Large-scale Internet Of Things Research Articles

An Intelligent Outlier Detection Method With One Class Support Tucker Machine and Genetic Algorithm Toward Big Sensor Data in Internet of Things

Various types of sensor data can be collected by the Internet of Things (IoT). Each sensor node has spatial attributes and may also be associated with a large number of measurement data that evolve over time; therefore, these high-dimensional sensor data are inherently large scale. Detecting outliers in large-scale IoT sensor data is a challenging task. Most existing anomaly detection methods are based on a vector representation. However, large-scale IoT sensor data have characteristics that make tensor methods more efficient for extracting information. The vector-based methods can destroy original structural information and correlation within large-scale sensor data, resulting in the problem of the “curse of dimensionality,” and some outliers hence cannot be detected. In this paper, we propose a one-class support Tucker machine (OCSTuM) and an OCSTuM based on tensor Tucker factorization and a genetic algorithm called GA-OCSTuM. These methods extend one-class support vector machines to tensor space. OCSTuM and GA-OCSTuM are unsupervised anomaly detection approaches for big sensor data. They retain the structural information of data while improving the accuracy and efficiency of anomaly detection. The experimental evaluations on real data sets demonstrate that our proposed method improves the accuracy and efficiency of anomaly detection while retaining the intrinsic structure of big sensor data.

Enabling Optimization-Based Localization for IoT Devices

In this paper, we propose an embedded optimization approach for the localization of Internet of Things (IoT) devices making use of range measurements from ultra-wideband (UWB) signals. Low-cost, low-power UWB radios provide time-of-arrival measurements with decimeter accuracy over large distances. UWB-based localization methods have been envisioned to enable feedback control in IoT applications, particularly, in GPS-denied environments, and large wireless sensor networks. In this paper, we formulate the localization task as a nonlinear least-squares optimization problem based on two-way time-of-arrival measurements between the IoT device and several UWB radios installed in a 3-D environment. For the practical implementation of large-scale IoT deployments we further assume only approximate knowledge of the UWB radio locations. We solve the resulting optimization problem directly on IoT devices equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for plug-and-play deployment of the nonlinear-programming algorithms. This paper further provides practical implementation details to improve the localization accuracy for feedback control in experimental IoT applications. The experimental results finally show that subdecimeter localization accuracy can be achieved using the proposed optimization-based approach, even when the majority of the UWB radio locations are unknown.

Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities

As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.

Lightweight Privacy Preservation for Securing Large-Scale Database-Driven Cognitive Radio Networks with Location Verification

The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

Radio-Silent Communications [Future Directions

The ability to open the radio-frequency (RF) spectrum in radio silence, where there is no interference with airwaves, is extremely valuable. By many estimates, even narrow slices of the spectrum are measured in billions of dollars. With the world on the precipice of largescale Internet of Things (IoT) and autonomous vehicle adoption, already crowded airwaves have reached their limits or soon will. Clever ways to maximize airwave channel capacities will not solve the ever-expanding need for more throughput and people's insatiable demand for more data. Pabellon, a Silicon Valley-based start-up, has developed Surface Plane Magnetics (SPM) technology to open the untapped and unregulated RF spectrum.

Efficient data handling in vehicular micro clouds

Wireless communication capabilities currently transform the automotive landscape. Short-range communication technologies enable a wide range of Information and Communication Technology (ICT) application for cars, drivers, and even large-scale Internet of Things (IoT) applications. Many of such applications have complex requirements in particular related to locality of data. Recently, the concept of the vehicular cloud has been proposed to address these issues, similar to what is currently investigated in the scope of Mobile Edge Computing (MEC). Forming what we call micro clouds of cars, we establish a virtual roadside infrastructure that can not only support other cars but also complex IoT applications. In this paper, we focus on data management in such micro clouds, i.e., clusters of cars organized in a hierarchical manner. Our micro clouds can provide services in their vicinity and together form macro clouds enabling more complex services and spanning entire cities. We first present an algorithm to form micro clouds at a specific geographic location using a map-based approach. Then, we develop data management services for such dynamic clusters. Concentrating on two services, namely collect data for collecting sensor data from vehicles within the micro cloud and forwarding these (possibly in aggregated form) to the macro cloud, and preserve data for keeping location-based data at the specified geo-location by continuously handing data from cars leaving to such joining the cluster. Our evaluation results clearly demonstrate the effectiveness of our approach including all the enhancements described in the paper.

UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm–Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm–Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.

Advancing Experimentation-as-a-Service Through Urban IoT Experiments

Smart cities are becoming a vibrant application domain for a number of science fields. As such, service providers and stakeholders are beginning to integrate co-creation aspects into current implementations to shape the future smart city solutions. In this context, holistic solutions are required to test such aspects in real city-scale Internet of Things (IoT) deployments, considering the complex city ecosystems. In this paper, we discuss OrganiCity’s implementation of an experimentation-as-a-service (EaaS) framework, presenting a toolset that allows developing, deploying, and evaluating smart city solutions in a one-stop shop manner. This is the first time such an integrated toolset is offered in the context of a large-scale IoT infrastructure, which spans across multiple European cities. We discuss the design and implementation of the toolset, presenting our view on what EaaS should provide, and how it is implemented. We present initial feedback from 25 experimenter teams that have utilized this toolset in the OrganiCity project, along with a discussion on two detailed actual use cases to validate our approach. Learnings from all experiments are discussed as well as architectural considerations for platform scaling. Our feedback from experimenters indicates that EaaS is a viable and useful approach.

A secure IoT cloud storage system with fine-grained access control and decryption key exposure resistance

Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in IoT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user secret keys due to different reasons. In this paper, we present a practical attribute-based access control system for IoT cloud by introducing an efficient revocable attribute-based encryption scheme that permits the data owner to efficiently manage the credentials of data users. Our proposed system can efficiently deal with both secret key revocation for corrupted users and accidental decryption key exposure for honest users. We analyze the security of our scheme with formal proofs, and demonstrate the high performance of the proposed system via experiments.

Activity Detection for Massive Connectivity Under Frequency Offsets via First-Order Algorithms

Activity detection in machine-type communication (MTC) has been recognized as an effective way to support massive connectivity of the Internet-of-Things (IoT) devices. However, due to the sporadic traffic pattern of the MTC, only a small portion of the massive potential devices are active, making the activity detection a challenging large-scale sparsity-constrained problem. On the other hand, since the low-cost IoT devices are commonly equipped with cheap crystal oscillators, the resulting frequency offsets would intensify the multi-user interference during the activity detection and invalidate existing detection methods that are designed under ideal frequency synchronization. To fill this gap, this paper proposes two methods for activity detection under unknown frequency offsets: a Lasso-based method and a sparsity-constrained method. Both the methods are first-order algorithms, making them suitable for large-scale IoT systems. Furthermore, the sparsity-constrained method can be executed in parallel and is proved to converge to a set of critical points. The simulation results show that both the proposed methods achieve much better detection performance than a two-stage approach that separately performs frequency synchronization and activity detection. Moreover, the proposed sparsity-constrained method is shown to perform better than two competing algorithms exploiting hierarchical sparsity.

Design and Analysis of a Distributed and Demand-Based Backscatter MAC Protocol for Internet of Things Networks

Backscatter communication is a new wireless communication technology, which can enable battery-free devices to communicate with others by backscattering ambient radio-frequency signals, and therefore has great potential to be deployed in the future Internet of Things (IoT) networks. In the existing related studies, the adopted centralized approach might be not suitable for a large-scale IoT network with sporadic backscatter communication, while the adopted distributed approach ignored the demand of the access point (AP). In this paper, we consider a large-scale IoT network consisting of the legacy Wi-Fi communication and the backscatter communication, and aim to propose an efficient distributed backscatter medium access control protocol that takes into account of the demand of the AP. In our protocol, when the AP has a demand to collect the information of backscatter devices, it enables backscatter devices to contend for the channel with Wi-Fi devices in a separate manner, where the backscatter devices are only allowed to participate in the contention in a limited time period. We then characterize this constrained contention process and develop a theoretical model to analyze the performance of the proposed protocol. With this model, we can exactly express the per-node throughput of Wi-Fi and backscatter devices. Finally, extensive simulations verify that our model is much accurate, and our protocol can outperform the related protocol in terms of both per-node throughput and system throughput.

Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things

Advances in Internet of Things (IoT) bring massive intelligent applications, many of which are computation intensive and time sensitive. With limited resources of IoT devices, mobile computation offloading can be exploited to offload part of the applications to nearby devices that have more powerful computing resources, thereby speeding up the applications and reducing the energy consumption. In this paper, we consider application partitioning and collaborative computation offloading in IoT networks, in order to meet the completion deadline of the applications while minimizing the overall energy consumption. The problem is formulated as a binary integer linear programming problem, which is transformed into a weighted bipartite matching problem and then solved by the centralized Kuhn-Munkres algorithm. To fit the large-scale IoT scenarios, three distributed algorithms are then introduced from different perspectives. The first one is referred to as the noncooperative matching (NCM) algorithm, where each node makes offloading decision based on its own interest in minimizing energy consumption. Afterward, an asynchronous greedy matching (AGM) algorithm is developed by considering the mutual interest of the requestor and collaborator pairs in terms of their energy consumptions. Finally, a maximum differential energy matching (MDEM) algorithm is devised by relaxing the network stability requirement, which can further benefit the energy efficiency for all network nodes. Theoretical analysis and simulation results demonstrate that both the NCM and AGM algorithms guarantee the network stability and improve the energy saving compared with entirely local execution, while the MDEM algorithm can further achieve near-optimal energy consumption at the expense of higher implementation overheads.

A best-fit routing algorithm for non-redundant communication in large-scale IoT based network

In this manuscript a self-disciplined path discovery and information transmitting routing is proposed for Internet of Things (IoT) enabled applications. This routing process works at device level for self-healing and organizing its communication routes in a large-scale network. The routing process is opportunistic in nature, requiring local device information other than global update. This opportunistic routing (OR) is based on best-fit traversing (BFT) algorithm to improve the accessibility of the device in a precise manner. The best-fit algorithm discovers flawless neighbors for resuming interrupted communications in prolonged manner. The opportunistic local information based searching process avoids frequent disconnection and redundancy that is common in a single device multi-application scenario. The local route searching process is constructed using a threshold metric to confine the greedy behavior of the devices at the time of neighbor discovery. The routing process is designed to support network scalability to adapt its benefits in the varying network dimension. The proposed opportunistic routing technique improves application performance by minimizing transmission delay, redundancy and increasing communication rates.

Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.

Blockchain Applications in Smart Grid–Review and Frameworks

Modern power systems face different challenges such as the ever-increasing electrical energy demand, the massive growth of renewable energy with distributed generations, the large-scale Internet of Things (IoT) devices adaptation, the emerging cyber-physical security threats, and the main goal of maintaining the system’s stability and reliability. These challenges pose extreme pressure on finding advanced technologies and sustainable solutions for secure and reliable operations of the power system. The blockchain is one of the recent technologies that have gained lots of attention in different applications including smart grid for its uniqueness and decentralized nature. In the last few years, this technology grew a momentum specifically with the cryptocurrencies’ industry such as the Bitcoin and Etherium. The Blockchain’s applications in the smart grids could offer many innovative and affordable solutions to some of the challenges that the future and the current smart grids will be facing. This paper reviews different prospects, advantages, approaches, and technical challenges of utilizing the blockchain technology in the smart grid, and presents frameworks for key smart grid blockchain-based applications; more specifically, it is shown that how the blockchain can be used as the smart grid’s cyber-physical layer.

