Battery operated systems are bulky, expensive, and often add unnecessary burden because of their maintenance. They are also harmful to the environment. However, the design and development of batteryless systems are highly challenging as the energy needs to be harvested from user's activities or the environment. The harvested energy also varies with the activity, environment, and other aspects. In this paper, we present a system employing an energy harvesting switch to power a low-power radio, which transmits data wirelessly in 2.4 GHz ISM band. We provide the details of the design of our system and modules. We evaluate our energy harvesting switch which we built in-house. With evaluations, we show that our system works well, and we demonstrate the transmission of 27 bytes at 200 kbps data rate. Further, by varying the transmission power between-10 dBm and 5 dBm, we transmit data packets of length between 19 and 27 bytes with a single press of the switch.

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.