# A Pervasive Collaborative Architectural Model at the Network's Periphery
###### Abstract
Because dynamic situations give these users more freedom in terms of place of work and time to continue their different jobs, the demand for the use of these devices or objects is steadily increasing. Likewise, the development of pervasive devices is leading to an intensification of their use in increasingly complex collaborative applications, as well as to an intensive use of resources. [2, 13, 14]. Unfortunately, technological progress does not follow this demand proportionately. For example, the number of extant mobile devices, including phones and tablets, has increased from more than 7.7 billion in 2014 to more than 12.1 billion in 2018 [15]. Moreover, some expect that, by 2025, there will be up to 74 billion connected IoT devices [16] or perhaps as many as 100 billion [17]. Therefore, since the technological advancement of these devices lags behind the increasing demand for their use, it can be compensated by improving the performance of the applications that these devices will achieve in terms of the speed of response and the completion of tasks. This can be done using job scheduling and load balancing techniques and by maintaining connectivity in networks, especially in harsh environments, to ensure the continuity of activities for the required time. Therefore, this paper seizes the opportunity of emerging architectures such as edge, fog, cloud, mobile ad hoc cloud computing and multi-group networking to build an autonomous architecture of pervasive, collaborative computing in the IoT paradigm. The main idea of the approach is to establish a peripheral autonomous network of mobile devices using their own P2P radio communication and the radio wireless enabled by Pycom's LoPy4. Therefore, the present work aims to:  
1. Reduce the risk of total or partial interruption of network connectivity caused by breakdowns or overwhelming (a huge number of) connections. This involves the improvement and continuity of maintaining the connectivity between mobile devices, regardless of the state of the network. 2. Increase the resilience and reliability of sharing data, information and computations between these devices (objects). To achieve these goals, we have designed a pervasive mobile network based on a collaborative layered architectural model [18] that allows users to communicate in various situations. For this purpose, we have taken into account the capacity limitations of their devices in terms of data storage, computing, and expected battery life. This model has been designed to enhance computing capacity, improve response time and increase the use of ubiquitous surrounding resources. The development of this architectural model includes the following steps:  
1. Design an architecture sample to maintain connectivity, even during extreme events and harsh environments. 2. Establish a multi-group network connected by device-to-device communication, such as Wi-Fi Direct and other communication technologies that have a larger range connection, such as those of Pycom's LoPy4. 3. Propose four new algorithms and validate them through the required applications needed to manage the proposed model, such as dividing tasks and balancing their load. Among these applications is one to program the Lopys as transmitter, bridge and receiver. Pervasive computing is the natural successor of mobile computing systems. It is an emerging field of research that offers revolutionary models for the most recent computing paradigms. Pervasive computing has led to tremendous advances in technologies such as computing and mobile devices, as well as technologies of communication, wireless networking, middleware programing, smart spaces and distributed and embedded systems. The present study fosters collaboration between groups of objects through intra-group and inter-group (of devices) communication in the IoT model, where most of these objects are no longer managed by a system administrator.