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Mobile Edge Computing (MEC) has become an attractive solution to enhance the computing and storage capacity of mobile devices by leveraging available resources on edge nodes. In MEC, the arrivals of tasks are highly dynamic and are hard to predict precisely. It is of great importance yet very challenging to assign the tasks to edge nodes with guaranteed system performance. In this article, we aim to optimize the revenue earned by each edge node by optimally offloading tasks to the edge nodes. We formulate the revenue-driven online task offloading (ROTO) problem, which is proved to be NP-hard. We first relax ROTO to a linear fractional programming problem, for which we propose the Level Balanced Allocation (LBA) algorithm. We then show the performance guarantee of LBA through rigorous theoretical analysis, and present the LB-Rounding algorithm for ROTO using the primal-dual technique. The algorithm achieves an approximation ratio of $2(1+\xi)\ln (d+1)$ 2 (1 + ξ) ln (d + 1) with a considerable probability, where $d$ d is the maximum number of process slots of an edge node and $\xi$ ξ is a small constant. The performance of the proposed algorithm is validated through both trace-driven simulations and testbed experiments. Results show that our proposed scheme is more efficient compared to baseline algorithms. [ABSTRACT FROM AUTHOR]

Caching popular videos at mobile edge servers (MESs) has been confirmed as a promising method to improve mobile users (MUs) perceived quality of experience (QoE) and to alleviate the server load. However, with the multiple bitrate encoding techniques prevalently employed in modern streaming services, caching deployment is challenging for the following three facts: (1) cooperative caching should be explored for MUs located at overlapped coverage areas of MESs; (2) there exists tradeoff consideration for caching either high bitrate videos or high diversity videos; and (3) the relationship between MU perceived QoE and MU received bitrate, known as QoE function, varies in different services. Aiming to maximize the MU perceived QoE, we formulate the multiple bitrate video caching problem, and prove this problem is NP-hard for any given positive and strictly increasing QoE function. We then propose a polynomial complexity algorithm based on a general QoE function, which can achieve an approximate ratio arbitrarily close to 1/2. Specifically, for a linear QoE function, we explore useful property of optimal solutions, based on which more efficient algorithms are proposed. We demonstrate the effectiveness of our solutions via both theoretical analysis and extensive simulations. [ABSTRACT FROM AUTHOR]

Wireless power transfer is a promising technology used to extend the lifetime of, and thus enhance the usability of, energy-hungry battery-powered devices. It enables energy to be wirelessly transmitted from power chargers to energy-receiving devices. Existing studies have mainly focused on maximizing network lifetime, optimizing charging efficiency, minimizing charging delay, etc. In this paper, we consider wireless charging service provision in a two-dimensional target area and focus on optimizing charging quality, where the power of each charger is adjustable. We first consider the charger Placement and Power allocation Problem with Stationary rechargeable devices (SP $^3$ ): Given a set of stationary devices and a set of candidate locations for placing chargers, find a charger placement and a corresponding power allocation to maximize the charging quality, subject to a power budget. We prove that SP$^3$ is NP-complete, and propose an approximation algorithm. We also show how to deal with mobile devices (MP $^3$), cost-constrained power reconfiguration (CRP), and optimization with more candidate locations. Extensive simulation results show that, the proposed algorithms perform very closely to the optimum (the gap is no more than 4.5, 4.4, and 5.0 percent of OPT in SP$^3$ , MP $^3$ , and CRP, respectively), and outperforms the baseline algorithms. [ABSTRACT FROM AUTHOR]

