for Power Constrained IoT Devices

Internet of Things (IoT) devices characterized by low power and low processing capabilities do not exactly fit into the provision of existing security techniques, due to their constrained nature. Classical security algorithms which are built on complex cryptographic functions often require a level of processing that low power IoT devices are incapable to effectively achieve due to limited power and processing resources. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones to be more suitable for constrained IoT devices. In this work, an Efficient security Algorithm for Constrained IoT devices; based on the Advanced Encryption Standard is proposed. We present a cryptanalytic overview of the consequence of complexity reduction together with a supporting mathematical justification, and provisioned a secure element (ATECC608A) as a trade-off. The ATECC608A doubles for authentication and guarding against implementation attacks on the associated IoT device (ARM Cortex M4 microprocessor) in line with our analysis. The software implementation of the efficient algorithm for constrained IoT devices shows up to 35% reduction in the time it takes to complete the encryption of a single block (16bytes) of plain text, in comparison to the currently used standard AES-128 algorithm, and in comparison to current results in literature at 26.6

Overview of Caching Mechanisms to Improve Hadoop Performance

Nowadays distributed computing environments, large amounts of data are generated from different resources with a high velocity, rendering the data difficult to capture, manage, and process within existing relational databases. Hadoop is a tool to store and process large datasets in a parallel manner across a cluster of machines in a distributed environment. Hadoop brings many benefits like flexibility, scalability, and high fault tolerance; however, it faces some challenges in terms of data access time, I/O operation, and duplicate computations resulting in extra overhead, resource wastage, and poor performance. Many researchers have utilized caching mechanisms to tackle these challenges. For example, they have presented approaches to improve data access time, enhance data locality rate, remove repetitive calculations, reduce the number of I/O operations, decrease the job execution time, and increase resource efficiency. In the current study, we provide a comprehensive overview of caching strategies to improve Hadoop performance. Additionally, a novel classification is introduced based on cache utilization. Using this classification, we analyze the impact on Hadoop performance and discuss the advantages and disadvantages of each group. Finally, a novel hybrid approach called Hybrid Intelligent Cache (HIC) that combines the benefits of two methods from different groups, H-SVM-LRU and CLQLMRS, is presented. Experimental results show that our hybrid method achieves an average improvement of 31.2% in job execution time

THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

Durant les quatre dernières décennies, la miniaturisation a permis la diffusion à large échelle des ordinateurs, les rendant omniprésents. Aujourd’hui, le nombre d’objets connectés à Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent être des téléphones mobiles, des véhicules ou des senseurs, génèrent de très grands volumes de données qui sont presque toujours associés à un contexte spatiotemporel. Le volume de ces données est souvent si grand que leur traitement requiert la création de système distribués qui impliquent la coopération de plusieurs ordinateurs. La capacité de traiter ces données revêt une importance sociétale. Par exemple: les données collectées lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son véhicule. Un autre exemple: dans un avenir proche, les données collectées à l’aide de gyroscopes capables de détecter les trous dans la chaussée permettront de mieux planifier les interventions de maintenance à effectuer sur le réseau routier. Les domaines d’applications sont par conséquent nombreux, de même que les problèmes qui y sont associés. Les articles qui composent cette thèse traitent de systèmes qui partagent deux caractéristiques clés: un contexte spatiotemporel et une architecture décentralisée. De plus, les systèmes décrits dans ces articles s’articulent autours de trois axes temporels: le présent, le passé, et le futur. Les systèmes axés sur le présent permettent à un très grand nombre d’objets connectés de communiquer en fonction d’un contexte spatial avec des temps de réponses proche du temps réel. Nos contributions dans ce domaine permettent à ce type de système décentralisé de s’adapter au volume de donnée à traiter en s’étendant sur du matériel bon marché. Les systèmes axés sur le passé ont pour but de faciliter l’accès a de très grands volumes données spatiotemporelles collectées par des objets connectés. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particulièrement denses, ce qui n’avait pas été fait auparavant. Enfin, les systèmes axés sur le futur utilisent les trajectoires passées pour prédire les trajectoires que des objets connectés suivront dans l’avenir. Nos contributions permettent de prédire les trajectoires suivies par des objets connectés avec une granularité jusque là inégalée. Bien qu’impliquant des domaines différents, ces contributions s’articulent autour de dénominateurs communs des systèmes sous-jacents, ouvrant la possibilité de pouvoir traiter ces problèmes avec plus de généricité dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

