# Towards Trust and Friendliness Approaches in the Social Internet of Things
## The Social IoT Paradigm
### The Social IoT and Cluster-Based IoT Nodes  
IoT-based applications usually rely on machine-to-machine (M2M) communications [19], such as a smart grid or smart meter. In these applications, objects usually use point-to-point connections for communications, and a unique, embedded hardware module is also required to connect tiny devices. The advantage of M2M communications is to provide good connectivity among a large number of low-cost devices with the least amount of human intervention, or no intervention [19]. According to recent survey statistics, more than 100 billion IoT devices are connected worldwide. By 2025, that number will increase (i.e., an addition of more than US$11 trillion [20]). Due to the huge number of  
Figure 4: Model of crowdsourcing in the social IoT. connected devices, energy efficiency and wireless access congestion issues arise [19]. In order to solve these problems in parallel, and improve energy efficiency, numerous joint clustering schemes have been proposed. These methods are mainly based on resource management and the interconnection of devices [20], according to a recent study [19]. Numerous M2M methods have been proposed in the past and are based on different criteria, such as M2M-achievable signal-to-interference-plus-noise ratio. There are few studies in which a scheme is handled by using priority [19]. Data priority and the proposing of energy-efficient congestion-based methods are mitigated by using various clustering algorithms. These algorithms introduce various ways to enhance energy conservation issues [19]. Data priority is the flow of data from specific M2M devices. The transmitted and collected data have a higher priority. The joint consideration of clustering formation and power demonstrates better results. [PERSON] et al. [19] discussed an energy, interest, and physical aware-based framework for coalition formation and resource distribution among wireless IoT applications [19]. In their proposed framework they assigned a holistic utility-based function to various IoT devices. This framework is divided into two stages. In the first stage, a coalition head will be determined for each coalition in a time-based slot. In the second stage, QoS prerequisites of M2M devices are formulated via holistic utility functions. The authors simulated numerical results and found the framework to be an energy-efficient approach that can be used for the coordination of various devices. Similar work was investigated by [PERSON] and [PERSON] et al. in [20]. In this study, a number of M2M devices are initialized from different clusters based on a low-complexity Chinese restaurant process (CRP). Afterwards, a cluster head is elected from among the members of each cluster. The contribution of this study is to select a cluster head. The selected cluster head can harvest and store energy in a stable manner. The authors used RF signals by adopting the wireless powered communication (WPC) paradigm. Finally, it is good to prolong the operation of the network [20]. [PERSON] et al. [21] demonstrated an adaptive self-organizing multihop cluster-based approach for mobile wireless networks. The radio network relies on a code-division multiple access (CDMA)-based scheme and has a multimedia support. The nodes are organized into a number of nonoverlapping clusters. The clusters are formed independently so they can be controlled and reconfigured dynamically (nodes can even move easily) [20]. [PERSON] et al. [22] proposed a heuristic routing strategy by using a fading channel for cluster-based multihop mobile wireless networks. Usually, frequent change of a cluster head will affect the performance of various scheduling allocation protocols. So in order to tackle this problem, the authors designed the least cluster change (LCC) algorithm. This framework has two conditions. In the first, two cluster heads come within range of each other; the second might be only when the nodes become disconnected from any other cluster.