Generation Internet Of Things Research Articles

A generic Internet of things (IoT) platform supporting plug-and-play device management based on the semantic web

Abstract There is a large variety of Internet of things (IoT) devices and their peripherals available in consumer markets, and IoT deployers should work on customizing device drivers that are compatible with their peripherals. Implementing compatible device drivers, however, often requires a burden of work. This paper proposes a generic platform that enables plug-and-play (PnP) integration for sensors and actuators to allow the addition and removal of IoT device peripherals without re-customizing all the device drivers. To this end, we employ IoT ontologies and semantics to represent IoT device characteristics and to infer IoT device behaviors. IoT device behavior is then passed to the generic device driver to cover device-specific operation. Since the generic device driver selectively operates most of the available function calls required in IoT devices, most of the programming work that is normally required for device customization is removed, and management overhead for software installation and maintenance can be minimized. To this end, we employ IoT ontologies and semantics as well as generic programming techniques in the generic platform in order to configure and control IoT devices. In the proposed platform, IoT device characteristics, including I/O functions and configuration rules, are defined using custom-built IoT ontologies, and operational behaviors are inferred through SPARQL queries. The generic platform then passes function-call name and configuration rules corresponding to the newly added peripheral device’s specification. The experimental results show that our generic platform covers most of the popular sensors available in the market. Our solution therefore enables a true PnP experience of sensors and actuator peripherals in IoT devices.

Architectural Considerations for Next Generation IoT Processors

Currently, the growth of the first generation Internet of Things (IoT) system for low-end devices having less memory and infrastructure is challenged due to the lack of engineering support in terms of architecture, technology, hardware, privacy, security, and business. This paper discusses system engineering issues relevant in IoT processors and builds a case for exploring rough set as an approximate computing technique in next generation IoT hardware. The proposed approach has a potential of dealing with inconsistencies arising at layers in hardware infrastructure for IoT applications with a dominance at cloud and fog processing. The authors bring out a research direction in IoT as an engineering discipline with an emphasis on the impact of processor hardware extension.

Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture.

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.

Generic IoT Platform for Analytics

Internet, since its root has reclassified the manners by which the world collaborates today, countless devices speak with each other which make our life simple and thus improve this world a place to dwell. The Internet of Things (IoT) is the system of physical gadgets, vehicles, home machines and different things implanted with hardware, programming, sensors and network which empowers these objects to interface and trade information. Today the world has swung to a place where each and everything should be associated. This paper discusses, how a generic IoT platform has been developed to get the data from various sensors from agricultural lands and conversely control the devices from distant places using Internet. For e.g., a smart plug can help you save and monitor your energy consumption by showing energy statistics. Sensors fitted in agricultural lands has been chosen to test the quality of the IoT platform.

Design and Implementation of an Integrated IoT Blockchain Platform for Sensing Data Integrity.

With the rapid development of communication technologies, the Internet of Things (IoT) is getting out of its infancy, into full maturity, and tends to be developed in an explosively rapid way, with more and more data transmitted and processed. As a result, the ability to manage devices deployed worldwide has been given more and advanced requirements in practical application performances. Most existing IoT platforms are highly centralized architectures, which suffer from various technical limitations, such as a cyber-attack and single point of failure. A new solution direction is essential to enhance data accessing, while regulating it with government mandates in privacy and security. In this paper, we propose an integrated IoT platform using blockchain technology to guarantee sensing data integrity. The aim of this platform is to afford the device owner a practical application that provides a comprehensive, immutable log and allows easy access to their devices deployed in different domains. It also provides characteristics of general IoT systems, allows for real-time monitoring, and control between the end user and device. The business logic of the application is defined by the smart contract, which contains rules and conditions. The proposed approach is backed by a proof of concept implementation in realistic IoT scenarios, utilizing Raspberry Pi devices and a permissioned network called Hyperledger Fabric. Lastly, a benchmark study using various performance metrics is made to highlight the significance of the proposed work. The analysis results indicate that the designed platform is suitable for the resource-constrained IoT architecture and is scalable to be extended in various IoT scenarios.

