## 1 Introduction
Meanwhile, with the accelerated growth of 5G technologies and the current development of emerging applications, it can be thought that the densely deployed small-cell networks (DDSCNs) will be the mode of future 5G communication networks which will have the capacity to deal with the requirements of excessive data traffic [19]. Generally, in sparse edge deployment, a user can only connect to the local SBS-MEC server, and it is not always the best decision to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technical Aspect** & **MCC** & **Fog Computing** & **MEC** \\ \hline Proposed by & Not specific & Cisco & ISG, ETSI \\ Deployment & Centralized & Distributed & Dense and distributed \\ Computing capabilities & Higher & Lower & Lower \\ Latency & High & Low & Low \\ Storage capacity & Ample & Limited & Limited \\ Location & Large data center & Between user devices and data center & Radio access network \\ Hierarchy & 2 tiers & 3 or more tiers & 3 tiers \\ Context awareness & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different mobile computing architectures [11; 16; 17]. connect this server; however, in densely deployment environment [20; 21], there will be multiple SBS-MEC servers in the nearest places such as user residences, airports, and workplaces. The user may, therefore, have multiple options to connect to neighboring SBS-MEC servers for the services. In DDSCNs, the access nodes are deployed close to the end users and the access nodes are densified per unit area with immense probability of line-of-sight transmission [22]. In a novel collaborative task offloading scheme, fuzzy logic is used because it has already been significantly applied for congestion mitigation, handoff control, network, and workload-management related problems [23; 24; 25; 26; 27; 28]. In this study, a fuzzy-logic based technique is proposed to deal with the collaborative task offloading problem in MEC-enabled SCNs. When the exact mathematical model is difficult to develop in a rapidly changing system that is dynamic and uncertain, fuzzy logic is the most employed method because its computation complexity is lower with respect to different decision-making algorithms [29; 30; 31]. In [32], we consider the collaboration approach between mobile device with the SBS-MEC server and the collaboration between SBS-MEC servers with the cloud; however, in our proposed work, the collaboration approach adopts the fuzzy rules to determine the task offloading location among the SBS-MEC servers. In particular, the main contributions of this study are summarized as follows:  
* We investigate an innovative collaborative task offloading framework for MEC-enabled DDSCNs that can easily compute various service requests based on the user's demand. * We develop a fuzzy-based algorithm, called fuzzy-based collaborative task offloading (FCTO), that analyzes the under-utilized and over-utilized SBS-MEC servers. Based on the fuzzy rules which select a target SBS-MEC server that can efficiently process dynamic flow of service requests, each task can be assigned among SBS-MEC servers. * By employing our proposed fuzzy-based model, one can handle uncertainty in rapidly changing environments. Our proposed model also helps in solving the dynamic resource management problem as well as the overload problem of a single SBS-MEC server. * To analyze the performance of our proposed collaborative task offloading scheme, we conducted some experiments. The experimental results validate that our proposed model achieves outstanding performance in terms of reducing the average task duration and task failure and also provides more server utilization when compared to other two reference schemes. The rest of this study is organized as follows. In Section 2, we review some relevant research works. The motivational scenario, different collaborative execution models, and our proposed collaborative architecture are narrated in Section 3. Section 4 introduces fuzzy logic and the proposed FCTO algorithm.