Channel Assignment Method for Maximizing Throughput in the Internet of Things System

The growth of the number of interconnected wireless devices such as the Internet of Things (IoT) is continuously increasing across various sectors, including smart buildings, smart offices, smart cities, and others. According to estimates, by the year 2030, there will be at least 50 billion devices interconnected through networks. The escalating number of uncontrolled wireless devices can lead to various issues such as interference, collisions, and data loss, resulting in an overall decline in network system performance. This study aims to propose a scenario as an alternative solution to optimize the overall network performance in the system by assigning channels to each interconnected wireless pair to reduce the impact of interference. This research indicates that the proposed method successfully enhances the system throughput performance by 39.75% compared to the condition where all wireless pairs operate on the same channel, thereby outperforming several other comparative methods.The growth in the number of wireless devices, such as the Internet of Things (IoT), interconnectedly continues to rise across various sectors like smart buildings, smart cities, and others. It is estimated that by 2030, at least 50 billion devices will be interconnected through networks. The escalating number of uncontrollable wireless devices can lead to various issues such as interference, collisions, and data loss, resulting in an overall system performance decline. This research aims to propose scenarios as an alternative solution to optimize the overall network performance through channel assignment for each interconnected wireless pair, reducing the impact of interference through a computational approach. Minimizing this impact will directly enhance the overall system throughput performance. The research results demonstrate that the proposed scenarios successfully improved the system throughput performance by 39.75% compared to the condition where all wireless pairs operate on the same channel, surpassing several other comparative algorithms

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Channel acquisition and routing system for real-time cognitive radio sensor networks

The need for efficient spectrum utilization and routing has ignited interest in the Cognitive Radio Sensor Network (CRSN) paradigm among researchers. CRSN ensures efficient spectrum utilization for wireless sensor network. However, the main challenge faced by CRSN users have to deal with is the issue of service quality in terms of interference when using channels and degradation in multi-hop communication. This thesis proposes to overcome the interference due to contention and routing issues through the design of an efficient Channel Acquisition and Reliable routing System (CARS). CARS is designed to reduce carrier sense multiple access contention and enhance routing in CRSNs. CARS comprises of Lightweight Distributed Geographical (LDG), and Reliable Opportunists Routing (ROR) modules. LDG is a medium access control centric; cross-layer designed protocol to acquire a common control channel for signalling to determine the data channel. ROR is a network-centric cross-layer designed protocol to decide on a path for routing data packets. The result shows that LDG significantly reduces the overhead of media access contention and energy cost by at an average of 70% and 80% respectively compared to other approaches that use common control channel acquisition like Efficient Recovery Control Channel (ERCC) protocol. In addition, LDG achieves a 16.3% boost in the time to rendezvous on the control channel above ERCC and a 36.9% boost above Coordinated Channel Hopping (CCH) protocol. On the other hand, the virtual clustering framework inspired by ROR has further improved network performance. The proposed ROR significantly increases packet received at the sink node by an average of over 20%, reduces end-to-end latency by an average of 37% and minimizes energy consumption by an average of 22% as compared to Spectrum-aware Clustering for Efficient Multimedia routing (SCEEM) protocol. In brief, the design of CARS which takes the intrinsic characteristics of CRSNs into consideration helps to significantly reduce the energy needed for securing a control channel and to guarantee that end-to-end, real-time conditions are preserved in terms of latency and media content. Thus, LDG and ROR are highly recommended for real-time data transmission such as multimedia data transfer in CRSN

one6G white paper, 6G technology overview:Second Edition, November 2022

6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond. These are characterized by a variety of diverse, often conflicting requirements, from technical ones such as extremely high data rates, unprecedented scale of communicating devices, high coverage, low communicating latency, flexibility of extension, etc., to non-technical ones such as enabling sustainable growth of the society as a whole, e.g., through energy efficiency of deployed networks. On the one hand, 6G is expected to fulfil all these individual requirements, extending thus the limits set by the previous generations of mobile networks (e.g., ten times lower latencies, or hundred times higher data rates than in 5G). On the other hand, 6G should also enable use cases characterized by combinations of these requirements never seen before, e.g., both extremely high data rates and extremely low communication latency). In this white paper, we give an overview of the key enabling technologies that constitute the pillars for the evolution towards 6G. They include: terahertz frequencies (Section 1), 6G radio access (Section 2), next generation MIMO (Section 3), integrated sensing and communication (Section 4), distributed and federated artificial intelligence (Section 5), intelligent user plane (Section 6) and flexible programmable infrastructures (Section 7). For each enabling technology, we first give the background on how and why the technology is relevant to 6G, backed up by a number of relevant use cases. After that, we describe the technology in detail, outline the key problems and difficulties, and give a comprehensive overview of the state of the art in that technology. 6G is, however, not limited to these seven technologies. They merely present our current understanding of the technological environment in which 6G is being born. Future versions of this white paper may include other relevant technologies too, as well as discuss how these technologies can be glued together in a coherent system

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected