Internet Of Things Space Research Articles

Unveiling IoT Customer Behaviour: Segmentation and Insights for Enhanced IoT-CRM Strategies: A Real Case Study.

In today's competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.

An Algorithm for Designing Value Propositions in the IoT Space: Addressing the Challenges of Selecting the Initial Class in Reference Class Forecasting

Developing products and entering new markets in the Internet of Things (IoT) and industry 4.0 space are challenging because value propositions and business models are complex. IoT products are fundamentally platform products, whose embedded components can measure a disparate set of characteristics for several user types. Deciding in advance which combination of characteristics and users to focus on is daunting, and further complicated wherever the user is not the payer. It requires prediction. A common predictive technique, such as reference class forecasting (RCF), often suffers from the biased selection of initial reference classes. In this article, we propose combining RCF with our new biclustering algorithm, ECrfBimax, built from 200 use cases we distilled. ECrfBimax uniquely makes biclustering predictive. Our combined biclustering/RCF approach helps managers and designers design value propositions for IoT devices through a data-driven selection of initial classes. Applying the algorithm to readily available qualitative data from a high-dimensional dataset comprising 10 competitors of a real-life IoT company, 200 customers, and 230 product characteristics, we identify solution characteristics deployable by that company in different sectors. ECrfBimax identifies how far competitors are targeting users using similar/dissimilar combinations of characteristics. Quantitative proxies for size, success, and performance based on funding raised empirically support and statistically validate the algorithm. Uniquely too, our bicluster hierarchically groups customers with characteristics which either should or should not be jointly developed to target them. We also uniquely compare RCF and biclustering as methods for analyzing objects’ similarity.

Attack and Anomaly Detection in IoT Sites Using Machine Learning Techniques

Abstract: A growing problem in the IoT space is the attack and anomaly detection in the infrastructure of the Internet of Things (IoT). Every domain is using IoT infrastructure more and more, and with that use comes a surge in risks and attacks against those infrastructures. Such attacks and anomalies that can lead to an IoT system failure include Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying, and Wrong Setup. Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) are the machine learning (ML) methods that have been employed in this. Accuracy, precision, recall, f1 score, and area under the receiver operating characteristic curve are the evaluation measures used in performance comparison. For Decision Tree and Random Forest, the system received test accuracy results of 99.4 %. Despite the same accuracy of these algorithms, other criteria show that Random Forest performs significantly better.

A survey on blockchain security for cloud and IoT environment

In traditional Internet of Things (IoT) ecosystems, fog devices transmit data from sensors to a centralized cloud server. Issues with security and upkeep while updating firmware for millions of smart devices, single points of failure, as well as third-party Cloud server administration, the difficulty of frequently updating the firmware on millions of smart devices presents security and maintenance issues as well as a bottleneck in information flows, all raise privacy concerns. Blockchain technology eliminates the need for trusted third parties while also preventing single points of failure and other issues. As a result, academics are looking into the usage of blockchain in the IoT space. The most recent state-of-the-art developments in blockchain for IoT, blockchain for Cloud, blockchain for eHealth, smart cities, transportation, and other programmes are examined in this article.

On the Reuse of a Matching Network for IoT Devices Operating at 900 MHz Embedding Antenna Boosters

The Internet of Things (IoT) is evolving rapidly, enabling more new applications than ever before. Some radio systems in the IoT space used to connect things with things operate at the 863–928 MHz frequency band (e.g., LoRa, ISM). IoT wireless devices at 900 MHz need an efficient antenna to send data into the cloud, and ensure range and long battery life. Moreover, the antenna should be easy to use across different device sizes. In this regard, antenna booster technology is proposed, which relies on a tiny element called an antenna booster, able to excite radiation currents on the ground plane of the IoT device. The antenna booster in this paper is only 12 mm × 3 mm × 2.4 mm (height), representing only ~λ/30 at 863 MHz. Such an antenna booster is reactive across the 863–928 MHz frequency range and, thus, a matching network is needed. The paper first proposes a matching network on a 120 mm × 60 mm ground plane. Afterward, an analysis is carried out to find the set of different ground plane sizes where the same matching network as the one used for the 120 mm × 60 mm ground plane can be reused, ensuring an S11 < −6 dB. The goal is to find a map representing a cluster of ground planes where the same antenna system can be used (the same antenna booster and the same matching network) to simplify the design of IoT devices embedding antenna boosters. The analysis is addressed using MoM (Method of Moments) electromagnetic software (IE3D) and validated through measurements. Results indicate that a broad set of device sizes can reuse the same matching network with the same antenna booster, thus, simplifying and accelerating the design of IoT devices since no adjustment is needed on the antenna system.

