# A Pervasive Collaborative Architectural Model at the Network's Periphery
## 2 Related Works on Pervasive Architectural Models and Their Limits
### Pervasive Architectural Models  
Maintaining connectivity between devices in pervasive computing, and particularly in the IoT paradigm, is important for serving people in various situations, especially in rural and harsh environments. For example, connectivity must be maintained with a smart car while it is driving as well as during a live and continuous transfer of data if it is necessary to make quick decisions. In addition, maximizing coverage areas, especially in these environments, is needed to ensure communications between pairs of devices over at least one connection. Many approaches and algorithms are designed to increase connectivity and maximize coverage in networks. Most require a central station [22, 23] with advanced resources to control the devices in the network, or they require homogenous devices [24, 25]. Recently, [PERSON] et al. [26] designed an algorithm CMHWN to provide connectivity between at least two devices and to add flexibility in controlling the overall displacement in the network. [PERSON] et al. [27] designed and validated a connectivity approach to minimize the movement of devices to save energy and support the self-organization of each object (device). [PERSON] et al. proposed a communication system using only Wi-Fi Direct technology [28]. However, these algorithms and systems do not handle heterogeneous communication technologies and require a central station to maintain connectivity. Therefore, many existing technologies can be considered in contemporary architectures to maximize coverage and maintain connectivity in pervasive computing. In addition, major research efforts have been conducted in the fields of cloud, fog, edge and mobile edge computing and their branching at the peripheral level. The fundamental structure in this area is cloud computing. The main problem of the cloud is that its services are centralized, which sometimes slows down processes and can cause total or partial shutdowns due to a huge number of connections. This problem is the basis for other approaches, which transform these centralized services into decentralized ones through intermediate and peripheral service centers. In this way, several architectural models were developed: fog, edge and mobile edge computing, cloudlet, and collaborative fog computing [29, 30]. Table 1 reviews the most recent and widely used of these architectural models. **QoS and Fault tolerance:** Some issues with the Mac framework are caused by the limitations of the mobile devices. These problems are related to service administration in terms of efficiency, availability, and fault tolerance. In [47] the fault tolerance mechanism and the requirement, to incorporate more aspects of QoS management, are raised in relation to requiring computational resources, low bandwidth, and frequent absence of connection. MAC Self-Management relies on self-repair mechanisms to enhance the quality of service provided while considering the fault tolerance levels and device churn. **Scalability:** Mac can maintain providing services on a large number of resources with limited capabilities and availability. Challenges in this area have been identified as: distribution processing, networking and storage capabilities, and administrating trade-offs in terms of cost and experience quality [48]. **Incentives for participation:** An essential key element of MAC, and in general of any voluntary computer-based computing system, is the incentive for temporary and voluntary sharing of its various resources as well as the exchange of data and services. **Resource Heterogeneity:** Management frames should take in count the device heterogeneity as a key feature because resources set-in up the Ad hoc Cloud environment are not limited to the boundaries of the data center, but instead are taken from available resources sets. **Resource Discovery:** The need to manage the dynamics of the environment and to interact closely with the incentive mechanisms necessitates specific requirements of resource discovery processes in MAC. Figure 1: MAC specific challenges [47, 48].