Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios, and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose the first online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely FML, with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates received data to learn from and adapt to its environment. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 minutes of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes such as blockage and traffic.

Millimeter-Wave (mmWave) bands have become the de-facto candidate for 5G vehicle-to-everything (V2X) since future vehicular systems demand Gbps links to acquire the necessary sensory information for (semi)-autonomous driving. Nevertheless, the directionality of mmWave communications and its susceptibility to blockage raise severe questions on the feasibility of mmWave vehicular communications. The dynamic nature of 5G vehicular scenarios and the complexity of directional mmWave communication calls for higher context-awareness and adaptability. To this aim, we propose an online learning algorithm addressing the problem of beam selection with environment-awareness in mmWave vehicular systems. In particular, we model this problem as a contextual multi-armed bandit problem. Next, we propose a lightweight context-aware online learning algorithm, namely fast machine learning (FML), with proven performance bound and guaranteed convergence. FML exploits coarse user location information and aggregates the received data to learn from and adapt to its environment. Furthermore, we demonstrate the feasibility of a real-world implementation of FML by proposing a standard-compliant protocol based on the existing architecture of cellular networks and the forthcoming features of 5G. We also perform an extensive evaluation using realistic traffic patterns derived from Google Maps. Our evaluation shows that FML enables mmWave base stations to achieve near-optimal performance on average within 33 mins of deployment by learning from the available context. Moreover, FML remains within ~ 5% of the optimal performance by swift adaptation to system changes (i.e., blockage, traffic).

One of the key challenges in wireless communications is handling the ever growing traffic demand. A large fraction of this traffic is induced by popular content. One way to face this challenge is mobile content caching which improves the system performance by caching content closer to the user. The benefit of content caching depends on the applied content delivery strategy. In this paper, we investigate a scenario where multiple destinations are concurrently requesting a content, which is already cached at mobile devices and then delivered over a wireless multihop network. We propose a content delivery framework which jointly exploits content already cached at mobile devices as well as switching between mechanisms at the physical layer and the network layer in order to optimally deliver the content to all destinations under changing network conditions. In our framework, we use a unified graph model to jointly model the network, the cached content and different mechanisms at the lower three layers. From the unified graph model, an optimization problem is formulated, which is used to find the optimal content delivery strategy. In our numerical evaluation, we show the combined gain of caching and the capability of switching between mechanisms by comparing with conventional schemes which either cannot switch between mechanisms or do not exploit caching.

Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations, since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user's context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14%.

Computation offloading is a promising approach for reducing the computational load and extending the battery lifetime of mobile nodes. A network consisting of several wireless nodes accessing the cloud in a multi-hop fashion is considered. In multi-hop networks, offloading a computational task requires relaying the task by the intermediate nodes along the path towards the cloud. If the nodes are autonomous and rational, the intermediate nodes need to be incentivized for forwarding the tasks of other nodes. In this paper, a distributed decision algorithm which determines the set of tasks to be offloaded and the set of tasks to be locally computed for total energy minimization is proposed. Since a task needs to be sequentially forwarded by multiple nodes, each of which decides independently, decision conflicts on forwarding a task can occur. Accordingly, a novel coordination mechanism is proposed by which the forwarding nodes resolve their decision conflicts. In this coordination mechanism, nodes need only to exchange their forwarding decisions to resolve the conflicts. The results show that the proposed distributed algorithm achieves a performance close to the performance of the centralized algorithm.

We consider a data dissemination scenario in a wireless network with selfish nodes. A message available at a source node has to be disseminated through the network in a multi-hop manner. In order to incentivize a node to forward the source's message to others, a forwarding cost is paid to a forwarder by its respective receiver. In the case of multicast transmission, the cost is shared among the receivers using the Shapley value (SV). Moreover, a node may exploit the maximal ratio combining (MRC) technique to receive the message from multiple transmitting nodes. In this paper, we show that in a game theoretic framework, the optimal decision of a node for receiving the message with minimum cost can be achieved by solving a linear optimization problem. In addition, we propose an algorithm by which truthfulness is a dominant strategy for the nodes and thus, fair cost allocation is guaranteed. Simulation results show that our proposed algorithm shares the cost of data dissemination among the nodes of a network in a fair manner. Compared to previous algorithms, the proposed algorithm can reduce the total cost paid by the nodes in the network for receiving messages.

