 in a more fundamental way. In cloud computing era, even though the data transmission speed is not high, the bottleneck comes mainly from the computation capacity. With Moore's law still being effective, performance of integrated circuit chips grows exponentially. On the other hand, communication technology makes the speed increase almost linearly. Since the goal is to reduce processing speed, it is more beneficial to perform task execution both locally and remotely. 1.2 Enabling Technologies Cloud service Media server Cache server App server Backhaul Cloud service ((i-))) ((p)) Media server Cache server App server ((a)) ((HH)) Edge server w/ BS Edge server w/ BS (((-))) MBS w/Edge (((-))) User devices (((-))) User devices Figure 1.3 Paradigm shift from cloud computing to mobile edge computing. In order to reduce latency as well as to improve system efficiency, we propose a joint processing scheme in which the total task can be divided into two parts, one for local computing and the other for offloading. To cope with the ever-increasing concerns on energy efficiency, we evaluate the system performance by a new metric, computational efficiency (CE). It is defined as the total number of bits computed with the total energy consumption. The objective is to maximize each user's CE with time constraints (users should finish their task before a required time), energy constraint (each user is powered by battery; hence, the total energy should be below a threshold), and task constraint (each user should finish a minimum number of data bits). Later we show CE is a more appropriate method in terms of finding the balance of more tasks and less energy. 1.2.3 Hybrid and Heterogeneous Communication Architecture for Pervasive loTs Recent years have witnessed the unprecedented growth of wearable devices owing to the swift advances in chip design, computing, sensing, and communications technologies. While wearable devices are not new, the past few years have seen a surge in their large-scale use and popularity. A wearable device o