ABSTRACT Owing to the growing Internet‐of‐things (IoT) infrastructure and the vast amounts of data involved, the demands of the IoT ecosystem are growing. Sixth generation (6G) networks are essential for meeting these demands. Due to its ability to provide high data rates with extended network coverage, visible light communication (VLC) in 6G optical networks is attracting more attention. The primary issue, though, is that these VLC networks are susceptible to signal obstructions that lower line‐of‐sight (LoS) link quality. Intelligent reflecting surfaces (IRSs), which provide improved non‐LoS channel gains, are used in the optical domain to get around this. This paper presents a novel framework for the IRS‐aided VLC system and optimizes it for maximum data rate. The mathematical formulations for the presented optimization methodology is also provided. Further, an association algorithm is proposed that associates each IRS element with each transmitter‐receiver pair. The time‐space complexity analysis is also carried out. It is observed that the proposed association scheme increases the achievable data rate in the IRS‐assisted VLC network by 7%. For various transmit powers , the effect of increasing the number of user nodes and blockages in the system model on the achievable data rate is assessed. Additionally, the suggested association scheme and random association scheme are used to analyse the outage performance of the IRS‐assisted VLC system. At of 10 W, the suggested association outperforms random association by 46.6% in outage performance. Finally, an IRS‐assisted VLC system use case scenario for indoor communication in a corporate office is also covered.

Modern vehicles involve large number of sensors, cameras and communication systems for real-time traffic management, collision avoidance and vehicle health monitoring. As the Internet of Vehicles (IoV) ecosystem evolve with more number of connected vehicles, handling of the enormous data is a challenge. This is overcome with the promising distributed edge intelligence (DEI) approach in which the computational tasks are distributed among the intelligent road side units (RSUs) at the network edge. The edge servers cooperate among themselves so as not to overload the central cloud server. This article presents a cooperative vehicular communication network which exploits the existing 5G infrastructure in roadside building as the edge/relay nodes. To overcome the communication and energy overhead, network resource management is enabled through proposed edge node selection algorithm. Further, a joint edge node and antenna selection algorithm is proposed for enhanced energy efficiency (EE) and reduced outage. The closed-form expression for the outage probability of the proposed cooperative communication scheme is derived. Our analysis shows that the proposed selection approach achieves improved outage probability and energy-efficiency. In particular, the proposed edge node selection approach improves the EE by 10.46% at total transmit power to noise power ratio of 16 dB. Moreover, the overall system performance is further enhanced by proposing a joint selection scheme. Specifically, the analysis shows that the energy-efficiency improves by 27.87% with the joint selection scheme. In the end, a use case scenario of DEI empowered IoVs in emergency healthcare support is discussed along with the future research directions.

There is an explosive growth of intelligent devices in the IoT ecosystem over the years. Owing to the massive multiple access at the network edge, there is increased latency and transmission overhead. Multiaccess edge computing (MEC) is a key technology used to offload the wireless devices from the computational tasks. But the wireless signal propagation is subject to fading, attenuation, obstructions, and other disturbances thereby affecting the performance of edge network. Reconfigurable intelligent surface (RIS) technology improves the quality of wireless propagation links through controlled reflection. This article presents an RIS-aided framework for a heterogenous edge network to offload the computation tasks of the resource constraint user equipment to the small access points (APs). A resource control algorithm is proposed which enables selection of an RIS-AP pair for each node in the edge network. The proposed algorithm selects the RIS-AP pair using maximum channel gain criteria such that the system sum throughput is maximized. Also, enabling reflection through the multiple RISs, the shortest path is selected using the graph theory to obtain the tradeoff between latency and reflection loss. It is observed that the proposed approach improves the achieved sum throughput of the system by 21.7% and the latency is reduced by 13.8%. The network performance is evaluated for varied RIS size and number of reflecting elements under different RIS phase shift design. It is shown that RIS with 1000 reflecting elements each of size${}({\lambda }/{2})\times {}({\lambda }/{2})$with equal phase shifts achieve sum throughput gain of 25.2% over randomly chosen phase shifts. Further, the comparison of intelligent reflecting the surface-aided MEC system with the conventional MEC system and the clustered MEC system is performed.

The expanding number of low cost sensors and smart devices drives the internet-of-things (IoT) ecosystem of the future. These sensing devices are connected to the internet for information exchange. The location and positioning of these nodes is very important information required in vast range of location based services like smart homes , smart healthcare , environmental monitoring, personal navigation and smart transportation. This paper presents an intelligent solution for node localization in a 6G enabled IoT network. An indoor communication network scenario is proposed in which reconfigurable intelligent surfaces (RISs) are installed to locate the sensor nodes operating in that network. The performance evaluation of the proposed scheme is carried out with optimum number of reflecting elements and optimum phase shifts. It is observed that optimized RISs with 100 reflecting elements improve the estimated localization error by 7.4% over non-optimum RISs. Also, the minimum gain of 6% in localization error is offered using equal phase shifts over random phase shifts. Further, the effect of channel conditions on the average estimation error in node locations is also elaborated. In the end, the explainable artificial intelligence (XAI) empowered indoor localization is discussed as a use case scenario and the performance comparison of the algorithms is evaluated.