Internet Of Things End Research Articles

Development of a cycling safety services system and its deep learning bicycle crash model

This research developed an Internet of things (IoT) services system for cyclists to keep them safe when cycling — from terminal to back end — called the cycling safety services system. The proposed system consists of wearable devices, a service mobile app, and back-end services, primarily providing three categories of services: (1) The cycling team services, (2) the physiological status services, and (3) the environmental information service, incorporating the technologies of deep learning, IoT end device development, mobile app programming, RESTful API implementation, open data exploitation, etc. The proposed system aims at protecting the cyclists from being left out when cycling as a team, warning them when their physiological status is going to be abnormal or when there is a possibility of a bicycle crash, as well as proactively sending out urgent messages with location information when a crash occurs. Moreover, to enable the mobile app to recognize crash events, this research trained a deep learning bicycle crash model of 87.8049% accuracy and implemented a procedure in the mobile app based on this model to detect bicycle crashes automatically. The proposed system also provides a way for the cyclists to report any false crash alarm so that the crash model can be re-trained to reduce its false alarm ratio after the system is distributed to the consumers in the future. This research confirmed that the proposed system works well, and suggests that the proposed system, the crash model, the data collection method with its associated mobile apps, and the anonymous crash locations collected in the future can be valuable and contribute to the cycling society and relevant researchers in a positive way.

Analysis of Web-Based IoT through Heterogeneous Networks: Swarm Computing over LoRaWAN.

The Internet of Things (IoT) leverages added valued services by the wide spread of connected smart devices. The Swarm Computing paradigm considers a single abstraction layer that connects all kinds of devices globally, from sensors to super computers. In this context, the Low-Power Wide-Area Network (LPWAN) emerges, spreading out connection to the IoT end devices. With the upsides of long-range, low power and low cost, LPWAN presents major limitations regarding data transmission capacity, throughput, supported packet length and quantity per day limitation. This situation makes LPWAN systems with limited interoperability integrate with systems based on REpresentational State Transfer (REST). This work investigates how to connect web-based IoT applications with LPWANs. The analysis was carried out studying the number of packets generated for a use case of REST-based IoT over LPWAN, specifically the Swarm OS over LoRaWAN. The work also presents an analysis of the impact of using promising schemes for lower communication load. We evaluated Constrained Application Protocol (CoAP), Static Context Header Compression (SCHC) and Concise Binary Object Representation (CBOR) to make transmission over the restricted links of LPWANs possible. The attained results show the reduction of 98.18% packet sizes while using SCHC and CBOR compared to HTTP and JSON by sending fewer packets with smaller sizes.

The Role of MANET in Collaborating IoT End Devices: A New Era of Smart Communication

<p class="0abstract"><span lang="EN-US">Merging of MANET with IOT utilized in inevitable knowledge situations opens new possibility in checking and makes another communication stage for various applications in Internet of Things (IoT). Sensors utilized for IoT applications, sense nature and send the data to the gateway hub, which then send the congregated information to the MANET hub, particularly used for information gathering. MANET plays a key function, which is used as the foundation of the IoT arrange. Because of its intrinsic properties, the MANET hubs can assemble unconstrained associations with different hubs without the need of any base. MANET hubs can turn over the IoT procedure and accumulate information from the sensors and or any fixed Radiocommunication nodes. MANET cores can be utilized as key advances in a few applications in IoT. Because of the idea of self-designing, MANET hubs just as the sensor hubs can be sent in huge possibility. In this paper we have taken a survey of MANET and IOT various applications in running era and corelating various computing techniques like cloud, fog and edge with MANET and IOT. This examination is an improvement and usage of existing versatile specially selected procedure communication exploiting the in the structure of the IoT.</span></p>

Lightweight Failover Authentication Mechanism for IoT-Based Fog Computing Environment

