IoT and Wireless Communication: Computer Science Approaches to Scalability and Reliability

With billions of devices adding up to the internet, broadly termed as Internet of Things (IoT), the need for reliable communication, distributed storage and computation has seamlessly increased. At this juncture, need for reliable communication, distributed storage and computation, adoption of error correcting codes and erasure codes plays a significant role. In this chapter, we give an overview of construction of erasure codes required for reliable communication with emphasis on Internet of Things (IoT) communication which have wireless sensors or sensor networks as its core. Wireless sensors form an integral part of Internet of Things (IoT) devices bridging the virtual world and the real world. Achieving reliability in such networks is highly desirable due to their broad range of applications. The discussed erasure codes in this chapter can be directly employed or with little modification in the context of reliable communication in Internet of Things. In this chapter, the two methods of information transmission, end-to-end transmission and hop-by-hop transmission, prevalent in digital communication scenario is discussed in detail with emphasis on with and without erasure coding. Also, the erasure codes used extensively in the context of achieving reliability in wireless sensor communication namely Reed-Solomon codes, Fountain codes and Decentralized Erasure codes are discussed and compared. This chapter serves as a starting point for researchers interested in working on reliability aspects of communication in Internet of Things embedded with wireless sensors and cyber physical systems.

The Internet of Things (IoT) has emerged as a transformative technological paradigm enabling seamless interconnection between physical devices, digital platforms, and intelligent decision-making systems. IoT integrates sensing technologies, embedded systems, wireless communication, edge computing, and cloud infrastructures to support large-scale smart ecosystems. Despite rapid adoption across healthcare, transportation, smart cities, agriculture, and industrial automation, IoT systems face persistent research challenges including cybersecurity vulnerabilities, scalability constraints, interoperability barriers, energy efficiency limitations, and complex data governance requirements. This paper presents a detailed examination of IoT architecture, systematically analyzes critical research challenges, and explores emerging future directions such as Artificial Intelligence-enabled IoT, Blockchain-integrated trust management, Digital Twin modeling, and 6G-enabled ultra-reliable communication. The study provides an in-depth academic perspective aimed at supporting future research and large-scale deployment of secure, scalable, and sustainable IoT infrastructures

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

<p>This paper investigates the reliability assessment and availability prediction techniques used in modeling of advanced (next generation) wireless communication networks. The 5G, 5G+, beyond 5G (B5G) and 6G communication technologies are leading to emerging applications of wireless communication that use cloud computing, edge computing, and fog computing. In the last decade, various user-centric and service-oriented networks such as internet of things (IoT), smart cities, smart homes, smart grids, drones, and unmanned aerial vehicles (UAV) have been deployed that use technologies such as network function virtualization (NVF), software defined networking (SDN), and 5G. This has led to proliferation of IoT devices and IoT applications in various critical usage systems such as intelligent transportation systems, smart healthcare, and e-commerce. The availability and reliability of wireless connectivity in IoT devices and nodes is of significant importance as unavailability of nodes or end-user devices even for a millisecond could cause failure of healthcare systems or lead to malfunction of connected and autonomous vehicles, or compromise the smart grids power generation and distribution, leading to fatal outcomes. </p>

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

With the increasing use of Internet of Things (IoT) devices in various industries, the challenge of efficient use of energy and reducing the amount of device downtime is a key concern in sustainable and reliable systems usage. The paper deals with energy consumption optimization and the forecasting of device failures in the IoT systems in industrial automation. More precisely, we suggest the hybrid solution consisting of reinforcement learning (RL) to optimize energy consumption, Long Short-Memory (LSTM) networks to predictive maintenance, and edge-cloud offloading solutions to improve the performance of devices and their sustainability. We use the modeling of heterogeneous IoT devices in our methodology, but with different energy profiles, different workloads, and operational modes. We use RL to dynamically tweak devices modes, which saves energy and does not impact task completion. We combine predictive maintenance, in which we use LSTM to forecast device failures using historical data to give timely warnings on maintenance. Moreover, the edge-cloud offloading decisions are also integrated into our system to further optimize the energy consumption and optimize the efficiency of performing tasks. The principal results are that our method is highly energy efficient and device uptime, and reinforcement learning is the most energy saving method. The predictive maintenance model is an effective way of cutting on the downtime, as the correct prediction of the occurrence of failure results in the taking of preventive measures. Task offloading, especially when there are resource constraints, improves system reliability without affecting the performance. Such findings indicate that adaptive IoT systems could be used to optimize energy use and system performance and, therefore, make industrial automation more sustainable. Our solution is informative to the design of future IoT systems in which energy efficiency and predictable stability will be the most crucial attributes. Keywords: IoT Energy Optimization, Predictive Maintenance, Reinforcement Learning, LSTM Networks, Edge-Cloud Offloading.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

Electronic gadgets, appliances, medical equipment, cameras, sensors, and cars are all part of the Internet of Things (IoT), which enables smart cities and infrastructure. It is predicted based on evidences that a minimum of forty nine billion IoT devices shall definitely be linked or connected with the Internet by or before 2020. Those devices will generate a huge and unprecedented amount of information, which conventional systems and cloud systems may find challenging to handle. Fog computing was created to address the shortcomings of existing technologies. Fog offers storage, computation, data, and application services at the network's edge, comparable to cloud computing. This article discusses the features that generally makes fog or edge computing a good stage for the recently evolved technology “IoT”, as well as application, characterization and interrelated research in the trending fields including IoT and Fog. Edge computing is a suitable platform for IoT because of its heterogeneous nature, mobility support, very low latency, and huge number of nodes. Rather of transmitting data to the cloud, data will be processed at the network edge.

