The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities [a.k.a fog nodes (FNs)]. In other words, VFC proposes to complement stationary FNs co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available FNs. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatiotemporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of FNs. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile FNs to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatiotemporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile FNs and create more savings in operational costs in the long term.

Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.

The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client’s field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.

Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.

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.

Unlike the existing sink-based data collection in energy harvesting wireless sensor networks (EH-WSNs) in which the mobile sink maintains a constant speed during its trajectory, the allocation of optimal sink speed can further improve the achievable network throughput. Inspired by this fact, in this paper, we formulate the optimal sink speed allocation problem with the objective of maximizing the network throughput (NTM-OSS) in delay-tolerant EH-WSNs. A mixed integer nonlinear programming(MINLP) optimization model is proposed for NTM-OSS formulation which takes into account the time duration for the acceleration and deceleration of mobile sink. With the sensors' information available in advance, an efficient algorithm named as optimal sink speed allocation (OSSAA) is designed which works based on an iteratively speed updating mechanism and runs by the mobile sink within consecutive time intervals. We show that the proposed algorithm guarantees the convergence to near-optimal solutions and has both time and message complexities of polynomial order in the worst case. The results of conducted simulations reveal that the proposed algorithm improves the network throughput by in average 1.6 × 103data unit and achieves an average of 3.3 % energy saving compared to its competitor.

MPEG has recently proposed Server and Network Assisted Dynamic Adaptive Streaming over HTTP (SANDDASH) for video streaming over the Internet. In contrast to the purely client-based video streaming in which each client makes its own decision to adjust its bitrate, SAND-DASH enables a group of simultaneous clients to select their bitrates in a coordinated fashion in order to improve resource utilization and quality of experience. In this paper, we study the performance of such an adaptation strategy compared to the traditional approach with large number of clients having mobile Internet access. We propose a multi-servers multi-coordinators (MSsMCs) framework to model groups of remote clients accessing video content replicated to spatially distributed edge servers. We then formulate an optimization problem to maximize jointly the QoE of individual clients, proportional fairness in allocating the limited resources of base stations as well as balancing the utilized resources among multiple serves. We then present an efficient heuristic-based solution to the problem and perform simulations in order to explore parameter space of the scheme as well as to compare the performance to purely client-based DASH.

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.

In this paper, we focus on the issue of maximizing the data collected by a mobile sink with unlimited energy which traverses a given straight path in an energy harvesting wireless sensor network (EH-WSN). Sensors are assumed to harvest energy periodically from the solar resources in their surrounding. Due to limitations on energy replenished within different time intervals, the sensors can reduce their transmission period in time slots with lesser available data in order to conserve energy for the possibility of higher data transmission in consecutive slots. Aiming at improving the overall network throughput, we formulate the problem of finding the effective data transmission period as a mixed integer linear programming (MILP) optimization model. With prior knowledge of the maximum transmission range, we design an online centralized algorithm for the problem which is run by the sink and easily scalable to large network sizes. Corroborated by extensive simulation results, we demonstrate that the proposed algorithm outperforms the existing work with the mean of throughput improvement being inversely proportional to that of the energy harvested while exhibiting the same order of computational complexity.

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.

In this paper, we introduce the problem of road condition surveillance using a mobile vehicle as a concrete practical application of data collection paradigm on a direct path in energy harvesting wireless sensor networks (EH-WSNs). The application components together with its associated challenges are discussed throughout the paper. An optimization model is introduced for the network throughput maximization problem. In contrast to the previous models, the proposed optimization model considers the effective and heterogeneous duration of sensor's transmission together with the dynamic aspect of energy harvesting over different time intervals. Towards the improvement of the network throughput under a proposed condition, an online centralized algorithm with less complexity is designed. Finally, simulations on both Random and Equal-Distance deployment of sensor nodes are conducted to compare the performance of the proposed algorithm with the previous approaches and to observe the effect of different energy harvesting distributions on the throughput achieved by the algorithm.