Typical Internet Of Things Research Articles

Whisper: Programmable and Flexible Control on Industrial IoT Networks

Software Defined Networking (SDN) centralizes network control to improve network programmability and flexibility. Contrary to wired settings, it is unclear how to support SDN in low power and lossy networks like typical Internet of Things (IoT) ones. Challenges encompass providing reliable in-band connectivity between the centralized controller and out-of-range nodes, and coping with physical limitations of the highly resource-constrained IoT devices. In this work, we present Whisper, an enabler for SDN in low power and lossy networks. The centralized Whisper controller of a network remotely controls nodes’ forwarding and cell allocation. To do so, the controller sends carefully computed routing and scheduling messages that are fully compatible with the protocols run in the network. This mechanism ensures the best possible in-band connectivity between the controller and all network nodes, capitalizing on an interface which is already supported by network devices. Whisper’s internal algorithms further reduce the number of messages sent by the controller, to make the exerted control as lightweight as possible for the devices. Beyond detailing Whisper’s design, we discuss compelling use cases that Whisper unlocks, including rerouting around low-battery devices and providing runtime defense to jamming attacks. We also describe how to implement Whisper in current IoT open standards (RPL and 6TiSCH) without modifying IoT devices’ firmware. This shows that Whisper can implement an SDN-like control for distributed low power networks with no specific support for SDN, from legacy to next generation IoT devices. Our testbed experiments show that Whisper successfully controls the network in both the scheduling and routing plane, with significantly less overhead than other SDN-IoT solutions, no additional latency and no packet loss.

Compressive Sensing-Based IoT Applications: A Review

The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

Automata-Based Generic Model for Interoperating Context-Aware Ad-Hoc Devices in Internet of Things

Internet of Things (IoT) applications can be seen as network-controlled systems with a distributed architecture connecting many smart objects through the Internet. A typical IoT application aims to connect a set of nodes, capable of processing data and making decisions regarding the goal of the IoT application, with other nodes containing smart objects with robotic components to collect data from their environment. In this paper, a generic model for novel context-aware and adaptive IoT architecture is presented. A group of nodes contains a controller interface (CI) that is used to control other nodes containing a controller unit (CU) which communicates randomly. CU nodes have complex control systems comprising both actuators and sensors, however CI nodes typically do not have detailed knowledge of such individual functionalities. Instead, at run time, control program logic (CPL) is downloaded by the CI to facilitate control and communication. The system also allows for different CPLs for any particular CU. In such an open and adaptive IoT environment, an architecture is required that can provide sufficient flexibility in designing nodes while maintaining uniformity in the IoT system to allow for interoperability and reliability. Using the notion of automata, a generalized mathematical description is presented that allows modeling of the CI and CU nodes and their operation. This model can then be used to formulate and solve problems regarding the interaction between CIs and CUs. The application of this model is demonstrated through a case study involving remote access laboratories where the CI–CU model is used.

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time.

A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes

With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea of Blockchain (BC) theory has brought about a potential solution to the IoT security problem. The emergence of blockchain technology has brought about a change of decentralized management, providing an effective solution for the protection of network security and privacy. On the other hand, the smart devices in IoT are always lightweight and have less energy and memory. This makes the application of blockchain difficult. Against this background, this paper proposes a blockchain model based on hypergraphs. The aims of this model are to reduce the storage consumption and to solve the additional security issues. In the model, we use the hyperedge as the organization of storage nodes and convert the entire networked data storage into part network storage. We discuss the design of the model and security strategy in detail, introducing some use cases in a smart home network and evaluating the storage performance of the model through simulation experiments and an evaluation of the network.

BES: Differentially private event aggregation for large-scale IoT-based systems

The emergence of Internet of Things (IoT) offers many advantages, but it also raises significant challenges with respect to efficient and distributed processing of large data and also privacy concerns related to large data disclosure.We investigate the above problems from a system-perspective and study how differential privacy can be used to complement other privacy-enhancing technologies to allow for controlled large data disclosure. We present a streaming-based framework, Bes, where we leverage the often distributed nature of typical IoT systems for efficient computation of differentially private aggregates. We also propose methods to limit the noise that is commonly introduced for differential privacy in real-world applications, by bounding the outliers based on (differentially private) parameters of the actual system at hand or data from other similar systems.We also provide a thorough evaluation based on a fully implemented Bes prototype using real-world data from of a concrete IoT system, namely an Advanced Metering Infrastructure (AMI). We show how a large number of events can be aggregated in a private fashion with low processing latency, even when the processing is made by a single-board device, with similar capabilities to the devices deployed in AMIs. Moreover, by implementing a de-pseudonymization attack known from the literature, we also show the strong complementary protection offered by Bes’ differentially private aggregation, compared to other privacy-enhancing technologies.

