Pervasive Internet Of Things Research Articles

Strengthening IoT Network Protocols: A Model Resilient Against Cyber Attacks

The pervasive Internet of Things (IoT) integration has revolutionized industries such as medicine, environmental care, and urban development. The synergy between IoT devices and 5G cellular networks has further accelerated this transformation, providing ultra-high data rates and ultra-low latency. This connectivity enables various applications, including remote surgery, autonomous driving, virtual reality gaming, and AI-driven smart manufacturing. However, IoT devices’ real-time and high-volume messaging nature exposes them to potential malicious attacks. The implementation of encryption in such networks is challenging due to the constraints of IoT devices, including limited memory, storage, and processing bandwidth. In a previous work [1], we proposed an ongoing key construction process, introducing a pivotal pool to enhance network security. The protocol is designed with a probability analysis to ensure the existence of a shared key between any pair of IoT devices, with the predefined probability set by the system designer. However, our earlier model faced vulnerabilities such as the “parking lot attack” and physical attacks on devices, as highlighted in the conclusion section. We present a complementary solution to address these issues, fortifying our previous protocol against cyber threats. Our approach involves the implementation of an internal Certification Authority (CA) that issues certificates for each IoT device before joining the network. Furthermore, all encryption keys are distributed by the primary IoT device using the Unix OS ‘passwd’ mechanism. If a device “disappears,” all encryption keys are promptly replaced, ensuring continuous resilience against potential security breaches. This enhanced protocol establishes a robust security framework for IoT networks, safeguarding against internal and external threats.

An AI-Enabled Framework to Defend Ingenious MDT-Based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

Applying software‐defined ambient intelligence techniques for cognitive and emotional awareness in the Internet of Things

SummarySmart “discrete” technologies are taking over everyday activities with an intended shift towards complete automation. With this, ambient intelligence (AmI) has come under the limelight, to make these routines subtle and automated, enabled through the widespread adoption of the ubiquitous and pervasive Internet of Things (IoT). AmI is context‐driven depending largely on user input through various interfaces, which is nothing but the interplay between IoT and artificial intelligence (AI). Despite extensive incorporation of personal digital assistants and smart home systems, AI is not yet capable of accurately interpreting and responding to human emotions. This paper aims to present one such application to gauge the emotional and cognitive state of the user and respond appropriately, be it ambient lighting or an emergency SOS. The proposed model extracts network packets to work with audio messages on the real‐time transport protocol (RTP) layer and presents a lightweight multilayer perceptron (MLP)‐based speech emotion recognition (SER) model for emotional analysis with an accuracy of 80.52%. It also features a simple confidence rating system for further optimization.

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

Formal security analysis of lightweight authenticated key agreement protocol for IoT in cloud computing

The internet of things (IoT) and cloud computing are evolving technologies in the information technology field. Merging the pervasive IoT technology with cloud computing is an innovative solution for better analytics and decision-making. Deployed IoT devices offload different types of data to the cloud, while cloud computing converges the infrastructure, links up the servers, analyzes information obtained from the IoT devices, reinforces processing power, and offers huge storage capacity. However, this merging is prone to various cyber threats that affect the IoT-Cloud environment. Mutual authentication is considered as the forefront mechanism for cyber-attacks as the IoT-Cloud participants have to ensure the authenticity of each other and generate a session key for securing the exchanged traffic. While designing these mechanisms, the constrained nature of the IoT devices must be taken into consideration. We proposed a novel lightweight protocol (Light-AHAKA) for authenticating IoT-Cloud elements and establishing a key agreement for encrypting the exchanged sensitive data was proposed. In this paper, the formal verification of (Light-AHAKA) was presented to prove and verify the correctness of our proposed protocol to ensure that the protocol is free from design flaws before the deployment phase. The verification is performed based on two different approaches, the strand space model and the automated validation of internet security protocols and applications (AVISPA) tool.

Development of Pervasive IoT Based Healthcare Monitoring System for Alzheimer Patients

Alzheimer is a neurological degenerative disease that steadily and gradually progresses. This disease is incurable at present and those who suffer from it have the hesitation to travel beyond their homes in comparison to normal people. The main aim of this paper is to offer a continuous mechanism that enhances the Alzheimer patient’s quality of life as well as that of their caregivers. Proposed work is based on the Internet of things (IoT) based sensors data that figure out different parameters of the patient body like temperature, blood pressure, striding action, striding speed, and so on. All these sensory data will be obtained with the help of the Atmega Microcontroller. All the collected data are then sent to the cloud server with the help of parallel communication for data analysis. The desired parameter of the patient is retrieved which will help in providing real time support to the patients.

