# A Pervasive Collaborative Architectural Model at the Network's Periphery
## 2 Related Works on Pervasive Architectural Models and Their Limits
### Issues of Pervasive Architectural Models at the Periphery of a Network

Table 1: Classification of contemporary architectural models. cloud, (ii) peripherally centralized including fog, edge computing and mobile edge computing, and (iii) other peripheral architectures such as emerging technologies introduced in recent years, especially mobile ad hoc networks at the periphery (MANET, VANET, multi-groups networks, mobile cloudlet, etc.) [49]. The peripheral architectures introduced most recently make smart mobile systems more efficient in terms of communication, power consumption, data storage and computation capability while they reduce peripheral device discharge to cloud service centers. Note that cloud computing was created as a solution for mega data exchange and distributed collaborative and ubiquitous computing. Fog and edge computing were also designed as system architectures to address the problems of cloud computing near peripheral devices. Still other architectures have been designed as solutions for more distant peripheral devices including cloudlet, mobile cloud computing, mobile cloudlet, mobile ad hoc cloud computing, smart P2P and multi-group networks. These peripheral architectures provide access to a large number of devices. The main issue is to maintain and enhance connectivity using the offered technologies, which include (i) P2P technology such as WI-FI-Direct, and LTE-Direct and (ii) wireless technology using base stations such as Wi-Fi and Lora. The emergence of the Internet of Things (IoT) and its widespread deployment as a new model that processes a huge amount of data has led to problems with collaborative structures. These include many applications, such as management, communicating and exchanging data between remote users at the periphery of networks away from their hubs or centers. Centralized cloud computing and other computing architectures at the levels closest to the periphery were, in their turn, effective solutions to these problems because of the proliferation of data centers and smart micro-devices that reach the network edges. However, the efficiency of these architectures has declined in proportion to the increase in requests from a huge number of users and the colossal amount of data to be processed. Therefore, it has caused a serious bottleneck in terms of connectivity and communication, especially for device users at the periphery. The networks in these paradigms generally comprise small mobile devices that suffer from the problem of limited capacity, as mentioned earlier. Following a thorough study and analysis of these architectural models, we highlight the following facts about this area: (i) there is a strong trend towards the development and evolution of small, smart mobile devices; (ii) there is excessive demand to use them, and (iii) researchers have made diligent efforts to improve their performance and functionality and overcome their disadvantages. The main research questions to address on the limits of these structures are:  
* How to maintain the connectivity of network devices in various situations? * How to reduce the tasks of loading and unloading these small peripheral devices with central or intermediate service centers (cloud, fog, edge) to avoid the communication bottleneck? * How to reduce the energy consumption of these devices by minimizing their time of use and improving task scheduling and load balancing algorithms? We identified five emerging issues in this area of intelligent and collaborative mobile computing at the periphery of networks:  
1. Limited connection bandwidth because of congestion from loading and unloading messages (tasks and data) to fixed or mobile center servers [29]. 2. The limitations of mobile devices in terms of power, computing ability and data storage [39]. 3. The reduced range of mobile network devices in terms of device-to-device connection when wireless connectivity with base stations is lost. 4. The complete loss of operability or effectiveness of these devices during severe events such as natural disasters and terrorist attacks because of malfunctions or failures in the base network stations. 5.