Data trust in Consumer Internet of Things assemblages in the mobile and fixed telecommunication operators in South Africa

Background: Devices can be connected through the Internet of Things (IoT) technology to create a smart ecosystem. From the connection, various stakeholders share personal data with companies in the consumer IoT (CIoT) space for marketing and other reasons. Customers download and use applications without mulling over the type of personal information exposed to the rest of the world.Objective: The main objective of this study was to explore data trust in CIoT assemblages in the mobile and fixed telecommunication operators in South Africa.Method: This qualitative study triangulated narrative enquiry with the Delphi technique to explore data trust in CIoT assemblages in South Africa. The primary data collection techniques used in this study were unstructured interviews (in the case of narrative enquiry), questionnaires and semi-structured interviews (in the Delphi technique). For the Delphi technique, five experts were chosen purposively based on their involvement in IoT, selling and on-selling IoT services or those providing support services to the IoT ecosystems, while six narrative enquiry participants were selected through snowball technique based on their exposure to using consumer IoT solutions, ability to provide detailed descriptions of their experiences and willingness to articulate those experiences.Results: The study established that the choice of system to use varied from consumer to consumer. The consumer’s original decision may be influenced by many factors, such as devices sponsored by one’s health insurance or security company. However, the constant use of a specific system makes it personal and more comfortable for the consumer. The level of trust in the CIoT system increases with constant interactions.Conclusion: The study concludes that there should be a very high level of stakeholders trust for faster adoption of CIoT in South Africa. Therefore, regulators such as the Independent Communication Authority of South Africa should ensure that IoT devices in the South African market are trustworthy.

Personal Data and the Assemblage Security in Consumer Internet of Things

The rise of the Internet of Things (IoT) has driven mobile applications development. As a result, in the consumer IoT (CIoT) space, any product usually comes with a smartphone application to either control, programme, or just view what is happening with the product. The IoT brings with it opportunities and challenges to consumers. For example, IoT technology makes it possible to connect all of a person’s devices to create a smart eco-system or assemblage. However, the biggest threat in CIoT is personal data security. This study utilized a qualitative narrative inquiry and Delphi technique to explore data security issues that come with CIoT assemblages and associated mobile applications in South Africa. The study established that consumers enjoy the convenience and benefits that IoT technology brings. It is concluded that for CIoT to thrive, safety is crucial, and all the stakeholders in the IoT assemblage need to ensure the protection of consumers.

Smart Home Energy Management System based on the Internet of Things (IoT)

The global increasing demand for energy has brought attention to the need for energy efficiency. Markedly noticeable in developing areas, energy challenges can be at-tributed to the losses in the distribution and transmission sys-tems, and insufficient demand-side energy management. Demand-oriented systems have been widely proposed as feasible solutions. Smart Home Energy Management Systems have been proposed to include smart Internet of Things (IoT)-capable devices in an ecosystem programmed to achieve energy efficiency. However, these systems apply only to already-smart devices and are not appropriate for the many locales where a majority of appliances are not yet IoT-capable. In this paper, we establish the need to pay attention to non-smart appliances, and propose a solution for incorporating such devices into the energy-efficient IoT space. As a solution, we propose Homergy, a smart IoT-based Home Energy Management Solution that is useful for any market –advanced and developing. Homergy consists of the Homergy Box (which is an IoT device with Internet connectivity, an in-built microcontroller and opto-coupled relays), a NoSQL cloud-based database with streaming capabilities, and a secure cross-platform mobile app (Homergy Mobile App). To validate and illustrate the effectiveness of Homergy, the system was deployed and tested in 3 different consumer scenarios: a low-consuming house, a single-user office and a high-consuming house. The results indicated that Homergy produced weekly energy savings of 0.5 kWh for the low-consuming house, 0.35 kWh for the single-user office, and a 13-kWh improvement over existing smart-devices-only systems in the high-consuming house.

Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy

This paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in creating sensor-based awareness using the Internet of Things (IoT) aboard naval vessels in the context of the US Navy’s Trident Warrior 2019 exercise. Funded by DARPA through the Brandeis program, the team built an integrated IoT data management middleware, entitled TIPPERS, that supports privacy by design and integrates a variety of Privacy Enhancing Technologies (PETs), including differential privacy, computation on encrypted data, and fine-grained policies. We describe the architecture of TIPPERS and its use in creating a smart ship that offers IoT-enabled services such as occupancy analysis, fall detection, detection of unauthorized access to spaces, and other situational awareness scenarios. We describe the privacy implications of creating IoT spaces that collect data that might include individuals’ data (e.g., location) and analyze the tradeoff between privacy and utility of the supported PETs in this context.

IoT-HarPSecA: A Framework and Roadmap for Secure Design and Development of Devices and Applications in the IoT Space

The past couple of years have marked continued growth in the applications and services of the Internet of Things (IoT). This has attracted the attention of new operators as well as institutional, corporate, and private investors in every sector of the economy, and as a result, new businesses are springing up rapidly. These include many start-up companies that are producing various kinds of useful IoT devices and Smart Applications (smart apps). While this can be seen as a boost for innovation in the IoT, some of these companies produce IoT devices and smart apps with security vulnerabilities. In this paper, we propose the IoT Hardware Platform Security Advisor (IoT-HarPSecA), a security framework intended to provide support to such IoT producers. IoT-HarPSecA offers three functionality features, namely security requirement elicitation, security best practice guidelines for secure development, and above all, a feature that recommends specific LightWeight Cryptographic Algorithms (LWCAs) for both software and hardware implementations. Accordingly, IoT-HarPSecA is composed of three main components, namely Security Requirements Elicitation (SRE) component, Security Best Practice Guidelines (SBPG) component, and LightWeight Cryptographic Algorithms Recommendation (LWCAR) component, each of them servicing one of the aforementioned features. We implement a command-line tool in C++ to serve as an interface between users and the proposed framework. IoT-HarPSecA can be employed during the early stages of IoT systems design, and it can also be used to facilitate the implementation of security in existing IoT systems. This paper presents a detailed description, design, and implementation of the SRE, SBPG, and LWCAR components of the proposed framework. Using real-world practical scenarios, we show how IoT-HarPSecA can be used to elicit security requirements and recommend appropriate LWCAs based on user inputs. While a full performance evaluation of the SRE and SBPG components is beyond the scope of this paper, we present a detailed performance evaluation of the LWCAR component, which shows that IoT-HarPSecA can serve as a roadmap for secure IoT development.

Scheduling software updates for connected cars with limited availability

The current and the new generation of Internet of Things (IoT) devices present several challenges, among them the software update of legacy and new devices using wireless connections. In this paper, we study a problem of scheduling massive Firmware-Over-The-Air updates for millions of connected cars. We model this problem as a new generic problem called Time- and Machine-Dependent Scheduling Problem (TMDSP) that resembles project scheduling problems with variable-intensity activities. In the TMDSP, jobs, machine utilization, and production rates vary over time. In each period, a given job can be executed by a subset of machines with different capacities and production rates. The objective is to deploy a feasible schedule with minimum total completion time. We explore solving the problem using several approaches among than Biased Random-Key Genetic Algorithm (BRKGA), Iterated Local Search (ILS), Simulating Annealing (SA), two variations of Tabu Search (TB), and traditional genetic algorithms (TGA), in addition to a Mixed Integer Programming (MIP) model. We test the proposed approaches on a synthetic benchmark and very large real instances in IoT space. While using a commercial solver and the MIP model, we are able to solve only a few real instances (with, at most, 1,976 cars and 9,116 sectors); on the other hand, BRKGA presents significantly better results when compared to ILS, MIP solver and a simple multi-start heuristic.