Fog computing as an extension to the cloud computing infrastructure has been invaluable in enhancing the applicability of the Internet of Things (IoT) paradigm. IoT based Fog systems magnify the range and minimize the latency of IoT applications. However, as fog nodes are considered transient and they offer authenticated services, when an IoT end device loses connectivity with a fog node, it must authenticate freshly with a secondary fog node. In this work, we present a new security mechanism to leverage the initial authentication to perform fast lightweight secondary authentication to ensure smooth failover among fog nodes. The proposed scheme is secure in the presence of a current de-facto Canetti and Krawczyk (CK)-adversary. We demonstrate the security of the proposed scheme with a detailed security analysis using formal security under the broadly recognized Real-Or-Random (ROR) model, informal security analysis as well as through formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool. A testbed experiment for measuring computational time for different cryptographic primitives using the Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) has been done. Finally, through comparative analysis with other related schemes, we show how the presented approach is uniquely advantageous over other schemes.

An Efficient Signature Scheme Based on Mobile Edge Computing in the NDN-IoT Environment

Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

Comparing the Performance of NB-IoT, LTE Cat-M1, Sigfox, and LoRa for IoT End Devices Moving at High Speeds in the Air

Recently, NB-IoT, LTE Cat-M1, Sigfox, and LoRa have been proposed as promising Low-Power Wide Area Networks (LPWANs) technologies for Internet-of-Things (IoT) applications. These technologies are aimed for IoT applications such as smart meters that can tolerate long transmission delays and only need a narrow band to periodically transmit a small amount of data. Although the performances of these technologies have been studied or compared in the literature, most comparisons were conducted in non-mobile conditions. In this work, we used a drone flying at 70 km/hr along a 10 km motorway to act as a mobile IoT end device to do the performance measurements. In this paper, we report and compare the performances of these technologies in such high-speed mobile conditions in the air.

Public Blockchains for Resource-Constrained IoT Devices—A State-of-the-Art Survey

Although blockchain offers a lot of benefits for the Internet-of-Things (IoT) network in terms of security, there are a number of barriers to its widespread adoption. Since many IoT networks consist of battery-powered devices with limited computing capabilities, one of those adoption barriers is the IoT end node itself. Several articles provided a general survey of the blockchain integration with IoT applications. However, a detailed analysis of this integration is missing, possible challenges are omitted, and no solutions that would enable the integration are presented. Furthermore, none of the surveys focused on reviewing public blockchains for IoT devices with low computational capabilities and limited energy resources. Therefore, the aim of this article is to provide the results of an in-depth study that covers challenges faced by existing public blockchains with IoT device integration. This article looks into resource-constrained IoT device classification and the wireless communication protocols used by them. It examines challenges that are required to be overcome for the successful integration of resource-constrained IoT end nodes with existing public blockchains. It then looks at the proposed solutions and their feasibility. A detailed review of possible security attacks on an IoT blockchain network is provided. As a result, a list of important characteristics that a public blockchain should possess in order to be integrated with a resource-constrained IoT network is proposed. Finally, the requirements for the IoT end nodes themselves are specified.

Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.

Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions.

Combination of Task Allocation and Approximate Computing for Fog-Architecture-Based IoT

Achieving energy efficiency is always a primary concern for fog-architecture-based Internet of Things (IoT) applications. As the IoT devices are typically of small sizes and powered by battery energy, it is essential to address the energy consumption at all levels from the circuit to the system. Two of the promising solutions at circuit and system levels are approximate computing and energy-aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. In this work, we fill this gap and aim to maximize the network lifetime subject to the accuracy requirements of the applications. By considering both the approximate computing and task allocation simultaneously, a nonlinear problem is obtained to allocate the tasks for the devices (fog nodes and IoT end devices) and to select the corresponding execution modes (tasks in approximate or exact modes). To efficiently solve this problem, a centralized algorithm is first proposed by transferring the above nonlinear problem as a linear programming (LP) problem. As executing the centralized algorithm is a challenge for the resource-limited IoT devices, this work further proposes an optimal distributed algorithm based on Dantzig-Wolfe decomposition to solve the problem of tasks distribution and execution modes selection. The centralized large-scaled LP problem is decomposed into small-scaled subproblems, which can be efficiently solved by each IoT device. The proposed algorithms are tested by extensive simulations. The results demonstrate that the distributed algorithm achieves the same results as the centralized algorithm, and both of them significantly outperform the previous approaches.

