Social Internet of Things (SIoT) is a specialized form of adaptive networks, where any change in one node can affect other nodes in the network. Millions of devices, including vehicles, buildings, healthcare, industries, and several other types of devices, share and exchange sensitive data among each other. Interconnection of these devices changes the paradigms of the world around us and puts a great effect on the overall society. The number of nodes, types of devices, layout of the overall network, and sensitivity of the data adds more and more complexity in the domain of SIoT. In this paper, our contribution is twofold, first, we provide a brief and comprehensive overview of the social aspects of the IoT by selecting the most recent papers to investigate the complexity, adaptivity, and other social aspects of the IoT and their implications. The main aim of this paper is to analyze the benefits of IoT to the society, social acceptance of IoTs, and social connectivity of different age groups. Second, we forecast the future trends and predict the world in the year 2020. We provide a detailed analysis by using several different data tables to present the future trends of different aspects of SIoT. With this survey, we aim to provide an easy and concise view of different challenges in implementing SIoT and how it will affect the future smart cities and overall impact on the society.

Cellular vehicle-to-everything (C-V2X) is the first standard introduced by the 3rd generation partnership project (3GPP) in its Rel. 14 to enable V2X communication. This standard is endorsed by the industries, scientists, and researchers. In C-V2X, mode 4 is designed to enable direct V2V communication via the PC-5 interface. In mode 4 vehicles select the resources which are over two-dimensional frequency and time domain for their packet transmission. These resources are scheduled based on a semi-persistent scheduling algorithm introduced by 3GPP to avoid resource contention. However, when the density of vehicles is high resolving resource contention is a major concern. To this end, 3GPP introduced NR-V2X which is the evolution of C-V2X in its Release 16. NR-V2X mode 2 is introduced as an alternative to C-V2X mode 4 for distributed resource scheduling. NR-numerologies and semi-persistent scheduling with re-evaluation mechanism is introduced by 3GPP in NR-V2X play a vital role in resolving resource contention. This paper presents first the analytical models for NR-V2X mode 2 considering the impact of NR-numerologies and re-evaluation mechanism on resource selection. The analytical models are designed considering the four transmission errors that could occur in NR-V2X mode 2. Moreover, the model validation is performed by comparing the proposed analytical models with the NR-V2X simulator implemented in ns-3. The model validation shows that the proposed analytical models are accurate.

5G revolutionizes connectivity with network slicing, but non-uniform slice deployment poses challenges. Efficient slice handover mechanisms and predictive learning offer solutions. We propose Long Short-Term Memory (LSTM) based prediction of network slice traffic demands for proactive resource management to resolve challenges of mobility management and non-uniform slice deployment within a registration area. Thereby, allowing seamless service provision throughout the registration area. Our proposed model shows excellent results in predicting the number of received requests for all available slices. These results are then utilized to deduce the average slice demand for each service in the network. The obtained results are beneficial for configuring the slice resources proactively in order to provide seamless service to mobile users throughout the registration area, regardless of the non-uniformity in slice deployment among different tracking areas.