(Invited) Substrate and Device Engineering for Iot and Automotive

The main challenges of the semiconductor industry have long revolved around density scaling and performance improvement of ICs. However, a growing proportion of semiconductor consumption is now driven by factors other than pure performance and density. The Internet Of Things (IoT) & Automotive markets are perfect examples of this, where increased energy efficiency combined with enhanced reliability and heterogeneous functions integration capabilities have become major hurdles to overcome. Advanced substrates such as Silicon-On-Insulator (SOI) are progressively emerging as key platforms to address these pain points. In fact, the choice of advanced substrates results from a tradeoff between device and substrate complexity with regards to system level challenges. Bringing all the complexity of device implementation and integration on the device manufacturing process - rather than the substrate - turns out to be less and less relevant to address the challenges of heterogeneous functions integration, reliability and power efficiency. In this talk, we will discuss how three engineered substrate platforms, namely Power-SOI, RF-SOI and FD-SOI, are currently reshaping IoT and Automotive with new industry standards. Power-SOI Power-SOI substrates are based on thick BOX and top silicon (typically 1µm or more). They address high voltage device integration challenges. They allow for the replacement of all leaky and area consuming junction isolation techniques by oxide isolation, bringing significant advantages in terms of device characteristics (higher RDSON, lower leakage, lower area), but also in terms of reliability (latch-up free operation) and integration with high voltage power devices up to 600V integrated with digital functions on the same substrate. Power-SOI offers therefore tremendous benefits for power management ICs and is particularly advantageous for automotive applications. RF-SOI RF-SOI is based on a high resistivity base material often combined with a low mobility layer just beneath the BOX. RF-SOI allows the integration of ultra high linear and low loss RF components within the same substrate. This unique capability has made RF-SOI the de facto technology for a growing number of smartphone Front-End-Modules (FEM) components (switch, LNA, PA, duplexers…), i.e. all the RF components linking the antenna and the transceiver. RF-SOI will continue to expand into new applications like automotive where high throughput and low latency connectivity will be key. RF-SOI will also bring major value in the IoT space, as its integration capability offers a single FEM chip solutionwhich is particularly beneficialat system level. FD-SOI FD-SOI is the natural technology for the continuation of Moore’s law for battery-powered and cost-sensitive applications. FD-SOI is actually the simplest platform enabling fully depleted operation of transistors, which is a fundamental advantage to go below the 28nm node. FD-SOI substrates havean ultra thin top layer and BOX. The benefits of FD-SOI are numerous: simple device manufacturing, high Fmax compatible with mmWave spectrum, ultra low voltage operation down to 0.4V, All these characteristics have made FD-SOI a key technology for all applications not purely driven by density and digital performance. In automotive, it brings huge differentiation in terms of reliability and power consumption, enabling the possibility of highly reliable fanless solutions. In IoT, significant battery life gains can be expected, while providing the best cost per transistor of all CMOS technologies. Conclusion Advanced substrates such as SOI appear more and more as key platforms to ease integration, while exacerbating device advantages such as power and reliability for a growing number of applications not purely driven by density and performance. These key features will probably impose SOI in the future as a new industry standard for IoT and automotive.

Continuously Power Delivering for Passive Backscatter Wireless Sensor Networks

Nowadays wireless sensor networks (WSNs) depend on the battery duration and there is a lot of interest in creating a passive sensor network scheme in the area of Internet of Things (IoT) and space-oriented WSN systems. Radio frequency (RF) energy harvesting enables the control and the delivery of wireless power to RF devices. All the devices made with this technology can be sealed, embedded with these structures, or made mobile, so they can be battery-independent. In this paper, we show that it is possible to have a complete passive WSN that could potentiate the IoT area and space craft sensing. This WSN should be capable of receiving a continuous flow of energy, so that the sensing mechanisms can be continuously powered. For this, we propose a combination of backscatter modulation with wireless power transfer, using the same diode based approach as in a traditional RF identification circuit. This way the cost is maintained low, but the amount of gathered energy is maximized during the backscatter communication. This paper also presents all the steps that are needed in order to implement such a circuit.