Fog data management: A vision, challenges, and future directions

Cloud computing with its key facets and its inherent advantages still faces several challenges in the Internet of Things (IoT) ecosystem. The distance among the IoT end devices and cloud computing might be a problem for latency-sensitive applications such as catastrophe management and content transference applications. Fog computing is a novel paradigm to address such issues that playacts a significant role in massive and real-time data management systems in an IoT environment. Particularly IoT data management by fog computing is one important phase for latency reduction in latency-sensitive applications and necessary to generate more skilled knowledge and intelligent decisions. In this study, we used the SLR (systematic literature review) method to survey fog data management to understand the various topics and main contexts in this domain that have been newly offered. The target of this article is classifying and analyzing the researches about the fog data management domain which has been released from 2014 to 2019. A context-based taxonomy is offered for fog data management including data processing, data storage and data security based on the context of papers that are elected with the SLR method in our study. Based on presented technical taxonomy, the grouped papers in any context are compared with each other pursuant to some metrics of fog data management reference model. Then, for any selected research, the new findings, advantages, and weaknesses are debated. Finally, based on studies the open issues in fog data management and their related challenges for future researches are highlighted.

Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor–critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

Developing TEI-Aware Ultralow-Power SoC Platforms for IoT End Nodes

Ranging from circuit-level characterization to designing a platform architecture, developing a design automation tool, and fabricating a System on Chip (SoC), this article deals with the entire development process for ultralow-power (ULP) SoCs for Internet-of-Things (IoT) end nodes. More precisely, this article first focuses on the unique characteristics of the ULP circuits, the temperature effect inversion (TEI), i.e., the delay of the ULP circuits decreases with increasing temperature. Existing TEI-aware low-power (TEI-LP) techniques have incredible potential to further reduce the power consumption of conventional ULP SoCs, but there is a critical limitation to be widely adopted in real SoCs. To address this limitation and realize the ULP SoCs that can fully benefit from the TEI-LP techniques, this article proposes a new TEI-inspired SoC platform (TIP) architecture. On top of that, taking into account that the highly complex, time consuming, and labor-intensive development process of these ULP SoCs may hinder their widespread use for IoT end nodes, this article presents a new electronic design automation tool to accelerate ULP SoC development, RISC-V express (RVX). Finally, by using the RVX, this article introduces a TIP prototyping chip fabricated in 28-nm FD-SOI technology. This chip demonstrates that power savings of up to 35% can be achieved by lowering the supply voltage from 0.54 to 0.48 V at 25 °C and 0.44 V at 80 °C while continuing to operate at a target 50-MHz clock frequency.

Threats and Vulnerabilities to IoT End Devices Architecture and suggested remedies

Due to decentralization of Internet of Things(IoT) applications and anything, anytime, anywhere connectivity has increased burden of data processing and decision making at IoT end devices. This overhead initiated new bugs and vulnerabilities thus security threats are emerging and presenting new challenges on these end devices. IoT End Devices rely on Trusted Execution Environments (TEEs) by implementing Root of trust (RoT) as soon as power is on thus forming Chain of trust (CoT) to ensure authenticity, integrity and confidentiality of every bit and byte of Trusted Computing Base (TCB) but due to un-trusted external world connectivity and security flaws such as Spectre and meltdown vulnerabilities present in the TCB of TEE has made CoT unstable and whole TEE are being misutilized. This paper suggests remedial solutions for the threats arising due to bugs and vulnerabilities present in the different components of TCB so as to ensure the stable CoT resulting into robust TEE.