A survey on application of machine learning for Internet of Things

Internet of Things (IoT) has become an important network paradigm and there are lots of smart devices connected by IoT. IoT systems are producing massive data and thus more and more IoT applications and services are emerging. Machine learning, as an another important area, has obtained a great success in several research fields such as computer vision, computer graphics, natural language processing, speech recognition, decision-making, and intelligent control. It has also been introduced in networking research. Many researches study how to utilize machine learning to solve networking problems, including routing, traffic engineering, resource allocation, and security. Recently, there has been a rising trend of employing machine learning to improve IoT applications and provide IoT services such as traffic engineering, network management, security, Internet traffic classification, and quality of service optimization. This survey paper focuses on providing an overview of the application of machine learning in the domain of IoT. We provide a comprehensive survey highlighting the recent progresses in machine learning techniques for IoT and describe various IoT applications. The application of machine learning for IoT enables users to obtain deep analytics and develop efficient intelligent IoT applications. This paper is different from the previously published survey papers in terms of focus, scope, and breadth; specifically, we have written this paper to emphasize the application of machine learning for IoT and the coverage of most recent advances. This paper has made an attempt to cover the major applications of machine learning for IoT and the relevant techniques, including traffic profiling, IoT device identification, security, edge computing infrastructure, network management and typical IoT applications. We also make a discussion on research challenges and open issues.

A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm

In the midst of all the applications empowered by the Internet of Things (IoT), constant health surveying framework is an especially essential one. The leg movement monitoring framework is utilized to recognize the beginning time of joint pain illness. Wearable sensor gadgets and Uric acid (UA) sensor utilized as a part of IoT health monitoring framework for joint inflammation ailment have been creating a gigantic measure of information on a nonstop premise with rapid, change of huge health facts, and identification of these infections present some significant issues. These can be overwhelmed by utilizing the proposed framework for health data transformation and alongside the calculation utilized for the discovery of human foot movement for the GOUT arthritis diagnosis. Sensitivity and specificity are computed to assess the Time wrapping calculation-based characterization display for Arthritis ailment. The exploratory outcomes demonstrated the adequacy of the dynamic time wrapping technique. At last, ROC examination is conducted to distinguish the most significance clinical and laboratory assessments to get joint inflammation (GOUT arthritis) and kidney ailment.

Toward Unified Control of Networks of Switches and Sensors Through a Network Operating System

Wireless sensor networks (WSNs) are an integral part of an Internet of Things (IoT) ecosystem. In fact, a typical IoT service deployment considers an integrated network of switches and sensors (as well as actuators). In order to manage this heterogeneous network infrastructure, the use of software defined networking (SDN) technologies is anticipated. However, existing SDN-enabling technologies consider only isolated networks and there has been no concrete solution that treats the whole network in a unified way. In this paper, a network operating system is proposed as a way to address the above issue. Starting from the open network operating system, which currently supports only OpenFlow networks, the proposed solution leverages SDN-WISE to integrate WSNs and achieves unified system representation and treatment. The proposed system has been prototyped and is publicly available in open source. Experiments have been executed demonstrating that, by exploiting the proposed solution, it is also possible to improve efficiency of the overall network.

Future Edge Cloud and Edge Computing for Internet of Things Applications

The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially connect billions or even trillions of edge devices which could generate huge amount of data at a very high speed and some of the applications may require very low latency. The traditional cloud infrastructure will run into a series of difficulties due to centralized computation, storage, and networking in a small number of datacenters, and due to the relative long distance between the edge devices and the remote datacenters. To tackle this challenge, edge cloud and edge computing seem to be a promising possibility which provides resources closer to the resource-poor edge IoT devices and potentially can nurture a new IoT innovation ecosystem. Such prospect is enabled by a series of emerging technologies, including network function virtualization and software defined networking. In this survey paper, we investigate the key rationale, the state-of-the-art efforts, the key enabling technologies and research topics, and typical IoT applications benefiting from edge cloud. We aim to draw an overall picture of both ongoing research efforts and future possible research directions through comprehensive discussions.