A game-theoretic learning approach to QoE-driven resource allocation scheme in 5G-enabled IoT

To significantly promote Internet of Things (IoT) development, 5G network is enabled for supporting IoT communications without the limitation of distance and location. This paper investigates the channel allocation problem for IoT uplink communications in the 5G network, with the aim of improving the quality of experience (QoE) of smart objects (SOs). To begin with, we define a mean opinion score (MOS) function of transmission delay to measure QoE of each SO. For the sum-MOS maximization problem, we leverage a game-theoretic learning approach to solve it. Specifically, the original optimization problem is equivalently transformed into a tractable form. Then, we formulate the converted problem as a game-theoretical framework and define a potential function which has a near-optimum as the optimization objective. To optimize the potential function, a distributed channel allocation algorithm is proposed to converge to the best Nash equilibrium solution which is the global optimum of maximizing the potential function. Finally, numerical results verify the effectiveness of the proposed scheme.

Guest Editorial Special Issue on IoT on the Move: Enabling Technologies and Driving Applications for Internet of Intelligent Vehicles (IoIV)

The new era of the Internet of Things (IoT) is prompting the evolution of conventional vehicle ad-hoc networks (VANETs) into the Internet of Intelligent Vehicles (IoIV). Different from VANETs, where a vehicle is essentially considered as a node disseminating messages, the emerging IoIV paradigm is expected to regard each vehicle as a smart object equipped with a powerful multisensor platform, unprecedented communication capability, computing units, and Internet protocol (IP)-based connectivity. As such, the vehicles in IoIV are highly efficient in a broad array of vehicular and transportation applications. As a unique subset of general purpose IoT, IoIV can benefit from the existing research on VANET, which lays the foundation toward a more pervasive and ubiquitous communications and networking core that is essential for IoIV. Nevertheless, research in many aspects of IoIV, especially those that are application-driven and data-oriented ones, is still at its infancy.

Designing Secure Lightweight Blockchain-Enabled RFID-Based Authentication Protocol for Supply Chains in 5G Mobile Edge Computing Environment

Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.

Application Specific Internet of Things (ASIoTs): Taxonomy, Applications, Use Case and Future Directions

As more and more applications are deployed using the Internet of Things (IoT) technologies, the fragmentation of general purpose IoT technologies to target particular sectors with different requirements is becoming necessary. In this paper, we summarize the latest developments of application-specific IoTs (ASIoTs) (a term to conceptualize the development of IoTs targeted toward specific domains, communications mediums, and industry sectors) in eight representative studies (Internet of Battlefield Things (IoBT), Internet of Medical Things (IoMT), Internet of Animal Things (IoAT), Internet of Waste Things (IoWT), Internet of Underwater Things (IoUWT), Internet of Underground Things (IoUGT), Internet of Nano Things (IoNT), and Internet of Mobile Things (IoMobT) such as the Internet of Vehicles). The paper gives contributions to ASIoTs from three perspectives: First, we offer a basic classification taxonomy for ASIoTs and discuss various representative studies and applications which can be found in the literature; Second, we discuss a use case for a biometrics-based ASIoT (termed IoBioT) for illustration and experiments of face-based biometric recognition on IoBioT are also performed; and Third, we give discussions and future directions for ASIoTs. An objective of this paper is to spur researchers and facilitate the development of ASIoTs for the different user-defined domains, communication mediums, and technology constrained platforms.

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.

Self-powered flexible electronics beyond thermal limits

Self-powered flexible electronics using high-performance inorganic materials have been studied and developed for the essence of future electronics due to the thing, lightweight, self-sustainable, and biocompatible characteristics, which can be applied to body sensor network and next generation Internet of Things (IoT). However, most of inorganic materials should be processed in the high-temperature processes such as the semiconductor fabrication, which is not compatible flexible plastic substrates. Therefore, the new approaches must be demonstrated to overcome the thermal limits of previous methodology and achieve the flexible inorganic electronics on various flexible plastic substrates. In this review paper, we introduce the recent progress of technologies to realize flexible and high-performance inorganic electronics on plastic substrates over the thermal limits, i.e., laser-assisted procedure, chemical or mechanical exfoliation approaches. They are compatible not only to flexible plastic substrates but also to conventional device processes. We also explain the novel application devices such as flexible optoelectronics, flexible large-scale integration (LSI) devices, flexible energy harvesters, and flexible sensors using the recent-developed technologies beyond the previous thermal limit. This paper highlights the proper direction to complete future flexible inorganic electronics for high-performance self-powered systems.

Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks

It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.

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.

The BIG IoT API - Semantically Enabling IoT Interoperability

Today, internet of things (IoT) platforms offer proprietary interfaces and protocols. To enable interoperable interaction with those platforms we present the generic BIG IoT API that employs a novel approach for self-description and semantic annotation to fully adapt arbitrary IoT platforms. We have deployed this approach for multiple platforms from the mobility domain.

A Heterogeneous Multicore System on Chip for Energy Efficient Brain Inspired Computing

Convolutional neural networks (CNNs) have revolutionized computer vision, speech recognition, and other fields requiring strong classification capabilities. These strengths make CNNs appealing in edge node Internet-of-Things (IoT) applications requiring near-sensors processing. Specialized CNN accelerators deliver significant performance per watt and satisfy the tight constraints of deeply embedded devices, but they cannot be used to implement arbitrary CNN topologies or nonconventional sensory algorithms where CNNs are only a part of the processing stack. A higher level of flexibility is desirable for next generation IoT nodes. Here, we present Mia Wallace, a 65-nm system-on-chip integrating a near-threshold parallel processor cluster tightly coupled with a CNN accelerator: it achieves peak energy efficiency of 108 GMAC/s/W at 0.72 V and peak performance of 14 GMAC/s at 1.2 V, leaving 1.2 GMAC/s available for general-purpose parallel processing.

Distributed Localization: A Linear Theory

Fifth-generation (5G) networks providing much higher bandwidth and faster data rates will allow connecting vast number of stationary and mobile devices, sensors, agents, users, machines, and vehicles, supporting Internet-of-Things (IoT), real-time dynamic networks of mobile things . Positioning and location awareness will become increasingly important, enabling deployment of new services and contributing to significantly improving the overall performance of the 5G system. Many of the currently talked about solutions to positioning in 5G are centralized, mostly requiring direct communication to the access nodes (or anchors, i.e., nodes with known locations), which in turn requires a high density of anchors. But such centralized positioning solutions may become unwieldy as the number of users and devices continues to grow without limit in sight. As an alternative to the centralized solutions, this paper discusses distributed localization in a 5G-enabled IoT environment where many low power devices, users, or agents are to locate themselves without a direct access to anchors. Even though positioning is essentially a nonlinear problem (solving circle equations by trilateration or triangulation), we discuss a cooperative linear distributed iterative solution with only local measurements, local communication, and local computation needed at each agent. Linearity is obtained by reparametrization of the agent location through barycentric coordinate representations based on local neighborhood geometry that may be computed in terms of certain Cayley–Menger determinants involving relative local inter-agent distance measurements. After a brief introduction to the localization problem, and other available distributed solutions primarily based on directly addressing the nonlinear formulation, we present the distributed linear solution for stationary agent networks and study its convergence, its robustness to noise, and extensions to mobile scenarios, in which agents, users, and (possibly) anchors are dynamic.

A Lightweight Continuous Authentication Protocol for the Internet of Things.

Modern societies are moving toward an information-oriented environment. To gather and utilize information around people’s modern life, tiny devices with all kinds of sensing devices and various sizes of gateways need to be deployed and connected with each other through the Internet or proxy-based wireless sensor networks (WSNs). Within this kind of Internet of Things (IoT) environment, how to authenticate each other between two communicating devices is a fundamental security issue. As a lot of IoT devices are powered by batteries and they need to transmit sensed data periodically, it is necessary for IoT devices to adopt a lightweight authentication protocol to reduce their energy consumption when a device wants to authenticate and transmit data to its targeted peer. In this paper, a lightweight continuous authentication protocol for sensing devices and gateway devices in general IoT environments is introduced. The concept of valid authentication time period is proposed to enhance robustness of authentication between IoT devices. To construct the proposed lightweight continuous authentication protocol, token technique and dynamic features of IoT devices are adopted in order to reach the design goals: the reduction of time consumption for consecutive authentications and energy saving for authenticating devices through by reducing the computation complexity during session establishment of continuous authentication. Security analysis is conducted to evaluate security strength of the proposed protocol. In addition, performance analysis has shown the proposed protocol is a strong competitor among existing protocols for device-to-device authentication in IoT environments.

Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays: An Experimental Case Study

Both computational performances and energy efficiency are required for the development of any mobile or embedded information processing system. The Internet of Things (IoT) is the latest evolution of these systems, paving the way for advancements in ubiquitous computing. In a context in which a large amount of data is often analyzed and processed, it is mandatory to adapt node logic and processing capabilities with respect to the available energy resources. This paper investigates under which conditions a partially reconfigurable hardware accelerator can provide energy saving in complex processing tasks. The paper also presents a useful analysis of how the dynamic partial reconfiguration technique can be used to enable energy efficiency in a generic IoT node that exploits a Field Programmable Gate Array (FPGA) device. Furthermore, this work introduces a hardware infrastructure and new energy metrics tailored for the energy efficiency evaluation of the dynamic partial reconfiguration process in embedded FPGA based devices. Exploiting the ability of reconfiguring circuit portions at runtime, the latest generation of FPGAs can be used to foster a better balance between energy consumption and performance. More specifically, the design methodology for the implemented digital signal processing application was adapted for the ZedBoard. To this aim, a case study of a video filtering system is proposed and analyzed by dynamically loading three different hardware filters from the management software running on a Linux-based device. With more details, the presented analytical framework allows for a direct comparison between the energy efficiency of a dynamic partially reconfigurable device and a static non-reconfigurable one. The estimated timing conditions that allow the dynamic partially reconfigurable process to achieve relevant energy efficiency with respect to the corresponding static architecture are also outlined.

Efficient and Secure Service-Oriented Authentication Supporting Network Slicing for 5G-Enabled IoT

5G network is considered as a key enabler in meeting continuously increasing demands for the future Internet of Things (IoT) services, including high data rate, numerous devices connection, and low service latency. To satisfy these demands, network slicing and fog computing have been envisioned as the promising solutions in service-oriented 5G architecture. However, security paradigms enabling authentication and confidentiality of 5G communications for IoT services remain elusive, but indispensable. In this paper, we propose an efficient and secure service-oriented authentication framework supporting network slicing and fog computing for 5G-enabled IoT services. Specifically, users can efficiently establish connections with 5G core network and anonymously access IoT services under their delegation through proper network slices of 5G infrastructure selected by fog nodes based on the slice/service types of accessing services. The privacy-preserving slice selection mechanism is introduced to preserve both configured slice types and accessing service types of users. In addition, session keys are negotiated among users, local fogs and IoT servers to guarantee secure access of service data in fog cache and remote servers with low latency. We evaluate the performance of the proposed framework through simulations to demonstrate its efficiency and feasibility under 5G infrastructure.

USA: Faster update for SDN-based internet of things sensory environments

Internet of Things (IoT) require ubiquitous and seamless network connectivity. Meanwhile, it also asks for effective service management, data transmission and analysis. Towards this end, software defined networks (SDN) technology is introduced as a key solution to enable IoT network management. When IoT requires an efficient forwarding policy reconfiguration as the response to the alteration of system requirement (e.g., network environment or user demand), SDN is able to adjust the configurations easily to meet its demand. Previous research efforts tried to complete the reconfiguration as quickly as possible, since the update speed is critical to the performance of network management. However, update time can be further reduced. In this paper, we propose a novel update mechanism, which is referred to as rule co-existence update. It is able to enlarge the solution space and obtain more individual solutions, without any negative effect to the packet headers or throughput of flows. Then, we propose a scheme called USA (i.e., Update Speed Accelerator), as a novel algorithm for network updates based on the above-mentioned rule co-existence, that accelerates the update of forwarding policy with spare rule space on current network switches for general IoT environments. Based on the obtained suboptimal greedy solution, USA shows a novel way for rule co-existence to accelerate the update of forwarding policies. It also proposes a simplified algorithm to ensure the forwarding correctness during the update process. Extensive simulation results show that USA can clearly reduce the update time for nearly half of policies in our experiments with few rule space overhead, which is less than 8% of all policies, and maximum update time is less than previous works when spare rule space is limited.