http://www.ronpub.com/index.php/journals/OJIOT/issues?volume=5&issue=1 All papers of this issue en-us https://www.ronpub.com/ojiot/OJIOT_2019v5i1n01e_VLIoT2019.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919330960165487 We are proud of presenting the outcome of this third edition of the "Very Large Internet of Things" (VLIoT) workshop, which was held in Los Angeles (USA) in August 2019, in conjunction with the 45th International Conference on Very Large Data Bases (VLDB). Following the success path of the two previous workshop editions - in Munich (2017) and in Rio de Janeiro (2018) - VLIoT 2019 kept its tradition to be a vivid and high-quality technical forum for researchers and practitioners working with Internet of Things to share their experiences, visions and latest findings, most of them regarding the design, implementation, deployment and management of IoT systems at very large and scale. This editorial of the special issue introduces and introduces all papers presented at the workshop. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n02_YiXu.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919332044579216 Opposite to the original cloudlet approach in which an edge is utilized to bring the cloud and its benefits closer to the applications, in cloud- and edge-connected IoT systems where the applications are deployed and run in the cloud, we exploit the edge somewhat differently, either by bringing the physical world and its data up closer to the cloud or by caching parts of the applications down closer to the physical world. Aggressive optimizations seeking substantial IoT energy savings are needed to maintain the scalability of large-scale IoT deployments and to stay within cloud cost constraints (avoiding costly elasticity when working with a budget limit). In this paper, we present a novel optimization approach that relies on the simple principle of minimizing all movements: movements of data from the IoT up to the Edge and Cloud, and movements of application fragments from the cloud down to the edge and the IoT itself. Our approach is novel in that it involves and utilizes the dynamic characteristics and variability of both the data and applications simultaneously. Another novelty of our approach is the definition and use of "sentience-efficiency" as a precursor to "energy-efficiency" for achieving truly aggressive savings in energy. We present our bi-directional optimization approach and its implementation in terms of algorithms within an architecture we name the cloud-edge-beneath architecture (CEB). We present a performance evaluation study to measure the impact of our optimization approach on energy saving. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n03_Santos.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919334248197873 A major challenge in emergent scenarios such as the Cloud-assisted Internet of Things is efficiently managing the resources involved in the system while meeting requirements of applications. From the acquisition of physical data to its transformation into valuable services or information, several steps must be performed, involving the various players in such a complex ecosystem. Support for decentralized data processing on IoT devices and other devices near the edge of the network, in combination with the benefits of cloud technologies has been identified as a promising approach to reduce communication overhead, thus reducing delay for time sensitive IoT applications. The interplay of IoT, edge and cloud to achieve the final goal of producing useful information and value-added services to end user gives rise to a management problem that needs to be wisely tackled. The goal of this work is to propose a novel resource management framework for edge-cloud systems that supports heterogeneity of both devices and application requirements. The framework aims to promote the efficient usage of the system resources while leveraging the Edge Computing features, to meet the low latency requirements of emergent IoT applications. The proposed framework encompasses (i) a lightweight and data-centric virtualization model for edge devices, (ii) a set of components responsible for the resource management and the provisioning of services from the virtualized edge-cloud resources. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n04_Semmler.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919335387371884 The rate at which data is produced at the network edge, e.g., collected from sensors and Internet of Things (IoT) devices, will soon exceed the storage and processing capabilities of a single system and the capacity of the network. Thus, data will need to be collected and preprocessed in distributed data stores - as part of a distributed database - at the network edge. Yet, even in this setup, the transfer of query results will incur prohibitive costs. To further reduce the data transfers, patterns in the workloads must be exploited. Particularly in IoT scenarios, we expect data access to be highly skewed. Most data will be store-only, while a fraction will be popular. Here, the replication of popular, raw data, as opposed to the shipment of partially redundant query results, can reduce the volume of data transfers over the network. In this paper, we design online strategies to decide between replicating data from data stores or forwarding the queries and retrieving their results. Our insight is that by profiling access patterns of the data we can lower the data transfer cost and the corresponding response times. We evaluate the benefit of our strategies using two real-world datasets. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n05_Segura.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919340426551900 Wireless sensor networks (WSN) are formed from restricted devices and are known to be vulnerable to denial of service (DoS) security attacks. In parallel, software-defined networking has been identified as a solution for many WSN challenges with respect to flexibility and reuse. Conversely, the SDN control plane centralization may bring about new security threats and vulnerabilities. In this work, we perform a traffic analysis of software-defined WSN (SDWSN) in order to gain understanding of the network's performance when it is under certain types of DoS attacks. In particular, we consider three different DoS scenarios of increasing aggressiveness: (i) false flow requests DoS, (ii) false data flow forwarding DoS, and, (iii) false neighbor information passing DoS. Our simulation results for the latter two types of attack showed significant changes both in the average value and the variance of the delivery rate and the overall overhead. These results demonstrate that it is possible to identify when a SDWSN is under a particular type of DoS, by monitoring the respective quantities. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n06_TaoPeng.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919341561784402 In this paper, we address the problem related to missing data imputation in the IoT domain. More specifically, we propose an Incremental Space-Time-based model (ISTM) for repairing missing values in IoT real-time data streams. ISTM is based on Incremental Multiple Linear Regression, which processes data as follows: Upon data arrival, ISTM updates the model after reading again the intermediary data matrix instead of accessing all historical information. If a missing value is detected, ISTM will provide an estimation for the missing value based on nearly historical data and the observations of neighboring sensors of the default one. Experiments conducted with real traffic data show the performance of ISTM in comparison with known techniques. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n07_Ramachandran.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919345869785889 Internet of Things applications has been deployed and managed in a small to a medium scale deployments in industries and small segments of cities in the last decade. These real-world deployments not only helped the researchers and application developers to create protocols, standards, and frameworks but also helped them understand the challenges associated with the maintenance and management of IoT deployments in all kinds of operational environments. Despite the technological advancements and the deployment experiences, the technology failed to create a notable momentum towards large scale IoT applications involving thousands of IoT devices. We argue the reasons behind the lack of large scale deployments and the limitations of contemporary IoT deployment model. In addition, we present an approach involving multiple stakeholders as a means to scale IoT applications to hundreds of devices. Besides, we argue that the partnership, incentive mechanisms, privacy, and security frameworks are the critical factors for large scale IoT deployments of the future. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n08_Tourani.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919342634548084 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. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n09_Carvalho.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919343755312186 So far, most of research and development for the Internet of Things has been focused at systems where the smart objects, WPAN beacons, sensors, and actuators are mainly stationary and associated with a fixed location (such as appliances in a home or office, an energy meter for a building), and are not capable of handling unrestricted/arbitrary forms of mobility. However, our current lifestyle and economy are increasingly mobile, as people, vehicles, and goods move independently in public and private areas (e.g., automated logistics, retail). Therefore, we are witnessing an increasing need to support Machine to Machine (M2M) communication, data collection, and processing and actuation control for mobile smart things, establishing what is called the Internet of Mobile Things (IoMT). Examples of mobile smart things that fit in the definition of IoMT include Unmanned Aerial Vehicles (UAVs), all sorts of human-crewed vehicles (e.g., cars, buses), and even people with wearable devices such as smart watches or fitness and health monitoring devices. Among these mobile IoT applications, there are several that only require occasional data probes from a mobile sensor, or need to control a smart device only in some specific conditions, or context, such as only when any user is in the ambient. While IoT systems still lack some general programming concepts and abstractions, this is even more so for IoMT. This paper discusses the definition and implementation of suitable programming concepts for mobile smart things - given several examples and scenarios of mobility-specific sensoring and actuation control, both regarding smart things individually, or in terms of collective smart things behaviors. We then show a proposal of programming constructs and language, and show how we will implement an IoMT application programming model, namely OBSACT, on the top of our current middleware ContextNet. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n10_Sotres.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919344775371207 For some years already, there has been a plethora of research initiatives throughout the world that have deployed diverse experimentation facilities for Future Internet technologies research and development. While access to these testbeds has been sometimes restricted to the specific research community supporting them, opening them to different communities can not only help those infrastructures to achieve a wider impact, but also to better identify new possibilities based on novel considerations brought by those external users. On top of the individual testbeds, supporting experiments that employs several of them in a combined and seamless fashion has been one of the main objectives of different transcontinental research initiatives, such as FIRE in Europe or GENI in United States. In particular, Fed4FIRE project and its continuation, Fed4FIRE+, have emerged as "best-in-town" projects to federate heterogeneous experimentation platforms. This paper presents the most relevant aspects of the integration of a large scale testbed on the IoT domain within the Fed4FIRE+ federation. It revolves around the adaptation carried out on the SmartSantander smart city testbed. Additionally, the paper offers an overview of the different federation models that Fed4FIRE+ proposes to testbed owners in order to provide a complete view of the involved technologies. The paper is also presenting a survey of how several specific research platforms from different experimentation domains have fulfilled the federation task following Fed4FIRE+ concepts. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n11_Mauldin.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919352344146661 This paper presents an experimental study of Ensemble Deep Learning (DL) techniques for the analysis of time series data on IoT devices. We have shown in our earlier work that DL demonstrates superior performance compared to traditional machine learning techniques on fall detection applications due to the fact that important features in time series data can be learned and need not be determined manually by the domain expert. However, DL networks generally require large datasets for training. In the health care domain, such as the real-time smartwatch-based fall detection, there are no publicly available large annotated datasets that can be used for training, due to the nature of the problem (i.e. a fall is not a common event). Moreover, fall data is also inherently noisy since motions generated by the wrist-worn smartwatch can be mistaken for a fall. This paper explores combing DL (Recurrent Neural Network) with ensemble techniques (Stacking and AdaBoosting) using a fall detection application as a case study. We conducted a series of experiments using two different datasets of simulated falls for training various ensemble models. Our results show that an ensemble of deep learning models combined by the stacking ensemble technique, outperforms a single deep learning model trained on the same data samples, and thus, may be better suited for small-size datasets. https://www.ronpub.com/ojiot/OJIOT_2019v5i1n12_Misic.html http://nbn-resolving.de/urn:nbn:de:101:1-2019092919351017303648 In this work we consider kernel-based record lifetime estimation in a proactive Internet of Things (IoT) proxy with multicast based cache management. Multicast refreshment requests were based on lifetime expiration for a predefined number of records. To reduce the traffic volume in the IoT domain, we assume that only nodes where the observed physical variable has changed its value will respond to the multicast request. For estimating the data lifetime at the proxy, we use Gaussian kernels, assuming that the intrinsic data lifetime probability distribution was taken from Erlang-k family of sub-exponential distributions. In this setup, we consider that the proxy connects to the IoT domain using an IEEE 802.15.4-compatible wireless network. Results indicate that narrow and symmetrical lifetime probability distributions require more frequent multicasting refreshments compared to wider and asymmetric ones. This increases traffic intensity and energy consumption in IoT domain. We quantify finding with numerical results.