Energy-efficient query management scheme for a wireless sensor database system

Minimizing the communication overhead to reduce the energy consumption is an essential consideration in sensor network applications, and existing research has mostly concentrated on data aggregation and in-network processing. However, effective query management to optimize the query aggregation plan at the gateway side is also a significant approach to energy saving in practice. In this paper, we present a multiquery management framework to support historical and continuous queries, where the key idea is to reduce common tasks in a collection of queries through merging and aggregation, according to query region, attribute, time duration, and frequency, by executing the common subqueries only once. In this framework, we propose a query management scheme to support query partitioning, region aggregation and approximate processing, time partitioning and aggregation rules, multirate queries, and historical database. In order to validate the performance of our algorithm, a heuristic routing protocol is also described. The performance simulation results show that the overall energy consumption for forwarding and answering a collection of queries can be significantly reduced by applying our query management scheme. The advantages and disadvantages of the proposed scheme are discussed, together with open research issues

Distributed routing in networks and its application

Tato diplomová práce popisuje decentralizovaný systém jménem NodeSkipper určený pro kteroukoli spojitou neorientovanou síť. Uzly v této síti mohou posílat nebo vyhledávat jiné uzly nebo vyvolat proces "consensus", kdy se celá síť shodne na hodnotě zvolené veličiny tak, aby byl výsledek ovlivněn každým uzlem a byl pro všechny uzly stejný. NodeSkipper je inspirovaný datovou strukturou Skip List, která díky náhodnosti své struktury, která se přes postupné přidávání a ubírání uzlů přibližuje zvolenému pravděpodobnostnímu rozdělení, nabízí velmi všestranný výkon a vysokou robustnost. Protokol NodeSkipper pracuje nejlépe pro sítě s efektem malého světa, který se vyskytuje ve skutečných sítích přirozeně. Díky tomuto efektu roste průměr sítě pouze logaritmicky vzhledem k množství uzlů. V takové síti je NodeSkipper schopný doručit zprávu nebo hledat uzel v logaritmickém čase. Díky své necentralizovanosti a absenci konkrétní struktury funguje velmi dobře s velkými sítěmi, kde jsou nové uzly nepředvídatelně přidávány a odebírány a přímá spojení navazována a ztrácena, jako například vozidla v silniční dopravě, doručovací roboti, stroje v továrně, bezpečnostní systémy pro velká území, počítače spolupracující na výpočetně náročné úloze nebo roboti účastnící se boje. Protože tento systém nemá žádné uzly s vyšší důležitostí, je odolný vůči cíleným útokům a vzhledem k tomu, že funguje na kterémkoli spojitém grafu, je odolný vůči náhodným útokům a selháním. Díky schopnosti dojít ke shodě může dobře koordinovat své prostředkyThis thesis describes a decentralized system that can work over any connected undirected network called NodeSkipper. Each node in this system can send a message to another node, look-up any node or request the system to reach consensus, which means that every node in the system will agree on a quantity of interest in a manner where each node influences the result. The system is designed after the Skip List data structure, which uses randomized structure that over successive entries and removals converges towards its probability distribution, while providing great all-rounded performance and robustness. The NodeSkipper protocol works best over networks with small-world effect, which occurs naturally on real networks. This effect manifests itself by network diameter scales logarithmically with the number of nodes. On such network, NodeSkipper can deliver messages and look-up nodes in logarithmical time as well. Thanks to its decentralized nature and no rigid structure, it works well with large networks where new nodes are unpredictably added and removed and direct connections gained and lost, such as cars on the road, delivery robots, machines in a manufacturing plant, large scale security system, computers working together on computationally demanding task or battle units in armed conflict. Because this system does not have any nodes of special importance, it is resistant to targeted attacks. Because it works as long as the graph is connected, it is resistant to random attacks and failures. Thanks to its ability to reach network wide consensus, it can coordinate its efforts