PREVENT THE TRANSMISSION OF USELESS / REPEATED DATA TRANSMISSION IN THE INTERNET OF THINGS NETWORK

Internet of Things (IoT) is a new and advanced network technology that incorporates inter-object communication and sensor technology that connects almost every controllable electronic device to be used in daily life with a different Internet Protocol (IP) address in a network environment. In a such large-scale network, data transmission, process monitoring, data processing and network traffic are also required to be addressed. Today's Internet architecture uses the TCP / IP protocol stack for communication and is insufficient to meet the needs of the large-scale IoT network. Therefore, it will be a big advantage to reduce the data traffic in an IoT network where large amounts of data will be generated. In this study, a new method for reducing the data traffic in a designed IoT network is proposed and the data traffic on the network is reduced by preventing the transfer of useless / repetitive data in the network environment. A four layered (sensing layer, internet/network layer, transport layer and application layer) IoT architecture has been proposed by Çavdar and Öztürk in 2018. In the sensing layer, it is aimed to prevent unnecessary and repeated data from being transferred in the network environment by analyzing and eliminating the data. In addition, preventing the transmission of unnecessary and repetitive data to the network environment will bring many advantages such as storage, data traffic speed and storage cost.

ICCF: An Information-Centric Collaborative Fog Platform for Building Energy Management Systems

In order to construct future large-scale Internet of Things (IoT) networks, Fog computing is a promising paradigm that brings big data processing capability, storage, and control from a remote cloud closer to the end users/things. However, the majority of prior studies have focused on the data connection to realize a vertical Cloud-Fog-devices' continuum. In this paper, we propose an information-centric collaborative Fog (ICCF) platform, empowered by a novel horizontal Fog-to-Fog layer. Specifically, the ICCF enhances sensor data processing performance by enabling horizontal data transfer in the Fog layer through connectionless name-based Fog-to-Fog data transmission. It utilizes the Fog node's distributed data processing power to achieve a satisfactory data processing performance, while communication with the Cloud is only required to report detected anomalies. Moreover, because the connectionless name-based scheme significantly reduces data connection overhead, this guarantees real-time communication and the ability of processing large-scale IoT data. Building energy management system (BEMS) for detecting abnormal sensor data is adopted as a case study to illustrate our design philosophy and, more importantly, to validate the advantages of the proposed ICCF by conducting a variety of experiments based on the sensor data collected from a real-world indoor environment.

SAHCI: Scheduling Approach for Heterogeneous Content-Centric IoT Applications

The Internet of Things (IoT) is one of the dominating paradigms of the new era with its abilities to provide ubiquitous intelligence and pervasive interconnections to diverse physical objects. With the advancements, such as new generation 5G communication and cloud/edge computing-based paradigms, the degree of domination is expected to further increase. Therefore, improvements for quality of service have become a critical issue. Traditional packets scheduling algorithms cannot meet the requirements of the large-scale IoT systems. In this paper, a novel scheduling approach is proposed for different data classes (types) to be exchanged between heterogeneous nodes of a generic IoT infrastructure. The possible ways of organizing these classes depending on different metrics, such as time latency, reliability, and data loss, are investigated. The proposed approach has the advantage of being able to tune the priorities and network characteristics to reach a specifically desired performance state. Since each type of packets is considered separately, it is possible to prioritize them, by tuning the related parameters, which changes the priorities between packets. The numerical results presented show that the new approach performs better than the existing typical scheduling approaches. The developed approach can be used in the various IoT applications with the support of 5G communication and edge computing, such as agriculture, wearables, connected cars, smart retail, and smart cities.

Blockchain-Driven on-Demand Control Loops Over Iot Environments

The feedback control loop is the atomic unit in a control system. Typically, feedback control loops are rigid objects that involve a dynamical system, or plant, which has a set of its output states measured by dedicated sensors, which in turn feed a processing unit, known as controller, that calculates actions to be applied as inputs to the plant, via elements known as actuators, in order to drive the outputs to a desired value or trajectory. The appearance of the Internet of Things (IoT) paradigm, where a large number of sensors and processing units interact over a communication network, offers an underlying infrastructure to operate and configure control loops using a different logic: an on-demand strategy. This work introduces the concept of on-demand control loop, and proposes the use of blockchain technology as the enabling infrastructure for generating on-demand control loops over large-scale IoT environments. General design guidelines are given and a simple implementation example over the Ethereum blockchain is presented, which shows the feasibility of the proposed technique.