Study on Trust Management System for Ubiquitous and Pervasive IoT Application

IoT (Internet of things) is one of prominent technologies for provisioning of services through smart, intelligent and heterogeneous devices connected to each other via internet. With environmental influences, the widely used IoT applications are much prone to security and privacy related problems. In order to address these issues, many solutions were proposed in the recent past but most of these models have not fulfilled yet all the promises of IoT requirements. Furthermore, considering the characteristics of IoT involving heterogeneity, dynamic, mobility of nodes, energy constraint, less computing power devices, then it’s challenging to propose dynamic trust management system (TMS). This paper contributes the comparison study of existing trust management approaches applied to various IoT based applications.Further, it highlights open issues, research challenges and provides the future direction to address the current research challenges through research model, holistic trust management.

Resource Efficient Deployment and Data Aggregation in Pervasive IoT Applications (Smart Agriculture)

Aims: Internet of Things (IoT) is the evolution of the Internet designed to sense, collect, analyze and distribute the data via IoT devices that form its core component. An important aspect of pervasive IoT applications is its resource-constrained devices. Most of the real-time Edge-IoT applications generate a huge amount of data, which add to the resource consumption in these devices. To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Objectives: To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Methods: For heterogeneous scenarios, we propose a clustering approach, Superior Aggregator Resource Efficient Clustering (SAREC), to address the resource constraints in pervasive Edge-IoT applications. The comparison of homogeneous and heterogeneous networks is based on LEACH and SAREC protocols, respectively. Results: The results show that SAREC is 25% more efficient in energy utilization and network lifetime than LEACH. The results also show that SAREC is more efficient in terms of storage and processing time as compared to LEACH. Conclusion: Node deployment is an important aspect in determining the architecture, which plays an important role in resource management in pervasive applications of IoT. The IoT nodes are distributed in a selected geographical location and the topology of the network is pre-decided to form an Edge-IoT network. In such an environment, the nodes are deployed to sense, aggregate and analyze the data. This paper presents a pervasive Edge-IoT network along with the mathematical modeling consisting of deployment and aggregation models. An Edge-IoT network for smart agriculture has been deployed and analysis of resource utilization has been performed in homogeneous and heterogeneous scenarios of the network. The resource limitations in pervasive IoT network motivated us to develop a SAREC approach for such Edge-IoT applications that optimizes the use of resources. The comparison of the proposed SAREC protocol is made with respect to LEACH protocol on the basis of energy, network lifetime, number of alive nodes, storage and processing time. The results show that SAREC protocol is 25% more efficient in energy utilization and network lifetime than LEACH. It is also evident from the results that the SAREC is more efficient in terms of storage and processing time as compared to LEACH.

Dynamic Contract Design for Federated Learning in Smart Healthcare Applications

Currently, the data collected by the Internet of Healthcare Things, i.e., healthcare oriented Internet of Things (IoT), still rely on cloud-based centralized data aggregation and processing. To reduce the need for transmission of data to the cloud, the edge computing architecture may be adopted to facilitate machine learning at the edge of the network through leveraging on the amassed computation resources of pervasive IoT devices. In this article, federated learning (FL) is proposed to enable privacy-preserving collaborative model training at the edge of the network across distributed IoT users. However, the users in the FL network may have different willingness to participate (WTP), a hidden information unknown to the model owner. Furthermore, the development of healthcare applications typically requires sustainable user participation, e.g., for the continuous collection of data during which a user’s WTP may change over time. As such, we leverage on the dynamic contract design to consider a two-period incentive mechanism that satisfies the intertemporal incentive compatibility (IIC), such that the self-revealing mechanism of the contract holds across both periods. The performance evaluation shows that our contract design satisfies the IIC constraints and derives greater profits than that of the uniform pricing scheme, thus validating its effectiveness in mitigating the adverse impacts of the information asymmetry.

Secure and Efficient Data Sharing in Dynamic Vehicular Networks

With the development of wireless communication and the pervasive Internet of Things, vehicular ad hoc networks (VANETs) have attracted much attention in recent years. As a platform for vehicular communication and management, VANETs require the guarantee of security and efficiency for the resources constrained and unreliable networks. In this article, a data-sharing scheme is proposed for VANETs where both efficient key updating and dynamic property of VANETs are supported. In particular, the symmetric balanced incomplete block design (SBIBD) in combinatorics is introduced to ensure secure and efficient VANETs while the concept of indistinguishability obfuscation is employed to support efficient key updating. In order to simultaneously satisfy the resources constrained and real-time requirements of VANETs, both serial and parallel construction of the SBIBD is implemented by the shift register. Taking advantage of this efficient sequential logical circuit, we eliminate the complicated construction of the SBIBD, thereby, making it effective for the resource-constrained environment. Theoretical and experimental analyses indicate that the proposed scheme is practical for VANETs with high security and efficiency.

