Commercial Internet Of Things Research Articles

Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems.

SensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.

A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques

The Internet of Things (IoT) is growing rapidly in an exponential manner due to its versatility in technology. This has led to many challenges in securing the IoT environment. Devices in IoT environments are vulnerable to various cyberattacks. Botnet-based attacks are predominant and widespread in nature. Due to insufficient memory and computational power, the IoT environment cannot handle the botnet attack that affects security. Identifying intrusions in IoT environments is another challenge for researchers. Finding unknown patterns in the data generated through IoT networks helps improve security in the IoT environment. Machine learning (ML) is a platform that helps identify patterns in the provided data. In this study, we present our research on classifying incoming data from the IoT as malicious or benign using machine learning techniques. We propose an ML-based botnet attack detection framework for nine commercial IoT devices that primarily target BASHLITE and Mirai botnet attacks. Rigorous pragmatic research was conducted on the N-BaIoT dataset, which was extracted from realtime IoT devices connected to a network. Using this framework, the results have been depicted, which can efficiently detect botnet attacks and can also be applied to any other types of attacks.

Unraveling the Dynamics of User Acceptance on the Internet of Things: A Systematic Literature Review on the Theories and Elements of Acceptance and Adoption

The Internet of Things (IoT) connects various devices to the internet, enabling seamless communication and coordination. Its sensor and action capabilities aid in early fire detection and response. This article investigates the theory and elements of Internet of Things (IoT) acceptance and acceptance to see how IoT can assist in monitoring and controlling fire safety management. This study uses a systematic literature review (SLR) methodology to search, identify, appraise, synthesize, analyze, and summarize studies on the Internet of Things (IoT). The study highlights the importance of theoretical frameworks like TPB, TAM, UTAUT, and TOE in understanding the Internet of Things (IoT) acceptance and adoption. TPB focuses on attitudes, subjective standards, and perceived behavioral control, TAM emphasizes ease of use, UTAUT focuses on performance expectancy, effort expectancy, social influence, and facilitating conditions, and TOE examines organizational, technological, and environmental aspects affecting commercial IoT adoption. These frameworks provide actionable insights for successful IoT implementation and integration in individual and organizational settings.

Evaluation and Challenges of IoT Simulators for Intelligent Transportation System Applications

The Internet-of-Things (IoT) constructs a vast, intricate, and perpetually evolving ecosystem exerting profound societal implications. This labyrinthine nature often culminates in errors that directly impact human lives. A significant domain where this complexity materializes is Intelligent Transportation Systems (ITS). Present tools and methodologies inadequately accommodate the complex task of testing and validation, underscoring the urgency for comprehensive review and enhancement. This study aims to present a broad analysis of existing simulators utilized for ITS simulations. It delves into the role and effectiveness of such simulation tools, highlighting their limitations and proposing research directions. This paper scrutinizes both commercial and research-oriented IoT simulators for ITS, evaluating their features and simulation environment tools. We have detailed various ITS scenarios simulated within these frameworks, intending to gauge their readiness for real-world ITS applications and to elaborate on the challenges involved in ITS infrastructure implementation. The findings suggest that despite numerous simulators aiding the evolution of solutions for IoT challenges in recent years, their utility in actual ITS implementations remain uncertain. Consequently, we explore public cloud platforms offering IoT simulation capabilities, focusing particularly on the capabilities provided by the Amazon Web Services (AWS) IoT simulation for this study. Our research outlines the pressing challenges in this field, while proposing potential solutions and flagging opportunities for further research. This study paves the way towards improving the reliability and accuracy of IoT simulators in the context of ITS, which has immense potential to enhance the quality of human life.