The Internet of Things (IoT) is revolutionizing numerous industries, including healthcare services, known as the Internet of Medical Things (IoMT). A large amount of generated data in IoMT applications need to be transmitted, analyzed, and stored. Consequently, the cloud-only architecture was proposed as being the best-fit organizational infrastructure. Indeed, cloud capabilities of processing, networking, and storage are overwhelming properties that make it outperform classical solutions for decades when it comes to healthcare applications. Nevertheless, this architecture could not keep up with the ever-growing amount of biomedical data. One of the main drawbacks of cloud architecture is the large latency, which prevents it from delivering real-time alerts to save the patient's life in critical situations. In this context, edge and fog computing become good alternatives to reduce health data management complexity and latency and therefore increase their reliability. This paper proposes a review of the most recent healthcare applications based on edge and fog computing, respectively. The selected research works of this survey are classified into four categories depending on their use case application. Furthermore, a comparison study helps extract relevant insights from several recent papers in the current literature to highlight their methodologies and purposes. The main concern is to emphasize what features and properties are to be considered most when designing a distributed healthcare system in an edge-fog architecture. Finally, we present some challenges to be addressed in healthcare applications along with the uprising of new technologies.

Internet of Medical Things (IoMT) is a breakthrough technology in the transfer of medical data via a communication system. Wearable sensor devices collect patient data and transfer them through mobile internet, that is, the IoMT. Recently, the shift in paradigm from manual data storage to electronic health recording on fog, edge, and cloud computing has been noted. These advanced computing technologies have facilitated medical services with minimum cost and available conditions. However, the IoMT raises a high concern on network security and patient data privacy in the health care system. The main issue is the transmission of health data with high security in the fog computing model. In today's market, the best solution is blockchain technology. This technology provides high-end security and authentication in storing and transferring data. In this research, a blockchain-based fog computing model is proposed for the IoMT. The proposed technique embeds a block chain with the yet another consensus (YAC) protocol building security infrastructure into fog computing for storing and transferring IoMT data in the network. YAC is a consensus protocol that authenticates the input data in the block chain. In this scenario, the patients and their family members are allowed to access the data. The empirical outcome of the proposed technique indicates high reliability and security against dangerous threats. The major advantages of using the blockchain model are high transparency, good traceability, and high processing speed. The technique also exhibits high reliability and efficiency in accessing data with secure transmission. The proposed technique achieves 95% reliability in transferring a large number of files up to 10,000.

Wireless communication in Internet of Things (IoT) systems involves several devices communicating in the limited available frequency spectrum using different wireless communication standards. To achieve efficient spectrum usage in the IoT systems, it is desired to have the ability of dynamic spectrum allocation. Cognitive radios (CR) enable efficient spectrum utilization by performing spectrum sensing to gain the knowledge of current spectrum occupancy and dynamically reconfiguring the communication parameters, making them highly suitable for IoT systems. In this paper, we present the efficient hardware implementation of a spectrum sensing scheme for CR based IoT applications that require large bandwidth. We show that the proposed implementation on field programmable gate arrays (FPGAs) can achieve over 1.56x improvement in performance, while consuming 77% less generic resources and at a fractionally higher power consumption.

The arrival of 5G technology presents substantial opportunities for industrial automation through enhanced connectivity and real-time data exchange. This integration enables seamless communication among Internet of Things (IoT) devices, heralding a new era of smart manufacturing characterized by increased productivity, reduced downtime, and improved decision-making. This study explores the synergy between 5G and IoT in optimizing industrial processes, emphasizing critical areas such as massive machine-type communications (mMTC), ultra-reliable low-latency communication (URLLC), and edge computing capabilities. The integration of the Internet of Things (IoT) with 5G technology is revolutionizing industrial automation by offering unprecedented levels of connectivity and real-time data sharing. With its support for massive machine-type communications (mMTC) and ultra-reliable low-latency communication (URLLC), 5G facilitates the seamless operation of IoT devices, enabling applications such as autonomous systems, remote monitoring, and predictive maintenance. This convergence enhances data analytics, leading to more informed decision-making and improved operational efficiency while reducing downtime. However, challenges such as security vulnerabilities, interoperability issues, and the need for robust infrastructure must be addressed. As more industries embrace this transformative technology, the combination of 5G and IoT is poised to enhance agility, scalability, and sustainability in the digital age.