Approach To Building A Virtual Assistant Or Bot Almost From Scratch

V.A.* (Virtual Assistants) are computer programs designed to simulate a conversation with a human via textual or voice interactions. We will focus on text-based interactions only in this paper. Voice-based V.A. consist in the combination of an Automatic Speech Recognition or Speech-To-Text and Text-To-Speech layers on top of a text-based interactions layer. By now you must have heard of Apple SIRI, Amazon Alexa, Microsoft Cortana, IBM Watson, Viv.ai, X.ai Amy, Kasisto Kai etc. They are becoming ubiquitous with the rise of Mobiles, IoT (Internet of Things) and Messaging platforms.V.A. can take multiple forms: simple command interface to interact with one specific service, e.g. in the IoT space to activate a device; basic chatbots to complex ones handling users’ requests, e.g. help-desk or customer service chatbots (e.g. Nuance Nina) or personal banking assistants (e.g. Kasisto Kai) providing information tied to transactions or specific assistants like X.ai Amy helping you schedule meetings. For multiple services V.A., we generally speak about V.A. with “skills”. A skill is a basic resource or service that the V.A. can access to answer the user’s request.We will explore in this paper some key aspects involved in building a V.A. We will also detail our parsing approach based on the concept of “contract” which provides an abstraction of the resource API (“skill”) that will be used to identify intents and related contexts.This paper is not meant to get into the details of parsing natural language using heavy linguistic engineering but rather it provides an alternative approach that is simple yet adaptable to any language. The main objective is to provide a text-based interface to any structured dataset or analytics service or to build a multi-skills virtual assistant with a clear focus on getting domain-specific actions executed while maintaining a smooth communication with the users.Also, this paper provides an introduction only. Going into further details may require additional papers specific to the: tokenizer, fuzzy parsing, handling of “Skills” compositionality, segmentation of sub-queries, online-learning auto-completion & suggestion model, graph-based conversation & knowledge base, expression evaluation with live code compilation, patterns recognizer approach, actual architecture flows, automatic testing based on automatic user’s requests generation module, mixing texts & clicks interactions etc.Most of the described concepts have been implemented in an advanced c#.Net 4.5 end-to-end prototype. The prototype itself has been used for a PoC with a major company to build a virtual assistant to access analytics and structured datasets. Most requests are usually parsed in less than 100ms based on dozens of skills, dozens of conversations and a dictionary with thousands of words.

Toward a Global Data Infrastructure

The Internet of Things (IoT) represents a new class of applications that can benefit from cloud infrastructure. However, directly connecting smart devices to the cloud has multiple disadvantages and is unlikely to keep up with the growing speed of the IoT or the diverse needs of IoT applications. Here, the authors argue that fundamental IoT properties prevent the current approach from scaling. What's missing is a well-architected system extending cloud functionality and providing seamless interplay among heterogeneous components closer to the edge in the IoT space. Raising the level of abstraction to a data-centric design -- focused around the distribution, preservation, and protection of information -- better matches the IoT. To address such problems with the cloud-centric architecture, the authors present their early work on a distributed platform, the Global Data Plane.

System-Level Analysis of Far-Field Radio Frequency Power Delivery for mm-Sized Sensor Nodes

Millimeter-sized and low-cost sensor nodes can enable future applications of the Internet of Things (IoT), for which the number of sensors is projected to grow to a trillion within the next decades. RF far-field power transfer is a potential technique for wirelessly powering these sensors since it offers flexible configuration of sensor networks, beamforming capability and a large power transfer range compared to near-field approaches. However, system design for RF power transfer needs to be completely rethought to enable this new paradigm of a trillion IoT sensors. This paper, therefore, presents a comprehensive, system-level analysis strategy and a modular framework for investigating the fundamental efficiency components in an RF power transfer chain. Through this detailed analysis, it is demonstrated that the optimal frequency is primarily determined by the antenna size and the quality factors $(Q)$ of components in the matching network. Millimeter-wave frequencies are shown to be optimal for powering mm-sized sensors for practical matching component $Q$ values. An intuitive explanation of our results is also provided, along with insights for the design and practical implementation of RF power transfer systems for the IoT space.

“We Have Problems for Solutions”: The State of Cybersecurity in Bosnia and Herzegovina

The Internet and information communication technologies (ICT) became the most important components in everyday life, given the fact that they have altered the behaviour patterns and in many aspects made our lives simpler. Upcoming Internet of Things (IoT) will additionally have a positive influence on our Quality of Life (QoL). However, even now, the society is extremely vulnerable to disturbances that may affect the functioning of the Internet and ICT systems, and thereby jeopardise the reliability and security of the information they contain. The situation becomes even more complicated when IoT takes effect and cyber threats exceed the perimeter of information security and include physical security, existence and health. The world has shyly started to raise questions and perceive problems regarding the IoT, QoL and security issues that it will bring to the cyber space in order to find the appropriate solutions in time. Bosnia and Herzegovina, as well as any other modern country, needs to take place in these processes and newly opened research fields. This paper gives an overview of Bosnia and Herzegovina’s existing cybersecurity infrastructure and capacities in terms of legislation, security management structure, corresponding cybersecurity units, as well as their cooperation and qualification level. On that basis it is concluded that cybersecurity is not among the priorities in Bosnia and Herzegovina. The country “offers” problems for adequate and generally accepted solutions in the cybersecurity domain and needs to work on its readiness to contribute to the safety of IoT and cyber space, and consequently improving citizens’ QoL.