Energy-Efficient Secure Transmission Design for the Internet of Things With an Untrusted Relay

In this paper, we investigate secure uplink transmission in a typical Internet of Things (IoT) deployment, where multiple sensors communicate with a controller via the assistance of an untrusted relay. By taking both the relay and direct links into account, three different scheduling schemes, e.g., optimal scheduling (OS) scheme, threshold-based scheduling (TS) scheme, and random scheduling (RS) scheme, are proposed to cope with the implementation complexity of user scheduling in IoT communications. For each scheduling scheme, we first derive the closed-form expressions for the secrecy outage probability and the secrecy throughput, as well as characterizing the secure energy efficiency to help facilitate an energy-efficient secure transmission design. Finally, numerical simulations demonstrate that increasing the number of sensors is an efficient method to boost the security and energy efficiency under the OS scheme. Moreover, the TS scheme provides a good tradeoff between implementation complexity and secrecy performance.

Towards Mapping the Security Challenges of the Internet of Things (IoT) Supply Chain

The flow complexity in today’s IoT supply chain is enormous. To produce an item in a global network of suppliers requires a delicate dance of people, assets, systems, information, companies, and even governments. The opportunities for security vulnerabilities abound. In this paper, we describe the security challenges facing an IoT supply chain. We first start by describing what an IoT endpoint is, their various types together with their concomitant security challenges. An IoT endpoint is an output of an IoT supply chain, and the enumeration of its security challenges is then used to describe what IoT attack surfaces are, together with the threats and vulnerabilities facing a typical IoT supply chain. We use the iPhone/Apple supply chain as an exemplar, creating a map, and using that map to depict likely vulnerabilities and attacks. We conclude by discussing the mitigations against potential vulnerabilities in such a supply chain.

On the Use of LoRaWAN for Indoor Industrial IoT Applications

Low‐Power Wide‐Area Networks (LPWANs) have recently emerged as appealing communication systems in the context of the Internet of Things (IoT). Particularly, they proved effective in typical IoT applications such as environmental monitoring and smart metering. Such networks, however, have a great potential also in the industrial scenario and, hence, in the context of the Industrial Internet of Things (IIoT), which represents a dramatically growing field of application. In this paper we focus on a specific LPWAN, namely, LoRaWAN, and provide an assessment of its performance for typical IIoT employments such as those represented by indoor industrial monitoring applications. In detail, after a general description of LoRaWAN, we discuss how to set some of its parameters in order to achieve the best performance in the considered industrial scenario. Subsequently we present the outcomes of a performance assessment, based on realistic simulations, aimed at evaluating the behavior of LoRaWAN for industrial monitoring applications. Moreover, the paper proposes a comparison with the IEEE 802.15.4 network protocol, which is often adopted in similar application contexts. The obtained results confirm that LoRaWAN can be considered as a strongly viable opportunity, since it is able to provide high reliability and timeliness, while ensuring very low energy consumption.

LeanBench: comparing software stacks for batch and query processing of IoT data

Companies produce data in large volumes and in multiple varieties. This trend is intensifying with the deployment of Internet of Things (IoT) devices. Companies need to process data more efficiently and at the edge of the network if they are to remain capable of making timely business decisions based on data. Apache Hadoop and Hive are two widely used data processing systems. However, they rely on complex software stacks that cannot run in a typical IoT gateway device, i.e., a computer with low hardware specifications. An approach to solve this problem is to replace the software with a leaner system with the same functionality. This approach is the value proposition of Unicage, a data processing system based on Unix shell scripting, that promises better performance by using the operating system directly for execution and inter-process communication.In this paper, we benchmark data processing systems with workloads that compare Unicage with Hadoop and with Hive. The results show that the complexity of the software stack is indeed a significant bottleneck in data processing.

