The Internet of Things (IoT) is transforming the surrounding everyday physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage and is driven by various sensors. Sensors detect and measure changes in position, temperature, light, and many others; furthermore, they are necessary to turn billions of objects into data-generating “things” that can report on their status and often interact with their environment. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. This paper introduces the SensorHUB framework that utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and service development. SensorHUB is both a method and an environment to support IoT related application and service development; furthermore, it supports the data monetization approach, that is, provides a method to define data views on top of different data sources and analyzed data. The framework is available in a Platform as a Service (PaaS) model and has been applied for the vehicle, health, production lines, and smart city domains.

Datasets that appear in publications are curated and split into training, testing, and validation sub-datasets by domain experts. Consequently, machine learning models typically perform well on such split-by-hand datasets, whereas preparing real-world datasets into curated splits, i.e., training, testing, and validation sub-datasets, require extensive effort. Usually, random repetitive splitting is carried out, practiced, and evaluated until a better score is reached on the evaluation metrics. In this paper, a novel algorithmic method is proposed for splitting datasets for machine learning models. Algorithmic Splitting utilizes deterministic dimension reduction and density-based clustering techniques for estimating the data distribution. Afterward, splitting the dataset is carried out based on the estimated data distribution. Our objective is to achieve evenly representative splits of a given dataset in a standard and algorithmic way that reduces the perplexity of random splitting using the thorough splitting method. Experiments demonstrate the potential of Algorithmic Splitting through qualitative and quantitative evaluation of MNIST, Fashion MNIST, CIFAR-10, SmallNORB, and Shapes3D.

No abstract

No abstract

In this paper, we propose a selective-repeat (SR) automatic repeat-request (ARQ) model for multi-source download scenarios and analyze their useful throughput that we refer to as goodput. The multi-source scenario comprises a set of transmitters that send packets to a receiver. We characterize the forward channels from the transmitters to the receiver via a general hidden Markov model (HMM) and assume that the reverse channels from the receiver to the transmitter are lossless. To find the average goodput of the network, we exploit the probability-generation function. We consider different packet transmission schemes, including uncoded random, network coded and sliding window-based network coded packets, and contrast their performance. Our calculations show that using network coding in a multi-source scenario can increase the average goodput, while sliding window-based coding may also archive the theoretical maximum goodput. We show that our multi-source approach avoids the straggler problem, therefore adding more transmitters to the network increases its throughout and the system does not get limited by the weakest transmitter. We also verify our analytic results with extensive simulations.

Energy efficiency is one of the key aspects of IoT and Wireless Sensor Networks (WSNs) since the nodes of the network are running on battery power and the lifespan of the system is an important missioncritical parameter. In WSNs, where the energy consumption mainly depends on the radio interface and the transmission protocols, reliable packet forwarding from the source node to Base Station (BS) is crucial. In this paper, we focus on developing new routing algorithms which extend the lifespan of WSNs by achieving optimal energy balancing subject to the criterion that the packets must reach the BS with a predefined probability. We will propose novel two-hop and multi-hop routing algorithms to achieve this objective. The performance of the novel algorithms is compared with the LEACH routing protocol. Extensive simulations prove that the new routing methods are indeed energy efficient, and they are able to meet the predefined reliability criteria as well. The results given in this paper can contribute to reliable communication in IoT or WSN networks and result in lower energy consumption.

No abstract

JSON Web Tokens provide a scalable solution with significant performance benefits for user access control in decentralized, large-scale distributed systems. Such examples would entail cloud-based, micro-services styled systems or typical Internet of Things solutions. One of the obstacles still preventing the wide-spread use of JSON Web Token–based access control is the problem of invalidating the issued tokens upon clients leaving the system. Token invalidation presently takes a considerable processing overhead or a drastically increased architectural complexity. Solving this problem without losing the main benefits of JSON Web Tokens still remains an open challenge which will be addressed in the article. We are going to propose some solutions to implement low-complexity token revocations and compare their characteristics in different environments with the traditional solutions. The proposed solutions have the benefit of preserving the advantages of JSON Web Tokens, while also adhering to stronger security constraints and possessing a finely tuneable performance cost.