A promising architecture for content caching in wireless small cell networks is storing popular files at small base stations (sBSs) with limited storage capacities. Using localized communication, an sBS serves local user requests, while reducing the load on the macro cellular network. The sBS should cache the most popular files to maximize the number of cache hits. Content popularity is described by a popularity profile containing the expected demand of each file. Assuming a fixed popularity profile of which the sBS has complete knowledge, the optimal content placement problem reduces to ranking the files according to their expected demands and caching the highest ranked ones. Instead, we assume that the popularity profile is varying, for example depending on fluctuating types of users in the vicinity of the sBS, and it is unknown a priori. We present a novel algorithm based on contextual multi-armed bandits, in which the sBS regularly updates its cache content and observes the demands for cached files in different contexts, thereby learning context-dependent popularity profiles over time. We derive a sub-linear regret bound, proving that our algorithm learns smart caching. Our numerical results confirm that by exploiting contextual information, our algorithm outperforms reference algorithms in various scenarios.

The handover from WiFi to mobile networks is known to lead to TCP connection drops due to changing IP addresses. Multipath TCP (MPTCP), a recent TCP extension, enables a transparent mobile handover by combining subflows on multiple interfaces, such as WiFi and LTE, to one logical connection. MPTCP provides multiple handover modes, which differ in their energy consumption and the performance during the handover. The Full-MPTCP mode uses permanently both WiFi and the mobile network, which increases energy consumption. The Single-Path mode establishes the mobile network connection after the WiFi connection broke, which leads to a short performance degradation. In this paper, we argue that this trade-off is not necessary. We propose to use the available (sensor) information to forecast the mobile handover. This allows switching to the Full-MPTCP mode before the WiFi connection breaks, providing both low energy consumption and high performance during the handover. For a first experimental evaluation, we use a declining WiFi link quality to forecast a handover. Our real world measurements show that both low energy consumption and high performance during the handover are possible at the same time.

Video streaming constitutes the dominant portion of today’s traffic on the Internet and will grow in the coming years. In order to provide for a low cost distribution of bulky video content, Peer-to-Peer (P2P) approaches are a viable way to cut down server bandwidth cost by utilizing user’s upstream bandwidth to redistribute data. However, users need an incentive to participate in such a system. The related work on incentive schemes has focused on using bandwidth contribution as a measure for contribution to the system’s performance, ignoring that user perceived Quality of Experience (QoE) is not necessarily maximized by maximizing bandwidth, but by delivering the right data in the right order. Consequently, this work presents QTrade, a topology agnostic incentive scheme for adaptive P2P video streaming systems based on user-validated video quality metrics. QTrade is evaluated on top of an existing adaptive streaming overlay. The results show that QTrade utilizes bandwidth more efficiently by providing incentive to distribute parts of the video with a high QoE. Moreover, up to 70% less and shorter rebuffering events are observed for cooperative peers while maintaining a 10 to 11 times worse performance in terms of rebuffering events for non-cooperative peers.

Computation offloading is an upcoming approach to increase battery life of mobile devices overburdened by resource-consuming applications. In multi-hop networks, computation offloading poses new challenges since intermediate devices are required to relay tasks of others along the path to the server. The decision of a device about whether to offload or not depends thus on the provided energy of relay devices and on the decisions of other offloading devices since relay resources need to be shared. This also implies that for energy minimization, optimal decisions are topology-dependent. This paper introduces a novel theoretical framework for energy minimization of computation offloading in multi-hop wireless networks which formulates the energy minimization problem as a binary linear problem. Proving its equivalence to a multi-dimensional knapsack problem allows us to specify a greedy heuristic, which shows very good performance, with a maximal deviation of less than 5% from the optimal results. From simulations and analytical results for different topologies, we derive under which conditions computation offloading in multi-hop networks is beneficial.