How to Obtain Common Criteria Certification of Smart TV for Home IoT Security and Reliability

In the new era of IoT (Internet of Things), numerous gadgets and services include innovative IoT technologies that provide customers with convenience and improve their quality of life. Smart TVs are typical IoT devices that offer broadcasting services. However, they are susceptible to security intrusions via e-mail, media players, cameras, and internet connectivity. The frequency of hacking through malicious applications installed in Smart TV has rapidly increased. Therefore, appropriate countermeasures should be developed immediately. In April 2017, we (with LG electronics) received the ‘world-first’ Common Criteria EAL2 (Evaluation Assurance Level 2) certification for Smart TVs. As far as we know, no Smart TV has received a Common Criteria EAL2 security certification until now. This article describes our experience with the certification process and examines several security and reliability aspects of Smart TVs.

Internet of animals: characterisation of LoRa sub‐GHz off‐body wireless channel in dairy barns

Advances in wireless sensor technologies and MEMS have made it possible to automatically monitor the health status of dairy cows using Internet of things (IoT) and wireless body area networks. Since on-cow measuring devices are energy constrained, a proper characterisation of the off-body wireless channel between the on-cow sensor nodes and the back-end base station is required for an optimised deployment of these networks in barns. In this Letter, the long range (LoRa) off-body wireless channel has been characterised at 868 MHz, a typical IoT frequency. Both path loss and temporal fading were investigated using LoRa motes. Based on this characterisation, network planning and energy consumption optimisation of the on-body nodes could be performed, which enables the deployment of reliable dairy cow monitoring systems.

Evaluating Performance of Containerized IoT Services for Clustered Devices at the Network Edge

The constant and fast increase in the number of heterogeneous Internet of Things (IoT) devices that populate everyday life environments brings new challenges to the full exploitation of the computation, memory, sensing, and actuation resources associated to them. In this context, device virtualization solutions and platforms may definitely play a key role in enabling the desired tradeoff between flexibility and performance. This paper focuses on lightweight virtualization technologies for IoT devices, suitably thought to effectively deploy new integrated applications and to create a novel distributed and virtualized ecosystem. Two different frameworks for container-based IoT service provisioning are compared, the one based on a direct interaction between two cooperating devices and the other based on the presence of a manager supervising the operations between cooperating devices forming a cluster. In the latter case, accounting for the growing impetus to move intelligence toward the edge of the network, management features are implemented at the network access point to provide short latency responses. We also introduce the outcomes of a thorough performance evaluation campaign conducted via a real IoT testbed. The measurements, performed by accounting for the constraints of typical IoT nodes, shed light on the actual feasibility of container-based IoT frameworks.

IoTChecker: A data-driven framework for security analytics of Internet of Things configurations

The advent of the Internet of Things (IoT) has revolutionized networks by transforming legacy dumb devices into smart connected “things”; observing, interacting and impacting the environment with minimal human intervention. These features, while promising a variety of innovative solutions and business benefits, are vulnerable to a host of new threat vectors and security risks. A typical IoT network comprises thousands of IoT devices using heterogeneous protocols, having varying resources, complex interdependencies and diverse networking and security requirements. The configuration data of IoT systems is mostly unstructured, lacking machine interpretable semantics and thus, traditional analysis techniques cannot tackle the IoT-specific configuration challenges of scalability, interoperability and security. In this paper, we present IoTChecker, a novel data-driven framework to semantically model IoT configurations and then employ that model to automatically arrest security configuration anomalies and analyze IoT-specific threat vectors. The approach leverages a combination of newly constructed as well as extended and aligned versions of existing ontologies. Configuration analytics are performed automatically by describing the context of complex IoT interactions and dependencies through rules-supported reasoning and queries. The evaluation involves ontology-based security classification of 954 real-world IoT products and security analysis of their practically-deployed system configurations. Our automated approach has proven to be scalable, easily manageable, formally verifiable and free from errors induced by tedious manual configurations.

80‐2: Evaluation of Displays and HMIs for Internet of Things (IoT)

We studied low cost Internet of Things (IoT) systems with displays regarding microprocessor (μP) interfaces (parallel or serial). An evaluation was made by comparing status indicators, character and graphics display regarding Human Machine Interfaces (HMI). We also measured and compared displays ambient lighting performance in typical IoT environments.