Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially.

In energy harvesting wireless sensor networks (EH-WSNs), maximizing the data collection throughput is one of the most challenging issues. In this paper, we consider the problem of data collection on a pre-specified path using a mobile sink which has a fixed-mobility pattern. As a generalization of the previous works, we propose an optimization model for the problem which incorporates the effective and heterogeneous duration of sensors' transmission in each time slot. To improve the network throughput, a simple condition is proposed which determines the maximum number of available time slots to each sensor node. Accordingly, the proposed condition specifies the constant velocity of the mobile sink. The NP-Hardness of the problem under the proposed condition is proved and an online centralized algorithm with less complexity is designed to handle the problem. Its complexity is in polynomial order and is easily scalable to the networks with large number of sensor nodes. Furthermore, we address the effect of increase in time slot period on the total amount of collected data which has not been yet exploited well. Finally, through extensive simulations on different set of deployed nodes, we observe that the proposed algorithm significantly increases the network throughput when the travelled distance by sink per time slot is reduced down to the adjusted point.

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging=discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically largescale smart energy distribution system where EVs have the flexibility of charging=discharging at spatiallydeployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management.

Plug-in electric vehicles are becoming one of indispensable prosumer electronics components for smart households and therefore, their cost efficient energy scheduling is one of the main challenging issues. In the current schemas, the charging and discharging interval of the vehicles are normally announced by the owners in advance leading to the suboptimal profit gain in some situations and hence consumers dissatisfaction. In this paper, we propose an efficient charging/discharging scheduling mechanism for electric vehicles in multiple homes common parking lot for smart households prosumers. The proposed mechanism takes into account the optimal interval allocation considering the instantaneous electricity load and the vehicles request pattern. Based on the data from the vehicles, a mixed optimization model is formulated by the central scheduler which aims to maximize the profit of consumers and is then solved using an effective algorithm. The optimization results are then sent to the system controller determining the interval and energy trading patterns between the power grid and the vehicles. The proposed algorithm has low complexity and ensures the energy satisfaction for all consumers. The performance of the scheduling schema is verified through multiple simulation scenarios.

Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.

Due to the advancement in energy harvesting wireless sensor networks (EH-WSNs), the data collection from one-hop stationary sensor nodes using a path-constrained mobile sink has become one of the challenging issues. Toward the throughput improvement, we propose a general framework for network throughput maximization (NTM) problem by optimizing practically feasible parameters. For each proposed scenario, a mixed integer linear programming (MILP) optimization model is introduced for the problem formulation. Due to the NP-Hardness of the MILP models, we design two efficient algorithms namely as ODSAA and ODAA for two practically implementable scenarios. Having a preknowledge about the deployed location of nodes, the proposed algorithms run centrally by sink and find the sub-optimal solutions within a reasonable computation time. Furthermore, under the uniform distribution of energy harvesting, we find out two threshold points on, respectively, energy harvesting mean and battery capacity of nodes after which the network throughput reaches a stable point. Finally, simulations are conducted on a different set of node deployments, which the results confirm that the proposed algorithms significantly improve the data throughput collected by sink and also the theoretical thresholds provide a confidence interval of 90 percent.