## 3 Related Work  
Existing surveys on edge, fog, and cloud computing have comprehensively discussed the synergy between IoT and edge, fog, and cloud computing paradigms, explaining how edge and fog computing can bridge the gap between IoT and cloud computing by moving the computation closer to the end-user IoT devices, thereby addressing issues with energy, latency, and context awareness [4, 5, 15, 16, 17, 18]. Numerous scholarly papers have extensively addressed diverse facets of edge, fog, and cloud tiers, taken as separate tiers. Specifically, for device-enhanced MEC, additional complexities need to be considered due to the dynamic nature of device-based resource providers and their intrinsic characteristics, such as distributed ownership, mobility, finite energy, resource constraints, increased proximity to other device-based resource providers, and Device-to-Device (D2D) communication capacity [5, 15, 19, 20, 21, 22]. Various aspects of the technical feasibility of the lowest tier of device-enhanced MEC, where devices such as smartphones function as edge resource providers, have been demonstrated in frameworks such as MMPI [23], Hyrax [24], MClouds [25], Aura [26], and Honeybee [6]. However, these existing works have not investigated how the device-based resource providers can collaborate and share work with nodes at fog and cloud layers, considering various overheads, impacts on performance, and battery and various offloading parameters. As highlighted in a recent work on the convergence of edge, fog, and cloud, only a few papers have yet investigated the interactions between these three paradigms in a 3-tier edge-fog-cloud architecture [27]. Below, we discuss related work that has explored this under-researched area. Here, we only focus on work that has investigated the interactions and collaborations between at least two of the edge, fog, and cloud tiers, and do not consider work that only focuses on one tier. One of the first papers to explore **edge-fog-cloud** collaboration is [PERSON] et al. [20], who proposed the HyMobi framework, which allows a mobile application to interoperate between device-enhanced MEC, fog, and cloud tiers. HyMobi has an incentive mechanism based on credit and reputation, and allows users to lease the resources of their devices as an open commodity in the edge tier, exploiting the social characteristics of the devices to form offloading communities. The effectiveness of HyMobi is demonstrated via a proof-of-concept implementation and a testbed, evaluating the performance and energy consumption of mobile applications and infrastructure awareness in a social-aware environment. However, the performance results are not explored thoroughly with different edge-fog-cloud combinations. Other papers that discuss all three tiers of edge-fog-cloud include [21, 28, 29]. In [21], a task can be offloaded to either edge, fog or cloud, or executed on the device itself; however, no collaboration amongst the tiers is mentioned. Although the algorithm in [28] can be applied to all three layers of edge-fog-cloud to reduce latency and power consumption, the paper only considers the fog layer. In [29], a Min-Min algorithm, considering cost, makespan, energy, and load balancing, is used to schedule tasks amongst the edge-fog-cloud tiers. The coordination of work offloading amongst the **edge and cloud** tiers is discussed in [22; 30; 31]. In [22], the authors propose the HyFog framework, which considers interactions between device-enhanced MEC and cloud, and allows devices to choose the execution mode among local mode, D2D mode, and cloud mode. Simulations show that HyFog's three-layer graph-matching algorithm-based solution is able to minimise the power consumption while ensuring latency requirements. However, there is no discussion about adapting to dynamic conditions, such as device mobility. In comparison, refs. [19; 32] only consider collaborated work offloading inside the device-enhanced MEC layer itself, between the devices as resource providers and the conventional edge server/s.