Juliet-PUF: Enhancing the Security of IoT-Based SRAM-PUFs Using the Remanence Decay Effect

The cloud-based Internet of Things (IoT) enables the creation of innovative computer applications based on sensing, analyzing, and controlling the physical world. IoT deployments, however, are at a particular risk of counterfeiting, through which an adversary can corrupt the entire ecosystem. Therefore, entity authentication of edge devices is considered an essential part of the security of IoT systems. This research addresses the challenge of generating a unique ID in IoT devices. Unique IDs allow the IoT system maker to identify each edge device, and to ensure that only genuine devices can upload data to the cloud. Traditional ID mechanisms are not feasible in IoT, due to the edge device’s constrained runtime environment, or the additional costs and the deployment difficulties that they introduce. In this work, we present JULIET-PUF, a novel PUF-based method for IoT identification, which relies on SRAM content retrieval after power glitches with time differences. Our scheme comes with no added hardware cost on the edge device. We evaluate JULIET-PUF using a dataset of 24 units of a popular commercial IoT device, and show that it is on average 95.58 times more secure than the common use of SRAM-PUF.

More than two decades of research on IoT in agriculture: a systematic literature review

PurposeAgriculture is one sector where the Internet of things (IoT) is expected to make a major impact. Yet, its adoption in the sector falls behind expectations. The purpose of this paper is to present the state-of-the-art of IoT in agriculture and investigate its slow adoption in the sector.Design/methodology/approachThe authors have undertaken a systematic review and a synthesis of 1355 relevant publications over the last decade.FindingsThis literature review reveals that the “big three” barriers for the overall sector are cost, skills and standardization. The lack of connectivity and data governance are two key reasons why most of the proposed IoT solutions are standalone systems of limited scope, while the majority of commercial IoT efforts focus on practices in the protected indoor environment. Lastly, the analysis of past research along the five layers of the IoT system architecture reveals limited attention to barriers and solutions at the business layer, which represents a research opportunity for information systems scholars.Research limitations/implicationsIt is possible that some of relevant publications were missed in the literature search, although the search queries were kept as broad as possible to avoid the exclusion of any relevant work. Any publication written in any other language other than English was excluded from the review. Given the geographical distribution of the reviewed English publications (see section 4.1), it is highly likely that important works written by Chinese and European scholars in their native language were overlooked.Practical implicationsThis study provides practical insights into the technical and organisational challenges on the ground. It is the hope that this literature review lays the groundwork for IS researchers who are well positioned to investigate technology adoption challenges in the relatively understudied agriculture sector.Originality/valueTo the best of the authors’ knowledge, this is the first comprehensive review of adoption barriers and solutions across all five layers of the IoT system architecture.

Living in the Dark: MQTT-Based Exploitation of IoT Security Vulnerabilities in ZigBee Networks for Smart Lighting Control

The Internet of Things (IoT) has provided substantial enhancements to the communication of sensors, actuators, and their controllers, particularly in the field of home automation. Home automation is experiencing a huge rise in the proliferation of IoT devices such as smart bulbs, smart switches, and control gateways. However, the main challenge for such control systems is how to maximize security under limited resources such as low-processing power, low memory, low data rate, and low-bandwidth IoT networks. In order to address this challenge the adoption of IoT devices in automation has mandated the adoption of secure communication protocols to ensure that compromised key security objectives, such as confidentiality, integrity, and availability are addressed. In light of this, this work evaluates the feasibility of MQTT-based Denial of Service (DoS) attacks, Man-in-the-Middle (MitM), and masquerade attacks on a ZigBee network, an IoT standard used in wireless mesh networks. Performed through MQTT, the attacks extend to compromise neighboring Constrained Application Protocol (CoAP) nodes, a specialized service layer protocol for resource-constrained Internet devices. By demonstrating the attacks on an IKEA TRÅDFRI lighting system, the impact of exploiting ZigBee keys, the basis of ZigBee security, is shown. The reduction of vulnerabilities to prevent attacks is imperative for application developers in this domain. Two Intrusion Detection Systems (IDSs) are proposed to mitigate against the proposed attacks, followed by recommendations for solution providers to improve IoT firmware security. The main motivation and purpose of this work is to demonstrate that conventional attacks are feasible and practical in commercial home automation IoT devices, regardless of the manufacturer. Thus, the contribution to the state-of-the-art is the design of attacks that demonstrate how known vulnerabilities can be exploited in commercial IoT devices for the purpose of motivating manufacturers to produce IoT systems with improved security.