SCRA: Structured Compressive Random Access for Efficient Information Collection in IoT

It is a fundamental issue to achieve efficient information collection in Internet of Things (IoT), where random (channel) access plays an indispensable role, especially when coordination among IoT end nodes is unachievable. Compressive sensing (CS) has been widely used in random access to facilitate energy efficient and accurate data collection. However, a joint sparsity structure, which commonly exists among signals acquired by different end nodes, has been long ignored by existing CS-based random access schemes, leading to insufficient energy efficiency and accuracy. In this article, capitalizing on this joint sparsity structure, we propose a structured compressive random access (SCRA) mechanism in order to achieve maximum energy efficiency with accuracy guarantee for data collection. Specifically, we first model the data loss induced by packet collisions during random access as an independent CS measurement process for each node, where the corresponding CS projection matrix is determined by the data loss pattern. Furthermore, in order to control the amount of data transmitted in the channel and alleviate the packet collisions, we employ the concept of sensing probability to perform random subsampling at each end node before transmission, where the optimal sensing probability is derived. Finally, we propose to jointly recover the set of original signals at all nodes based on the concept of group sparsity by formulating the data collection process as a single-measurement-vector problem in CS. The evaluation results validate the effectiveness of SCRA in utilizing the joint sparsity structure to obtain superior performance compared to the benchmark methods.

On the Bound of Energy Consumption in Cellular IoT Networks

Billions of sensors are expected to be connected to the Internet through the emerging Internet of Things (IoT) technologies. Many of these sensors will primarily be connected using wireless technologies powered using batteries as their sole energy source which makes it paramount to optimize their energy consumption. In this paper, we provide an analytic framework of the energy-consumption profile and its lower bound for an IoT end device formulated based on Shannon capacity. We extend the study to model the average energy-consumption performance based on the random geometric distribution of IoT gateways by utilizing tools from stochastic geometry and real measurements of interference in the ISM-band. Experimental data, interference measurements and Monte-Carlo simulations are presented to validate the plausibility of the proposed analytic framework, where results demonstrate that the current network infrastructures performance is bounded between two extreme geometric models. This study considers interference seen by a gateway regardless of its source.

A New Planning-Based Collision-Prevention Mechanism in Long-Range IoT Networks

Wireless communication is prone to collisions, resulting from multiple devices transmitting at the same time. It implies subsequent retransmissions, which increase energy consumption of communicating devices and reduce throughput of the network. This is especially critical in Internet of Things (IoT) networks, in which the number of connected devices grows rapidly and the available energy of IoT end devices is rather limited (e.g., energy harvesting and battery powered). The number of retransmissions must be reduced in order the networks to be sustainable. However, in long-range wireless IoT networks, the most effective collision-resolution techniques using a transmission-channel listening to detect collisions cannot be reliably used due to various problems, such as a hidden-node problem or environment interference. In this article, a new solution of this problem is proposed, which consists of a new communication-planning mechanism for low-speed long-range IoT networks with a huge number of communicating energy-constrained devices. The access points (APs) (or IoT gateways) are used to plan the periodically repeated communication into a transmission schedule, allowing only a single IoT device to communicate at a time. This approach results in reduction of collisions, which leads to the increased network throughput, smaller delays, and lower power requirements of energy-constrained devices. The experiments indicate that the proposed approach provides better communication efficiency than the LoRaWAN and Sigfox collision-resolution techniques, when more than 15, respectively 125, end devices communicate with a single AP.

A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management.

Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.

Disclosing and Locating Concurrency Bugs of Interrupt-Driven IoT Programs

The Internet of Things (IoT) is envisioned as a distributed network formed by many end devices, e.g., the motes of wireless sensor network (WSN). These important IoT end devices enable ubiquitous sensing of environments and provide reliable services for mission-critical applications. However, programs running on WSN devices are typically interrupt-driven and prone to interrupt-induced concurrency bugs, which are primarily caused by erroneous interleavings among interrupt procedure instances (IPIs) (namely, executions of interrupt processing logic). In this paper, we use a set of dynamic bug patterns to characterize the concurrency bugs due to buggy access-interleavings among IPIs to shared resources, including shared memory locations and shared communication channels. By matching the above bug patterns, a dynamic analysis approach called disclosing and locating concurrency bugs of interrupt-driven IoT programs based on dynamic bug patterns (Daemon) is proposed to automatically detect and locate concurrency bugs in WSN programs. A GUI tool of Daemon is developed. As the empirical studies exhibit, the tool can discover concurrency bugs effectively and locate the buggy source lines visually.