Recently, the edge resource provisioning schemes were defined considering the low-latency Mobile Edge Computing (MEC) paradigm. Most of these models only consider batterypowered devices like smart-phones, thus are agnostic to the
energy harvesting techniques that achieves a green MEC system. Further, most of the studies on MEC assume unlimited edge resources which is not the case as it is with the conventional data-centers (public clouds). Hence, unrestricted use of edge resources is not ideal. This work mainly considers two problems: (1) the offloading of data traffic from the Internet of Things (IoT) devices that rely on energy harvesting to the MEC entities and (2) assignment of the resources at the MEC. The novelty of this paper lies in the energy scavenging based architecture
that is developed over the Contiki OS. Secondly, saving the energy for computations to maximize the lifetime of the sensing nodes by performing the execution of the computationallyintensive tasks at the edge which is a single hop away. The proposed architecture uses the ambient triggers to form the sensor network and establish links with computationally capable resources located at the edge. Further, a mathematical model to manage the resources at the edge is proposed. Finally, we evaluate a threshold-policy for optimizing the resources participating in an edge computation service for an IoT scenario and discuss the
improvements achieved.

The Internet of Things (IoT) is an enabler of the digital transformation dictating new needs and trends in the domains of business and technology. Ecosystems of IoT devices are often organized in networks, using wireless technology and sharing access infrastructure. These networks are used to monitor a wide range of systems, from simple household activities to fully-interconnected smart cities. In many usage scenarios, the IoT devices are resource-constrained. Thus, energy scavenging is utilized to meet their expanding longevity requirements. In this paper, we study the local resource dynamics of IoT devices in an ecosystem, i.e., a set of different IoT devices that co-exist in spatiotemporal level to coordinate the use of available common resources for their individual goals. To this end, we model an ecosystem of IoT devices as a time-varying graph and provide a theoretical foundation for resource distribution using Graph Theory. We show that simple graph-theoretic metrics, such as, the clustering coefficient and degree distribution, can provide rich information about the priority policy that is followed for the distribution of resources among different IoT devices. We take the case of micro grids; with some nodes having harvesting potential and smart meters measuring the current consumption/generation and being connected to the control unit. We use this notion in our example use-case, appropriating this to micro-grids with enough harvested energy. Even one link per node can describe an ecosystem as a connected component with more than 60% of its total energy needs covered. Additionally, the nodes presenting harvesting potential are formed into unipartite graphs of affiliation networks. Studying their clustering coefficient we infer the priority policy that ia applied when excess energy is shared within their ecosystem.