Implementation and characterization of flash‐based hardware security primitives for cryptographic key generation

AbstractHardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off‐the‐shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware‐based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min‐entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high‐entropy requirements, such as cryptographic key generation for IoT devices.

A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future Trends

The Internet of Things (IoT) has grown over decades to encompass many forms of sensing modalities, and continues to improve in terms of sophistication and lower costs. The trend of hardware miniaturization and emphasis on user convenience has inspired numerous studies to integrate more varied devices within the IoT into modernizing healthcare systems, facilitating applications, such as activity recognition, fitness assistance, vital signs monitoring, daily dietary tracking, and sleep monitoring. These applications are vital for prevention, detection, and treatment of ailments and can be realized using both dedicated health sensors as well as general-purpose sensors not originally designed for health monitoring. This article surveys such studies, detailing smart health monitoring systems, and the types of sensor components utilized within the IoT. We categorize and analyze these works based on their leverage of device-based techniques (i.e., use of sensors worn or carried by the person) and device-free techniques (i.e., wireless sensing without need to carry hardware), as well as signal processing and classification techniques utilized. In particular, we discuss how different combinations of these techniques can be creatively applied to support professional and commercial health-monitoring IoT networks. We also identify limitations and potential directions that future research may explore.

Contextualized Design of IoT (Internet of Things) Finance for Edge Artificial Intelligence Computing.

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively.

On the Use of LoRaWAN and Cloud Platforms for Diversification of Mobility-as-a-Service Infrastructure in Smart City Scenarios

Many different application domains are taking advantage from the adoption of the Internet of Things (IoT) paradigm. Among them, intelligent transportation systems can improve efficiency and sustainability by exploiting increased awareness about the actual mobility resource availability. For this reason, the idea of mobility as a service is gaining consensus. However, satisfying the requirements of very heterogeneous scenarios can be really challenging. In this work, we propose adding redundancy and diversity to the IoT platforms to improve overall resilience and flexibility, other than mitigating unstable behaviors. A proof-of-concept prototype is realized around a commercial IoT platform natively intended for the automotive domain. New “paths” from the vehicles to the end users, different from the original one, are added for retrieving the information: one additional LoRaWAN trunk and the other two via IBM Cloud service. A methodology for performance evaluation is suggested, computing purposely defined metrics. When applied to a sample use case, experimental results show that the redundancy and diversity strategies, compared to the (best) single-path approach, halve the overall average transfer delay (from about 1.2 s down to about 0.5 s) and the standard deviation (from about 0.5 to about 0.3 s).

TAPInspector: Safety and Liveness Verification of Concurrent Trigger-Action IoT Systems

Trigger-action programming (TAP) is a popular end-user programming framework that can simplify the Internet of Things (IoT) automation with simple trigger-action rules. However, it also introduces new security and safety threats. A lot of advanced techniques have been proposed to address this problem. Rigorously reasoning about the security of a TAP-based IoT system requires a well-defined model and verification method both against rule semantics and physical-world features, e.g., concurrency, rule latency, extended action, tardy attributes, and connection-based rule interactions, which has been missing until now. By analyzing these features, we find 9 new types of rule interaction vulnerabilities and validate them on two commercial IoT platforms. We then present TAPInspector, a novel system to detect these interaction vulnerabilities in concurrent TAP-based IoT systems. It automatically extracts TAP rules from IoT apps, translates them into a hybrid model by model slicing and state compression, and performs semantic analysis and model checking with various safety and liveness properties. Our experiments corroborate that TAPInspector is practical: it identifies 533 violations related to rule interaction from 1108 real-world market IoT apps and is at least 60000 times faster than the baseline without optimization.

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection.

Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics.

Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

Combining IOTA and Attribute-Based Encryption for Access Control in the Internet of Things.

