Recent development toward innovative applications and technologies like self-driving, augmented reality, smart cities, and various other applications leads to excessive growth in the number of devices. These devices have finite computation resources and cannot handle the applications that require extensive computation with minimal delay. To overcome this, the mobile edge cloud (MEC) emerges as a practical solution that allows devices to offload their extensive computation to MEC located in their vicinity; this will lead to succeeding the arduous delay of the millisecond scale: requirement of $5^{th}$ generation communication system. This work examines the convex optimization problem. The objective is to minimize the task duration by optimal allocation of the resources like local and edge computational capabilities, transmission power, and optimal task segmentation. For optimal allocation of resources, an algorithm name E stimation of O ptimal R esource A llocator (EORA) is designed to optimize the function by keeping track of statistics of each candidate of the population. Using EORA, a comparative analysis of the hybrid approach (partial offloading) and edge computation only is performed. Results reveal the fundamental trade-off between both of these models. Simultaneously, the impact of devices’ computational capability, data volume, and computational cycles requirement on task segmentation is analyzed. Simulation results demonstrate that the hybrid approach: partial offloading scheme reduces the task's computation time and outperforms edge computing only.

In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of internet of things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power Mobile Edge Cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and Mobile Edge Cloud (MEC) to address low power devices’ battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes.

Within the Lysekil wave energy research project at the Swedish west coast, more than ten Wave Energy Converters (WECs) prototypes have been developed and installed in an ocean based test site. Since 2006 various experiments have been conducted and the generated electricity was delivered to shore at a nearby island. While experiments are essential for the development of wave energy converters, theoretical studies and simulations are an important complement – not only in the search for advanced designs with higher efficiency, but also for improving the economic viability of the studied concepts. In this paper a WEC model is presented. The model consists of three subsystems: i) the hydrodynamic source, ii) the linear generator model, and iii) the electrical conversion system. After the validation with the experimental results at the research site, the generator model is connected to three passive load strategies – linear resistive load, passive rectification and resonance circuit. The paper focuses on analysing the operation of the model coupled with three load cases. The results prove that the WEC model correctly simulates the linear generator developed in the Lysekil Project. Moreover, the comparison among different load cases is made and discussed. The results gives an indication of the efficiency of energy production as well as the force ripples and resulting mechanical loads on the wave energy converters.

The Lysekil wave energy converter (WEC), developed by the wave energy research group of Uppsala University, has evolved through a variety of mechanical designs since the first prototype was installed in 2006. The hundreds of engineering decisions made throughout the design processes have been based on a combination of theory, know-how from previous experiments, and educated guesses. One key parameter in the design of the WECs linear generator is the stroke length. A long stroke requires a taller WEC with associated economical and mechanical challenges, but a short stroke limits the power production. The 2-m stroke of the current WECs has been an educated guess for the Swedish wave climate, though the consequences of this choice on energy absorption have not been studied. When the WEC technology is considered for international waters, with larger waves and challenges of energy absorption and survivability, the subject of stroke length becomes even more relevant. This paper studies the impact of generator stroke length on energy absorption for three sites off the coasts of Sweden, Chile and Scotland. 2-m, 4-m, and unlimited stroke are considered. Power matrices for the studied WEC prototype are presented for each of the studied stroke lengths. Presented results quantify the losses incurred by a limited stroke. The results indicate that a 2-m stroke length is likely to be a good choice for Sweden, but 4-m is likely to be necessary in more energetic international waters.