A Parallelization based Data Management Framework for Pervasive IoT Applications

Pervasive Internet of Things (IoT) is a research paradigm that has attracted considerable attention nowadays. The main aim of pervasive IoT is that in the future, the everyday objects (devices) would be accessible, sensed, and interconnected inside the global structure of the Internet. But in most of the pervasive IoT applications, the resources of an IoT device such as storage, processing, and energy are limited; as such there is a need for management of resources in such applications. Multiple aspects related to the data such as the type of data, size of data, number of transmission and reception of data packets, the structure of data, etc are taken into consideration while managing the resources of pervasive IoT applications. Therefore data management is essential for the management of limited resources in such applications. This paper presents the recent studies and related information in data management for pervasive IoT applications having limited resources. This paper also proposes a parallelization based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach.

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Tag Localization with Asynchronous Inertial-Based Shifting and Trilateration.

Personal Area Networks (PAN) are key topologies in pervasive Internet of Things (IoT) localization applications. In the numerous object localization techniques, centralization and synchronization between the elements are assumed. In this paper, we leverage crowdsourcing from multiple fixed and mobile elements to enhance object localization. A cooperative crowdsourcing scheme is proposed to localize mobile low power tags using distributed and mobile/fixed readers for GPS assisted environments (i.e., outdoor) and fixed readers for indoors. We propose Inertial-Based Shifting and Trilateration (IBST) technique to provide an accurate reckoning of the absolute location of mobile tags. The novelty in our technique is its capability to estimate tag locations even when the tag is not covered by three readers to perform trilateration. In addition, IBST provides scalability since no processing is required by the low power tags. IBST technique is validated through extensive simulations using MATLAB. Simulation results show that IBST consistently estimates location, while other indoor localization solutions fail to provide such estimates as the state-of-the-art techniques require localization data to be available simultaneously to provide location estimation.

Chip off IoT Devices: Attacks and Mitigations

Internet of Things (IoT) is the most emerging field in Information Technology. IoT will make real-world objects into virtual objects and allows them to be sensed remotely. The IoT biological community is changing and getting to be appealing over a more extensive scope of controls, consequently their development in prevalent innovation. Security and protection are the key issues for IoT applications and still face some huge difficulties. Pervasive IoT gadgets have genuine security suggestions as they happen in extensive numbers, are topographically dispersed and can be hard to physically verify. These gadgets may contain touchy or economically profitable information making them appealing to different types of assault. It is discovered that a large portion of the gadgets is defenseless against assaults on hardware, firmware as well as web interface level. In this paper, vulnerabilities due to Insufficient Security Configurability, Insecure Firmware, Security Misconfiguration of IoT devices, recommendations for those vulnerabilities and an approach to reproduce those attacks is explained so as to provide better security to IoT Devices.

Secure Short-Packet Communications for Mission-Critical IoT Applications

In pervasive Internet of Things (IoT) applications, the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications; however, the incurred security issues and the impact of finite blocklength coding on the physical-layer security have not been well understood. This paper comprehensively investigates the performance of secure short-packet communications in a mission-critical IoT system with an external multi-antenna eavesdropper. An analytical framework is proposed to approximate the average achievable secrecy throughput of the system with finite blocklength coding. To gain more insight, a simple case with a single-antenna access point (AP) is considered first, in which the secrecy throughput is approximated in a closed form. Based on that result, the optimal blocklengths to maximize the secrecy throughput with and without the reliability and latency constraints, respectively, are derived. For the case with a multi-antenna AP, following the proposed analytical framework, closed-form approximations for the secrecy throughput are obtained under both beamforming and artificial-noise-aided transmission schemes. Numerical results verify the accuracy of the proposed approximations and illustrate the impact of the system parameters on the tradeoff between transmission latency and reliability under the secrecy constraint.

The Last Byte: I Have Met the IoT Security Enemy and He Is Us

Much of this issue of IEEE Design&Test is about pervasive Internet of Things (IoT). Ali Keshavarzi and William van den Hoek write about how Edge Intelligence will enable trillions of IoT devices. They quote the CEO of SoftBank in Japan as stating that combining IoT devices with artificial intelligence (AI) can lead to a “Cambrian-like explosion.” Sounds good until you remember that the Cambrian period ended with a mass extinction.

An Investigation on Pervasive Technologies for IoT-based Thermal Monitoring.