Unauthorized resource access represents a typical security threat in the Internet of Things (IoT), while distributed ledger technologies (e.g., blockchain and IOTA) hold great promise to address this threat. Although blockchain-based IoT access control schemes have been the most popular ones, they suffer from several significant limitations, such as high monetary cost and low throughput of processing access requests. To overcome these limitations, this paper proposes a novel IoT access control scheme by combining the fee-less IOTA technology and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology. To control the access to a resource, a token, which records access permissions to this resource, is encrypted by the CP-ABE technology and uploaded to the IOTA Tangle (i.e., the underlying database of IOTA). Any user can fetch the encrypted token from the Tangle, while only those who can decrypt this token are authorized to access the resource. In this way, the proposed scheme enables not only distributed, fee-less and scalable access control thanks to the IOTA but also fine-grained attribute-based access control thanks to the CP-ABE. We show the feasibility of our scheme by implementing a proof-of-concept prototype system using smart phones (Google Pixel 3XL) and a commercial IoT gateway (NEC EGW001). We also evaluate the performance of the proposed scheme in terms of access request processing throughput. The experimental results show that our scheme enables object owners to authorize access rights to a large number of subjects in a much (about 5 times) shorter time than the existing access control scheme called Decentralized Capability-based Access Control framework using IOTA (DCACI), significantly improving the access request processing throughput.

MoSIoT: Modeling and Simulating IoT Healthcare-Monitoring Systems for People with Disabilities.

The need to remotely monitor people with disabilities has increased due to growth in their number in recent years. The democratization of Internet of Things (IoT) devices facilitates the implementation of healthcare-monitoring systems (HMSs) that are capable of supporting disabilities and diseases. However, to achieve their full potential, these devices must efficiently address the customization demanded by different IoT HMS scenarios. This work introduces a new approach, called Modeling Scenarios of Internet of Things (MoSIoT), which allows healthcare experts to model and simulate IoT HMS scenarios defined for different disabilities and diseases. MoSIoT comprises a set of models based on the model-driven engineering (MDE) paradigm, which first allows simulation of a complete IoT HMS scenario, followed by generation of a final IoT system. In the current study, we used a real scenario defined by a recognized medical publication for a patient with Alzheimer’s disease to validate this proposal. Furthermore, we present an implementation based on an enterprise cloud architecture that provides the simulation data to a commercial IoT hub, such as Azure IoT Central.

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications spanning from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consumer</i> domain to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">commercial</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">industrial</i> systems. The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life cycles. IoT-related vulnerabilities, if successfully exploited can affect, not only the device itself but also the application field in which the IoT device operates. Evidently, identifying and addressing every single vulnerability are an arduous, if not impossible, task. Attack taxonomies can assist in classifying attacks and their corresponding vulnerabilities. Security countermeasures and best practices can then be leveraged to mitigate threats and vulnerabilities before they emerge into catastrophic attacks and ensure overall secure IoT operation. Therefore, in this article, we provide an attack taxonomy, which takes into consideration the different layers of the IoT stack, i.e., device, infrastructure, communication, and service, and each layer’s designated characteristics, which can be exploited by adversaries. Furthermore, using nine real-world cybersecurity incidents that had targeted IoT devices deployed in the consumer, commercial, and industrial sectors, we describe the IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and potential mitigation mechanisms and protection strategies. These (and many other) incidents highlight the underlying security concerns of IoT systems and demonstrate the potential attack impacts of such connected ecosystems, while the proposed taxonomy provides a systematic procedure to categorize attacks based on the affected layer and corresponding impact.

Performance of distributed coordination function for serving IoT applications

The Internet of Things (IoT) interconnects “things” and autonomously exchanges data between them. It is expected that there are more than seven billion of connections interconnected by low-power wide-area (LPWA) networks to support IoT services before 2025. Several LPWA technologies are gaining popularity recently, such as LoRa and NB-IoT. Despite of their advertised advantages over conventional technologies such as low power, long range, and cheap module, several studies and field tests shows that they cannot communicate in long-range, the DIY supporting modules are not really power efficient, and the development are rather expensive. While commercial IoT networks have yet to be widely deployed, many smart connected devices use WiFi-based connectivity. This motivates us to study and evaluate the performance of WiFi’s medium access protocol for this new use case of IoT. In the experiment, our proposed analytical model is evaluated against a custom-made Monte Carlo simulation. The result shows that our model is accurate in estimating the system performance in term of normalized throughput.