Wireless power transfer provides a promising way to extend the battery lifetime of our energy-hungry rechargeable devices. Previous studies have envisioned using mobile vehicles/robots/drones equipped with high capacity batteries as mobile chargers to replenish those devices, and they mainly focus on maximizing network lifetime, optimizing efficiency of charging scheduling, minimizing total charging delay, etc. However, existing methods may be insufficient and inflexible when the energy consumption of rechargeable devices fluctuates over time, or when rechargeable devices are sparse. In this paper, we consider how to efficiently provide flexible wireless charging using pre-planned charging itineraries. We present the Itinerary Selection and Charging Association (ISCA) problem: given a set of rechargeable devices and a set of candidate charging itineraries, how can we select itineraries and determine a corresponding charging association to minimize the amount of energy which is due to mobile chargers’ movement and wireless charging loss, so that every device gets its required energy. We prove that ISCA is NP-complete by reducing the set cover problem to it. We start solving this problem by first looking at the case in which an itinerary can only be used once, and we propose an algorithm with approximation ratio of $O(\ln M)$ and a practical heuristic algorithm, where $M$ is the number of devices. For the general case in which an itinerary may be used multiple times, we propose an approximation algorithm of factor 10 using the Primal-Dual schema. Evaluations results from real field experiments and extensive simulations show that the proposed algorithms have near-optimal performance and PDA reduces the amount of wasted energy by up to 65 percent compared with a set cover-based algorithm. [ABSTRACT FROM AUTHOR]

In wireless networks, a recent trend is to make spectrum access dynamic for efficient utilization of spectrum. In such a scenario, the spectrum is periodically allocated to wireless users using an auction-based market mechanism. A critical property required for designing such a mechanism is truthfulness, which could avoid market manipulation. Such mechanism design typically involves solving NP-hard problems; hence, approximation algorithms are always resorted to in real systems. However, recent results have suggested that it is impossible to implement reasonable approximations without losing robustness to manipulation. In this paper, we solve the problem in a novel perspective by relaxing the constraints of ensuring strong truthfulness. We discuss the concepts of approximate truthfulness and provide approximately truthful mechanisms to improve efficiency (in terms of social welfare and spectrum utilization). We first develop a computationally efficient mechanism that achieves truthful in expectation. This mechanism is based on the assumption that bidders are risk neutral. Following that, we break the assumption by proposing a hard-to-manipulate auction (HMA), which makes it hard to manipulate the auction for profit gains. Our extensive simulation results show that our mechanisms can achieve significant improvement over the state-of-the-art mechanisms. [ABSTRACT FROM PUBLISHER]

In recent years, OFDMA relay networks have become a key component in the 4G standards (e.g., IEEE 802.16j, 3GPP LTE-Advanced) for broadband wireless access. When numerous bidirectional flows pass through the relay stations in an OFDMA relay network that supports various interactive applications, plenty of network coding opportunities arise and can be leveraged to enhance the throughput. In this paper, we study the proportional-fair scheduling problem in the presence of network coding in OFDMA relay networks. Considering the tradeoff between performance and overhead, we propose two models, global approach (GA) and local approach (LA), under which the corresponding problems are shown both NP-hard. For the GA model, we show that it cannot be approximated within some constant factor. Hence, we propose a heuristic algorithm with low time complexity. For the LA model, we propose a theoretical polynomial time approximation scheme (PTAS), and also present a practical greedy algorithm with approximation factor of (1\over 2). Simulation results show that our algorithms can achieve significant throughput improvement over a state-of-the-art noncoding scheme. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are key for various applications that involve long-term and low-cost monitoring and actuating. In these applications, sensor nodes use batteries as the sole energy source. Therefore, energy efficiency becomes critical. We observe that many WSN applications require redundant sensor nodes to achieve fault tolerance and Quality of Service (QoS) of the sensing. However, the same redundancy may not be necessary for multihop communication because of the light traffic load and the stable wireless links. In this paper, we present a novel sleep-scheduling technique called Virtual Backbone Scheduling (VBS). VBS is designed for WSNs has redundant sensor nodes. VBS forms multiple overlapped backbones which work alternatively to prolong the network lifetime. In VBS, traffic is only forwarded by backbone sensor nodes, and the rest of the sensor nodes turn off their radios to save energy. The rotation of multiple backbones makes sure that the energy consumption of all sensor nodes is balanced, which fully utilizes the energy and achieves a longer network lifetime compared to the existing techniques. The scheduling problem of VBS is formulated as the Maximum Lifetime Backbone Scheduling (MLBS) problem. Since the MLBS problem is NP-hard, we propose approximation algorithms based on the Schedule Transition Graph (STG) and Virtual Scheduling Graph (VSG). We also present an Iterative Local Replacement (ILR) scheme as a distributed implementation. Theoretical analyses and simulation studies verify that VBS is superior to the existing techniques. [ABSTRACT FROM AUTHOR]