## 3 Research Process  
To better understand task allocation in edge computing, in the following review, we will examine the most recent papers that refer to the specific topic. To achieve this, we conducted an extensive search in the Scopus search engine [84] for papers relevant to \"task allocation on edge computing\". Therefore, search terms such as \"task allocation\" and \"edge computing\" are used to search in the titles, abstracts, and keywords of publications. We narrowed the search to the years after 2020 to catch out on the latest technologies in the field we are examining. The initial search yielded back 218 documents. Consequently, using the following inclusion/exclusion criteria:  
* In cases where the study has a conference version and a journal version of a study, the journal version is retained while the conference version is discarded. * If there are multiple published versions of a study, only the latest or most recent version is retained. * If a study is available in multiple sources, only one version is included. Exclusion criteria were also used to omit research that did not meet the requirements for inclusion. The following are the specific exclusion criteria:
* Papers not available on an open access basis or through our university access systems that are opening the vast majority of the reputable editor houses and journal titles were not included in the final literature corpus. * Only documents written in the English language were used. * Only papers related to computer science were included; works that were only peripheral to edge computing were excluded. * Papers dealing with very specific datasets were excluded. We focused on conceptual papers, case studies, methodological papers, and position papers. We do acknowledge that potentially missing a relatively minor number of papers that we excluded from our systematic review approach due to the unavailability of access to the sources, these were published, and may have a minimal impact on the completeness of the corpus and thus on our results. We would argue, however, that our Universities' journal access agreements in Greece and the UK are very inclusive and only miss very specialized and small publishers. We anticipate that this had an extremely limited impact on our work. In this research, we used the following keywords (with logical connectives) and their combinations: edge computing, task allocation, IoT, energy utilization, mobile edge computing, computation offloading, integer programming, resource allocation, resource management, antennas, reinforcement learning, task analysis, fog computing, energy efficiency, green computing, task offloading, UAV, 5G mobile communication systems, cloud computing, data handling, job analysis, MEC, network architecture, cloud-computing, computing environments, deep learning, edge server, fog, learning algorithms, big data, computation-intensive task, computational modeling, computing resource, energy consumption, learning systems, multi-agent systems, optimization, QoS, servers, task allocation algorithm, and total energy consumption. To answer RQ1, the task allocation methods are categorized according to the following methods:  
1. Resource-aware task allocation: Allocating tasks depending on the resources that are currently accessible on edge devices, such as processing power, memory, and battery life. 2. Distributed task allocation: Allocating tasks across a network of edge devices to optimize the performance and reduce latency. 3. Dynamic task allocation: Adapting task allocation in real-time based on changes in device performance or network conditions. 4. Machine learning-based task allocation: Using machine learning algorithms to predict resource utilization and allocate tasks accordingly. 5. Energy-efficient task allocation: Allocating tasks in a way that minimizes energy consumption in edge devices. 6. Quality of service (QoS)-aware task allocation: Allocating tasks based on the required QoS levels for different tasks. 7.