Indoor thermal monitoring is a crucial requirement for home automation, which fits inside the ever-growing Internet of Things (IoT) paradigm. The IoT ecosystem aims at connecting every device exploiting specific functions, deployed in a particular place, in order to give the chance to the users to monitor and/or control some aspects of their life, or to demand this task to a proper software. In the thermal monitoring context, IoT provides new opportunities for a dense and/or large-scale distribution of sensors, which have to gather data in order to effectively control the Heating, Ventilation and Air Conditioning (HVAC) system. Several wireless technologies can be exploited for this scope. However, they involve different benefits and drawbacks. In particular, this study is focused on Radio Frequency Identification (RFID) and Bluetooth®, which represent two well-known wireless technological standards used by commercial electronics but suitable also for pervasive IoT systems. These technologies are discussed and compared from several points of view, i.e., flexibility, reliability, battery life and cost of the system. A theoretical analysis highlights their benefits for the application context and evaluates their suitability to dense and large-scale monitoring systems. The theoretical results are supported by an experimental analysis based on the implementation and test of two different systems, one using RFID and the other using Bluetooth technology.

Ultrasounds-Based Context Sensing Method and Applications Over the Internet of Things

Nowadays, Internet of Things (IoT) devices can collect a large amount of data and infer the context they are operating in. One of the most pervasive IoT device is the smartphone. Among the plethora of sensors that such device has, microphone is probably the most versatile. It can be used to infer information about the context by acquiring the environmental sound. In this paper, we propose an active ultrasonic-based method able to sense context information. The approach is based on the emission of periodic ultrasonic impulses, called pings, whose echoes are continuously acquired to extract a proper set of features. Using a classifier the context information is then retrieved. Two applications are presented: 1) an indoor/outdoor detector and 2) an earphone wearing state detector. In both cases a smartphone is the employed device. The former senses if the smartphone is in an indoor or outdoor environment while the latter detects if a user is wearing or not his earphones. The obtained results are encouraging for both the solutions that have been stressed in different working conditions and employed within proper application frameworks.

Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT

Precise occupancy information is extremely valuable for energy savings and optimization in the built environment. Building management systems (BMSs) can automatically turn off the HVAC systems in unoccupied spaces and adjust the ventilation rate based on the number of occupants in each zone for energy saving. Popular commercial occupancy detection and crowd counting approaches, such as the infrared sensor, camera, and mobile devices, either require the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are intrusive and inconvenient for pervasive implementation. In this paper, we propose WiFree, a novel device-free occupancy detection and crowd counting scheme using only commercial WiFi enabled the Internet of Things (IoT) devices. Firstly, we design a novel IoT platform with which the fine-grand channel state information (CSI) measurements can be obtained directly from pervasive IoT devices. Given the CSI data, we propose an effective occupancy detection scheme by measuring the shape similarity between adjacent time series CSI curves. For crowd counting, we first design an information theory based feature selection scheme to select the most representative features that are sensitive to human motion. After that, we propose a crowd counting classifier based on transfer kernel learning and information fusion, which is robust to temporal and environmental disparities. We implemented WiFree with commodity WiFi routers and conducted extensive experiments in three indoor environments of disparate sizes. Experimental results have validated that WiFree is able to achieve 99.1% occupancy detection accuracy and 92.8% crowd counting accuracy over temporal variation in a device-free and privacy-preserving manner.

An intelligent, uncertainty driven management scheme for software updates in pervasive IoT applications

The era of the Internet of Things (IoT) involves a huge number of autonomous devices (nodes) capable of monitoring and interacting with their environment. The autonomous devices are also able of being interconnected, thus, they can exchange data. Pervasive computing applications can be built on top of this infrastructure offering efficient solutions for multiple domains. Nodes can execute intelligent, light-weight processing of the collected data being capable of responding in case of events. Apart from the software necessary to perform the discussed processing tasks, nodes are coming with pre-installed software necessary to perform basic functionalities e.g., communication. When nodes act in dynamic environments, it is necessary to update the software necessary for their functionalities. Updates involve software extensions and patches important to secure a high level performance of the IoT nodes. In this paper, we propose a distributed updates management scheme enhancing the autonomous nature of nodes. Legacy models deal with centralized approaches (i.e., a central server) where complex algorithms are adopted to derive the protocols for the distribution of the updates. In our approach, each node is responsible to, independently, initiate and conclude the update process. The central server is responsible only for indicating when the updates are available to the nodes. Every node monitors a set of performance metrics (either for the node itself or the network) and based on an intelligent scheme decides the appropriate time to conclude the update process. We adopt an ensemble forecasting model on top of a pool of estimators and an optimization model to derive the right time for initiating the update process. We are based on the solution of the known Santa Fe bar problem to perform load balancing in the retrieval of the updates. The aim is to have the nodes deciding the conclusion of the update process in different time intervals, thus, to keep the load of the network at low levels. We provide specific formulations and the analysis of our problem while extensive simulations and a comparison assessment reveal the advantages of the proposed solution.